Skip to content

Introduction

This vignette describes the use of the new pivot_longer() and pivot_wider() functions. Their goal is to improve the usability of gather() and spread(), and incorporate state-of-the-art features found in other packages.

For some time, it’s been obvious that there is something fundamentally wrong with the design of spread() and gather(). Many people don’t find the names intuitive and find it hard to remember which direction corresponds to spreading and which to gathering. It also seems surprisingly hard to remember the arguments to these functions, meaning that many people (including me!) have to consult the documentation every time.

There are two important new features inspired by other R packages that have been advancing reshaping in R:

  • pivot_longer() can work with multiple value variables that may have different types, inspired by the enhanced melt() and dcast() functions provided by the data.table package by Matt Dowle and Arun Srinivasan.

  • pivot_longer() and pivot_wider() can take a data frame that specifies precisely how metadata stored in column names becomes data variables (and vice versa), inspired by the cdata package by John Mount and Nina Zumel.

In this vignette, you’ll learn the key ideas behind pivot_longer() and pivot_wider() as you see them used to solve a variety of data reshaping challenges ranging from simple to complex.

To begin we’ll load some needed packages. In real analysis code, I’d imagine you’d do with the library(tidyverse), but I can’t do that here since this vignette is embedded in a package.

Longer

pivot_longer() makes datasets longer by increasing the number of rows and decreasing the number of columns. I don’t believe it makes sense to describe a dataset as being in “long form”. Length is a relative term, and you can only say (e.g.) that dataset A is longer than dataset B.

pivot_longer() is commonly needed to tidy wild-caught datasets as they often optimise for ease of data entry or ease of comparison rather than ease of analysis. The following sections show how to use pivot_longer() for a wide range of realistic datasets.

String data in column names

The relig_income dataset stores counts based on a survey which (among other things) asked people about their religion and annual income:

relig_income
#> # A tibble: 18 × 11
#>    religion      `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k`
#>    <chr>           <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
#>  1 Agnostic           27        34        60        81        76       137
#>  2 Atheist            12        27        37        52        35        70
#>  3 Buddhist           27        21        30        34        33        58
#>  4 Catholic          418       617       732       670       638      1116
#>  5 Don’t know/r…      15        14        15        11        10        35
#>  6 Evangelical …     575       869      1064       982       881      1486
#>  7 Hindu               1         9         7         9        11        34
#>  8 Historically…     228       244       236       238       197       223
#>  9 Jehovah's Wi…      20        27        24        24        21        30
#> 10 Jewish             19        19        25        25        30        95
#> # ℹ 8 more rows
#> # ℹ 4 more variables: `$75-100k` <dbl>, `$100-150k` <dbl>, `>150k` <dbl>,
#> #   `Don't know/refused` <dbl>

This dataset contains three variables:

  • religion, stored in the rows,
  • income spread across the column names, and
  • count stored in the cell values.

To tidy it we use pivot_longer():

relig_income %>% 
  pivot_longer(
    cols = !religion, 
    names_to = "income", 
    values_to = "count"
  )
#> # A tibble: 180 × 3
#>    religion income             count
#>    <chr>    <chr>              <dbl>
#>  1 Agnostic <$10k                 27
#>  2 Agnostic $10-20k               34
#>  3 Agnostic $20-30k               60
#>  4 Agnostic $30-40k               81
#>  5 Agnostic $40-50k               76
#>  6 Agnostic $50-75k              137
#>  7 Agnostic $75-100k             122
#>  8 Agnostic $100-150k            109
#>  9 Agnostic >150k                 84
#> 10 Agnostic Don't know/refused    96
#> # ℹ 170 more rows
  • The first argument is the dataset to reshape, relig_income.

  • cols describes which columns need to be reshaped. In this case, it’s every column apart from religion.

  • names_to gives the name of the variable that will be created from the data stored in the column names, i.e. income.

  • values_to gives the name of the variable that will be created from the data stored in the cell value, i.e. count.

Neither the names_to nor the values_to column exists in relig_income, so we provide them as strings surrounded by quotes.

Numeric data in column names

The billboard dataset records the billboard rank of songs in the year 2000. It has a form similar to the relig_income data, but the data encoded in the column names is really a number, not a string.

billboard
#> # A tibble: 317 × 79
#>    artist     track date.entered   wk1   wk2   wk3   wk4   wk5   wk6   wk7
#>    <chr>      <chr> <date>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 2 Pac      Baby… 2000-02-26      87    82    72    77    87    94    99
#>  2 2Ge+her    The … 2000-09-02      91    87    92    NA    NA    NA    NA
#>  3 3 Doors D… Kryp… 2000-04-08      81    70    68    67    66    57    54
#>  4 3 Doors D… Loser 2000-10-21      76    76    72    69    67    65    55
#>  5 504 Boyz   Wobb… 2000-04-15      57    34    25    17    17    31    36
#>  6 98^0       Give… 2000-08-19      51    39    34    26    26    19     2
#>  7 A*Teens    Danc… 2000-07-08      97    97    96    95   100    NA    NA
#>  8 Aaliyah    I Do… 2000-01-29      84    62    51    41    38    35    35
#>  9 Aaliyah    Try … 2000-03-18      59    53    38    28    21    18    16
#> 10 Adams, Yo… Open… 2000-08-26      76    76    74    69    68    67    61
#> # ℹ 307 more rows
#> # ℹ 69 more variables: wk8 <dbl>, wk9 <dbl>, wk10 <dbl>, wk11 <dbl>,
#> #   wk12 <dbl>, wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>,
#> #   wk17 <dbl>, wk18 <dbl>, wk19 <dbl>, wk20 <dbl>, wk21 <dbl>,
#> #   wk22 <dbl>, wk23 <dbl>, wk24 <dbl>, wk25 <dbl>, wk26 <dbl>,
#> #   wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>, wk31 <dbl>,
#> #   wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>, …

We can start with the same basic specification as for the relig_income dataset. Here we want the names to become a variable called week, and the values to become a variable called rank. I also use values_drop_na to drop rows that correspond to missing values. Not every song stays in the charts for all 76 weeks, so the structure of the input data force the creation of unnecessary explicit NAs.

billboard %>% 
  pivot_longer(
    cols = starts_with("wk"), 
    names_to = "week", 
    values_to = "rank",
    values_drop_na = TRUE
  )
#> # A tibble: 5,307 × 5
#>    artist  track                   date.entered week   rank
#>    <chr>   <chr>                   <date>       <chr> <dbl>
#>  1 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk1      87
#>  2 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk2      82
#>  3 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk3      72
#>  4 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk4      77
#>  5 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk5      87
#>  6 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk6      94
#>  7 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk7      99
#>  8 2Ge+her The Hardest Part Of ... 2000-09-02   wk1      91
#>  9 2Ge+her The Hardest Part Of ... 2000-09-02   wk2      87
#> 10 2Ge+her The Hardest Part Of ... 2000-09-02   wk3      92
#> # ℹ 5,297 more rows

It would be nice to easily determine how long each song stayed in the charts, but to do that, we’ll need to convert the week variable to an integer. We can do that by using two additional arguments: names_prefix strips off the wk prefix, and names_transform converts week into an integer:

billboard %>% 
  pivot_longer(
    cols = starts_with("wk"), 
    names_to = "week", 
    names_prefix = "wk",
    names_transform = as.integer,
    values_to = "rank",
    values_drop_na = TRUE,
  )

Alternatively, you could do this with a single argument by using readr::parse_number() which automatically strips non-numeric components:

billboard %>% 
  pivot_longer(
    cols = starts_with("wk"), 
    names_to = "week", 
    names_transform = readr::parse_number,
    values_to = "rank",
    values_drop_na = TRUE,
  )

Many variables in column names

A more challenging situation occurs when you have multiple variables crammed into the column names. For example, take the who dataset:

who
#> # A tibble: 7,240 × 60
#>    country     iso2  iso3   year new_sp_m014 new_sp_m1524 new_sp_m2534
#>    <chr>       <chr> <chr> <dbl>       <dbl>        <dbl>        <dbl>
#>  1 Afghanistan AF    AFG    1980          NA           NA           NA
#>  2 Afghanistan AF    AFG    1981          NA           NA           NA
#>  3 Afghanistan AF    AFG    1982          NA           NA           NA
#>  4 Afghanistan AF    AFG    1983          NA           NA           NA
#>  5 Afghanistan AF    AFG    1984          NA           NA           NA
#>  6 Afghanistan AF    AFG    1985          NA           NA           NA
#>  7 Afghanistan AF    AFG    1986          NA           NA           NA
#>  8 Afghanistan AF    AFG    1987          NA           NA           NA
#>  9 Afghanistan AF    AFG    1988          NA           NA           NA
#> 10 Afghanistan AF    AFG    1989          NA           NA           NA
#> # ℹ 7,230 more rows
#> # ℹ 53 more variables: new_sp_m3544 <dbl>, new_sp_m4554 <dbl>,
#> #   new_sp_m5564 <dbl>, new_sp_m65 <dbl>, new_sp_f014 <dbl>,
#> #   new_sp_f1524 <dbl>, new_sp_f2534 <dbl>, new_sp_f3544 <dbl>,
#> #   new_sp_f4554 <dbl>, new_sp_f5564 <dbl>, new_sp_f65 <dbl>,
#> #   new_sn_m014 <dbl>, new_sn_m1524 <dbl>, new_sn_m2534 <dbl>,
#> #   new_sn_m3544 <dbl>, new_sn_m4554 <dbl>, new_sn_m5564 <dbl>, …

country, iso2, iso3, and year are already variables, so they can be left as is. But the columns from new_sp_m014 to newrel_f65 encode four variables in their names:

  • The new_/new prefix indicates these are counts of new cases. This dataset only contains new cases, so we’ll ignore it here because it’s constant.

  • sp/rel/ep describe how the case was diagnosed.

  • m/f gives the gender.

  • 014/1524/2535/3544/4554/65 supplies the age range.

We can break these variables up by specifying multiple column names in names_to, and then either providing names_sep or names_pattern. Here names_pattern is the most natural fit. It has a similar interface to extract: you give it a regular expression containing groups (defined by ()) and it puts each group in a column.

who %>% 
  pivot_longer(
    cols = new_sp_m014:newrel_f65,
    names_to = c("diagnosis", "gender", "age"), 
    names_pattern = "new_?(.*)_(.)(.*)",
    values_to = "count"
  )
#> # A tibble: 405,440 × 8
#>    country     iso2  iso3   year diagnosis gender age   count
#>    <chr>       <chr> <chr> <dbl> <chr>     <chr>  <chr> <dbl>
#>  1 Afghanistan AF    AFG    1980 sp        m      014      NA
#>  2 Afghanistan AF    AFG    1980 sp        m      1524     NA
#>  3 Afghanistan AF    AFG    1980 sp        m      2534     NA
#>  4 Afghanistan AF    AFG    1980 sp        m      3544     NA
#>  5 Afghanistan AF    AFG    1980 sp        m      4554     NA
#>  6 Afghanistan AF    AFG    1980 sp        m      5564     NA
#>  7 Afghanistan AF    AFG    1980 sp        m      65       NA
#>  8 Afghanistan AF    AFG    1980 sp        f      014      NA
#>  9 Afghanistan AF    AFG    1980 sp        f      1524     NA
#> 10 Afghanistan AF    AFG    1980 sp        f      2534     NA
#> # ℹ 405,430 more rows

We could go one step further use readr functions to convert the gender and age to factors. I think this is good practice when you have categorical variables with a known set of values.

who %>% 
  pivot_longer(
    cols = new_sp_m014:newrel_f65,
    names_to = c("diagnosis", "gender", "age"), 
    names_pattern = "new_?(.*)_(.)(.*)",
    names_transform = list(
      gender = ~ readr::parse_factor(.x, levels = c("f", "m")),
      age = ~ readr::parse_factor(
        .x,
        levels = c("014", "1524", "2534", "3544", "4554", "5564", "65"), 
        ordered = TRUE
      )
    ),
    values_to = "count",
)

Doing it this way is a little more efficient than doing a mutate after the fact, pivot_longer() only has to transform one occurence of each name where a mutate() would need to transform many repetitions.

Multiple observations per row

So far, we have been working with data frames that have one observation per row, but many important pivoting problems involve multiple observations per row. You can usually recognise this case because name of the column that you want to appear in the output is part of the column name in the input. In this section, you’ll learn how to pivot this sort of data.

The following example is adapted from the data.table vignette, as inspiration for tidyr’s solution to this problem.

household
#> # A tibble: 5 × 5
#>   family dob_child1 dob_child2 name_child1 name_child2
#>    <int> <date>     <date>     <chr>       <chr>      
#> 1      1 1998-11-26 2000-01-29 Susan       Jose       
#> 2      2 1996-06-22 NA         Mark        NA         
#> 3      3 2002-07-11 2004-04-05 Sam         Seth       
#> 4      4 2004-10-10 2009-08-27 Craig       Khai       
#> 5      5 2000-12-05 2005-02-28 Parker      Gracie

Note that we have two pieces of information (or values) for each child: their name and their dob (date of birth). These need to go into separate columns in the result. Again we supply multiple variables to names_to, using names_sep to split up each variable name. Note the special name .value: this tells pivot_longer() that that part of the column name specifies the “value” being measured (which will become a variable in the output).

household %>% 
  pivot_longer(
    cols = !family, 
    names_to = c(".value", "child"), 
    names_sep = "_", 
    values_drop_na = TRUE
  )
#> # A tibble: 9 × 4
#>   family child  dob        name  
#>    <int> <chr>  <date>     <chr> 
#> 1      1 child1 1998-11-26 Susan 
#> 2      1 child2 2000-01-29 Jose  
#> 3      2 child1 1996-06-22 Mark  
#> 4      3 child1 2002-07-11 Sam   
#> 5      3 child2 2004-04-05 Seth  
#> 6      4 child1 2004-10-10 Craig 
#> 7      4 child2 2009-08-27 Khai  
#> 8      5 child1 2000-12-05 Parker
#> 9      5 child2 2005-02-28 Gracie

Note the use of values_drop_na = TRUE: the input shape forces the creation of explicit missing variables for observations that don’t exist.

A similar problem problem also exists in the anscombe dataset built in to base R:

anscombe
#>    x1 x2 x3 x4    y1   y2    y3    y4
#> 1  10 10 10  8  8.04 9.14  7.46  6.58
#> 2   8  8  8  8  6.95 8.14  6.77  5.76
#> 3  13 13 13  8  7.58 8.74 12.74  7.71
#> 4   9  9  9  8  8.81 8.77  7.11  8.84
#> 5  11 11 11  8  8.33 9.26  7.81  8.47
#> 6  14 14 14  8  9.96 8.10  8.84  7.04
#> 7   6  6  6  8  7.24 6.13  6.08  5.25
#> 8   4  4  4 19  4.26 3.10  5.39 12.50
#> 9  12 12 12  8 10.84 9.13  8.15  5.56
#> 10  7  7  7  8  4.82 7.26  6.42  7.91
#> 11  5  5  5  8  5.68 4.74  5.73  6.89

This dataset contains four pairs of variables (x1 and y1, x2 and y2, etc) that underlie Anscombe’s quartet, a collection of four datasets that have the same summary statistics (mean, sd, correlation etc), but have quite different data. We want to produce a dataset with columns set, x and y.

anscombe %>% 
  pivot_longer(
    cols = everything(), 
    cols_vary = "slowest",
    names_to = c(".value", "set"), 
    names_pattern = "(.)(.)"
  )
#> # A tibble: 44 × 3
#>    set       x     y
#>    <chr> <dbl> <dbl>
#>  1 1        10  8.04
#>  2 1         8  6.95
#>  3 1        13  7.58
#>  4 1         9  8.81
#>  5 1        11  8.33
#>  6 1        14  9.96
#>  7 1         6  7.24
#>  8 1         4  4.26
#>  9 1        12 10.8 
#> 10 1         7  4.82
#> # ℹ 34 more rows

Setting cols_vary to "slowest" groups the values from columns x1 and y1 together in the rows of the output before moving on to x2 and y2. This argument often produces more intuitively ordered output when you are pivoting every column in your dataset.

A similar situation can arise with panel data. For example, take this example dataset provided by Thomas Leeper. We can tidy it using the same approach as for anscombe:

pnl <- tibble(
  x = 1:4,
  a = c(1, 1,0, 0),
  b = c(0, 1, 1, 1),
  y1 = rnorm(4),
  y2 = rnorm(4),
  z1 = rep(3, 4),
  z2 = rep(-2, 4),
)

pnl %>% 
  pivot_longer(
    cols = !c(x, a, b), 
    names_to = c(".value", "time"), 
    names_pattern = "(.)(.)"
  )
#> # A tibble: 8 × 6
#>       x     a     b time         y     z
#>   <int> <dbl> <dbl> <chr>    <dbl> <dbl>
#> 1     1     1     0 1     -0.995       3
#> 2     1     1     0 2      1.53       -2
#> 3     2     1     1 1      0.241       3
#> 4     2     1     1 2      0.00493    -2
#> 5     3     0     1 1      0.245       3
#> 6     3     0     1 2      0.452      -2
#> 7     4     0     1 1     -0.433       3
#> 8     4     0     1 2      0.800      -2

Wider

pivot_wider() is the opposite of pivot_longer(): it makes a dataset wider by increasing the number of columns and decreasing the number of rows. It’s relatively rare to need pivot_wider() to make tidy data, but it’s often useful for creating summary tables for presentation, or data in a format needed by other tools.

Capture-recapture data

The fish_encounters dataset, contributed by Myfanwy Johnston, describes when fish swimming down a river are detected by automatic monitoring stations:

fish_encounters
#> # A tibble: 114 × 3
#>    fish  station  seen
#>    <fct> <fct>   <int>
#>  1 4842  Release     1
#>  2 4842  I80_1       1
#>  3 4842  Lisbon      1
#>  4 4842  Rstr        1
#>  5 4842  Base_TD     1
#>  6 4842  BCE         1
#>  7 4842  BCW         1
#>  8 4842  BCE2        1
#>  9 4842  BCW2        1
#> 10 4842  MAE         1
#> # ℹ 104 more rows

Many tools used to analyse this data need it in a form where each station is a column:

fish_encounters %>% 
  pivot_wider(
    names_from = station, 
    values_from = seen
  )
#> # A tibble: 19 × 12
#>    fish  Release I80_1 Lisbon  Rstr Base_TD   BCE   BCW  BCE2  BCW2   MAE
#>    <fct>   <int> <int>  <int> <int>   <int> <int> <int> <int> <int> <int>
#>  1 4842        1     1      1     1       1     1     1     1     1     1
#>  2 4843        1     1      1     1       1     1     1     1     1     1
#>  3 4844        1     1      1     1       1     1     1     1     1     1
#>  4 4845        1     1      1     1       1    NA    NA    NA    NA    NA
#>  5 4847        1     1      1    NA      NA    NA    NA    NA    NA    NA
#>  6 4848        1     1      1     1      NA    NA    NA    NA    NA    NA
#>  7 4849        1     1     NA    NA      NA    NA    NA    NA    NA    NA
#>  8 4850        1     1     NA     1       1     1     1    NA    NA    NA
#>  9 4851        1     1     NA    NA      NA    NA    NA    NA    NA    NA
#> 10 4854        1     1     NA    NA      NA    NA    NA    NA    NA    NA
#> # ℹ 9 more rows
#> # ℹ 1 more variable: MAW <int>

This dataset only records when a fish was detected by the station - it doesn’t record when it wasn’t detected (this is common with this type of data). That means the output data is filled with NAs. However, in this case we know that the absence of a record means that the fish was not seen, so we can ask pivot_wider() to fill these missing values in with zeros:

fish_encounters %>% 
  pivot_wider(
    names_from = station, 
    values_from = seen,
    values_fill = 0
  )
#> # A tibble: 19 × 12
#>    fish  Release I80_1 Lisbon  Rstr Base_TD   BCE   BCW  BCE2  BCW2   MAE
#>    <fct>   <int> <int>  <int> <int>   <int> <int> <int> <int> <int> <int>
#>  1 4842        1     1      1     1       1     1     1     1     1     1
#>  2 4843        1     1      1     1       1     1     1     1     1     1
#>  3 4844        1     1      1     1       1     1     1     1     1     1
#>  4 4845        1     1      1     1       1     0     0     0     0     0
#>  5 4847        1     1      1     0       0     0     0     0     0     0
#>  6 4848        1     1      1     1       0     0     0     0     0     0
#>  7 4849        1     1      0     0       0     0     0     0     0     0
#>  8 4850        1     1      0     1       1     1     1     0     0     0
#>  9 4851        1     1      0     0       0     0     0     0     0     0
#> 10 4854        1     1      0     0       0     0     0     0     0     0
#> # ℹ 9 more rows
#> # ℹ 1 more variable: MAW <int>

Aggregation

You can also use pivot_wider() to perform simple aggregation. For example, take the warpbreaks dataset built in to base R (converted to a tibble for the better print method):

warpbreaks <- warpbreaks %>% 
  as_tibble() %>% 
  select(wool, tension, breaks)
warpbreaks
#> # A tibble: 54 × 3
#>    wool  tension breaks
#>    <fct> <fct>    <dbl>
#>  1 A     L           26
#>  2 A     L           30
#>  3 A     L           54
#>  4 A     L           25
#>  5 A     L           70
#>  6 A     L           52
#>  7 A     L           51
#>  8 A     L           26
#>  9 A     L           67
#> 10 A     M           18
#> # ℹ 44 more rows

This is a designed experiment with nine replicates for every combination of wool (A and B) and tension (L, M, H):

warpbreaks %>% 
  count(wool, tension)
#> # A tibble: 6 × 3
#>   wool  tension     n
#>   <fct> <fct>   <int>
#> 1 A     L           9
#> 2 A     M           9
#> 3 A     H           9
#> 4 B     L           9
#> 5 B     M           9
#> 6 B     H           9

What happens if we attempt to pivot the levels of wool into the columns?

warpbreaks %>% 
  pivot_wider(
    names_from = wool, 
    values_from = breaks
  )
#> Warning: Values from `breaks` are not uniquely identified; output will contain
#> list-cols.
#>  Use `values_fn = list` to suppress this warning.
#>  Use `values_fn = {summary_fun}` to summarise duplicates.
#>  Use the following dplyr code to identify duplicates.
#>   {data} |>
#>   dplyr::summarise(n = dplyr::n(), .by = c(tension, wool)) |>
#>   dplyr::filter(n > 1L)
#> # A tibble: 3 × 3
#>   tension A         B        
#>   <fct>   <list>    <list>   
#> 1 L       <dbl [9]> <dbl [9]>
#> 2 M       <dbl [9]> <dbl [9]>
#> 3 H       <dbl [9]> <dbl [9]>

We get a warning that each cell in the output corresponds to multiple cells in the input. The default behaviour produces list-columns, which contain all the individual values. A more useful output would be summary statistics, e.g. mean breaks for each combination of wool and tension:

warpbreaks %>% 
  pivot_wider(
    names_from = wool, 
    values_from = breaks,
    values_fn = mean
  )
#> # A tibble: 3 × 3
#>   tension     A     B
#>   <fct>   <dbl> <dbl>
#> 1 L        44.6  28.2
#> 2 M        24    28.8
#> 3 H        24.6  18.8

For more complex summary operations, I recommend summarising before reshaping, but for simple cases it’s often convenient to summarise within pivot_wider().

Generate column name from multiple variables

Imagine, as in https://stackoverflow.com/questions/24929954, that we have information containing the combination of product, country, and year. In tidy form it might look like this:

production <- 
  expand_grid(
    product = c("A", "B"), 
    country = c("AI", "EI"), 
    year = 2000:2014
  ) %>%
  filter((product == "A" & country == "AI") | product == "B") %>% 
  mutate(production = rnorm(nrow(.)))
production
#> # A tibble: 45 × 4
#>    product country  year production
#>    <chr>   <chr>   <int>      <dbl>
#>  1 A       AI       2000     0.262 
#>  2 A       AI       2001     0.555 
#>  3 A       AI       2002     0.183 
#>  4 A       AI       2003     1.10  
#>  5 A       AI       2004    -1.82  
#>  6 A       AI       2005     0.667 
#>  7 A       AI       2006    -0.577 
#>  8 A       AI       2007     0.0660
#>  9 A       AI       2008     0.681 
#> 10 A       AI       2009     0.177 
#> # ℹ 35 more rows

We want to widen the data so we have one column for each combination of product and country. The key is to specify multiple variables for names_from:

production %>% 
  pivot_wider(
    names_from = c(product, country), 
    values_from = production
  )
#> # A tibble: 15 × 4
#>     year    A_AI   B_AI   B_EI
#>    <int>   <dbl>  <dbl>  <dbl>
#>  1  2000  0.262   1.43   0.157
#>  2  2001  0.555  -1.04  -2.54 
#>  3  2002  0.183   1.83  -0.615
#>  4  2003  1.10   -1.94   1.18 
#>  5  2004 -1.82    1.66  -1.97 
#>  6  2005  0.667  -0.379 -1.75 
#>  7  2006 -0.577  -0.126 -0.452
#>  8  2007  0.0660  0.947  0.353
#>  9  2008  0.681  -0.925  1.81 
#> 10  2009  0.177   1.05   1.84 
#> # ℹ 5 more rows

When either names_from or values_from select multiple variables, you can control how the column names in the output constructed with names_sep and names_prefix, or the workhorse names_glue:

production %>% 
  pivot_wider(
    names_from = c(product, country), 
    values_from = production,
    names_sep = ".",
    names_prefix = "prod."
  )
#> # A tibble: 15 × 4
#>     year prod.A.AI prod.B.AI prod.B.EI
#>    <int>     <dbl>     <dbl>     <dbl>
#>  1  2000    0.262      1.43      0.157
#>  2  2001    0.555     -1.04     -2.54 
#>  3  2002    0.183      1.83     -0.615
#>  4  2003    1.10      -1.94      1.18 
#>  5  2004   -1.82       1.66     -1.97 
#>  6  2005    0.667     -0.379    -1.75 
#>  7  2006   -0.577     -0.126    -0.452
#>  8  2007    0.0660     0.947     0.353
#>  9  2008    0.681     -0.925     1.81 
#> 10  2009    0.177      1.05      1.84 
#> # ℹ 5 more rows

production %>% 
  pivot_wider(
    names_from = c(product, country), 
    values_from = production,
    names_glue = "prod_{product}_{country}"
  )
#> # A tibble: 15 × 4
#>     year prod_A_AI prod_B_AI prod_B_EI
#>    <int>     <dbl>     <dbl>     <dbl>
#>  1  2000    0.262      1.43      0.157
#>  2  2001    0.555     -1.04     -2.54 
#>  3  2002    0.183      1.83     -0.615
#>  4  2003    1.10      -1.94      1.18 
#>  5  2004   -1.82       1.66     -1.97 
#>  6  2005    0.667     -0.379    -1.75 
#>  7  2006   -0.577     -0.126    -0.452
#>  8  2007    0.0660     0.947     0.353
#>  9  2008    0.681     -0.925     1.81 
#> 10  2009    0.177      1.05      1.84 
#> # ℹ 5 more rows

Tidy census

The us_rent_income dataset contains information about median income and rent for each state in the US for 2017 (from the American Community Survey, retrieved with the tidycensus package).

us_rent_income
#> # A tibble: 104 × 5
#>    GEOID NAME       variable estimate   moe
#>    <chr> <chr>      <chr>       <dbl> <dbl>
#>  1 01    Alabama    income      24476   136
#>  2 01    Alabama    rent          747     3
#>  3 02    Alaska     income      32940   508
#>  4 02    Alaska     rent         1200    13
#>  5 04    Arizona    income      27517   148
#>  6 04    Arizona    rent          972     4
#>  7 05    Arkansas   income      23789   165
#>  8 05    Arkansas   rent          709     5
#>  9 06    California income      29454   109
#> 10 06    California rent         1358     3
#> # ℹ 94 more rows

Here both estimate and moe are values columns, so we can supply them to values_from:

us_rent_income %>% 
  pivot_wider(
    names_from = variable, 
    values_from = c(estimate, moe)
  )
#> # A tibble: 52 × 6
#>    GEOID NAME            estimate_income estimate_rent moe_income moe_rent
#>    <chr> <chr>                     <dbl>         <dbl>      <dbl>    <dbl>
#>  1 01    Alabama                   24476           747        136        3
#>  2 02    Alaska                    32940          1200        508       13
#>  3 04    Arizona                   27517           972        148        4
#>  4 05    Arkansas                  23789           709        165        5
#>  5 06    California                29454          1358        109        3
#>  6 08    Colorado                  32401          1125        109        5
#>  7 09    Connecticut               35326          1123        195        5
#>  8 10    Delaware                  31560          1076        247       10
#>  9 11    District of Co…           43198          1424        681       17
#> 10 12    Florida                   25952          1077         70        3
#> # ℹ 42 more rows

Note that the name of the variable is automatically appended to the output columns.

Implicit missing values

Occasionally, you’ll come across data where your names variable is encoded as a factor, but not all of the data will be represented.

weekdays <- c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")

daily <- tibble(
  day = factor(c("Tue", "Thu", "Fri", "Mon"), levels = weekdays),
  value = c(2, 3, 1, 5)
)

daily
#> # A tibble: 4 × 2
#>   day   value
#>   <fct> <dbl>
#> 1 Tue       2
#> 2 Thu       3
#> 3 Fri       1
#> 4 Mon       5

pivot_wider() defaults to generating columns from the values that are actually represented in the data, but you might want to include a column for each possible level in case the data changes in the future.

daily %>%
  pivot_wider(
    names_from = day, 
    values_from = value
  )
#> # A tibble: 1 × 4
#>     Tue   Thu   Fri   Mon
#>   <dbl> <dbl> <dbl> <dbl>
#> 1     2     3     1     5

The names_expand argument will turn implicit factor levels into explicit ones, forcing them to be represented in the result. It also sorts the column names using the level order, which produces more intuitive results in this case.

daily %>% 
  pivot_wider(
    names_from = day, 
    values_from = value, 
    names_expand = TRUE
  )
#> # A tibble: 1 × 7
#>     Mon   Tue   Wed   Thu   Fri   Sat   Sun
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1     5     2    NA     3     1    NA    NA

If multiple names_from columns are provided, names_expand will generate a Cartesian product of all possible combinations of the names_from values. Notice that the following data has omitted some rows where the percentage value would be 0. names_expand allows us to make those explicit during the pivot.

percentages <- tibble(
  year = c(2018, 2019, 2020, 2020),
  type = factor(c("A", "B", "A", "B"), levels = c("A", "B")),
  percentage = c(100, 100, 40, 60)
)

percentages
#> # A tibble: 4 × 3
#>    year type  percentage
#>   <dbl> <fct>      <dbl>
#> 1  2018 A            100
#> 2  2019 B            100
#> 3  2020 A             40
#> 4  2020 B             60

percentages %>% 
  pivot_wider(
    names_from = c(year, type),
    values_from = percentage,
    names_expand = TRUE,
    values_fill = 0
  )
#> # A tibble: 1 × 6
#>   `2018_A` `2018_B` `2019_A` `2019_B` `2020_A` `2020_B`
#>      <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
#> 1      100        0        0      100       40       60

A related problem can occur when there are implicit missing factor levels or combinations in the id_cols. In this case, there are missing rows (rather than columns) that you’d like to explicitly represent. For this example, we’ll modify our daily data with a type column, and pivot on that instead, keeping day as an id column.

daily <- mutate(daily, type = factor(c("A", "B", "B", "A")))
daily
#> # A tibble: 4 × 3
#>   day   value type 
#>   <fct> <dbl> <fct>
#> 1 Tue       2 A    
#> 2 Thu       3 B    
#> 3 Fri       1 B    
#> 4 Mon       5 A

All of our type levels are represented in the columns, but we are missing some rows related to the unrepresented day factor levels.

daily %>%
  pivot_wider(
    names_from = type, 
    values_from = value,
    values_fill = 0
  )
#> # A tibble: 4 × 3
#>   day       A     B
#>   <fct> <dbl> <dbl>
#> 1 Tue       2     0
#> 2 Thu       0     3
#> 3 Fri       0     1
#> 4 Mon       5     0

We can use id_expand in the same way that we used names_expand, which will expand out (and sort) the implicit missing rows in the id_cols.

daily %>% 
  pivot_wider(
    names_from = type, 
    values_from = value,
    values_fill = 0,
    id_expand = TRUE
  )
#> # A tibble: 7 × 3
#>   day       A     B
#>   <fct> <dbl> <dbl>
#> 1 Mon       5     0
#> 2 Tue       2     0
#> 3 Wed       0     0
#> 4 Thu       0     3
#> 5 Fri       0     1
#> 6 Sat       0     0
#> 7 Sun       0     0

Unused columns

Imagine you’ve found yourself in a situation where you have columns in your data that are completely unrelated to the pivoting process, but you’d still like to retain their information somehow. For example, in updates we’d like to pivot on the system column to create one row summaries of each county’s system updates.

updates <- tibble(
  county = c("Wake", "Wake", "Wake", "Guilford", "Guilford"),
  date = c(as.Date("2020-01-01") + 0:2, as.Date("2020-01-03") + 0:1),
  system = c("A", "B", "C", "A", "C"),
  value = c(3.2, 4, 5.5, 2, 1.2)
)

updates
#> # A tibble: 5 × 4
#>   county   date       system value
#>   <chr>    <date>     <chr>  <dbl>
#> 1 Wake     2020-01-01 A        3.2
#> 2 Wake     2020-01-02 B        4  
#> 3 Wake     2020-01-03 C        5.5
#> 4 Guilford 2020-01-03 A        2  
#> 5 Guilford 2020-01-04 C        1.2

We could do that with a typical pivot_wider() call, but we completely lose all information about the date column.

updates %>% 
  pivot_wider(
    id_cols = county, 
    names_from = system, 
    values_from = value
  )
#> # A tibble: 2 × 4
#>   county       A     B     C
#>   <chr>    <dbl> <dbl> <dbl>
#> 1 Wake       3.2     4   5.5
#> 2 Guilford   2      NA   1.2

For this example, we’d like to retain the most recent update date across all systems in a particular county. To accomplish that we can use the unused_fn argument, which allows us to summarize values from the columns not utilized in the pivoting process.

updates %>% 
  pivot_wider(
    id_cols = county, 
    names_from = system, 
    values_from = value,
    unused_fn = list(date = max)
  )
#> # A tibble: 2 × 5
#>   county       A     B     C date      
#>   <chr>    <dbl> <dbl> <dbl> <date>    
#> 1 Wake       3.2     4   5.5 2020-01-03
#> 2 Guilford   2      NA   1.2 2020-01-04

You can also retain the data but delay the aggregation entirely by using list() as the summary function.

updates %>% 
  pivot_wider(
    id_cols = county, 
    names_from = system, 
    values_from = value,
    unused_fn = list(date = list)
  )
#> # A tibble: 2 × 5
#>   county       A     B     C date      
#>   <chr>    <dbl> <dbl> <dbl> <list>    
#> 1 Wake       3.2     4   5.5 <date [3]>
#> 2 Guilford   2      NA   1.2 <date [2]>

Contact list

A final challenge is inspired by Jiena Gu. Imagine you have a contact list that you’ve copied and pasted from a website:

contacts <- tribble(
  ~field, ~value,
  "name", "Jiena McLellan",
  "company", "Toyota", 
  "name", "John Smith", 
  "company", "google", 
  "email", "john@google.com",
  "name", "Huxley Ratcliffe"
)

This is challenging because there’s no variable that identifies which observations belong together. We can fix this by noting that every contact starts with a name, so we can create a unique id by counting every time we see “name” as the field:

contacts <- contacts %>% 
  mutate(
    person_id = cumsum(field == "name")
  )
contacts
#> # A tibble: 6 × 3
#>   field   value            person_id
#>   <chr>   <chr>                <int>
#> 1 name    Jiena McLellan           1
#> 2 company Toyota                   1
#> 3 name    John Smith               2
#> 4 company google                   2
#> 5 email   john@google.com          2
#> 6 name    Huxley Ratcliffe         3

Now that we have a unique identifier for each person, we can pivot field and value into the columns:

contacts %>% 
  pivot_wider(
    names_from = field, 
    values_from = value
  )
#> # A tibble: 3 × 4
#>   person_id name             company email          
#>       <int> <chr>            <chr>   <chr>          
#> 1         1 Jiena McLellan   Toyota  NA             
#> 2         2 John Smith       google  john@google.com
#> 3         3 Huxley Ratcliffe NA      NA

Longer, then wider

Some problems can’t be solved by pivoting in a single direction. The examples in this section show how you might combine pivot_longer() and pivot_wider() to solve more complex problems.

World bank

world_bank_pop contains data from the World Bank about population per country from 2000 to 2018.

world_bank_pop
#> # A tibble: 1,064 × 20
#>    country indicator        `2000`  `2001`  `2002`  `2003`  `2004`  `2005`
#>    <chr>   <chr>             <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#>  1 ABW     SP.URB.TOTL      4.16e4 4.20e+4 4.22e+4 4.23e+4 4.23e+4 4.24e+4
#>  2 ABW     SP.URB.GROW      1.66e0 9.56e-1 4.01e-1 1.97e-1 9.46e-2 1.94e-1
#>  3 ABW     SP.POP.TOTL      8.91e4 9.07e+4 9.18e+4 9.27e+4 9.35e+4 9.45e+4
#>  4 ABW     SP.POP.GROW      2.54e0 1.77e+0 1.19e+0 9.97e-1 9.01e-1 1.00e+0
#>  5 AFE     SP.URB.TOTL      1.16e8 1.20e+8 1.24e+8 1.29e+8 1.34e+8 1.39e+8
#>  6 AFE     SP.URB.GROW      3.60e0 3.66e+0 3.72e+0 3.71e+0 3.74e+0 3.81e+0
#>  7 AFE     SP.POP.TOTL      4.02e8 4.12e+8 4.23e+8 4.34e+8 4.45e+8 4.57e+8
#>  8 AFE     SP.POP.GROW      2.58e0 2.59e+0 2.61e+0 2.62e+0 2.64e+0 2.67e+0
#>  9 AFG     SP.URB.TOTL      4.31e6 4.36e+6 4.67e+6 5.06e+6 5.30e+6 5.54e+6
#> 10 AFG     SP.URB.GROW      1.86e0 1.15e+0 6.86e+0 7.95e+0 4.59e+0 4.47e+0
#> # ℹ 1,054 more rows
#> # ℹ 12 more variables: `2006` <dbl>, `2007` <dbl>, `2008` <dbl>,
#> #   `2009` <dbl>, `2010` <dbl>, `2011` <dbl>, `2012` <dbl>, `2013` <dbl>,
#> #   `2014` <dbl>, `2015` <dbl>, `2016` <dbl>, `2017` <dbl>

My goal is to produce a tidy dataset where each variable is in a column. It’s not obvious exactly what steps are needed yet, but I’ll start with the most obvious problem: year is spread across multiple columns.

pop2 <- world_bank_pop %>% 
  pivot_longer(
    cols = `2000`:`2017`, 
    names_to = "year", 
    values_to = "value"
  )
pop2
#> # A tibble: 19,152 × 4
#>    country indicator   year  value
#>    <chr>   <chr>       <chr> <dbl>
#>  1 ABW     SP.URB.TOTL 2000  41625
#>  2 ABW     SP.URB.TOTL 2001  42025
#>  3 ABW     SP.URB.TOTL 2002  42194
#>  4 ABW     SP.URB.TOTL 2003  42277
#>  5 ABW     SP.URB.TOTL 2004  42317
#>  6 ABW     SP.URB.TOTL 2005  42399
#>  7 ABW     SP.URB.TOTL 2006  42555
#>  8 ABW     SP.URB.TOTL 2007  42729
#>  9 ABW     SP.URB.TOTL 2008  42906
#> 10 ABW     SP.URB.TOTL 2009  43079
#> # ℹ 19,142 more rows

Next we need to consider the indicator variable:

pop2 %>% 
  count(indicator)
#> # A tibble: 4 × 2
#>   indicator       n
#>   <chr>       <int>
#> 1 SP.POP.GROW  4788
#> 2 SP.POP.TOTL  4788
#> 3 SP.URB.GROW  4788
#> 4 SP.URB.TOTL  4788

Here SP.POP.GROW is population growth, SP.POP.TOTL is total population, and SP.URB.* are the same but only for urban areas. Let’s split this up into two variables: area (total or urban) and the actual variable (population or growth):

pop3 <- pop2 %>% 
  separate(indicator, c(NA, "area", "variable"))
pop3
#> # A tibble: 19,152 × 5
#>    country area  variable year  value
#>    <chr>   <chr> <chr>    <chr> <dbl>
#>  1 ABW     URB   TOTL     2000  41625
#>  2 ABW     URB   TOTL     2001  42025
#>  3 ABW     URB   TOTL     2002  42194
#>  4 ABW     URB   TOTL     2003  42277
#>  5 ABW     URB   TOTL     2004  42317
#>  6 ABW     URB   TOTL     2005  42399
#>  7 ABW     URB   TOTL     2006  42555
#>  8 ABW     URB   TOTL     2007  42729
#>  9 ABW     URB   TOTL     2008  42906
#> 10 ABW     URB   TOTL     2009  43079
#> # ℹ 19,142 more rows

Now we can complete the tidying by pivoting variable and value to make TOTL and GROW columns:

pop3 %>% 
  pivot_wider(
    names_from = variable, 
    values_from = value
  )
#> # A tibble: 9,576 × 5
#>    country area  year   TOTL   GROW
#>    <chr>   <chr> <chr> <dbl>  <dbl>
#>  1 ABW     URB   2000  41625 1.66  
#>  2 ABW     URB   2001  42025 0.956 
#>  3 ABW     URB   2002  42194 0.401 
#>  4 ABW     URB   2003  42277 0.197 
#>  5 ABW     URB   2004  42317 0.0946
#>  6 ABW     URB   2005  42399 0.194 
#>  7 ABW     URB   2006  42555 0.367 
#>  8 ABW     URB   2007  42729 0.408 
#>  9 ABW     URB   2008  42906 0.413 
#> 10 ABW     URB   2009  43079 0.402 
#> # ℹ 9,566 more rows

Multi-choice

Based on a suggestion by Maxime Wack, https://github.com/tidyverse/tidyr/issues/384), the final example shows how to deal with a common way of recording multiple choice data. Often you will get such data as follows:

multi <- tribble(
  ~id, ~choice1, ~choice2, ~choice3,
  1, "A", "B", "C",
  2, "C", "B",  NA,
  3, "D",  NA,  NA,
  4, "B", "D",  NA
)

But the actual order isn’t important, and you’d prefer to have the individual questions in the columns. You can achieve the desired transformation in two steps. First, you make the data longer, eliminating the explicit NAs, and adding a column to indicate that this choice was chosen:

multi2 <- multi %>% 
  pivot_longer(
    cols = !id, 
    values_drop_na = TRUE
  ) %>% 
  mutate(checked = TRUE)
multi2
#> # A tibble: 8 × 4
#>      id name    value checked
#>   <dbl> <chr>   <chr> <lgl>  
#> 1     1 choice1 A     TRUE   
#> 2     1 choice2 B     TRUE   
#> 3     1 choice3 C     TRUE   
#> 4     2 choice1 C     TRUE   
#> 5     2 choice2 B     TRUE   
#> 6     3 choice1 D     TRUE   
#> 7     4 choice1 B     TRUE   
#> 8     4 choice2 D     TRUE

Then you make the data wider, filling in the missing observations with FALSE:

multi2 %>% 
  pivot_wider(
    id_cols = id,
    names_from = value, 
    values_from = checked, 
    values_fill = FALSE
  )
#> # A tibble: 4 × 5
#>      id A     B     C     D    
#>   <dbl> <lgl> <lgl> <lgl> <lgl>
#> 1     1 TRUE  TRUE  TRUE  FALSE
#> 2     2 FALSE TRUE  TRUE  FALSE
#> 3     3 FALSE FALSE FALSE TRUE 
#> 4     4 FALSE TRUE  FALSE TRUE

Manual specs

The arguments to pivot_longer() and pivot_wider() allow you to pivot a wide range of datasets. But the creativity that people apply to their data structures is seemingly endless, so it’s quite possible that you will encounter a dataset that you can’t immediately see how to reshape with pivot_longer() and pivot_wider(). To gain more control over pivoting, you can instead create a “spec” data frame that describes exactly how data stored in the column names becomes variables (and vice versa). This section introduces you to the spec data structure, and show you how to use it when pivot_longer() and pivot_wider() are insufficient.

Longer

To see how this works, lets return to the simplest case of pivoting applied to the relig_income dataset. Now pivoting happens in two steps: we first create a spec object (using build_longer_spec()) then use that to describe the pivoting operation:

spec <- relig_income %>% 
  build_longer_spec(
    cols = !religion, 
    names_to = "income",
    values_to = "count"
  )
pivot_longer_spec(relig_income, spec)
#> # A tibble: 180 × 3
#>    religion income             count
#>    <chr>    <chr>              <dbl>
#>  1 Agnostic <$10k                 27
#>  2 Agnostic $10-20k               34
#>  3 Agnostic $20-30k               60
#>  4 Agnostic $30-40k               81
#>  5 Agnostic $40-50k               76
#>  6 Agnostic $50-75k              137
#>  7 Agnostic $75-100k             122
#>  8 Agnostic $100-150k            109
#>  9 Agnostic >150k                 84
#> 10 Agnostic Don't know/refused    96
#> # ℹ 170 more rows

(This gives the same result as before, just with more code. There’s no need to use it here, it is presented as a simple example for using spec.)

What does spec look like? It’s a data frame with one row for each column in the wide format version of the data that is not present in the long format, and two special columns that start with .:

  • .name gives the name of the column.
  • .value gives the name of the column that the values in the cells will go into.

There is also one column in spec for each column present in the long format of the data that is not present in the wide format of the data. This corresponds to the names_to argument in pivot_longer() and build_longer_spec() and the names_from argument in pivot_wider() and build_wider_spec(). In this example, the income column is a character vector of the names of columns being pivoted.

spec
#> # A tibble: 10 × 3
#>    .name              .value income            
#>    <chr>              <chr>  <chr>             
#>  1 <$10k              count  <$10k             
#>  2 $10-20k            count  $10-20k           
#>  3 $20-30k            count  $20-30k           
#>  4 $30-40k            count  $30-40k           
#>  5 $40-50k            count  $40-50k           
#>  6 $50-75k            count  $50-75k           
#>  7 $75-100k           count  $75-100k          
#>  8 $100-150k          count  $100-150k         
#>  9 >150k              count  >150k             
#> 10 Don't know/refused count  Don't know/refused

Wider

Below we widen us_rent_income with pivot_wider(). The result is ok, but I think it could be improved:

us_rent_income %>% 
  pivot_wider(
    names_from = variable, 
    values_from = c(estimate, moe)
  )
#> # A tibble: 52 × 6
#>    GEOID NAME            estimate_income estimate_rent moe_income moe_rent
#>    <chr> <chr>                     <dbl>         <dbl>      <dbl>    <dbl>
#>  1 01    Alabama                   24476           747        136        3
#>  2 02    Alaska                    32940          1200        508       13
#>  3 04    Arizona                   27517           972        148        4
#>  4 05    Arkansas                  23789           709        165        5
#>  5 06    California                29454          1358        109        3
#>  6 08    Colorado                  32401          1125        109        5
#>  7 09    Connecticut               35326          1123        195        5
#>  8 10    Delaware                  31560          1076        247       10
#>  9 11    District of Co…           43198          1424        681       17
#> 10 12    Florida                   25952          1077         70        3
#> # ℹ 42 more rows

I think it would be better to have columns income, rent, income_moe, and rent_moe, which we can achieve with a manual spec. The current spec looks like this:

spec1 <- us_rent_income %>% 
  build_wider_spec(
    names_from = variable, 
    values_from = c(estimate, moe)
  )
spec1
#> # A tibble: 4 × 3
#>   .name           .value   variable
#>   <chr>           <chr>    <chr>   
#> 1 estimate_income estimate income  
#> 2 estimate_rent   estimate rent    
#> 3 moe_income      moe      income  
#> 4 moe_rent        moe      rent

For this case, we mutate spec to carefully construct the column names:

spec2 <- spec1 %>%
  mutate(
    .name = paste0(variable, ifelse(.value == "moe", "_moe", ""))
  )
spec2
#> # A tibble: 4 × 3
#>   .name      .value   variable
#>   <chr>      <chr>    <chr>   
#> 1 income     estimate income  
#> 2 rent       estimate rent    
#> 3 income_moe moe      income  
#> 4 rent_moe   moe      rent

Supplying this spec to pivot_wider() gives us the result we’re looking for:

us_rent_income %>% 
  pivot_wider_spec(spec2)
#> # A tibble: 52 × 6
#>    GEOID NAME                 income  rent income_moe rent_moe
#>    <chr> <chr>                 <dbl> <dbl>      <dbl>    <dbl>
#>  1 01    Alabama               24476   747        136        3
#>  2 02    Alaska                32940  1200        508       13
#>  3 04    Arizona               27517   972        148        4
#>  4 05    Arkansas              23789   709        165        5
#>  5 06    California            29454  1358        109        3
#>  6 08    Colorado              32401  1125        109        5
#>  7 09    Connecticut           35326  1123        195        5
#>  8 10    Delaware              31560  1076        247       10
#>  9 11    District of Columbia  43198  1424        681       17
#> 10 12    Florida               25952  1077         70        3
#> # ℹ 42 more rows

By hand

Sometimes it’s not possible (or not convenient) to compute the spec, and instead it’s more convenient to construct the spec “by hand”. For example, take this construction data, which is lightly modified from Table 5 “completions” found at https://www.census.gov/construction/nrc/index.html:

construction
#> # A tibble: 9 × 9
#>    Year Month  `1 unit` `2 to 4 units` `5 units or more` Northeast Midwest
#>   <dbl> <chr>     <dbl> <lgl>                      <dbl>     <dbl>   <dbl>
#> 1  2018 Janua…      859 NA                           348       114     169
#> 2  2018 Febru…      882 NA                           400       138     160
#> 3  2018 March       862 NA                           356       150     154
#> 4  2018 April       797 NA                           447       144     196
#> 5  2018 May         875 NA                           364        90     169
#> 6  2018 June        867 NA                           342        76     170
#> 7  2018 July        829 NA                           360       108     183
#> 8  2018 August      939 NA                           286        90     205
#> 9  2018 Septe…      835 NA                           304       117     175
#> # ℹ 2 more variables: South <dbl>, West <dbl>

This sort of data is not uncommon from government agencies: the column names actually belong to different variables, and here we have summaries for number of units (1, 2-4, 5+) and regions of the country (NE, NW, midwest, S, W). We can most easily describe that with a tibble:

spec <- tribble(
  ~.name,            ~.value, ~units,  ~region,     
  "1 unit",          "n",     "1",     NA,          
  "2 to 4 units",    "n",     "2-4",   NA,          
  "5 units or more", "n",     "5+",    NA,          
  "Northeast",       "n",     NA,      "Northeast", 
  "Midwest",         "n",     NA,      "Midwest",   
  "South",           "n",     NA,      "South",     
  "West",            "n",     NA,      "West",      
)

Which yields the following longer form:

construction %>% pivot_longer_spec(spec)
#> # A tibble: 63 × 5
#>     Year Month    units region        n
#>    <dbl> <chr>    <chr> <chr>     <dbl>
#>  1  2018 January  1     NA          859
#>  2  2018 January  2-4   NA           NA
#>  3  2018 January  5+    NA          348
#>  4  2018 January  NA    Northeast   114
#>  5  2018 January  NA    Midwest     169
#>  6  2018 January  NA    South       596
#>  7  2018 January  NA    West        339
#>  8  2018 February 1     NA          882
#>  9  2018 February 2-4   NA           NA
#> 10  2018 February 5+    NA          400
#> # ℹ 53 more rows

Note that there is no overlap between the units and region variables; here the data would really be most naturally described in two independent tables.

Theory

One neat property of the spec is that you need the same spec for pivot_longer() and pivot_wider(). This makes it very clear that the two operations are symmetric:

construction %>% 
  pivot_longer_spec(spec) %>% 
  pivot_wider_spec(spec)
#> # A tibble: 9 × 9
#>    Year Month  `1 unit` `2 to 4 units` `5 units or more` Northeast Midwest
#>   <dbl> <chr>     <dbl>          <dbl>             <dbl>     <dbl>   <dbl>
#> 1  2018 Janua…      859             NA               348       114     169
#> 2  2018 Febru…      882             NA               400       138     160
#> 3  2018 March       862             NA               356       150     154
#> 4  2018 April       797             NA               447       144     196
#> 5  2018 May         875             NA               364        90     169
#> 6  2018 June        867             NA               342        76     170
#> 7  2018 July        829             NA               360       108     183
#> 8  2018 August      939             NA               286        90     205
#> 9  2018 Septe…      835             NA               304       117     175
#> # ℹ 2 more variables: South <dbl>, West <dbl>

The pivoting spec allows us to be more precise about exactly how pivot_longer(df, spec = spec) changes the shape of df: it will have nrow(df) * nrow(spec) rows, and ncol(df) - nrow(spec) + ncol(spec) - 2 columns.