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Type:

Package

Title:

Data Science for Psychologists

Version:

1.2.0

Date:

2025-11-05

Maintainer:

Hansjoerg Neth <h.neth@uni.kn>

Description:

All datasets and functions required for the examples and exercises of the book "Data Science for Psychologists" (by Hansjoerg Neth, Konstanz University, 2025, <doi:10.5281/zenodo.7229812>), freely available athttps://bookdown.org/hneth/ds4psy/. The book and corresponding courses introduce principles and methods of data science to students of psychology and other biological or social sciences. The 'ds4psy' package primarily provides datasets, but also functions for data generation and manipulation (e.g., of text and time data) and graphics that are used in the book and its exercises. All functions included in 'ds4psy' are designed to be explicit and instructive, rather than efficient or elegant.

Depends:

R (≥ 3.5.0)

Imports:

ggplot2, unikn

Suggests:

knitr, rmarkdown, spelling, testthat (≥ 3.0.0)

Collate:

'util_fun.R' 'num_util_fun.R' 'text_util_fun.R''time_util_fun.R' 'color_fun.R' 'data.R' 'data_fun.R''text_fun.R' 'time_fun.R' 'num_fun.R' 'theme_fun.R''plot_fun.R' 'start.R'

Encoding:

UTF-8

LazyData:

true

License:

CC BY-SA 4.0

URL:

https://bookdown.org/hneth/ds4psy/,https://github.com/hneth/ds4psy/

BugReports:

https://github.com/hneth/ds4psy/issues

VignetteBuilder:

knitr

RoxygenNote:

7.3.3

Language:

en-US

NeedsCompilation:

Packaged:

2025-11-05 18:24:32 UTC; hneth

Author:

Hansjoerg Neth

[aut, cre]

Repository:

CRAN

Date/Publication:

2025-11-05 23:20:13 UTC

Data: Bushisms

Description

Bushisms contains some phrases uttered by or attributed to U.S. president George W. Bush (the 43rd president of the United States of America, in office from January 2001 to January 2009).

Usage

Bushisms

Format

A vector of typecharacter withlength(Bushisms) = 22.

Source

Data based onhttps://en.wikipedia.org/wiki/Bushism.

Data: Trumpisms

Description

Trumpisms contains characteristic words and phrases used by U.S. president Donald J. Trump (the 45th and 47th president of the United States of America) during his first presidency (ranging from January 20, 2017, to January 20, 2021).

Usage

Trumpisms

Format

A vector of typecharacter withlength(Trumpisms) = 168 (on 2021-01-28).

Details

Seehttps://en.wikiquote.org/wiki/Donald_Trump for a more recent collection of attributed and disputed quotes.

Source

Data originally based on a collection ofDonald Trump's 20 most frequently used words onhttps://www.yourdictionary.com and expanded by interviews, public speeches, and Twitter tweets fromhttps://twitter.com/realDonaldTrump.

Umlaut provides German Umlaut letters (as Unicode characters).

Description

Umlaut provides the German Umlaut letters (aka. diaeresis/diacritic) as a named character vector.

Usage

Umlaut

Format

An object of classcharacter of length 7.

Details

For Unicode details, seehttps://home.unicode.org/,

For details on German Umlaut letters (aka. diaeresis/diacritic), seehttps://en.wikipedia.org/wiki/Diaeresis_(diacritic) andhttps://en.wikipedia.org/wiki/Germanic_umlaut.

Examples

Umlautnames(Umlaut)paste0("Hansj", Umlaut["o"], "rg i", Umlaut["s"], "t s", Umlaut["u"], "sse ", Umlaut["A"], "pfel.")paste0("Das d", Umlaut["u"], "nne M", Umlaut["a"], "dchen l", Umlaut["a"], "chelt.")paste0("Der b", Umlaut["o"], "se Mann macht ", Umlaut["u"], "blen ", Umlaut["A"], "rger.")paste0("Das ", Umlaut["U"], "ber-Ich ist ", Umlaut["a"], "rgerlich.")

Convert a string of numeral digits from some base into decimal notation

Description

base2dec converts a sequence of numeral symbols (digits) from its notation as positional numerals (with some base or radix)into standard decimal notation (using the base or radix of 10).

Usage

base2dec(x, base = 2)

Arguments

x

A (required) sequence of numeric symbols (as a character sequence or vector of digits).

base

The base or radix of the symbols inseq. Default:base = 2 (binary).

Details

The individual digits provided inx (e.g., from "0" to "9", "A" to "F") must be defined in the specified base (i.e., every digit value must be lower than the base or radix value). Seebase_digits for the sequence of default digits.

base2dec is the complement ofdec2base.

Value

An integer number (in decimal notation).

Examples

# (a) single string input:base2dec("11")   # default base = 2base2dec("0101")base2dec("1010")base2dec("11", base = 3)base2dec("11", base = 5)base2dec("11", base = 10)base2dec("11", base = 12)base2dec("11", base = 14)base2dec("11", base = 16)# (b) numeric vectors as inputs:base2dec(c(0, 1, 0))base2dec(c(0, 1, 0), base = 3)# (c) character vector as inputs:base2dec(c("0", "1", "0"))base2dec(c("0", "1", "0"), base = 3)# (d) multi-digit vectors:base2dec(c(1, 1))base2dec(c(1, 1), base = 3)# Extreme values:base2dec(rep("1", 32))          # 32 x "1"base2dec(c("1", rep("0", 32)))  # 2^32base2dec(rep("1", 33))          # 33 x "1"base2dec(c("1", rep("0", 33)))  # 2^33 # Non-standard inputs:base2dec("  ", 2)      # no non-spaces: NAbase2dec(" ?! ", 2)    # no base digits: NAbase2dec(" 100  ", 2)  # remove leading and trailing spacesbase2dec("-  100", 2)  # handle negative inputs (value < 0)base2dec("- -100", 2)  # handle double negationsbase2dec("---100", 2)  # handle multiple negations# Special cases:base2dec(NA)base2dec(0)base2dec(c(3, 3), base = 3)  # Note message!# Note: base2dec(dec2base(012340, base =  9), base =  9)dec2base(base2dec(043210, base = 11), base = 11)

Base digits: Sequence of numeric symbols (as named vector)

Description

base_digits provides numeral symbols (digits) for notational place-value systems with arbitrary bases (as a named character vector).

Usage

base_digits

Format

An object of classcharacter of length 62.

Details

Note that the elements (digits) are character symbols (i.e., numeral digits "0"-"9", "A"-"F", etc.), whereas their names correspond to their numeric values (from 0 tolength(base_digits) - 1).

Thus, the maximum base value in conversions bybase2dec ordec2base islength(base_digits).

Examples

base_digits          # named character vector, zero-indexed nameslength(base_digits)  # 62 (maximum base value)base_digits[10]      # 10. element ("9" with name "9") base_digits["10"]    # named element "10" ("A" with name "10")base_digits[["10"]]  # element named "10" ("A")

Capitalize initial characters in a string of text

Description

capitalize converts the firstn initial characters of each element of a text stringx (i.e., characters or words) to upper- or lowercase.

Usage

capitalize(x, n = 1, upper = TRUE, as_text = FALSE)

Arguments

x

A string of text (required).

n

Number of initial characters to convert.Default:n = 1.

upper

Convert to uppercase?Default:upper = TRUE.

as_text

Treat and returnx as a text (i.e., one character string)? Default:as_text = FALSE.

Details

Ifas_text = TRUE, the inputx is merged into one string of text and the arguments are applied to each word.

Value

A character vector.

Examples

x <- c("Hello world!", "this is a TEST sentence.", "the end.")capitalize(x)capitalize(tolower(x))# Options: capitalize(x, n = 3)                  # leaves strings intactcapitalize(x, n = 3, as_text = TRUE)  # treats strings as textcapitalize(x, n = 3, upper = FALSE)   # first n in lowercase

Flip the case of characters in a string of text

Description

caseflip flips the case of all characters in a string of textx.

Usage

caseflip(x)

Arguments

x

A string of text (required).

Details

Internally,caseflip uses theletters andLETTERS constants ofbase R and thechartr function for replacing characters in strings of text.

Value

A character vector.

Examples

x <- c("Hello world!", "This is a 1st sentence.", "This is the 2nd sentence.", "The end.")caseflip(x)

cclass provides character classes (as a named vector).

Description

cclass provides different character classes (as a named character vector).

Usage

cclass

Format

An object of classcharacter of length 6.

Details

cclass allows illustrating matching character classes via regular expressions.

See?base::regex for details on regular expressions and?"'" for a list of character constants/quotes in R.

Examples

cclass["hex"]  # select by namewriteLines(cclass["pun"])grep("[[:alpha:]]", cclass, value = TRUE)

Change time and time zone (without changing time display)

Description

change_time changes the time and time zone without changing the time display.

Usage

change_time(time, tz = "")

Arguments

time

Time (as a scalar or vector).Iftime is not a local time (of the "POSIXlt" class) the function first tries coercingtime into "POSIXlt" without changing the time display.

tz

Time zone (as character string).Default:tz = "" (i.e., current system time zone,Sys.timezone()). SeeOlsonNames() for valid options.

Details

change_time expects inputs totime to be local time(s) (of the "POSIXlt" class) and a valid time zone argumenttz (as a string)and returns the same time display (but different actual times) as calendar time(s) (of the "POSIXct" class).

Value

A calendar time of class "POSIXct".

Examples

change_time(as.POSIXlt(Sys.time()), tz = "UTC")# from "POSIXlt" time:t1 <- as.POSIXlt("2020-01-01 10:20:30", tz = "Europe/Berlin")change_time(t1, "Pacific/Auckland")change_time(t1, "America/Los_Angeles")# from "POSIXct" time:tc <- as.POSIXct("2020-07-01 12:00:00", tz = "UTC")change_time(tc, "Pacific/Auckland")# from "Date":dt <- as.Date("2020-12-31", tz = "Pacific/Honolulu")change_time(dt, tz = "Pacific/Auckland")# from time "string":ts <- "2020-12-31 20:30:45"change_time(ts, tz = "America/Los_Angeles")# from other "string" times:tx <- "7:30:45"change_time(tx, tz = "Asia/Calcutta")ty <- "1:30"change_time(ty, tz = "Europe/London")# convert into local times:(l1 <- as.POSIXlt("2020-06-01 10:11:12"))change_tz(change_time(l1, "Pacific/Auckland"), tz = "UTC")change_tz(change_time(l1, "Europe/Berlin"), tz = "UTC")change_tz(change_time(l1, "America/New_York"), tz = "UTC")# with vector of "POSIXlt" times:(l2 <- as.POSIXlt("2020-12-31 23:59:55", tz = "America/Los_Angeles"))(tv <- c(l1, l2))              # uses tz of l1change_time(tv, "America/Los_Angeles")  # change time and tz

Change time zone (without changing represented time).

Description

change_tz changes the nominal time zone (i.e., the time display) without changing the actual time.

Usage

change_tz(time, tz = "")

Arguments

time

Time (as a scalar or vector).Iftime is not a calendar time (of the "POSIXct" class) the function first tries coercingtime into "POSIXct" without changing the denoted time.

tz

Time zone (as character string).Default:tz = "" (i.e., current system time zone,Sys.timezone()). SeeOlsonNames() for valid options.

Details

change_tz expects inputs totime to be calendar time(s) (of the "POSIXct" class) and a valid time zone argumenttz (as a string)and returns the same time(s) as local time(s) (of the "POSIXlt" class).

Value

A local time of class "POSIXlt".

Examples

change_tz(Sys.time(), tz = "Pacific/Auckland")change_tz(Sys.time(), tz = "Pacific/Honolulu")# from "POSIXct" time:tc <- as.POSIXct("2020-07-01 12:00:00", tz = "UTC")change_tz(tc, "Australia/Melbourne")change_tz(tc, "Europe/Berlin")change_tz(tc, "America/Los_Angeles")# from "POSIXlt" time:tl <- as.POSIXlt("2020-07-01 12:00:00", tz = "UTC")change_tz(tl, "Australia/Melbourne")change_tz(tl, "Europe/Berlin")change_tz(tl, "America/Los_Angeles")# from "Date":dt <- as.Date("2020-12-31")change_tz(dt, "Pacific/Auckland")change_tz(dt, "Pacific/Honolulu")  # Note different date!# with a vector of "POSIXct" times:t2 <- as.POSIXct("2020-12-31 23:59:55", tz = "America/Los_Angeles")tv <- c(tc, t2)tv  # Note: Both times in tz of tcchange_tz(tv, "America/Los_Angeles")

Combine character inputs`x` into a single string of text.

Description

chars_to_text combines multi-element character inputsx into a single string of text (i.e., a character object of length 1), while preserving punctuation and spaces.

Usage

chars_to_text(x, sep = "")

Arguments

x

A vector (required), typically a character vector.

sep

Character to insert between the elements of a multi-element character vector as inputx? Default:sep = "" (i.e., add nothing).

Details

chars_to_text is an inverse function oftext_to_chars.

Note that usingpaste(x, collapse = "") would remove spaces. Seecollapse_chars for a simpler alternative.

Value

A character vector (of length 1).

Examples

# (a) One string (with spaces and punctuation):t1 <- "Hello world! This is _A   TEST_. Does this work?"(cv <- unlist(strsplit(t1, split = "")))(t2 <- chars_to_text(cv))t1 == t2# (b) Multiple strings (nchar from 0 to >1):s <- c("Hi", " ", "", "there!", " ", "", "Does  THIS  work?")chars_to_text(s)# Note: Using sep argument: chars_to_text(c("Hi there!", "How are you today?"), sep = "  ")chars_to_text(1:3, sep = " | ")

Flip a fair coin (with 2 sides "H" and "T") n times

Description

coin generates a sequence of events that represent the results of flipping a fair coinn times.

Usage

coin(n = 1, events = c("H", "T"))

Arguments

n

Number of coin flips.Default:n = 1.

events

Possible outcomes (as a vector). Default:events = c("H", "T").

Details

By default, the 2 possibleevents for each flip are "H" (for "heads") and "T" (for "tails").

Examples

# Basics: coin()table(coin(n = 100))table(coin(n = 100, events = LETTERS[1:3]))# Note an oddity:coin(10, events = 8:9)  # works as expected, but coin(10, events = 9:9)  # odd: see sample() for an explanation.# Limits:coin(2:3)coin(NA)coin(0)coin(1/2)coin(3, events = "X")coin(3, events = NA)coin(NULL, NULL)

Collapse character inputs`x` into a single string.

Description

collapse_chars converts multi-element character inputsx into a single string of text (i.e., a character object of length 1), separating its elements bysep.

Usage

collapse_chars(x, sep = " ")

Arguments

x

A vector (required), typically a character vector.

sep

A character inserted as separator/delimiter between elements when collapsing multi-element strings ofx. Default:sep = " " (i.e., insert 1 space between elements).

Details

Ascollapse_chars is a wrapper aroundpaste(x, collapse = sep). It preserves spaces within the elements ofx.

The separatorsep is only used when collapsing multi-element vectors and inserted between elements.

Seechars_to_text for combining character vectors into text.

Value

A character vector (of length 1).

Examples

collapse_chars(c("Hello", "world", "!"))collapse_chars(c("_", " _ ", "  _  "), sep = "|")  # preserves spaceswriteLines(collapse_chars(c("Hello", "world", "!"), sep = "\n"))collapse_chars(1:3, sep = "")

Count the frequency of characters in a string of text

Description

count_chars provides frequency counts of the characters in a string of textx as a named numeric vector.

Usage

count_chars(x, case_sense = TRUE, rm_specials = TRUE, sort_freq = TRUE)

Arguments

x

A string of text (required).

case_sense

Boolean: Distinguish lower- vs. uppercase characters? Default:case_sense = TRUE.

rm_specials

Boolean: Remove special characters? Default:rm_specials = TRUE.

sort_freq

Boolean: Sort output by character frequency? Default:sort_freq = TRUE.

Details

Ifrm_specials = TRUE (as per default), most special (or non-word) characters are removed and not counted. (Note that this currently works without using regular expressions.)

The quantification is case-sensitive and the resulting vector is sorted by name (alphabetically) or by frequency (per default).

Value

A named numeric vector.

Examples

# Default: x <- c("Hello world!", "This is a 1st sentence.",        "This is the 2nd sentence.", "THE END.")count_chars(x)# Options: count_chars(x, case_sense = FALSE)count_chars(x, rm_specials = FALSE)count_chars(x, sort_freq = FALSE)

Count the frequency of characters and words in a string of text

Description

count_chars_words provides frequency counts of the characters and words of a string of textx on a per character basis.

Usage

count_chars_words(x, case_sense = TRUE, sep = "|", rm_sep = TRUE)

Arguments

x

A string of text (required).

case_sense

Boolean: Distinguish lower- vs. uppercase characters? Default:case_sense = TRUE.

sep

Dummy character(s) to insert between elements/lines when parsing a multi-element character vectorx as input. This character is inserted to mark word boundaries in multi-element inputsx (without punctuation at the boundary). It should NOT occur anywhere inx, so that it can be removed again (byrm_sep = TRUE). Default:sep = "|" (i.e., insert a vertical bar between lines).

rm_sep

Shouldsep be removed from output? Default:rm_sep = TRUE.

Details

count_chars_words calls bothcount_chars andcount_words and maps their results to a data frame that contains a row for each character ofx.

The quantifications are case-sensitive. Special characters (e.g., parentheses, punctuation, and spaces) are counted as characters, but removed from word counts.

If inputx consists of multiple text strings, they are collapsed with an added " " (space) between them.

Value

A data frame with 4 variables (char,char_freq,word,word_freq).

Examples

s1 <- ("This test is to test this function.")head(count_chars_words(s1))head(count_chars_words(s1, case_sense = FALSE))s3 <- c("A 1st sentence.", "The 2nd sentence.",         "A 3rd --- and also THE  FINAL --- SENTENCE.")tail(count_chars_words(s3))tail(count_chars_words(s3, case_sense = FALSE))

Count the frequency of words in a string of text

Description

count_words provides frequency counts of the words in a string of textx as a named numeric vector.

Usage

count_words(x, case_sense = TRUE, sort_freq = TRUE)

Arguments

x

A string of text (required).

case_sense

Boolean: Distinguish lower- vs. uppercase characters? Default:case_sense = TRUE.

sort_freq

Boolean: Sort output by word frequency? Default:sort_freq = TRUE.

Details

Special (or non-word) characters are removed and not counted.

The quantification is case-sensitive and the resulting vector is sorted by name (alphabetically) or by frequency (per default).

Value

A named numeric vector.

Examples

# Default: s3 <- c("A first sentence.", "The second sentence.",         "A third --- and also THE FINAL --- SENTENCE.")count_words(s3)  # case-sensitive, sorts by frequency # Options: count_words(s3, case_sense = FALSE)  # case insensitivecount_words(s3, sort_freq = FALSE)   # sorts alphabetically

Data: Names of countries

Description

countries is a dataset containing the names of 197 countries (as a vector of text strings).

Usage

countries

Format

A vector of typecharacter withlength(countries) = 197.

Source

Data fromhttps://www.gapminder.org: Original data athttps://www.gapminder.org/data/documentation/gd004/.

Get current date (in yyyy-mm-dd or dd-mm-yyyy format)

Description

cur_date provides a relaxed version ofSys.time() that is sufficient for most purposes.

Usage

cur_date(rev = FALSE, as_string = TRUE, sep = "-")

Arguments

rev

Boolean: Reverse from "yyyy-mm-dd" to "dd-mm-yyyy" format?Default:rev = FALSE.

as_string

Boolean: Return as character string? Default:as_string = TRUE. Ifas_string = FALSE, a "Date" object is returned.

sep

Character: Separator to use. Default:sep = "-".

Details

By default,cur_date returnsSys.Date as a character string (using current system settings andsep for formatting). Ifas_string = FALSE, a "Date" object is returned.

Alternatively, consider usingSys.Date orSys.time() to obtain the "format according to the ISO 8601 standard.

For more options, see the documentations of thedate andSys.Date functions ofbase R and the formatting options forSys.time().

Value

A character string or object of class "Date".

Examples

cur_date()cur_date(sep = "/")cur_date(rev = TRUE)cur_date(rev = TRUE, sep = ".")# return a "Date" object:from <- cur_date(as_string = FALSE)class(from)

Get current time (in hh:mm or hh:mm:ss format)

Description

cur_time provides a satisficing version ofSys.time() that is sufficient for most purposes.

Usage

cur_time(seconds = FALSE, as_string = TRUE, sep = ":")

Arguments

seconds

Boolean: Show time with seconds?Default:seconds = FALSE.

as_string

Boolean: Return as character string? Default:as_string = TRUE. Ifas_string = FALSE, a "POSIXct" object is returned.

sep

Character: Separator to use. Default:sep = ":".

Details

By default,cur_time returns aSys.time() as a character string (in "using current system settings. Ifas_string = FALSE, a "POSIXct" (calendar time) object is returned.

For a time zone argument, see thewhat_time function, or thenow() function of thelubridate package.

Value

A character string or object of class "POSIXct".

Examples

cur_time() cur_time(seconds = TRUE)cur_time(sep = ".")# return a "POSIXct" object:t <- cur_time(as_string = FALSE)format(t, "%T %Z")

Data import data_1.

Description

data_1 is a fictitious dataset to practice importing data(from a DELIMITED file).

Usage

data_1

Format

A table with 100 cases (rows) and 4 variables (columns).

Source

See DELIMITED data athttp://rpository.com/ds4psy/data/data_1.dat.

Data import data_2.

Description

data_2 is a fictitious dataset to practice importing data (from a FWF file).

Usage

data_2

Format

A table with 100 cases (rows) and 4 variables (columns).

Source

See FWF data athttp://rpository.com/ds4psy/data/data_2.dat.

Data table data_t1.

Description

data_t1 is a fictitious dataset to practice importing and joining data (from a CSV file).

Usage

data_t1

Format

A table with 20 cases (rows) and 4 variables (columns).

Source

See CSV data athttp://rpository.com/ds4psy/data/data_t1.csv.

Data import data_t1_de.

Description

data_t1_de is a fictitious dataset to practice importing data (from a CSV file, de/European style).

Usage

data_t1_de

Format

A table with 20 cases (rows) and 4 variables (columns).

Source

See CSV data athttp://rpository.com/ds4psy/data/data_t1_de.csv.

Data import data_t1_tab.

Description

data_t1_tab is a fictitious dataset to practice importing data (from a TAB file).

Usage

data_t1_tab

Format

A table with 20 cases (rows) and 4 variables (columns).

Source

See TAB-delimited data athttp://rpository.com/ds4psy/data/data_t1_tab.csv.

Data table data_t2.

Description

data_t2 is a fictitious dataset to practice importing and joining data (from a CSV file).

Usage

data_t2

Format

A table with 20 cases (rows) and 4 variables (columns).

Source

See CSV data athttp://rpository.com/ds4psy/data/data_t2.csv.

Data table data_t3.

Description

data_t3 is a fictitious dataset to practice importing and joining data (from a CSV file).

Usage

data_t3

Format

A table with 20 cases (rows) and 4 variables (columns).

Source

See CSV data athttp://rpository.com/ds4psy/data/data_t3.csv.

Data table data_t4.

Description

data_t4 is a fictitious dataset to practice importing and joining data (from a CSV file).

Usage

data_t4

Format

A table with 20 cases (rows) and 4 variables (columns).

Source

See CSV data athttp://rpository.com/ds4psy/data/data_t4.csv.

How many days are in a month (of given date)?

Description

days_in_month computes the number of days in the months of given dates (provided as a date or timedt, or number/string denoting a 4-digit year).

Usage

days_in_month(dt = Sys.Date(), ...)

Arguments

dt

Date or time (scalar or vector). Default:dt = Sys.Date(). Numbers or strings with dates are parsed into 4-digit numbers denoting the year.

...

Other parameters (passed toas.Date()).

Details

The function requiresdt as "Dates", rather than month names or numbers, to check for leap years (in which February has 29 days).

Value

A named (numeric) vector.

Examples

days_in_month() # Robustness: days_in_month(Sys.Date())    # Datedays_in_month(Sys.time())    # POSIXctdays_in_month("2020-07-01")  # stringdays_in_month(20200901)      # numberdays_in_month(c("2020-02-10 01:02:03", "2021-02-11", "2024-02-12"))  # vectors of strings# For leap years:ds <- as.Date("2020-02-20") + (365 * 0:4)  days_in_month(ds)  # (2020/2024 are leap years)

Convert an integer from decimal notation into a string of numeric digits in some base

Description

dec2base converts an integer from its standard decimal notation (i.e., using positional numerals with a base or radix of 10) into a sequence of numeric symbols (digits) in some other base. Seebase_digits for the sequence of default digits.

Usage

dec2base(x, base = 2)

Arguments

x

A (required) integer in decimal (base 10) notation or corresponding string of digits (i.e., digits 0-9).

base

The base or radix of the digits in the output. Default:base = 2 (binary).

Details

To prevent erroneous interpretations of numeric outputs,dec2base returns a sequence of digits (as a character string).

dec2base is the complement ofbase2dec.

Value

A character string of digits (in base notation).

Examples

# (a) single numeric input:dec2base(3)  # base = 2dec2base(8, base = 2)dec2base(8, base = 3)dec2base(8, base = 7)dec2base(100, base = 5)dec2base(100, base = 10)dec2base(100, base = 15)dec2base(14, base = 14)dec2base(15, base = 15)dec2base(16, base = 16)dec2base(15, base = 16)dec2base(31, base = 16)dec2base(47, base = 16)# (b) single string input:dec2base("7", base = 2)dec2base("8", base = 3)# Extreme values:dec2base(base2dec(rep("1", 32)))          # 32 x "1"dec2base(base2dec(c("1", rep("0", 32))))  # 2^32dec2base(base2dec(rep("1", 33)))          # 33 x "1"dec2base(base2dec(c("1", rep("0", 33))))  # 2^33# Non-standard inputs:dec2base("  ")          # only spaces: NAdec2base("?")           # no decimal digits: NAdec2base(" 10 ", 2)     # remove leading and trailing spacesdec2base("-10", 2)      # handle negative inputs (in character strings)dec2base(" -- 10", 2)   # handle multiple negationsdec2base("xy -10 ", 2)  # ignore non-decimal digit prefixes# Note: base2dec(dec2base(012340, base =  9), base =  9)dec2base(base2dec(043210, base = 11), base = 11)

Throw a fair dice (with a given number of sides) n times

Description

dice generates a sequence of events that represent the results of throwing a fair dice (with a given number ofevents or number of sides)n times.

Usage

dice(n = 1, events = 1:6)

Arguments

n

Number of dice throws. Default:n = 1.

events

Events to draw from (or number of sides).Default:events = 1:6.

Details

By default, the 6 possibleevents for each throw of the dice are the numbers from 1 to 6.

Examples

# Basics:dice()table(dice(10^4))# 5-sided dice:dice(events = 1:5)table(dice(100, events = 5))# Strange dice:dice(5, events = 8:9)table(dice(100, LETTERS[1:3]))# Note:dice(10, 1)table(dice(100, 2))# Note an oddity:dice(10, events = 8:9)  # works as expected, but dice(10, events = 9:9)  # odd: see sample() for an explanation.# Limits:dice(NA)dice(0)dice(1/2)dice(2:3)dice(5, events = NA)dice(5, events = 1/2)dice(NULL, NULL)

Throw a questionable dice (with a given number of sides) n times

Description

dice_2 is a variant ofdice that generates a sequence of events that represent the results of throwing a dice (with a given number ofsides)n times.

Usage

dice_2(n = 1, sides = 6)

Arguments

n

Number of dice throws.Default:n = 1.

sides

Number of sides.Default:sides = 6.

Details

Something is wrong with this dice. Can you examine it and measure its problems in a quantitative fashion?

Examples

# Basics:dice_2()table(dice_2(100))# 10-sided dice:dice_2(sides = 10)table(dice_2(100, sides = 10))# Note:dice_2(10, 1)table(dice_2(5000, sides = 5))# Note an oddity:dice_2(n = 10, sides = 8:9)  # works, but dice_2(n = 10, sides = 9:9)  # odd: see sample() for an explanation.

Get the difference between two dates (in human units).

Description

diff_dates computes the difference between two dates (i.e., from somefrom_date to someto_date) in human measurement units (periods).

Usage

diff_dates(  from_date,  to_date = Sys.Date(),  unit = "years",  as_character = TRUE)

Arguments

from_date

From date (required, scalar or vector, as "Date"). Date of birth (DOB), assumed to be of class "Date", and coerced into "Date" when of class "POSIXt".

to_date

To date (optional, scalar or vector, as "Date"). Default:to_date = Sys.Date(). Maximum date/date of death (DOD), assumed to be of class "Date", and coerced into "Date" when of class "POSIXt".

unit

Largest measurement unit for representing results. Units represent human time periods, rather than chronological time differences. Default:unit = "years" for completed years, months, and days. Options available:

unit = "years": completed years, months, and days (default)
unit = "months": completed months, and days
unit = "days": completed days

Units may be abbreviated.

as_character

Boolean: Return output as character? Default:as_character = TRUE. Ifas_character = FALSE, results are returned as columns of a data frame and includefrom_date andto_date.

Details

diff_dates answers questions like "How much time has elapsed between two dates?" or "How old are you?" in human time periods of (full) years, months, and days.

Key characteristics:

Ifto_date orfrom_date are not "Date" objects,diff_dates aims to coerce them into "Date" objects.
Ifto_date is missing (i.e.,NA),to_date is set to today's date (i.e.,Sys.Date()).
Ifto_date is specified, any intermittent missing values (i.e.,NA) are set to today's date (i.e.,Sys.Date()). Thus, dead people (with both birth dates and death dates specified) do not age any further, but people still alive (withis.na(to_date), are measured to today's date (i.e.,Sys.Date()).
Ifto_date precedesfrom_date (i.e.,from_date > to_date) computations are performed on swapped days and the result is marked as negative (by a character"-") in the output.
If the lengths offrom_date andto_date differ, the shorter vector is recycled to the length of the longer one.

By default,diff_dates provides output as (signed) character strings. For numeric outputs, useas_character = FALSE.

Value

A character vector or data frame (with dates, sign, and numeric columns for units).

Examples

y_100 <- Sys.Date() - (100 * 365.25) + -1:1diff_dates(y_100)# with "to_date" argument: y_050 <- Sys.Date() - (50 * 365.25) + -1:1 diff_dates(y_100, y_050)diff_dates(y_100, y_050, unit = "d") # days (with decimals)# Time unit and output format:ds_from <- as.Date("2010-01-01") + 0:2ds_to   <- as.Date("2020-03-01")  # (2020 is leap year)diff_dates(ds_from, ds_to, unit = "y", as_character = FALSE)  # yearsdiff_dates(ds_from, ds_to, unit = "m", as_character = FALSE)  # monthsdiff_dates(ds_from, ds_to, unit = "d", as_character = FALSE)  # days# Robustness:days_cur_year <- 365 + is_leap_year(Sys.Date())diff_dates(Sys.time() - (1 * (60 * 60 * 24) * days_cur_year))  # for POSIXt timesdiff_dates("10-08-11", "20-08-10")   # for stringsdiff_dates(20200228, 20200301)       # for numbers (2020 is leap year)# Recycling "to_date" to length of "from_date":y_050_2 <- Sys.Date() - (50 * 365.25)diff_dates(y_100, y_050_2)# Note maxima and minima: diff_dates("0000-01-01", "9999-12-31")  # max. d + m + ydiff_dates("1000-06-01", "1000-06-01")  # min. d + m + y# If from_date == to_date:diff_dates("2000-01-01", "2000-01-01")# If from_date > to_date:diff_dates("2000-01-02", "2000-01-01")  # Note negation "-"diff_dates("2000-02-01", "2000-01-01", as_character = TRUE)diff_dates("2001-02-02", "2000-02-02", as_character = FALSE)# Test random date samples:f_d <- sample_date(size = 10)t_d <- sample_date(size = 10)diff_dates(f_d, t_d, as_character = TRUE)# Using 'fame' data:dob <- as.Date(fame$DOB, format = "%B %d, %Y")dod <- as.Date(fame$DOD, format = "%B %d, %Y")head(diff_dates(dob, dod))  # Note: Deceased people do not age further.head(diff_dates(dob, dod, as_character = FALSE))  # numeric outputs

Get the difference between two times (in human units).

Description

diff_times computes the difference between two times (i.e., from somefrom_time to someto_time) in human measurement units (periods).

Usage

diff_times(from_time, to_time = Sys.time(), unit = "days", as_character = TRUE)

Arguments

from_time

From time (required, scalar or vector, as "POSIXct"). Origin time, assumed to be of class "POSIXct", and coerced into "POSIXct" when of class "Date" or "POSIXlt.

to_time

To time (optional, scalar or vector, as "POSIXct"). Default:to_time = Sys.time(). Maximum time, assumed to be of class "POSIXct", and coerced into "POSIXct" when of class "Date" or "POSIXlt".

unit

Largest measurement unit for representing results. Units represent human time periods, rather than chronological time differences. Default:unit = "days" for completed days, hours, minutes, and seconds. Options available:

unit = "years": completed years, months, and days (default)
unit = "months": completed months, and days
unit = "days": completed days
unit = "hours": completed hours
unit = "minutes": completed minutes
unit = "seconds": completed seconds

Units may be abbreviated.

as_character

Boolean: Return output as character? Default:as_character = TRUE. Ifas_character = FALSE, results are returned as columns of a data frame and includefrom_date andto_date.

Details

diff_times answers questions like "How much time has elapsed between two dates?" or "How old are you?" in human time periods of (full) years, months, and days.

Key characteristics:

Ifto_time orfrom_time are not "POSIXct" objects,diff_times aims to coerce them into "POSIXct" objects.
Ifto_time is missing (i.e.,NA),to_time is set to the current time (i.e.,Sys.time()).
Ifto_time is specified, any intermittent missing values (i.e.,NA) are set to the current time (i.e.,Sys.time()).
Ifto_time precedesfrom_time (i.e.,from_time > to_time) computations are performed on swapped times and the result is marked as negative (by a character"-") in the output.
If the lengths offrom_time andto_time differ, the shorter vector is recycled to the length of the longer one.

By default,diff_times provides output as (signed) character strings. For numeric outputs, useas_character = FALSE.

Value

A character vector or data frame (with times, sign, and numeric columns for units).

Examples

t1 <- as.POSIXct("1969-07-13 13:53 CET")  # (before UNIX epoch)diff_times(t1, unit = "years", as_character = TRUE)diff_times(t1, unit = "secs", as_character = TRUE)

Get the time zone difference between two times.

Description

diff_tz computes the time difference between two timest1 andt2 that is exclusively due to both times being in different time zones.

Usage

diff_tz(t1, t2, in_min = FALSE)

Arguments

t1

First time (required, as "POSIXt" time point/moment).

t2

Second time (required, as "POSIXt" time point/moment).

in_min

Return time-zone based time difference in minutes (Boolean)? Default:in_min = FALSE.

Details

diff_tz ignores all differences in nominal times, but allows adjusting time-based computations for time shifts that are due to time zone differences (e.g., different locations, or changes to/from daylight saving time, DST), rather than differences in actual times.

Internally,diff_tz determines and contrasts the POSIX conversion specifications "(in numeric form).

If the lengths oft1 andt2 differ, the shorter vector is recycled to the length of the longer one.

Value

A character (in "HH:MM" format) or numeric vector (number of minutes).

Examples

# Time zones differences:tm <- "2020-01-01 01:00:00"  # nominal timet1 <- as.POSIXct(tm, tz = "Pacific/Auckland")t2 <- as.POSIXct(tm, tz = "Europe/Berlin")t3 <- as.POSIXct(tm, tz = "Pacific/Honolulu")# as character (in "HH:MM"):diff_tz(t1, t2)diff_tz(t2, t3)diff_tz(t1, t3)# as numeric (in minutes):diff_tz(t1, t3, in_min = TRUE)# Compare local times (POSIXlt): t4 <- as.POSIXlt(Sys.time(), tz = "Pacific/Auckland")t5 <- as.POSIXlt(Sys.time(), tz = "Europe/Berlin")diff_tz(t4, t5)diff_tz(t4, t5, in_min = TRUE)# DSL shift: Spring ahead (on 2020-03-29: 02:00:00 > 03:00:00):s6 <- "2020-03-29 01:00:00 CET"   # before DSL switchs7 <- "2020-03-29 03:00:00 CEST"  # after DSL switcht6 <- as.POSIXct(s6, tz = "Europe/Berlin")  # CETt7 <- as.POSIXct(s7, tz = "Europe/Berlin")  # CESTdiff_tz(t6, t7)  # 1 hour forwarddiff_tz(t6, t7, in_min = TRUE)

Open theds4psy package's user guide.

Description

Theds4psy package currently only contains a default vignette that provides general information and links.

Usage

ds4psy.guide()

Data from 10 Danish people

Description

dt_10 contains precise DOB information of 10 non-existent, but definitely Danish people.

Usage

dt_10

Format

A table with 10 cases (rows) and 7 variables (columns).

Source

See CSV data file athttp://rpository.com/ds4psy/data/dt_10.csv.

Data from an experiment with numeracy and date-time variables

Description

exp_num_dt is a fictitious set of data describing 1000 non-existing, but surprisingly friendly people.

Usage

exp_num_dt

Format

A table with 1000 cases (rows) and 15 variables (columns).

Details

Codebook The data characterize 1000 individuals (rows) in 15 variables (columns):

1.name: Participant initials.
2.gender: Self-identified gender (as a binary variable).
3.bday: Day (within month) of DOB.
4.bmonth: Month (within year) of DOB.
5.byear: Year of DOB.
6.height: Height (in cm).
7.blood_type: Blood type.
8.bnt_1 to 11.bnt_4: Correct response to corresponding BNT question? (1: correct, 0: incorrect).
12.g_iq and 13.s_iq: Scores from two IQ tests (general vs. social).
14.t_1 and 15.t_2: Study start and end time.

exp_num_dt was generated for practice purposes. It allows (1) converting data tables from wider into longer format, (2) dealing with date- and time-related variables, and (3) computing, analyzing, and visualizing test scores (e.g., numeracy, IQ).

Thegender variable was converted into a binary variable (i.e., using 2 categories "female" and "not female").

Source

See CSV data files athttp://rpository.com/ds4psy/data/numeracy.csv andhttp://rpository.com/ds4psy/data/dt.csv.

Data exp_wide.

Description

exp_wide is a fictitious dataset to practice tidying data (here: converting from wide to long format).

Usage

exp_wide

Format

A table with 10 cases (rows) and 7 variables (columns).

Source

See CSV data athttp://rpository.com/ds4psy/data/exp_wide.csv.

Data: False Positive Psychology

Description

falsePosPsy_all is a dataset containing the data from 2 studies designed to highlight problematic research practices within psychology.

Usage

falsePosPsy_all

Format

A table with 78 cases (rows) and 19 variables (columns):

Details

Simmons, Nelson and Simonsohn (2011) published a controversial article with a necessarily false finding. By conducting simulations and 2 simple behavioral experiments, the authors show that flexibility in data collection, analysis, and reporting dramatically increases the rate of false-positive findings.

study: Study ID.
id: Participant ID.
aged: Days since participant was born (based on their self-reported birthday).
aged365: Age in years.
female: Is participant a woman? 1: yes, 2: no.
dad: Father's age (in years).
mom: Mother's age (in years).
potato: Did the participant hear the song 'Hot Potato' by The Wiggles? 1: yes, 2: no.
when64: Did the participant hear the song 'When I am 64' by The Beatles? 1: yes, 2: no.
kalimba: Did the participant hear the song 'Kalimba' by Mr. Scrub? 1: yes, 2: no.
cond: In which condition was the participant? control: Subject heard the song 'Kalimba' by Mr. Scrub; potato: Subject heard the song 'Hot Potato' by The Wiggles; 64: Subject heard the song 'When I am 64' by The Beatles.
root: Could participant report the square root of 100? 1: yes, 2: no.
bird: Imagine a restaurant you really like offered a 30 percent discount for dining between 4pm and 6pm. How likely would you be to take advantage of that offer? Scale from 1: very unlikely, 7: very likely.
political: In the political spectrum, where would you place yourself? Scale: 1: very liberal, 2: liberal, 3: centrist, 4: conservative, 5: very conservative.
quarterback: If you had to guess who was chosen the quarterback of the year in Canada last year, which of the following four options would you choose? 1: Dalton Bell, 2: Daryll Clark, 3: Jarious Jackson, 4: Frank Wilczynski.
olddays: How often have you referred to some past part of your life as “the good old days”? Scale: 11: never, 12: almost never, 13: sometimes, 14: often, 15: very often.
feelold: How old do you feel? Scale: 1: very young, 2: young, 3: neither young nor old, 4: old, 5: very old.
computer: Computers are complicated machines. Scale from 1: strongly disagree, to 5: strongly agree.
diner: Imagine you were going to a diner for dinner tonight, how much do you think you would like the food? Scale from 1: dislike extremely, to 9: like extremely.

Seehttps://bookdown.org/hneth/ds4psy/B.2-datasets-false.html for codebook and more information.

Source

Articles

Simmons, J.P., Nelson, L.D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant.Psychological Science,22(11), 1359–1366. doi:10.1177/0956797611417632
Simmons, J.P., Nelson, L.D., & Simonsohn, U. (2014). Data from paper "False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant".Journal of Open Psychology Data,2(1), e1. doi:10.5334/jopd.aa

See files athttps://openpsychologydata.metajnl.com/articles/10.5334/jopd.aa/ and the archive athttps://zenodo.org/record/7664 for original dataset.

Data: fame

Description

fame is a dataset to practice working with dates.

fame contains the names, areas, dates of birth (DOB), and — if applicable — the dates of death (DOD) of famous people.

Usage

fame

Format

A table with 67 cases (rows) and 4 variables (columns).

Source

Student solutions to exercises, dates mostly fromhttps://www.wikipedia.org/.

Data: Flowery phrases

Description

flowery contains versions and variations of Gertrude Stein's popular phrase "A rose is a rose is a rose".

Usage

flowery

Format

A vector of typecharacter withlength(flowery) = 60.

Details

The phrase stems from Gertrude Stein's poem "Sacred Emily" (written in 1913 and published in 1922, in "Geography and Plays"). The verbatim line in the poem actually reads "Rose is a rose is a rose is a rose".

Seehttps://en.wikipedia.org/wiki/Rose_is_a_rose_is_a_rose_is_a_rose for additional variations and sources.

Source

Data based onhttps://en.wikipedia.org/wiki/Rose_is_a_rose_is_a_rose_is_a_rose.

Data: Names of fruits

Description

fruits is a dataset containing the names of 122 fruits (as a vector of text strings).

Usage

fruits

Format

A vector of typecharacter withlength(fruits) = 122.

Details

Botanically, "fruits" are the seed-bearing structures of flowering plants (angiosperms) formed from the ovary after flowering.

In common usage, "fruits" refer to the fleshy seed-associated structures of a plant that taste sweet or sour, and are edible in their raw state.

Source

Data based onhttps://simple.wikipedia.org/wiki/List_of_fruits.

Get a set of x-y coordinates (from Anscombe's Quartet)

Description

get_set obtains a set of x/y coordinates and returns it (as a data frame).

Usage

get_set(n = 1)

Arguments

n

Number of set (as an integer from 1 to 4)).Default:n = 1.

Details

Each set stems from Anscombe's Quartet (seedatasets::anscombe, hence1 <= n <= 4) and is returned as an11 x 2 data frame.

Source

See?datasets:anscombe for details and references.

Examples

get_set(1)plot(get_set(2), col = "red")

Data from the i2ds online survey

Description

i2ds_survey contains pre-processed data from the i2ds online survey.

Usage

i2ds_survey

Format

On 2025-11-02, this data contains 60 participants (rows) and 116 variables (columns).

Details

Prefix codes

Many variable names have prefixes that indicate a particular type of variable:

rv: A random variable
c(#): A choice variable (with # alternatives)
t: A text variable (with any input)
tn: A text variable (with numeric input)
crs: A course-related variable
combined: A composite variable created by averaging either 4 or 5 individual Likert-scale items.Depending on the item set, the resulting score was normalized (i.e., divided by 4 or 5), and stored as a new variable.

List of variables

After pre-processing the raw data and re-arranging its variables (columns), the variable names and their contents in thei2ds_survey tibble are as follows:

Key person-related variables:c4_gender A categorical (character) variable indicating the participant’s gender identity, with possible values including "female", "male", "non-binary" or "do not wish to respond". This variable is used for demographic analysis.
tn_year A numeric (double) variable indicating the year of birth (e.g.,1999, 2000, 2001, etc.).
tn_month A numeric (double) variable indicating the participant’s birth month (1–12). This variable also supports demographic profiling.
tn_day A numeric (double) variable indicating the day of birth provided by the participant (1–31). Used for demographic purposes and potential exploratory analyses. DOB-related variables can be used to calculate age and analyze age-related trends.
t_height A character variable indicating a participant's self-described height, using various formats and units (e.g., "1.80", "180 cm", "1,80m", or "5'11"). This variable requires pre-processing for analysis.
t_pid An optional character variable capturing a participant ID, pseudonym, or other identifying entry. This variable allows participants to recognize their own data without disclosing their identity.
Variables indicating informed consent and willingness to share data:c2_informed_consent A logical variable indicating whether the participant provided informed consent before starting the study (TRUE = consent provided,FALSE = no consent provided). This variable is a pre-requisite for ethical compliance (i.e., should beTRUE for all participants).
c2_use_data_2 A logical variable indicating whether a participant still agrees to allow their data to be shared after having finished the survey (TRUE = consent provided,FALSE = no consent provided). This variable is a pre-requisite for data re-usability in research (and should beTRUE for all cases included here).
Variables indicating course membership:crs_i2ds_1 A logical variable indicating whether a participant is currently enrolled in the courseIntroduction to Data Science 1: Basics (i2ds 1:TRUE = enrolled).
crs_i2ds_2 A logical variable indicating whether a participant is enrolled in the courseIntroduction to Data Science 2: Applications (i2ds 2:TRUE = enrolled).
crs_ds4psy A logical variable indicating whether a participant is enrolled in the courseData Science for Psychology (ds4psy:TRUE = enrolled).
crs_diff_kn A logical variable indicating whether a participant is enrolled in a different course at theUniversity of Konstanz (TRUE = yes).
crs_diff_else A logical variable indicating whether a participant is enrolled in a coursenot at theUniversity of Konstanz (TRUE = yes). This variable helps identifying external learners.
crs_self_study A logical variable indicating whether a participant is engaging with course materials without formal enrollment (TRUE = yes). This variable reflects informal learning engagement.
crs_only_study A logical variable indicating whether a participant is taking the survey only, without engaging with course materials (TRUE = yes). This variable identifies participants not studying R or data science.
t_crs_other A character variable capturing free-text input describing any other course a participant is taking.
v_crs_other_dept A character variable indicating the department of the other course(s) mentioned int_crs_other. This variable may facilitate grouping participants by academic discipline.
Variables indicating (randomized) survey conditions:rv_anchor_high_low A randomized (character) variable that indicates whether a person is to keep a relatively large or small number in memory (i.e., assignment to either242 or42, respectively). This manipulation is used to examine anchoring effects on later responses.
rv_scale_randomization A randomized (character) variable that indicates whether a person was asked to rate their personality (from "serious" to "humorous") on a 4-point or on a 5-point Likert scale. The variable controls for the influence of scale granularity on ratings.
rv_barnum_pos_neg A randomized (character) variable that indicates whether the participant is to receive a positive or negative Barnum statement ("positive" vs. "negative"). This is used to measure sensitivity to vague or generic personality feedback.
rv_sc_false_dicho_3 A randomized (character) variable indicating which version of the scale is to be shown: a dichotomous comparison between admiration vs. respect, fear vs. love, admiration vs. love and fear, or a single undivided scale (values: "admir_resp" "fear_love", "admir_love" fear_resp", "single_scale"). Used to examine how scale format affects evaluative judgments.
rv_wait_time A randomized (character) variable that indicates whether the participant waited 10 seconds ("short") or 30 seconds ("long") before continuing. This manipulation aims to examine whether a longer waiting period increases the perceived credibility or value of a following personality feedback, in line with mechanisms underlying the Barnum effect.
rv_political_orientation A randomized (character) variable indicating the order in which the two political orientation scales ("left–right" and "liberal–conservative") were presented. Possible values include "left_right, lib_cons", "left_cons, lib_right", etc. This variable is used to control for potential order effects in political self-placement tasks.
rv_thinkingstyle A randomized (character) variable that indicates the order in which pairs of thinking styles are to be presented ("deliberative vs. intuitive"; "reflective vs. spontaneous";" deliberative vs. spontaneous";"reflective vs. Intuitive"). The order is counterbalanced to reduce presentation bias in self-assessment tasks.
Binary choices on art preference:c2_img_sel_1 A numeric (double) variable that represents the participant's preferred choice between 2 images in choice Set 1. The binary variable indicates the participant's image preference:
- 1 corresponds to thecubist paintingLes Baigneurs (the bathers), by Roger de La Fresnaye, 1912
- 2 corresponds to theexpressionist paintingBadende Mädchen (bathing girls), by August Macke, 1913
c2_img_sel_2 A numeric (double) variable that represents the participant's preferred choice between 2 images in choice Set 2. The binary variable indicates the participant's image preference:
- 1 corresponds to thecubist paintingLe Gouter (the taster, aka. tea time), by Jean Metzinger, 1911
- 2 corresponds to theexpressionist paintingLa petite Jeanne, by Amedeo Modigliani, 1909
c2_img_sel_3 A numeric (double) variable that represents the participant's preferred choice between 2 images in choice Set 3. The binary variable indicates the participant's image preference:
- 1 corresponds to thecubist paintingEdtaonisl Ecclesiastic (the 1st word being an acronym made by alternating the French words for 'star' and 'dance'), by Francis Picabia, 1913
- 2 corresponds to theimpressionist paintingFemme avec parasol dans un jardin (woman with parasol in a garden), by Pierre-Auguste Renoir, 1875
c2_img_sel_4 A numeric (double) variable that represents the participant's preferred choice between 2 images in choice Set 4. The binary variable indicates the participant's image preference:
- 1 corresponds to theexpressionist paintingSolitude, by Alexej von Jawlensky, 1912
- 2 corresponds to theimpressionist paintingPont dans le Jardin de Monet (bridge in Monet’s garden), by Claude Monet, 1895–96
Variables describing habits and preferences:c7_eating_habits A categorical (character) variable that indicates which dietary lifestyle an individual assigns to itself(1 = "vegetarian";2 = "omnivore";3 = "vegan";4 = "pescetarian";5 = "flexitarian";6 = "carnivore";7 = "other").
t_eating_habits_other A character variable intended to capture free-text input for other dietary descriptions; usuallyNA unless "other" was selected. May appear as logical if no responses were entered.
c7_apple A numeric (double) variable indicating how much a participant likes apples on a1-7 ranking scale (1 = highest preference,7 = lowest preference,0 if not ranked).
c7_cherry A numeric (double) variable indicating how much a participant likes cherries on a1-7 ranking scale (1 = highest preference,7 = lowest preference,0 if not ranked).
c7_broccoli A numeric (double) variable indicating how much a participant likes broccoli on a1-7 ranking scale (1 = highest preference,7 = lowest preference,0 if not ranked).
c7_asparagus A numeric (double) variable indicating how much a participant likes asparagus on a1-7 ranking scale (1 = highest preference,7 = lowest preference,0 if not ranked).
c7_spinach A numeric (double) variable indicating how much a participant likes spinach on a1-7 ranking scale (1 = highest preference,7 = lowest preference,0 if not ranked).
c7_mud A numeric (double) variable indicating how much a participant likes mud on a1-7 ranking scale (1 = highest preference,7 = lowest preference,0 if not ranked).
c7_banana A numeric (double) variable indicating how much a participant likes bananas on a1-7 ranking scale (1 = highest preference,7 = lowest preference,0 if not ranked).
Note: Variablesc7_apple toc7_banana were derived from a sorting/ranking task in which each participant sorted/ranked food items by preference. Each item was subsequently coded as a numeric value between1 and7 (0 if not ranked).
Responses to binary choice items:c2_decsleep_instant A categorical (character) variable indicating whether a participant prefers to sleep before making important decisions ("sleep") or to make them instantly ("instant").
c2_shopperson_online A categorical (character) variable indicating whether a participant prefers shopping in person ("person") or online ("online").
c2_town_city A categorical (character) variable indicating whether a participant prefers living in a town ("town") or in a city ("city").
c2_club_house A categorical (character) variable indicating whether a participant prefers to party in a club ("club") or to attend an house party ("house").
c2_hotel_camping A categorical (character) variable capturing a participant's preference for staying in a hotel ("hotel") versus going camping ("camping").
c2_photo_being A categorical (character) variable indicating whether a participant prefers photographing ("photo") or being in a moment ("being").
c2_spring_fall A categorical (character) variable indicating whether a participant prefers the spring season ("spring") or the fall/autumn season ("fall").
c2_beach_mount A categorical (character) variable reflecting whether a participant prefers the beach ("beach") or the mountains ("mount").
c2_cats_dogs A categorical (character) variable indicating preference for cats ("cats") versus dogs ("dogs").
c2_indiv_team A categorical (character) variable indicating whether a participant prefers individual ("indiv") or team sports ("team").
c2_movies_books A categorical (character) variable indicating a participant's preference for movies ("movies") or books ("books").
c2_board_video A categorical (character) variable indicating whether a participant prefers board games ("board") or video games ("video").
c2_ios_android A categorical (character) variable indicating whether a participant prefers iOS ("ios") or Android ("android") as a mobile operating system.
c2_text_voice A categorical (character) variable indicating whether a participant prefers texting ("text") or sending voice messages ("voice").
c2_cook_bake A categorical (character) variable indicating whether a participant prefers cooking ("cook") or baking ("bake").
c2_pinapple_no A categorical (character) variable that records whether a participant likes pineapple on pizza ("yes") or not ("no").
c2_ketchup_mayo A categorical (character) variable indicating whether a participant prefers ketchup ("ketchup") or mayonnaise ("mayo").
c2_coffee_tea A categorical (character) variable indicating whether a participant prefers coffee ("coffee") or tea ("tea").
c2_math_lang A categorical (character) variable indicating whether a participant prefers mathematics ("math") or language-related subjects ("lang").
c2_odd_even A categorical (character) variable indicating whether a participant prefers odd numbers ("odd") or even numbers ("even").
c3_diff_bin A categorical (character) variable indicating how difficult it was for a participant to make their previous preference decisions (items 22–41) . Response options include "yes", "a little", and "no". This item captures perceived decisional difficulty and may serve as an indicator of response certainty, thinking style, or task engagement.
Variables on political opinions:politics_left A numeric (double) variable representing the participant’s self-placement on a left–right political spectrum. Values range from1 (left) to6 (right).
politics_liberal A numeric (double) variable representing self-placement on a liberal to conservative scale, ranging from1 (liberal) to6 (conservative).
Miscellaneous estimates, choices, opinions, and preferences:tn_estimate_sun A numeric (double) variable capturing the participant’s estimate of how many times larger the sun’s diameter is compared to that of the earth. This item serves as a manipulation check for the anchoring effect, based on previously presented numeric anchors (e.g.,42 or242).
t_att_check_1 A character variable containing the participant’s open-text response to an attention check prompt ("Please type: 'I read the instructions'"). This attention check allows detecting inattentive or automated responses.
c2_fly_invisible A categorical (character) variable indicating whether the participant would prefer the superpower of flying ("fly") or becoming invisible ("invisible").
t_fly_invisible_explain A character variable where participants explain their choice between flying and invisibility. This free text answer allows for qualitative analysis of a participant's justifications and motivations.
combined_c_ser_hum_self A numeric (double) variable reflecting a participant’s self-assessment on a "serious vs. humorous" scale. The score is based on a 4-point or 5-point Likert scale, depending on random assignment. This variable is used to test how perspective (self vs. others) and scale format (presence vs. absence of a middle option) influences self-ratings.
combined_c_ser_hum_others A combined numeric (double) variable reflecting how humorous or serious participants believe others to perceive them. This score is derived from either a 4-point or 5-point scale and is used to examine the effect of perspective and scale design on perceived external ratings.
c4_chronotype A categorical (character) variable indicating whether the participant identifies as a morning person ("morning"), evening person ("evening") mid-day person ("mid-day") or a never person ("never").
tn_sleep A numeric (double) variable indicating the typical number of hours the participant typically sleeps per night.
tn_bedtime A character variable representing the participant’s usual bedtime, to be entered in 24-hour format (e.g., "22:30", "00:00").
tn_anchor_recall_1 A numeric (double) variable recording the number (either42 or242) that the participant was previously asked to memorize and later recall. It is used to test memory for the anchor manipulation.
combined_admired A combined numeric (double) variable reflecting how much a participant wants to be admired by others, rated on a1–6 Likert scale (1 = not at all,6 = very much).
combined_feared A combined numeric (double) variable reflecting how much a participant wants to be feared by others, rated on a1–6 Likert scale (1 = not at all,6 = very much).
combined_loved A combined numeric (double) variable reflecting how much a participant wants to be loved by others, rated on a1–6 Likert scale (1 = not at all,6 = very much).
combined_respected A combined numeric (double) variable reflecting how much a participant wants to be respected by others, rated on a1–6 Likert scale (1 = not at all,6 = very much).
c7_pess_opti A numeric (double) variable capturing a participant’s self-rated tendency toward pessimism versus optimism, on a 7-point scale (1 = very pessimistic,7 = very optimistic).
c7_story_list A numeric (double) variable indicating how much a participant enjoys listening to or reading stories, rated from1 (not at all) to7 (very much).
c7_stab_adv A numeric (double) variable indicating a participant’s self-assessed position on a stability versus adventurousness spectrum, rated on a scale from1 (very stable) to7 (very adventurous). This variable may indicate personality traits related to risk-taking.
think_reflect A numeric (double) variable representing a participant’s placement on a bipolar scale ranging from1 ("reflective") to6 (either "spontaneous" or " intuitive"). The specific version of the 2nd scale anchor is randomly assigned.
think_delib A numeric (double) variable representing a participant’s placement on a bipolar scale ranging from1 ("deliberative") to6 (either "intuitive" or " spontaneous". The specific version of the 2nd scale anchor is randomly assigned.
c4_intro_extrovert A categorical (character) variable indicating a participant's self-rated social orientation: "introverted", "extroverted", or mixed variants such as "extro-intro" or "intro-extro".
tn_favorit_number A numeric (double) variable capturing a participant’s favorite number, in free answer format.
c3_cutlery A categorical (character) variable indicating which piece of cutlery a participant most identifies with. The 3 possible values include "knife", "fork", and "spoon".
c3_rock_paper_scissors A categorical (character) variable capturing a participant's selection in a rock–paper–scissors scenario. The 3 possible values are "rock", "paper", or "scissors".
c5_att_check_2 A numeric (double) variable used as an attention check. Participants were asked to select the number that most resembles the shape of a circle. The correct response is0, which corresponds to scale option 5. Responses deviating from this may indicate inattentiveness.
c6_barnum_accuracy A numeric (double) variable indicating how accurately a participant rated a generic personality description (i.e., a Barnum statement), on a scale from1 (poor) to6 (perfect). This variable is used to assess susceptibility to the so-calledBarnum effect (i.e., the tendency to perceive vague and general statements as highly accurate).
t_anchor_recall_2 A numeric (double) variable recording whether a participant correctly remembered a previously presented number (either42 or242). This assesses memory and anchoring manipulation success (for a 2nd time).
Other person-related variables:c9_occupation A categorical (character) variable indicating a participant’s current occupational status (e.g., "student", "employed", "other"). This variable may be used for demographic segmentation.
t_occupation_other A logical variable for free-text input if a participant selected "other" for occupation. This variable captures detailed occupational descriptions not covered by the pre-defined options.
c7_education A categorical (character) variable indicating a participant’s highest completed education level (e.g., "high school", "bachelor", "master"). This variable may be used for demographic segmentation.
t_education_other A logical variable to allow participants to enter their education level in free text (if "other" was selected). This variable enables open-format responses for less common education paths.
c3_current_degree A categorical (character) variable indicating the type of academic degree a participant is currently pursuing (e.g, "bachelor", "master"). This variable provides educational context for other academic measures.
tn_semester A numeric (double) variable indicating the current semester of study reported by a participant (e.g., 1, 6, 10). This variable helps contextualize course experience and academic progress.
c14_studyfield A categorical (character) variable indicating the participant’s field of study (e.g., "psychology", "data science"). This variable is used to examine field-specific attitudes and skills.
t_studyfield_other A character variable capturing free-text responses if the participant selected "other" as their study field. This variable allows classification of less common disciplines.
Preferences for course contents:c5_pref_stats A numeric (double) variable indicating a participant’s interest in preparing data for statistical analysis, rated on a scale from1 (no interest) to5 (absolutely essential).
c5_pref_visualize A numeric (double) variable indicating a participant's interest in data visualization in R, rated on a scale from1 (no interest) to5 (absolutely essential).
c5_pref_sims A numeric (double) variable indicating a participant’s interest in using R for simulations and modeling, rated on a scale from1 (no interest) to5 (absolutely essential).
c5_pref_shiny A numeric (double) variable capturing how essential a participant considers learning to build interactive web applications using R Shiny. Responses range from1 (no interest) to5 (absolutely essential).
c5_pref_scrape A numeric (double) variable capturing how essential a participant considers learning web scraping with R. Responses range from1 (no interest) to5 (absolutely essential).
c5_pref_arts A numeric (double) variable capturing how essential a participant considers exploring artistic or creative aspects of data science (e.g., generative art in R). Responses range from1 (no interest) to5 (absolutely essential).
Course-related expectations and worries:t_crs_expect_i2ds_1 A character variable containing free-text input describing a participant’s expectations and hopes for the courseIntroduction to Data Science 1: Basics (i2ds 1).
t_crs_worry_i2ds_1 A character variable capturing free-text responses describing a participant’s worries and reservations related to the courseIntroduction to Data Science 1: Basics (i2ds 1).
t_crs_expect_i2ds_2 A character variable containing free-text input describing a participant’s expectations and hopes for the courseIntroduction to Data Science 2: Applications (i2ds 2).
t_crs_worry_i2ds_2 A character variable capturing free-text input describing a participant’s worries and reservations concerns related to the courseIntroduction to Data Science 2: Applications (i2ds 2).
t_crs_expect_ds4psy A logical variable containing free-text input describing a participant’s expectations and hopes for the courseData Science for Psychology (ds4psy).
t_crs_worry_ds4psy A logical variable describing a participant’s worries and reservations regarding the courseData Science for Psychology (ds4psy), in free text format.
Variables on prior experience:c6_exp_math A numeric (double) variable indicating a participant’s self-assessed experience with mathematics, rated on a scale from1 (no experience) to6 (extremely experienced).
c6_exp_statistics A numeric (double) variable measuring a participant’s self-assessed experience with statistics, rated on a scale from1 (no experience) to6 (extremely experienced).
c6_exp_program A numeric (double) variable indicating a participant’s experience with programming (any programming language), rated on a scale from1 (no experience) to6 (extremely experienced).
c6_exp_r A numeric (double) variable indicating a participant’s experience with R programming, rated on a scale from1 (no experience) to6 (extremely experienced).
c6_exp_datavisual A numeric (double) variable capturing a participant’s prior experience with data visualization, rated on a scale from1 (no experience) to6 (extremely experienced).
Survey feedback:t_feedback An optional character variable containing general feedback provided by the participant regarding the survey or course. This is an open-ended text field for final comments, impressions, or suggestions.
Session info:referer URL of referring page.
datetime Date and time of initial survey access.
duration Session duration (in seconds).
date_of_last_access Date and time of final survey access.

See thecodebook andprint version for additional coding details.

Missing values are represented asNA values in the data. These can be due to a participant not providing a response to an item or to an item not being applicable to this participant.

Source

See online survey athttps://ww3.unipark.de/uc/i2ds_survey/.

invert_rules inverts a set of encoding rules.

Description

invert_rules allows decoding messages that were encoded by a set of rulesx.

Usage

invert_rules(x)

Arguments

x

The rules used for encoding a message(as a named vector).

Details

x is assumed to be a named vector.

invert_rules replaces the elements ofx by the names ofx, and vice versa.

A message is issued if the elements ofx are repeated (i.e., decoding is non-unique).

Value

A character vector.

Examples

invert_rules(l33t_rul35)  # Note repeated elements# Encoding and decoding a message:(txt_0 <- "Hello world! How are you doing today?")             # message(txt_1 <- transl33t(txt_0, rules = l33t_rul35))                # encoding (txt_2 <- transl33t(txt_1, rules = invert_rules(l33t_rul35)))  # decoding

Test two vectors for pairwise (near) equality

Description

is_equal tests if two vectorsx andy are pairwise equal.

Usage

is_equal(x, y, ...)

Arguments

x

1st vector to compare (required).

y

2nd vector to compare (required).

...

Other parameters (passed tonum_equal()).

Details

If bothx andy are numeric,is_equal callsnum_equal(x, y, ...) (allowing for a tolerance thresholdtol). Otherwise,x andy are compared byx == y.

is_equal provides a wrapper aroundnum_equal (for numeric objectsx andy) and== (otherwise).

Examples

# numeric data: is_equal(2, sqrt(2)^2)is_equal(2, sqrt(2)^2, tol = 0)is_equal(c(2, 3), c(sqrt(2)^2, sqrt(3)^2, 4/2, 9/3))# other data types:is_equal((1:3 > 1), (1:3 > 2))                         # logicalis_equal(c("A", "B", "c"), toupper(c("a", "b", "c")))  # characteris_equal(as.Date("2023-10-30"), Sys.Date())            # dates# factors:is_equal((1:3 > 1), as.factor((1:3 > 2)))  is_equal(c(1, 2, 3), as.factor(c(1, 2, 3)))is_equal(c("A", "B", "C"), as.factor(c("A", "B", "C")))

Is some year a so-called leap year?

Description

is_leap_year checks whether a given year (provided as a date or timedt, or number/string denoting a 4-digit year) lies in a so-called leap year (i.e., a year containing a date of Feb-29).

Usage

is_leap_year(dt)

Arguments

dt

Date or time (scalar or vector). Numbers or strings with dates are parsed into 4-digit numbers denoting the year.

Details

Whendt is not recognized as "Date" or "POSIXt" object(s),is_leap_year aims to parse a stringdt as describing year(s) in a "dddd" (4-digit year) format, as a valid "Date" string (to retrieve the 4-digit year "%Y"), or a numericdt as 4-digit integer(s).

is_leap_year then solves the task by verifying the numeric definition of a "leap year" (seehttps://en.wikipedia.org/wiki/Leap_year).

An alternative solution that tried usingas.Date() for defining a "Date" of Feb-29 in the corresponding year(s) was removed, as it evaluatedNA values asFALSE.

Value

Boolean vector.

Source

Seehttps://en.wikipedia.org/wiki/Leap_year for definition.

Examples

is_leap_year(2020)(days_this_year <- 365 + is_leap_year(Sys.Date()))# from dates:is_leap_year(Sys.Date())is_leap_year(as.Date("2022-02-28"))# from times:is_leap_year(Sys.time())is_leap_year(as.POSIXct("2022-10-11 10:11:12"))is_leap_year(as.POSIXlt("2022-10-11 10:11:12"))# from non-integers:is_leap_year(2019.5)# For vectors:is_leap_year(2020:2028)# with dt as strings:is_leap_year(c("2020", "2021"))is_leap_year(c("2020-02-29 01:02:03", "2021-02-28 01:02"))# Note: Invalid date string yields error: # is_leap_year("2021-02-29")

Test for a vector (i.e., atomic vector or list).

Description

is_vect tests ifx is a vector.

Usage

is_vect(x)

Arguments

x

Vector(s) to test (required).

Details

is_vect does what thebase R functionis.vector isnot designed to do:

is_vect() returns TRUE ifx is an atomic vector or a list (irrespective of its attributes).
is.vector() returns TRUE ifx is a vector of the specifiedmode having no attributes other than names, otherwise FALSE.

Internally, the function is a wrapper foris.atomic(x) | is.list(x).

Note that data frames are also vectors.

See theis_vector function of thepurrr package and thebase R functionsis.atomic,is.list, andis.vector, for details.

Examples

# Define 3 types of vectors:v1 <- 1:3  # (a) atomic vectornames(v1) <- LETTERS[v1]  # with namesv2 <- v1   # (b) copy vectorattr(v2, "my_attr") <- "foo"  # add an attributels <- list(1, 2, "C")  # (c) list# Compare:is.vector(v1)is.list(v1)is_vect(v1)is.vector(v2)  # FALSEis.list(v2)is_vect(v2)  # TRUEis.vector(ls)is.list(ls)is_vect(ls)# Data frames are also vectors: df <- as.data.frame(1:3)is_vect(df)  # is TRUE

Test for whole numbers (i.e., integers)

Description

is_wholenumber tests ifx contains only integer numbers.

Usage

is_wholenumber(x, tol = .Machine$double.eps^0.5)

Arguments

x

Number(s) to test (required, accepts numeric vectors).

tol

Numeric tolerance value.Default:tol = .Machine$double.eps^0.5 (see?.Machine for details).

Details

is_wholenumber does what thebase R functionis.integer isnot designed to do:

is_wholenumber() returns TRUE or FALSE depending on whether its numeric argumentx is an integer value (i.e., a "whole" number).
is.integer() returns TRUE or FALSE depending on whether its argument is of integer type, and FALSE if its argument is a factor.

See the documentation ofis.integer for definition and details.

Examples

is_wholenumber(1)    # is TRUEis_wholenumber(1/2)  # is FALSEx <- seq(1, 2, by = 0.5)is_wholenumber(x)# Compare:is.integer(1+2) is_wholenumber(1+2)

Rules for translating text into leet/l33t slang

Description

l33t_rul35 specifies rules for translating characters into other characters (typically symbols) to mimic leet/l33t slang (as a named character vector).

Usage

l33t_rul35

Format

An object of classcharacter of length 13.

Details

Old (i.e., to be replaced) characters arepaste(names(l33t_rul35), collapse = "").

New (i.e., replaced) characters arepaste(l33t_rul35, collapse = "").

Seehttps://en.wikipedia.org/wiki/Leet for details.

Generate a grid of x-y coordinates.

Description

make_grid generates a grid of x/y coordinates and returns it (as a data frame).

Usage

make_grid(x_min = 0, x_max = 2, y_min = 0, y_max = 1)

Arguments

x_min

Minimum x coordinate.Default:x_min = 0.

x_max

Maximum x coordinate.Default:x_max = 2.

y_min

Minimum y coordinate.Default:y_min = 0.

y_max

Maximum y coordinate.Default:y_max = 1.

Examples

make_grid()make_grid(x_min = -3, x_max = 3, y_min = -2, y_max = 2)

map_text_chars maps the characters of a text string into a table (with x/y coordinates).

Description

map_text_chars parses text (from a text stringx) into a table that contains a row for each character and x/y-coordinates corresponding to the character positions inx.

Usage

map_text_chars(x, flip_y = FALSE)

Arguments

x

The text string(s) to map (required). Iflength(x) > 1, elements are mapped to different lines (i.e., y-coordinates).

flip_y

Boolean: Should y-coordinates be flipped, so that the lowest line in the text file becomesy = 1, and the top line in the text file becomesy = n_lines? Default:flip_y = FALSE.

Details

map_text_chars creates a data frame with 3 variables: Each character'sx- andy-coordinates (from top to bottom) and a variablechar for the character at these coordinates.

Note thatmap_text_chars was originally a part ofread_ascii, but has been separated to enable independent access to separate functionalities.

Note thatmap_text_chars is replaced by the simplermap_text_coord function.

Value

A data frame with 3 variables: Each character'sx- andy-coordinates (from top to bottom) and a variablechar for the character at this coordinate.

map_text_coord maps the characters of a text string into a table (with x/y-coordinates).

Description

map_text_coord parses text (from a text stringx) into a table that contains a row for each character and x/y-coordinates corresponding to the character positions inx.

Usage

map_text_coord(x, flip_y = FALSE, sep = "")

Arguments

x

The text string(s) to map (required). Iflength(x) > 1, elements are mapped to different lines (i.e., y-coordinates).

flip_y

Boolean: Should y-coordinates be flipped, so that the lowest line in the text file becomesy = 1, and the top line in the text file becomesy = n_lines? Default:flip_y = FALSE.

sep

Character to insert between the elements of a multi-element character vector as inputx? Default:sep = "" (i.e., add nothing).

Details

map_text_coord creates a data frame with 3 variables: Each character'sx- andy-coordinates (from top to bottom) and a variablechar for the character at these coordinates.

Note thatmap_text_coord was originally a part ofread_ascii, but has been separated to enable independent access to separate functionalities.

Value

A data frame with 3 variables: Each character'sx- andy-coordinates (from top to bottom) and a variablechar for the character at this coordinate.

Examples

map_text_coord("Hello world!")             # 1 line of textmap_text_coord(c("Hello", "world!"))       # 2 lines of textmap_text_coord(c("Hello", " ", "world!"))  # 3 lines of text ## Read text from file:## Create a temporary file "test.txt":# cat("Hello world!", "This is a test.",#     "Can you see this text?", "Good! Please carry on...",#      file = "test.txt", sep = "\n") # txt <- read_ascii("test.txt")# map_text_coord(txt)# unlink("test.txt")  # clean up (by deleting file).

Map text to character table (allowing for matching patterns)

Description

map_text_regex parses text (from a file or user input) into a data frame that contains a row for each character ofx.

Usage

map_text_regex(  x = NA,  file = "",  lbl_hi = NA,  lbl_lo = NA,  bg_hi = NA,  bg_lo = "[[:space:]]",  lbl_rotate = NA,  case_sense = TRUE,  lbl_tiles = TRUE,  col_lbl = "black",  col_lbl_hi = pal_ds4psy[[1]],  col_lbl_lo = pal_ds4psy[[9]],  col_bg = pal_ds4psy[[7]],  col_bg_hi = pal_ds4psy[[4]],  col_bg_lo = "white",  col_sample = FALSE,  rseed = NA,  angle_fg = c(-90, 90),  angle_bg = 0)

Arguments

x

The text to map or plot (as a character vector). Different elements denote different lines of text. Ifx = NA (as per default), thefile argument is used to read a text file or user input from the Console.

file

A text file to read (or its path). Iffile = "" (as per default),scan is used to read user input from the Console. If a text file is stored in a sub-directory, enter its path and name here (without any leading or trailing "." or "/").

lbl_hi

Labels to highlight (as regex). Default:lbl_hi = NA.

lbl_lo

Labels to de-emphasize (as regex). Default:lbl_lo = NA.

bg_hi

Background tiles to highlight (as regex). Default:bg_hi = NA.

bg_lo

Background tiles to de-emphasize (as regex). Default:bg_lo = "[[:space:]]".

lbl_rotate

Labels to rotate (as regex). Default:lbl_rotate = NA.

case_sense

Boolean: Distinguish lower- vs. uppercase characters in pattern matches? Default:case_sense = TRUE.

lbl_tiles

Are character labels shown? This enables pattern matching for (fg) color and angle aesthetics. Default:lbl_tiles = TRUE (i.e., show labels).

col_lbl

Default color of text labels.Default:col_lbl = "black".

col_lbl_hi

Highlighting color of text labels.Default:col_lbl_hi = pal_ds4psy[[1]].

col_lbl_lo

De-emphasizing color of text labels.Default:col_lbl_lo = pal_ds4psy[[9]].

col_bg

Default color to fill background tiles.Default:col_bg = pal_ds4psy[[7]].

col_bg_hi

Highlighting color to fill background tiles.Default:col_bg_hi = pal_ds4psy[[4]].

col_bg_lo

De-emphasizing color to fill background tiles.Default:col_bg_lo = "white".

col_sample

Boolean: Sample color vectors (within category)?Default:col_sample = FALSE.

rseed

Random seed (number).Default:rseed = NA (using random seed).

angle_fg

Angle(s) for rotating character labels matching the pattern of thelbl_rotate expression. Default:angle_fg = c(-90, 90). Iflength(angle_fg) > 1, a random value in uniformrange(angle_fg) is used for every character.

angle_bg

Angle(s) of rotating character labels not matching the pattern of thelbl_rotate expression. Default:angle_bg = 0 (i.e., no rotation). Iflength(angle_bg) > 1, a random value in uniformrange(angle_bg) is used for every character.

Details

map_text_regex allows for regular expression (regex) to match text patterns and create corresponding variables (e.g., for color or orientation).

Five regular expressions and corresponding color and angle arguments allow identifying, marking (highlighting or de-emphasizing), and rotating those sets of characters (i.e, their text labels or fill colors).that match the provided patterns.

The plot generated byplot_chars is character-based: Individual characters are plotted at equidistant x-y-positions and the aesthetic settings provided for text labels and tile fill colors.

map_text_regex returns a plot description (as a data frame). Using this output as an input toplot_charmap plots text in a character-based fashion (i.e., individual characters are plotted at equidistant x-y-positions). Together, both functions replace the over-specializedplot_chars andplot_text functions.

Value

A data frame describing a plot.

Examples

## (1) From text string(s): ts <- c("Hello world!", "This is a test to test this splendid function",        "Does this work?", "That's good.", "Please carry on.")sum(nchar(ts))# (a) simple use:map_text_regex(ts) # (b) matching patterns (regex):map_text_regex(ts, lbl_hi = "\\b\\w{4}\\b", bg_hi = "[good|test]",               lbl_rotate = "[^aeiou]", angle_fg = c(-45, +45))## (2) From user input:# map_text_regex()  # (enter text in Console) ## (3) From text file:# cat("Hello world!", "This is a test file.",#     "Can you see this text?",#     "Good! Please carry on...",#     file = "test.txt", sep = "\n")# # map_text_regex(file = "test.txt")  # default# map_text_regex(file = "test.txt", lbl_hi = "[[:upper:]]", lbl_lo = "[[:punct:]]",#                col_lbl_hi = "red", col_lbl_lo = "blue")# # map_text_regex(file = "test.txt", lbl_hi = "[aeiou]", col_lbl_hi = "red",#                col_bg = "white", bg_hi = "see")  # mark vowels and "see" (in bg)# map_text_regex(file = "test.txt", bg_hi = "[aeiou]", col_bg_hi = "gold")  # mark (bg of) vowels# # # Label options:# map_text_regex(file = "test.txt", bg_hi = "see", lbl_tiles = FALSE)# map_text_regex(file = "test.txt", angle_bg = c(-20, 20))# # unlink("test.txt")  # clean up (by deleting file).

metachar provides metacharacters (as a character vector).

Description

metachar provides the metacharacters of extended regular expressions (as a character vector).

Usage

metachar

Format

An object of classcharacter of length 12.

Details

metachar allows illustrating the notion of meta-characters in regular expressions (and provides corresponding exemplars).

See?base::regex for details on regular expressions and?"'" for a list of character constants/quotes in R.

Examples

metacharlength(metachar)  # 12nchar(paste0(metachar, collapse = ""))  # 12

Convert a number into a character sequence

Description

num_as_char converts a number into a character sequence (of a specific length).

Usage

num_as_char(x, n_pre_dec = 2, n_dec = 2, sym = "0", sep = ".")

Arguments

x

Number(s) to convert (required, accepts numeric vectors).

n_pre_dec

Number of digits before the decimal separator. Default:n_pre_dec = 2. This value is used to add zeros to the front of numbers. If the number of meaningful digits prior to decimal separator is greater thann_pre_dec, this value is ignored.

n_dec

Number of digits after the decimal separator. Default:n_dec = 2.

sym

Symbol to add to front or back. Default:sym = 0. Usingsym = " " orsym = "_" can make sense, digits other than"0" do not.

sep

Decimal separator to use.Default:sep = ".".

Details

The argumentsn_pre_dec andn_dec set a number of desired digits before and after the decimal separatorsep.num_as_char tries to meet these digit numbers by adding zeros to the front and end ofx. However, whenn_pre_dec is lower than the number of relevant (pre-decimal) digits, all relevant digits are shown.

n_pre_dec also works for negative numbers, but the minus symbol is not counted as a (pre-decimal) digit.

Caveat: Note that this function illustrates how numbers, characters,for loops, andpaste() can be combined when writing functions. It is not written efficiently or well.

Examples

num_as_char(1)num_as_char(10/3)num_as_char(1000/6) # rounding down:num_as_char((1.3333), n_pre_dec = 0, n_dec = 0)num_as_char((1.3333), n_pre_dec = 2, n_dec = 0)num_as_char((1.3333), n_pre_dec = 2, n_dec = 1)# rounding up: num_as_char(1.6666, n_pre_dec = 1, n_dec = 0)num_as_char(1.6666, n_pre_dec = 1, n_dec = 1)num_as_char(1.6666, n_pre_dec = 2, n_dec = 2)num_as_char(1.6666, n_pre_dec = 2, n_dec = 3)# Note: If n_pre_dec is too small, actual number is kept:num_as_char(11.33, n_pre_dec = 0, n_dec = 1)num_as_char(11.66, n_pre_dec = 1, n_dec = 1)# Note:num_as_char(1, sep = ",")num_as_char(2, sym = " ")num_as_char(3, sym = " ", n_dec = 0)# for vectors:num_as_char(1:10/1, n_pre_dec = 1, n_dec = 1)num_as_char(1:10/3, n_pre_dec = 2, n_dec = 2)# for negative numbers (adding relevant pre-decimals):mix <- c(10.33, -10.33, 10.66, -10.66)num_as_char(mix, n_pre_dec = 1, n_dec = 1)num_as_char(mix, n_pre_dec = 1, n_dec = 0)# Beware of bad inputs:num_as_char(4, sym = "8")num_as_char(5, sym = "99")

Convert a number into an ordinal character sequence

Description

num_as_ordinal converts a given (cardinal) number into an ordinal character sequence.

Usage

num_as_ordinal(x, sep = "")

Arguments

x

Number(s) to convert (required, scalar or vector).

sep

Decimal separator to use. Default:sep = "" (i.e., no separator).

Details

The function currently only works for the English language and does not accepts inputs that are characters, dates, or times.

Note that thetoOrdinal() function of thetoOrdinal package works for multiple languages and provides atoOrdinalDate() function.

Caveat: Note that this function illustrates how numbers, characters,for loops, andpaste() can be combined when writing functions. It is instructive, but not written efficiently or well (see the function definition for an alternative solution using vector indexing).

Examples

num_as_ordinal(1:4)num_as_ordinal(10:14)    # all with "th"num_as_ordinal(110:114)  # all with "th"num_as_ordinal(120:124)  # 4 different suffixesnum_as_ordinal(1:15, sep = "-")  # using sep# Note special cases:num_as_ordinal(NA)num_as_ordinal("1")num_as_ordinal(Sys.Date())num_as_ordinal(Sys.time())num_as_ordinal(seq(1.99, 2.14, by = .01))

Test two numeric vectors for pairwise (near) equality

Description

num_equal tests if two numeric vectorsx andy are pairwise equal (within a tolerance value 'tol').

Usage

num_equal(x, y, tol = .Machine$double.eps^0.5)

Arguments

x

1st numeric vector to compare (required, assumes a numeric vector).

y

2nd numeric vector to compare (required, assumes a numeric vector).

tol

Numeric tolerance value.Default:tol = .Machine$double.eps^0.5 (see?.Machine for details).

Details

num_equal verifies thatx andy are numeric and then evaluatesabs(x - y) < tol. Thus,num_equal provides a safer way to verify the (near) equality of numeric vectors than== (due to possible floating point effects).

Examples

num_equal(2, sqrt(2)^2)# Recycling: num_equal(c(2, 3), c(sqrt(2)^2, sqrt(3)^2, 4/2, 9/3))# Contrast:.1 == .3/3num_equal(.1, .3/3)# Contrast:v <- c(.9 - .8, .8 - .7, .7 - .6, .6 - .5,        .5 - .4, .4 - .3, .3 - .2, .2 -.1, .1)unique(v).1 == vnum_equal(.1, v)

Outlier data.

Description

outliers is a fictitious dataset containing the id, sex, and height of 1000 non-existing, but otherwise normal people.

Usage

outliers

Format

A table with 100 cases (rows) and 3 variables (columns).

Details

Codebook

id: Participant ID (as character code)
sex: Gender (female vs. male)
height: Height (in cm)

Source

See CSV data athttp://rpository.com/ds4psy/data/out.csv.

ds4psy default color palette.

Description

pal_ds4psy provides a dedicated color palette.

Usage

pal_ds4psy

Format

An object of classdata.frame with 1 rows and 11 columns.

Details

By default,pal_ds4psy is based onpal_unikn of theunikn package.

Get n-by-n dedicated colors of a color palette

Description

pal_n_sq returnsn^2 dedicated colors of a color palettepal (up to a maximum ofn = "all" colors).

Usage

pal_n_sq(n = "all", pal = pal_ds4psy)

Arguments

n

The desired number colors of pal (as a number) or the character string"all" (to get all colors ofpal). Default:n = "all".

pal

A color palette (as a data frame). Default:pal =pal_ds4psy.

Details

Use the more specialized functionunikn::usecol for choosingn dedicated colors of a known color palette.

Examples

pal_n_sq(1)  #  1 color: seeblau3pal_n_sq(2)  #  4 colorspal_n_sq(3)  #  9 colors (5: white)pal_n_sq(4)  # 11 colors (6: white)

Data: 100k digits of pi.

Description

pi_100k is a dataset containing the first 100k digits of pi.

Usage

pi_100k

Format

A character ofnchar(pi_100k) = 100001.

Source

See TXT data athttp://rpository.com/ds4psy/data/pi_100k.txt.

Original data athttp://www.geom.uiuc.edu/~huberty/math5337/groupe/digits.html.

Plot a character map as a tile plot with text labels

Description

plot_charmap plots a character map and some aesthetics as a tile plot with text labels (usingggplot2).

Usage

plot_charmap(  x = NA,  file = "",  lbl_tiles = TRUE,  col_lbl = "black",  angle = 0,  cex = 3,  fontface = 1,  family = "sans",  col_bg = "grey80",  borders = FALSE,  border_col = "white",  border_size = 0.5)

Arguments

x

A character map, as generated bymap_text_coord ormap_text_regex (as df). Alternatively, some text to map or plot (as a character vector). Different elements denote different lines of text. Ifx = NA (as per default), thefile argument is used to read a text file or user input from the Console.

file

lbl_tiles

Add character labels to tiles? Default:lbl_tiles = TRUE (i.e., show labels).

col_lbl

Default color of text labels (unless specified as a columncol_fg ofx).Default:col_lbl = "black".

angle

Default angle of text labels (unless specified as a column ofx). Default:angle = 0.

cex

Character size (numeric). Default:cex = 3.

fontface

Font face of text labels (numeric). Default:fontface = 1, (from 1 to 4).

family

Font family of text labels (name).Default:family = "sans". Alternative options: "sans", "serif", or "mono".

col_bg

Default color to fill background tiles (unless specified as a columncol_bg ofx).Default:col_bg = "grey80".

borders

Boolean: Add borders to tiles? Default:borders = FALSE (i.e., no borders).

border_col

Color of tile borders. Default:border_col = "white".

border_size

Size of tile borders. Default:border_size = 0.5.

Details

plot_charmap is based onplot_chars. As it only contains the plotting-related parts, it assumes a character map generated bymap_text_regex as input.

The plot generated byplot_charmap is character-based: Individual characters are plotted at equidistant x-y-positions and aesthetic variables are used for text labels and tile fill colors.

Value

A plot generated byggplot2.

Examples

# (0) Prepare: ts <- c("Hello world!", "This is a test to test this splendid function",         "Does this work?", "That's good.", "Please carry on.")sum(nchar(ts))  # (1) From character map:# (a) simple: cm_1 <- map_text_coord(x = ts, flip_y = TRUE)plot_charmap(cm_1)# (b) pattern matching (regex): cm_2 <- map_text_regex(ts, lbl_hi = "\\b\\w{4}\\b", bg_hi = "[good|test]",                        lbl_rotate = "[^aeiou]", angle_fg = c(-45, +45))plot_charmap(cm_2)                      # (2) Alternative inputs:     # (a) From text string(s):plot_charmap(ts)# (b) From user input:# plot_charmap()  # (enter text in Console) # (c) From text file:# cat("Hello world!", "This is a test file.",#      "Can you see this text?",#      "Good! Please carry on...",#      file = "test.txt", sep = "\n")# plot_charmap(file = "test.txt")# unlink("test.txt")  # clean up (by deleting file).

Plot text characters (from file or user input) and match patterns

Description

plot_chars parses text (from a file or user input) into a table and then plots its individual characters as a tile plot (usingggplot2).

Usage

plot_chars(  x = NA,  file = "",  lbl_hi = NA,  lbl_lo = NA,  bg_hi = NA,  bg_lo = "[[:space:]]",  lbl_rotate = NA,  case_sense = TRUE,  lbl_tiles = TRUE,  angle_fg = c(-90, 90),  angle_bg = 0,  col_lbl = "black",  col_lbl_hi = pal_ds4psy[[1]],  col_lbl_lo = pal_ds4psy[[9]],  col_bg = pal_ds4psy[[7]],  col_bg_hi = pal_ds4psy[[4]],  col_bg_lo = "white",  col_sample = FALSE,  rseed = NA,  cex = 3,  fontface = 1,  family = "sans",  borders = FALSE,  border_col = "white",  border_size = 0.5)

Arguments

x

The text to plot (as a character vector). Different elements denote different lines of text. Ifx = NA (as per default), thefile argument is used to read a text file or user input from the Console.

file

lbl_hi

Labels to highlight (as regex). Default:lbl_hi = NA.

lbl_lo

Labels to de-emphasize (as regex). Default:lbl_lo = NA.

bg_hi

Background tiles to highlight (as regex). Default:bg_hi = NA.

bg_lo

Background tiles to de-emphasize (as regex). Default:bg_lo = "[[:space:]]".

lbl_rotate

Labels to rotate (as regex). Default:lbl_rotate = NA.

case_sense

Boolean: Distinguish lower- vs. uppercase characters in pattern matches? Default:case_sense = TRUE.

lbl_tiles

Add character labels to tiles? Default:lbl_tiles = TRUE (i.e., show labels).

angle_fg

angle_bg

col_lbl

Default color of text labels.Default:col_lbl = "black".

col_lbl_hi

Highlighting color of text labels.Default:col_lbl_hi = pal_ds4psy[[1]].

col_lbl_lo

De-emphasizing color of text labels.Default:col_lbl_lo = pal_ds4psy[[9]].

col_bg

Default color to fill background tiles.Default:col_bg = pal_ds4psy[[7]].

col_bg_hi

Highlighting color to fill background tiles.Default:col_bg_hi = pal_ds4psy[[4]].

col_bg_lo

De-emphasizing color to fill background tiles.Default:col_bg_lo = "white".

col_sample

Boolean: Sample color vectors (within category)?Default:col_sample = FALSE.

rseed

Random seed (number).Default:rseed = NA (using random seed).

cex

Character size (numeric). Default:cex = 3.

fontface

Font face of text labels (numeric). Default:fontface = 1, (from 1 to 4).

family

Font family of text labels (name).Default:family = "sans". Alternative options: "sans", "serif", or "mono".

borders

Boolean: Add borders to tiles? Default:borders = FALSE (i.e., no borders).

border_col

Color of tile borders. Default:border_col = "white".

border_size

Size of tile borders. Default:border_size = 0.5.

Details

plot_chars blurs the boundary between a text and its graphical representation by combining options for matching patterns of text with visual features for displaying characters (e.g., their color or orientation).

plot_chars is based onplot_text, but provides additional support for detecting and displaying characters (i.e., text labels, their orientation, and color options) based on matching regular expression (regex).

Internally,plot_chars is a wrapper that calls (1)map_text_regex for creating a character map (allowing for matching patterns for some aesthetics) and (2)plot_charmap for plotting this character map.

However, in contrast toplot_charmap,plot_chars invisibly returns a description of the plot (as a data frame).

The plot generated byplot_chars is character-based: Individual characters are plotted at equidistant x-y-positions and the aesthetic settings provided for text labels and tile fill colors.

Value

An invisible data frame describing the plot.

Examples

# (A) From text string(s):plot_chars(x = c("Hello world!", "Does this work?",                  "That's good.", "Please carry on..."))# (B) From user input:# plot_chars()  # (enter text in Console)# (C) From text file:# Create and use a text file: # cat("Hello world!", "This is a test file.", #     "Can you see this text?", #     "Good! Please carry on...", #     file = "test.txt", sep = "\n")# plot_chars(file = "test.txt")  # default# plot_chars(file = "test.txt", lbl_hi = "[[:upper:]]", lbl_lo = "[[:punct:]]", #            col_lbl_hi = "red", col_lbl_lo = "blue") # plot_chars(file = "test.txt", lbl_hi = "[aeiou]", col_lbl_hi = "red", #            col_bg = "white", bg_hi = "see")  # mark vowels and "see" (in bg)# plot_chars(file = "test.txt", bg_hi = "[aeiou]", col_bg_hi = "gold")  # mark (bg of) vowels## Label options:# plot_chars(file = "test.txt", bg_hi = "see", lbl_tiles = FALSE)# plot_chars(file = "test.txt", cex = 5, family = "mono", fontface = 4, lbl_angle = c(-20, 20))## Note: plot_chars() invisibly returns a description of the plot (as df):# tb <- plot_chars(file = "test.txt", lbl_hi = "[aeiou]", lbl_rotate = TRUE)# head(tb)# unlink("test.txt")  # clean up (by deleting file).## (B) From text file (in subdir):# plot_chars(file = "data-raw/txt/hello.txt")  # requires txt file# plot_chars(file = "data-raw/txt/ascii.txt", lbl_hi = "[2468]", bg_lo = "[[:digit:]]", #            col_lbl_hi = "red", cex = 10, fontface = 2)           ## (C) User input:# plot_chars()  # (enter text in Console)

Plot objects (as points) arranged on a circle

Description

plot_circ_points arranges a number ofn on a circle (defined by its origin coordinates and radius).

Usage

plot_circ_points(  n = 4,  x_org = 0,  y_org = 0,  radius = 1,  show_axes = FALSE,  show_label = FALSE,  ...)

Arguments

n

The number of points (or shapes defined bypch) to plot.

x_org

The x-value of circle origin.

y_org

The y-value of circle origin.

radius

The circle radius.

show_axes

Show axes? Default:show_axes = FALSE.

show_label

Show a point label? Default:show_label = FALSE.

...

Additional aesthetics (passed topoints ofgraphics).

Details

The... is passed topoints of thegraphics package.

Examples

plot_circ_points(8)  # default# with aesthetics of points():plot_circ_points(n =  8, r = 10, cex = 8,                  pch = sample(21:25, size = 8, replace = TRUE), bg = "deeppink")plot_circ_points(n = 12, r = 8, show_axes = TRUE, show_label = TRUE,                 cex = 6, pch = 21, lwd = 5, col = "deepskyblue", bg = "gold")

A function to plot a plot

Description

plot_fn is a function that uses parameters for plotting a plot.

Usage

plot_fn(  x = NA,  y = 1,  A = TRUE,  B = FALSE,  C = TRUE,  D = FALSE,  E = FALSE,  F = FALSE,  f = c(rev(pal_seeblau), "white", pal_pinky),  g = "white")

Arguments

x

Numeric (integer > 0). Default:x = NA.

y

Numeric (double).Default:y = 1.

A

Boolean. Default:A = TRUE.

B

Boolean. Default:B = FALSE.

C

Boolean. Default:C = TRUE.

D

Boolean. Default:D = FALSE.

E

Boolean. Default:E = FALSE.

F

Boolean. Default:F = FALSE.

f

A color palette (as a vector). Default:f = c(rev(pal_seeblau), "white", pal_pinky). Note: Using colors of theunikn package by default.

g

A color (e.g., a color name, as a character). Default:g = "white".

Details

plot_fn is deliberately kept cryptic and obscure to illustrate how function parameters can be explored.

plot_fn also shows that brevity in argument names should not come at the expense of clarity. In fact, transparent argument names are absolutely essential for understanding and using a function.

plot_fn currently requirespal_seeblau andpal_pinky (from theunikn package) for its default colors.

Examples

# Basics: plot_fn()# Exploring options: plot_fn(x = 2, A = TRUE)plot_fn(x = 3, A = FALSE, E = TRUE)plot_fn(x = 4, A = TRUE,  B = TRUE, D = TRUE)plot_fn(x = 5, A = FALSE, B = TRUE, E = TRUE, f = c("black", "white", "gold"))plot_fn(x = 7, A = TRUE,  B = TRUE, F = TRUE, f = c("steelblue", "white", "forestgreen"))

An example function to plot some plot

Description

plot_fun provides options for plotting a plot.

Usage

plot_fun(  a = NA,  b = TRUE,  c = TRUE,  d = 1,  e = FALSE,  f = FALSE,  g = FALSE,  c1 = c(rev(pal_seeblau), "white", pal_grau, "black", Bordeaux),  c2 = "black")

Arguments

a

Numeric (integer > 0). Default:a = NA.

b

Boolean. Default:b = TRUE.

c

Boolean. Default:c = TRUE.

d

Numeric (double). Default:d = 1.0.

e

Boolean. Default:e = FALSE.

f

Boolean. Default:f = FALSE.

g

Boolean. Default:g = FALSE.

c1

A color palette (as a vector). Default:c1 = c(rev(pal_seeblau), "white", pal_grau, "black", Bordeaux) (i.e., using colors of theunikn package by default).

c2

A color (e.g., color name, as character). Default:c2 = "black".

Details

plot_fun is deliberately kept cryptic and obscure to illustrate how function parameters can be explored.

plot_fun also shows that brevity in argument names should not come at the expense of clarity. In fact, transparent argument names are absolutely essential for understanding and using a function.

plot_fun currently requirespal_seeblau,pal_grau, andBordeaux (from theunikn package) for its default colors.

Examples

# Basics: plot_fun()# Exploring options: plot_fun(a = 3, b = FALSE, e = TRUE)plot_fun(a = 4, f = TRUE, g = TRUE, c1 = c("steelblue", "white", "firebrick"))

Plot n tiles

Description

plot_n plots a row or column ofn tiles on fixed or polar coordinates.

Usage

plot_n(  n = NA,  row = TRUE,  polar = FALSE,  pal = pal_ds4psy,  sort = TRUE,  borders = TRUE,  border_col = "black",  border_size = 0,  lbl_tiles = FALSE,  lbl_title = FALSE,  rseed = NA,  save = FALSE,  save_path = "images/tiles",  prefix = "",  suffix = "")

Arguments

n

Basic number of tiles (on either side).

row

Plot as a row? Default:row = TRUE (else plotted as a column).

polar

Plot on polar coordinates? Default:polar = FALSE (i.e., using fixed coordinates).

pal

A color palette (automatically extended ton colors). Default:pal =pal_ds4psy.

sort

Sort tiles? Default:sort = TRUE (i.e., sorted tiles).

borders

Add borders to tiles? Default:borders = TRUE (i.e., use borders).

border_col

Color of borders (ifborders = TRUE). Default:border_col = "black".

border_size

Size of borders (ifborders = TRUE). Default:border_size = 0 (i.e., invisible).

lbl_tiles

Add numeric labels to tiles? Default:lbl_tiles = FALSE (i.e., no labels).

lbl_title

Add numeric label (of n) to plot? Default:lbl_title = FALSE (i.e., no title).

rseed

Random seed (number).Default:rseed = NA (using random seed).

save

Save plot as png file? Default:save = FALSE.

save_path

Path to save plot (ifsave = TRUE).Default:save_path = "images/tiles".

prefix

Prefix to plot name (ifsave = TRUE).Default:prefix = "".

suffix

Suffix to plot name (ifsave = TRUE).Default:suffix = "".

Details

Note that a polar row makes a tasty pie, whereas a polar column makes a target plot.

Examples

# (1) Basics (as ROW or COL): plot_n()  # default plot (random n, row = TRUE, with borders, no labels)plot_n(row = FALSE)  # default plot (random n, with borders, no labels)plot_n(n = 4, sort = FALSE)      # random orderplot_n(n = 6, borders = FALSE)   # no bordersplot_n(n = 8, lbl_tiles = TRUE,  # with tile +        lbl_title = TRUE)         # title labels # Set colors: plot_n(n = 5, row = TRUE, lbl_tiles = TRUE, lbl_title = TRUE,       pal = c("orange", "white", "firebrick"),       border_col = "white", border_size = 2)  # Fixed rseed:plot_n(n = 4, sort = FALSE, borders = FALSE,        lbl_tiles = TRUE, lbl_title = TRUE, rseed = 101)# (2) polar plot (as PIE or TARGET):    plot_n(polar = TRUE)  # PIE plot (with borders, no labels)plot_n(polar = TRUE, row = FALSE)  # TARGET plot (with borders, no labels)plot_n(n = 4, polar = TRUE, sort = FALSE)      # PIE in random orderplot_n(n = 5, polar = TRUE, row = FALSE, borders = FALSE)   # TARGET no bordersplot_n(n = 5, polar = TRUE, lbl_tiles = TRUE)  # PIE with tile labels plot_n(n = 5, polar = TRUE, row = FALSE, lbl_title = TRUE)  # TARGET with title label # plot_n(n = 4, row = TRUE, sort = FALSE, borders = TRUE,  #        border_col = "white", border_size = 2, #        polar = TRUE, rseed = 132)# plot_n(n = 4, row = FALSE, sort = FALSE, borders = TRUE,  #        border_col = "white", border_size = 2, #        polar = TRUE, rseed = 134)

Plot text characters (from file or user input)

Description

plot_text parses text (from a file or from user input) and plots its individual characters as a tile plot (usingggplot2).

Usage

plot_text(  x = NA,  file = "",  char_bg = " ",  lbl_tiles = TRUE,  lbl_rotate = FALSE,  cex = 3,  fontface = 1,  family = "sans",  col_lbl = "black",  col_bg = "white",  pal = pal_ds4psy[1:5],  pal_extend = TRUE,  case_sense = FALSE,  borders = TRUE,  border_col = "white",  border_size = 0.5)

Arguments

x

The text to plot (as a character vector). Different elements denote different lines of text. Ifx = NA (as per default), thefile argument is used to read a text file or scan user input (entering text in Console).

file

char_bg

Character used as background. Default:char_bg = " ". Ifchar_bg = NA, the most frequent character is used.

lbl_tiles

Add character labels to tiles? Default:lbl_tiles = TRUE (i.e., show labels).

lbl_rotate

Rotate character labels? Default:lbl_rotate = FALSE (i.e., no rotation).

cex

Character size (numeric). Default:cex = 3.

fontface

Font face of text labels (numeric). Default:fontface = 1, (from 1 to 4).

family

Font family of text labels (name).Default:family = "sans". Alternative options: "sans", "serif", or "mono".

col_lbl

Color of text labels.Default:col_lbl = "black" (iflbl_tiles = TRUE).

col_bg

Color ofchar_bg (if defined), or the most frequent character in text (typically" "). Default:col_bg = "white".

pal

Color palette for filling tiles of text (used in order of character frequency). Default:pal = pal_ds4psy[1:5] (i.e., shades ofSeeblau).

pal_extend

Boolean: Shouldpal be extended to match the number of different characters in text? Default:pal_extend = TRUE. Ifpal_extend = FALSE, only the tiles of thelength(pal) most frequent characters will be filled by the colors ofpal.

case_sense

Boolean: Distinguish lower- vs. uppercase characters? Default:case_sense = FALSE.

borders

Boolean: Add borders to tiles? Default:borders = TRUE (i.e., use borders).

border_col

Color of borders (ifborders = TRUE). Default:border_col = "white".

border_size

Size of borders (ifborders = TRUE). Default:border_size = 0.5.

Details

plot_text blurs the boundary between a text and its graphical representation by adding visual options for coloring characters based on their frequency counts. (Note thatplot_chars provides additional support for matching regular expressions.)

plot_text is character-based: Individual characters are plotted at equidistant x-y-positions with color settings for text labels and tile fill colors.

By default, the color palettepal (used for tile fill colors) is scaled to indicate character frequency.

plot_text invisibly returns a description of the plot (as a data frame).

Value

An invisible data frame describing the plot.

Examples

# (A) From text string(s):plot_text(x = c("Hello", "world!"))plot_text(x = c("Hello world!", "How are you today?"))# (B) From user input:# plot_text()  # (enter text in Console)# (C) From text file:## Create a temporary file "test.txt":# cat("Hello world!", "This is a test file.", #     "Can you see this text?", #     "Good! Please carry on...", #     file = "test.txt", sep = "\n")# plot_text(file = "test.txt")## Set colors, pal_extend, and case_sense:# cols <- c("steelblue", "skyblue", "lightgrey")# cols <- c("firebrick", "olivedrab", "steelblue", "orange", "gold")# plot_text(file = "test.txt", pal = cols, pal_extend = TRUE)# plot_text(file = "test.txt", pal = cols, pal_extend = FALSE)# plot_text(file = "test.txt", pal = cols, pal_extend = FALSE, case_sense = TRUE)## Customize text and grid options:# plot_text(file = "test.txt", col_lbl = "darkblue", cex = 4, family = "sans", fontface = 3,#           pal = "gold1", pal_extend = TRUE, border_col = NA)# plot_text(file = "test.txt", family = "serif", cex = 6, lbl_rotate = TRUE,  #           pal = NA, borders = FALSE)# plot_text(file = "test.txt", col_lbl = "white", pal = c("green3", "black"),#           border_col = "black", border_size = .2)## Color ranges:# plot_text(file = "test.txt", pal = c("red2", "orange", "gold"))# plot_text(file = "test.txt", pal = c("olivedrab4", "gold"))# unlink("test.txt")  # clean up. ## (B) From text file (in subdir):# plot_text(file = "data-raw/txt/hello.txt")  # requires txt file# plot_text(file = "data-raw/txt/ascii.txt", cex = 5, #           col_bg = "grey", char_bg = "-")         ## (C) From user input:# plot_text()  # (enter text in Console)

Plot n-by-n tiles.

Description

plot_tiles plots an area ofn-by-n tiles on fixed or polar coordinates.

Usage

plot_tiles(  n = NA,  pal = pal_ds4psy,  sort = TRUE,  borders = TRUE,  border_col = "black",  border_size = 0.2,  lbl_tiles = FALSE,  lbl_title = FALSE,  polar = FALSE,  rseed = NA,  save = FALSE,  save_path = "images/tiles",  prefix = "",  suffix = "")

Arguments

n

Basic number of tiles (on either side).

pal

Color palette (automatically extended ton x n colors). Default:pal =pal_ds4psy.

sort

Boolean: Sort tiles? Default:sort = TRUE (i.e., sorted tiles).

borders

Boolean: Add borders to tiles? Default:borders = TRUE (i.e., use borders).

border_col

Color of borders (ifborders = TRUE). Default:border_col = "black".

border_size

Size of borders (ifborders = TRUE). Default:border_size = 0.2.

lbl_tiles

Boolean: Add numeric labels to tiles? Default:lbl_tiles = FALSE (i.e., no labels).

lbl_title

Boolean: Add numeric label (of n) to plot? Default:lbl_title = FALSE (i.e., no title).

polar

Boolean: Plot on polar coordinates? Default:polar = FALSE (i.e., using fixed coordinates).

rseed

Random seed (number).Default:rseed = NA (using random seed).

save

Boolean: Save plot as png file? Default:save = FALSE.

save_path

Path to save plot (ifsave = TRUE).Default:save_path = "images/tiles".

prefix

Prefix to plot name (ifsave = TRUE).Default:prefix = "".

suffix

Suffix to plot name (ifsave = TRUE).Default:suffix = "".

Examples

# (1) Tile plot:plot_tiles()  # default plot (random n, with borders, no labels)plot_tiles(n = 4, sort = FALSE)      # random orderplot_tiles(n = 6, borders = FALSE)   # no bordersplot_tiles(n = 8, lbl_tiles = TRUE,  # with tile +            lbl_title = TRUE)         # title labels # Set colors: plot_tiles(n = 4, pal = c("orange", "white", "firebrick"),           lbl_tiles = TRUE, lbl_title = TRUE,           sort = TRUE)plot_tiles(n = 6, sort = FALSE, border_col = "white", border_size = 2)# Fixed rseed:plot_tiles(n = 4, sort = FALSE, borders = FALSE,            lbl_tiles = TRUE, lbl_title = TRUE,            rseed = 101)# (2) polar plot:  plot_tiles(polar = TRUE)  # default polar plot (with borders, no labels)plot_tiles(n = 4, polar = TRUE, sort = FALSE)   # random orderplot_tiles(n = 6, polar = TRUE, sort = TRUE,    # sorted and with            lbl_tiles = TRUE, lbl_title = TRUE)  # tile + title labels plot_tiles(n = 4, sort = FALSE, borders = TRUE,             border_col = "white", border_size = 2,            polar = TRUE, rseed = 132)           # fixed rseed

Positive Psychology: AHI CESD data

Description

posPsy_AHI_CESD is a dataset containing answers to the 24 items of the Authentic Happiness Inventory (AHI) and answers to the 20 items of the Center for Epidemiological Studies Depression (CES-D) scale (Radloff, 1977) for multiple (1 to 6) measurement occasions.

Usage

posPsy_AHI_CESD

Format

A table with 992 cases (rows) and 50 variables (columns).

Details

Codebook

1.id: Participant ID.
2.occasion: Measurement occasion: 0: Pretest (i.e., at enrolment), 1: Posttest (i.e., 7 days after pretest), 2: 1-week follow-up, (i.e., 14 days after pretest, 7 days after posttest), 3: 1-month follow-up, (i.e., 38 days after pretest, 31 days after posttest), 4: 3-month follow-up, (i.e., 98 days after pretest, 91 days after posttest), 5: 6-month follow-up, (i.e., 189 days after pretest, 182 days after posttest).
3.elapsed.days: Time since enrolment measured in fractional days.
4.intervention: Type of intervention: 3 positive psychology interventions (PPIs), plus 1 control condition: 1: "Using signature strengths", 2: "Three good things", 3: "Gratitude visit", 4: "Recording early memories" (control condition).
5.-28. (fromahi01 toahi24): Responses on 24 AHI items.
29.-48. (fromcesd01 tocesd20): Responses on 20 CES-D items.
49.ahiTotal: Total AHI score.
50.cesdTotal: Total CES-D score.

See codebook and references athttps://bookdown.org/hneth/ds4psy/B.1-datasets-pos.html.

Source

Articles

Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R., & Schüz, B. (2017). Web-based positive psychology interventions: A reexamination of effectiveness.Journal of Clinical Psychology,73(3), 218–232. doi:10.1002/jclp.22328
Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R. and Schüz, B. (2018). Data from, ‘Web-based positive psychology interventions: A reexamination of effectiveness’.Journal of Open Psychology Data,6(1). doi:10.5334/jopd.35

Seehttps://openpsychologydata.metajnl.com/articles/10.5334/jopd.35/ for details anddoi:10.6084/m9.figshare.1577563.v1 for original dataset.

Additional references athttps://bookdown.org/hneth/ds4psy/B.1-datasets-pos.html.

Positive Psychology: AHI CESD corrected data (in long format)

Description

posPsy_long is a dataset containing answers to the 24 items of the Authentic Happiness Inventory (AHI) and answers to the 20 items of the Center for Epidemiological Studies Depression (CES-D) scale (see Radloff, 1977) for multiple (1 to 6) measurement occasions.

Usage

posPsy_long

Format

A table with 990 cases (rows) and 50 variables (columns).

Details

This dataset is a corrected version ofposPsy_AHI_CESD and in long-format.

Source

Articles

Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R., & Schüz, B. (2017). Web-based positive psychology interventions: A reexamination of effectiveness.Journal of Clinical Psychology,73(3), 218–232. doi:10.1002/jclp.22328
Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R. and Schüz, B. (2018). Data from, ‘Web-based positive psychology interventions: A reexamination of effectiveness’.Journal of Open Psychology Data,6(1). doi:10.5334/jopd.35

Seehttps://openpsychologydata.metajnl.com/articles/10.5334/jopd.35/ for details anddoi:10.6084/m9.figshare.1577563.v1 for original dataset.

Additional references athttps://bookdown.org/hneth/ds4psy/B.1-datasets-pos.html.

Positive Psychology: Participant data

Description

posPsy_p_info is a dataset containing details of 295 participants.

Usage

posPsy_p_info

Format

A table with 295 cases (rows) and 6 variables (columns).

Details

id: Participant ID.
intervention: Type of intervention: 3 positive psychology interventions (PPIs), plus 1 control condition: 1: "Using signature strengths", 2: "Three good things", 3: "Gratitude visit", 4: "Recording early memories" (control condition).
sex: Sex: 1 = female, 2 = male.
age: Age (in years).
educ: Education level: Scale from 1: less than 12 years, to 5: postgraduate degree.
income: Income: Scale from 1: below average, to 3: above average.

See codebook and references athttps://bookdown.org/hneth/ds4psy/B.1-datasets-pos.html.

Source

Articles

Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R., & Schüz, B. (2017). Web-based positive psychology interventions: A reexamination of effectiveness.Journal of Clinical Psychology,73(3), 218–232. doi:10.1002/jclp.22328
Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R. and Schüz, B. (2018). Data from, ‘Web-based positive psychology interventions: A reexamination of effectiveness’.Journal of Open Psychology Data,6(1). doi:10.5334/jopd.35

Seehttps://openpsychologydata.metajnl.com/articles/10.5334/jopd.35/ for details anddoi:10.6084/m9.figshare.1577563.v1 for original dataset.

Additional references athttps://bookdown.org/hneth/ds4psy/B.1-datasets-pos.html.

Positive Psychology: All corrected data (in wide format)

Description

posPsy_wide is a dataset containing answers to the 24 items of the Authentic Happiness Inventory (AHI) and answers to the 20 items of the Center for Epidemiological Studies Depression (CES-D) scale (see Radloff, 1977) for multiple (1 to 6) measurement occasions.

Usage

posPsy_wide

Format

An object of classspec_tbl_df (inherits fromtbl_df,tbl,data.frame) with 295 rows and 294 columns.

Details

This dataset is based onposPsy_AHI_CESD andposPsy_long, but is in wide format.

Source

Articles

Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R., & Schüz, B. (2017). Web-based positive psychology interventions: A reexamination of effectiveness.Journal of Clinical Psychology,73(3), 218–232. doi:10.1002/jclp.22328
Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R. and Schüz, B. (2018). Data from, ‘Web-based positive psychology interventions: A reexamination of effectiveness’.Journal of Open Psychology Data,6(1). doi:10.5334/jopd.35

Seehttps://openpsychologydata.metajnl.com/articles/10.5334/jopd.35/ for details anddoi:10.6084/m9.figshare.1577563.v1 for original dataset.

Additional references athttps://bookdown.org/hneth/ds4psy/B.1-datasets-pos.html.

Parse text (from file or user input) into string(s) of text

Description

read_ascii parses text inputs (from a file or from user input in the Console) into a character vector.

Usage

read_ascii(file = "", quiet = FALSE)

Arguments

file

The text file to read (or its path). Iffile = "" (the default),scan is used to read user input from the Console. If a text file is stored in a sub-directory, enter its path and name here (without any leading or trailing "." or "/"). Default:file = "".

quiet

Boolean: Provide user feedback? Default:quiet = FALSE.

Details

Different lines of text are represented by different elements of the character vector returned.

Thegetwd function is used to determine the current working directory. This replaces thehere package, which was previously used to determine an (absolute) file path.

Note thatread_ascii originally containedmap_text_coord, but has been separated to enable independent access to separate functionalities.

Value

A character vector, with its elements denoting different lines of text.

Examples

## Create a temporary file "test.txt":# cat("Hello world!", "This is a test.", #     "Can you see this text?", #     "Good! Please carry on...", #     file = "test.txt", sep = "\n")## (a) Read text (from file): # read_ascii("test.txt")# read_ascii("test.txt", quiet = TRUE)  # y flipped# unlink("test.txt")  # clean up (by deleting file). ## (b) Read text (from file in subdir):# read_ascii("data-raw/txt/ascii.txt")  # requires txt file## (c) Scan user input (from console):# read_ascii()

Draw a sample of n random characters (from given characters)

Description

sample_char draws a sample ofn random characters from a given range of characters.

Usage

sample_char(x_char = c(letters, LETTERS), n = 1, replace = FALSE, ...)

Arguments

x_char

Population of characters to sample from. Default:x_char = c(letters, LETTERS).

n

Number of characters to draw. Default:n = 1.

replace

Boolean: Sample with replacement? Default:replace = FALSE.

...

Other arguments.(Use for specifyingprob, as passed tosample().)

Details

By default,sample_char drawsn = 1 a random alphabetic character fromx_char = c(letters, LETTERS).

As withsample(), the sample sizen must not exceed the number of available charactersnchar(x_char), unlessreplace = TRUE (i.e., sampling with replacement).

Value

A text string (scalar character vector).

Examples

sample_char()  # defaultsample_char(n = 10)sample_char(x_char = "abc", n = 10, replace = TRUE)sample_char(x_char = c("x y", "6 9"), n =  6, replace = FALSE)sample_char(x_char = c("x y", "6 9"), n = 20, replace = TRUE)# Biased sampling: sample_char(x_char = "abc", n = 20, replace = TRUE,              prob = c(3/6, 2/6, 1/6))# Note: By default, n must not exceed nchar(x_char):sample_char(n = 52, replace = FALSE)    # works, but# sample_char(n = 53, replace = FALSE)  # would yield ERROR; sample_char(n = 53, replace = TRUE)     # works again.

Draw a sample of n random dates (from a given range).

Description

sample_date draws a sample ofn random dates from a given range.

Usage

sample_date(from = "1970-01-01", to = Sys.Date(), size = 1, ...)

Arguments

from

Earliest date (as "Date" or string). Default:from = "1970-01-01" (as a scalar).

to

Latest date (as "Date" or string). Default:to = Sys.Date() (as a scalar).

size

Size of date samples to draw. Default:size = 1.

...

Other arguments.(Use for specifyingreplace, as passed tosample().)

Details

By default,sample_date drawsn = 1 random date (as a "Date" object) in the rangefrom = "1970-01-01"to = Sys.Date() (current date).

Bothfrom andto currently need to be scalars (i.e., with a length of 1).

Value

A vector of class "Date".

Examples

sample_date()sort(sample_date(size = 10))sort(sample_date(from = "2020-02-28", to = "2020-03-01",      size = 10, replace = TRUE))  # 2020 is a leap year# Note: Oddity with sample():sort(sample_date(from = "2020-01-01", to = "2020-01-01", size = 10, replace = TRUE))  # range of 0!# see sample(9:9, size = 10, replace = TRUE)

Draw a sample of n random times (from a given range).

Description

sample_time draws a sample ofn random times from a given range.

Usage

sample_time(  from = "1970-01-01 00:00:00",  to = Sys.time(),  size = 1,  as_POSIXct = TRUE,  tz = "",  ...)

Arguments

from

Earliest date-time (as string). Default:from = "1970-01-01 00:00:00" (as a scalar).

to

Latest date-time (as string). Default:to = Sys.time() (as a scalar).

size

Size of time samples to draw. Default:size = 1.

as_POSIXct

Boolean: Return calendar time ("POSIXct") object? Default:as_POSIXct = TRUE. Ifas_POSIXct = FALSE, a local time ("POSIXlt") object is returned (as a list).

tz

Time zone.Default:tz = "" (i.e., current system time zone, seeSys.timezone()). Usetz = "UTC" for Universal Time, Coordinated.

...

Other arguments.(Use for specifyingreplace, as passed tosample().)

Details

By default,sample_time drawsn = 1 random calendar time (as a "POSIXct" object) in the rangefrom = "1970-01-01 00:00:00"to = Sys.time() (current time).

Bothfrom andto currently need to be scalars (i.e., with a length of 1).

Ifas_POSIXct = FALSE, a local time ("POSIXlt") object is returned (as a list).

Thetz argument allows specifying time zones (seeSys.timezone() for current setting andOlsonNames() for options.)

Value

A vector of class "POSIXct" or "POSIXlt".

Examples

# Basics:sample_time()sample_time(size = 10)# Specific ranges:sort(sample_time(from = (Sys.time() - 60), size = 10))  # within last minutesort(sample_time(from = (Sys.time() - 1 * 60 * 60), size = 10))  # within last hoursort(sample_time(from = Sys.time(), to = (Sys.time() + 1 * 60 * 60),      size = 10, replace = FALSE))  # within next hoursort(sample_time(from = "2020-12-31 00:00:00 CET", to = "2020-12-31 00:00:01 CET",                 size = 10, replace = TRUE))  # within 1 sec range                            # Local time (POSIXlt) objects (as list):(lt_sample <- sample_time(as_POSIXct = FALSE))unlist(lt_sample)# Time zones:sample_time(size = 3, tz = "UTC")sample_time(size = 3, tz = "America/Los_Angeles") # Note: Oddity with sample(): sort(sample_time(from = "2020-12-31 00:00:00 CET", to = "2020-12-31 00:00:00 CET",     size = 10, replace = TRUE))  # range of 0!# see sample(9:9, size = 10, replace = TRUE)

Data: t3

Description

t3 is a fictitious dataset to practice importing and joining data (from a CSV file).

Usage

t3

Format

A table with 10 cases (rows) and 4 variables (columns).

Source

See CSV data athttp://rpository.com/ds4psy/data/t3.csv.

Data: t4

Description

t4 is a fictitious dataset to practice importing and joining data (from a CSV file).

Usage

t4

Format

A table with 10 cases (rows) and 4 variables (columns).

Source

See CSV data athttp://rpository.com/ds4psy/data/t4.csv.

Data: t_1

Description

t_1 is a fictitious dataset to practice tidying data.

Usage

t_1

Format

A table with 8 cases (rows) and 9 variables (columns).

Source

See CSV data athttp://rpository.com/ds4psy/data/t_1.csv.

Data: t_2

Description

t_2 is a fictitious dataset to practice tidying data.

Usage

t_2

Format

A table with 8 cases (rows) and 5 variables (columns).

Source

See CSV data athttp://rpository.com/ds4psy/data/t_2.csv.

Data: t_3

Description

t_3 is a fictitious dataset to practice tidying data.

Usage

t_3

Format

A table with 16 cases (rows) and 6 variables (columns).

Source

See CSV data athttp://rpository.com/ds4psy/data/t_3.csv.

Data: t_4

Description

t_4 is a fictitious dataset to practice tidying data.

Usage

t_4

Format

A table with 16 cases (rows) and 8 variables (columns).

Source

See CSV data athttp://rpository.com/ds4psy/data/t_4.csv.

Data: table6

Description

table6 is a fictitious dataset to practice reshaping and tidying data.

Usage

table6

Format

A table with 6 cases (rows) and 2 variables (columns).

Details

This dataset is a further variant of thetable1 totable5 datasets of thetidyr package.

Source

See CSV data athttp://rpository.com/ds4psy/data/table6.csv.

Data: table7

Description

table7 is a fictitious dataset to practice reshaping and tidying data.

Usage

table7

Format

A table with 6 cases (rows) and 1 (horrendous) variable (column).

Details

This dataset is a further variant of thetable1 totable5 datasets of thetidyr package.

Source

See CSV data athttp://rpository.com/ds4psy/data/table7.csv.

Data: table8

Description

table9 is a fictitious dataset to practice reshaping and tidying data.

Usage

table8

Format

A table with 3 cases (rows) and 5 variables (columns).

Details

This dataset is a further variant of thetable1 totable5 datasets of thetidyr package.

Source

See CSV data athttp://rpository.com/ds4psy/data/table8.csv.

Data table9.

Description

table9 is a fictitious dataset to practice reshaping and tidying data.

Usage

table9

Format

A 3 x 2 x 2 array (of type "xtabs") with 2940985206 elements (frequency counts).

Details

This dataset is a further variant of thetable1 totable5 datasets of thetidyr package.

Source

Generated by usingstats::xtabs(formula = count ~., data = tidyr::table2).

Data table tb.

Description

tb is a fictitious set of data describing 100 non-existing, but otherwise ordinary people.

Usage

tb

Format

A table with 100 cases (rows) and 5 variables (columns).

Details

Codebook

The table contains 5 columns/variables:

1.id: Participant ID.
2.age: Age (in years).
3.height: Height (in cm).
4.shoesize: Shoe size (EU standard).
5.IQ: IQ score (according Raven's Regressive Tables).

tb was originally created to practice loops and iterations (as a CSV file).

Source

See CSV data file athttp://rpository.com/ds4psy/data/tb.csv.

Split string(s) of text`x` into its characters.

Description

text_to_chars splits a string of textx (consisting of one or more character strings) into a vector of its individual characters.

Usage

text_to_chars(x, rm_specials = FALSE, sep = "")

Arguments

x

A string of text (required).

rm_specials

Boolean: Remove special characters? Default:rm_specials = TRUE.

sep

Character to insert between the elements of a multi-element character vector as inputx? Default:sep = "" (i.e., add nothing).

Details

Ifrm_specials = TRUE, most special (or non-word) characters are removed. (Note that this currently works without using regular expressions.)

text_to_chars is an inverse function ofchars_to_text.

Value

A character vector (containing individual characters).

Examples

s3 <- c("A 1st sentence.", "The 2nd sentence.",        "A 3rd --- and  FINAL --- sentence.")text_to_chars(s3)text_to_chars(s3, sep = "\n")text_to_chars(s3, rm_specials = TRUE)

Split strings of text`x` into sentences.

Description

text_to_sentences splits textx (consisting of one or more character strings) into a vector of its constituting sentences.

Usage

text_to_sentences(  x,  sep = " ",  split_delim = "\\.|\\?|!",  force_delim = FALSE)

Arguments

x

A string of text (required), typically a character vector.

sep

A character inserted as separator/delimiter between elements when collapsing multi-element strings ofx. Default:sep = " " (i.e., insert 1 space between elements).

split_delim

Sentence delimiters (as regex) used to split the collapsed string ofx into substrings. Default:split_delim = "\.|\?|!" (rather than"[[:punct:]]").

force_delim

Boolean: Enforce splitting atsplit_delim? Ifforce_delim = FALSE (as per default), a standard sentence-splitting pattern is assumed:split_delim is followed by one or more blank spaces and a capital letter. Ifforce_delim = TRUE, splits atsplit_delim are enforced (without considering spacing or capitalization).

Details

The splits ofx will occur at given punctuation marks (provided as a regular expression, default:split_delim = "\.|\?|!"). Empty leading and trailing spaces are removed before returning a vector of the remaining character sequences (i.e., the sentences).

The Boolean argumentforce_delim distinguishes between two splitting modes:

Ifforce_delim = FALSE (as per default), a standard sentence-splitting pattern is assumed: A sentence delimiter insplit_delim must be followed by one or more blank spaces and a capital letter starting the next sentence. Sentence delimiters insplit_delim are not removed from the output.
Ifforce_delim = TRUE, the function enforces splits at each delimiter insplit_delim. For instance, any dot (i.e., the metacharacter"\.") is interpreted as a full stop, so that sentences containing dots mid-sentence (e.g., for abbreviations, etc.) are split into parts. Sentence delimiters insplit_delim are removed from the output.

Internally,text_to_sentences first usespaste to collapse strings (addingsep between elements) and thenstrsplit to split strings atsplit_delim.

Value

A character vector (of sentences).

Examples

x <- c("A first sentence. Exclamation sentence!",        "Any questions? But etc. can be tricky. A fourth --- and final --- sentence.")text_to_sentences(x)text_to_sentences(x, force_delim = TRUE)# Changing split delimiters:text_to_sentences(x, split_delim = "\\.")  # only split at "."text_to_sentences("Buy apples, berries, and coconuts.")text_to_sentences("Buy apples, berries; and coconuts.",                   split_delim = ",|;|\\.", force_delim = TRUE)                  text_to_sentences(c("123. 456? 789! 007 etc."), force_delim = TRUE)# Split multi-element strings (w/o punctuation):e3 <- c("12", "34", "56")text_to_sentences(e3, sep = " ")  # Default: Collapse strings adding 1 space, but: text_to_sentences(e3, sep = ".", force_delim = TRUE)  # insert sep and force split.# Punctuation within sentences:text_to_sentences("Dr. who is left intact.")text_to_sentences("Dr. Who is problematic.")

Split string(s) of text`x` into words.

Description

text_to_words splits a string of textx (consisting of one or more character strings) into a vector of its constituting words.

Usage

text_to_words(x)

Arguments

x

A string of text (required), typically a character vector.

Details

text_to_words removes all (standard) punctuation marks and empty spaces in the resulting text parts, before returning a vector of the remaining character symbols (as its words).

Internally,text_to_words usesstrsplit to split strings at punctuation marks (split = "[[:punct:]]") and blank spaces (split = "( ){1,}").

Value

A character vector (of words).

Examples

# Default: x <- c("Hello!", "This is a 1st sentence.", "This is the 2nd sentence.", "The end.")text_to_words(x)

A clean alternative theme forggplot2

Description

theme_clean provides an alternativeds4psy theme to use inggplot2 commands.

Usage

theme_clean(  base_size = 11,  base_family = "",  base_line_size = base_size/22,  base_rect_size = base_size/22,  col_title = grey(0, 1),  col_panel = grey(0.85, 1),  col_gridx = grey(1, 1),  col_gridy = grey(1, 1),  col_ticks = grey(0.1, 1))

Arguments

base_size

Base font size (optional, numeric). Default:base_size = 11.

base_family

Base font family (optional, character). Default:base_family = "". Options include"mono","sans" (default), and "serif".

base_line_size

Base line size (optional, numeric). Default:base_line_size = base_size/22.

base_rect_size

Base rectangle size (optional, numeric). Default:base_rect_size = base_size/22.

col_title

Color of plot title (and tag). Default:col_title = grey(.0, 1) (i.e., "black").

col_panel

Color of panel background(s). Default:col_panel = grey(.85, 1) (i.e., light "grey").

col_gridx

Color of (major) panel lines (through x/vertical). Default:col_gridx = grey(1.0, 1) (i.e., "white").

col_gridy

Color of (major) panel lines (through y/horizontal). Default:col_gridy = grey(1.0, 1) (i.e., "white").

col_ticks

Color of axes text and ticks. Default:col_ticks = grey(.10, 1) (i.e., near "black").

Details

theme_clean is more minimal thantheme_ds4psy and fills panel backgrounds with a colorcol_panel.

This theme works well for plots with multiple panels, strong colors and bright color accents, but is of limited use with transparent colors.

Value

Aggplot2 theme.

Examples

# Plotting iris dataset (using ggplot2, theme_grau, and unikn colors):  library('ggplot2')  # theme_clean() requires ggplot2library('unikn')    # for colors and usecol() function   ggplot(datasets::iris) +  geom_jitter(aes(x = Sepal.Length, y = Sepal.Width, color = Species), size = 3, alpha = 3/4) +  facet_wrap(~Species) +  scale_color_manual(values = usecol(pal = c(Pinky, Karpfenblau, Seegruen))) +  labs(tag = "B",       title = "Iris sepals",       caption = "Data from datasets::iris") +   coord_fixed(ratio = 3/2) +   theme_clean()

A basic and flexible plot theme

Description

theme_ds4psy provides a genericds4psy theme to use inggplot2 commands.

Usage

theme_ds4psy(  base_size = 11,  base_family = "",  base_line_size = base_size/22,  base_rect_size = base_size/22,  col_title = grey(0, 1),  col_txt_1 = grey(0.1, 1),  col_txt_2 = grey(0.2, 1),  col_txt_3 = grey(0.1, 1),  col_bgrnd = "transparent",  col_panel = grey(1, 1),  col_strip = "transparent",  col_axes = grey(0, 1),  col_gridx = grey(0.75, 1),  col_gridy = grey(0.75, 1),  col_brdrs = "transparent")

Arguments

base_size

Base font size (optional, numeric). Default:base_size = 11.

base_family

Base font family (optional, character). Default:base_family = "". Options include"mono","sans" (default), and "serif".

base_line_size

Base line size (optional, numeric). Default:base_line_size = base_size/22.

base_rect_size

Base rectangle size (optional, numeric). Default:base_rect_size = base_size/22.

col_title

Color of plot title (and tag). Default:col_title = grey(.0, 1) (i.e., "black").

col_txt_1

Color of primary text (headings and axis labels).Default:col_title = grey(.1, 1).

col_txt_2

Color of secondary text (caption, legend, axes labels/ticks). Default:col_title = grey(.2, 1).

col_txt_3

Color of other text (facet strip labels).Default:col_title = grey(.1, 1).

col_bgrnd

Color of plot background.Default:col_bgrnd = "transparent".

col_panel

Color of panel background(s).Default:col_panel = grey(1.0, 1) (i.e., "white").

col_strip

Color of facet strips. Default:col_strip = "transparent".

col_axes

Color of (x and y) axes. Default:col_axes = grey(.00, 1) (i.e., "black").

col_gridx

Color of (major and minor) panel lines (through x/vertical). Default:col_gridx = grey(.75, 1) (i.e., light "grey").

col_gridy

Color of (major and minor) panel lines (through y/horizontal). Default:col_gridy = grey(.75, 1) (i.e., light "grey").

col_brdrs

Color of (panel and strip) borders. Default:col_brdrs = "transparent".

Details

The theme is lightweight and no-nonsense, but somewhat opinionated (e.g., in using transparency and grid lines, and relying on grey tones for emphasizing data with color accents).

Basic sizes and the colors of text elements, backgrounds, and lines can be specified. However, excessive customization rarely yields aesthetic improvements over the standardggplot2 themes.

Value

Aggplot2 theme.

Examples

# Plotting iris dataset (using ggplot2 and unikn):library('ggplot2')  # theme_ds4psy() requires ggplot2library('unikn')    # for colors and usecol() function   ggplot(datasets::iris) +  geom_jitter(aes(x = Petal.Length, y = Petal.Width, color = Species), size = 3, alpha = 2/3) +  scale_color_manual(values = usecol(pal = c(Pinky, Seeblau, Seegruen))) +  labs(title = "Iris petals",       subtitle = "The subtitle of this plot",        caption = "Data from datasets::iris") +  theme_ds4psy()ggplot(datasets::iris) +  geom_jitter(aes(x = Sepal.Length, y = Sepal.Width, color = Species), size = 3, alpha = 2/3) +  facet_wrap(~Species) +  scale_color_manual(values = usecol(pal = c(Pinky, Seeblau, Seegruen))) +  labs(tag = "A",       title = "Iris sepals",       subtitle = "Demo plot with facets and default colors",        caption = "Data from datasets::iris") +   coord_fixed(ratio = 3/2) +   theme_ds4psy()# A unikn::Seeblau look:ggplot(datasets::iris) +  geom_jitter(aes(x = Sepal.Length, y = Sepal.Width, color = Species), size = 3, alpha = 2/3) +  facet_wrap(~Species) +  scale_color_manual(values = usecol(pal = c(Pinky, Seeblau, Seegruen))) +  labs(tag = "B",       title = "Iris sepals",       subtitle = "Demo plot in unikn::Seeblau colors",        caption = "Data from datasets::iris") +   coord_fixed(ratio = 3/2) +   theme_ds4psy(col_title = pal_seeblau[[4]], col_strip = pal_seeblau[[1]], col_brdrs = Grau)

A basic and flexible plot theme (usingggplot2)

Description

theme_empty provides an empty (blank) theme to use inggplot2 commands.

Usage

theme_empty(  font_size = 12,  font_family = "",  rel_small = 12/14,  plot_mar = c(0, 0, 0, 0))

Arguments

font_size

Overall font size. Default:font_size = 12.

font_family

Base font family.Default:font_family = "".

rel_small

Relative size of smaller text. Default:rel_small = 10/12.

plot_mar

Plot margin sizes (on top, right, bottom, left). Default:plot_mar = c(0, 0, 0, 0) (in lines).

Details

theme_empty shows nothing but the plot panel.

theme_empty is based ontheme_nothing of thecowplot package and usestheme_void of theggplot2 package.

Value

Aggplot2 theme.

Examples

# Plotting iris dataset (using ggplot2):library('ggplot2')  # theme_empty() requires ggplot2   ggplot(datasets::iris) +  geom_point(aes(x = Petal.Length, y = Petal.Width, color = Species), size = 4, alpha = 1/2) +  scale_color_manual(values = c("firebrick3", "deepskyblue3", "olivedrab3")) +  labs(title = "NOT SHOWN: Title",       subtitle = "NOT SHOWN: Subtitle",        caption = "NOT SHOWN: Data from datasets::iris") +  theme_empty(plot_mar = c(2, 0, 1, 0))  # margin lines (top, right, bot, left)

Translate text into leet slang

Description

transl33t translates text into leet (or l33t) slang given a set of rules.

Usage

transl33t(txt, rules = l33t_rul35, in_case = "no", out_case = "no")

Arguments

txt

The text (character string) to translate.

rules

Rules which existing character intxt is to be replaced by which new character (as a named character vector). Default:rules =l33t_rul35.

in_case

Change case of input stringtxt. Default:in_case = "no". Set to"lo" or"up" for lower or uppercase, respectively.

out_case

Change case of output string. Default:out_case = "no". Set to"lo" or"up" for lower or uppercase, respectively.

Details

The current version oftransl33t only usesbase R commands, rather than thestringr package.

Value

A character vector.

Examples

# Use defaults:transl33t(txt = "hello world")transl33t(txt = c(letters))transl33t(txt = c(LETTERS))# Specify rules:transl33t(txt = "hello world",           rules = c("e" = "3", "l" = "1", "o" = "0"))# Set input and output case:transl33t(txt = "hello world", in_case = "up",           rules = c("e" = "3", "l" = "1", "o" = "0"))  # e only capitalizedtransl33t(txt = "hEllo world", in_case = "lo", out_case = "up",           rules = c("e" = "3", "l" = "1", "o" = "0"))  # e transl33ted

What date is it?

Description

what_date provides a satisficing version ofSys.Date() that is sufficient for most purposes.

Usage

what_date(  when = NA,  rev = FALSE,  as_string = TRUE,  sep = "-",  month_form = "m",  tz = "")

Arguments

when

Date(s) (as a scalar or vector).Default:when = NA. Usingas.Date(when) to convert strings into dates, andSys.Date(), ifwhen = NA.

rev

Boolean: Reverse date (to Default:rev = FALSE.

as_string

Boolean: Return as character string? Default:as_string = TRUE. Ifas_string = FALSE, a "Date" object is returned.

sep

Character: Separator to use. Default:sep = "-".

month_form

Character: Month format. Default:month_form = "m" for numeric month (01-12). Usemonth_form = "b" for short month name andmonth_form = "B" for full month name (in current locale).

tz

Time zone.Default:tz = "" (i.e., current system time zone, seeSys.timezone()). Usetz = "UTC" for Coordinated Universal Time.

Details

By default,what_date returns eitherSys.Date() or the dates provided bywhen as a character string (using current system settings andsep for formatting). Ifas_string = FALSE, a "Date" object is returned.

Thetz argument allows specifying time zones (seeSys.timezone() for current setting andOlsonNames() for options.)

However,tz is merely used to represent the dates provided to thewhen argument. Thus, there currently is no active conversion of dates into other time zones (see thetoday function oflubridate package).

Value

A character string or object of class "Date".

Examples

what_date()  what_date(sep = "/")what_date(rev = TRUE)what_date(rev = TRUE, sep = ".")what_date(rev = TRUE, sep = " ", month_form = "B")# with "POSIXct" times:what_date(when = Sys.time())# with time vector (of "POSIXct" objects):ts <- c("1969-07-13 13:53 CET", "2020-12-31 23:59:59")what_date(ts)what_date(ts, rev = TRUE, sep = ".")what_date(ts, rev = TRUE, month_form = "b")# return a "Date" object:dt <- what_date(as_string = FALSE)class(dt)# with time zone: ts <- ISOdate(2020, 12, 24, c(0, 12))  # midnight and midday UTCwhat_date(when = ts, tz = "Pacific/Honolulu", as_string = FALSE)

What month is it?

Description

what_month provides a satisficing version of to determine the month corresponding to a given date.

Usage

what_month(when = Sys.Date(), abbr = FALSE, as_integer = FALSE)

Arguments

when

Date (as a scalar or vector).Default:when = NA. Usingas.Date(when) to convert strings into dates, andSys.Date(), ifwhen = NA.

abbr

Boolean: Return abbreviated?Default:abbr = FALSE.

as_integer

Boolean: Return as integer? Default:as_integer = FALSE.

Details

what_month returns the month ofwhen orSys.Date() (as a name or number).

Examples

what_month()what_month(abbr = TRUE)what_month(as_integer = TRUE)# with date vector (as characters):ds <- c("2020-01-01", "2020-02-29", "2020-12-24", "2020-12-31")what_month(when = ds)what_month(when = ds, abbr = TRUE, as_integer = FALSE)what_month(when = ds, abbr = TRUE, as_integer = TRUE)# with time vector (strings of POSIXct times):ts <- c("2020-02-29 10:11:12 CET", "2020-12-31 23:59:59")what_month(ts)

What time is it?

Description

what_time provides a satisficing version ofSys.time() that is sufficient for most purposes.

Usage

what_time(when = NA, seconds = FALSE, as_string = TRUE, sep = ":", tz = "")

Arguments

when

Time (as a scalar or vector).Default:when = NA. ReturningSys.time(), ifwhen = NA.

seconds

Boolean: Show time with seconds?Default:seconds = FALSE.

as_string

Boolean: Return as character string? Default:as_string = TRUE. Ifas_string = FALSE, a "POSIXct" object is returned.

sep

Character: Separator to use. Default:sep = ":".

tz

Time zone.Default:tz = "" (i.e., current system time zone, seeSys.timezone()). Usetz = "UTC" for Coordinated Universal Time.

Details

By default,what_time prints a simple version ofwhen orSys.time() as a character string (in "using current default system settings. Ifas_string = FALSE, a "POSIXct" (calendar time) object is returned.

Thetz argument allows specifying time zones (seeSys.timezone() for current setting andOlsonNames() for options.)

However,tz is merely used to represent the times provided to thewhen argument. Thus, there currently is no active conversion of times into other time zones (see thenow function oflubridate package).

Value

A character string or object of class "POSIXct".

Examples

what_time()  # with vector (of "POSIXct" objects): tm <- c("2020-02-29 01:02:03", "2020-12-31 14:15:16")what_time(tm)# with time zone: ts <- ISOdate(2020, 12, 24, c(0, 12))  # midnight and midday UTCt1 <- what_time(when = ts, tz = "Pacific/Honolulu")t1  # time display changed, due to tz# return "POSIXct" object(s):# Same time in differen tz:t2 <- what_time(as.POSIXct("2020-02-29 10:00:00"), as_string = FALSE, tz = "Pacific/Honolulu")format(t2, "%F %T %Z (UTF %z)")# from string:t3 <- what_time("2020-02-29 10:00:00", as_string = FALSE, tz = "Pacific/Honolulu")format(t3, "%F %T %Z (UTF %z)")

What day of the week is it?

Description

what_wday provides a satisficing version of to determine the day of the week corresponding to a given date.

Usage

what_wday(when = Sys.Date(), abbr = FALSE)

Arguments

when

Date (as a scalar or vector).Default:when = Sys.Date(). Aiming to convertwhen into "Date" if a different object class is provided.

abbr

Boolean: Return abbreviated?Default:abbr = FALSE.

Details

what_wday returns the name of the weekday ofwhen or ofSys.Date() (as a character string).

Examples

what_wday()what_wday(abbr = TRUE)what_wday(Sys.Date() + -1:1)  # Date (as vector)what_wday(Sys.time())         # POSIXctwhat_wday("2020-02-29")       # string (of valid date)what_wday(20200229)           # number (of valid date)# date vector (as characters):ds <- c("2020-01-01", "2020-02-29", "2020-12-24", "2020-12-31")what_wday(when = ds)what_wday(when = ds, abbr = TRUE)# time vector (strings of POSIXct times):ts <- c("1969-07-13 13:53 CET", "2020-12-31 23:59:59")what_wday(ts)# fame data:greta_dob <- as.Date(fame[grep(fame$name, pattern = "Greta") , ]$DOB, "%B %d, %Y")what_wday(greta_dob)  # Friday, of course.

What week is it?

Description

what_week provides a satisficing version of to determine the week corresponding to a given date.

Usage

what_week(when = Sys.Date(), unit = "year", as_integer = FALSE)

Arguments

when

Date (as a scalar or vector).Default:when = Sys.Date(). Usingas.Date(when) to convert strings into dates if a differentwhen is provided.

unit

Character: Unit of week?Possible values are"month", "year". Default:unit = "year" (for week within year).

as_integer

Boolean: Return as integer? Default:as_integer = FALSE.

Details

what_week returns the week ofwhen orSys.Date() (as a name or number).

Examples

what_week()what_week(as_integer = TRUE)# Other dates/times:d1 <- as.Date("2020-12-24")what_week(when = d1, unit = "year")what_week(when = d1, unit = "month")what_week(Sys.time())  # with POSIXct time # with date vector (as characters):ds <- c("2020-01-01", "2020-02-29", "2020-12-24", "2020-12-31")what_week(when = ds)what_week(when = ds, unit = "month", as_integer = TRUE)what_week(when = ds, unit = "year", as_integer = TRUE)# with time vector (strings of POSIXct times):ts <- c("2020-12-25 10:11:12 CET", "2020-12-31 23:59:59")what_week(ts)

What year is it?

Description

what_year provides a satisficing version of to determine the year corresponding to a given date.

Usage

what_year(when = Sys.Date(), abbr = FALSE, as_integer = FALSE)

Arguments

when

Date (as a scalar or vector).Default:when = NA. Usingas.Date(when) to convert strings into dates, andSys.Date(), ifwhen = NA.

abbr

Boolean: Return abbreviated?Default:abbr = FALSE.

as_integer

Boolean: Return as integer? Default:as_integer = FALSE.

Details

what_year returns the year ofwhen orSys.Date() (as a name or number).

Examples

what_year()what_year(abbr = TRUE)what_year(as_integer = TRUE)# with date vectors (as characters):ds <- c("2020-01-01", "2020-02-29", "2020-12-24", "2020-12-31")what_year(when = ds)what_year(when = ds, abbr = TRUE, as_integer = FALSE)what_year(when = ds, abbr = TRUE, as_integer = TRUE)# with time vector (strings of POSIXct times):ts <- c("2020-02-29 10:11:12 CET", "2020-12-31 23:59:59")what_year(ts)

Paste or collapse words`x` into a text.

Description

words_to_text pastes or collapses a character stringx into a single text string.

Usage

words_to_text(x, collapse = " ")

Arguments

x

A string of text (required), typically a character vector.

collapse

A character string to separate the elements ofx in the resulting text. Default:collapse = " ".

Details

words_to_text is essentially identical tocollapse_chars. Internally, both functions are wrappers aroundpaste with acollapse argument.

Value

A text (as a collapsed character vector).

Examples

s <- c("Hello world!", "A 1st sentence.", "A 2nd sentence.", "The end.")words_to_text(s)cat(words_to_text(s, collapse = "\n"))

Get zodiac corresponding to date(s)

Description

zodiac provides the tropical zodiac sign or symbol (aka. astrological sign) for given date(s)x.

Usage

zodiac(  x,  out = "en",  zodiac_swap_mmdd = c(120, 219, 321, 421, 521, 621, 723, 823, 923, 1023, 1123, 1222))

Arguments

x

Date (as a scalar or vector, required).Ifx is not a date (of class "Date"), the function tries to coercex into a "Date".

out

Output format (as character). Available output formats are: English/Latin (out = "en", by default), German/Deutsch (out = "de"), HTML (out = "html"), or Unicode (out = "Unicode") symbols.

zodiac_swap_mmdd

Monthly dates on which the 12 zodiac signs switch (inmmdd format, ordered chronologically within a calendar year). Default:zodiac_swap_mmdd = c(0120, 0219, 0321, 0421, 0521, 0621, 0723, 0823, 0923, 1023, 1123, 1222).

Details

zodiac is flexible by providing different output formats (in Latin/English, German, or Unicode/HTML, seeout) and allowing to adjust the calendar dates on which a new zodiac is assigned (viazodiac_swap_mmdd).

Value

Zodiac label or symbol (as a factor).

Source

Seehttps://en.wikipedia.org/wiki/Zodiac orhttps://de.wikipedia.org/wiki/Tierkreiszeichen for alternative date ranges.

Examples

zodiac(Sys.Date())# Works with vectors:dt <- sample_date(size = 10)zodiac(dt)levels(zodiac(dt))# Alternative outputs:zodiac(dt, out = "de")  # German/deutschzodiac(dt, out = "Unicode")  # Unicodezodiac(dt, out = "HTML")     # HTML# Alternative date breaks:zodiac("2000-08-23")  # 0823 is "Virgo" by defaultzodiac("2000-08-23",  # change to 0824 (i.e., August 24):        zodiac_swap_mmdd = c(0120, 0219, 0321, 0421, 0521, 0621,                             0723, 0824, 0923, 1023, 1123, 1222))

Movatterモバイル変換

Data: Bushisms

Description

Usage

Format

Source

See Also

Data: Trumpisms

Description

Usage

Format

Details

Source

See Also

Umlaut provides German Umlaut letters (as Unicode characters).

Description

Usage

Format

Details

See Also

Examples

Convert a string of numeral digits from some base into decimal notation

Description

Usage

Arguments

Details

Value

See Also

Examples

Base digits: Sequence of numeric symbols (as named vector)

Description

Usage

Format

Details

See Also

Examples

Capitalize initial characters in a string of text

Description

Usage

Arguments

Details

Value

See Also

Examples

Flip the case of characters in a string of text

Description

Usage

Arguments

Details

Value

See Also

Examples

cclass provides character classes (as a named vector).

Description

Usage

Format

Details

See Also

Examples

Change time and time zone (without changing time display)

Description

Usage

Arguments

Details

Value

See Also

Examples

Change time zone (without changing represented time).

Description

Usage

Arguments

Details

Value

See Also

Examples

Combine character inputsx into a single string of text.

Description

Usage

Arguments

Details

Combine character inputs`x` into a single string of text.

Collapse character inputs`x` into a single string.