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Subset rows in nested data frames using their positions.

Source: R/nest_slice.R
nest_slice.Rd

nest_slice() lets you index rows in nested data frames by their (integer) locations. It allows you to select, remove, and duplicate rows. It is accompanied by a number of helpers for common use cases:

  • nest_slice_head() and nest_slice_tail() select the first or last rows of each nested data frame in .nest_data.

  • nest_slice_sample() randomly selects rows from each data frame in .nest_data.

  • nest_slice_min() and nest_slice_max() select the rows with the highest or lowest values of a variable within each nested data frame in .nest_data.

If .nest_data is a grouped data frame, the operation will be performed on each group, so that (e.g.) nest_slice_head(df, nested_dfs, n = 5) will return the first five rows in each group for each nested data frame.

Usage

nest_slice(.data, .nest_data, ..., .preserve = FALSE)

nest_slice_head(.data, .nest_data, ...)

nest_slice_tail(.data, .nest_data, ...)

nest_slice_min(.data, .nest_data, order_by, ..., with_ties = TRUE)

nest_slice_max(.data, .nest_data, order_by, ..., with_ties = TRUE)

nest_slice_sample(.data, .nest_data, ..., weight_by = NULL, replace = FALSE)

Arguments

.data

A data frame, data frame extension (e.g., a tibble), or a lazy data frame (e.g., from dbplyr or dtplyr).

.nest_data

A list-column containing data frames

...

For nest_slice(): Integer row values.

Provide either positive values to keep, or negative values to drop. The values provided must be either all positive or all negative. Indices beyond the number of rows in the input are silently ignored.

For nest_slice_helpers(), these arguments are passed on to methods.

Additionally:

  • n,prop Provide either n, the number of rows, or prop, the proportion of rows to select. If neither are supplied, n = 1 will be used.

    If a negative value of n or prop is provided, the specified number or proportion of rows will be removed.

    If n is greater than the number of rows in the group (or prop > 1), the result will be silently truncated to the group size. If the proportion of a group size does not yield an integer number of rows, the absolute value of prop*nrow(.nest_data) is rounded down.

.preserve

Relevant when .nest_data is grouped. If .preserve = FALSE (the default), the grouping structure is recalculated based on the resulting data, otherwise the grouping data is kept as is.

order_by

Variable or function of variables to order by.

with_ties

Should ties be kept together? The default, TRUE, may return more rows than you request. Use FALSE to ignore ties and return the first n rows.

weight_by

Sampling weights. This must evaluate to a vector of non-negative numbers the same length as the input. Weights are automatically standardised to sum to 1.

replace

Should sampling be performed with (TRUE) or without (FALSE, the default) replacement?

Value

An object of the same type as .data. Each object in the column .nest_data

will also be of the same type as the input. Each object in .nest_data has the following properties:

  • Each row may appear 0, 1, or many times in the output.

  • Columns are not modified.

  • Groups are not modified.

  • Data frame attributes are preserved.

Details

nest_slice() and its helpers are largely wrappers for dplyr::slice() and its helpers and maintains the functionality of slice() and its helpers within each nested data frame. For more information on slice() or its helpers, please refer to the documentation in dplyr.

See also

Other single table verbs: nest_arrange(), nest_filter(), nest_mutate(), nest_rename(), nest_select(), nest_summarise()

Examples

gm_nest <- gapminder::gapminder %>% tidyr::nest(country_data = -continent)

# select the 1st, 3rd, and 5th rows in each data frame in country_data
gm_nest %>% nest_slice(country_data, 1, 3, 5)
#> # A tibble: 5 × 2
#>   continent country_data    
#>   <fct>     <list>          
#> 1 Asia      <tibble [3 × 5]>
#> 2 Europe    <tibble [3 × 5]>
#> 3 Africa    <tibble [3 × 5]>
#> 4 Americas  <tibble [3 × 5]>
#> 5 Oceania   <tibble [3 × 5]>

# or select all but the 1st, 3rd, and 5th rows:
gm_nest %>% nest_slice(country_data, -1, -3, -5)
#> # A tibble: 5 × 2
#>   continent country_data      
#>   <fct>     <list>            
#> 1 Asia      <tibble [393 × 5]>
#> 2 Europe    <tibble [357 × 5]>
#> 3 Africa    <tibble [621 × 5]>
#> 4 Americas  <tibble [297 × 5]>
#> 5 Oceania   <tibble [21 × 5]> 

# first and last rows based on existing order:
gm_nest %>% nest_slice_head(country_data, n = 5)
#> # A tibble: 5 × 2
#>   continent country_data    
#>   <fct>     <list>          
#> 1 Asia      <tibble [5 × 5]>
#> 2 Europe    <tibble [5 × 5]>
#> 3 Africa    <tibble [5 × 5]>
#> 4 Americas  <tibble [5 × 5]>
#> 5 Oceania   <tibble [5 × 5]>
gm_nest %>% nest_slice_tail(country_data, n = 5)
#> # A tibble: 5 × 2
#>   continent country_data    
#>   <fct>     <list>          
#> 1 Asia      <tibble [5 × 5]>
#> 2 Europe    <tibble [5 × 5]>
#> 3 Africa    <tibble [5 × 5]>
#> 4 Americas  <tibble [5 × 5]>
#> 5 Oceania   <tibble [5 × 5]>

# rows with minimum and maximum values of a variable:
gm_nest %>% nest_slice_min(country_data, lifeExp, n = 5)
#> # A tibble: 5 × 2
#>   continent country_data    
#>   <fct>     <list>          
#> 1 Asia      <tibble [5 × 5]>
#> 2 Europe    <tibble [5 × 5]>
#> 3 Africa    <tibble [5 × 5]>
#> 4 Americas  <tibble [5 × 5]>
#> 5 Oceania   <tibble [5 × 5]>
gm_nest %>% nest_slice_max(country_data, lifeExp, n = 5)
#> # A tibble: 5 × 2
#>   continent country_data    
#>   <fct>     <list>          
#> 1 Asia      <tibble [5 × 5]>
#> 2 Europe    <tibble [5 × 5]>
#> 3 Africa    <tibble [5 × 5]>
#> 4 Americas  <tibble [5 × 5]>
#> 5 Oceania   <tibble [5 × 5]>

# randomly select rows with or without replacement:
gm_nest %>% nest_slice_sample(country_data, n = 5)
#> # A tibble: 5 × 2
#>   continent country_data    
#>   <fct>     <list>          
#> 1 Asia      <tibble [5 × 5]>
#> 2 Europe    <tibble [5 × 5]>
#> 3 Africa    <tibble [5 × 5]>
#> 4 Americas  <tibble [5 × 5]>
#> 5 Oceania   <tibble [5 × 5]>
gm_nest %>% nest_slice_sample(country_data, n = 5, replace = TRUE)
#> # A tibble: 5 × 2
#>   continent country_data    
#>   <fct>     <list>          
#> 1 Asia      <tibble [5 × 5]>
#> 2 Europe    <tibble [5 × 5]>
#> 3 Africa    <tibble [5 × 5]>
#> 4 Americas  <tibble [5 × 5]>
#> 5 Oceania   <tibble [5 × 5]>