Title:	A Traceability Focused Grammar of Clinical Data Summary
Version:	1.2.1
Description:	A traceability focused tool created to simplify the data manipulation necessary to create clinical summaries.
License:	MIT + file LICENSE
URL:	https://github.com/atorus-research/Tplyr
BugReports:	https://github.com/atorus-research/Tplyr/issues
Encoding:	UTF-8
Depends:	R (≥ 3.5.0)
Imports:	rlang (≥ 0.4.6), assertthat (≥ 0.2.1), magrittr (≥ 1.5), dplyr (≥ 1.0.0), purrr (≥ 0.3.3), stringr (≥ 1.4.0), tidyr (≥ 1.0.2), tidyselect (≥ 1.1.0), tibble (≥ 3.0.1), lifecycle, forcats (≥ 1.0.0)
Suggests:	testthat (≥ 2.1.0), haven (≥ 2.2.0), knitr, rmarkdown, huxtable, tidyverse, readr, kableExtra, pharmaRTF, withr
VignetteBuilder:	knitr
RoxygenNote:	7.2.3
RdMacros:	lifecycle
Config/testthat/edition:	3
LazyData:	true
NeedsCompilation:	no
Packaged:	2024-02-19 21:00:25 UTC; mike.stackhouse
Author:	Eli Miller [aut], Mike Stackhouse [aut, cre], Ashley Tarasiewicz [aut], Nathan Kosiba [ctb], Sadchla Mascary [ctb], Andrew Bates [ctb], Shiyu Chen [ctb], Oleksii Mikryukov [ctb], Atorus Research LLC [cph]
Maintainer:	Mike Stackhouse <mike.stackhouse@atorusresearch.com>
Repository:	CRAN
Date/Publication:	2024-02-20 08:00:02 UTC

A grammar of summary data for clinical reports

Description

'r lifecycle::badge("experimental")'

Details

'Tplyr' is a package dedicated to simplifying the data manipulation necessary to create clinical reports. Clinical data summaries can often be broken down into two factors - counting discrete variables (or counting shifts in state), and descriptive statistics around a continuous variable. Many of the reports that go into a clinical report are made up of these two scenarios. By abstracting this process away, 'Tplyr' allows you to rapidly build these tables without worrying about the underlying data manipulation.

'Tplyr' takes this process a few steps further by abstracting away most of the programming that goes into proper presentation, which is where a great deal of programming time is spent. For example, 'Tplyr' allows you to easily control:

String formatting

Different reports warrant different presentation of your strings. Programming this can get tedious, as you typically want to make sure that your decimals properly align. 'Tplyr' abstracts this process away and provides you with a simple interface to specify how you want your data presented

Treatment groups

Need a total column? Need to group summaries of multiple treatments? 'Tplyr' makes it simple to add additional treatment groups into your report

Denominators

n (%) counts often vary based on the summary being performed. 'Tplyr' allows you to easily control what denominators are used based on a few common scenarios

Sorting

Summarizing data is one thing, but ordering it for presentation. Tplyr automatically derives sorting variable to give you the data you need to order your table properly. This process is flexible so you can easily get what you want by leveraging your data or characteristics of R.

Another powerful aspect of 'Tplyr' are the objects themselves. 'Tplyr' does more than format your data. Metadata about your table is kept under the hood, and functions allow you to access information that you need. For example, 'Tplyr' allows you to calculate and access the raw numeric data of calculations as well, and easily pick out just the pieces of information that you need.

Lastly, 'Tplyr' was built to be flexible, yet intuitive. A common pitfall of building tools like this is over automation. By doing to much, you end up not doing enough. 'Tplyr' aims to hit the sweet spot in between. Additionally, we designed our function interfaces to be clean. Modifier functions offer you flexibility when you need it, but defaults can be set to keep the code concise. This allows you to quickly assemble your table, and easily make changes where necessary.

Author(s)

Maintainer: Mike Stackhouse mike.stackhouse@atorusresearch.com (ORCID)

Authors:

• Eli Miller Eli.Miller@AtorusResearch.com (ORCID)

• Ashley Tarasiewicz Ashley.Tarasiewicz@atorusresearch.com

Other contributors:

• Nathan Kosiba Nathan.Kosiba@atorusresearch.com (ORCID) [contributor]

• Sadchla Mascary sadchla.mascary@atorusresearch.com [contributor]

• Andrew Bates andrew.bates@atorusresearch.com [contributor]

• Shiyu Chen shiyu.chen@atorusresearch.com [contributor]

• Oleksii Mikryukov alex.mikryukov@atorusresearch.com [contributor]

• Atorus Research LLC [copyright holder]

See Also

Useful links:

• https://github.com/atorus-research/Tplyr

• Report bugs at https://github.com/atorus-research/Tplyr/issues

Examples

# Load in pipe
library(magrittr)

# Use just the defaults
tplyr_table(mtcars, gear) %>%
  add_layer(
    group_desc(mpg, by=cyl)
  ) %>%
  add_layer(
    group_count(carb, by=cyl)
  ) %>%
  build()

# Customize and modify
tplyr_table(mtcars, gear) %>%
  add_layer(
    group_desc(mpg, by=cyl) %>%
      set_format_strings(
        "n"         = f_str("xx", n),
        "Mean (SD)" = f_str("a.a+1 (a.a+2)", mean, sd, empty='NA'),
        "Median"    = f_str("a.a+1", median),
        "Q1, Q3"    = f_str("a, a", q1, q3, empty=c(.overall='NA')),
        "Min, Max"  = f_str("a, a", min, max),
        "Missing"   = f_str("xx", missing)
      )
  ) %>%
  add_layer(
    group_count(carb, by=cyl) %>%
      add_risk_diff(
        c('5', '3'),
        c('4', '3')
      ) %>%
      set_format_strings(
        n_counts = f_str('xx (xx%)', n, pct),
        riskdiff = f_str('xx.xxx (xx.xxx, xx.xxx)', dif, low, high)
      ) %>%
      set_order_count_method("bycount") %>%
      set_ordering_cols('4') %>%
      set_result_order_var(pct)
  ) %>%
  build()

# A Shift Table
tplyr_table(mtcars, am) %>%
  add_layer(
    group_shift(vars(row=gear, column=carb), by=cyl) %>%
    set_format_strings(f_str("xxx (xx.xx%)", n, pct))
  ) %>%
  build()

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling 'rhs(lhs)'.

Add an anti-join onto a tplyr_meta object

Description

An anti-join allows a tplyr_meta object to refer to data that should be extracted from a separate dataset, like the population data of a Tplyr table, that is unavailable in the target dataset. The primary use case for this is the presentation of missing subjects, which in a Tplyr table is presented using the function add_missing_subjects_row(). The missing subjects themselves are not present in the target data, and are thus only available in the population data. The add_anti_join() function allows you to provide the meta information relevant to the population data, and then specify the on variable that should be used to join with the target dataset and find the values present in the population data that are missing from the target data.

Usage

add_anti_join(meta, join_meta, on)

Arguments

Value

A tplyr_meta object

Examples

tm <- tplyr_meta(
  rlang::quos(TRT01A, SEX, ETHNIC, RACE),
  rlang::quos(TRT01A == "Placebo", TRT01A == "SEX", ETHNIC == "HISPANIC OR LATINO")
)

tm %>%
  add_anti_join(
    tplyr_meta(
      rlang::quos(TRT01A, ETHNIC),
      rlang::quos(TRT01A == "Placebo", ETHNIC == "HISPANIC OR LATINO")
    ),
    on = rlang::quos(USUBJID)
  )

Attach column headers to a Tplyr output

Description

When working with 'huxtable' tables, column headers can be controlled as if they are rows in the data frame. add_column_headers eases the process of introducing these headers.

Usage

add_column_headers(.data, s, header_n = NULL)

Arguments

Details

Headers are created by providing a single string. Columns are specified by delimitting each header with a '|' symbol. Instead of specifying the destination of each header, add_column_headers assumes that you have organized the columns of your data frame before hand. This means that after you use Tplyr::build(), if you'd like to reorganize the default column order (which is simply alphabetical), simply pass the build output to a dplyr::select or dplyr::relocate statement before passing into add_column_headers.

Spanning headers are also supported. A spanning header is an overarching header that sits across multiple columns. Spanning headers are introduced to add_column_header by providing the spanner text (i.e. the text that you'd like to sit in the top row), and then the spanned text (the bottom row) within curly brackets ('{}). For example, take the iris dataset. We have the names:

"Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"

If we wanted to provide a header string for this dataset, with spanners to help with categorization of the variables, we could provide the following string:

"Sepal {Length | Width} | Petal {Length | Width} | Species"

Value

A data.frame with the processed header string elements attached as the top rows

Important note

Make sure you are aware of the order of your variables prior to passing in to add_column_headers. The only requirement is that the number of column match. The rest is up to you.

Development notes

There are a few features of add_column_header that are intended but not yet supported:

• Nested spanners are not yet supported. Only a spanning row and a bottom row can currently be created

• Different delimiters and indicators for a spanned group may be used in the future. The current choices were intuitive, but based on feedback it could be determined that less common characters may be necessary.

Token Replacement

This function has support for reading values from the header_n object in a Tplyr table and adding them in the column headers. Note: The order of the parameters passed in the token is important. They should be first the treatment variable then any cols variables in the order they were passed in the table construction.

Use a double asterisk "**" at the begining to start the token and another double asterisk to close it. You can separate column parameters in the token with a single underscore. For example, **group1_flag2_param3** will pull the count from the header_n binding for group1 in the treat_var, flag2 in the first cols argument, and param3 in the second cols argument.

You can pass fewer arguments in the token to get the sum of multiple columns. For example, **group1** would get the sum of the group1 treat_var, and all cols from the header_n.

Examples

# Load in pipe
library(magrittr)
library(dplyr)
header_string <- "Sepal {Length | Width} | Petal {Length | Width} | Species"

iris2 <- iris %>%
  mutate_all(as.character)

iris2 %>% add_column_headers(header_string)

# Example with counts
mtcars2 <- mtcars %>%
  mutate_all(as.character)

t <- tplyr_table(mtcars2, vs, cols = am) %>%
  add_layer(
    group_count(cyl)
  )

b_t <- build(t) %>%
  mutate_all(as.character)

count_string <- paste0(" | V N=**0** {auto N=**0_0** | man N=**0_1**} |",
                       " S N=**1** {auto N=**1_0** | man N=**1_1**} | | ")

add_column_headers(b_t, count_string, header_n(t))

Attach a layer to a tplyr_table object

Description

add_layer attaches a tplyr_layer to a tplyr_table object. This allows for a tidy style of programming (using magrittr piping, i.e. %>%) with a secondary advantage - the construction of the layer object may consist of a series of piped functions itself.

Tplyr encourages a user to view the construction of a table as a series of "layers". The construction of each of these layers are isolated and independent of one another - but each of these layers are children of the table itself. add_layer isolates the construction of an individual layer and allows the user to construct that layer and insert it back into the parent. The syntax for this is intuitive and allows for tidy piping. Simply pipe the current table object in, and write the code to construct your layer within the layer parameter.

add_layers is another approach to attaching layers to a tplyr_table. Instead of constructing the entire table at once, add_layers allows you to construct layers as different objects. These layers can then be attached into the tplyr_table all at once.

add_layer and add_layers both additionally allow you to name the layers as you attach them. This is helpful when using functions like get_numeric_data or get_stats_data when you can access information from a layer directly. add_layer has a name parameter, and layers can be named in add_layers by submitting the layer as a named argument.

Usage

add_layer(parent, layer, name = NULL)

add_layers(parent, ...)

Arguments

Value

A tplyr_table or tplyr_layer/tplyr_subgroup_layer with a new layer inserted into the layer binding

See Also

[tplyr_table(), tplyr_layer(), group_count(), group_desc(), group_shift()]

Examples

# Load in pipe
library(magrittr)

## Single layer
t <- tplyr_table(mtcars, cyl) %>%
  add_layer(
    group_desc(target_var=mpg)
  )

## Single layer with name
t <- tplyr_table(mtcars, cyl) %>%
  add_layer(name='mpg',
    group_desc(target_var=mpg)
  )

# Using add_layers
t <- tplyr_table(mtcars, cyl)
l1 <- group_desc(t, target_var=mpg)
l2 <- group_count(t, target_var=cyl)

t <- add_layers(t, l1, 'cyl' = l2)

Add a missing subject row into a count summary.

Description

This function calculates the number of subjects missing from a particular group of results. The calculation is done by examining the total number of subjects potentially available from the Header N values within the result column, and finding the difference with the total number of subjects present in the result group. Note that for accurate results, the subject variable needs to be defined using the 'set_distinct_by()' function. As with other methods, this function instructs how distinct results should be identified.

Usage

add_missing_subjects_row(e, fmt = NULL, sort_value = NULL)

Arguments

Examples

tplyr_table(mtcars, gear) %>%
  add_layer(
    group_count(cyl) %>%
      add_missing_subjects_row(f_str("xxxx", n))
   ) %>%
   build()

Add risk difference to a count layer

Description

A very common requirement for summary tables is to calculate the risk difference between treatment groups. add_risk_diff allows you to do this. The underlying risk difference calculations are performed using the Base R function prop.test - so prior to using this function, be sure to familiarize yourself with its functionality.

Usage

add_risk_diff(layer, ..., args = list(), distinct = TRUE)

Arguments

Details

add_risk_diff can only be attached to a count layer, so the count layer must be constructed first. add_risk_diff allows you to compare the difference between treatment group, so all comparisons should be based upon the values within the specified treat_var in your tplyr_table object.

Comparisons are specified by providing two-element character vectors. You can provide as many of these groups as you want. You can also use groups that have been constructed using add_treat_grps or add_total_group. The first element provided will be considered the 'reference' group (i.e. the left side of the comparison), and the second group will be considered the 'comparison'. So if you'd like to see the risk difference of 'T1 - Placebo', you would specify this as c('T1', 'Placebo').

Tplyr forms your two-way table in the background, and then runs prop.test appropriately. Similar to way that the display of layers are specified, the exact values and format of how you'd like the risk difference display are set using set_format_strings. This controls both the values and the format of how the risk difference is displayed. Risk difference formats are set within set_format_strings by using the name 'riskdiff'.

You have 5 variables to choose from in your data presentation:

comp

Probability of the left hand side group (i.e. comparison)

ref

Probability of the right hand side group (i.e. reference)

dif

Difference of comparison - reference

low

Lower end of the confidence interval (default is 95%, override with the args paramter)

high

Upper end of the confidence interval (default is 95%, override with the args paramter)

Use these variable names when forming your f_str objects. The default presentation, if no string format is specified, will be:

f_str('xx.xxx (xx.xxx, xx.xxx)', dif, low, high)

Note - within Tplyr, you can account for negatives by allowing an extra space within your integer side settings. This will help with your alignment.

If columns are specified on a Tplyr table, risk difference comparisons still only take place between groups within the treat_var variable - but they are instead calculated treating the cols variables as by variables. Just like the tplyr layers themselves, the risk difference will then be transposed and display each risk difference as separate variables by each of the cols variables.

If distinct is TRUE (the default), all calculations will take place on the distinct counts, if they are available. Otherwise, non-distinct counts will be used.

One final note - prop.test may throw quite a few warnings. This is natural, because it alerts you when there's not enough data for the approximations to be correct. This may be unnerving coming from a SAS programming world, but this is R is trying to alert you that the values provided don't have enough data to truly be statistically accurate.

Examples

library(magrittr)

## Two group comparisons with default options applied
t <- tplyr_table(mtcars, gear)

# Basic risk diff for two groups, using defaults
l1 <- group_count(t, carb) %>%
  # Compare 3 vs. 4, 3 vs. 5
  add_risk_diff(
    c('3', '4'),
    c('3', '5')
  )

# Build and show output
add_layers(t, l1) %>% build()

## Specify custom formats and display variables
t <- tplyr_table(mtcars, gear)

# Create the layer with custom formatting
l2 <- group_count(t, carb) %>%
  # Compare 3 vs. 4, 3 vs. 5
  add_risk_diff(
    c('3', '4'),
    c('3', '5')
  ) %>%
  set_format_strings(
    'n_counts' = f_str('xx (xx.x)', n, pct),
    'riskdiff' = f_str('xx.xxx, xx.xxx, xx.xxx, xx.xxx, xx.xxx', comp, ref, dif, low, high)
  )

# Build and show output
add_layers(t, l2) %>% build()

## Passing arguments to prop.test
t <- tplyr_table(mtcars, gear)

# Create the layer with args option
l3 <- group_count(t, carb) %>%
  # Compare 3 vs. 4, 4 vs. 5
  add_risk_diff(
    c('3', '4'),
    c('3', '5'),
    args = list(conf.level = 0.9, correct=FALSE, alternative='less')
  )

# Build and show output
add_layers(t, l3) %>% build()

Add a Total row into a count summary.

Description

Adding a total row creates an additional observation in the count summary that presents the total counts (i.e. the n's that are summarized). The format of the total row will be formatted in the same way as the other count strings.

Usage

add_total_row(e, fmt = NULL, count_missings = TRUE, sort_value = NULL)

Arguments

Details

Totals are calculated using all grouping variables, including treat_var and cols from the table level. If by variables are included, the grouping of the total and the application of denominators becomes ambiguous. You will be warned specifically if a percent is included in the format. To rectify this, use set_denoms_by(), and the grouping of add_total_row() will be updated accordingly.

Note that when using add_total_row() with set_pop_data(), you should call add_total_row() AFTER calling set_pop_data(), otherwise there is potential for unexpected behaivior with treatment groups.

Examples

# Load in Pipe
library(magrittr)

tplyr_table(mtcars, gear) %>%
  add_layer(
    group_count(cyl) %>%
      add_total_row(f_str("xxxx", n))
   ) %>%
   build()

Combine existing treatment groups for summary

Description

Summary tables often present individual treatment groups, but may additionally have a "Treatment vs. Placebo" or "Total" group added to show grouped summary statistics or counts. This set of functions offers an interface to add these groups at a table level and be consumed by subsequent layers.

Usage

add_treat_grps(table, ...)

add_total_group(table, group_name = "Total")

treat_grps(table)

Arguments

Details

add_treat_grps allows you to specify specific groupings. This is done by supplying named arguments, where the name becomes the new treatment group's name, and those treatment groups are made up of the argument's values.

add_total_group is a simple wrapper around add_treat_grps. Instead of producing custom groupings, it produces a "Total" group by the supplied name, which defaults to "Total". This "Total" group is made up of all existing treatment groups within the population dataset.

Note that when using add_treat_grps or add_total_row() with set_pop_data(), you should call add_total_row() AFTER calling set_pop_data(), otherwise there is potential for unexpected behaivior with treatment groups.

The function treat_grps allows you to see the custom treatment groups available in your tplyr_table object

Value

The modified table object

Examples

tab <- tplyr_table(iris, Species)

# A custom group
add_treat_grps(tab, "Not Setosa" = c("versicolor", "virginica"))

# Add a total group
add_total_group(tab)

treat_grps(tab)
# Returns:
# $`Not Setosa`
#[1] "versicolor" "virginica"
#
#$Total
#[1] "setosa"     "versicolor" "virginica"

Add variables to a tplyr_meta object

Description

Add additional variable names to a tplyr_meta() object.

Usage

add_variables(meta, names)

add_filters(meta, filters)

Arguments

Value

tplyr_meta object

Examples

m <- tplyr_meta()
m <- add_variables(m, rlang::quos(a, b, c))
m <- add_filters(m, rlang::quos(a==1, b==2, c==3))
m

Append the Tplyr table metadata dataframe

Description

append_metadata() allows a user to extend the Tplyr metadata data frame with user provided data. In some tables, Tplyr may be able to provided most of the data, but a user may have to extend the table with other summaries, statistics, etc. This function allows the user to extend the tplyr_table's metadata with their own metadata content using custom data frames created using the tplyr_meta object.

Usage

append_metadata(t, meta)

Arguments

Details

As this is an advanced feature of Tplyr, ownership is on the user to make sure the metadata data frame is assembled properly. The only restrictions applied by append_metadata() are that meta must have a column named row_id, and the values in row_id cannot be duplicates of any row_id value already present in the Tplyr metadata dataframe. tplyr_meta() objects align with constructed dataframes using the row_id and output dataset column name. As such, tplyr_meta() objects should be inserted into a data frame using a list column.

Value

A tplyr_table object

Examples

t <- tplyr_table(mtcars, gear) %>%
  add_layer(
    group_desc(wt)
  )

t %>%
  build(metadata=TRUE)

m <- tibble::tibble(
  row_id = c('x1_1'),
  var1_3 = list(tplyr_meta(rlang::quos(a, b, c), rlang::quos(a==1, b==2, c==3)))
)

append_metadata(t, m)

Conditional reformatting of a pre-populated string of numbers

Description

This function allows you to conditionally re-format a string of numbers based on a numeric value within the string itself. By selecting a "format group", which is targeting a specific number within the string, a user can establish a condition upon which a provided replacement string can be used. Either the entire replacement can be used to replace the entire string, or the replacement text can refill the "format group" while preserving the original width and alignment of the target string.

Usage

apply_conditional_format(
  string,
  format_group,
  condition,
  replacement,
  full_string = FALSE
)

Arguments

Value

A character vector

Examples

string <- c(" 0  (0.0%)", " 8  (9.3%)", "78 (90.7%)")

apply_conditional_format(string, 2, x == 0, " 0        ", full_string=TRUE)

apply_conditional_format(string, 2, x < 1, "(<1%)")

Apply Format Strings outside of a Tplyr table

Description

The f_str object in Tplyr is used to drive formatting of the outputs strings within a Tplyr table. This function allows a user to use the same interface to apply formatted string on any data frame within a dplyr::mutate() context.

Usage

apply_formats(format_string, ..., empty = c(.overall = ""))

Arguments

Details

Note that auto-precision is not currently supported within apply_formats()

Value

Character vector of formatted values

Examples

library(dplyr)

mtcars %>%
  head() %>%
  mutate(
    fmt_example = apply_formats('xxx (xx.x)', hp, wt)
  )

Replace repeating row label variables with blanks in preparation for display.

Description

Depending on the display package being used, row label values may need to be blanked out if they are repeating. This gives the data frame supporting the table the appearance of the grouping variables being grouped together in blocks. apply_row_masks does this work by blanking out the value of any row_label variable where the current value is equal to the value before it. Note - apply_row_masks assumes that the data frame has already be sorted and therefore should only be applied once the data frame is in its final sort sequence.

Usage

apply_row_masks(dat, row_breaks = FALSE, ...)

Arguments

Details

Additionally, apply_row_masks can add row breaks for you between each layer. Row breaks are inserted as blank rows. This relies on the "break by" variables (submitted via ...) constructed in build still being attached to the dataset. An additional order variable is attached named ord_break, but the output dataset is sorted to properly insert the row breaks between layers.

Value

tibble with blanked out rows where values are repeating

Trigger the execution of the tplyr_table

Description

The functions used to assemble a tplyr_table object and each of the layers do not trigger the processing of any data. Rather, a lazy execution style is used to allow you to construct your table and then explicitly state when the data processing should happen. build triggers this event.

Usage

build(x, metadata = FALSE)

Arguments

Details

When the build command is executed, all of the data processing commences. Any pre-processing necessary within the table environment takes place first. Next, each of the layers begins executing. Once the layers complete executing, the output of each layer is stacked into the resulting data frame.

Once this process is complete, any post-processing necessary within the table environment takes place, and the final output can be delivered. Metadata and traceability information are kept within each of the layer environments, which allows an investigation into the source of the resulting datapoints. For example, numeric data from any summaries performed is maintained and accessible within a layer using get_numeric_data.

The 'metadata' option of build will trigger the construction of traceability metadata for the constructed data frame. Essentially, for every "result" that Tplyr produces, Tplyr can also generate the steps necessary to obtain the source data which produced that result from the input. For more information, see vignette("metadata").

Value

An executed tplyr_table

See Also

tplyr_table, tplyr_layer, add_layer, add_layers, layer_constructors

Examples

# Load in Pipe
library(magrittr)

tplyr_table(iris, Species) %>%
  add_layer(
    group_desc(Sepal.Length, by = "Sepal Length")
  ) %>%
  add_layer(
    group_desc(Sepal.Width, by = "Sepal Width")
  ) %>%
  build()

Collapse row labels into a single column

Description

This is a generalized post processing function that allows you to take groups of by variables and collapse them into a single column. Repeating values are split into separate rows, and for each level of nesting, a specified indentation level can be applied.

Usage

collapse_row_labels(x, ..., indent = "  ", target_col = row_label)

Arguments

Value

data.frame with row labels collapsed into a single column

Examples

x <- tibble::tribble(
~row_label1, ~row_label2, ~row_label3, ~row_label4, ~var1,
  "A",         "C",         "G",         "M",        1L,
  "A",         "C",         "G",         "N",        2L,
  "A",         "C",         "H",         "O",        3L,
  "A",         "D",         "H",         "P",        4L,
  "A",         "D",         "I",         "Q",        5L,
  "A",         "D",         "I",         "R",        6L,
  "B",         "E",         "J",         "S",        7L,
  "B",         "E",         "J",         "T",        8L,
  "B",         "E",         "K",         "U",        9L,
  "B",         "F",         "K",         "V",        10L,
  "B",         "F",         "L",         "W",        11L
)


collapse_row_labels(x, row_label1, row_label2, row_label3, row_label4)

collapse_row_labels(x, row_label1, row_label2, row_label3)

collapse_row_labels(x, row_label1, row_label2, indent = "    ", target_col = rl)

Create a f_str object

Description

f_str objects are intended to be used within the function set_format_strings. The f_str object carries information that powers a significant amount of layer processing. The format_string parameter is capable of controlling the display of a data point and decimal precision. The variables provided in ... control which data points are used to populate the string formatted output.

Usage

f_str(format_string, ..., empty = c(.overall = ""))

Arguments

Details

Format strings are one of the most powerful components of 'Tplyr'. Traditionally, converting numeric values into strings for presentation can consume a good deal of time. Values and decimals need to align between rows, rounding before trimming is sometimes forgotten - it can become a tedious mess that is realistically not an important part of the analysis being performed. 'Tplyr' makes this process as simple as we can, while still allowing flexibility to the user.

Tplyr provides both manual and automatic decimal precision formatting. The display of the numbers in the resulting data frame is controlled by the format_string parameter. For manual precision, just like dummy values may be presented on your mocks, integer and decimal precision is specified by the user providing a string of 'x's for how you'd like your numbers formatted. If you'd like 2 integers with 3 decimal places, you specify your string as 'xx.xxx'. 'Tplyr' does the work to get the numbers in the right place.

To take this a step further, automatic decimal precision can also be obtained based on the collected precision within the data. When creating tables where results vary by some parameter, different results may call for different degrees of precision. To use automatic precision, use a single 'a' on either the integer and decimal side. If you'd like to use increased precision (i.e. you'd like mean to be collected precision +1), use 'a+1'. So if you'd like both integer and and decimal precision to be based on the data as collected, you can use a format like 'a.a' - or for collected+1 decimal precision, 'a.a+1'. You can mix and match this with manual formats as well, making format strings such as 'xx.a+1'.

If you want two numbers on the same line, you provide two sets of x's. For example, if you're presenting a value like "mean (sd)" - you could provide the string 'xx.xx (xx.xxx)', or perhaps 'a.a+1 (a.a+2). Note that you're able to provide different integer lengths and different decimal precision for the two values. Each format string is independent and relates only to the format specified.

As described above, when using 'x' or 'a', any other character within the format string will stay stationary. So for example, if your format string is 'xx (xxx.x)', your number may format as '12 ( 34.5)'. So the left side parenthesis stays fixed. In some displays, you may want the parenthesis to 'hug' your number. Following this example, when allotting 3 spaces for the integer within parentheses, the parentehsis should shift to the right, making the numbers appear '12 (34.5)'. Using f_str() you can achieve this by using a capital 'X' or 'A'. For this example, the format string would be 'xx (XXX.x)'.

There are a two rules when using 'parenthesis hugging':

• Capital letters should only be used on the integer side of a number

• A character must precede the capital letter, otherwise there's no character to 'hug'

The other parameters of the f_str call specify what values should fill the x's. f_str objects are used slightly differently between different layers. When declaring a format string within a count layer, f_str() expects to see the values n or distinct_n for event or distinct counts, pct or distinct_pct for event or distinct percentages, or total or distinct_total for denominator calculations. Note that in an f_str() for a count layer 'A' or 'a' are based on n counts, and therefore don't make sense to use in percentages. But in descriptive statistic layers, f_str parameters refer to the names of the summaries being performed, either by built in defaults, or custom summaries declared using set_custom_summaries(). See set_format_strings() for some more notes about layers specific implementation.

An f_str() may also be used outside of a Tplyr table. The function apply_formats() allows you to apply an f_str within the context of dplyr::mutate() or more generally a vectorized function.

Value

A f_str object

Valid f_str() Variables by Layer Type

Valid variables allowed within the ... parameter of f_str() differ by layer type.

• Count layers

  • n

  • pct

  • total

  • distinct_n

  • distinct_pct

  • distinct_total

• Shift layers

  • n

  • pct

  • total

• Desc layers

  • n

  • mean

  • sd

  • median

  • var

  • min

  • max

  • iqr

  • q1

  • q3

  • missing

  • Custom summaries created by set_custom_summaries()

Examples

f_str("xx.x (xx.x)", mean, sd)

f_str("a.a+1 (a.a+2)", mean, sd)

f_str("xx.a (xx.a+1)", mean, sd)

f_str("xx.x, xx.x, xx.x", q1, median, q3)

f_str("xx (XXX.x%)", n, pct)

f_str("a.a+1 (A.a+2)", mean, sd)

Set or return by layer binding

Description

Set or return by layer binding

Usage

get_by(layer)

set_by(layer, by)

Arguments

Value

For get_by, the by binding of the supplied layer. For set_by the modified layer environment.

Examples

# Load in pipe
library(magrittr)
iris$Species2 <- iris$Species
lay <- tplyr_table(iris, Species) %>%
  group_count(Species) %>%
  set_by(vars(Species2, Sepal.Width))

Get Data Labels

Description

Get labels for data sets included in Tplyr.

Usage

get_data_labels(data)

Arguments

Value

A data.frame with columns 'name' and 'label' containing the names and labels of each column.

Get or set the default format strings for descriptive statistics layers

Description

Tplyr provides you with the ability to set table-wide defaults of format strings. You may wish to reuse the same format strings across numerous layers. set_desc_layer_formats and set_count_layer_formats allow you to apply your desired format strings within the entire scope of the table.

Usage

get_desc_layer_formats(obj)

set_desc_layer_formats(obj, ...)

get_count_layer_formats(obj)

set_count_layer_formats(obj, ...)

get_shift_layer_formats(obj)

set_shift_layer_formats(obj, ...)

Arguments

Details

For descriptive statistic layers, you can also use set_format_strings and set_desc_layer_formats together within a table, but not within the same layer. In the absence of specified format strings, first the table will be checked for any available defaults, and otherwise the tplyr.desc_layer_default_formats option will be used. set_format_strings will always take precedence over either. Defaults cannot be combined between set_format_strings, set_desc_layer_formats, and the tplyr.desc_layer_default_formats because the order of presentation of results is controlled by the format strings, so relying on combinations of these setting would not be intuitive.

For count layers, you can override the n_counts or riskdiff format strings separately, and the narrowest scope available will be used from layer, to table, to default options.

Extract the result metadata of a Tplyr table

Description

Given a row_id value and a result column, this function will return the tplyr_meta object associated with that 'cell'.

Usage

get_meta_result(x, row_id, column, ...)

Arguments

Details

If a Tplyr table is built with the metadata=TRUE option specified, then metadata is assembled behind the scenes to provide traceability on each result cell derived. The functions get_meta_result() and get_meta_subset() allow you to access that metadata by using an ID provided in the row_id column and the column name of the result you'd like to access. The purpose is of the row_id variable instead of a simple row index is to provide a sort resistant reference of the originating column, so the output Tplyr table can be sorted in any order but the metadata are still easily accessible.

The tplyr_meta object provided a list with two elements - names and filters. The metadata contain every column from the target data.frame of the Tplyr table that factored into the specified result cell, and the filters contains all the necessary filters to subset to data summarized to create the specified result cell. get_meta_subset() additionally provides a parameter to specify any additional columns you would like to include in the returned subset data frame.

Value

A tplyr_meta object

Examples

t <- tplyr_table(mtcars, cyl) %>%
  add_layer(
    group_desc(hp)
  )

dat <- t %>% build(metadata = TRUE)

get_meta_result(t, 'd1_1', 'var1_4')

m <- t$metadata
dat <- t$target

get_meta_result(t, 'd1_1', 'var1_4')

Extract the subset of data based on result metadata

Description

Given a row_id value and a result column, this function will return the subset of data referenced by the tplyr_meta object associated with that 'cell', which provides traceability to tie a result to its source.

Usage

get_meta_subset(x, row_id, column, add_cols = vars(USUBJID), ...)

## S3 method for class 'data.frame'
get_meta_subset(
  x,
  row_id,
  column,
  add_cols = vars(USUBJID),
  target = NULL,
  pop_data = NULL,
  ...
)

## S3 method for class 'tplyr_table'
get_meta_subset(x, row_id, column, add_cols = vars(USUBJID), ...)

Arguments

Details

If a Tplyr table is built with the metadata=TRUE option specified, then metadata is assembled behind the scenes to provide traceability on each result cell derived. The functions get_meta_result() and get_meta_subset() allow you to access that metadata by using an ID provided in the row_id column and the column name of the result you'd like to access. The purpose is of the row_id variable instead of a simple row index is to provide a sort resistant reference of the originating column, so the output Tplyr table can be sorted in any order but the metadata are still easily accessible.

The tplyr_meta object provided a list with two elements - names and filters. The metadata contain every column from the target data.frame of the Tplyr table that factored into the specified result cell, and the filters contains all the necessary filters to subset to data summarized to create the specified result cell. get_meta_subset() additionally provides a parameter to specify any additional columns you would like to include in the returned subset data frame.

Value

A data.frame

Examples

t <- tplyr_table(mtcars, cyl) %>%
  add_layer(
    group_desc(hp)
  )


dat <- t %>% build(metadata = TRUE)

get_meta_subset(t, 'd1_1', 'var1_4', add_cols = dplyr::vars(carb))

m <- t$metadata
dat <- t$target

get_meta_subset(t, 'd1_1', 'var1_4', add_cols = dplyr::vars(carb), target = target)

Get the metadata dataframe from a tplyr_table

Description

Pull out the metadata dataframe from a tplyr_table to work with it directly

Usage

get_metadata(t)

Arguments

Value

Tplyr metadata dataframe

Examples

t <- tplyr_table(mtcars, gear) %>%
  add_layer(
    group_desc(wt)
  )

t %>%
  build(metadata=TRUE)

get_metadata(t)

Retrieve the numeric data from a tplyr objects

Description

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.

Usage

get_numeric_data(x, layer = NULL, where = TRUE, ...)

Arguments

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.

Value

Numeric data from the Tplyr layer

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)

 # Get the data from a specific layer
 get_numeric_data(t, layer='drat')
 get_numeric_data(t, layer=1)

 # Choose multiple layers by name or index
 get_numeric_data(t, layer=c('cyl', 'drat'))
 get_numeric_data(t, layer=c(2, 1))

 # Get the data and filter it
 get_numeric_data(t, layer='drat', where = gear==3)

Set or return precision_by layer binding

Description

The precision_by variables are used to collect the integer and decimal precision when auto-precision is used. These by variables are used to group the input data and identify the maximum precision available within the dataset for each by group. The precision_by variables must be a subset of the by variables

Usage

get_precision_by(layer)

set_precision_by(layer, precision_by)

Arguments

Value

For get_precision_by, the precision_by binding of the supplied layer. For set_precision_by the modified layer environment.

Examples

# Load in pipe
library(magrittr)
lay <- tplyr_table(mtcars, gear) %>%
  add_layer(
    group_desc(mpg, by=vars(carb, am)) %>%
    set_precision_by(carb)
  )

Set or return precision_on layer binding

Description

The precision_on variable is the variable used to establish numeric precision. This variable must be included in the list of target_var variables.

Usage

get_precision_on(layer)

set_precision_on(layer, precision_on)

Arguments

Value

For get_precision_on, the precision_on binding of the supplied layer. For set_precision_on the modified layer environment.

Examples

# Load in pipe
library(magrittr)
lay <- tplyr_table(mtcars, gear) %>%
  add_layer(
    group_desc(vars(mpg, disp), by=vars(carb, am)) %>%
    set_precision_on(disp)
  )

Get statistics data

Description

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

Usage

get_stats_data(x, layer = NULL, statistic = NULL, where = TRUE, ...)

Arguments

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.

Value

The statistics data of the supplied layer

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)

 # Returns just the riskdiff statistics from each layer - NULL
 # for layers without riskdiff
 get_stats_data(t, statistic="riskdiff")

 # Return the statistic data for just the "am" layer - a list
 get_stats_data(t, layer="am")
 get_stats_data(t, layer=3)

 # Return the statistic data for just the "am" and "cyl", layer - a
 # list of lists
 get_stats_data(t, layer=c("am", "cyl"))
 get_stats_data(t, layer=c(3, 2))

 # Return just the statistic data for "am" and "cyl" - a list
 get_stats_data(t, layer=c("am", "cyl"), statistic="riskdiff")
 get_stats_data(t, layer=c(3, 2), statistic="riskdiff")


 # Return the riskdiff for the "am" layer - a data frame
 get_stats_data(t, layer="am", statistic="riskdiff")

 # Return and filter the riskdiff for the am layer - a data frame
 get_stats_data(t, layer="am", statistic="riskdiff", where = summary_var==1)

Set or return treat_var binding

Description

Set or return treat_var binding

Usage

get_target_var(layer)

set_target_var(layer, target_var)

Arguments

Value

For treat_var, the treatment variable binding of the layer object. For set_treat_var, the modified layer environment.

Examples

# Load in pipe
library(magrittr)
iris$Species2 <- iris$Species
lay <- tplyr_table(iris, Species) %>%
  group_count(Species) %>%
  set_target_var(Species2)

Retrieve one of Tplyr's regular expressions

Description

This function allows you to extract important regular expressions used inside Tplyr.

Usage

get_tplyr_regex(rx = c("format_string", "format_group"))

Arguments

Details

There are two important regular expressions used within Tplyr. The format_string expression is the expression to parse format strings. This is what is used to make sense out of strings like 'xx (XX.x%)' or 'a+1 (A.a+2)' by inferring what the user is specifying about number formatting.

The 'format_group' regex is the opposite of this, and when given a string of numbers, such as ' 5 (34%) [9]' will return the separate segments of numbers broken into their format groups, which in this example would be ' 5', '(34%)', and '[9]'.

Value

A regular expression object

Examples

get_tplyr_regex('format_string')

get_tplyr_regex('format_group')

Set or return where binding for layer or table

Description

Set or return where binding for layer or table

Usage

## S3 method for class 'tplyr_layer'
get_where(obj)

## S3 method for class 'tplyr_layer'
set_where(obj, where)

get_where(obj)

## S3 method for class 'tplyr_table'
get_where(obj)

set_where(obj, where)

## S3 method for class 'tplyr_table'
set_where(obj, where)

set_pop_where(obj, where)

get_pop_where(obj)

Arguments

Value

For where, the where binding of the supplied object. For set_where, the modified object

Examples

# Load in pipe
library(magrittr)

iris$Species2 <- iris$Species
lay <- tplyr_table(iris, Species) %>%
  group_count(Species) %>%
  set_where(Petal.Length > 3) %>%
  # Set logic for pop_data as well
  set_pop_where(Petal.Length > 3)

Create a count, desc, or shift layer for discrete count based summaries, descriptive statistics summaries, or shift count summaries

Description

This family of functions specifies the type of summary that is to be performed within a layer. count layers are used to create summary counts of some discrete variable. desc layers create summary statistics, and shift layers summaries the counts of different changes in states. See the "details" section below for more information.

Usage

group_count(parent, target_var, by = vars(), where = TRUE, ...)

group_desc(parent, target_var, by = vars(), where = TRUE, ...)

group_shift(parent, target_var, by = vars(), where = TRUE, ...)

Arguments

Details

Count Layers

Count layers allow you to create summaries based on counting values with a variable. Additionally, this layer allows you to create n (%) summaries where you're also summarizing the proportion of instances a value occurs compared to some denominator. Count layers are also capable of producing counts of nested relationships. For example, if you want to produce counts of an overall outside group, and then the subgroup counts within that group, you can specify the target variable as vars(OutsideVariable, InsideVariable). This allows you to do tables like Adverse Events where you want to see the Preferred Terms within Body Systems, all in one layer. Further control over denominators is available using the function set_denoms_by and distinct counts can be set using set_distinct_by

Descriptive Statistics Layers

Descriptive statistics layers perform summaries on continuous variables. There are a number of summaries built into Tplyr already that you can perform, including n, mean, median, standard deviation, variance, min, max, inter-quartile range, Q1, Q3, and missing value counts. From these available summaries, the default presentation of a descriptive statistic layer will output 'n', 'Mean (SD)', 'Median', 'Q1, Q3', 'Min, Max', and 'Missing'. You can change these summaries using set_format_strings, and you can also add your own summaries using set_custom_summaries. This allows you to implement any additional summary statistics you want presented.

Shift Layers

A shift layer displays an endpoint's 'shift' throughout the duration of the study. It is an abstraction over the count layer, however we have provided an interface that is more efficient and intuitive. Targets are passed as named symbols using dplyr::vars. Generally the baseline is passed with the name 'row' and the shift is passed with the name 'column'. Both counts (n) and percentages (pct) are supported and can be specified with the set_format_strings function. To allow for flexibility when defining percentages, you can define the denominator using the set_denoms_by function. This function takes variable names and uses those to determine the denominator for the counts.

Value

An tplyr_layer environment that is a child of the specified parent. The environment contains the object as listed below.

A tplyr_layer object

See Also

[add_layer, add_layers, tplyr_table, tplyr_layer]

Examples

# Load in pipe
library(magrittr)

t <- tplyr_table(iris, Species) %>%
  add_layer(
    group_desc(target_var=Sepal.Width)
  )

t <- tplyr_table(iris, Species) %>%
  add_layer(
    group_desc(target_var=Sepal.Width)
  )

t <- tplyr_table(mtcars, am) %>%
  add_layer(
    group_shift(vars(row=gear, column=carb), by=cyl)
  )

Return or set header_n binding

Description

The 'header_n()' functions can be used to automatically pull the header_n derivations from the table or change them for future use.

Usage

header_n(table)

header_n(x) <- value

set_header_n(table, value)

Arguments

Details

The 'header_n' object is created by Tplyr when a table is built and intended to be used by the 'add_column_headers()' function when displaying table level population totals. These methods are intended to be used for calling the population totals calculated by Tplyr, and to overwrite them if a user chooses to.

If you have a need to change the header Ns that appear in your table headers, say you know you are working with a subset of the data that doesn't represent the totals, you can replace the data used with 'set_header_n()'.

Value

For tplyr_header_n the header_n binding of the tplyr_table object. For tplyr_header_n<- and set_tplyr_header_n the modified object.

Examples

tab <- tplyr_table(mtcars, gear)

header_n(tab) <- data.frame(
  gear = c(3, 4, 5),
  n = c(10, 15, 45)
)

Select levels to keep in a count layer

Description

In certain cases you only want a layer to include certain values of a factor. The 'keep_levels()' function allows you to pass character values to be included in the layer. The others are ignored. **NOTE: Denominator calculation is unaffected by this function, see the examples on how to include this logic in your percentages'**

Usage

keep_levels(e, ...)

Arguments

Value

The modified Tplyr layer object

Examples

library(dplyr)
mtcars <- mtcars %>%
  mutate_all(as.character)

t <- tplyr_table(mtcars, gear) %>%
  add_layer(
    group_count(cyl) %>%
      keep_levels("4", "8") %>%
      set_denom_where(cyl %in% c("4", "8"))
 ) %>%
 build()

Create, view, extract, remove, and use Tplyr layer templates

Description

There are several scenarios where a layer template may be useful. Some tables, like demographics tables, may have many layers that will all essentially look the same. Categorical variables will have the same count layer settings, and continuous variables will have the same desc layer settings. A template allows a user to build those settings once per layer, then reference the template when the Tplyr table is actually built.

Usage

new_layer_template(name, template)

remove_layer_template(name)

get_layer_template(name)

get_layer_templates()

use_template(name, ..., add_params = NULL)

Arguments

Details

This suite of functions allows a user to create and use layer templates. Layer templates allow a user to pre-build and reuse an entire layer configuration, from the layer constructor down to all modifying functions. Furthermore, users can specify parameters they may want to be interchangeable. Additionally, layer templates are extensible, so a template can be use and then further extended with additional layer modifying functions.

Layers are created using new_layer_template(). To use a layer, use the function use_template() in place of group_count|desc|shift(). If you want to view a specific template, use get_layer_template(). If you want to view all templates, use get_layer_templates(). And to remove a layer template use remove_layer_template(). Layer templates themselves are stored in the option tplyr.layer_templates, but a user should not access this directly and instead use the Tplyr supplied functions.

When providing the template layer syntax, the layer must start with a layer constructor. These are one of the function group_count(), group_desc(), or group_shift(). Instead of passing arguments into these function, templates are specified using an ellipsis in the constructor, i.e. group_count(...). This is required, as after the template is built a user supplies these arguments via use_template()

use_template() takes the group_count|desc|shift() arguments by default. If a user specified additional arguments in the template, these are provided in a list throught the argument add_params. Provide these arguments exactly as you would in a normal layer. When creating the template, these parameters can be specified by using curly brackets. See the examples for details.

Examples

op <- options()

new_layer_template(
  "example_template",
  group_count(...) %>%
    set_format_strings(f_str('xx (xx%)', n, pct))
)

get_layer_templates()

get_layer_template("example_template")

tplyr_table(mtcars, vs) %>%
  add_layer(
    use_template("example_template", gear)
  ) %>%
  build()

remove_layer_template("example_template")

new_layer_template(
  "example_template",
  group_count(...) %>%
    set_format_strings(f_str('xx (xx%)', n, pct)) %>%
    set_order_count_method({sort_meth}) %>%
    set_ordering_cols({sort_cols})
)

get_layer_template("example_template")

tplyr_table(mtcars, vs) %>%
  add_layer(
    use_template("example_template", gear, add_params =
                   list(
                     sort_meth = "bycount",
                     sort_cols = `1`
                   ))
  ) %>%
  build()

remove_layer_template("example_template")

options(op)

Return or set population data bindings

Description

The population data is used to gather information that may not be available from the target dataset. For example, missing treatment groups, population N counts, and proper N counts for denominators will be provided through the population dataset. The population dataset defaults to the target dataset unless otherwise specified using set_pop_data.

Usage

pop_data(table)

pop_data(x) <- value

set_pop_data(table, pop_data)

Arguments

Value

For tplyr_pop_data the pop_data binding of the tplyr_table object. For tplyr_pop_data<- nothing is returned, the pop_data binding is set silently. For set_tplyr_pop_data the modified object.

Examples

tab <- tplyr_table(iris, Species)

pop_data(tab) <- mtcars

tab <- tplyr_table(iris, Species) %>%
  set_pop_data(mtcars)

Return or set pop_treat_var binding

Description

The treatment variable used in the target data may be different than the variable within the population dataset. set_pop_treat_var allows you to change this.

Usage

pop_treat_var(table)

set_pop_treat_var(table, pop_treat_var)

Arguments

Value

For tplyr_pop_treat_var the pop_treat_var binding of the tplyr_table object. For set_tplyr_pop_treat_var the modified object.

Examples

tab <- tplyr_table(iris, Species)

pop_data(tab) <- mtcars
set_pop_treat_var(tab, mpg)

Process layers to get formatted and pivoted tables.

Description

This is an internal method, but is exported to support S3 dispatch. Not intended for direct use by a user.

Usage

process_formatting(x, ...)

Arguments

Value

The formatted_table object that is bound to the layer

Process layers to get metadata tables

Description

This is an internal method, but is exported to support S3 dispatch. Not intended for direct use by a user.

Usage

process_metadata(x, ...)

Arguments

Value

The formatted_meta object that is bound to the layer

Process a tplyr_statistic object

Description

This is an internal function that is not meant for use externally, but must be exported. Use with caution.

Usage

process_statistic_data(x, ...)

Arguments

Value

Numeric statistc data from a tplyr statistc

Process string formatting on a tplyr_statistic object

Description

This is an internal function that is not meant for use externally, but must be exported. Use with caution.

Usage

process_statistic_formatting(x, ...)

Arguments

Value

Formatted tplyr_statistic data

Process layers to get numeric results of layer

Description

This is an internal method, but is exported to support S3 dispatch. Not intended for direct use by a user.

Usage

process_summaries(x, ...)

Arguments

Value

The tplyr_layer object with a 'built_table' binding

Reformat strings with leading whitespace for HTML

Description

Reformat strings with leading whitespace for HTML

Usage

replace_leading_whitespace(x, tab_width = 4)

Arguments

Value

String with   replaced for leading whitespace

Examples

x <- c(" Hello there", "  Goodbye Friend ",  "\tNice to meet you",
"  \t What are you up to? \t \t ")
replace_leading_whitespace(x)

replace_leading_whitespace(x, tab=2)

Set custom summaries to be performed within a descriptive statistics layer

Description

This function allows a user to define custom summaries to be performed in a call to dplyr::summarize(). A custom summary by the same name as a default summary will override the default. This allows the user to override the default behavior of summaries built into 'Tplyr', while also adding new desired summary functions.

Usage

set_custom_summaries(e, ...)

Arguments

Details

When programming the logic of the summary function, use the variable name .var to within your summary functions. This allows you apply the summary function to each variable when multiple target variables are declared.

An important, yet not immediately obvious, part of using set_custom_summaries is to understand the link between the named parameters you set in set_custom_summaries and the names called in f_str objects within set_format_strings. In f_str, after you supply the string format you'd like your numbers to take, you specify the summaries that fill those strings.

When you go to set your format strings, the name you use to declare a summary in set_custom_summaries is the same name that you use in your f_str call. This is necessary because set_format_strings needs some means of putting two summaries in the same value, and setting a row label for the summary being performed.

Review the examples to see this put into practice. Note the relationship between the name created in set_custom_summaries and the name used in set_format_strings within the f_str call

Value

Binds a variable custom_summaries to the specified layer

Examples

#Load in pipe
library(magrittr)

tplyr_table(iris, Species) %>%
  add_layer(
    group_desc(Sepal.Length, by = "Sepal Length") %>%
      set_custom_summaries(
        geometric_mean = exp(sum(log(.var[.var > 0]),
                                     na.rm=TRUE) / length(.var))
      ) %>%
      set_format_strings(
        'Geometric Mean' = f_str('xx.xx', geometric_mean)
      )
  ) %>%
  build()

Set values the denominator calculation will ignore

Description

'r lifecycle::badge("defunct")'

This is generally used for missing values. Values like "", NA, "NA" are common ways missing values are presented in a data frame. In certain cases, percentages do not use "missing" values in the denominator. This function notes different values as "missing" and excludes them from the denominators.

Usage

set_denom_ignore(e, ...)

Arguments

Value

The modified layer object

Examples

library(magrittr)
mtcars2 <- mtcars
mtcars2[mtcars$cyl == 6, "cyl"] <- NA
mtcars2[mtcars$cyl == 8, "cyl"] <- "Not Found"

tplyr_table(mtcars2, gear) %>%
  add_layer(
    group_count(cyl) %>%
      set_missing_count(f_str("xx ", n), Missing = c(NA, "Not Found"))
      # This function is currently deprecated. It was replaced with an
      # argument in set_missing_count
      # set_denom_ignore("Missing")
  ) %>%
  build()

Set Logic for denominator subsetting

Description

By default, denominators in count layers are subset based on the layer level where logic. In some cases this might not be correct. This functions allows the user to override this behavior and pass custom logic that will be used to subset the target dataset when calculating denominators for the layer.

Usage

set_denom_where(e, denom_where)

Arguments

Value

The modified Tplyr layer object

Examples

library(magrittr)
t10 <- tplyr_table(mtcars, gear) %>%
  add_layer(
    group_count(cyl, where = cyl != 6) %>%
    set_denom_where(TRUE)
    # The denominators will be based on all of the values, including 6
  ) %>%
 build()

Set variables used in pct denominator calculation

Description

This function is used when calculating pct in count or shift layers. The percentages default to the treatment variable and any column variables but can be calculated on any variables passed to target_var, treat_var, by, or cols.

Usage

set_denoms_by(e, ...)

Arguments

Value

The modified layer object

Examples

library(magrittr)

# Default has matrix of treatment group, additional columns,
# and by variables sum to 1
tplyr_table(mtcars, am) %>%
  add_layer(
    group_shift(vars(row=gear, column=carb), by=cyl) %>%
      set_format_strings(f_str("xxx (xx.xx%)", n, pct))
  ) %>%
  build()

tplyr_table(mtcars, am) %>%
  add_layer(
    group_shift(vars(row=gear, column=carb), by=cyl) %>%
      set_format_strings(f_str("xxx (xx.xx%)", n, pct)) %>%
      set_denoms_by(cyl, gear) # Row % sums to 1
  ) %>%
  build()

tplyr_table(mtcars, am) %>%
  add_layer(
    group_shift(vars(row=gear, column=carb), by=cyl) %>%
      set_format_strings(f_str("xxx (xx.xx%)", n, pct)) %>%
      set_denoms_by(cyl, gear, am) # % within treatment group sums to 1
  ) %>%
  build()

Set counts to be distinct by some grouping variable.

Description

In some situations, count summaries may want to see distinct counts by a variable like subject. For example, the number of subjects in a population who had a particular adverse event. set_distinct_by allows you to set the by variables used to determine a distinct count.

Usage

set_distinct_by(e, distinct_by)

Arguments

Details

When a distinct_by value is set, distinct counts will be used by default. If you wish to combine distinct and not distinct counts, you can choose which to display in your f_str() objects using n, pct, distinct_n, and distinct_pct. Additionally, denominators may be presented using total and distinct_total

Value

The layer object with

Examples

#Load in pipe
library(magrittr)

tplyr_table(mtcars, gear) %>%
  add_layer(
    group_count(cyl) %>%
      set_distinct_by(carb)
  ) %>%
  build()

Set the format strings and associated summaries to be performed in a layer

Description

'Tplyr' gives you extensive control over how strings are presented. set_format_strings allows you to apply these string formats to your layer. This behaves slightly differently between layers.

Usage

set_format_strings(e, ...)

## S3 method for class 'desc_layer'
set_format_strings(e, ..., cap = getOption("tplyr.precision_cap"))

## S3 method for class 'count_layer'
set_format_strings(e, ...)

Arguments

Details

Format strings are one of the most powerful components of 'Tplyr'. Traditionally, converting numeric values into strings for presentation can consume a good deal of time. Values and decimals need to align between rows, rounding before trimming is sometimes forgotten - it can become a tedious mess that, in the grand scheme of things, is not an important part of the analysis being performed. 'Tplyr' makes this process as simple as we can, while still allowing flexibility to the user.

In a count layer, you can simply provide a single f_str object to specify how you want your n's, percentages, and denominators formatted. If you are additionally supplying a statistic, like risk difference using add_risk_diff, you specify the count formats using the name 'n_counts'. The risk difference formats would then be specified using the name 'riskdiff'. In a descriptive statistic layer, set_format_strings allows you to do a couple more things:

• By naming parameters with character strings, those character strings become a row label in the resulting data frame

• The actual summaries that are performed come from the variable names used within the f_str calls

• Using multiple summaries (declared by your f_str calls), multiple summary values can appear within the same line. For example, to present "Mean (SD)" like displays.

• Format strings in the desc layer also allow you to configure how empty values should be presented. In the f_str call, use the empty parameter to specify how missing values should present. A single element character vector should be provided. If the vector is unnamed, that value will be used in the format string and fill the space similar to how the numbers will display. Meaning - if your empty string is 'NA' and your format string is 'xx (xxx)', the empty values will populate as 'NA ( NA)'. If you name the character vector in the 'empty' parameter '.overall', like empty = c(.overall=''), then that exact string will fill the value instead. For example, providing 'NA' will instead create the formatted string as 'NA' exactly.

See the f_str documentation for more details about how this implementation works.

Value

The layer environment with the format string binding added

tplyr_layer object with formats attached

Returns the modified layer object.

Examples

# Load in pipe
library(magrittr)

# In a count layer
tplyr_table(mtcars, gear) %>%
  add_layer(
    group_count(cyl) %>%
      set_format_strings(f_str('xx (xx%)', n, pct))
  ) %>%
  build()

# In a descriptive statistics layer
tplyr_table(mtcars, gear) %>%
  add_layer(
    group_desc(mpg) %>%
      set_format_strings(
        "n"         = f_str("xx", n),
        "Mean (SD)" = f_str("xx.x", mean, empty='NA'),
        "SD"        = f_str("xx.xx", sd),
        "Median"    = f_str("xx.x", median),
        "Q1, Q3"    = f_str("xx, xx", q1, q3, empty=c(.overall='NA')),
        "Min, Max"  = f_str("xx, xx", min, max),
        "Missing"   = f_str("xx", missing)
      )
  ) %>%
  build()

# In a shift layer
tplyr_table(mtcars, am) %>%
  add_layer(
    group_shift(vars(row=gear, column=carb), by=cyl) %>%
    set_format_strings(f_str("xxx (xx.xx%)", n, pct))
  ) %>%
  build()

Set the option to prefix the row_labels in the inner count_layer

Description

When a count layer uses nesting (i.e. triggered by set_nest_count), the indentation argument's value will be used as a prefix for the inner layer's records

Usage

set_indentation(e, indentation)

Arguments

Value

The modified count_layer environment

Set variables to limit reported data values only to those that exist rather than fully completing all possible levels

Description

This function allows you to select a combination of by variables or potentially target variables for which you only want to display values present in the data. By default, Tplyr will create a cartesian combination of potential values of the data. For example, if you have 2 by variables present, then each potential combination of those by variables will have a row present in the final table. set_limit_data_by() allows you to choose the by variables whose combination you wish to limit to values physically present in the available data.

Usage

set_limit_data_by(e, ...)

Arguments

Value

a tplyr_table

Examples

tplyr_table(tplyr_adpe, TRT01A) %>%
  add_layer(
    group_desc(AVAL, by = vars(PECAT, PARAM, AVISIT))
  ) %>%
  build()

tplyr_table(tplyr_adpe, TRT01A) %>%
  add_layer(
    group_desc(AVAL, by = vars(PECAT, PARAM, AVISIT)) %>%
      set_limit_data_by(PARAM, AVISIT)
  ) %>%
  build()

tplyr_table(tplyr_adpe, TRT01A) %>%
  add_layer(
    group_count(AVALC, by = vars(PECAT, PARAM, AVISIT)) %>%
      set_limit_data_by(PARAM, AVISIT)
  ) %>%
  build()

tplyr_table(tplyr_adpe, TRT01A) %>%
  add_layer(
    group_count(AVALC, by = vars(PECAT, PARAM, AVISIT)) %>%
      set_limit_data_by(PECAT, PARAM, AVISIT)
  ) %>%
  build()

Set the display for missing strings

Description

Controls how missing counts are handled and displayed in the layer

Usage

set_missing_count(e, fmt = NULL, sort_value = NULL, denom_ignore = FALSE, ...)

Arguments

Value

The modified layer

Examples

library(magrittr)
library(dplyr)
  mtcars2 <- mtcars %>%
mutate_all(as.character)
mtcars2[mtcars$cyl == 6, "cyl"] <- NA

tplyr_table(mtcars2, gear) %>%
  add_layer(
    group_count(cyl) %>%
      set_missing_count(f_str("xx ", n), Missing = NA)
  ) %>%
  build()

Set the label for the missing subjects row

Description

Set the label for the missing subjects row

Usage

set_missing_subjects_row_label(e, missing_subjects_row_label)

Arguments

Value

The modified count_layer object

Examples

t <- tplyr_table(mtcars, gear) %>%
  add_layer(
    group_count(cyl) %>%
      add_missing_subjects_row() %>%
      set_missing_subjects_row_label("Missing")
  )
build(t)

Set the option to nest count layers

Description

If set to TRUE, the second variable specified in target_var will be nested inside of the first variable. This allows you to create displays like those commonly used in adverse event tables, where one column holds both the labels of the outer categorical variable and the inside event variable (i.e. AEBODSYS and AEDECOD).

Usage

set_nest_count(e, nest_count)

Arguments

Value

The modified layer

Set a numeric cutoff

Description

In certain tables, it may be necessary to only include rows that meet numeric conditions. Rows that are less than a certain cutoff can be suppressed from the output. This function allows you to pass a cutoff, a cutoff stat(n, distinct_n, pct, or distinct_pct) to supress values that are lesser than the cutoff.

Usage

set_numeric_threshold(e, numeric_cutoff, stat, column = NULL)

Arguments

Value

The modified Tplyr layer object

Examples

mtcars %>%
tplyr_table(gear) %>%
  add_layer(
    group_count(cyl) %>%
      set_numeric_threshold(10, "n") %>%
      add_total_row() %>%
      set_order_count_method("bycount")
  )

Set the ordering logic for the count layer

Description

The sorting of a table can greatly vary depending on the situation at hand. For count layers, when creating tables like adverse event summaries, you may wish to order the table by descending occurrence within a particular treatment group. But in other situations, such as AEs of special interest, or subject disposition, there may be a specific order you wish to display values. Tplyr offers solutions to each of these situations.

Instead of allowing you to specify a custom sort order, Tplyr instead provides you with order variables that can be used to sort your table after the data are summarized. Tplyr has a default order in which the table will be returned, but the order variables will always persist. This allows you to use powerful sorting functions like arrange to get your desired order, and in double programming situations, helps your validator understand the how you achieved a particular sort order and where discrepancies may be coming from.

When creating order variables for a layer, for each 'by' variable Tplyr will search for a <VAR>N version of that variable (i.e. VISIT <-> VISITN, PARAM <-> PARAMN). If available, this variable will be used for sorting. If not available, Tplyr will created a new ordered factor version of that variable to use in alphanumeric sorting. This allows the user to control a custom sorting order by leaving an existing <VAR>N variable in your dataset if it exists, or create one based on the order in which you wish to sort - no custom functions in Tplyr required.

Ordering of results is where things start to differ. Different situations call for different methods. Descriptive statistics layers keep it simple - the order in which you input your formats using set_format_strings is the order in which the results will appear (with an order variable added). For count layers, Tplyr offers three solutions: If there is a <VAR>N version of your target variable, use that. If not, if the target variable is a factor, use the factor orders. Finally, you can use a specific data point from your results columns. The result column can often have multiple data points, between the n counts, percent, distinct n, and distinct percent. Tplyr allows you to choose which of these values will be used when creating the order columns for a specified result column (i.e. based on the treat_var and cols arguments). See the 'Sorting a Table' section for more information.

Shift layers sort very similarly to count layers, but to order your row shift variable, use an ordered factor.

Usage

set_order_count_method(e, order_count_method, break_ties = NULL)

set_ordering_cols(e, ...)

set_result_order_var(e, result_order_var)

Arguments

Value

Returns the modified layer object. The 'ord_' columns are added during the build process.

Sorting a Table

When a table is built, the output has several ordering(ord_) columns that are appended. The first represents the layer index. The index is determined by the order the layer was added to the table. Following are the indices for the by variables and the target variable. The by variables are ordered based on:

1. The 'by' variable is a factor in the target dataset

2. If the variable isn't a factor, but has a <VAR>N variable (i.e. VISIT -> VISITN, TRT -> TRTN)

3. If the variable is not a factor in the target dataset, it is coerced to one and ordered alphabetically.

The target variable is ordered depending on the type of layer. See more below.

Ordering a Count Layer

There are many ways to order a count layer depending on the preferences of the table programmer. Tplyr supports sorting by a descending amount in a column in the table, sorting by a <VAR>N variable, and sorting by a custom order. These can be set using the 'set_order_count_method' function.

Sorting by a numeric count

A selected numeric value from a selected column will be indexed based on the descending numeric value. The numeric value extracted defaults to 'n' but can be changed with 'set_result_order_var'. The column selected for sorting defaults to the first value in the treatment group variable. If there were arguments passed to the 'cols' argument in the table those must be specified with 'set_ordering_columns'.

Sorting by a 'varn' variable

If the treatment variable has a <VAR>N variable. It can be indexed to that variable.

Sorting by a factor(Default)

If a factor is found for the target variable in the target dataset that is used to order, if no factor is found it is coerced to a factor and sorted alphabetically.

Sorting a nested count layer

If two variables are targeted by a count layer, two methods can be passed to 'set_order_count'. If two are passed, the first is used to sort the blocks, the second is used to sort the "inside" of the blocks. If one method is passed, that will be used to sort both.

Ordering a Desc Layer

The order of a desc layer is mostly set during the object construction. The by variables are resolved and index with the same logic as the count layers. The target variable is ordered based on the format strings that were used when the layer was created.

Examples

library(dplyr)

# Default sorting by factor
t <- tplyr_table(mtcars, gear) %>%
  add_layer(
    group_count(cyl)
  )
build(t)

# Sorting by <VAR>N
mtcars$cylN <- mtcars$cyl
t <- tplyr_table(mtcars, gear) %>%
  add_layer(
    group_count(cyl) %>%
      set_order_count_method("byvarn")
  )

# Sorting by row count
t <- tplyr_table(mtcars, gear) %>%
  add_layer(
    group_count(cyl) %>%
      set_order_count_method("bycount") %>%
      # Orders based on the 6 gear group
      set_ordering_cols(6)
  )

# Sorting by row count by percentages
t <- tplyr_table(mtcars, gear) %>%
  add_layer(
    group_count(cyl) %>%
      set_order_count_method("bycount") %>%
      set_result_order_var(pct)
  )

# Sorting when you have column arguments in the table
t <- tplyr_table(mtcars, gear, cols = vs) %>%
  add_layer(
    group_count(cyl) %>%
      # Uses the fourth gear group and the 0 vs group in ordering
      set_ordering_cols(4, 0)
  )

# Using a custom factor to order
mtcars$cyl <- factor(mtcars$cyl, c(6, 4, 8))
t <- tplyr_table(mtcars, gear) %>%
  add_layer(
    group_count(cyl) %>%
      # This is the default but can be used to change the setting if it is
      #set at the table level.
      set_order_count_method("byfactor")
  )

Set the value of a outer nested count layer to Inf or -Inf

Description

Set the value of a outer nested count layer to Inf or -Inf

Usage

set_outer_sort_position(e, outer_sort_position)

Arguments

Value

The modified count layer.

Set precision data

Description

In some cases, there may be organizational standards surrounding decimal precision. For example, there may be a specific standard around the representation of precision relating to lab results. As such, set_precision_data() provides an interface to provide integer and decimal precision from an external data source.

Usage

set_precision_data(layer, prec, default = c("error", "auto"))

Arguments

Details

The ultimate behavior of this feature is just that of the existing auto precision method, except that the precision is specified in the provided precision dataset rather than inferred from the source data. At a minimum, the precision dataset must contain the integer variables max_int and max_dec. If by variables are provided, those variables must be available in the layer by variables.

When the table is built, by default Tplyr will error if the precision dataset is missing by variable groupings that exist in the target dataset. This can be overriden using the default parameter. If default is set to "auto", any missing values will be automatically inferred from the source data.

Examples

prec <- tibble::tribble(
  ~vs, ~max_int, ~max_dec,
  0,        1,        1,
  1,        2,        2
)

tplyr_table(mtcars, gear) %>%
  add_layer(
    group_desc(wt, by = vs) %>%
      set_format_strings(
        'Mean (SD)' = f_str('a.a+1 (a.a+2)', mean, sd)
      ) %>%
      set_precision_data(prec) %>%
      set_precision_on(wt)
  ) %>%
  build()

Set descriptive statistics as columns

Description

In many cases, treatment groups are represented as columns within a table. But some tables call for a transposed presentation, where the treatment groups displayed by row, and the descriptive statistics are represented as columns. set_stats_as_columns() allows Tplyr to output a built table using this transposed format and deviate away from the standard representation of treatment groups as columns.

Usage

set_stats_as_columns(e, stats_as_columns = TRUE)

Arguments

Details

This function leaves all specified by variables intact. The only switch that happens during the build process is that the provided descriptive statistics are transposed as columns and the treatment variable is left as rows. Column variables will remain represented as columns, and multiple target variables will also be respected properly.

Value

The input tplyr_layer

Examples

dat <- tplyr_table(mtcars, gear) %>%
  add_layer(
    group_desc(wt, by = vs) %>%
      set_format_strings(
        "n"        = f_str("xx", n),
        "sd"       = f_str("xx.x", sd, empty = c(.overall = "BLAH")),
        "Median"   = f_str("xx.x", median),
        "Q1, Q3"   = f_str("xx, xx", q1, q3),
        "Min, Max" = f_str("xx, xx", min, max),
        "Missing"  = f_str("xx", missing)
      ) %>%
      set_stats_as_columns()
  ) %>%
  build()

Set the label for the total row

Description

The row label for a total row defaults to "Total", however this can be overriden using this function.

Usage

set_total_row_label(e, total_row_label)

Arguments

Value

The modified count_layer object

Examples

# Load in pipe
library(magrittr)

t <- tplyr_table(mtcars, gear) %>%
  add_layer(
    group_count(cyl) %>%
      add_total_row() %>%
      set_total_row_label("Total Cyl")
  )
build(t)

Extract format group strings or numbers

Description

These functions allow you to extract segments of information from within a result string by targetting specific format groups. str_extract_fmt_group() allows you to pull out the individual format group string, while str_extract_num() allows you to pull out that specific numeric result.

Usage

str_extract_fmt_group(string, format_group)

str_extract_num(string, format_group)

Arguments

Details

Format groups refer to individual segments of a string. For example, given the string ' 5 (34.4%) [9]', there are three separate format groups, which are ' 5', '(34.4%)', and '[9]'.

Value

A character vector

Examples

string <- c(" 0  (0.0%)", " 8  (9.3%)", "78 (90.7%)")

str_extract_fmt_group(string, 2)

str_extract_num(string, 2)

Wrap strings to a specific width with hyphenation while preserving indentation

Description

str_indent_wrap() leverages stringr::str_wrap() under the hood, but takes some extra steps to preserve any indentation that has been applied to a character element, and use hyphenated wrapping of single words that run longer than the allotted wrapping width.

Usage

str_indent_wrap(x, width = 10, tab_width = 5)

Arguments

Details

The function stringr::str_wrap() is highly efficient, but in the context of table creation there are two select features missing - hyphenation for long running strings that overflow width, and respect for pre-indentation of a character element. For example, in an adverse event table, you may have body system rows as an un-indented column, and preferred terms as indented columns. These strings may run long and require wrapping to not surpass the column width. Furthermore, for crowded tables a single word may be longer than the column width itself.

This function takes steps to resolve these two issues, while trying to minimize additional overhead required to apply the wrapping of strings.

Note: This function automatically converts tabs to spaces. Tab width varies depending on font, so width cannot automatically be determined within a data frame. As such, users can specify the width

Value

A character vector with string wrapping applied

Examples

ex_text1 <- c("RENAL AND URINARY DISORDERS", "   NEPHROLITHIASIS")
ex_text2 <- c("RENAL AND URINARY DISORDERS", "\tNEPHROLITHIASIS")

cat(paste(str_indent_wrap(ex_text1, width=8), collapse="\n\n"),"\n")
cat(paste(str_indent_wrap(ex_text2, tab_width=4), collapse="\n\n"),"\n")

ADAE Data

Description

A subset of the PHUSE Test Data Factory ADAE data set.

Usage

tplyr_adae

Format

A data.frame with 276 rows and 55 columns.

Source

https://github.com/phuse-org/TestDataFactory

See Also

[get_data_labels()]

ADAS Data

Description

A subset of the PHUSE Test Data Factory ADAS data set.

Usage

tplyr_adas

Format

A data.frame with 1,040 rows and 40 columns.

Source

https://github.com/phuse-org/TestDataFactory

See Also

[get_data_labels()]

ADLB Data

Description

A subset of the PHUSE Test Data Factory ADLB data set.

Usage

tplyr_adlb

Format

A data.frame with 311 rows and 46 columns.

Source

https://github.com/phuse-org/TestDataFactory

See Also

[get_data_labels()]

ADPE Data

Description

A mock-up dataset that is fit for testing data limiting

Usage

tplyr_adpe

Format

A data.frame with 21 rows and 8 columns.

ADSL Data

Description

A subset of the PHUSE Test Data Factory ADSL data set.

Usage

tplyr_adsl

Format

A data.frame with 254 rows and 49 columns.

Source

https://github.com/phuse-org/TestDataFactory

See Also

[get_data_labels()]

Create a tplyr_layer object

Description

This object is the workhorse of the tplyr package. A tplyr_layer can be thought of as a block, or "layer" of a table. Summary tables typically consist of different sections that require different summaries. When programming these section, your code will create different layers that need to be stacked or merged together. A tplyr_layer is the container for those isolated building blocks.

When building the tplyr_table, each layer will execute independently. When all of the data processing has completed, the layers are brought together to construct the output.

tplyr_layer objects are not created directly, but are rather created using the layer constructor functions group_count, group_desc, and group_shift.

Usage

tplyr_layer(parent, target_var, by, where, type, ...)

Arguments

Value

A tplyr_layer environment that is a child of the specified parent. The environment contains the object as listed below.

tplyr_layer Core Object Structure

type

This is an attribute. A string indicating the layer type, which controls the summary that will be performed.

target_var

A quosure of a name, which is the variable on which a summary will be performed.

by

A list of quosures representing either text labels or variable names used in grouping. Variable names must exist within the target dataset Text strings submitted do not need to exist in the target dataset.

cols

A list of quosures used to determine the variables that are used to display in columns.

where

A quosure of a call that containers the filter logic used to subset the target dataset. This filtering is in addition to any subsetting done based on where criteria specified in tplyr_table

layers

A list with class tplyr_layer_container. Initialized as empty, but serves as the container for any sublayers of the current layer. Used internally.

Different layer types will have some different bindings specific to that layer's needs.

See Also

tplyr_table

Examples

tab <- tplyr_table(iris, Sepal.Width)

l <- group_count(tab, by=vars('Label Text', Species),
                 target_var=Species, where= Sepal.Width < 5.5,
                 cols = Species)

Tplyr Metadata Object

Description

If a Tplyr table is built with the 'metadata=TRUE' option specified, then metadata is assembled behind the scenes to provide traceability on each result cell derived. The functions 'get_meta_result()' and 'get_meta_subset()' allow you to access that metadata by using an ID provided in the row_id column and the column name of the result you'd like to access. The purpose is of the row_id variable instead of a simple row index is to provide a sort resistant reference of the originating column, so the output Tplyr table can be sorted in any order but the metadata are still easily accessible.

Usage

tplyr_meta(names = list(), filters = exprs())

Arguments

Details

The 'tplyr_meta' object provided a list with two elements - names and filters. The names contain every column from the target data.frame of the Tplyr table that factored into the specified result cell, and the filters contains all the necessary filters to subset the target data to create the specified result cell. 'get_meta_subset()' additionally provides a parameter to specify any additional columns you would like to include in the returned subset data frame.

Value

tplyr_meta object

Examples

tplyr_meta(
   names = rlang::quos(x, y, z),
   filters = rlang::quos(x == 1, y==2, z==3)
 )

Create a Tplyr table object

Description

The tplyr_table object is the main container upon which a Tplyr table is constructed. Tplyr tables are made up of one or more layers. Each layer contains an instruction for a summary to be performed. The tplyr_table object contains those layers, and the general data, metadata, and logic necessary.

Usage

tplyr_table(target, treat_var, where = TRUE, cols = vars())

Arguments

Details

When a tplyr_table is created, it will contain the following bindings:

• target - The dataset upon which summaries will be performed

• pop_data - The data containing population information. This defaults to the target dataset

• cols - A categorical variable to present summaries grouped by column (in addition to treat_var)

• table_where - The where parameter provided, used to subset the target data

• treat_var - Variable used to distinguish treatment groups.

• header_n - Default header N values based on treat_var

• pop_treat_var - The treatment variable for pop_data (if different)

• layers - The container for individual layers of a tplyr_table

• treat_grps - Additional treatment groups to be added to the summary (i.e. Total)

tplyr_table allows you a basic interface to instantiate the object. Modifier functions are available to change individual parameters catered to your analysis. For example, to add a total group, you can use the add_total_group.

In future releases, we will provide vignettes to fully demonstrate these capabilities.

Value

A tplyr_table object

Examples

tab <- tplyr_table(iris, Species, where = Sepal.Length < 5.8)

Return or set the treatment variable binding

Description

Return or set the treatment variable binding

Usage

treat_var(table)

set_treat_var(table, treat_var)

Arguments

Value

For tplyr_treat_var the treat_var binding of the tplyr_table object. For set_tplyr_treat_var the modified object.

Examples

tab <- tplyr_table(mtcars, cyl)

set_treat_var(tab, gear)