get_numeric_data provides access to the un-formatted numeric data for
each of the layers within a tplyr_table, with options to allow you to
extract distinct layers and filter as desired.
Arguments
- x
A tplyr_table or tplyr_layer object
- layer
Layer name or index to select out specifically
- where
Subset criteria passed to dplyr::filter
- ...
Additional arguments to pass forward
Details
When used on a tplyr_table object, this method will aggregate the
numeric data from all Tplyr layers. The data will be returned to the user in
a list of data frames. If the data has already been processed (i.e.
build has been run), the numeric data is already available and will be
returned without reprocessing. Otherwise, the numeric portion of the layer
will be processed.
Using the layer and where parameters, data for a specific layer can be extracted and subset. This is most clear when layers are given text names instead of using a layer index, but a numeric index works as well.
Examples
# Load in pipe
library(magrittr)
t <- tplyr_table(mtcars, gear) %>%
add_layer(name='drat',
group_desc(drat)
) %>%
add_layer(name='cyl',
group_count(cyl)
)
# Return a list of the numeric data frames
get_numeric_data(t)
#> $drat
#> # A tibble: 27 × 4
#> summary_var gear stat value
#> <chr> <fct> <chr> <dbl>
#> 1 drat 3 n 15
#> 2 drat 3 mean 3.13
#> 3 drat 3 sd 0.274
#> 4 drat 3 median 3.08
#> 5 drat 3 q1 3.04
#> 6 drat 3 q3 3.18
#> 7 drat 3 min 2.76
#> 8 drat 3 max 3.73
#> 9 drat 3 missing 0
#> 10 drat 4 n 12
#> # ℹ 17 more rows
#>
#> $cyl
#> # A tibble: 9 × 6
#> gear summary_var n distinct_n total distinct_total
#> <chr> <chr> <dbl> <dbl> <int> <int>
#> 1 3 4 1 1 15 1
#> 2 3 6 2 1 15 1
#> 3 3 8 12 1 15 1
#> 4 4 4 8 1 12 1
#> 5 4 6 4 1 12 1
#> 6 4 8 0 0 12 1
#> 7 5 4 2 1 5 1
#> 8 5 6 1 1 5 1
#> 9 5 8 2 1 5 1
#>
# Get the data from a specific layer
get_numeric_data(t, layer='drat')
#> # A tibble: 27 × 4
#> summary_var gear stat value
#> <chr> <fct> <chr> <dbl>
#> 1 drat 3 n 15
#> 2 drat 3 mean 3.13
#> 3 drat 3 sd 0.274
#> 4 drat 3 median 3.08
#> 5 drat 3 q1 3.04
#> 6 drat 3 q3 3.18
#> 7 drat 3 min 2.76
#> 8 drat 3 max 3.73
#> 9 drat 3 missing 0
#> 10 drat 4 n 12
#> # ℹ 17 more rows
get_numeric_data(t, layer=1)
#> # A tibble: 27 × 4
#> summary_var gear stat value
#> <chr> <fct> <chr> <dbl>
#> 1 drat 3 n 15
#> 2 drat 3 mean 3.13
#> 3 drat 3 sd 0.274
#> 4 drat 3 median 3.08
#> 5 drat 3 q1 3.04
#> 6 drat 3 q3 3.18
#> 7 drat 3 min 2.76
#> 8 drat 3 max 3.73
#> 9 drat 3 missing 0
#> 10 drat 4 n 12
#> # ℹ 17 more rows
# Choose multiple layers by name or index
get_numeric_data(t, layer=c('cyl', 'drat'))
#> $cyl
#> # A tibble: 9 × 6
#> gear summary_var n distinct_n total distinct_total
#> <chr> <chr> <dbl> <dbl> <int> <int>
#> 1 3 4 1 1 15 1
#> 2 3 6 2 1 15 1
#> 3 3 8 12 1 15 1
#> 4 4 4 8 1 12 1
#> 5 4 6 4 1 12 1
#> 6 4 8 0 0 12 1
#> 7 5 4 2 1 5 1
#> 8 5 6 1 1 5 1
#> 9 5 8 2 1 5 1
#>
#> $drat
#> # A tibble: 27 × 4
#> summary_var gear stat value
#> <chr> <fct> <chr> <dbl>
#> 1 drat 3 n 15
#> 2 drat 3 mean 3.13
#> 3 drat 3 sd 0.274
#> 4 drat 3 median 3.08
#> 5 drat 3 q1 3.04
#> 6 drat 3 q3 3.18
#> 7 drat 3 min 2.76
#> 8 drat 3 max 3.73
#> 9 drat 3 missing 0
#> 10 drat 4 n 12
#> # ℹ 17 more rows
#>
get_numeric_data(t, layer=c(2, 1))
#> $cyl
#> # A tibble: 9 × 6
#> gear summary_var n distinct_n total distinct_total
#> <chr> <chr> <dbl> <dbl> <int> <int>
#> 1 3 4 1 1 15 1
#> 2 3 6 2 1 15 1
#> 3 3 8 12 1 15 1
#> 4 4 4 8 1 12 1
#> 5 4 6 4 1 12 1
#> 6 4 8 0 0 12 1
#> 7 5 4 2 1 5 1
#> 8 5 6 1 1 5 1
#> 9 5 8 2 1 5 1
#>
#> $drat
#> # A tibble: 27 × 4
#> summary_var gear stat value
#> <chr> <fct> <chr> <dbl>
#> 1 drat 3 n 15
#> 2 drat 3 mean 3.13
#> 3 drat 3 sd 0.274
#> 4 drat 3 median 3.08
#> 5 drat 3 q1 3.04
#> 6 drat 3 q3 3.18
#> 7 drat 3 min 2.76
#> 8 drat 3 max 3.73
#> 9 drat 3 missing 0
#> 10 drat 4 n 12
#> # ℹ 17 more rows
#>
# Get the data and filter it
get_numeric_data(t, layer='drat', where = gear==3)
#> # A tibble: 9 × 4
#> summary_var gear stat value
#> <chr> <fct> <chr> <dbl>
#> 1 drat 3 n 15
#> 2 drat 3 mean 3.13
#> 3 drat 3 sd 0.274
#> 4 drat 3 median 3.08
#> 5 drat 3 q1 3.04
#> 6 drat 3 q3 3.18
#> 7 drat 3 min 2.76
#> 8 drat 3 max 3.73
#> 9 drat 3 missing 0