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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.