knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(tidyverse) library(magrittr) library(Tplyr) library(knitr) op <- options()
One thing that we wanted to build into Tplyr to make it a user friendly package is the ability to eliminate redundant code where possible. This is why there are several options available in Tplyr to allow you to control things at your session level instead of each individual table, or even each individual layer.
The following are the options available in Tplyr and their descriptions:
suppressMessages(read_csv('tplyr_options.csv')) %>% kable(align="cl", col.names = c('Option', 'Description'))
Each of these options allows you to set these settings in one place, and every Tplyr table you create will inherit your option settings as defaults. This allows your table code to be more concise, and centralizes where you need to make an update when your code has to be adjusted. This vignette is dedicated to helping you understand how to leverage each of these options properly.
Declaring string formats and summaries that need to be performed is one of the more verbose parts of Tplyr. Furthermore, this is something that will often be fairly consistent within a study, as you'll likely want to look across a consistent set of descriptive statistics, or your count/shift tables will likely require the same sort of "n (%)" formatting.
Using the format options is very similar to setting a string format. The only difference is that you need to enter the string formats as a named list instead of as separate parameters to a function call.
options( # Count layer defaults tplyr.count_layer_default_formats = list(n_counts = f_str("xxx (xx%)", n, pct), riskdiff = f_str('xx.xxx', dif) ), # Desc layer defaults tplyr.desc_layer_default_formats = list("n" = f_str("xxx", n), "Mean (SD)"= f_str("a.a+1 (a.a+2)", mean, sd), "Median" = f_str("a.a+4", median) ), # Shift layer defaults tplyr.shift_layer_default_formats = list(f_str("xxx", n)) )
Here you can see that Tplyr picks up these option changes. In the table below, we didn't use set_format_strings()
anywhere - instead we let Tplyr pick up the default formats from the options.
tplyr_table(tplyr_adsl, TRT01P) %>% add_layer( group_desc(AGE, by = "Age (years)") ) %>% add_layer( group_count(AGEGR1, by = "Categorical Age Groups") ) %>% build() %>% kable()
One important thing to understand about how these options work in particular is the scoping.
tplyr_table()
level will override the Tplyr options and extends to any layer of the specified type in the current table.tplyr_layer()
level will always be prioritized over Tplyr options and any tplyr_table()
formats set. This has the narrowest scope and will always be used when specified.To demonstrate, consider the following. The Tplyr options remain set from the block above.
tplyr_table(tplyr_adsl, TRT01P) %>% set_count_layer_formats(n_counts = f_str("xx (xxx%)", n, pct)) %>% set_desc_layer_formats("Mean (SD)" = f_str("a.a+1 (a.a+2)", mean, sd)) %>% add_layer( group_desc(AGE, by = "Age (Years)") ) %>% add_layer( group_count(AGEGR1, by = "Categorical Age Groups") ) %>% add_layer( group_count(ETHNIC, by = "Ethnicity") %>% set_format_strings(f_str("xxxxx (xx.xxx%)", n, pct)) ) %>% build() %>% kable()
In the above output:
set_desc_layer_formats()
set_count_layer_formats()
set_format_strings()
Each of the outputs ignores the Tplyr option defaults.
Tplyr defaults to avoiding capping precision. We do this because capping precision should be a conscious decision, where you as the user specifically set the limit of how many decimal places are relevant to a specific result. One way to cap precision is by using the cap
parameter within set_format_strings()
. But perhaps you have a specific limit to what you'd like to see on any output. Here, we offer the tplyr.precision_cap
option to set whatever cap you wish.
options(tplyr.precision_cap = c('int'=2, 'dec'=2))
Similar to the layer defaults, setting a precision cap at the layer level will override the tplyr.precision_cap
option.
tplyr_table(tplyr_adsl, TRT01P) %>% add_layer( group_desc(HEIGHTBL, by = "Height at Baseline") %>% set_format_strings( 'Mean (SD)' = f_str('a.a (a.a+1)', mean, sd) ) ) %>% add_layer( group_desc(HEIGHTBL, by = "Height at Baseline (Limited)") %>% set_format_strings( 'Mean (SD)' = f_str('a.a (a.a+1)', mean, sd), cap = c('int'= 1, 'dec'=0) ) ) %>% build() %>% kable()
Both layers in the above example are summarizing the same data. The top layer is using the tplyr.precision_cap
option set above, which limits to 2 integer place and 2 decimal places. To bottom layer applies its own cap to override the option. Recall a few things about auto precision:
The bottom layer overrides the Tplyr option. Instead, integers are capped at 1 space, and decimals are capped at 0.
Custom summaries allow you to extend the capabilities of descriptive statistics layers in Tplyr. Maybe our defaults don't work how you'd like them to, or maybe you have some custom functions within your organization that you commonly would like to use. Specifying the custom summaries you wish to use in every table would prove quite tedious - therefore, the tplyr.custom_summaries
option is a better choice.
options(tplyr.custom_summaries = rlang::quos( geometric_mean = exp(sum(log(.var[.var > 0]),na.rm=TRUE) / length(.var)) ))
Note that the table code used to produce the output is the same. Now Tplyr used the custom summary function for geometric_mean
as specified in the tplyr.custom_summaries
option. Also note the use of rlang::quos()
. We've done our best to mask this from the user everywhere possible and make the interfaces clean and intuitive, but a great deal of Tplyr is built using 'rlang' and non-standard evaluation. Within this option is one of the very few instances where a user needs to concern themselves with the use of quosures. If you'd like to learn more about non-standard evaluation and quosures, we recommend Section IV in Advanced R.
Now that geometric mean is set within the Tplyr options, you can use it within your descriptive statistics layers, just like it was one of the built-in summaries.
tplyr_table(tplyr_adsl, TRT01P) %>% add_layer( group_desc(AGE) %>% set_format_strings('Geometric Mean' = f_str('xx.xx', geometric_mean)) ) %>% build() %>% kable()
By default, R will switch to scientific notation for a number less than .001. This is controlled by the scipen
option. The default value of scipen
is 0. If you'd like to increase the decimal places required for scientific notation to be triggered, increase the value of scipen
. The value of scipen
is the number of orders of ten smaller (i.e. decimal places preceded by 0's) required to switch to scientific notation. Decreasing the value of scipen
will cause R to switch to scientific location for larger numbers.
This is easier to understand with an example.
options(scipen = 0) # This is the default .0001 options(scipen = 1) # Require 5 decimal places instead .0001 .00001 options(scipen = -1) # Only require 3 decimal places .001
In Tplyr, we have the option tplyr.scipen
. This is the scipen
setting that will be used only while the Tplyr table is being built. This allows you to use a different scipen
setting within Tplyr than your R session. The default value we use in Tplyr is 9999, which is intended to totally prevent numbers from switching to scientific notation. We want this to be a conscious decision that you make in order to prevent any unexpected outputs.
options(op)
options(tplyr.scipen = -3) t <- tplyr_table(tplyr_adae, TRTA) %>% add_layer( group_count(AEDECOD) %>% add_risk_diff(c('Xanomeline Low Dose', 'Placebo')) ) suppressWarnings(build(t)) %>% # Chi-squared warnings occur with small samples head() %>% kable()
options(op)
Note that the risk-difference variables above have mostly shifted to scientific notation. This is because the limit has been shifted to .1 within the Tplyr build.
There are many algorithms available to compute quantile R has 9, controlled by the type
parameter in quantile()
. The descriptive statistics offer built-in summaries for Q1, Q3, and Interquartile range, all three of which use the quantile()
in the underlying implementation. Given that we offer this default, we felt it was important to offer you the flexibility to change the algorithm. You can do this with tplyr.quantile_type
.
The default we chose to use is the R default of Type 7:
$$ m = 1-p. p[k] = (k - 1) / (n - 1). \textrm{In this case, } p[k] = mode[F(x[k])]. \textrm{This is used by S.} $$ The example below demonstrates using the default quantile algorithm in R
tplyr_table(tplyr_adsl, TRT01P) %>% add_layer( group_desc(CUMDOSE) %>% set_format_strings("Q1, Q3" = f_str('xxxxx, xxxxx', q1, q3)) ) %>% build() %>% kable()
Within the clinical world, you may wish to match the way that SAS calculates quantiles. To match SAS's definition, use Type 3:
$$ \textrm{Nearest even order statistic. γ = 0 if g = 0 and j is even, and 1 otherwise.} $$
options(tplyr.quantile_type = 3) tplyr_table(tplyr_adsl, TRT01P) %>% add_layer( group_desc(CUMDOSE) %>% set_format_strings("Q1, Q3" = f_str('xxxxx, xxxxx', q1, q3)) ) %>% build() %>% select(-starts_with("ord")) %>% kable()
In certain cases users may want to match tables produced by other languages that IBM rounding.Tplyr offers the option 'tplyr.IBMRounding' to change the default rounding behavior of Tplyr tables. Review var1_4 in the tables below.
Using the default R behavior
tplyr_table(mtcars, gear) %>% add_layer( group_desc(qsec) %>% set_format_strings(mean = f_str("xx.xx", mean)) ) %>% build()
Using IBM rounding
withr::with_options( list(tplyr.IBMRounding = TRUE), { tplyr_table(mtcars, gear) %>% add_layer( group_desc(qsec) %>% set_format_strings(mean = f_str("xx.xx", mean)) ) %>% build() } )
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