summariser

summariser provides simple functions for calculating the most common summary statistics, particularly confidence intervals.

Installation

You can install the released version of summariser from CRAN with:

install.packages("summariser")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("condwanaland/summariser")

Using summariser

summariser is designed to fit into the tidyverse ‘piping’ style. Just pass a dataframe, and your measurement variable of interest into summary_stats.

library(summariser)
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
iris %>% 
  summary_stats(Sepal.Length)
#>       mean        sd   n         se        ci
#> 1 5.843333 0.8280661 150 0.06761132 0.1325157

If you want to group your dataframe by categorical factors, simply use dplyrs group_by before piping to summary_stats

iris %>%
  group_by(Species) %>% 
  summary_stats(Sepal.Length)
#> # A tibble: 3 × 6
#>   Species     mean    sd     n     se     ci
#>   <fct>      <dbl> <dbl> <int>  <dbl>  <dbl>
#> 1 setosa      5.01 0.352    50 0.0498 0.0977
#> 2 versicolor  5.94 0.516    50 0.0730 0.143 
#> 3 virginica   6.59 0.636    50 0.0899 0.176

By default, summariser uses a normal distribution to calculate confidence intervals. If you would rather use a t distribution, just pass this to the type parameter.

iris %>%
  group_by(Species) %>% 
  summary_stats(Sepal.Length, type = "t")