At surface level - sorting a table may seem easy, and in many cases it is. But in a handful of cases it can get quite tricky, with some odd situations that need to be handled carefully. For this reason, we found it necessary to dedicate an entire vignette to just sorting and handling columns output by ‘Tplyr’.

Let’s start by looking at an example.

t <- tplyr_table(adsl, TRT01A) %>%
  add_total_group() %>%
  add_treat_grps(Treated = c("Xanomeline Low Dose", "Xanomeline High Dose")) %>%
  add_layer(
    group_count(EOSSTT, by = SEX)
  ) %>%
  add_layer(
    group_desc(HEIGHTBL, by = SEX)
  ) %>%
  build()

kable(t)
row_label1 row_label2 var1_Placebo var1_Total var1_Treated var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index ord_layer_1 ord_layer_2
F COMPLETED 34 ( 39.5%) 64 ( 25.2%) 30 ( 17.9%) 13 ( 15.5%) 17 ( 20.2%) 1 1 1
F DISCONTINUED 19 ( 22.1%) 79 ( 31.1%) 60 ( 35.7%) 27 ( 32.1%) 33 ( 39.3%) 1 1 2
M COMPLETED 24 ( 27.9%) 46 ( 18.1%) 22 ( 13.1%) 14 ( 16.7%) 8 ( 9.5%) 1 2 1
M DISCONTINUED 9 ( 10.5%) 65 ( 25.6%) 56 ( 33.3%) 30 ( 35.7%) 26 ( 31.0%) 1 2 2
F n 53 143 90 40 50 2 1 1
F Mean (SD) 156.06 ( 8.010) 157.25 ( 7.374) 157.94 ( 6.924) 158.02 ( 6.370) 157.88 ( 7.401) 2 1 2
F Median 156.20 157.50 157.50 157.50 157.85 2 1 3
F Q1, Q3 149.90, 162.60 152.40, 162.60 154.00, 162.60 154.28, 164.12 154.00, 162.60 2 1 4
F Min, Max 137.2, 174.0 135.9, 175.3 135.9, 175.3 146.1, 170.2 135.9, 175.3 2 1 5
F Missing 0 0 0 0 0 2 1 6
M n 33 111 78 44 34 2 2 1
M Mean (SD) 173.03 ( 8.088) 172.55 ( 7.946) 172.34 ( 7.929) 172.91 ( 7.304) 171.60 ( 8.729) 2 2 2
M Median 174.00 172.70 172.70 172.70 172.10 2 2 3
M Q1, Q3 170.20, 177.80 168.25, 177.80 167.60, 177.80 170.15, 177.80 165.42, 177.48 2 2 4
M Min, Max 144.8, 185.4 144.8, 195.6 147.3, 195.6 147.3, 190.5 157.5, 195.6 2 2 5
M Missing 0 0 0 0 0 2 2 6

In this table, we have:

  • Added a ‘Total’ treatment group
  • Added a ‘Treated’ group made up of the two treated cohorts
  • Created a count layer for End of Study Status, reported by sex
  • Created a descriptive statistics layer for Height at Baseline, reported by sex

Now let’s dig in.

Sorting Table Columns

Ordering Helpers

Ordering helpers are columns added into ‘Tplyr’ tables to make sure that you can sort the display to your preference. In general, ‘Tplyr’ will create:

  • One order variable to order layers
  • One order variable for each by variable
  • At least one order variable for the target variables

In the example above, the t table outputs with three columns:

  • ord_layer_index indexes the layer itself.
  • ord_layer_1 indexes the first by variable, SEX. No options were presented so sorting was done alphabetically.
  • ord_layer_2 indexes the values of the EOSSTT variable in the count layer, and the names of the summaries in the desc layer.
t %>%
  select(starts_with("ord")) %>% 
  kable()
ord_layer_index ord_layer_1 ord_layer_2
1 1 1
1 1 2
1 2 1
1 2 2
2 1 1
2 1 2
2 1 3
2 1 4
2 1 5
2 1 6
2 2 1
2 2 2
2 2 3
2 2 4
2 2 5
2 2 6

Reordering and Dropping Columns

Column selection from data frames is something that is already very well done in R. The functions dplyr::select(), magrittr::extract(), and [ can all be used to reorder and drop column cleanly and concisely based on a user’s preference.

To drop the ordering helpers, you can easily subtract them with ‘dplyr’ and ‘tidyselect’.

t %>% 
  select(-starts_with("ord_")) %>% 
  kable()
row_label1 row_label2 var1_Placebo var1_Total var1_Treated var1_Xanomeline High Dose var1_Xanomeline Low Dose
F COMPLETED 34 ( 39.5%) 64 ( 25.2%) 30 ( 17.9%) 13 ( 15.5%) 17 ( 20.2%)
F DISCONTINUED 19 ( 22.1%) 79 ( 31.1%) 60 ( 35.7%) 27 ( 32.1%) 33 ( 39.3%)
M COMPLETED 24 ( 27.9%) 46 ( 18.1%) 22 ( 13.1%) 14 ( 16.7%) 8 ( 9.5%)
M DISCONTINUED 9 ( 10.5%) 65 ( 25.6%) 56 ( 33.3%) 30 ( 35.7%) 26 ( 31.0%)
F n 53 143 90 40 50
F Mean (SD) 156.06 ( 8.010) 157.25 ( 7.374) 157.94 ( 6.924) 158.02 ( 6.370) 157.88 ( 7.401)
F Median 156.20 157.50 157.50 157.50 157.85
F Q1, Q3 149.90, 162.60 152.40, 162.60 154.00, 162.60 154.28, 164.12 154.00, 162.60
F Min, Max 137.2, 174.0 135.9, 175.3 135.9, 175.3 146.1, 170.2 135.9, 175.3
F Missing 0 0 0 0 0
M n 33 111 78 44 34
M Mean (SD) 173.03 ( 8.088) 172.55 ( 7.946) 172.34 ( 7.929) 172.91 ( 7.304) 171.60 ( 8.729)
M Median 174.00 172.70 172.70 172.70 172.10
M Q1, Q3 170.20, 177.80 168.25, 177.80 167.60, 177.80 170.15, 177.80 165.42, 177.48
M Min, Max 144.8, 185.4 144.8, 195.6 147.3, 195.6 147.3, 190.5 157.5, 195.6
M Missing 0 0 0 0 0

Or you can reorder columns. In this example the “Total” result column is moved to the front of the results.

t %>%
  select( starts_with("row"), var1_Total, starts_with("var1")) %>% 
  kable()
row_label1 row_label2 var1_Total var1_Placebo var1_Treated var1_Xanomeline High Dose var1_Xanomeline Low Dose
F COMPLETED 64 ( 25.2%) 34 ( 39.5%) 30 ( 17.9%) 13 ( 15.5%) 17 ( 20.2%)
F DISCONTINUED 79 ( 31.1%) 19 ( 22.1%) 60 ( 35.7%) 27 ( 32.1%) 33 ( 39.3%)
M COMPLETED 46 ( 18.1%) 24 ( 27.9%) 22 ( 13.1%) 14 ( 16.7%) 8 ( 9.5%)
M DISCONTINUED 65 ( 25.6%) 9 ( 10.5%) 56 ( 33.3%) 30 ( 35.7%) 26 ( 31.0%)
F n 143 53 90 40 50
F Mean (SD) 157.25 ( 7.374) 156.06 ( 8.010) 157.94 ( 6.924) 158.02 ( 6.370) 157.88 ( 7.401)
F Median 157.50 156.20 157.50 157.50 157.85
F Q1, Q3 152.40, 162.60 149.90, 162.60 154.00, 162.60 154.28, 164.12 154.00, 162.60
F Min, Max 135.9, 175.3 137.2, 174.0 135.9, 175.3 146.1, 170.2 135.9, 175.3
F Missing 0 0 0 0 0
M n 111 33 78 44 34
M Mean (SD) 172.55 ( 7.946) 173.03 ( 8.088) 172.34 ( 7.929) 172.91 ( 7.304) 171.60 ( 8.729)
M Median 172.70 174.00 172.70 172.70 172.10
M Q1, Q3 168.25, 177.80 170.20, 177.80 167.60, 177.80 170.15, 177.80 165.42, 177.48
M Min, Max 144.8, 195.6 144.8, 185.4 147.3, 195.6 147.3, 190.5 157.5, 195.6
M Missing 0 0 0 0 0

For more information, it’s well worth your time to familiarize yourself with the select helpers that work with ‘dplyr’.

Sorting the Layers

Layers are one of the fundamental building blocks of ‘Tplyr’. Each layer executes independently, and at the end of a build they’re bound together. The ord_layer_index variable allows you differentiate and sort layers after the table is built. Layers are indexed in the order in which they were added to the table using add_layer() or add_layers(). For example, let’s say you wanted to reverse the order of the layers.

t %>%
  select(starts_with("row"), starts_with("ord")) %>%
  arrange(desc(ord_layer_index)) %>% 
  kable()
row_label1 row_label2 ord_layer_index ord_layer_1 ord_layer_2
F n 2 1 1
F Mean (SD) 2 1 2
F Median 2 1 3
F Q1, Q3 2 1 4
F Min, Max 2 1 5
F Missing 2 1 6
M n 2 2 1
M Mean (SD) 2 2 2
M Median 2 2 3
M Q1, Q3 2 2 4
M Min, Max 2 2 5
M Missing 2 2 6
F COMPLETED 1 1 1
F DISCONTINUED 1 1 2
M COMPLETED 1 2 1
M DISCONTINUED 1 2 2

Sorting the by Variables

Each by variable gets its own order column as well. These will be named ord_layer_<n> where <n> typically relates back to the row_label variable (this isn’t necessarily the case when count layers are nested - see vignette("count")).

These order variables will calculate based on the first applicable method below.

  1. If the variable has a VARN variable in the target dataset, (i.e. AVISIT has AVISITN, or PARAM has PARAMN), that variable will be extracted and used as the ordering variable associated with that row label.
  2. If the by variable is a factor, the values of the ordering column will be associated with the factor levels.
  3. If neither 1 or 2 are true, the values in the ordering column will be based on an alphabetical sorting. The resulting column will be numeric.

VARN

To demonstrate the use of VARN sorting, consider the variable RACE. In ADSL, RACE also has RACEN:

adsl %>% 
  distinct(RACEN, RACE) %>% 
  kable()
RACE RACEN
WHITE 1
BLACK OR AFRICAN AMERICAN 2
AMERICAN INDIAN OR ALASKA NATIVE 6

‘Tplyr’ will automatically figure this out for you, and pull the RACEN values into the variable ord_layer_1.

tplyr_table(adsl, TRT01A) %>%
  add_layer(
    group_count(EOSSTT, by = RACE)
  ) %>%
  build() %>%
  select(row_label1, row_label2, ord_layer_1) %>%
  arrange(ord_layer_1) %>% 
  kable()
row_label1 row_label2 ord_layer_1
WHITE COMPLETED 1
WHITE DISCONTINUED 1
BLACK OR AFRICAN AMERICAN COMPLETED 2
BLACK OR AFRICAN AMERICAN DISCONTINUED 2
AMERICAN INDIAN OR ALASKA NATIVE COMPLETED 6
AMERICAN INDIAN OR ALASKA NATIVE DISCONTINUED 6

Factor

If there’s no VARN variable in the target dataset, ‘Tplyr’ will then check if the variable you provided is a factor. If you’re new to R, spending some time trying to understand factor variables is quite worthwhile. Let’s look at example using the variable ETHNIC and see some of the advantages in practice.

adsl$ETHNIC <- factor(adsl$ETHNIC, levels=c("HISPANIC OR LATINO", "NOT HISPANIC OR LATINO", "DUMMMY"))
tplyr_table(adsl, TRT01A) %>%
  add_layer(
    group_count(EOSSTT, by = ETHNIC)
  ) %>%
  build() %>%
  select(row_label1, row_label2, ord_layer_1) %>%
  kable()
row_label1 row_label2 ord_layer_1
HISPANIC OR LATINO COMPLETED 2
HISPANIC OR LATINO DISCONTINUED 2
NOT HISPANIC OR LATINO COMPLETED 3
NOT HISPANIC OR LATINO DISCONTINUED 3
DUMMMY COMPLETED 1
DUMMMY DISCONTINUED 1

Factor variables have ‘levels’. These levels are essentially what the VARN variables are trying to achieve - they specify the order of the different values within the associated variable. The variable we set above specifies that “HISPANIC OR LATINO” should sort first, then “NOT HISPANIC OR LATINO”, and finally “DUMMY”. Notice how they’re not alphabetical?

A highly advantageous aspect of using factor variables in ‘Tplyr’ is that factor variables can be used to insert dummy values into your table. Consider this line of code from above:

adsl$ETHNIC <- factor(adsl$ETHNIC, levels=c("HISPANIC OR LATINO", "NOT HISPANIC OR LATINO", "DUMMMY"))

This is converting the variable ETHNIC to a factor, then setting the factor levels. But it doesn’t change any of the values in the dataset - there are no values of “dummy” within ETHNIC in ADSL. Yet in the output built above, you see rows for “DUMMY”. By using factors, you can insert rows into your ‘Tplyr’ table that don’t exist in the data. This is particularly helpful if you’re working with data early on in a study, where certain values are expected, yet do not currently exist in the data. This will help you prepare tables that are complete even when your data are not.

Alphabetical

Lastly, If the target doesn’t have a VARN variable in the target dataset and isn’t a factor, ‘Tplyr’ will sort the variable alphabetically. The resulting order variable will be numeric, simply numbering each of the variable values alphabetically. Nothing fancy to it!

Sorting Descriptive Statistic Summaries

After the by variables, each layer will sort results slightly differently. We’ll start with the most simple case - descriptive statistic layers. As the user, you have full control over the order in which results present using set_format_strings(). Results will be ordered based on the order in which you create your f_str() objects.

tplyr_table(adsl, TRT01A) %>%
  add_layer(
    group_desc(HEIGHTBL) %>% 
      set_format_strings(
        'Group 1' = f_str('xx.x', mean),
        'Group 2' = f_str('xx.x', median),
        'Group 3' = f_str('xx.x', sd)
      )
  ) %>% 
  build() %>% 
  select(starts_with("row"), starts_with("ord")) %>% 
  kable()
row_label1 ord_layer_index ord_layer_1
Group 1 1 1
Group 2 1 2
Group 3 1 3

Each of the separate “Groups” added above were indexed based on their position in set_format_strings(). If you’d like to change the order, all you need to do is update your set_format_strings() call.

Sorting Count Layers

The order in which results appear on a frequency table can be deceptively complex and depends on the situation at hand. With this in mind, ‘Tplyr’ has 3 different methods of ordering the results of a count layer using the function set_order_count_method():

  1. “byfactor” - The default method is to sort by a factor. If the input variable is not a factor, alphabetical sorting will be used.
  2. “byvarn” - Similar to a ‘by’ variable, a count target can be sorted with a VARN variable existing in the target dataset.
  3. “bycount” - This is the most complex method. Many tables require counts to be sorted based on the counts within a particular group, like a treatment variable. ‘Tplyr’ can populate the ordering column based on numeric values within any results column. This requires some more granular control, for which we’ve created the functions set_ordering_cols() and set_numeric_result_order_var() to specify the column and numeric value on which the ordering column should be based.

“byfactor” and “byvarn”

“byfactor” is the default ordering method of results for count layers. Both “byfactor” and “byvarn” behave exactly like the order variables associated with by variables in a ‘Tplyr’ table. For “byvarn”, you must set the sort method using set_order_count_method().

adsl$AGEGR1 <- factor(adsl$AGEGR1, c("<65", "65-80", ">80"))
# Warnings suppressed to remove 'forcats' implicit NA warning
suppressWarnings({
  tplyr_table(adsl, TRT01A) %>%
    add_layer(
      group_count(AGEGR1) %>%
        # This is the default and not needed
        set_order_count_method("byfactor")
    ) %>% 
    build() %>%
    select(row_label1, ord_layer_1) %>%
    kable()
})
row_label1 ord_layer_1
<65 1
65-80 2
>80 3
tplyr_table(adsl, TRT01A) %>%
  add_layer(
    group_count(RACE) %>%
      set_order_count_method("byvarn")
  ) %>%
  build() %>%
  select(row_label1, ord_layer_1) %>%
  kable()
row_label1 ord_layer_1
AMERICAN INDIAN OR ALASKA NATIVE 6
BLACK OR AFRICAN AMERICAN 2
WHITE 1

“bycount”

Using count-based sorting is where things get more complicated. There are multiple items to consider:

  • What column do you want to sort by?
  • If there are multiple numbers in the column, like “n (%) [event]” type tables, which number should be used to create the sort variable?

We’ve created helper functions to aid in making this step more intuitive from a user perspective, and to maintain the flexibility that you need. The two functions that you need here are set_ordering_cols() and set_result_order_var().

tplyr_table(adae, TRTA) %>%
  add_layer(
    group_count(AEDECOD) %>% 
      # This will present 3 numbers in a cell
      set_format_strings(f_str("xx (xx.x%) [x]", distinct, distinct_pct, n)) %>% 
      # This makes the distinct numbers available
      set_distinct_by(USUBJID) %>%
      # Choosing "bycount" ordering for the result variable
      set_order_count_method("bycount") %>%
      # This will target the results column for Xanomeline High Dose, or `var1_Xanomeline High Dose`
      set_ordering_cols("Xanomeline High Dose") %>% 
      # The number we want to pull out is the distinct N counts
      set_result_order_var(distinct_n)
  ) %>% 
  build() %>% 
  arrange(desc(ord_layer_1)) %>% 
  select(row_label1, `var1_Xanomeline High Dose`, ord_layer_1) %>% 
  head() %>% 
  kable()
row_label1 var1_Xanomeline High Dose ord_layer_1
PRURITUS 26 (61.9%) [38] 26
ERYTHEMA 14 (33.3%) [22] 14
RASH 11 (26.2%) [18] 11
HYPERHIDROSIS 8 (19.0%) [10] 8
SKIN IRRITATION 5 (11.9%) [8] 5
RASH PRURITIC 2 ( 4.8%) [3] 2

In the above example, the results columns of the output table actually contain three different numbers: the distinct counts, the distinct percentage, and the non-distinct counts. We want to use distinct counts, so we choose distinct_n.

The next question that we need to answer when sorting by counts is which result column to take counts out of. Here, we have three results columns - one for each treatment group in the dataset. We want to use the results for the treatment group “Xanomeline High Dose”, so we provide the name of the treatment group.

But what if you have an additional column variable on top of the treatment groups?

tplyr_table(adae, TRTA, cols=SEX) %>%
  add_layer(
    group_count(AEDECOD) %>% 
      # This will present 3 numbers in a cell
      set_format_strings(f_str("xx (xx.x%) [x]", distinct, distinct_pct, n)) %>% 
      # This makes the distinct numbers available
      set_distinct_by(USUBJID) %>%
      # Choosing "bycount" ordering for the result variable
      set_order_count_method("bycount") %>%
      # This will target the results column for Xanomeline High Dose, or `var1_Xanomeline High Dose`
      set_ordering_cols("Xanomeline High Dose", "F") %>% 
      # The number we want to pull out is the distinct N counts
      set_result_order_var(distinct_n)
  ) %>% 
  build() %>% 
  arrange(desc(ord_layer_1)) %>% 
  select(row_label1, `var1_Xanomeline High Dose_F`, ord_layer_1) %>% 
  head() %>% 
  kable()
row_label1 var1_Xanomeline High Dose_F ord_layer_1
PRURITUS 11 (78.6%) [14] 11
ERYTHEMA 7 (50.0%) [8] 7
RASH 3 (21.4%) [5] 3
HYPERHIDROSIS 2 (14.3%) [2] 2
RASH PRURITIC 1 ( 7.1%) [1] 1
SKIN IRRITATION 1 ( 7.1%) [2] 1

Here we’re ordering on the female subjects in the “Xanomeline High Dose” cohort. In set_result_order_var(), you need to enter the values from each variable between treat_var and any variable entered in cols that you’d like to extract.

Nested Sorting

Nested count layers add one more piece to the puzzle. As a reminder, nested count layers are count summaries that are summarizing both a grouping variable, and a variable that’s being grouped. The best example is probably Adverse Event tables, where we want to see adverse events that occurred within different body systems.

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

In a layer that uses nesting, we need one more order variable - as we’re now concerned with the sorting of both the outside and inside variable. Counts are being summarized for both - so we need to know how both should be sorted. Additionally, we need to make sure that, in this case, the adverse events within a body system stay within the rows of that body system.

These result variables will always be the last two order variables output by ‘Tplyr’. In the above example, ord_layer_1 is for AEBODSYS and ord_layer_2 is for AEDECOD. Note that ord_layer_2 has Inf where row_label1 and row_label2 are both equal. This is the row that summarizes the AEBODSYS counts. By default, ‘Tplyr’ is set to assume that you will use descending sort on the order variable associated with the inside count variable (i.e. AEDECOD). This is because in nested count layer you will often want to sort by descending occurrence of the inside target variable. If you’d like to use ascending sorting instead, we offer the function set_outer_sort_position().

tplyr_table(adae, TRTA) %>% 
  add_layer(
    group_count(vars(AEBODSYS, AEDECOD)) %>% 
      set_outer_sort_position("asc")
  ) %>% 
  build() %>% 
  arrange(ord_layer_1, ord_layer_2) %>% 
  select(starts_with("row"), starts_with("ord_layer")) %>% 
  head() %>% 
  kable()
row_label1 row_label2 ord_layer_index ord_layer_1 ord_layer_2
SKIN AND SUBCUTANEOUS TISSUE DISORDERS SKIN AND SUBCUTANEOUS TISSUE DISORDERS 1 1 -Inf
SKIN AND SUBCUTANEOUS TISSUE DISORDERS ACTINIC KERATOSIS 1 1 1
SKIN AND SUBCUTANEOUS TISSUE DISORDERS ALOPECIA 1 1 2
SKIN AND SUBCUTANEOUS TISSUE DISORDERS BLISTER 1 1 3
SKIN AND SUBCUTANEOUS TISSUE DISORDERS COLD SWEAT 1 1 4
SKIN AND SUBCUTANEOUS TISSUE DISORDERS DERMATITIS ATOPIC 1 1 5

Notice that the Inf has now switched to -Inf to ensure that the AEBODSYS row stays at the top of the group.

Another consideration of nested sorting is whether or not you want to sort both result variables the same way. Do you want to sort both by counts? Or do you want to sort one alphabetically and the other by count? Or maybe one has a VARN variable associated with it? For this reason, set_order_count_method() can take in a 2-element character vector, where the first element specifies the outside variable and the second the inside variable.

tplyr_table(adsl, TRT01A) %>%
  add_layer(
    group_count(vars(EOSSTT, DCDECOD)) %>%
      set_order_count_method(c("byfactor", "bycount"))
  ) %>%
  build() %>%
  select(starts_with("row"), starts_with("ord")) %>%
  kable()
row_label1 row_label2 ord_layer_index ord_layer_1 ord_layer_2
COMPLETED COMPLETED 1 1 Inf
COMPLETED COMPLETED 1 1 58
DISCONTINUED DISCONTINUED 1 2 Inf
DISCONTINUED ADVERSE EVENT 1 2 8
DISCONTINUED DEATH 1 2 2
DISCONTINUED LACK OF EFFICACY 1 2 3
DISCONTINUED LOST TO FOLLOW-UP 1 2 1
DISCONTINUED PHYSICIAN DECISION 1 2 1
DISCONTINUED PROTOCOL VIOLATION 1 2 2
DISCONTINUED STUDY TERMINATED BY SPONSOR 1 2 2
DISCONTINUED WITHDRAWAL BY SUBJECT 1 2 9

In the example above, EOSTT is ordered alphabetically (recall that using “byfactor” when the variable is not a factor will do alphabetical sorting), and DSDECOD is ordered by count.

If only one method is provided, that method will automatically be applied to both variables. So in the example below, “bycount” is applied to both EOSTT and DSDECOD.

tplyr_table(adsl, TRT01A) %>%
  add_total_group() %>%
  add_layer(
    group_count(vars(EOSSTT, DCDECOD)) %>%
      set_order_count_method("bycount") %>%
      #set_order_count_method("bycount", "bycount") %>% This is functionally the same.
      set_ordering_cols(Total)
  ) %>%
  build() %>%
  select(starts_with("row"),  var1_Total, starts_with("ord")) %>%
  kable()
row_label1 row_label2 var1_Total ord_layer_index ord_layer_1 ord_layer_2
COMPLETED COMPLETED 110 ( 43.3%) 1 110 Inf
COMPLETED COMPLETED 110 ( 43.3%) 1 110 110
DISCONTINUED DISCONTINUED 144 ( 56.7%) 1 144 Inf
DISCONTINUED ADVERSE EVENT 92 ( 36.2%) 1 144 92
DISCONTINUED DEATH 3 ( 1.2%) 1 144 3
DISCONTINUED LACK OF EFFICACY 4 ( 1.6%) 1 144 4
DISCONTINUED LOST TO FOLLOW-UP 2 ( 0.8%) 1 144 2
DISCONTINUED PHYSICIAN DECISION 3 ( 1.2%) 1 144 3
DISCONTINUED PROTOCOL VIOLATION 6 ( 2.4%) 1 144 6
DISCONTINUED STUDY TERMINATED BY SPONSOR 7 ( 2.8%) 1 144 7
DISCONTINUED WITHDRAWAL BY SUBJECT 27 ( 10.6%) 1 144 27

Sorting Shift Tables

Shift tables keep things relatively simple when it comes to sorting and use the “byfactor” method seen above. We encourage this primarily because you likely want the benefits of factor variables on a shift layer. For example, consider this table:

tplyr_table(adlb, TRTA, where=PARAMCD == "CK") %>%
  add_layer(
    group_shift(vars(row = BNRIND, column = ANRIND), by = vars(PARAM, AVISIT))
  ) %>%
  build() %>%
  select(-starts_with('var1')) %>% 
  head(20) %>% 
  kable()
row_label1 row_label2 row_label3 ord_layer_index ord_layer_1 ord_layer_2 ord_layer_3
Creatine Kinase (U/L) Week 12 H 1 35 12 1
Creatine Kinase (U/L) Week 12 N 1 35 12 3
Creatine Kinase (U/L) Week 24 H 1 35 24 1
Creatine Kinase (U/L) Week 24 N 1 35 24 3
Creatine Kinase (U/L) Week 8 H 1 35 8 1
Creatine Kinase (U/L) Week 8 N 1 35 8 3

There are a few problems here:

  • “H” sorts before “N” alphabetically
  • We’re missing the rows for “L” on most visits, even though “L” in in the data for BNRIND.

Using factor variables cleans this right up for us:

adlb$BNRIND <- factor(adlb$BNRIND, levels=c("L", "N", "H"))
adlb$ANRIND <- factor(adlb$ANRIND, levels=c("L", "N", "H"))

tplyr_table(adlb, TRTA, where=PARAMCD == "CK") %>%
  add_layer(
    group_shift(vars(row = BNRIND, column = ANRIND), by = vars(PARAM, AVISIT))
  ) %>%
  build() %>%
  select(-starts_with('var1')) %>% 
  head(20) %>% 
  kable()
row_label1 row_label2 row_label3 ord_layer_index ord_layer_1 ord_layer_2 ord_layer_3
Creatine Kinase (U/L) Week 12 L 1 35 12 1
Creatine Kinase (U/L) Week 12 N 1 35 12 2
Creatine Kinase (U/L) Week 12 H 1 35 12 3
Creatine Kinase (U/L) Week 24 L 1 35 24 1
Creatine Kinase (U/L) Week 24 N 1 35 24 2
Creatine Kinase (U/L) Week 24 H 1 35 24 3
Creatine Kinase (U/L) Week 8 L 1 35 8 1
Creatine Kinase (U/L) Week 8 N 1 35 8 2
Creatine Kinase (U/L) Week 8 H 1 35 8 3

Now we have the nice “L”, “N”, “H” order that we’d like to see. Other sort methods on a shift table are fairly unlikely, as the matrix structure of the counts displayed by shift tables is relevant to the presentation and interpreting results.

Happy sorting!