Data

We will generate some fake data for this test.

g1<-rep(c("a","b","c"), each=10)
g2<-rep(c("A","B"), each=5, length.out=30)
x<-rnorm(30,0,10)
dat<-data.frame(g1,g2,x)
kable(head(dat))

As you can see, this dataset includes 3 columns:

• g1: a category variable with 3 levels, “a”, “b”, and “c”

• g2: another variable with 2 levels, “A”,“B”, which are evenly balanced within each value of g1

• x: the value column, which will be statistically tested

Tests

We will run a statistical test for the value x between “A” and “B” (column g2) within each category of g1. For this, we will use dplyer’s group_by() function to divide data, and then run tests on subsets and each subset returns a data.frame. This data.frame can be multi-row or single-row, and see how dplyr handles the return results.

First, let’s use the summarize() function to collapse the results.

# this function return a single-row data.frame
my_test<-function(v, g) {
  res<-t.test(v ~ g)
  df<-data.frame(mean1=res$estimate[1], mean2=res$estimate[2], P=res$p.value)
  return(df)
}

dat %>%
  group_by(g1) %>%
  summarize(with(.data, my_test(x, g2) )) %>%
  kable()

As you can see, the functions do well, and return a new data.frame with group name and the result from each group.

Let’s also try a function returning multiple rows.

# this function returns multiple-row data.frame
my_quantiles<-function(v) {
  probs<-seq(0,1,0.25)
  qt<-quantile(x, probs = probs)
  data.frame(quant=qt, prob=probs)
}
dat %>%
  group_by(g1) %>%
  summarize(with(.data, my_quantiles(x) )) %>%
  kable()

## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
##   always returns an ungrouped data frame and adjust accordingly.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## `summarise()` has grouped output by 'g1'. You can override using the `.groups`
## argument.

The results remain good but with deprecating warning, which asks me to replace summarize() with reframe(), so let’s try this new function.

dat %>%
  group_by(g1) %>%
  reframe(with(.data, my_quantiles(x) )) %>%
  kable()

Great, everything looks good.

Conclusions

We can combine the function group_by(), summarize() and a function returning a data.frame to easily analyze data by groups via dplyr. When the returned data.frame is multi-row, the function summarize() should be replaced with reframe().

References

• reframe(): https://dplyr.tidyverse.org/reference/reframe.html

• mutate() with multi-row results: https://stackoverflow.com/questions/73398676/dplyrmutate-when-custom-function-return-a-vector