Like the layer numeric data, Tplyr also stores the numeric data produced from statistics like risk difference. This helper function gives you access to obtain that data from the environment
Arguments
- x
A tplyr_table or tplyr_layer object
- layer
Layer name or index to select out specifically
- statistic
Statistic name or index to select
- where
Subset criteria passed to dplyr::filter
- ...
Additional arguments passed to dispatch
Details
When used on a tplyr_table object, this method will aggregate the
numeric data from all Tplyr layers and calculate all statistics. 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 the statistic data will simply be returned. Otherwise,
the numeric portion of the layer will be processed.
Using the layer, where, and statistic parameters, data for a specific layer statistic can be extracted and subset, allowing you to directly access data of interest. This is most clear when layers are given text names instead of using a layer index, but a numeric index works as well. If just a statistic is specified, that statistic will be collected and returned in a list of data frames, allowing you to grab, for example, just the risk difference statistics across all layers.
Examples
library(magrittr)
t <- tplyr_table(mtcars, gear) %>%
add_layer(name='drat',
group_desc(drat)
) %>%
add_layer(name="cyl",
group_count(cyl)
) %>%
add_layer(name="am",
group_count(am) %>%
add_risk_diff(c('4', '3'))
) %>%
add_layer(name="carb",
group_count(carb) %>%
add_risk_diff(c('4', '3'))
)
# Returns a list of lists, containing stats data from each layer
get_stats_data(t)
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> $drat
#> list()
#>
#> $cyl
#> list()
#>
#> $am
#> $am$riskdiff
#> # A tibble: 10 × 3
#> summary_var measure `4_3`
#> <chr> <chr> <dbl>
#> 1 0 comp 0.333
#> 2 0 ref 1
#> 3 0 dif -0.667
#> 4 0 low -1
#> 5 0 high -0.325
#> 6 1 comp 0.667
#> 7 1 ref 0
#> 8 1 dif 0.667
#> 9 1 low 0.325
#> 10 1 high 1
#>
#>
#> $carb
#> $carb$riskdiff
#> # A tibble: 30 × 3
#> summary_var measure `4_3`
#> <chr> <chr> <dbl>
#> 1 1 comp 0.333
#> 2 1 ref 0.2
#> 3 1 dif 0.133
#> 4 1 low -0.277
#> 5 1 high 0.543
#> 6 2 comp 0.333
#> 7 2 ref 0.267
#> 8 2 dif 0.0667
#> 9 2 low -0.348
#> 10 2 high 0.481
#> # ℹ 20 more rows
#>
#>
# Returns just the riskdiff statistics from each layer - NULL
# for layers without riskdiff
get_stats_data(t, statistic="riskdiff")
#> $drat
#> NULL
#>
#> $cyl
#> NULL
#>
#> $am
#> # A tibble: 10 × 3
#> summary_var measure `4_3`
#> <chr> <chr> <dbl>
#> 1 0 comp 0.333
#> 2 0 ref 1
#> 3 0 dif -0.667
#> 4 0 low -1
#> 5 0 high -0.325
#> 6 1 comp 0.667
#> 7 1 ref 0
#> 8 1 dif 0.667
#> 9 1 low 0.325
#> 10 1 high 1
#>
#> $carb
#> # A tibble: 30 × 3
#> summary_var measure `4_3`
#> <chr> <chr> <dbl>
#> 1 1 comp 0.333
#> 2 1 ref 0.2
#> 3 1 dif 0.133
#> 4 1 low -0.277
#> 5 1 high 0.543
#> 6 2 comp 0.333
#> 7 2 ref 0.267
#> 8 2 dif 0.0667
#> 9 2 low -0.348
#> 10 2 high 0.481
#> # ℹ 20 more rows
#>
# Return the statistic data for just the "am" layer - a list
get_stats_data(t, layer="am")
#> $riskdiff
#> # A tibble: 10 × 3
#> summary_var measure `4_3`
#> <chr> <chr> <dbl>
#> 1 0 comp 0.333
#> 2 0 ref 1
#> 3 0 dif -0.667
#> 4 0 low -1
#> 5 0 high -0.325
#> 6 1 comp 0.667
#> 7 1 ref 0
#> 8 1 dif 0.667
#> 9 1 low 0.325
#> 10 1 high 1
#>
get_stats_data(t, layer=3)
#> $riskdiff
#> # A tibble: 10 × 3
#> summary_var measure `4_3`
#> <chr> <chr> <dbl>
#> 1 0 comp 0.333
#> 2 0 ref 1
#> 3 0 dif -0.667
#> 4 0 low -1
#> 5 0 high -0.325
#> 6 1 comp 0.667
#> 7 1 ref 0
#> 8 1 dif 0.667
#> 9 1 low 0.325
#> 10 1 high 1
#>
# Return the statistic data for just the "am" and "cyl", layer - a
# list of lists
get_stats_data(t, layer=c("am", "cyl"))
#> $am
#> $am$riskdiff
#> # A tibble: 10 × 3
#> summary_var measure `4_3`
#> <chr> <chr> <dbl>
#> 1 0 comp 0.333
#> 2 0 ref 1
#> 3 0 dif -0.667
#> 4 0 low -1
#> 5 0 high -0.325
#> 6 1 comp 0.667
#> 7 1 ref 0
#> 8 1 dif 0.667
#> 9 1 low 0.325
#> 10 1 high 1
#>
#>
#> $cyl
#> list()
#>
get_stats_data(t, layer=c(3, 2))
#> $am
#> $am$riskdiff
#> # A tibble: 10 × 3
#> summary_var measure `4_3`
#> <chr> <chr> <dbl>
#> 1 0 comp 0.333
#> 2 0 ref 1
#> 3 0 dif -0.667
#> 4 0 low -1
#> 5 0 high -0.325
#> 6 1 comp 0.667
#> 7 1 ref 0
#> 8 1 dif 0.667
#> 9 1 low 0.325
#> 10 1 high 1
#>
#>
#> $cyl
#> list()
#>
# Return just the statistic data for "am" and "cyl" - a list
get_stats_data(t, layer=c("am", "cyl"), statistic="riskdiff")
#> $am
#> # A tibble: 10 × 3
#> summary_var measure `4_3`
#> <chr> <chr> <dbl>
#> 1 0 comp 0.333
#> 2 0 ref 1
#> 3 0 dif -0.667
#> 4 0 low -1
#> 5 0 high -0.325
#> 6 1 comp 0.667
#> 7 1 ref 0
#> 8 1 dif 0.667
#> 9 1 low 0.325
#> 10 1 high 1
#>
#> $cyl
#> NULL
#>
get_stats_data(t, layer=c(3, 2), statistic="riskdiff")
#> $am
#> # A tibble: 10 × 3
#> summary_var measure `4_3`
#> <chr> <chr> <dbl>
#> 1 0 comp 0.333
#> 2 0 ref 1
#> 3 0 dif -0.667
#> 4 0 low -1
#> 5 0 high -0.325
#> 6 1 comp 0.667
#> 7 1 ref 0
#> 8 1 dif 0.667
#> 9 1 low 0.325
#> 10 1 high 1
#>
#> $cyl
#> NULL
#>
# Return the riskdiff for the "am" layer - a data frame
get_stats_data(t, layer="am", statistic="riskdiff")
#> # A tibble: 10 × 3
#> summary_var measure `4_3`
#> <chr> <chr> <dbl>
#> 1 0 comp 0.333
#> 2 0 ref 1
#> 3 0 dif -0.667
#> 4 0 low -1
#> 5 0 high -0.325
#> 6 1 comp 0.667
#> 7 1 ref 0
#> 8 1 dif 0.667
#> 9 1 low 0.325
#> 10 1 high 1
# Return and filter the riskdiff for the am layer - a data frame
get_stats_data(t, layer="am", statistic="riskdiff", where = summary_var==1)
#> # A tibble: 5 × 3
#> summary_var measure `4_3`
#> <chr> <chr> <dbl>
#> 1 1 comp 0.667
#> 2 1 ref 0
#> 3 1 dif 0.667
#> 4 1 low 0.325
#> 5 1 high 1