Type: Package
Title: Make Quick Descriptive Tables for Continuous Variables
Description: Quickly make tables of descriptive statistics (i.e., counts, means, confidence intervals) for continuous variables. This package is designed to work in a Tidyverse pipeline, and consideration has been given to get results from R to 'Microsoft Word' ® with minimal pain.
Version: 0.1.2
Maintainer: Brad Cannell <brad.cannell@gmail.com>
License: MIT + file LICENSE
Encoding: UTF-8
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
RoxygenNote: 7.1.2
Imports: dplyr, tibble, rlang, stringr
NeedsCompilation: no
Packaged: 2022-03-19 21:35:49 UTC; bradcannell
Author: Brad Cannell [aut, cre, cph]
Repository: CRAN
Date/Publication: 2022-03-19 21:50:02 UTC

Format mean_table Output for Publication and Dissemination

Description

The mean_format function is intended to make it quick and easy to format the output of the mean_table function for tables that may be used for publication. For example, a mean and 95 could be formatted as "24.00 (21.00 - 27.00)."

Usage

mean_format(.data, recipe, name = NA, digits = NA)

Arguments

.data

A data frame of class "mean_table" or "mean_table_grouped".

recipe

A recipe used to create a new column from existing mean_table columns. The recipe must be in the form of a quoted string. It may contain any combination of column names, spaces, and characters. For example: "mean (sd)" or "mean (lcl - ucl)".

name

An optional name to assign to the column created by the recipe. The default name is "formatted_stats"

digits

The number of decimal places to display.

Value

A tibble

Examples

## Not run: 
library(dplyr)
library(meantables)

data(mtcars)

# Overall mean table with defaults

mtcars %>%
  mean_table(mpg) %>%
  mean_format("mean (sd)") %>%
  select(response_var, formatted_stats)

# A tibble: 1 × 2
  response_var formatted_stats
  <chr>        <chr>
1 mpg          20.09 (6.03)

# Grouped means table with defaults

mtcars %>%
  group_by(cyl) %>%
  mean_table(mpg) %>%
  mean_format("mean (sd)") %>%
  select(response_var:group_cat, formatted_stats)

  # A tibble: 3 × 4
  response_var group_var group_cat formatted_stats
  <chr>        <chr>         <dbl> <chr>
1 mpg          cyl               4 26.66 (4.51)
2 mpg          cyl               6 19.74 (1.45)
3 mpg          cyl               8 15.1 (2.56)

## End(Not run)

Estimate Mean and 95 Percent Confidence Intervals in dplyr Pipelines

Description

The mean_table function produces overall and grouped tables of means with related statistics. In addition to means, the mean_table missing/non-missing frequencies, the standard error of the mean (sem), the 95 value, and the maximum value. For grouped tibbles, mean_table displays these statistics for each category of the group_by variable.

Usage

mean_table(.data, .x, t_prob = 0.975, output = default, digits = 2, ...)

Arguments

.data

A tibble or grouped tibble.

.x

The continuous response variable for which the statistics are desired.

t_prob

(1 - alpha / 2). Default value is 0.975, which corresponds to an alpha of 0.05. Used to calculate a critical value from Student's t distribution with n - 1 degrees of freedom.

output

Options for this parameter are "default" and "all".

Default output includes the n, mean, sem, and 95 the mean. Using output = "all" also returns the the number of missing values for .x and the critical t-value.

digits

Round mean, lcl, and ucl to digits. Default is 2.

...

Other parameters to be passed on.

Value

A tibble of class "mean_table" or "mean_table_grouped"

References

SAS documentation: http://support.sas.com/documentation/cdl/en/proc/65145/HTML/default/viewer.htm#p0klmrp4k89pz0n1p72t0clpavyx.htm

Examples

## Not run: 
library(dplyr)
library(meantables)

data(mtcars)

# Overall mean table with defaults

mtcars %>%
  mean_table(mpg)

# A tibble: 1 x 9
  response_var     n  mean    sd   sem   lcl   ucl   min   max
  <chr>        <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 mpg             32  20.1  6.03  1.07  17.9  22.3  10.4  33.9

# Grouped means table with defaults

mtcars %>%
  group_by(cyl) %>%
  mean_table(mpg)

# A tibble: 3 x 11
  response_var group_var group_cat     n  mean    sd   sem   lcl   ucl   min   max
  <chr>        <chr>         <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 mpg          cyl               4    11  26.7  4.51 1.36   23.6  29.7  21.4  33.9
2 mpg          cyl               6     7  19.7  1.45 0.549  18.4  21.1  17.8  21.4
3 mpg          cyl               8    14  15.1  2.56 0.684  13.6  16.6  10.4  19.2

## End(Not run)