rwetasks has simple functions for performing common tasks, such as calculating the number (and proportion) of people: in each age group, receiving a specific treatment, annualizing healthcare costs and how different inclusion criteria affects the sample size. It was built for anyone working with real-world data (RWDD) in mind, however experienced R users can already do everything covered here. rwetasks is aimed at improving speed of simple tasks, so you can use your brain power for the more complex stuff!
Status: Currently in development, not officially released yet (aka not ready for prime time)
The development version from GitHub with:
# install.packages("devtools")
devtools::install_github("battenr/rwetasks")mean_by_group provides a way to calculate the mean for each group. Can also be used across a dataframe when you want to calculate mean(sd) for each group for every variable
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
#> ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
#> ✓ tibble 3.1.2 ✓ dplyr 1.0.7
#> ✓ tidyr 1.1.3 ✓ stringr 1.4.0
#> ✓ readr 1.4.0 ✓ forcats 0.5.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
library(rwetasks)
rwetasks::mean_by_group(mtcars, mpg, gear)
#> # A tibble: 3 x 3
#> gear mean sd
#> <dbl> <dbl> <dbl>
#> 1 3 16.1 3.37
#> 2 4 24.5 5.28
#> 3 5 21.4 6.66
# Can also use across a dataframe like so
mtcars %>%
purrr::map(
~mean_by_group(mtcars, .x, gear)
)
#> $mpg
#> # A tibble: 3 x 3
#> gear mean sd
#> <dbl> <dbl> <dbl>
#> 1 3 20.1 6.03
#> 2 4 20.1 6.03
#> 3 5 20.1 6.03
#>
#> $cyl
#> # A tibble: 3 x 3
#> gear mean sd
#> <dbl> <dbl> <dbl>
#> 1 3 6.19 1.79
#> 2 4 6.19 1.79
#> 3 5 6.19 1.79
#>
#> $disp
#> # A tibble: 3 x 3
#> gear mean sd
#> <dbl> <dbl> <dbl>
#> 1 3 231. 124.
#> 2 4 231. 124.
#> 3 5 231. 124.
#>
#> $hp
#> # A tibble: 3 x 3
#> gear mean sd
#> <dbl> <dbl> <dbl>
#> 1 3 147. 68.6
#> 2 4 147. 68.6
#> 3 5 147. 68.6
#>
#> $drat
#> # A tibble: 3 x 3
#> gear mean sd
#> <dbl> <dbl> <dbl>
#> 1 3 3.60 0.535
#> 2 4 3.60 0.535
#> 3 5 3.60 0.535
#>
#> $wt
#> # A tibble: 3 x 3
#> gear mean sd
#> <dbl> <dbl> <dbl>
#> 1 3 3.22 0.978
#> 2 4 3.22 0.978
#> 3 5 3.22 0.978
#>
#> $qsec
#> # A tibble: 3 x 3
#> gear mean sd
#> <dbl> <dbl> <dbl>
#> 1 3 17.8 1.79
#> 2 4 17.8 1.79
#> 3 5 17.8 1.79
#>
#> $vs
#> # A tibble: 3 x 3
#> gear mean sd
#> <dbl> <dbl> <dbl>
#> 1 3 0.438 0.504
#> 2 4 0.438 0.504
#> 3 5 0.438 0.504
#>
#> $am
#> # A tibble: 3 x 3
#> gear mean sd
#> <dbl> <dbl> <dbl>
#> 1 3 0.406 0.499
#> 2 4 0.406 0.499
#> 3 5 0.406 0.499
#>
#> $gear
#> # A tibble: 3 x 3
#> gear mean sd
#> <dbl> <dbl> <dbl>
#> 1 3 3.69 0.738
#> 2 4 3.69 0.738
#> 3 5 3.69 0.738
#>
#> $carb
#> # A tibble: 3 x 3
#> gear mean sd
#> <dbl> <dbl> <dbl>
#> 1 3 2.81 1.62
#> 2 4 2.81 1.62
#> 3 5 2.81 1.62count_percent provides a way to quickly calculate the number (n) and proportion of each value of a variable, arranged by proportion.
library(tidyverse)
library(rwetasks)
rwetasks::count_percent(mtcars, gear)
#> # A tibble: 3 x 3
#> # Groups: gear [3]
#> gear n prop
#> <dbl> <int> <dbl>
#> 1 3 15 0.469
#> 2 4 12 0.375
#> 3 5 5 0.156count_percent_demo provides a way to quickly calculate the number (n) and proportion of each value of a variable, when you have multiple measures per participants (i.e., if you have longitudinal data, but want to calculate proportion of females for participants).
library(tidyverse)
library(rwetasks)
rwetasks::count_percent_demo(iris, Species, Petal.Width)
#> # A tibble: 3 x 3
#> # Groups: Species [3]
#> Species n prop
#> <fct> <int> <dbl>
#> 1 versicolor 9 0.409
#> 2 virginica 7 0.318
#> 3 setosa 6 0.273
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent