furniture (version 1.5.4)

table1: Table 1 for Simple and Stratified Descriptive Statistics

Description

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)].

Usage

table1(.data, ..., splitby = NULL, FUN = NULL, FUN2 = NULL,
  second = NULL, row_wise = FALSE, test = FALSE, type = "pvalues",
  output = "text", rounding_perc = 1, var_names = NULL,
  format_number = FALSE, NAkeep = FALSE, booktabs = TRUE,
  caption = NULL, align = NULL, export = 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.

splitby

the categorical variable to stratify by in formula form (e.g., splitby = ~gender) or quoted (e.g., splitby = "gender"); not too surprisingly, it requires that the number of levels be > 0

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

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").

rounding_perc

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

var_names

custom variable names to be printed in the table

format_number

default in 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

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

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")

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

# NOT RUN {
## Ficticious Data ##
library(furniture)
library(tidyverse)

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)

## Stratified
## both below are the same
table1(df, x, y, z,
       splitby = ~ a)
table1(df, x, y, z,
       splitby = "a")

## With Piping
df %>%
  table1(x, y, z, 
         splitby = ~a) %>%
  summarise(count = n())

## Adjust variables within function
table1(df, ifelse(x > 0, 1, 0), z,
       var_names = c("X2", "Z"))
         

# }