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Strip a level of headers from a pivot table

Source: R/behead.R
behead.Rd

behead() takes one level of headers from a pivot table and makes it part of the data. Think of it like tidyr::gather(), except that it works when there is more than one row of headers (or more than one column of row-headers), and it only works on tables that have first come through as_cells() or tidyxl::xlsx_cells().

Usage

behead(
  cells,
  direction,
  name,
  values = NULL,
  types = data_type,
  formatters = list(),
  drop_na = TRUE
)

behead_if(
  cells,
  ...,
  direction,
  name,
  values = NULL,
  types = data_type,
  formatters = list(),
  drop_na = TRUE
)

Arguments

cells

Data frame. The cells of a pivot table, usually the output of as_cells() or tidyxl::xlsx_cells(), or of a subsequent operation on those outputs.

direction

The direction between a data cell and its header, one of "up", "right", "down", "left", "up-left", "up-right", "right-up", "right-down", "down-right", "down-left", "left-down", "left-up". See ?direction. "up-ish", "down-ish", "left-ish" and "right-ish" aren't available because they require certain ambiguities that are better handled by using enhead() directly rather than via behead().

name

A name to give the new column that will be created, e.g. "location" if the headers are locations. Quoted ("location", not location) because it doesn't refer to an actual object.

values

Optional. The column of cells to use as the values of each header. Given as a bare variable name. If omitted (the default), the types argument will be used instead.

types

The name of the column that names the data type of each cell. Usually called data_types (the default), this is a character column that names the other columns in cells that contain the values of each cell. E.g. a cell with a character value will have "character" in this column. Unquoted(data_types, not "data_types") because it refers to an actual object.

formatters

A named list of functions for formatting each data type in a set of headers of mixed data types, e.g. when some headers are dates and others are characters. These can be given as character = toupper or character = ~ toupper(.x), similar to purrr::map.

drop_na

logical Whether to filter out headers that have NA in the value column. Default: TRUE. This can happen with the output of tidyxl::xlsx_cells(), when an empty cell exists because it has formatting applied to it, but should be ignored.

...

Passed to dplyr::filter. logical predicates defined in terms of the variables in .data. Multiple conditions are combined with &. Only rows where the condition evaluates to TRUE are kept.

The arguments in ... are automatically quoted and evaluated in the context of the data frame. They support unquoting and splicing. See the dplyr vignette("programming") for an introduction to these concepts.

Value

A data frame

Examples

# A simple table with a row of headers
(x <- data.frame(a = 1:2, b = 3:4))
#>   a b
#> 1 1 3
#> 2 2 4

# Make a tidy representation of each cell
(cells <- as_cells(x, col_names = TRUE))
#> # A tibble: 6 × 5
#>     row   col data_type chr     int
#>   <int> <int> <chr>     <chr> <int>
#> 1     1     1 chr       a        NA
#> 2     2     1 int       NA        1
#> 3     3     1 int       NA        2
#> 4     1     2 chr       b        NA
#> 5     2     2 int       NA        3
#> 6     3     2 int       NA        4

# Strip the cells in row 1 (the original headers) and use them as data
behead(cells, "N", foo)
#> # A tibble: 4 × 6
#>     row   col data_type chr     int foo  
#>   <int> <int> <chr>     <chr> <int> <chr>
#> 1     2     1 int       NA        1 a    
#> 2     3     1 int       NA        2 a    
#> 3     2     2 int       NA        3 b    
#> 4     3     2 int       NA        4 b    

# More complex example: pivot table with several layers of headers
(x <- purpose$`up-left left-up`)
#>                            X2      X3     X4     X5    X6     X7
#> 1                        <NA>    <NA> Female   <NA>  Male   <NA>
#> 2                        <NA>    <NA>  0 - 6 7 - 10 0 - 6 7 - 10
#> 3           Bachelor's degree 15 - 24   7000  27000  <NA>  13000
#> 4                        <NA> 25 - 44  12000 137000  9000  81000
#> 5                        <NA> 45 - 64  10000  64000  7000  66000
#> 6                        <NA>     65+   <NA>  18000  7000  17000
#> 7                 Certificate 15 - 24  29000 161000 30000 190000
#> 8                        <NA> 25 - 44  34000 179000 31000 219000
#> 9                        <NA> 45 - 64  30000 210000 23000 199000
#> 10                       <NA>     65+  12000  77000  8000 107000
#> 11                    Diploma 15 - 24   <NA>  14000  9000  11000
#> 12                       <NA> 25 - 44  10000  66000  8000  47000
#> 13                       <NA> 45 - 64   6000  68000  5000  58000
#> 14                       <NA>     65+   5000  41000  1000  34000
#> 15           No Qualification 15 - 24  10000  43000 12000  37000
#> 16                       <NA> 25 - 44  11000  36000 21000  50000
#> 17                       <NA> 45 - 64  19000  91000 17000  75000
#> 18                       <NA>     65+  16000 118000  9000  66000
#> 19 Postgraduate qualification 15 - 24   <NA>   6000  <NA>   <NA>
#> 20                       <NA> 25 - 44   5000  86000  7000  60000
#> 21                       <NA> 45 - 64   6000  55000  6000  68000
#> 22                       <NA>     65+   <NA>  13000  <NA>  18000

# Make a tidy representation
cells <- as_cells(x)
head(cells)
#> # A tibble: 6 × 4
#>     row   col data_type chr              
#>   <int> <int> <chr>     <chr>            
#> 1     1     1 chr       NA               
#> 2     2     1 chr       NA               
#> 3     3     1 chr       Bachelor's degree
#> 4     4     1 chr       NA               
#> 5     5     1 chr       NA               
#> 6     6     1 chr       NA               
tail(cells)
#> # A tibble: 6 × 4
#>     row   col data_type chr  
#>   <int> <int> <chr>     <chr>
#> 1    17     6 chr       75000
#> 2    18     6 chr       66000
#> 3    19     6 chr       NA   
#> 4    20     6 chr       60000
#> 5    21     6 chr       68000
#> 6    22     6 chr       18000

# Strip the headers and make them into data
tidy <-
  cells %>%
  behead("up-left", Sex) %>%
  behead("up", `Sense of purpose`) %>%
  behead("left-up", `Highest qualification`) %>%
  behead("left", `Age group (Life-stages)`) %>%
  dplyr::mutate(count = as.integer(chr)) %>%
  dplyr::select(-row, -col, -data_type, -chr)
head(tidy)
#> # A tibble: 6 × 5
#>   Sex    `Sense of purpose` Highest qualificatio…¹ Age group (Life-stag…²  count
#>   <chr>  <chr>              <chr>                  <chr>                   <int>
#> 1 Female 0 - 6              Bachelor's degree      15 - 24                  7000
#> 2 Female 0 - 6              Bachelor's degree      25 - 44                 12000
#> 3 Female 0 - 6              Bachelor's degree      45 - 64                 10000
#> 4 Female 0 - 6              Bachelor's degree      65+                        NA
#> 5 Female 7 - 10             Bachelor's degree      15 - 24                 27000
#> 6 Female 7 - 10             Bachelor's degree      25 - 44                137000
#> # ℹ abbreviated names: ¹​`Highest qualification`, ²​`Age group (Life-stages)`

# Check against the provided 'tidy' version of the data.
dplyr::anti_join(tidy, purpose$Tidy)
#> Joining with `by = join_by(Sex, `Sense of purpose`, `Highest qualification`,
#> `Age group (Life-stages)`)`
#> # A tibble: 1 × 5
#>   Sex   `Sense of purpose` `Highest qualification`  Age group (Life-stag…¹ count
#>   <chr> <chr>              <chr>                    <chr>                  <int>
#> 1 Male  0 - 6              Postgraduate qualificat… 15 - 24                   NA
#> # ℹ abbreviated name: ¹​`Age group (Life-stages)`

# The provided 'tidy' data is missing a row for Male 15-24-year-olds with a
# postgraduate qualification and a sense of purpose between 0 and 6.  That
# seems to have been an oversight by Statistics New Zealand.

cells <- tibble::tribble(
       ~X1, ~adult, ~juvenile,
    "LION",    855,       677,
    "male",    496,       322,
  "female",    359,       355,
   "TIGER",    690,       324,
    "male",    381,       222,
  "female",    309,       102
  )
cells <- as_cells(cells, col_names = TRUE)

cells %>%
  behead_if(chr == toupper(chr), direction = "left-up", name = "species") %>%
  behead("left", "sex") %>%
  behead("up", "age") %>%
  dplyr::select(species, sex, age, population = dbl)
#> # A tibble: 12 × 4
#>    species sex    age      population
#>    <chr>   <chr>  <chr>         <dbl>
#>  1 LION    NA     adult           855
#>  2 LION    male   adult           496
#>  3 LION    female adult           359
#>  4 LION    NA     juvenile        677
#>  5 LION    male   juvenile        322
#>  6 LION    female juvenile        355
#>  7 TIGER   NA     adult           690
#>  8 TIGER   male   adult           381
#>  9 TIGER   female adult           309
#> 10 TIGER   NA     juvenile        324
#> 11 TIGER   male   juvenile        222
#> 12 TIGER   female juvenile        102