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Shift to the middle of each interval

Description

After thickening all the values are eithershifted to the first or the last value of their interval.This function creates a vector fromx, with the values shifted tothe (approximate) center of the interval. This can give a more accuratepicture of the aggregated data when plotting.

Usage

center_interval(x, shift = c("up", "down"), interval = NULL)

Arguments

Details

The interval will be translated to number of days whenx is of classDate, or number of seconds whenx is ofclassPOSIXt. For months and quarters this will be the averagelength of the interval. The translated units divided by twowill be added by or subtracted from each value ofx.

Value

Vector of the same class asx, with the values shifted to the(approximate) center.

Examples

library(dplyr)library(ggplot2)plot_set <- emergency %>%  thicken("hour", "h") %>%  count(h) %>%  head(24)ggplot(plot_set, aes(h, n)) + geom_col()plot_set %>%  mutate(h_center = center_interval(h)) %>%  ggplot(aes(h_center, n)) + geom_col()

Retrieve the closest given weekday

Description

Find the closest instance of the requested weekday tomin(x).Helper function forthicken with the interval "week", when the userdesires the start day of the weeks to be different from Sundays.

Usage

closest_weekday(x, wday = 1, direction = c("down", "up"))

Arguments

Value

Object of classDate, reflecting the closest desired weekdaytox.

Examples

closest_weekday(coffee$time_stamp)closest_weekday(coffee$time_stamp, 5)closest_weekday(coffee$time_stamp, 1, direction = "up")closest_weekday(coffee$time_stamp, 5, direction = "up")

Coffee Data Set

Description

Made-up data set for demonstratingpadr.

Usage

coffee

Format

A data frame with 4 rows and 2 variables:

time_stamp

YYYY-MM-DD HH:MM:SS

amount

Amount spent on coffee

Emergency Calls for Montgomery County, PA

Description

The emergency calls coming in at Montgomery County, PA since 2015-12-10.Data set was created at 2016-10-17 16:15:40 CEST from the API and containsevents until 2016-10-17 09:47:03 EST. From the original set the columnsdesc and e are not included.

Usage

emergency

Format

A data frame with 120450 rows and 6 variables:

lat

Latitude from Google maps, based on the address

lng

Longitude from Google maps, based on the address

zip

Zipcode from Google, when possible

title

Title, emergency category

time_stamp

YYYY-MM-DD HH:MM:SS

twp

Township

Fill missing values by a function of the nonmissings

Description

For each specified column inx replace the missing values by afunction of the nonmissing values.

Usage

fill_by_function(x, ..., fun = mean)

Arguments

Value

x with the altered columns.

Examples

library(dplyr) # for the pipe operatorx <- seq(as.Date('2016-01-01'), by = 'day', length.out = 366)x <- x[sample(1:366, 200)] %>% sortx_df <- data_frame(x  = x,                   y1 = runif(200, 10, 20) %>% round,                   y2 = runif(200, 1, 50) %>% round)x_df %>% pad %>% fill_by_function(y1, y2)x_df %>% pad %>% fill_by_function(y1, y2, fun = median)

Fill missing values by the most prevalent nonmissing value

Description

For each specified column inx replace the missing values by the mostprevalent nonmissing value.

Usage

fill_by_prevalent(x, ...)

Arguments

Value

x with the altered columns.

Examples

library(dplyr) # for the pipe operatorx <- seq(as.Date('2016-01-01'), by = 'day', length.out = 366)x <- x[sample(1:366, 200)] %>% sortx_df <- data_frame(x  = x,                  y1 = rep(letters[1:3], c(80, 70, 50)) %>% sample,                  y2 = rep(letters[2:5], c(60, 80, 40, 20)) %>% sample)x_df %>% pad %>% fill_by_prevalent(y1, y2)

Fill missing values by a single value

Description

Replace all missing values in the specified columns by the same value.

Usage

fill_by_value(x, ..., value = 0)

Arguments

Value

x with the altered columns.

Examples

library(dplyr) # for the pipe operatorx <- seq(as.Date('2016-01-01'), by = 'day', length.out = 366)x <- x[sample(1:366, 200)] %>% sortx_df <- data_frame(x  = x,                   y1 = runif(200, 10, 20) %>% round,                   y2 = runif(200, 1, 50) %>% round,                   y3 = runif(200, 20, 40) %>% round,                   y4 = sample(letters[1:5], 200, replace = TRUE))x_padded <- x_df %>% padx_padded %>% fill_by_value(y1)x_df %>% pad %>% fill_by_value(y1, y2, value = 42)

Make a period character vector

Description

After applyingthicken all the observations of a period are mappedto a single time point. This function will convert a datetime variable toa character vector that reflects the period, instead of a single time point.strftime is used to format the start and the end of the interval.

Usage

format_interval(  x,  start_format = "%Y-%m-%d",  end_format = start_format,  sep = " ",  end_offset = 0,  units_to_last = NULL)

Arguments

Details

The end of the periods will be determined by the next unique valueinx. It does so without regarding the interval ofx. If a specificinterval is desired,thicken and / orpad should first beapplied to create an equally spaced datetime variable.

Value

A character vector showing the interval.

Examples

library(dplyr)library(ggplot2)plot_set <- emergency %>%  head(500) %>%  thicken("hour", "h") %>%  count(h)# this will show the data on the full hourggplot(plot_set, aes(h, n)) + geom_col()# adding a character to indicate the hours of the interval.plot_set %>%  mutate(h_int = format_interval(h, "%H", sep = "-"))

Get the interval of a datetime variable

Description

The interval is the highest datetime unit that can explain all instances of avariable of classDate, classPOSIXct, or classPOSIXct.This function will determine what the interval of the variable is.

Usage

get_interval(x)

Arguments

Details

Seevignette("padr") for more information on intervals.

Value

A character string indicating the interval ofx.

Examples

x_month <- seq(as.Date('2016-01-01'), as.Date('2016-05-01'), by = 'month')get_interval(x_month)x_sec <- seq(as.POSIXct('2016-01-01 00:00:00'), length.out = 100, by = 'sec')get_interval(x_sec)get_interval(x_sec[seq(0, length(x_sec), by = 5)])

Pad the datetime column of a data frame

Description

pad will fill the gaps in incomplete datetime variables, by figuring outwhat the interval of the data is and what instances are missing. It will inserta record for each of the missing time points. For allother variables in the data frame a missing value will be inserted at the padded rows.

Usage

pad(  x,  interval = NULL,  start_val = NULL,  end_val = NULL,  by = NULL,  group = NULL,  break_above = 1)

Arguments

Details

The interval of a datetime variable is the time unit at which theobservations occur. The eight intervals inpadr are from high to lowyear,quarter,month,week,day,hour,min, andsec. Sincepadr v.0.3.0 theinterval is no longer limited to be of a single unit.(Intervals like 5 minutes, 6 hours, 10 days are possible).pad willfigure out the interval of the input variable and the step size, and willfill the gaps for the instances that would be expected from the interval andstep size, but are missing in the input data.Note that whenstart_val and/orend_val are specified, they areconcatenated with the datetime variable before the interval is determined.

Rows with missing values in the datetime variables will be retained.However, they will be moved to the end of the returned data frame.

Value

The data framex with the datetime variable padded. Allnon-grouping variables in the data frame will have missing values at the rowsthat are padded. The result will always be sorted on the datetime variable.Ifgroup is notNULL result is sorted on grouping variable(s)first, then on the datetime variable.

Examples

simple_df <- data.frame(day = as.Date(c('2016-04-01', '2016-04-03')),                        some_value = c(3,4))pad(simple_df)pad(simple_df, interval = "day")library(dplyr) # for the pipe operatormonth <- seq(as.Date('2016-04-01'), as.Date('2017-04-01'),              by = 'month')[c(1, 4, 5, 7, 9, 10, 13)]month_df <- data.frame(month = month,                       y = runif(length(month), 10, 20) %>% round)# forward fill the padded values with tidyr's fillmonth_df %>% pad %>% tidyr::fill(y)# or fill all y with 0month_df %>% pad %>% fill_by_value(y)# padding a data.frame on group levelday_var <- seq(as.Date('2016-01-01'), length.out = 12, by = 'month')x_df_grp <- data.frame(grp1 = rep(LETTERS[1:3], each =4),                       grp2 = letters[1:2],                       y    = runif(12, 10, 20) %>% round(0),                       date = sample(day_var, 12, TRUE)) %>% arrange(grp1, grp2, date)# pad by one grouping varx_df_grp %>% pad(group = 'grp1')# pad by two groups varsx_df_grp %>% pad(group = c('grp1', 'grp2'), interval = "month")# Using group argument the interval is determined over all the observations,# ignoring the groups.x <- data.frame(dt_var = as.Date(c("2017-01-01", "2017-03-01", "2017-05-01","2017-01-01", "2017-02-01", "2017-04-01")),id = rep(1:2, each = 3), val = round(rnorm(6)))pad(x, group = "id")# applying pad with do, interval is determined individualle for each groupx %>% group_by(id) %>% do(pad(.))

Pad with a custom spanning

Description

Pad the datetime variable afterthicken_cust is applied, using the samespanning.

Usage

pad_cust(x, spanned, by = NULL, group = NULL, drop_last_spanned = TRUE)

Arguments

Value

The data framex with the datetime column padded.

Examples

library(dplyr)# analysis of traffic accidents in traffic jam hours and other hours.accidents <- emergency %>% filter(title == "Traffic: VEHICLE ACCIDENT -")spanning <- span_time("20151210 16", "20161017 17", tz = "EST") %>%  subset_span(list(hour = c(6, 9, 16, 19)))thicken_cust(accidents, spanning, "period") %>%  count(period) %>%  pad_cust(spanning)

Pad the integer column of a data frame

Description

pad_int fills the gaps in incomplete integer variables. It will inserta record for each of the missing value. For allother variables in the data frame a missing value will be inserted at thepadded rows.

Usage

pad_int(x, by, start_val = NULL, end_val = NULL, group = NULL, step = 1)

Arguments

Value

The data framex with the specified variable padded. Allnon-grouping variables in the data frame will have missing values at the rowsthat are padded.

Examples

int_df <- data.frame(x = c(2005, 2007, 2008, 2011),                     val = c(3, 2, 6, 3))pad_int(int_df, 'x')pad_int(int_df, 'x', start_val = 2006, end_val = 2013)int_df2 <- data.frame(x = c(2005, 2015), val = c(3, 4))pad_int(int_df2, 'x', step = 2)pad_int(int_df2, 'x', step = 5)int_df3 <- data.frame(x = c(2005, 2006, 2008, 2006, 2007, 2009),                      g = rep(LETTERS[1:2], each = 3),                      val = c(6, 6, 3, 5, 4, 3))pad_int(int_df3, 'x', group = 'g')pad_int(int_df3, 'x', group = 'g', start_val = 2005, end_val = 2009)

Span an equally spaced vector around a datetime variable

Description

Span a vector of specified interval around a variable of classDate,POSIXct, orPOSIXlt..

Usage

span_around(x, interval, start_shift = NULL, end_shift = start_shift)

Arguments

Details

Note that use of thestart_shift andend_shiftarguments change the entire spanning when they are not in line withthe interval. It is not checked for.

Value

A datetime vector, with the first observation smaller or equal thanmin(x) and the last observation larger or equal thanmax(x).Spaces between points are equal tointerval.

Examples

span_around(coffee$time_stamp, "hour")span_around(coffee$time_stamp, "hour", end_shift = "2 hour")span_around(coffee$time_stamp, "2 day")span_around(coffee$time_stamp, "2 day", start_shift = "2 day")span_around(emergency$time_stamp, "week")span_around(emergency$time_stamp, "2 month")

Wrapper aroundseq.Date.

Description

Quickly create a sequence of dates from minimal specifications.

Usage

span_date(from, to = NULL, len_out = NULL, by = NULL)

Arguments

Details

Minimal specification of dates, sets unspecified date parts todefault values. These are 01 for both month and day.

In addition tofrom, eitherto orlen_out must be specified.Ifby is not specified,span_date will set the interval to thehighest of the specified date parts in eitherfrom orto.For example, if they are 2011 and 2015 it will be "year", if they are 2011and 201501 it will be "month".

Value

An object of class Date.

Examples

# using "to" argumentspan_date(2011, 2015)span_date(201101, 201501)span_date(2011, 2015, by = "month")span_date(2011, 201501)span_date(20111225, 2012)# using "len_out" argumentspan_date(2011, len_out = 4)span_date(201101, len_out = 4)span_date(20110101, len_out = 4)span_date(20110101, len_out = 4, by = "month")

Wrapper aroundseq.POSIXct.

Description

Quickly create a sequence of datetimes from minimal specifications.

Usage

span_time(from, to = NULL, len_out = NULL, by = NULL, tz = "UTC")

Arguments

Details

Minimal specification of datetimes, sets unspecified date parts todefault values. These are 01 for both month and day and 00 for hour, minute,and second.

In addition tofrom, eitherto orlength must be specified.If theby is not specified,span_time will set the interval tothe highest of the specified datetime parts in eitherfrom orto. For example, if they are "20160103 01" and "20160108 05" it willbe "hour", if they are "2011" and "20110101 021823" it will be "second".

Value

An object of class POSIXct.

Examples

# using tospan_time(2011, 2013)span_time("2011", "2013")span_time(2011, 201301)span_time(2011, 20130101)span_time(2011, "20110101 0023")span_time(2011, "20110101 002300")# using len_outspan_time(2011, len_out = 3)span_time("2011", len_out = 3)span_time(2011, len_out = 10, by = "month")span_time(2011, len_out = 10, by = "day")span_time(2011, len_out = 10, by = "hour")span_time("20110101 00", len_out = 10)span_time("20110101 002300", len_out = 10)

Subset a spanned datetime vector

Description

Take aDate,POSIXct, orPOSIXlt vector and subset it bya pattern of date and/or time parts.

Usage

subset_span(spanned, pattern_list)

Arguments

Details

For subsetting weekdays, they run from 0 (Sunday) to 6 (Saturday).

Value

Vector of the same class asspanned, containing all the data points inspanned that meets the requirements inpattern_list.

Examples

date_span <- span_date(20170701, len_out = 100)subset_span(date_span, list(wday = 1:5))time_span <- span_time("20170101 00", 201702)subset_span(time_span, list(hour = 7:17))subset_span(time_span, list(hour = c(10, 16), mday = seq(5, 30, 5)))

Add a variable of a higher interval to a data frame

Description

Take the datetime variable in a data frame and map thisto a variable of a higher interval. The mapping is added to the data framein a new variable.

Usage

thicken(  x,  interval,  colname = NULL,  rounding = c("down", "up"),  by = NULL,  start_val = NULL,  drop = FALSE,  ties_to_earlier = FALSE)

Arguments

Details

When the datetime variable contains missing values, they are leftin place in the dataframe. The added column with the new datetime variable,will have a missing values for these rows as well.

Seevignette("padr") for more information onthicken.Seevignette("padr_implementation") for detailed information ondaylight savings time, different timezones, and the implementation ofthicken.

Value

The data framex with the variable added to it.

Examples

x_hour <- seq(lubridate::ymd_hms('20160302 000000'), by = 'hour',              length.out = 200)some_df <- data.frame(x_hour = x_hour)thicken(some_df, 'week')thicken(some_df, 'month')thicken(some_df, 'day', start_val = lubridate::ymd_hms('20160301 120000'))library(dplyr)x_df <- data.frame(  x = seq(lubridate::ymd(20130101), by = 'day', length.out = 1000) %>%    sample(500),  y = runif(500, 10, 50) %>% round) %>%  arrange(x)# get the max per monthx_df %>% thicken('month') %>% group_by(x_month) %>%  summarise(y_max = max(y))# get the average per week, but you want your week to start on Mondays# instead of Sundaysx_df %>% thicken('week',                 start_val = closest_weekday(x_df$x, 2)) %>%  group_by(x_week) %>% summarise(y_avg = mean(y))# rounding up instead of downx <- data.frame(dt = lubridate::ymd_hms('20171021 160000',                                        '20171021 163100'))thicken(x, interval = "hour", rounding = "up")thicken(x, interval = "hour", rounding = "up", ties_to_earlier = TRUE)

Thicken with a custom spanning

Description

Likethicken, it will find the datetime variable inxand add a variable of a higher periodicity to it. However, the variable towhich to map the observation is provided by the user. This enables mapping totime points that are unequally spaced.

Usage

thicken_cust(x, spanned, colname, by = NULL, drop = FALSE)

Arguments

Details

Only rounding down is available for custom thickening.

Value

The data framex with the variable added to it.

Examples

library(dplyr)# analysis of traffic accidents in traffic jam hours and other hours.accidents <- emergency %>% filter(title == "Traffic: VEHICLE ACCIDENT -")spanning <- span_time("20151210 16", "20161017 17", tz = "EST") %>%  subset_span(list(hour = c(6, 9, 16, 19)))thicken_cust(accidents, spanning, "period") %>%  count(period) %>%  pad_cust(spanning)