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This is a method for the tidyr pivot_wider() generic. It is translated to data.table::dcast()

Usage

# S3 method for dtplyr_step
pivot_wider(
  data,
  id_cols = NULL,
  names_from = name,
  names_prefix = "",
  names_sep = "_",
  names_glue = NULL,
  names_sort = FALSE,
  names_repair = "check_unique",
  values_from = value,
  values_fill = NULL,
  values_fn = NULL,
  ...
)

Arguments

data

A lazy_dt().

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 names_from and values_from. Typically used when you have redundant variables, i.e. variables whose values are perfectly correlated with existing variables.

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.

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_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.

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.

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 aggregations to different value columns.

values_fn

A function, the default is length(). Note this is different behavior than tidyr::pivot_wider(), which returns a list column by default.

...

Additional arguments passed on to methods.

Examples

library(tidyr)

fish_encounters_dt <- lazy_dt(fish_encounters)
fish_encounters_dt
#> Source: local data table [114 x 3]
#> Call:   `_DT29`
#> 
#>   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
#> # … with 108 more rows
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
fish_encounters_dt %>%
  pivot_wider(names_from = station, values_from = seen)
#> Source: local data table [19 x 12]
#> Call:   dcast(`_DT29`, formula = fish ~ station, value.var = "seen")
#> 
#>   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
#> # … with 13 more rows, and 1 more variable: MAW <int>
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
# Fill in missing values
fish_encounters_dt %>%
  pivot_wider(names_from = station, values_from = seen, values_fill = 0)
#> Source: local data table [19 x 12]
#> Call:   dcast(`_DT29`, formula = fish ~ station, value.var = "seen", 
#>     fill = 0)
#> 
#>   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
#> # … with 13 more rows, and 1 more variable: MAW <int>
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results

# Generate column names from multiple variables
us_rent_income_dt <- lazy_dt(us_rent_income)
us_rent_income_dt
#> Source: local data table [104 x 5]
#> Call:   `_DT30`
#> 
#>   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
#> # … with 98 more rows
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
us_rent_income_dt %>%
  pivot_wider(names_from = variable, values_from = c(estimate, moe))
#> Source: local data table [52 x 6]
#> Call:   dcast(`_DT30`, formula = GEOID + NAME ~ variable, value.var = c("estimate", 
#> "moe"))
#> 
#>   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
#> # … with 46 more rows
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results

# When there are multiple `names_from` or `values_from`, you can use
# use `names_sep` or `names_glue` to control the output variable names
us_rent_income_dt %>%
  pivot_wider(
    names_from = variable,
    names_sep = ".",
    values_from = c(estimate, moe)
  )
#> Source: local data table [52 x 6]
#> Call:   dcast(`_DT30`, formula = GEOID + NAME ~ variable, value.var = c("estimate", 
#> "moe"), sep = ".")
#> 
#>   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
#> # … with 46 more rows
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results

# Can perform aggregation with values_fn
warpbreaks_dt <- lazy_dt(as_tibble(warpbreaks[c("wool", "tension", "breaks")]))
warpbreaks_dt
#> Source: local data table [54 x 3]
#> Call:   `_DT31`
#> 
#>   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
#> # … with 48 more rows
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
warpbreaks_dt %>%
  pivot_wider(
    names_from = wool,
    values_from = breaks,
    values_fn = mean
  )
#> Source: local data table [3 x 3]
#> Call:   dcast(`_DT31`, formula = tension ~ wool, value.var = "breaks", 
#>     fun.aggregate = function (x, ...) 
#>     UseMethod("mean"))
#> 
#>   tension     A     B
#>   <fct>   <dbl> <dbl>
#> 1 L        44.6  28.2
#> 2 M        24    28.8
#> 3 H        24.6  18.8
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results