How ‘Tplyr’ Works

When you look at a summary table within a clinical report, you can often break it down into some basic pieces. Consider this output.

Different variables are being summarized in chunks of the table, which we refer to as “layers”. Additionally, this table really only contains a few different types of summaries, which makes many of the calculations rather redundant. This drives the motivation behind ‘Tplyr’. The containing table is encapsulated within the tplyr_table() object, and each section, or “layer”, within the summary table can be broken down into a tplyr_layer() object.

The tplyr_table() Object

The tplyr_table() object is the conceptual “table” that contains all of the logic necessary to construct and display the data. ‘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 to prepare the data before any layers are constructed.

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

  • target - The dataset upon which summaries will be performed
  • count_layer_formats - Default formats to be used on count layers in the table
  • shift_layer_formats - Default formats to be used on shift layers in the table
  • desc_layer_formats - Default formats to be used on descriptive statistics layers in the table
  • pop_data - The dataset containing population information. This defaults to the target dataset
  • cols - A categorical variable in the target dataset to present summaries grouped by column (in addition to the treat_var variable)
  • table_where - The where clause provided, used to subset the target dataset
  • treat_var - Variable used to distinguish treatment groups in the target dataset.
  • header_n - Default header N values based on treat_var and any cols variables
  • pop_treat_var - Variable used to distinguish treatment groups in pop_data dataset (if different than the treat_var variable in the target dataset)
  • layers - The container for individual layers of a tplyr_table()
  • treat_grps - Additional treatment groups to be added to the summary (i.e. Total)

The function tplyr_table() allows you a basic interface to instantiate the object. Modifier functions are available to change individual parameters catered to your analysis.

t <- tplyr_table(adsl, TRT01P, where = SAFFL == "Y")
t
#> *** tplyr_table ***
#> Target (data.frame):
#>  Name:  adsl
#>  Rows:  254
#>  Columns:  49 
#> treat_var variable (quosure)
#>  TRT01P
#> header_n:  header groups
#> treat_grps groupings (list)
#> Table Columns (cols):
#> where: == SAFFL Y
#> Number of layer(s): 0
#> layer_output: 0

The tplyr_layer Object

Users of ‘Tplyr’ interface with tplyr_layer() objects using the group_<type> family of functions. This family 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. shift layers summarize the counts for different changes in states. Lastly, desc layers create descriptive statistics.

  • Count Layers
    • Count layers allow you to easily create summaries based on counting distinct or non-distinct occurrences of values within 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 simply 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. Count layers can also distinguish between distinct and non-distinct counts. Using some specified by variable, you can count the unique occurrences of some variable within the specified by grouping, including the target. This allows you to do a summary like unique subjects and their proportion experiencing some adverse event, and the number of total occurrences of that adverse event.
  • 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, interquartile range, Q1, Q3, and missing value counts. From these available summaries, the default presentation of a descriptive statistics 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 easily implement any additional summary statistics you want presented.
  • Shift Layers
    • Shift layers are largely an abstraction of the count layer - and in fact, we re-use a lot of the same code to process these layers. In many shift tables, the “from” state is presented as rows in the table, and the “to” state is presented as columns. This clearly lays out how many subjects changed state between a baseline and some point in time. Shift layers give you an intuitive API to break these out, using a very similar interface as the other layers. There are also a number of modifier functions available to control nuanced aspects, such as how denominators should be applied.
cnt <- group_count(t, AGEGR1)
cnt
#> *** count_layer ***
#> Self:  count_layer < 0x7f8911809c10 >
#> Parent:  tplyr_table < 0x7f890fb78660 >
#> target_var: 
#>  AGEGR1
#> by: 
#> where: TRUE
#> Layer(s): 0

dsc <- group_desc(t, AGE)
dsc
#> *** desc_layer ***
#> Self:  desc_layer < 0x7f89109330d0 >
#> Parent:  tplyr_table < 0x7f890fb78660 >
#> target_var: 
#>  AGE
#> by: 
#> where: TRUE
#> Layer(s): 0

shf <- group_shift(t, vars(row=COMP8FL, column=COMP24FL))
shf
#> *** shift_layer ***
#> Self:  shift_layer < 0x7f8910a29d78 >
#> Parent:  tplyr_table < 0x7f890fb78660 >
#> target_var: 
#>  COMP8FL
#>  COMP24FL
#> by: 
#> where: TRUE
#> Layer(s): 0

Adding Layers to a Table

Everyone has their own style of coding - so we’ve tried to be flexible to an extent. Overall, ‘Tplyr’ is built around tidy syntax, so all of our object construction supports piping with magrittr (i.e. %>%).

There are two ways to add layers to a tplyr_table(): add_layer() and add_layers(). The difference is that add_layer() allows you to construct the layer within the call to add_layer(), whereas with add_layers() you can attach multiple layers that have already been constructed upfront:

t <- tplyr_table(adsl, TRT01P) %>% 
  add_layer(
    group_count(AGEGR1, by = "Age categories n (%)")
  )

Within add_layer(), the syntax to constructing the count layer for Age Categories was written on the fly. add_layer() is special in that it also allows you to use piping to use modifier functions on the layer being constructed

t <- tplyr_table(adsl, TRT01P) %>% 
  add_layer(
    group_count(AGEGR1, by = "Age categories n (%)") %>% 
      set_format_strings(f_str("xx (xx.x%)", n, pct)) %>% 
      add_total_row()
  )

add_layers(), on the other hand, lets you isolate the code to construct a particular layer if you wanted to separate things out more. Some might find this cleaner to work with if you have a large number of layers being constructed.

t <- tplyr_table(adsl, TRT01P) 

l1 <- group_count(t, AGEGR1, by = "Age categories n (%)")
l2 <- group_desc(t, AGE, by = "Age (years)")

t <- add_layers(t, l1, l2)

Notice that when you construct the layers separately, you need to specify the table to which they belong. add_layer() does this automatically. tplyr_table() and tplyr_layer() objects are built on environments, and the parent/child relationships are very important. This is why, even though the layer knows who its table parent is, the layers still need to be attached to the table (as the table doesn’t know who its children are). Advanced R does a very good job at explaining what environments in R are, their benefits, and how to use them.

A Note Before We Go Deeper

Notice that when you construct a tplyr_table() or a tplyr_layer() that what displays is a summary of information about the table or layer? That’s because when you create these objects - it constructs the metadata, but does not process the actual data. This allows you to construct and make sure the pieces of your table fit together before you do the data processing - and it gives you a container to hold all of this metadata, and use it later if necessary.

To generate the data from a tplyr_table() object, you use the function build():

t <- tplyr_table(adsl, TRT01P) %>% 
  add_layer(
    group_count(AGEGR1, by = "Age categories n (%)")
  )

t %>% 
  build() %>% 
  kable()
row_label1 row_label2 var1_Placebo var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index ord_layer_1 ord_layer_2
Age categories n (%) <65 14 ( 16.3%) 11 ( 13.1%) 8 ( 9.5%) 1 1 1
Age categories n (%) >80 30 ( 34.9%) 18 ( 21.4%) 29 ( 34.5%) 1 1 2
Age categories n (%) 65-80 42 ( 48.8%) 55 ( 65.5%) 47 ( 56.0%) 1 1 3

But there’s more you can get from ‘Tplyr’. It’s great to have the formatted numbers, but what about the numeric data behind the scenes? Maybe a number looks suspicious and you need to investigate how you got that number. What if you want to calculate your own statistics based off of the counts? You can get that information as well using get_numeric_data(). This returns the numeric data from each layer as a list of data frames:

get_numeric_data(t) %>% 
  head() %>% 
  kable()
TRT01P “Age categories n (%)” summary_var n total
Placebo Age categories n (%) <65 14 86
Placebo Age categories n (%) >80 30 86
Placebo Age categories n (%) 65-80 42 86
Xanomeline High Dose Age categories n (%) <65 11 84
Xanomeline High Dose Age categories n (%) >80 18 84
Xanomeline High Dose Age categories n (%) 65-80 55 84
Xanomeline Low Dose Age categories n (%) <65 8 84
Xanomeline Low Dose Age categories n (%) >80 29 84
Xanomeline Low Dose Age categories n (%) 65-80 47 84

By storing pertinent information, you can get more out of a ‘Tplyr’ object than processed data for display. And by specifying when you want to get data out of ‘Tplyr’, we can save you from repeatedly processing data while your constructing your outputs - which is particularly useful when that computation starts taking time.

Constructing Layers

The bulk of ‘Tplyr’ coding comes from constructing your layers and specifying the work you want to be done. Before we get into this, it’s important to discuss how ‘Tplyr’ handles string formatting.

String Formatting in ‘Tplyr’

String formatting in ‘Tplyr’ is controlled by an object called an f_str(), which is also the name of function you use to create these formats. To set these format strings into a tplyr_layer(), you use the function set_format_strings(), and this usage varies slightly between layer types (which is covered in other vignettes).

So - why is this object necessary. Consider this example:


t <- tplyr_table(adsl, TRT01P) %>% 
  add_layer(
    group_desc(AGE, by = "Age (years)") %>% 
      set_format_strings(
        'n' = f_str('xx', n),
        'Mean (SD)' = f_str('xx.xx (xx.xxx)', mean, sd)
      )
  )

t %>% 
  build() %>% 
  kable()
row_label1 row_label2 var1_Placebo var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index ord_layer_1 ord_layer_2
Age (years) n 86 84 84 1 1 1
Age (years) Mean (SD) 75.21 ( 8.590) 74.38 ( 7.886) 75.67 ( 8.286) 1 1 2

In a perfect world, the f_str() calls wouldn’t be necessary - but in reality they allow us to infer a great deal of information from very few user inputs. In the calls that you see above:

  • The row labels in the row_label2 column are taken from the left side of each = in set_format_strings()
  • The string formats, including integer length and decimal precision, and exact presentation formatting are taken from the strings within the first parameter of each f_str() call
  • The second and greater parameters within each f_str() call determine the descriptive statistic summaries that will be performed. This is connected to a number of default summaries available within ‘Tplyr’, but you can also create your own summaries (covered in other vignettes). The default summaries that are built in include:
    • n = Number of observations
    • mean = Mean
    • sd = Standard Deviation
    • var = Variance
    • iqr = Inter Quartile Range
    • q1 = 1st quartile
    • q3 = 3rd quartile
    • min = Minimum value
    • max = Maximum value
    • missing = Count of NA values
  • When two summaries are placed on the same f_str() call, then those two summaries are formatted into the same string. This allows you to do a “Mean (SD)” type format where both numbers appear.

This simple user input controls a significant amount of work in the back end of the data processing, and the f_str() object allows that metadata to be collected.

f_str() objects are also used with count layers as well to control the data presentation. Instead of specifying the summaries performed, you use n, pct, distinct, and distinct_pct for your parameters and specify how you would like the values displayed. Using distinct and distinct_pct should be combined with specifying a distinct_by() variable using set_distinct_by().

tplyr_table(adsl, TRT01P) %>% 
  add_layer(
    group_count(AGEGR1, by = "Age categories") %>% 
      set_format_strings(f_str('xx (xx.x)',n,pct))
  ) %>% 
  build() %>% 
  kable()
row_label1 row_label2 var1_Placebo var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index ord_layer_1 ord_layer_2
Age categories <65 14 (16.3) 11 (13.1) 8 ( 9.5) 1 1 1
Age categories >80 30 (34.9) 18 (21.4) 29 (34.5) 1 1 2
Age categories 65-80 42 (48.8) 55 (65.5) 47 (56.0) 1 1 3

tplyr_table(adsl, TRT01P) %>% 
  add_layer(
    group_count(AGEGR1, by = "Age categories") %>% 
      set_format_strings(f_str('xx',n))
  ) %>% 
  build() %>% 
  kable()
row_label1 row_label2 var1_Placebo var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index ord_layer_1 ord_layer_2
Age categories <65 14 11 8 1 1 1
Age categories >80 30 18 29 1 1 2
Age categories 65-80 42 55 47 1 1 3

Really - format strings allow you to present your data however you like.

tplyr_table(adsl, TRT01P) %>% 
  add_layer(
    group_count(AGEGR1, by = "Age categories") %>% 
      set_format_strings(f_str('xx (•◡•) xx.x%',n,pct))
  ) %>% 
  build() %>% 
  kable()
row_label1 row_label2 var1_Placebo var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index ord_layer_1 ord_layer_2
Age categories <65 14 (•◡•) 16.3% 11 (•◡•) 13.1% 8 (•◡•) 9.5% 1 1 1
Age categories >80 30 (•◡•) 34.9% 18 (•◡•) 21.4% 29 (•◡•) 34.5% 1 1 2
Age categories 65-80 42 (•◡•) 48.8% 55 (•◡•) 65.5% 47 (•◡•) 56.0% 1 1 3

But should you? Probably not.

Layer Types

Descriptive Statistic Layers

As covered under string formatting, set_format_strings() controls a great deal of what happens within a descriptive statistics layer. Note that there are some built in defaults to what’s output:

tplyr_table(adsl, TRT01P) %>% 
  add_layer(
    group_desc(AGE, by = "Age (years)")
  ) %>% 
  build() %>% 
  kable()
row_label1 row_label2 var1_Placebo var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index ord_layer_1 ord_layer_2
Age (years) n 86 84 84 1 1 1
Age (years) Mean (SD) 75.2 ( 8.59) 74.4 ( 7.89) 75.7 ( 8.29) 1 1 2
Age (years) Median 76.0 76.0 77.5 1 1 3
Age (years) Q1, Q3 69.2, 81.8 70.8, 80.0 71.0, 82.0 1 1 4
Age (years) Min, Max 52, 89 56, 88 51, 88 1 1 5
Age (years) Missing 0 0 0 1 1 6

To override these defaults, just specify the summaries that you want to be performed using set_format_strings() as described above. But what if ‘Tplyr’ doesn’t have a built in function to do the summary statistic that you want to see? Well - you can make your own! This is where set_custom_summaries() comes into play. Let’s say you want to derive a geometric mean.

tplyr_table(adsl, TRT01P) %>%
  add_layer(
    group_desc(AGE, by = "Sepal Length") %>%
      set_custom_summaries(
        geometric_mean = exp(sum(log(.var[.var > 0]), na.rm=TRUE) / length(.var))
      ) %>%
      set_format_strings(
        'Geometric Mean (SD)' = f_str('xx.xx (xx.xxx)', geometric_mean, sd)
      )
  ) %>% 
  build() %>% 
  kable()
row_label1 row_label2 var1_Placebo var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index ord_layer_1 ord_layer_2
Sepal Length Geometric Mean (SD) 74.70 ( 8.590) 73.94 ( 7.886) 75.18 ( 8.286) 1 1 1

In set_custom_summaries(), first you name the summary being performed. This is important - that name is what you use in the f_str() call to incorporate it into a format. Next, you program or call the function desired. What happens in the background is that this is used in a call to dplyr::summarize() - so use similar syntax. Use the variable name .var in your custom summary function. This is necessary because it allows a generic variable name to be used when multiple target variables are specified - and therefore the function can be applied to both target variables.

Sometimes there’s a need to present multiple variables summarized side by side. ‘Tplyr’ allows you to do this as well.

tplyr_table(adsl, TRT01P) %>% 
  add_layer(
    group_desc(vars(AGE, AVGDD), by = "Age and Avg. Daily Dose")
  ) %>% 
  build() %>% 
  kable()
row_label1 row_label2 var1_Placebo var1_Xanomeline High Dose var1_Xanomeline Low Dose var2_Placebo var2_Xanomeline High Dose var2_Xanomeline Low Dose ord_layer_index ord_layer_1 ord_layer_2
Age and Avg. Daily Dose n 86 84 84 86 84 84 1 1 1
Age and Avg. Daily Dose Mean (SD) 75.2 ( 8.59) 74.4 ( 7.89) 75.7 ( 8.29) 0.0 ( 0.00) 71.6 ( 8.11) 54.0 ( 0.00) 1 1 2
Age and Avg. Daily Dose Median 76.0 76.0 77.5 0.0 75.1 54.0 1 1 3
Age and Avg. Daily Dose Q1, Q3 69.2, 81.8 70.8, 80.0 71.0, 82.0 0.0, 0.0 70.2, 76.9 54.0, 54.0 1 1 4
Age and Avg. Daily Dose Min, Max 52, 89 56, 88 51, 88 0, 0 54, 79 54, 54 1 1 5
Age and Avg. Daily Dose Missing 0 0 0 0 0 0 1 1 6

‘Tplyr’ summarizes both variables and merges them together. This makes creating tables where you need to compare BASE, AVAL, and CHG next to each other nice and simple. Note the use of dplyr::vars() - in any situation where you’d like to use multiple variable names in a parameter, use dplyr::vars() to specify the variables. You can use text strings in the calls to dplyr::vars() as well.

Count Layers

Count layers generally allow you to create “n” and “n (%)” count type summaries. There are a few extra features here as well. Let’s say that you want a total row within your counts. This can be done with add_total_row():

tplyr_table(adsl, TRT01P) %>% 
  add_layer(
    group_count(AGEGR1, by = "Age categories") %>% 
      add_total_row()
  ) %>% 
  build() %>% 
  kable()
row_label1 row_label2 var1_Placebo var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index ord_layer_1 ord_layer_2
Age categories <65 14 ( 16.3%) 11 ( 13.1%) 8 ( 9.5%) 1 1 1
Age categories >80 30 ( 34.9%) 18 ( 21.4%) 29 ( 34.5%) 1 1 2
Age categories 65-80 42 ( 48.8%) 55 ( 65.5%) 47 ( 56.0%) 1 1 3
Age categories Total 86 (100.0%) 84 (100.0%) 84 (100.0%) 1 1 4

Sometimes it’s also necessary to count summaries based on distinct values. ‘Tplyr’ allows you to do this as well with set_distinct_by():

tplyr_table(adae, TRTA) %>% 
  add_layer(
    group_count('Subjects with at least one adverse event') %>% 
      set_distinct_by(USUBJID) %>% 
      set_format_strings(f_str('xx', n))
  ) %>% 
  build() %>% 
  kable()
row_label1 var1_Placebo var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index ord_layer_1
Subjects with at least one adverse event 47 111 118 1 NA

There’s another trick going on here - to create a summary with row label text like you see above, text strings can be used as the target variables. Here, we use this in combination with set_distinct_by() to count distinct subjects.

Adverse event tables often call for counting AEs of something like a body system and counting actual events within that body system. ‘Tplyr’ has means of making this simple for the user as well.

tplyr_table(adae, TRTA) %>% 
  add_layer(
    group_count(vars(AEBODSYS, AEDECOD))
  ) %>% 
  build() %>% 
  head() %>% 
  kable()
row_label1 row_label2 var1_Placebo var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index ord_layer_1 ord_layer_2
SKIN AND SUBCUTANEOUS TISSUE DISORDERS SKIN AND SUBCUTANEOUS TISSUE DISORDERS 47 (100.0%) 111 (100.0%) 118 (100.0%) 1 1 Inf
SKIN AND SUBCUTANEOUS TISSUE DISORDERS ACTINIC KERATOSIS 0 ( 0.0%) 1 ( 0.9%) 0 ( 0.0%) 1 1 1
SKIN AND SUBCUTANEOUS TISSUE DISORDERS ALOPECIA 1 ( 2.1%) 0 ( 0.0%) 0 ( 0.0%) 1 1 2
SKIN AND SUBCUTANEOUS TISSUE DISORDERS BLISTER 0 ( 0.0%) 2 ( 1.8%) 8 ( 6.8%) 1 1 3
SKIN AND SUBCUTANEOUS TISSUE DISORDERS COLD SWEAT 3 ( 6.4%) 0 ( 0.0%) 0 ( 0.0%) 1 1 4
SKIN AND SUBCUTANEOUS TISSUE DISORDERS DERMATITIS ATOPIC 1 ( 2.1%) 0 ( 0.0%) 0 ( 0.0%) 1 1 5

Here we again use dplyr::vars() to specify multiple target variables. When used in a count layer, ‘Tplyr’ knows automatically that the first variable is a grouping variable for the second variable, and counts shall be produced for both then merged together.

Shift Layers

Lastly, let’s talk about shift layers. A common example of this would be looking at a subject’s lab levels at baseline versus some designated evaluation point. This would tell us, for example, how many subjects were high at baseline for a lab test vs. after an intervention has been introduced. The shift layer in ‘Tplyr’ is intended for creating shift tables that show these data as a matrix, where one state will be presented in rows and the other in columns. Let’s look at an example.

# Tplyr can use factor orders to dummy values and order presentation
adlb$ANRIND <- factor(adlb$ANRIND, c("L", "N", "H"))
adlb$BNRIND <- factor(adlb$BNRIND, c("L", "N", "H"))

tplyr_table(adlb, TRTA, where = PARAMCD == "CK") %>%
  add_layer(
    group_shift(vars(row=BNRIND, column=ANRIND), by=PARAM) %>% 
      set_format_strings(f_str("xx (xxx%)", n, pct))
  ) %>% 
  build() %>% 
  kable()
row_label1 row_label2 var1_Placebo_L var1_Placebo_N var1_Placebo_H var1_Xanomeline High Dose_L var1_Xanomeline High Dose_N var1_Xanomeline High Dose_H var1_Xanomeline Low Dose_L var1_Xanomeline Low Dose_N var1_Xanomeline Low Dose_H ord_layer_index ord_layer_1 ord_layer_2
Creatine Kinase (U/L) L 0 ( 0%) 0 ( 0%) 0 ( 0%) 0 ( 0%) 0 ( 0%) 0 ( 0%) 0 ( 0%) 0 ( 0%) 0 ( 0%) 1 35 1
Creatine Kinase (U/L) N 0 ( 0%) 27 ( 87%) 4 ( 13%) 0 ( 0%) 17 ( 85%) 2 ( 10%) 0 ( 0%) 14 ( 93%) 1 ( 7%) 1 35 2
Creatine Kinase (U/L) H 0 ( 0%) 0 ( 0%) 0 ( 0%) 0 ( 0%) 0 ( 0%) 1 ( 5%) 0 ( 0%) 0 ( 0%) 0 ( 0%) 1 35 3

The underlying process of shift tables is the same as count layers - we’re counting the number of occurrences of something by a set of grouping variables. This differs in that ‘Tplyr’ uses the group_shift() API to use the same basic interface as other tables, but translate your target variables into the row variable and the column variable. Furthermore, there is some enhanced control over how denominators should behave that is necessary for a shift layer.

Where to go from here?

There’s quite a bit more to learn! And we’ve prepared a number of other vignettes to help you get what you need out of ‘Tplyr’.

References

In building ‘Tplyr’, we needed some additional resources in addition to our personal experience to help guide design. PHUSE has done some great work to create guidance for standard outputs with collaboration between multiple pharmaceutical companies and the FDA. You can find some of the resource that we referenced below.

Analysis and Displays Associated with Adverse Events

Analyses and Displays Associated with Demographics, Disposition, and Medications

Analyses and Displays Associated with Measures of Central Tendency