# Pivoting

## 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.

    library(tidyr)
    library(dplyr)
    library(readr)

## 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` $10-2…¹ $20-3…² $30-4…³ $40-5…⁴ $50-7…⁵ $75-1…⁶ $100-…⁷
    #>    <chr>           <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
    #>  1 Agnostic           27      34      60      81      76     137     122     109
    #>  2 Atheist            12      27      37      52      35      70      73      59
    #>  3 Buddhist           27      21      30      34      33      58      62      39
    #>  4 Catholic          418     617     732     670     638    1116     949     792
    #>  5 Don’t know/r…      15      14      15      11      10      35      21      17
    #>  6 Evangelical …     575     869    1064     982     881    1486     949     723
    #>  7 Hindu               1       9       7       9      11      34      47      48
    #>  8 Historically…     228     244     236     238     197     223     131      81
    #>  9 Jehovah's Wi…      20      27      24      24      21      30      15      11
    #> 10 Jewish             19      19      25      25      30      95      69      87
    #> # … with 8 more rows, 2 more variables: `>150k` <dbl>,
    #> #   `Don't know/refused` <dbl>, and abbreviated variable names ¹​`$10-20k`,
    #> #   ²​`$20-30k`, ³​`$30-40k`, ⁴​`$40-50k`, ⁵​`$50-75k`, ⁶​`$75-100k`, ⁷​`$100-150k`

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
    #> # … with 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.ent…¹   wk1   wk2   wk3   wk4   wk5   wk6   wk7   wk8   wk9
    #>    <chr>  <chr> <date>     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
    #>  1 2 Pac  Baby… 2000-02-26    87    82    72    77    87    94    99    NA    NA
    #>  2 2Ge+h… The … 2000-09-02    91    87    92    NA    NA    NA    NA    NA    NA
    #>  3 3 Doo… Kryp… 2000-04-08    81    70    68    67    66    57    54    53    51
    #>  4 3 Doo… Loser 2000-10-21    76    76    72    69    67    65    55    59    62
    #>  5 504 B… Wobb… 2000-04-15    57    34    25    17    17    31    36    49    53
    #>  6 98^0   Give… 2000-08-19    51    39    34    26    26    19     2     2     3
    #>  7 A*Tee… Danc… 2000-07-08    97    97    96    95   100    NA    NA    NA    NA
    #>  8 Aaliy… I Do… 2000-01-29    84    62    51    41    38    35    35    38    38
    #>  9 Aaliy… Try … 2000-03-18    59    53    38    28    21    18    16    14    12
    #> 10 Adams… Open… 2000-08-26    76    76    74    69    68    67    61    58    57
    #> # … with 307 more rows, 67 more variables: 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>,
    #> #   wk37 <dbl>, wk38 <dbl>, wk39 <dbl>, wk40 <dbl>, wk41 <dbl>, wk42 <dbl>,
    #> #   wk43 <dbl>, wk44 <dbl>, wk45 <dbl>, wk46 <dbl>, wk47 <dbl>, wk48 <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 `NA`s.

    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
    #> # … with 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_s…¹ new_s…² new_s…³ new_s…⁴ new_s…⁵ new_s…⁶
    #>    <chr>       <chr> <chr> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
    #>  1 Afghanistan AF    AFG    1980      NA      NA      NA      NA      NA      NA
    #>  2 Afghanistan AF    AFG    1981      NA      NA      NA      NA      NA      NA
    #>  3 Afghanistan AF    AFG    1982      NA      NA      NA      NA      NA      NA
    #>  4 Afghanistan AF    AFG    1983      NA      NA      NA      NA      NA      NA
    #>  5 Afghanistan AF    AFG    1984      NA      NA      NA      NA      NA      NA
    #>  6 Afghanistan AF    AFG    1985      NA      NA      NA      NA      NA      NA
    #>  7 Afghanistan AF    AFG    1986      NA      NA      NA      NA      NA      NA
    #>  8 Afghanistan AF    AFG    1987      NA      NA      NA      NA      NA      NA
    #>  9 Afghanistan AF    AFG    1988      NA      NA      NA      NA      NA      NA
    #> 10 Afghanistan AF    AFG    1989      NA      NA      NA      NA      NA      NA
    #> # … with 7,230 more rows, 50 more variables: 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>, new_sn_m65 <dbl>, new_sn_f014 <dbl>,
    #> #   new_sn_f1524 <dbl>, new_sn_f2534 <dbl>, new_sn_f3544 <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
    #> # … with 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
    #> # … with 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     -0.713     3
    #> 2     1     1     0 2      0.386    -2
    #> 3     2     1     1 1      0.220     3
    #> 4     2     1     1 2     -0.306    -2
    #> 5     3     0     1 1      0.167     3
    #> 6     3     0     1 2     -0.779    -2
    #> 7     4     0     1 1      1.54      3
    #> 8     4     0     1 2      0.844    -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
    #> # … with 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   MAW
    #>    <fct>   <int> <int>  <int> <int>   <int> <int> <int> <int> <int> <int> <int>
    #>  1 4842        1     1      1     1       1     1     1     1     1     1     1
    #>  2 4843        1     1      1     1       1     1     1     1     1     1     1
    #>  3 4844        1     1      1     1       1     1     1     1     1     1     1
    #>  4 4845        1     1      1     1       1    NA    NA    NA    NA    NA    NA
    #>  5 4847        1     1      1    NA      NA    NA    NA    NA    NA    NA    NA
    #>  6 4848        1     1      1     1      NA    NA    NA    NA    NA    NA    NA
    #>  7 4849        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
    #>  8 4850        1     1     NA     1       1     1     1    NA    NA    NA    NA
    #>  9 4851        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
    #> 10 4854        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
    #> # … with 9 more rows

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 `NA`s. 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   MAW
    #>    <fct>   <int> <int>  <int> <int>   <int> <int> <int> <int> <int> <int> <int>
    #>  1 4842        1     1      1     1       1     1     1     1     1     1     1
    #>  2 4843        1     1      1     1       1     1     1     1     1     1     1
    #>  3 4844        1     1      1     1       1     1     1     1     1     1     1
    #>  4 4845        1     1      1     1       1     0     0     0     0     0     0
    #>  5 4847        1     1      1     0       0     0     0     0     0     0     0
    #>  6 4848        1     1      1     1       0     0     0     0     0     0     0
    #>  7 4849        1     1      0     0       0     0     0     0     0     0     0
    #>  8 4850        1     1      0     1       1     1     1     0     0     0     0
    #>  9 4851        1     1      0     0       0     0     0     0     0     0     0
    #> 10 4854        1     1      0     0       0     0     0     0     0     0     0
    #> # … with 9 more rows

### 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
    #> # … with 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::group_by(tension, wool) %>%
    #>   dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
    #>   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    0.225
    #>  2 A       AI       2001   -0.821
    #>  3 A       AI       2002   -2.01
    #>  4 A       AI       2003    0.259
    #>  5 A       AI       2004   -0.00412
    #>  6 A       AI       2005    0.0530
    #>  7 A       AI       2006    0.689
    #>  8 A       AI       2007    0.385
    #>  9 A       AI       2008   -0.538
    #> 10 A       AI       2009    0.0187
    #> # … with 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  0.225    0.224    0.0440
    #>  2  2001 -0.821   -0.00660  0.507
    #>  3  2002 -2.01    -0.00240 -0.926
    #>  4  2003  0.259   -0.886   -1.39
    #>  5  2004 -0.00412 -0.816    1.31
    #>  6  2005  0.0530   0.357   -0.747
    #>  7  2006  0.689   -0.422   -0.191
    #>  8  2007  0.385    0.585    0.313
    #>  9  2008 -0.538    1.53     0.739
    #> 10  2009  0.0187   1.31     1.71
    #> # … with 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   0.225     0.224      0.0440
    #>  2  2001  -0.821    -0.00660    0.507
    #>  3  2002  -2.01     -0.00240   -0.926
    #>  4  2003   0.259    -0.886     -1.39
    #>  5  2004  -0.00412  -0.816      1.31
    #>  6  2005   0.0530    0.357     -0.747
    #>  7  2006   0.689    -0.422     -0.191
    #>  8  2007   0.385     0.585      0.313
    #>  9  2008  -0.538     1.53       0.739
    #> 10  2009   0.0187    1.31       1.71
    #> # … with 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   0.225     0.224      0.0440
    #>  2  2001  -0.821    -0.00660    0.507
    #>  3  2002  -2.01     -0.00240   -0.926
    #>  4  2003   0.259    -0.886     -1.39
    #>  5  2004  -0.00412  -0.816      1.31
    #>  6  2005   0.0530    0.357     -0.747
    #>  7  2006   0.689    -0.422     -0.191
    #>  8  2007   0.385     0.585      0.313
    #>  9  2008  -0.538     1.53       0.739
    #> 10  2009   0.0187    1.31       1.71
    #> # … with 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
    #> # … with 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 Columbia           43198          1424        681       17
    #> 10 12    Florida                        25952          1077         70        3
    #> # … with 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`  `2006`
    #>    <chr>   <chr>           <dbl>   <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 4.26e+4
    #>  2 ABW     SP.URB.GROW    1.66e0 9.56e-1 4.01e-1 1.97e-1 9.46e-2 1.94e-1 3.67e-1
    #>  3 ABW     SP.POP.TOTL    8.91e4 9.07e+4 9.18e+4 9.27e+4 9.35e+4 9.45e+4 9.56e+4
    #>  4 ABW     SP.POP.GROW    2.54e0 1.77e+0 1.19e+0 9.97e-1 9.01e-1 1.00e+0 1.18e+0
    #>  5 AFE     SP.URB.TOTL    1.16e8 1.20e+8 1.24e+8 1.29e+8 1.34e+8 1.39e+8 1.44e+8
    #>  6 AFE     SP.URB.GROW    3.60e0 3.66e+0 3.72e+0 3.71e+0 3.74e+0 3.81e+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 4.70e+8
    #>  8 AFE     SP.POP.GROW    2.58e0 2.59e+0 2.61e+0 2.62e+0 2.64e+0 2.67e+0 2.70e+0
    #>  9 AFG     SP.URB.TOTL    4.31e6 4.36e+6 4.67e+6 5.06e+6 5.30e+6 5.54e+6 5.83e+6
    #> 10 AFG     SP.URB.GROW    1.86e0 1.15e+0 6.86e+0 7.95e+0 4.59e+0 4.47e+0 5.03e+0
    #> # … with 1,054 more rows, and 11 more variables: `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
    #> # … with 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
    #> # … with 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
    #> # … with 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 `NA`s, 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
    #> # … with 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 Columbia           43198          1424        681       17
    #> 10 12    Florida                        25952          1077         70        3
    #> # … with 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
    #> # … with 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 …¹ North…² Midwest South  West
    #>   <dbl> <chr>        <dbl> <lgl>               <dbl>   <dbl>   <dbl> <dbl> <dbl>
    #> 1  2018 January        859 NA                    348     114     169   596   339
    #> 2  2018 February       882 NA                    400     138     160   655   336
    #> 3  2018 March          862 NA                    356     150     154   595   330
    #> 4  2018 April          797 NA                    447     144     196   613   304
    #> 5  2018 May            875 NA                    364      90     169   673   319
    #> 6  2018 June           867 NA                    342      76     170   610   360
    #> 7  2018 July           829 NA                    360     108     183   594   310
    #> 8  2018 August         939 NA                    286      90     205   649   286
    #> 9  2018 September      835 NA                    304     117     175   560   296
    #> # … with abbreviated variable names ¹​`5 units or more`, ²​Northeast

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
    #> # … with 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 …¹ North…² Midwest South  West
    #>   <dbl> <chr>        <dbl>          <dbl>      <dbl>   <dbl>   <dbl> <dbl> <dbl>
    #> 1  2018 January        859             NA        348     114     169   596   339
    #> 2  2018 February       882             NA        400     138     160   655   336
    #> 3  2018 March          862             NA        356     150     154   595   330
    #> 4  2018 April          797             NA        447     144     196   613   304
    #> 5  2018 May            875             NA        364      90     169   673   319
    #> 6  2018 June           867             NA        342      76     170   610   360
    #> 7  2018 July           829             NA        360     108     183   594   310
    #> 8  2018 August         939             NA        286      90     205   649   286
    #> 9  2018 September      835             NA        304     117     175   560   296
    #> # … with abbreviated variable names ¹​`5 units or more`, ²​Northeast

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.