Pivot a DataFrame from long to wide
Usage
# S3 method for class 'RPolarsDataFrame'
pivot_wider(
data,
...,
id_cols = NULL,
names_from = name,
values_from = value,
names_prefix = "",
names_sep = "_",
names_glue = NULL,
values_fill = NULL
)Arguments
- data
A Polars DataFrame (LazyFrames are not supported).
- ...
Not used.
- id_cols
A set of columns that uniquely identify each observation. Typically used when you have redundant variables, i.e. variables whose values are perfectly correlated with existing variables.
Defaults to all columns in data except for the columns specified through
names_fromandvalues_from. If a tidyselect expression is supplied, it will be evaluated on data after removing the columns specified throughnames_fromandvalues_from.- names_from
The (quoted or unquoted) column names whose values will be used for the names of the new columns.
- values_from
The (quoted or unquoted) column names whose values will be used to fill the new columns.
- names_prefix
String added to the start of every variable name. This is particularly useful if
names_fromis a numeric vector and you want to create syntactic variable names.- names_sep
If
names_fromorvalues_fromcontains 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_sepandnames_prefix, you can supply agluespecification that uses thenames_fromcolumns to create custom column names.- values_fill
A scalar that will be used to replace missing values in the new columns. Note that the type of this value will be applied to new columns. For example, if you provide a character value to fill numeric columns, then all these columns will be converted to character.
Examples
pl_fish_encounters <- polars::pl$DataFrame(tidyr::fish_encounters)
#> Warning: Passing a `data.frame` or `RPolarsDataFrame` to `pl$DataFrame()` is deprecated and will be removed in the future. Use `as_polars_df()` instead.
pl_fish_encounters |>
pivot_wider(names_from = station, values_from = seen)
#> shape: (19, 12)
#> ┌──────┬─────────┬───────┬────────┬───┬──────┬──────┬──────┬──────┐
#> │ fish ┆ Release ┆ I80_1 ┆ Lisbon ┆ … ┆ BCE2 ┆ BCW2 ┆ MAE ┆ MAW │
#> │ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
#> │ cat ┆ i32 ┆ i32 ┆ i32 ┆ ┆ i32 ┆ i32 ┆ i32 ┆ i32 │
#> ╞══════╪═════════╪═══════╪════════╪═══╪══════╪══════╪══════╪══════╡
#> │ 4842 ┆ 1 ┆ 1 ┆ 1 ┆ … ┆ 1 ┆ 1 ┆ 1 ┆ 1 │
#> │ 4843 ┆ 1 ┆ 1 ┆ 1 ┆ … ┆ 1 ┆ 1 ┆ 1 ┆ 1 │
#> │ 4844 ┆ 1 ┆ 1 ┆ 1 ┆ … ┆ 1 ┆ 1 ┆ 1 ┆ 1 │
#> │ 4845 ┆ 1 ┆ 1 ┆ 1 ┆ … ┆ null ┆ null ┆ null ┆ null │
#> │ 4847 ┆ 1 ┆ 1 ┆ 1 ┆ … ┆ null ┆ null ┆ null ┆ null │
#> │ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
#> │ 4861 ┆ 1 ┆ 1 ┆ 1 ┆ … ┆ 1 ┆ 1 ┆ 1 ┆ 1 │
#> │ 4862 ┆ 1 ┆ 1 ┆ 1 ┆ … ┆ 1 ┆ 1 ┆ null ┆ null │
#> │ 4863 ┆ 1 ┆ 1 ┆ null ┆ … ┆ null ┆ null ┆ null ┆ null │
#> │ 4864 ┆ 1 ┆ 1 ┆ null ┆ … ┆ null ┆ null ┆ null ┆ null │
#> │ 4865 ┆ 1 ┆ 1 ┆ 1 ┆ … ┆ null ┆ null ┆ null ┆ null │
#> └──────┴─────────┴───────┴────────┴───┴──────┴──────┴──────┴──────┘
pl_fish_encounters |>
pivot_wider(names_from = station, values_from = seen, values_fill = 0)
#> shape: (19, 12)
#> ┌──────┬─────────┬───────┬────────┬───┬──────┬──────┬─────┬─────┐
#> │ fish ┆ Release ┆ I80_1 ┆ Lisbon ┆ … ┆ BCE2 ┆ BCW2 ┆ MAE ┆ MAW │
#> │ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
#> │ cat ┆ f64 ┆ f64 ┆ f64 ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
#> ╞══════╪═════════╪═══════╪════════╪═══╪══════╪══════╪═════╪═════╡
#> │ 4842 ┆ 1.0 ┆ 1.0 ┆ 1.0 ┆ … ┆ 1.0 ┆ 1.0 ┆ 1.0 ┆ 1.0 │
#> │ 4843 ┆ 1.0 ┆ 1.0 ┆ 1.0 ┆ … ┆ 1.0 ┆ 1.0 ┆ 1.0 ┆ 1.0 │
#> │ 4844 ┆ 1.0 ┆ 1.0 ┆ 1.0 ┆ … ┆ 1.0 ┆ 1.0 ┆ 1.0 ┆ 1.0 │
#> │ 4845 ┆ 1.0 ┆ 1.0 ┆ 1.0 ┆ … ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
#> │ 4847 ┆ 1.0 ┆ 1.0 ┆ 1.0 ┆ … ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
#> │ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
#> │ 4861 ┆ 1.0 ┆ 1.0 ┆ 1.0 ┆ … ┆ 1.0 ┆ 1.0 ┆ 1.0 ┆ 1.0 │
#> │ 4862 ┆ 1.0 ┆ 1.0 ┆ 1.0 ┆ … ┆ 1.0 ┆ 1.0 ┆ 0.0 ┆ 0.0 │
#> │ 4863 ┆ 1.0 ┆ 1.0 ┆ 0.0 ┆ … ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
#> │ 4864 ┆ 1.0 ┆ 1.0 ┆ 0.0 ┆ … ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
#> │ 4865 ┆ 1.0 ┆ 1.0 ┆ 1.0 ┆ … ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
#> └──────┴─────────┴───────┴────────┴───┴──────┴──────┴─────┴─────┘
# be careful about the type of the replacement value!
pl_fish_encounters |>
pivot_wider(names_from = station, values_from = seen, values_fill = "a")
#> shape: (19, 12)
#> ┌──────┬─────────┬───────┬────────┬───┬──────┬──────┬─────┬─────┐
#> │ fish ┆ Release ┆ I80_1 ┆ Lisbon ┆ … ┆ BCE2 ┆ BCW2 ┆ MAE ┆ MAW │
#> │ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
#> │ cat ┆ str ┆ str ┆ str ┆ ┆ str ┆ str ┆ str ┆ str │
#> ╞══════╪═════════╪═══════╪════════╪═══╪══════╪══════╪═════╪═════╡
#> │ 4842 ┆ 1 ┆ 1 ┆ 1 ┆ … ┆ 1 ┆ 1 ┆ 1 ┆ 1 │
#> │ 4843 ┆ 1 ┆ 1 ┆ 1 ┆ … ┆ 1 ┆ 1 ┆ 1 ┆ 1 │
#> │ 4844 ┆ 1 ┆ 1 ┆ 1 ┆ … ┆ 1 ┆ 1 ┆ 1 ┆ 1 │
#> │ 4845 ┆ 1 ┆ 1 ┆ 1 ┆ … ┆ a ┆ a ┆ a ┆ a │
#> │ 4847 ┆ 1 ┆ 1 ┆ 1 ┆ … ┆ a ┆ a ┆ a ┆ a │
#> │ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
#> │ 4861 ┆ 1 ┆ 1 ┆ 1 ┆ … ┆ 1 ┆ 1 ┆ 1 ┆ 1 │
#> │ 4862 ┆ 1 ┆ 1 ┆ 1 ┆ … ┆ 1 ┆ 1 ┆ a ┆ a │
#> │ 4863 ┆ 1 ┆ 1 ┆ a ┆ … ┆ a ┆ a ┆ a ┆ a │
#> │ 4864 ┆ 1 ┆ 1 ┆ a ┆ … ┆ a ┆ a ┆ a ┆ a │
#> │ 4865 ┆ 1 ┆ 1 ┆ 1 ┆ … ┆ a ┆ a ┆ a ┆ a │
#> └──────┴─────────┴───────┴────────┴───┴──────┴──────┴─────┴─────┘
# using "names_glue" to specify the names of new columns
production <- expand.grid(
product = c("A", "B"),
country = c("AI", "EI"),
year = 2000:2014
) |>
filter((product == "A" & country == "AI") | product == "B") |>
mutate(production = 1:45) |>
as_polars_df()
production
#> shape: (45, 4)
#> ┌─────────┬─────────┬──────┬────────────┐
#> │ product ┆ country ┆ year ┆ production │
#> │ --- ┆ --- ┆ --- ┆ --- │
#> │ cat ┆ cat ┆ i32 ┆ i32 │
#> ╞═════════╪═════════╪══════╪════════════╡
#> │ A ┆ AI ┆ 2000 ┆ 1 │
#> │ B ┆ AI ┆ 2000 ┆ 2 │
#> │ B ┆ EI ┆ 2000 ┆ 3 │
#> │ A ┆ AI ┆ 2001 ┆ 4 │
#> │ B ┆ AI ┆ 2001 ┆ 5 │
#> │ … ┆ … ┆ … ┆ … │
#> │ B ┆ AI ┆ 2013 ┆ 41 │
#> │ B ┆ EI ┆ 2013 ┆ 42 │
#> │ A ┆ AI ┆ 2014 ┆ 43 │
#> │ B ┆ AI ┆ 2014 ┆ 44 │
#> │ B ┆ EI ┆ 2014 ┆ 45 │
#> └─────────┴─────────┴──────┴────────────┘
production |>
pivot_wider(
names_from = c(product, country),
values_from = production,
names_glue = "prod_{product}_{country}"
)
#> shape: (15, 4)
#> ┌──────┬───────────┬───────────┬───────────┐
#> │ year ┆ prod_A_AI ┆ prod_B_AI ┆ prod_B_EI │
#> │ --- ┆ --- ┆ --- ┆ --- │
#> │ i32 ┆ i32 ┆ i32 ┆ i32 │
#> ╞══════╪═══════════╪═══════════╪═══════════╡
#> │ 2000 ┆ 1 ┆ 2 ┆ 3 │
#> │ 2001 ┆ 4 ┆ 5 ┆ 6 │
#> │ 2002 ┆ 7 ┆ 8 ┆ 9 │
#> │ 2003 ┆ 10 ┆ 11 ┆ 12 │
#> │ 2004 ┆ 13 ┆ 14 ┆ 15 │
#> │ … ┆ … ┆ … ┆ … │
#> │ 2010 ┆ 31 ┆ 32 ┆ 33 │
#> │ 2011 ┆ 34 ┆ 35 ┆ 36 │
#> │ 2012 ┆ 37 ┆ 38 ┆ 39 │
#> │ 2013 ┆ 40 ┆ 41 ┆ 42 │
#> │ 2014 ┆ 43 ┆ 44 ┆ 45 │
#> └──────┴───────────┴───────────┴───────────┘