tidyverse is a set of packages based on Hadley Wickham's philosphy of doing
data science. It provides consistent and gracefully interacting methods to tidy
data, wrangle data, and visualize results; and is indispensable to almost any
analysis I carry out these days.
The tidyverse package also provides a set of convenient operators (through the
magrittr package) to manipulate data and make the code more readable. One of
the commonly used operators is the "pipe" operator (%>%) allows you to pipe
data through a chain of function calls.
Consider this pseudo example:
the_data <-
read.csv('/path/to/data/file.csv') %>%
subset(variable_a > x) %>%
transform(variable_c = variable_a/variable_b) %>%
head(100)
Four operations are performed to arrive at the desired data set, and they are written in a natural order: the same as the order of execution. Also, no temporary variables are needed. If yet another operation is required, it is straight-forward to add to the sequence of operations wherever it may be needed.
Pretty straight-forward.
However, what to do if you want to pipe data according to a conditional? That is, maybe use certain dataset when condition is true otherwise use the other one?
This can be done using . and if (or ifelse) as follows:
X %>%
add(1) %>%
{if(Y) add(1) else .}
Or perhaps,
X %>%
{ if (cond) group_by(., Z) else . } %>%
...