3.15 Pipes: Tee Pipe
20220511 Another useful operation is the tee-pipe
%T>% which causes the command that follows to be run as a side-pipe,
piping the same data into that command and also into the then
following command and the rest of the pipeline. The output from the
first command is ignored, except for its side-effect, which might be
to base::print() the intermediate results as shown below or to
store the intermediate results before further processing, as in
Section 3.16.
In the following example the tee-pipe processes the transformed dataset in two ways—once with base::print(), then continuing on with dplyr::select() and base::summary(). The tee-pipe splits the flow in these two directions with the second flow continuing the sequence of the pipeline.
ds %>%
select(rainfall, min_temp, max_temp, sunshine) %>%
filter(rainfall==0) %T>%
print() %>%
select(min_temp, max_temp, sunshine) %>%
summary()## # A tibble: 171,900 × 4
## rainfall min_temp max_temp sunshine
## <dbl> <dbl> <dbl> <dbl>
## 1 0 7.4 25.1 NA
## 2 0 12.9 25.7 NA
....## min_temp max_temp sunshine
## Min. :-8.70 Min. :-2.10 Min. : 0.000
## 1st Qu.: 7.40 1st Qu.:19.70 1st Qu.: 6.800
## Median :12.00 Median :24.50 Median : 9.700
## Mean :12.09 Mean :24.89 Mean : 8.708
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