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This is a low level interface to pivotting, inspired by the cdata package, that allows you to describe pivotting with a data frame.

Usage

pivot_wider_spec(
  data,
  spec,
  names_repair = "check_unique",
  id_cols = NULL,
  id_expand = FALSE,
  values_fill = NULL,
  values_fn = NULL,
  unused_fn = NULL
)

build_wider_spec(
  data,
  names_from = name,
  values_from = value,
  names_prefix = "",
  names_sep = "_",
  names_glue = NULL,
  names_sort = FALSE,
  names_vary = "fastest",
  names_expand = FALSE
)

Arguments

data

A data frame to pivot.

spec

A specification data frame. This is useful for more complex pivots because it gives you greater control on how metadata stored in the columns become column names in the result.

Must be a data frame containing character .name and .value columns. Additional columns in spec should be named to match columns in the long format of the dataset and contain values corresponding to columns pivoted from the wide format. The special .seq variable is used to disambiguate rows internally; it is automatically removed after pivotting.

names_repair

What happens if the output has invalid column names? The default, "check_unique" is to error if the columns are duplicated. Use "minimal" to allow duplicates in the output, or "unique" to de-duplicated by adding numeric suffixes. See vctrs::vec_as_names() for more options.

id_cols

<tidy-select> A set of columns that uniquely identifies each observation. Defaults to all columns in data except for the columns specified in spec$.value and the columns of the spec that aren't named .name or .value. Typically used when you have redundant variables, i.e. variables whose values are perfectly correlated with existing variables.

id_expand

Should the values in the id_cols columns be expanded by expand() before pivoting? This results in more rows, the output will contain a complete expansion of all possible values in id_cols. Implicit factor levels that aren't represented in the data will become explicit. Additionally, the row values corresponding to the expanded id_cols will be sorted.

values_fill

Optionally, a (scalar) value that specifies what each value should be filled in with when missing.

This can be a named list if you want to apply different fill values to different value columns.

values_fn

Optionally, a function applied to the value in each cell in the output. You will typically use this when the combination of id_cols and names_from columns does not uniquely identify an observation.

This can be a named list if you want to apply different aggregations to different values_from columns.

unused_fn

Optionally, a function applied to summarize the values from the unused columns (i.e. columns not identified by id_cols, names_from, or values_from).

The default drops all unused columns from the result.

This can be a named list if you want to apply different aggregations to different unused columns.

id_cols must be supplied for unused_fn to be useful, since otherwise all unspecified columns will be considered id_cols.

This is similar to grouping by the id_cols then summarizing the unused columns using unused_fn.

names_from

<tidy-select> A pair of arguments describing which column (or columns) to get the name of the output column (names_from), and which column (or columns) to get the cell values from (values_from).

If values_from contains multiple values, the value will be added to the front of the output column.

values_from

<tidy-select> A pair of arguments describing which column (or columns) to get the name of the output column (names_from), and which column (or columns) to get the cell values from (values_from).

If values_from contains multiple values, the value will be added to the front of the output column.

names_prefix

String added to the start of every variable name. This is particularly useful if names_from is a numeric vector and you want to create syntactic variable names.

names_sep

If names_from or values_from contains multiple variables, this will be used to join their values together into a single string to use as a column name.

names_glue

Instead of names_sep and names_prefix, you can supply a glue specification that uses the names_from columns (and special .value) to create custom column names.

names_sort

Should the column names be sorted? If FALSE, the default, column names are ordered by first appearance.

names_vary

When names_from identifies a column (or columns) with multiple unique values, and multiple values_from columns are provided, in what order should the resulting column names be combined?

  • "fastest" varies names_from values fastest, resulting in a column naming scheme of the form: value1_name1, value1_name2, value2_name1, value2_name2. This is the default.

  • "slowest" varies names_from values slowest, resulting in a column naming scheme of the form: value1_name1, value2_name1, value1_name2, value2_name2.

names_expand

Should the values in the names_from columns be expanded by expand() before pivoting? This results in more columns, the output will contain column names corresponding to a complete expansion of all possible values in names_from. Implicit factor levels that aren't represented in the data will become explicit. Additionally, the column names will be sorted, identical to what names_sort would produce.

Examples

# See vignette("pivot") for examples and explanation

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
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    

us_rent_income %>%
  pivot_wider_spec(spec1)
#> # 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
#> # … with 42 more rows

# Is equivalent to
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
#> # … with 42 more rows

# `pivot_wider_spec()` provides more control over column names and output format
# instead of creating columns with estimate_ and moe_ prefixes,
# keep original variable name for estimates and attach _moe as suffix
spec2 <- tibble(
  .name = c("income", "rent", "income_moe", "rent_moe"),
  .value = c("estimate", "estimate", "moe", "moe"),
  variable = c("income", "rent", "income", "rent")
)

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