`table1.Rd`

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,
total = FALSE,
second = NULL,
row_wise = FALSE,
test = FALSE,
param = TRUE,
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
)
```

- .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

- total
whether a total (not stratified with the

`splitby`

or`group_by()`

) should also be reported in the table- 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.- param
logical; if set to

`TRUE`

then the appropriate parametric bivariate tests of significance are performed (if `test = TRUE`). For continuous variables, it is a t-test or ANOVA (depending on the number of levels of the group). If set to`FALSE`

, the Kruskal-Wallis Rank Sum Test is performed for the continuous variables. Either way, the chi-square test of independence is performed for categorical variables.- 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

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.

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.

```
## Fictitious Data ##
library(furniture)
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> 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)
#>
#>
#> ────────────────────────
#> Mean/Count (SD/%)
#> n = 1000
#> x
#> 0.5 (0.3)
#> y
#> 0.0 (1.0)
#> z
#> 0 503 (50.3%)
#> 1 497 (49.7%)
#> a
#> 1 483 (48.3%)
#> 2 517 (51.7%)
#> ────────────────────────
## Stratified
## all three below are the same
table1(df, x, y, z,
splitby = ~ a)
#>
#>
#> ──────────────────────────────
#> a
#> 1 2
#> n = 483 n = 517
#> x
#> 0.5 (0.3) 0.5 (0.3)
#> y
#> 0.0 (1.0) -0.0 (1.0)
#> z
#> 0 236 (48.9%) 267 (51.6%)
#> 1 247 (51.1%) 250 (48.4%)
#> ──────────────────────────────
table1(df, x, y, z,
splitby = "a")
#>
#>
#> ──────────────────────────────
#> a
#> 1 2
#> n = 483 n = 517
#> x
#> 0.5 (0.3) 0.5 (0.3)
#> y
#> 0.0 (1.0) -0.0 (1.0)
#> z
#> 0 236 (48.9%) 267 (51.6%)
#> 1 247 (51.1%) 250 (48.4%)
#> ──────────────────────────────
## With Piping
df %>%
table1(x, y, z,
splitby = ~a)
#>
#>
#> ──────────────────────────────
#> a
#> 1 2
#> n = 483 n = 517
#> x
#> 0.5 (0.3) 0.5 (0.3)
#> y
#> 0.0 (1.0) -0.0 (1.0)
#> z
#> 0 236 (48.9%) 267 (51.6%)
#> 1 247 (51.1%) 250 (48.4%)
#> ──────────────────────────────
df %>%
group_by(a) %>%
table1(x, y, z)
#> Using dplyr::group_by() groups: a
#>
#>
#> ──────────────────────────────
#> a
#> 1 2
#> n = 483 n = 517
#> x
#> 0.5 (0.3) 0.5 (0.3)
#> y
#> 0.0 (1.0) -0.0 (1.0)
#> z
#> 0 236 (48.9%) 267 (51.6%)
#> 1 247 (51.1%) 250 (48.4%)
#> ──────────────────────────────
## Adjust variables within function and assign name
table1(df,
x2 = ifelse(x > 0, 1, 0), z = z)
#>
#>
#> ────────────────────────
#> Mean/Count (SD/%)
#> n = 1000
#> x2
#> 1.0 (0.0)
#> z
#> 0 503 (50.3%)
#> 1 497 (49.7%)
#> ────────────────────────
```