On 2019-03-22, I tweeted about a survey to help me pick names for the new pivot functions in the dev version of tidyr.
In the survey, I showed a picture of two tables containing the same data, and asked participants to describe their relative shapes. This document describes the results.
library(googlesheets)
library(tidyverse)
# This googlesheet is public if you want to do your own analysis
key <- gs_key("1Do5R1k5sEZrwU0N1KmIjKaapHDrf7eYLdIlcGNx-MsI")
results <- googlesheets::gs_read(key, col_types = list())
names(results) <- c("timestamp", "table_a", "table_b")
head(results)
#> # A tibble: 6 x 3
#> timestamp table_a table_b
#> <chr> <chr> <chr>
#> 1 3/22/2019 10:30:… Table A is tidier than Tab… Table B is deeper than Tab…
#> 2 3/22/2019 10:31:… Table A is wider than Tabl… Table B is taller than Tab…
#> 3 3/22/2019 10:32:… Table A is wider than Tabl… Table B is taller than Tab…
#> 4 3/22/2019 10:33:… Table A is wider than Tabl… Table B is longer than Tab…
#> 5 3/22/2019 10:34:… Table A is wider than Tabl… Table B is longer than Tab…
#> 6 3/22/2019 10:37:… Table A is shallower than … Table B is narrower than T…
nrow(results)
#> [1] 2649
# Capture for posterity
write_csv(results, "results.csv")Wider is the clear winner with ~80% of responses.
table_a <- results %>%
filter(!is.na(table_a)) %>%
mutate(top3 = table_a %>% fct_lump(3) %>% fct_infreq() %>% fct_rev()) %>%
count(top3) %>%
mutate(prop = n / sum(n))
table_a %>%
ggplot(aes(top3, prop)) +
geom_col() +
scale_y_continuous(labels = scales::percent) +
labs(
x = NULL,
y = "Percent of responses"
) +
coord_flip()
There were a wide range of write in respones. The most popular included concise, compact, condense, denser.
results %>%
mutate(
table_a = table_a %>%
str_remove("Table A is ") %>%
str_remove(" than Table B") %>%
str_trunc(50)
) %>%
count(table_a, sort = TRUE) %>%
print(n = Inf)
#> # A tibble: 99 x 2
#> table_a n
#> <chr> <int>
#> 1 wider 2121
#> 2 shorter 328
#> 3 shallower 90
#> 4 Compact 4
#> 5 more compact 4
#> 6 Denser 3
#> 7 <NA> 2
#> 8 Condensed 2
#> 9 fatter than table B 2
#> 10 Horizontal 2
#> 11 smaller 2
#> 12 wider and shorter 2
#> 13 %>% select(x, y, z) %>% 1
#> 14 3D 1
#> 15 "a \"condensed\" version of Table B" 1
#> 16 a gathered Table B 1
#> 17 A is a condense of B 1
#> 18 A is more collective than B 1
#> 19 A is the pivot shape of B 1
#> 20 A is the unpivot of B 1
#> 21 buxom 1
#> 22 By individual / by observation 1
#> 23 cases by variable, i.e. wider 1
#> 24 cleaner 1
#> 25 coiled up compared to table B 1
#> 26 colum heavy 1
#> 27 column-heavy as compared to Table B 1
#> 28 Compact. Far fewer data cells required in table A. 1
#> 29 concise version table B 1
#> 30 Concise, Row Complete, completeR, conciseR 1
#> 31 condensed than table B 1
#> 32 Denormslised 1
#> 33 Dense 1
#> 34 denser than table B. 1
#> 35 distinct, indexed on ID, fact table 1
#> 36 expanded by columns 1
#> 37 Fater 1
#> 38 fatter 1
#> 39 Flatter 1
#> 40 For this specific example, I would say shorter.... 1
#> 41 Funkier 1
#> 42 horizontal vs Table B is vertical 1
#> 43 horizontal, or column-wise 1
#> 44 implicit 1
#> 45 In A each observation repeats only once 1
#> 46 Keep it as gather/spread. 1
#> 47 Less melted 1
#> 48 more aggregated 1
#> 49 More compact 1
#> 50 more compact / succinct / concise 1
#> 51 more compact and easy to read 1
#> 52 more compact than table B 1
#> 53 More compact. 1
#> 54 more compressed. 1
#> 55 more concentrated than table B 1
#> 56 more concise 1
#> 57 more condensed 1
#> 58 More dense (more info per cell) 1
#> 59 more spread 1
#> 60 notidy 1
#> 61 pedantic 1
#> 62 pivot 1
#> 63 Pivoted 1
#> 64 pivoted on “n” as its columns 1
#> 65 Reshape 1
#> 66 row_condensed 1
#> 67 Russia 1
#> 68 Short fat 1
#> 69 short-form of Table B. 1
#> 70 short-wide 1
#> 71 Shorter and fatter 1
#> 72 shorter and fatter (wider) 1
#> 73 shorter and wider 1
#> 74 shorter and wider but not shallower 1
#> 75 shorter and wider than B 1
#> 76 skimpy 1
#> 77 spreaded on b 1
#> 78 squash 1
#> 79 Squatter 1
#> 80 stacked by id compared to Table B 1
#> 81 Stout 1
#> 82 "Table \"A\" covers less area than Table \"B\"" 1
#> 83 Table 1 is better organized for the human inter... 1
#> 84 Table A has grouped/summarized information from... 1
#> 85 Table A has more columns but less rows 1
#> 86 table A is of 2 dimensions, while B is of 1 in ... 1
#> 87 Table A looks like a waffle 1
#> 88 Table A spreads more horizontally than table B 1
#> 89 Table B is CR(Column to Row) transformation of ... 1
#> 90 Table B is taller than Table A 1
#> 91 Table is wide table b is long 1
#> 92 Tabular 1
#> 93 Te same table, but different presentation or da... 1
#> 94 the before picture of Table B. 1
#> 95 the unstacked form of Table B 1
#> 96 They are similar, containing the same information 1
#> 97 thicc while Table B is a sticc 1
#> 98 tidier 1
#> 99 unique on one variable and is wider 1Longer is the clear winner with ~70% of responses. Given the number of people who suggested taller to me, I had expected it to come in much higher. Interestingly narrower is much less common than shorter, it’s equivalent above.
table_b <- results %>%
filter(!is.na(table_b)) %>%
mutate(top3 = table_b %>% fct_lump(3) %>% fct_infreq() %>% fct_rev()) %>%
count(top3) %>%
mutate(prop = n / sum(n))
table_b %>%
ggplot(aes(top3, prop)) +
geom_col() +
scale_y_continuous(labels = scales::percent) +
labs(
x = NULL,
y = "Percent of responses"
) +
coord_flip()
There were a wide range of write in respones. The most popular included expanded and skinnier.
results %>%
mutate(
table_b = table_b %>%
str_remove("Table B is ") %>%
str_remove(" than Table A") %>%
str_trunc(50)
) %>%
count(table_b, sort = TRUE) %>%
print(n = Inf)
#> # A tibble: 96 x 2
#> table_b n
#> <chr> <int>
#> 1 longer 1844
#> 2 taller 419
#> 3 narrower 171
#> 4 deeper 116
#> 5 <NA> 7
#> 6 Vertical 2
#> 7 %>% filter(n=='x'|n=='y'|n=='z') %>% 1
#> 8 2d 1
#> 9 a detailed version of table A 1
#> 10 a panel, Table A is not 1
#> 11 a spread Table A 1
#> 12 "an \"expanded\" version of Table A" 1
#> 13 Atomic 1
#> 14 B is a sublimate of A 1
#> 15 B is more individual than A 1
#> 16 B is the itemized shape of A 1
#> 17 B is the pivot of A 1
#> 18 bigger 1
#> 19 By attribute / by key-value 1
#> 20 Chile 1
#> 21 clearer than table B. 1
#> 22 combonation of variable is unique and is longer 1
#> 23 Down 1
#> 24 Expanded 1
#> 25 expanded by rows 1
#> 26 Expanded. 1
#> 27 expansive than table A 1
#> 28 explicit 1
#> 29 Extended 1
#> 30 flatter than table A (i.e The value column is t... 1
#> 31 gaunt 1
#> 32 Groovier 1
#> 33 "I think if I hadn't heard of \"wide\" and \"long\",..." 1
#> 34 Keep it as gather/spread. 1
#> 35 less compact 1
#> 36 Less dense 1
#> 37 long and narrow 1
#> 38 longer and narrower 1
#> 39 Looser 1
#> 40 melter 1
#> 41 more diluted than table A 1
#> 42 more fragmented or elemental 1
#> 43 more likely to be drafted in the NBA. Jk, it's ... 1
#> 44 More melted 1
#> 45 more repetitive / verbose / redundant 1
#> 46 More slender 1
#> 47 more stretched out. 1
#> 48 more verbose 1
#> 49 more vertically stacked than table A 1
#> 50 narrow-long 1
#> 51 narrower and longer 1
#> 52 Needs more rows for each observation 1
#> 53 Normalised 1
#> 54 Not deeper 1
#> 55 outrolled 1
#> 56 pivot 1
#> 57 pivoted with “n” in its rows 1
#> 58 portrait vs. Table A is landscape 1
#> 59 redundundant 1
#> 60 repeated observation/measurement of cases by va... 1
#> 61 row heavy 1
#> 62 row_expanded 1
#> 63 row-heavy as compared to Table A 1
#> 64 skinnier. 1
#> 65 Skinnier/skinny 1
#> 66 Skinny 1
#> 67 Slack 1
#> 68 Sparser 1
#> 69 Sparser than table A 1
#> 70 stack 1
#> 71 Stacked 1
#> 72 stretched by n compared to Table A 1
#> 73 stretched out compared to Table A 1
#> 74 "Table \"B\" has creates options than Table \"A\"" 1
#> 75 Table A is RC transformation of Table B 1
#> 76 Table B has more rows but less columns 1
#> 77 Take B is skinnier 1
#> 78 Tall skinny 1
#> 79 taller and thinner 1
#> 80 Taller and thinner 1
#> 81 the after photo of Table A 1
#> 82 the long-form of Table A. 1
#> 83 the stacked form of Table A 1
#> 84 Thinner 1
#> 85 thinner and longer 1
#> 86 Tidier 1
#> 87 tidy 1
#> 88 transaction table, not indexed on ID 1
#> 89 transposed of table A 1
#> 90 Unbind variables for each id 1
#> 91 Unpivoted 1
#> 92 Vectorial 1
#> 93 verbose 1
#> 94 vertical 1
#> 95 wider 1
#> 96 worse 1The new functions will be called pivot_wider() and pivot_longer():
these are not the most natural names for everyone, but they are are the
most popular by a large margin. I like pivot because it suggests the
form of the underlying operation (a pivoting or rotation), and it is
evocative to excel users.
A few alternatives that were suggested, considered, and rejected:
-
VERB_long()/VERB_wide(): not obvious whether they take long/wide data or return long/wide data. -
VERB_to_long()/VERB_to_wider(): implies that long and wide are absolute terms. I don’t think it makes sense to talk about long or wide form data; you can only say one form is longer or wider than another form. -
to_long()/to_wide(): isn’t a verb, and implies that there’s only one operation that makes data longer/wider. The next version of tidyr will also contain functions that unnest list-columns of vectors, and that verb (name TBA) also needs directional suffixes. -
reshape_SHAPE: too much potential for confusion with the existingbase::resahpe() -
gather()/spread(): while some people clearly liked these functions they were not memorable to a large number of people I talked to.
I appreciate the enthusiasm that people have for naming functions!