dplyr tricks

A few lesser known dplyr functions that are super useful

The following is a list of functions that are super useful in the tidyverse that help solve a wide range of problems:

• add_count
• crossing
• across

# Get data from tidytues

library(tidyverse)
cocktails <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-26/cocktails.csv')

add_count

adds a count based on the variable supplied. Equivalent to group_by, summarize, ungroup.

cocktails %>% 
  add_count(drink) %>% 
  select(row_id, drink, n) %>% 
  filter(n >= 8) %>% 
  distinct() %>% 
  knitr::kable()

row_id	drink	n
1	1-900-FUK-MEUP	8
3	151 Florida Bushwacker	8
8	3-Mile Long Island Iced Tea	8
74	Amaretto Liqueur	11
88	Angelica Liqueur	12
91	Apple Cider Punch #1	8
106	Arizona Twister	9
110	Artillery Punch	8
190	Cherry Electric Lemonade	8
197	Chocolate Monkey	8
231	Egg Nog #4	11
366	Masala Chai	8
425	Radioactive Long Island Iced Tea	8
489	Sweet Sangria	8

crossing

generates all possible combinations of variables (like expand.grid, but returns a dataframe)

crossing(
  a = 1:2,
  b = c("a", "b"),
  c = c("x", "y")) %>% 
  knitr::kable()

a	b	c
1	a	x
1	a	y
1	b	x
1	b	y
2	a	x
2	a	y
2	b	x
2	b	y

across

character_visualization <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-30/character_visualization.csv')

(top_speech <- character_visualization %>% 
  filter(!(character %in% c("Editor narration", "Omnipresent narration"))) %>% 
  group_by(character) %>% 
  summarize(across(speech:depicted, sum)) %>% 
  slice_max(order_by = speech, n = 15) %>% 
  separate(character, into = c("hero", "real_name"), sep = " = ") %>% 
  mutate(pct_speech = speech/sum(speech)) %>% 
  arrange(desc(pct_speech)) %>% 
  select(-real_name) %>% 
  select(hero, pct_speech, everything()) %>% 
  mutate(pct_speech = round(pct_speech, 3)))

# A tibble: 15 x 6
   hero                   pct_speech speech thought narrative depicted
   <chr>                       <dbl>  <dbl>   <dbl>     <dbl>    <dbl>
 1 Wolverine                   0.157   3640     532       352     3537
 2 Storm                       0.152   3517    1900        99     4169
 3 Cyclops                     0.089   2052     729       193     2223
 4 Colossus                    0.078   1800     461         6     2712
 5 Rogue                       0.078   1800     611        49     1603
 6 Ariel/Sprite/Shadowcat      0.073   1690     737        66     1714
 7 Nightcrawler                0.064   1492     441         6     1839
 8 Psylocke                    0.05    1163     159       109      993
 9 Professor X                 0.049   1128     269       175     1095
10 Marvel Girl/Phoenix         0.041    957     195         2     1012
11 Havok                       0.039    902     238       172      779
12 Dazzler                     0.038    875     565         5      896
13 Forge                       0.035    818     131       118      588
14 Magneto                     0.028    651      74       176      552
15 Phoenix(2)                  0.028    651     469        40      680