Credibly Curious

In this post I describe how to use tally, the dplyr equivalent of table.

table gives you the frequencies of something in a category. Let’s use the iris dataset to illustrate. Let’s say we want to know how many are in each species in iris.

table(iris$Species)

## 
##     setosa versicolor  virginica 
##         50         50         50

So there happen to be 50 in each of the species.

But if you want to present this in a tidy dataframe, where each column is a variable, and each row is an observation, you’d have to do some annoying reformating. But need not dispair, dplyr has us covered.

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.tally <- iris %>%
  group_by(Species) %>% 
  tally

iris.tally

## # A tibble: 3 x 2
##   Species        n
##   <fct>      <int>
## 1 setosa        50
## 2 versicolor    50
## 3 virginica     50

This gives us a neat dataframe, where we get Species as a column, and the number of observations in each Species.

One of the reasons I like this is because it means I can do something like create a table using knitr::kable if I need to for a report.

So I could now do this:

library(knitr)

kable(iris.tally)

Thanks to this SO post for providing me with knowledge of tally and providing inspiration for this post.