ViewPipeSteps

Overview

ViewPipeSteps helps to debug pipe chains in a slightly more elegant fashion. Print/View debugging isn’t sexy, but instead of manually inserting %>% View() after each step, spice it up a bit by, e.g., highlighting the entire chain and calling the viewPipeChain addin:

Thanks to @batpigandme for the the gif!

Alternatively, you can:

• Print each pipe step of the selction to the console by using the printPipeChain addin.
• Print all pipe steps to the console by adding a print_pipe_steps() call to your pipe.

diamonds %>%
  select(carat, cut, color, clarity, price) %>%
  group_by(color) %>%
  summarise(n = n(), price = mean(price)) %>%
  arrange(desc(color)) %>%
  print_pipe_steps() -> result

## 1. diamonds

## # A tibble: 53,940 x 10
##    carat cut       color clarity depth table price     x     y     z
##    <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
##  1 0.23  Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
##  2 0.21  Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
##  3 0.23  Good      E     VS1      56.9    65   327  4.05  4.07  2.31
##  4 0.290 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
##  5 0.31  Good      J     SI2      63.3    58   335  4.34  4.35  2.75
##  6 0.24  Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
##  7 0.24  Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
##  8 0.26  Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
##  9 0.22  Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
## 10 0.23  Very Good H     VS1      59.4    61   338  4     4.05  2.39
## # … with 53,930 more rows

## 2. select(carat, cut, color, clarity, price)

## # A tibble: 53,940 x 5
##    carat cut       color clarity price
##    <dbl> <ord>     <ord> <ord>   <int>
##  1 0.23  Ideal     E     SI2       326
##  2 0.21  Premium   E     SI1       326
##  3 0.23  Good      E     VS1       327
##  4 0.290 Premium   I     VS2       334
##  5 0.31  Good      J     SI2       335
##  6 0.24  Very Good J     VVS2      336
##  7 0.24  Very Good I     VVS1      336
##  8 0.26  Very Good H     SI1       337
##  9 0.22  Fair      E     VS2       337
## 10 0.23  Very Good H     VS1       338
## # … with 53,930 more rows

## 4. summarise(n = n(), price = mean(price))

## # A tibble: 7 x 3
##   color     n price
##   <ord> <int> <dbl>
## 1 D      6775 3170.
## 2 E      9797 3077.
## 3 F      9542 3725.
## 4 G     11292 3999.
## 5 H      8304 4487.
## 6 I      5422 5092.
## 7 J      2808 5324.

## 5. arrange(desc(color))

## # A tibble: 7 x 3
##   color     n price
##   <ord> <int> <dbl>
## 1 J      2808 5324.
## 2 I      5422 5092.
## 3 H      8304 4487.
## 4 G     11292 3999.
## 5 F      9542 3725.
## 6 E      9797 3077.
## 7 D      6775 3170.

• Try your luck with the experimental %P>% pipe variant that prints the output of the pipe’s left hand side prior to piping it to the right hand side.

diamonds %>%
  select(carat, cut, color, clarity, price) %>%
  group_by(color) %>%
  summarise(n = n(), price = mean(price)) %P>%
  arrange(desc(color)) -> result

## Printing diamonds %>% select(carat, cut, color, clarity, price) %>% group_by(color) %>% summarise(n = n(), price = mean(price))

## # A tibble: 7 x 3
##   color     n price
##   <ord> <int> <dbl>
## 1 D      6775 3170.
## 2 E      9797 3077.
## 3 F      9542 3725.
## 4 G     11292 3999.
## 5 H      8304 4487.
## 6 I      5422 5092.
## 7 J      2808 5324.

Installation

devtools::install_github("daranzolin/ViewPipeSteps")

More Examples

Check tools/test_cases.R for more elaborate examples.

Future Work

• Verify that %P>% is implemented in a useful way and does it what it is supposed to do.