| 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. |
| Authors: | Brad Cannell [aut, cre, cph] |
| Maintainer: | Brad Cannell <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.1.2 |
| Built: | 2025-02-27 03:00:36 UTC |
| Source: | https://github.com/cran/meantables |
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)."
mean_format(.data, recipe, name = NA, digits = NA)mean_format(.data, recipe, name = NA, digits = NA)
.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. |
A tibble
## 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)## 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)
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.
mean_table(.data, .x, t_prob = 0.975, output = default, digits = 2, ...)mean_table(.data, .x, t_prob = 0.975, output = default, digits = 2, ...)
.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. |
A tibble of class "mean_table" or "mean_table_grouped"
SAS documentation: http://support.sas.com/documentation/cdl/en/proc/65145/HTML/default/viewer.htm#p0klmrp4k89pz0n1p72t0clpavyx.htm
## 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)## 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)