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Earlier this year my colleague Steve Vaisey was converting code in some course notes from Stata to R. He asked me a question about tidily converting from long to wide format when you have multiple value columns. This is a little more awkward than it should be, and I’ve run into the issue several times since then. I’m writing down the answer (or, an answer) here so that I can find it again myself. Maybe it will be of use to other people as well.
Here’s a motivating example, using some made-up data. What we have are measures of sex, race, stratum (from a survey, say), education, and income. Of these, everything is categorical except income. Here’s what it looks like:
library(tidyverse)
library(data.table)
#>
#> Attaching package: 'data.table'
#> The following objects are masked from 'package:dplyr':
#>
#> between, first, last
#> The following object is masked from 'package:purrr':
#>
#> transpose
gen_cats <- function(x, N = 1000) {
sample(x, N, replace = TRUE)
}
set.seed(101)
N <- 1000
income <- rnorm(N, 100, 50)
vars <- list(stratum = c(1:8),
sex = c("M", "F"),
race = c("B", "W"),
educ = c("HS", "BA"))
df <- as_tibble(map_dfc(vars, gen_cats))
df <- add_column(df, income)
df
#> A tibble: 1,000 x 5
#> stratum sex race educ income
#> <int> <chr> <chr> <chr> <dbl>
#> 1 6 F W BA 83.7
#> 2 7 F B HS 128.
#> 3 1 F B BA 66.3
#> 4 2 M B BA 111.
#> 5 4 M B BA 116.
#> 6 6 F W BA 159.
#> 7 7 F W BA 131.
#> 8 6 M W HS 94.4
#> 9 4 F W HS 146.
#> 10 7 M B BA 88.8
#> # ... with 990 more rows
In order to do a bit of modeling, what Steve wanted to do was to get a table with the data grouped by sex, race, and stratum, but with averages and totals for income by education. In particular, he needed to spread the average income and count values by education into columns. One way to do this, just to show what’s needed, is using the data.table library:
## Data Table
data.table::setDT(df)
dt_wide <- data.table::dcast(df, sex + race + stratum ~ educ,
fun = list(mean, length),
value.var = "income")
head(dt_wide)
#> sex race stratum income_mean_BA income_mean_HS income_length_BA
#> 1: F B 1 101.56368 115.51767 16
#> 2: F B 2 92.97993 90.54429 12
#> 3: F B 3 114.20984 103.88870 18
#> 4: F B 4 90.51281 103.28220 13
#> 5: F B 5 110.94754 70.65812 12
#> 6: F B 6 97.82804 88.66467 9
#> income_length_HS
#> 1: 23
#> 2: 15
#> 3: 11
#> 4: 15
#> 5: 10
#> 6: 17
As you can see, we stratify by sex, race, and stratum, and then in the new columns we have average income values and observation counts for BA and HS values of education.
The data.table library is great, and does the job nicely. What if we wanted to keep everything in the tidyverse, just for expositional purposes? A first cut gets us some of the way:
## Simple tidy summary
tv_wide1 <- df %>% group_by(sex, race, stratum, educ) %>%
summarize(mean_inc = mean(income), N = n())
tv_wide1
# A tibble: 64 x 6
# Groups: sex, race, stratum [?]
sex race stratum educ mean_inc N
<chr> <chr> <int> <chr> <dbl> <int>
1 F B 1 BA 102. 16
2 F B 1 HS 116. 23
3 F B 2 BA 93.0 12
4 F B 2 HS 90.5 15
5 F B 3 BA 114. 18
6 F B 3 HS 104. 11
7 F B 4 BA 90.5 13
8 F B 4 HS 103. 15
9 F B 5 BA 111. 12
10 F B 5 HS 70.7 10
# ... with 54 more rows
There the education variable is still tidily organized, and so the mean income and count variables are their own columns, rather than widened out. To widen them in the way we want, we will need to do a bit more work. In effect—and this is a general strategy when doing this kind of thing with tidyr—we gather() the data into a long-enough form, then temporarily re-aggregate it to the level we want using unite(), and finally spread() the result into columns. I’ll show the results of each of the additional steps cumulatively, so you can see what each stage of the pipeline produces.
First we gather the summaries (mean income and N observations) for each value of the education variable, still stratifying on sex, race, and stratum:
## 1. gather()
tv_wide2 <- df %>% group_by(sex, race, stratum, educ) %>%
summarize(mean_inc = mean(income), N = n()) %>%
gather(variable, value, -(sex:educ))
tv_wide2
# A tibble: 128 x 6
# Groups: sex, race, stratum [32]
sex race stratum educ variable value
<chr> <chr> <int> <chr> <chr> <dbl>
1 F B 1 BA mean_inc 102.
2 F B 1 HS mean_inc 116.
3 F B 2 BA mean_inc 93.0
4 F B 2 HS mean_inc 90.5
5 F B 3 BA mean_inc 114.
6 F B 3 HS mean_inc 104.
7 F B 4 BA mean_inc 90.5
8 F B 4 HS mean_inc 103.
9 F B 5 BA mean_inc 111.
10 F B 5 HS mean_inc 70.7
# ... with 118 more rows
The gather() step has converted the mean_inc and N columns into long form, with variable and value columns.
We then use unite() to create a temporary variable that unites the education variable with the means and counts for each row. In effect we’re sticking the educ and variable columns together:
## 2. unite()
tv_wide2 <- df %>% group_by(sex, race, stratum, educ) %>%
summarize(mean_inc = mean(income), N = n()) %>%
gather(variable, value, -(sex:educ)) %>%
unite(temp, educ, variable)
tv_wide2
# A tibble: 128 x 5
# Groups: sex, race, stratum [32]
sex race stratum temp value
<chr> <chr> <int> <chr> <dbl>
1 F B 1 BA_mean_inc 102.
2 F B 1 HS_mean_inc 116.
3 F B 2 BA_mean_inc 93.0
4 F B 2 HS_mean_inc 90.5
5 F B 3 BA_mean_inc 114.
6 F B 3 HS_mean_inc 104.
7 F B 4 BA_mean_inc 90.5
8 F B 4 HS_mean_inc 103.
9 F B 5 BA_mean_inc 111.
10 F B 5 HS_mean_inc 70.7
# ... with 118 more rows
As you can see, educ and variable are gone, replaced by a single new variable, temp that’s glued them together. Finally we spread() this temp variable into columns, giving us separate columns for BA Mean income, BA N observations, HS Mean income, and HS N observations:
## 3. spread()
tv_wide2 <- df %>% group_by(sex, race, stratum, educ) %>%
summarize(mean_inc = mean(income), N = n()) %>%
gather(variable, value, -(sex:educ)) %>%
unite(temp, educ, variable) %>%
spread(temp, value)
tv_wide2
# A tibble: 32 x 7
# Groups: sex, race, stratum [32]
sex race stratum BA_mean_inc BA_N HS_mean_inc HS_N
<chr> <chr> <int> <dbl> <dbl> <dbl> <dbl>
1 F B 1 102. 16 116. 23
2 F B 2 93.0 12 90.5 15
3 F B 3 114. 18 104. 11
4 F B 4 90.5 13 103. 15
5 F B 5 111. 12 70.7 10
6 F B 6 97.8 9 88.7 17
7 F B 7 70.2 17 99.0 21
8 F B 8 116. 15 101. 8
9 F W 1 86.4 20 93.0 8
10 F W 2 104. 14 93.4 12
# ... with 22 more rows
This table is the same as the data.table output, except that the naming conventions for the created columns are a little different.
Because we might be doing this gather-unite-spread step quite often, it’d be useful to have a function to bundle up the steps for us into something more convenient. Dan Sullivan has helpfully written one for us on the Rstudio community website. It uses tidyeval conventions for its internals.
multi_spread <- function(df, key, value) {
# quote key
keyq <- rlang::enquo(key)
# break value vector into quotes
valueq <- rlang::enquo(value)
s <- rlang::quos(!!valueq)
df %>% gather(variable, value, !!!s) %>%
unite(temp, !!keyq, variable) %>%
spread(temp, value)
}
The multi-spread function generalizes to more than two values, by the way. It lets us do this:
## Final version
tv_wide3 <- df %>% group_by(sex, race, stratum, educ) %>%
summarize(mean_inc = mean(income), N = n()) %>%
multi_spread(educ, c(mean_inc, N))
tv_wide3
# A tibble: 32 x 7
# Groups: sex, race, stratum [32]
sex race stratum BA_mean_inc BA_N HS_mean_inc HS_N
<chr> <chr> <int> <dbl> <dbl> <dbl> <dbl>
1 F B 1 102. 16 116. 23
2 F B 2 93.0 12 90.5 15
3 F B 3 114. 18 104. 11
4 F B 4 90.5 13 103. 15
5 F B 5 111. 12 70.7 10
6 F B 6 97.8 9 88.7 17
7 F B 7 70.2 17 99.0 21
8 F B 8 116. 15 101. 8
9 F W 1 86.4 20 93.0 8
10 F W 2 104. 14 93.4 12
# ... with 22 more rows
And there we are. A tidyverse-only way to spread() with multiple value columns.
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