Movatterモバイル変換

sumvar: Summarise Continuous and Categorical Variables in R

Description

The sumvar package explores continuous and categorical variables.sumvar brings the ease and simplicity of the "sum" and "tab" functions from Stata to R.

• To explore a continuous variable, usedist_sum(). You can stratify by a grouping variable:df %>% dist_sum(var, group)

• To explore dates, usedist_date(); usage is the same asdist_sum().

• To summarise a single categorical variable usetab1(), e.g.df %>% tab1(var). For a two-way table, usetab(), e.g.df %>% tab(var1, var2). Both include options for frequentist hypothesis tests.

• Explore duplicates and missing values with withdup().

All functions are tidyverse/dplyr-friendly and accept the%>% pipe, outputting results as a tibble. You can save outputs for further manipulation, e.g.summary <- df %>% dist_sum(var).

Author(s)

Maintainer: Alexander Stockdalea.stockdale@liverpool.ac.uk

See Also

Useful links:

• https://github.com/alstockdale/sumvar

• https://alstockdale.github.io/sumvar/

• Report bugs athttps://github.com/alstockdale/sumvar/issues

Pipe operator

Description

Seemagrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of callingrhs(lhs).

Summarize and visualize a date variable

Description

Summarises the minimum, maximum, median, and interquartile range of a date variable,optionally stratified by a grouping variable. Produces a histogram and (optionally) a density plot.

Usage

dist_date(data, var, by = NULL)

Arguments

Value

A tibble with summary statistics for the date variable.

See Also

dist_sum for continuous variables.

Examples

# Example ungroupeddf <- tibble::tibble(  dt = as.Date("2020-01-01") + sample(0:1000, 100, TRUE))dist_date(df, dt)# Example groupeddf2 <- tibble::tibble(  dt = as.Date("2020-01-01") + sample(0:1000, 100, TRUE),  grp = sample(1:2, 100, TRUE))dist_date(df2, dt, grp)# Note this function accepts a pipe from dplyr eg. df %>% dist_date(date_var, group_var)

Explore a continuous variable.

Description

Summarises the median, interquartile range, mean, standard deviation, confidence intervals of the mean and produces a density plot, stratified by a second grouping variable.

Provides frequentist hypothesis tests for comparison between groups: T test and Wilcoxon rank sum for 2 groups,Anova and Kruskall wallis test for 3 or more groups.

The function accepts an input from a dplyr pipe "%>%" and outputs the results as a tibble.

Usage

dist_sum(data, var, by = NULL)

Arguments

Value

A tibble with a summary of the variable frequency (n), number of missing observations (n_miss), median,interquartile range, mean, SD, 95% confidence intervals of the mean (using the Z distribution), and density plots.

Shows the T test (p_ttest) and Wilcoxon rank sum (p_wilcox) hypothesis tests when there are two groupsAnd an Anova test (p_anova) and Kruskal-Wallis test (p_kruskal) when there are three or more groups.

Examples

example_data <- dplyr::tibble(id = 1:100, age = rnorm(100, mean = 30, sd = 10),                              group = sample(c("a", "b", "c", "d"),                              size = 100, replace = TRUE))dist_sum(example_data, age, group)example_data <- dplyr::tibble(id = 1:100, age = rnorm(100, mean = 30, sd = 10),                             sex = sample(c("male", "female"),                             size = 100, replace = TRUE))dist_sum(example_data, age, sex)summary <- dist_sum(example_data, age, sex) # Save summary statistics as a tibble.

Explore duplicate and missing data

Description

Provides an integer value for the number of duplicates found within a variableThe function accepts an input from a dplyr pipe "%>%" and outputs the results as a tibble.

eg. example_data %>% dup(variable)

Usage

dup(data, var = NULL)

Arguments

Value

A tibble with the number and percentage of duplicate values found, and the number of missing values (NA), together with percentages.

Examples

example_data <- dplyr::tibble(id = 1:200, age = round(rnorm(200, mean = 30, sd = 50), digits=0))example_data$age[sample(1:200, size = 15)] <- NA  # Replace 15 values with missing.dup(example_data, age)# It is also possible to pass a whole database to dup and it will explore all variables.example_data <- dplyr::tibble(age = round(rnorm(200, mean = 30, sd = 50), digits=0),                              sex = sample(c("Male", "Female"), 200, TRUE),                              favourite_colour = sample(c("Red", "Blue", "Purple"), 200, TRUE))example_data$age[sample(1:200, size = 15)] <- NA  # Replace 15 values with missing.example_data$sex[sample(1:200, size = 32)] <- NA  # Replace 32 values with missing.dup(example_data)

Create a cross-tabulation of two categorial variables

Description

Creates a "n x n" cross-tabulation of two categorical variables, with row percentages.Includes options for adding frequentist hypothesis testing.

The function accepts an input from a dplyr pipe "%>%" and outputs the results as a tibble.

eg. example_data %>% tab(variable1, variable2)

Usage

tab(data, variable1, variable2, test = "none")

Arguments

Value

A tibble with a cross-tabulation of frequencies and row percentages

Examples

example_data <- dplyr::tibble(id = 1:100, group1 = sample(c("a", "b", "c", "d"),                                                  size = 100, replace = TRUE),                                                  group2= sample(c("male", "female"),                                                  size = 100, replace = TRUE))example_data$group1[sample(1:100, size = 10)] <- NA  # Replace 10 with missingtab(example_data, group1, group2)summary <- tab(example_data, group1, group2) # Save summary statistics as a tibble.

Summarise a categorial variable

Description

Summarises frequencies and percentages for a categorical variable.

The function accepts an input from a dplyr pipe "%>%" and outputs the results as a tibble.eg. example_data %>% tab1(variable)

Usage

tab1(data, variable, dp = 1)

Arguments

Value

A tibble with frequencies and percentages

Examples

example_data <- dplyr::tibble(id = 1:100, group = sample(c("a", "b", "c", "d"),                                                  size = 100, replace = TRUE))example_data$group[sample(1:100, size = 10)] <- NA  # Replace 10 with missingtab1(example_data, group)summary <- tab1(example_data, group) # Save summary statistics as a tibble.