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