Package 'describedata'

Create publication-style table across one categorical variable

Description

Descriptive statistics for categorical variables as well as normally and non-normally distributed continuous variables, split across levels of a categorical variable. Depending on the variable type, an appropriate statistical test is used to assess differences across levels of the comparison variable.

Usage

bivariate_compare(df, compare, normal_vars = NULL,
  non_normal_vars = NULL, cat_vars = NULL, display_round = 2,
  p = TRUE, p_round = 4, include_na = FALSE, col_n = TRUE,
  cont_n = FALSE, all_cont_mean = FALSE, all_cont_median = FALSE,
  iqr = TRUE, fisher = FALSE, workspace = NULL, var_order = NULL,
  var_label_df = NULL)

Arguments

df	A data.frame or tibble.
compare	Discrete variable. Separate statistics will be produced for each level, with statistical tests across levels. Must be quoted.
normal_vars	Character vector of normally distributed continuous variables that will be included in the descriptive table.
non_normal_vars	Character vector of non-normally distributed continuous variables that will be included in the descriptive table.
cat_vars	Character vector of categorical variables that will be included in the descriptive table.
display_round	Number of decimal places displayed values should be rounded to
p	Logical. Should p-values be calculated and displayed? Default TRUE.
p_round	Number of decimal places p-values should be rounded to.
include_na	Logical. Should NA values be included in the table and accompanying statistical tests? Default FALSE.
col_n	Logical. Should the total number of observations be displayed for each column? Default TRUE.
cont_n	Logical. Display sample n for continuous variables in the table. Default FALSE.
all_cont_mean	Logical. Display mean (sd) for all continuous variables. Default FALSE results in mean (sd) for normally distributed variables and median (IQR) for non-normally distributed variables. Must be FALSE if all_cont_median == TRUE.
all_cont_median	Logical. Display median (sd) for all continuous variables. Default FALSE results in mean (sd) for normally distributed variables and median (IQR) for non-normally distributed variables. Must be FALSE if all_cont_mean == TRUE.
iqr	Logical. If the median is displayed for a continuous variable, should interquartile range be displayed as well (TRUE), or should the values for the 25th and 75th percentiles be displayed (FALSE)? Default TRUE
fisher	Logical. Should Fisher's exact test be used for categorical variables? Default FALSE. Ignored if p == FALSE.
workspace	Numeric variable indicating the workspace to be used for Fisher's exact test. If NULL, the default, the default value of 2e5 is used. Ignored if fisher == FALSE.
var_order	Character vector listing the variable names in the order results should be displayed. If NULL, the default, continuous variables are displayed first, followed by categorical variables.
var_label_df	A data.frame or tibble with columns "variable" and "label" that contains display labels for each variable specified in normal_vars, non_normal_vars, and cat_vars.

Details

Statistical differences between normally distributed continuous variables are assessed using aov(), differences in non-normally distributed variables are assessed using kruskal.test(), and differences in categorical variables are assessed using chisq.test() by default, with a user option for fisher.test() instead.

Value

A data.frame with columns label, overall, a column for each level of compare, and p.value. For normal_vars, mean (SD) is displayed, for non_normal_vars median (IQR) is displayed, and for cat_vars n (percent) is displayed. For p values on continuous variables, a superscript 'a' denotes the Kruskal-Wallis test was used

Examples

bivariate_compare(iris, compare = "Species", normal_vars = c("Sepal.Length", "Sepal.Width"))

bivariate_compare(mtcars, compare = "cyl", non_normal_vars = "mpg")

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Calculate pairwise correlations

Description

Internal function to calculate pairwise correlations and return p values

Usage

cor.prob(df)

Arguments

df	A data frame or tibble.

Value

A data.frame with columns h_var, v_var, and p.value

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describedata: Miscellaneous descriptive and SAS/Stata duplicate functions

Description

The helpR package contains descriptive functions for tasks such as making print-friendly bivariate tables, sample size flow counts, and more. It also contains R approximations of some common, useful SAS/Stata functions.

Frequency functions

The helper functions bivariate_compare and univar_freq create frequency tables. univar_freq produces simple n and percent for categories of a single variable, while bivariate_compare compares continuous or categorical variables across categories of a comparison variable. This is particularly useful for generating a Table 1 or 2 for a publication manuscript.

Sample size functions

sample_flow produces tables illustrating how final sample size is determined and the number of participants excluded by each exclusion criteria.

Other helper functions

nagelkerke calculates the Nagelkerke pseudo r-squared for a logistic regression model.

Stata replica functions

ladder, gladder, and pwcorr are approximate replicas of the respective Stata functions. Not all functionality is currently incorporated. stata_tidy reformats R model output to a format similar to Stata.

SAS replica functions

proc_means is an approximate replica of the respective SAS function. Not all functionality is currently incorporated.

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Replica of Stata's gladder function

Description

Creates ladder-of-powers histograms to visualize nine common transformations and compare each to a normal distribution. The following transformations are included: identity, cubic, square, square root, natural logarithm, inverse square root, inverse, inverse square, and inverse cubic.

Usage

gladder(x)

Arguments

x	A continuous numeric vector.

Value

A ggplot object with plots of each transformation

Examples

gladder(iris$Sepal.Length)
gladder(mtcars$disp)

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Replica of Stata's ladder function

Description

Searches the ladder of powers histograms to find a transformation to make x normally distributed. The Shapiro-Wilkes test is used to assess for normality. The following transformations are included: identity, cubic, square, square root, natural logarithm, inverse square root, inverse, inverse square, and inverse cubic.

Usage

ladder(x)

Arguments

x	A continuous numeric vector.

Value

A data.frame

Examples

ladder(iris$Sepal.Length)
ladder(mtcars$disp)

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Calculate Nagelkerke pseudo r-squared

Description

Calculate Nagelkerke pseudo r-squared from a fitted model object.

Usage

nagelkerke(mod)

Arguments

mod	A glm model object, usually from logistic regression. The model must have been fit using the data option, in order to extract the data from the model object.

Value

Numeric value of Nagelkerke r-squared for the model

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Create density histogram with normal distribution overlaid

Description

Plots a simple density histogram for a continuous variable with a normal distribution overlaid. The overlaid normal distribution has the same mean and standard deviation as the provided variable, and the plot provides a visual means to assess the normality of the variable's distribution.

Usage

norm_dist_plot(df, vars)

Arguments

df	A data.frame or tibble.
vars	A character vector of continuous variable names.

Value

A ggplot object.

Examples

norm_dist_plot(df = iris, vars = "Sepal.Width")

norm_dist_plot(df = iris,
               vars = c("Sepal.Width", "Sepal.Length"))

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Replica of SAS's PROC MEANS

Description

Descriptive statistics for continuous variables, with the option of stratifying by a categorical variable.

Usage

proc_means(df, vars = NULL, var_order = NULL, by = NULL, n = T,
  mean = TRUE, sd = TRUE, min = TRUE, max = TRUE, median = FALSE,
  q1 = FALSE, q3 = FALSE, iqr = FALSE, nmiss = FALSE,
  nobs = FALSE, p = FALSE, p_round = 4, display_round = 3)

Arguments

df	A data frame or tibble.
vars	Character vector of numeric variables to generate descriptive statistics for. If the default (NULL), all variables are included, except for any specified in by.
var_order	Character vector listing the variable names in the order results should be displayed. If the default (NULL), variables are displayed in the order specified in vars.
by	Discrete variable. Separate statistics will be produced for each level. Default NULL provides statistics for all observations.
n	logical. Display number of rows with values. Default TRUE.
mean	logical. Display mean value. Default TRUE.
sd	logical. Display standard deviation. Default TRUE.
min	logical. Display minimum value. Default TRUE.
max	logical. Display maximum value. Default TRUE.
median	logical. Display median value. Default FALSE.
q1	logical. Display first quartile value. Default FALSE.
q3	logical. Display third quartile value. Default FALSE.
iqr	logical. Display interquartile range. Default FALSE.
nmiss	logical. Display number of missing values. Default FALSE.
nobs	logical. Display total number of rows. Default FALSE.
p	logical. Calculate p-value across by groups using aov. Ignored if no by variable specified. Default FALSE.
p_round	Number of decimal places p-values should be rounded to.
display_round	Number of decimal places displayed values should be rounded to

Value

A data.frame with columns variable, by variable, and a column for each summary statistic.

Examples

proc_means(iris, vars = c("Sepal.Length", "Sepal.Width"))
proc_means(iris, by = "Species")

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Replica of Stata's pwcorr function

Description

Calculate and return a matrix of pairwise correlation coefficients. Returns significance levels if method == "pearson"

Usage

pwcorr(df, vars = NULL, method = "pearson", var_label_df = NULL)

Arguments

df	A data.frame or tibble.
vars	A character vector of numeric variables to generate pairwise correlations for. If the default (NULL), all variables are included.
method	One of "pearson", "kendall", or "spearman" passed on to "cor".
var_label_df	A data.frame or tibble with columns "variable" and "label" that contains display labels for each variable specified in vars.

Value

A data.frame displaying the pairwise correlation coefficients between all variables in vars.

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Create table illustrating sample exclusions

Description

Generate a table illustrating sequential exclusion from an analytical sample due to user specified exclusions.

Usage

sample_flow(df, exclusions = c())

Arguments

df	A data.frame or tibble.
exclusions	Character vector of logical conditions indicating which rows should be excluded from the final sample. Exclusions occur in the order specified.

Value

A data.frame with columns Exclusion, 'Sequential Excluded', and 'Total Excluded' for display.

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Tidy model output into similar format from Stata

Description

Create a display data frame similar to Stata model output for a fitted R model.

Usage

stata_tidy(mod, var_label_df = NULL)

Arguments

mod	A fitted model object
var_label_df	A data.frame or tibble with columns "variable" and "label" that contains display labels for each variable in mod.

Value

A data.frame with columns term and display

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Univariate statistics for a discrete variable

Description

Descriptive statistics (N,

Usage

univar_freq(df, var, na.rm = FALSE)

Arguments

df	A data frame or tibble.
var	A discrete, numeric variable.
na.rm	logical. Should missing values (including NaN) be removed?)

Value

A data.frame with columns var, NObs, and Percent

Examples

univar_freq(iris, var = "Species")
univar_freq(mtcars, var = "cyl")

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