Provides a pipe-friendly framework to perform a Friedman rank sum test, which is the non-parametric alternative to the one-way repeated measures ANOVA test. Wrapper around the function friedman.test(). Read more: Friedman test in R.

friedman_test(data, formula, ...)

## Arguments

data

a data.frame containing the variables in the formula.

formula

a formula of the form a ~ b | c, where a (numeric) is the dependent variable name; b is the within-subjects factor variables; and c (factor) is the column name containing individuals/subjects identifier. Should be unique per individual.

...

other arguments to be passed to the function friedman.test.

## Value

return a data frame with the following columns:

• .y.: the y (dependent) variable used in the test.

• n: sample count.

• statistic: the value of Friedman's chi-squared statistic, used to compute the p-value.

• p: p-value.

• method: the statistical test used to compare groups.

## Examples

# Load data
#:::::::::::::::::::::::::::::::::::::::
data("ToothGrowth")
df <- ToothGrowth %>%
filter(supp == "VC") %>%
mutate(id = rep(1:10, 3))
#>    len supp dose id
#> 1  4.2   VC  0.5  1
#> 2 11.5   VC  0.5  2
#> 3  7.3   VC  0.5  3
#> 4  5.8   VC  0.5  4
#> 5  6.4   VC  0.5  5
#> 6 10.0   VC  0.5  6

# Friedman rank sum test
#:::::::::::::::::::::::::::::::::::::::::
df %>% friedman_test(len ~ dose | id)
#> # A tibble: 1 × 6
#>   .y.       n statistic    df         p method
#> * <chr> <int>     <dbl> <dbl>     <dbl> <chr>
#> 1 len      10        20     2 0.0000454 Friedman test