Produces a descriptive table, stratified by an optional categorical variable, providing means/frequencies and standard deviations/percentages. It is well-formatted for easy transition to academic article or report. Can be used within the piping framework [see library(magrittr)].

table1(.data, ..., splitby = NULL, FUN = NULL, FUN2 = NULL,
  second = NULL, row_wise = FALSE, test = FALSE,
  header_labels = NULL, type = "pvalues", output = "text",
  rounding_perc = 1, digits = 1, var_names = NULL,
  format_number = FALSE, NAkeep = NULL, na.rm = TRUE,
  booktabs = TRUE, caption = NULL, align = NULL, float = "ht",
  export = NULL, label = NULL)

Arguments

.data

the data.frame that is to be summarized

...

variables in the data set that are to be summarized; unquoted names separated by commas (e.g. age, gender, race) or indices. If indices, it needs to be a single vector (e.g. c(1:5, 8, 9:20) instead of 1:5, 8, 9:20). As it is currently, it CANNOT handle both indices and unquoted names simultaneously. Finally, any empty rows (where the row is NA for each variable selected) will be removed for an accurate n count.

splitby

the categorical variable to stratify (in formula form splitby = ~gender) or quoted splitby = "gender"; instead, dplyr::group_by(...) can be used within a pipe (this is the default when the data object is a grouped data frame from dplyr::group_by(...)).

FUN

the function to be applied to summarize the numeric data; default is to report the means and standard deviations

FUN2

a secondary function to be applied to summarize the numeric data; default is to report the medians and 25% and 75% quartiles

second

a vector or list of quoted continuous variables for which the FUN2 should be applied

row_wise

how to calculate percentages for factor variables when splitby != NULL: if FALSE calculates percentages by variable within groups; if TRUE calculates percentages across groups for one level of the factor variable.

test

logical; if set to TRUE then the appropriate bivariate tests of significance are performed if splitby has more than 1 level. A message is printed when the variances of the continuous variables being tested do not meet the assumption of Homogeneity of Variance (using Breusch-Pagan Test of Heteroskedasticity) and, therefore, the argument `var.equal = FALSE` is used in the test.

header_labels

a character vector that renames the header labels (e.g., the blank above the variables, the p-value label, and test value label).

type

what is displayed in the table; a string or a vector of strings. Two main sections can be inputted: 1. if test = TRUE, can write "pvalues", "full", or "stars" and 2. can state "simple" and/or "condense". These are discussed in more depth in the details section below.

output

how the table is output; can be "text" or "text2" for regular console output or any of kable()'s options from knitr (e.g., "latex", "markdown", "pandoc"). A new option, 'latex2', although more limited, allows the variable name to show and has an overall better appearance.

rounding_perc

the number of digits after the decimal for percentages; default is 1

digits

the number of significant digits for the numerical variables (if using default functions); default is 1.

var_names

custom variable names to be printed in the table. Variable names can be applied directly in the list of variables.

format_number

default is FALSE; if TRUE, then the numbers are formatted with commas (e.g., 20,000 instead of 20000)

NAkeep

when set to TRUE it also shows how many missing values are in the data for each categorical variable being summarized (deprecated; use na.rm)

na.rm

when set to FALSE it also shows how many missing values are in the data for each categorical variable being summarized

booktabs

when output != "text"; option is passed to knitr::kable

caption

when output != "text"; option is passed to knitr::kable

align

when output != "text"; option is passed to knitr::kable

float

the float applied to the table in Latex when output is latex2, default is "ht".

export

character; when given, it exports the table to a CSV file to folder named "table1" in the working directory with the name of the given string (e.g., "myfile" will save to "myfile.csv")

label

for output == "latex2", this provides a table reference label for latex

Value

A table with the number of observations, means/frequencies and standard deviations/percentages is returned. The object is a table1 class object with a print method. Can be printed in LaTex form.

Details

In defining type, 1. options are "pvalues" that display the p-values of the tests, "full" which also shows the test statistics, or "stars" which only displays stars to highlight significance with *** < .001 ** .01 * .05; and 2. "simple" then only percentages are shown for categorical variable and "condense" then continuous variables' means and SD's will be on the same line as the variable name and dichotomous variables only show counts and percentages for the reference category.

Examples

## Fictitious Data ## library(furniture) library(dplyr)
#> #> Attaching package: ‘dplyr’
#> The following object is masked from ‘package:testthat’: #> #> matches
#> The following objects are masked from ‘package:stats’: #> #> filter, lag
#> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union
x <- runif(1000) y <- rnorm(1000) z <- factor(sample(c(0,1), 1000, replace=TRUE)) a <- factor(sample(c(1,2), 1000, replace=TRUE)) df <- data.frame(x, y, z, a) ## Simple table1(df, x, y, z, a)
#> Warning: Not all variables have at least 2 unique values. Functionality of the following will be limited: #> -- type = 'condense' will not work #> -- test = TRUE will not work
#>
#> #> ──────────────────────── #> Mean/Count (SD/%) #> n = 1000 #> x #> 0.5 (0.3) #> y #> 0.1 (1.0) #> z #> 0 510 (51%) #> 1 490 (49%) #> a #> 1 525 (52.5%) #> 2 475 (47.5%) #> ────────────────────────
## Stratified ## all three below are the same table1(df, x, y, z, splitby = ~ a)
#>
#> #> ──────────────────────────── #> a #> 1 2 #> n = 525 n = 475 #> x #> 0.5 (0.3) 0.5 (0.3) #> y #> 0.0 (1.0) 0.1 (1.0) #> z #> 0 257 (49%) 253 (53.3%) #> 1 268 (51%) 222 (46.7%) #> ────────────────────────────
table1(df, x, y, z, splitby = "a")
#>
#> #> ──────────────────────────── #> a #> 1 2 #> n = 525 n = 475 #> x #> 0.5 (0.3) 0.5 (0.3) #> y #> 0.0 (1.0) 0.1 (1.0) #> z #> 0 257 (49%) 253 (53.3%) #> 1 268 (51%) 222 (46.7%) #> ────────────────────────────
## With Piping df %>% table1(x, y, z, splitby = ~a)
#>
#> #> ──────────────────────────── #> a #> 1 2 #> n = 525 n = 475 #> x #> 0.5 (0.3) 0.5 (0.3) #> y #> 0.0 (1.0) 0.1 (1.0) #> z #> 0 257 (49%) 253 (53.3%) #> 1 268 (51%) 222 (46.7%) #> ────────────────────────────
df %>% group_by(a) %>% table1(x, y, z)
#> Using dplyr::group_by() groups: a
#>
#> #> ──────────────────────────── #> a #> 1 2 #> n = 525 n = 475 #> x #> 0.5 (0.3) 0.5 (0.3) #> y #> 0.0 (1.0) 0.1 (1.0) #> z #> 0 257 (49%) 253 (53.3%) #> 1 268 (51%) 222 (46.7%) #> ────────────────────────────
## Adjust variables within function and assign name table1(df, x2 = ifelse(x > 0, 1, 0), z = z)
#> Warning: Not all variables have at least 2 unique values. Functionality of the following will be limited: #> -- type = 'condense' will not work #> -- test = TRUE will not work
#>
#> #> ──────────────────────── #> Mean/Count (SD/%) #> n = 1000 #> x2 #> 1.0 (0.0) #> z #> 0 510 (51%) #> 1 490 (49%) #> ────────────────────────