Titles, Footers, and Referential Footnotes

Gabriel Becker and Adrian Waddell

2023-12-07

Page-by splitting

We often want to split tables based on the values of one or more variables (e.g., lab measurement) and then paginate separately within each of those table subsections. In rtables we do this via page by row splits.

Row splits can be declared page by splits by setting page_by = TRUE in the split_rows_by*() call, as below.

When page by splits are present, page titles are generated automatically by appending the split value (typically a factor level, though it need not be), to the page_prefix, separated by a :. By default, page_prefix is name of the variable being split.

lyt2 <- basic_table(
  title = "Study XXXXXXXX",
  subtitles = c("subtitle YYYYYYYYYY", "subtitle2 ZZZZZZZZZ"),
  main_footer = "Analysis was done using cool methods that are correct",
  prov_footer = "file: /path/to/stuff/that/lives/there HASH:1ac41b242a"
) %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX", page_by = TRUE, page_prefix = "Patient Subset - Gender", split_fun = drop_split_levels) %>%
  split_rows_by("STRATA1") %>%
  analyze("AGE", mean, format = "xx.x")

tbl2 <- build_table(lyt2, DM)
cat(export_as_txt(tbl2, paginate = TRUE, page_break = "\n\n~~~~ Page Break ~~~~\n\n"))
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: F
# 
# ——————————————————————————————————————————————————
#            A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————————
# A                                                 
#   mean       30.9         32.9           36.0     
# B                                                 
#   mean       34.9         32.9           34.4     
# C                                                 
#   mean       35.2         36.0           34.3     
# ——————————————————————————————————————————————————
# 
# Analysis was done using cool methods that are correct
# 
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a
# 
# 
# ~~~~ Page Break ~~~~
# 
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: M
# 
# ——————————————————————————————————————————————————
#            A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————————
# A                                                 
#   mean       35.1         31.1           35.6     
# B                                                 
#   mean       36.6         32.1           34.4     
# C                                                 
#   mean       37.4         32.8           32.8     
# ——————————————————————————————————————————————————
# 
# Analysis was done using cool methods that are correct
# 
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a

Page by row splits can be nested, but only within other page_by splits, they cannot be nested within traditional row splits. In this case, a page title for each page by split will be present on every resulting page, as seen below:

lyt3 <- basic_table(
  title = "Study XXXXXXXX",
  subtitles = c("subtitle YYYYYYYYYY", "subtitle2 ZZZZZZZZZ"),
  main_footer = "Analysis was done using cool methods that are correct",
  prov_footer = "file: /path/to/stuff/that/lives/there HASH:1ac41b242a"
) %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX", page_by = TRUE, page_prefix = "Patient Subset - Gender", split_fun = drop_split_levels) %>%
  split_rows_by("STRATA1", page_by = TRUE, page_prefix = "Stratification - Strata") %>%
  analyze("AGE", mean, format = "xx.x")

tbl3 <- build_table(lyt3, DM)
cat(export_as_txt(tbl3, paginate = TRUE, page_break = "\n\n~~~~ Page Break ~~~~\n\n"))
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: F
# Stratification - Strata: A
# 
# ——————————————————————————————————————————————————
#            A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————————
# mean         30.9         32.9           36.0     
# ——————————————————————————————————————————————————
# 
# Analysis was done using cool methods that are correct
# 
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a
# 
# 
# ~~~~ Page Break ~~~~
# 
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: F
# Stratification - Strata: B
# 
# ——————————————————————————————————————————————————
#            A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————————
# mean         34.9         32.9           34.4     
# ——————————————————————————————————————————————————
# 
# Analysis was done using cool methods that are correct
# 
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a
# 
# 
# ~~~~ Page Break ~~~~
# 
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: F
# Stratification - Strata: C
# 
# ——————————————————————————————————————————————————
#            A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————————
# mean         35.2         36.0           34.3     
# ——————————————————————————————————————————————————
# 
# Analysis was done using cool methods that are correct
# 
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a
# 
# 
# ~~~~ Page Break ~~~~
# 
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: M
# Stratification - Strata: A
# 
# ——————————————————————————————————————————————————
#            A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————————
# mean         35.1         31.1           35.6     
# ——————————————————————————————————————————————————
# 
# Analysis was done using cool methods that are correct
# 
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a
# 
# 
# ~~~~ Page Break ~~~~
# 
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: M
# Stratification - Strata: B
# 
# ——————————————————————————————————————————————————
#            A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————————
# mean         36.6         32.1           34.4     
# ——————————————————————————————————————————————————
# 
# Analysis was done using cool methods that are correct
# 
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a
# 
# 
# ~~~~ Page Break ~~~~
# 
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: M
# Stratification - Strata: C
# 
# ——————————————————————————————————————————————————
#            A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————————
# mean         37.4         32.8           32.8     
# ——————————————————————————————————————————————————
# 
# Analysis was done using cool methods that are correct
# 
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a

Referential Footnotes

Referential footnotes are footnotes associated with a particular component of a table: a column, a row, or a cell. They can be added during tabulation via analysis functions, but they can also be added post-hoc once a table is created.

They are rendered as a number within curly braces within the table body, row, or column labels, followed by a message associated with that number printed below the table during rendering.

Adding Cell- and Analysis-row Referential Footnotes At Tabulation Time

afun <- function(df, .var, .spl_context) {
  val <- .spl_context$value[NROW(.spl_context)]
  rw_fnotes <- if (val == "C") list("This is strata level C for these patients") else list()
  cl_fnotes <- if (val == "B" && df[1, "ARM", drop = TRUE] == "C: Combination") {
    list("these Strata B patients got the drug combination")
  } else {
    list()
  }

  in_rows(
    mean = mean(df[[.var]]),
    .row_footnotes = rw_fnotes,
    .cell_footnotes = cl_fnotes,
    .formats = c(mean = "xx.x")
  )
}

lyt <- basic_table(
  title = "Study XXXXXXXX",
  subtitles = c("subtitle YYYYYYYYYY", "subtitle2 ZZZZZZZZZ"),
  main_footer = "Analysis was done using cool methods that are correct",
  prov_footer = "file: /path/to/stuff/that/lives/there HASH:1ac41b242a"
) %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX", page_by = TRUE, page_prefix = "Patient Subset - Gender", split_fun = drop_split_levels) %>%
  split_rows_by("STRATA1") %>%
  analyze("AGE", afun, format = "xx.x")

tbl <- build_table(lyt, DM)
cat(export_as_txt(tbl, paginate = TRUE, page_break = "\n\n\n"))
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: F
# 
# ——————————————————————————————————————————————————————
#                A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————————————
# A                                                     
#   mean           30.9         32.9           36.0     
# B                                                     
#   mean           34.9         32.9         34.4 {1}   
# C                                                     
#   mean {2}       35.2         36.0           34.3     
# ——————————————————————————————————————————————————————
# 
# {1} - these Strata B patients got the drug combination
# {2} - This is strata level C for these patients
# ——————————————————————————————————————————————————————
# 
# Analysis was done using cool methods that are correct
# 
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a
# 
# 
# 
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: M
# 
# ——————————————————————————————————————————————————————
#                A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————————————
# A                                                     
#   mean           35.1         31.1           35.6     
# B                                                     
#   mean           36.6         32.1         34.4 {1}   
# C                                                     
#   mean {2}       37.4         32.8           32.8     
# ——————————————————————————————————————————————————————
# 
# {1} - these Strata B patients got the drug combination
# {2} - This is strata level C for these patients
# ——————————————————————————————————————————————————————
# 
# Analysis was done using cool methods that are correct
# 
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a

We note that typically the type of footnote added within the analysis function would be dependent on the computations done to calculate the cell value(s), e.g., a model not converging. We simply use context information as an illustrative proxy for that.

The procedure for adding footnotes to content (summary row) rows or cells is identical to the above, when done within a content function.

Annotating an Existing Table with Referential Footnotes

In addition to inserting referential footnotes at tabulation time within our analysis functions, we can also annotate our tables with them post-hoc.

This is also the only way to add footnotes to column labels, as those cannot be controlled within an analysis or content function.

## from ?tolower example slightly modified
.simpleCap <- function(x) {
  if (length(x) > 1) {
    return(sapply(x, .simpleCap))
  }
  s <- strsplit(tolower(x), " ")[[1]]
  paste(toupper(substring(s, 1, 1)), substring(s, 2), sep = "", collapse = " ")
}

adsl2 <- ex_adsl %>%
  filter(SEX %in% c("M", "F") & RACE %in% (levels(RACE)[1:3])) %>%
  ## we trim the level names here solely due to space considerations
  mutate(ethnicity = .simpleCap(gsub("(.*)OR.*", "\\1", RACE)), RACE = factor(RACE))

lyt2 <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_cols_by("SEX", split_fun = drop_split_levels) %>%
  split_rows_by("RACE", labels_var = "ethnicity", split_fun = drop_split_levels) %>%
  summarize_row_groups() %>%
  analyze(c("AGE", "STRATA1"))

tbl2 <- build_table(lyt2, adsl2)
tbl2
#                    A: Drug X                B: Placebo              C: Combination     
#                 F            M            F            M            F            M     
# ———————————————————————————————————————————————————————————————————————————————————————
# Asian       41 (53.9%)   25 (54.3%)   36 (52.2%)   30 (60.0%)   39 (60.9%)   32 (57.1%)
#   AGE                                                                                  
#     Mean      31.22        34.60        35.06        38.63        36.44        37.66   
#   STRATA1                                                                              
#     A           11           10           14           10           11           7     
#     B           11           9            15           7            11           14    
#     C           19           6            7            13           17           11    
# Black       18 (23.7%)   12 (26.1%)   16 (23.2%)   12 (24.0%)   14 (21.9%)   14 (25.0%)
#   AGE                                                                                  
#     Mean      34.06        34.58        33.88        36.33        33.21        34.21   
#   STRATA1                                                                              
#     A           5            2            5            6            3            7     
#     B           6            5            3            4            4            4     
#     C           7            5            8            2            7            3     
# White       17 (22.4%)   9 (19.6%)    17 (24.6%)   8 (16.0%)    11 (17.2%)   10 (17.9%)
#   AGE                                                                                  
#     Mean      34.12        40.00        32.41        34.62        33.00        30.80   
#   STRATA1                                                                              
#     A           5            3            3            3            3            5     
#     B           5            4            8            4            5            2     
#     C           7            2            6            1            3            3

We do this with the fnotes_at_path<- function which accepts a row path, a column path, and a value for the full set of footnotes for the defined locations (NULL or a character vector).

A non-NULL row path with a NULL column path specifies the footnote(s) should be attached to the row, while NULL row path with non-NULL column path indicates they go with the column. Both being non-NULL indicates a cell (and must resolve to an individual cell).

fnotes_at_path(tbl2, c("RACE", "ASIAN")) <- c("hi", "there")
tbl2
#                       A: Drug X                B: Placebo              C: Combination     
#                    F            M            F            M            F            M     
# ——————————————————————————————————————————————————————————————————————————————————————————
# Asian {1, 2}   41 (53.9%)   25 (54.3%)   36 (52.2%)   30 (60.0%)   39 (60.9%)   32 (57.1%)
#   AGE                                                                                     
#     Mean         31.22        34.60        35.06        38.63        36.44        37.66   
#   STRATA1                                                                                 
#     A              11           10           14           10           11           7     
#     B              11           9            15           7            11           14    
#     C              19           6            7            13           17           11    
# Black          18 (23.7%)   12 (26.1%)   16 (23.2%)   12 (24.0%)   14 (21.9%)   14 (25.0%)
#   AGE                                                                                     
#     Mean         34.06        34.58        33.88        36.33        33.21        34.21   
#   STRATA1                                                                                 
#     A              5            2            5            6            3            7     
#     B              6            5            3            4            4            4     
#     C              7            5            8            2            7            3     
# White          17 (22.4%)   9 (19.6%)    17 (24.6%)   8 (16.0%)    11 (17.2%)   10 (17.9%)
#   AGE                                                                                     
#     Mean         34.12        40.00        32.41        34.62        33.00        30.80   
#   STRATA1                                                                                 
#     A              5            3            3            3            3            5     
#     B              5            4            8            4            5            2     
#     C              7            2            6            1            3            3     
# ——————————————————————————————————————————————————————————————————————————————————————————
# 
# {1} - hi
# {2} - there
# ——————————————————————————————————————————————————————————————————————————————————————————
fnotes_at_path(tbl2, rowpath = NULL, c("ARM", "B: Placebo")) <- c("this is a placebo")
tbl2
#                       A: Drug X              B: Placebo {NA}           C: Combination     
#                    F            M            F            M            F            M     
# ——————————————————————————————————————————————————————————————————————————————————————————
# Asian {1, 2}   41 (53.9%)   25 (54.3%)   36 (52.2%)   30 (60.0%)   39 (60.9%)   32 (57.1%)
#   AGE                                                                                     
#     Mean         31.22        34.60        35.06        38.63        36.44        37.66   
#   STRATA1                                                                                 
#     A              11           10           14           10           11           7     
#     B              11           9            15           7            11           14    
#     C              19           6            7            13           17           11    
# Black          18 (23.7%)   12 (26.1%)   16 (23.2%)   12 (24.0%)   14 (21.9%)   14 (25.0%)
#   AGE                                                                                     
#     Mean         34.06        34.58        33.88        36.33        33.21        34.21   
#   STRATA1                                                                                 
#     A              5            2            5            6            3            7     
#     B              6            5            3            4            4            4     
#     C              7            5            8            2            7            3     
# White          17 (22.4%)   9 (19.6%)    17 (24.6%)   8 (16.0%)    11 (17.2%)   10 (17.9%)
#   AGE                                                                                     
#     Mean         34.12        40.00        32.41        34.62        33.00        30.80   
#   STRATA1                                                                                 
#     A              5            3            3            3            3            5     
#     B              5            4            8            4            5            2     
#     C              7            2            6            1            3            3     
# ——————————————————————————————————————————————————————————————————————————————————————————
# 
# {1} - hi
# {2} - there
# {NA} - this is a placebo
# ——————————————————————————————————————————————————————————————————————————————————————————

Note to step into a content row we must add that to the path, even though we didn’t need it to put a footnote on the full row.

Currently, content rows by default are named with the label rather than name of the corresponding facet. This is reflected in the output of, e.g., row_paths_summary.

row_paths_summary(tbl2)
# rowname      node_class    path                                            
# ———————————————————————————————————————————————————————————————————————————
# Asian        ContentRow    RACE, ASIAN, @content, Asian                    
#   AGE        LabelRow      RACE, ASIAN, AGE                                
#     Mean     DataRow       RACE, ASIAN, AGE, Mean                          
#   STRATA1    LabelRow      RACE, ASIAN, STRATA1                            
#     A        DataRow       RACE, ASIAN, STRATA1, A                         
#     B        DataRow       RACE, ASIAN, STRATA1, B                         
#     C        DataRow       RACE, ASIAN, STRATA1, C                         
# Black        ContentRow    RACE, BLACK OR AFRICAN AMERICAN, @content, Black
#   AGE        LabelRow      RACE, BLACK OR AFRICAN AMERICAN, AGE            
#     Mean     DataRow       RACE, BLACK OR AFRICAN AMERICAN, AGE, Mean      
#   STRATA1    LabelRow      RACE, BLACK OR AFRICAN AMERICAN, STRATA1        
#     A        DataRow       RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A     
#     B        DataRow       RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B     
#     C        DataRow       RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C     
# White        ContentRow    RACE, WHITE, @content, White                    
#   AGE        LabelRow      RACE, WHITE, AGE                                
#     Mean     DataRow       RACE, WHITE, AGE, Mean                          
#   STRATA1    LabelRow      RACE, WHITE, STRATA1                            
#     A        DataRow       RACE, WHITE, STRATA1, A                         
#     B        DataRow       RACE, WHITE, STRATA1, B                         
#     C        DataRow       RACE, WHITE, STRATA1, C

So we can add our footnotes to the cell like so:

fnotes_at_path(
  tbl2,
  rowpath = c("RACE", "ASIAN", "@content", "Asian"),
  colpath = c("ARM", "B: Placebo", "SEX", "F")
) <- "These asian women got placebo treatments"
tbl2
#                       A: Drug X                B: Placebo {NA}             C: Combination     
#                    F            M              F              M            F            M     
# ——————————————————————————————————————————————————————————————————————————————————————————————
# Asian {1, 2}   41 (53.9%)   25 (54.3%)   36 (52.2%) {3}   30 (60.0%)   39 (60.9%)   32 (57.1%)
#   AGE                                                                                         
#     Mean         31.22        34.60          35.06          38.63        36.44        37.66   
#   STRATA1                                                                                     
#     A              11           10             14             10           11           7     
#     B              11           9              15             7            11           14    
#     C              19           6              7              13           17           11    
# Black          18 (23.7%)   12 (26.1%)     16 (23.2%)     12 (24.0%)   14 (21.9%)   14 (25.0%)
#   AGE                                                                                         
#     Mean         34.06        34.58          33.88          36.33        33.21        34.21   
#   STRATA1                                                                                     
#     A              5            2              5              6            3            7     
#     B              6            5              3              4            4            4     
#     C              7            5              8              2            7            3     
# White          17 (22.4%)   9 (19.6%)      17 (24.6%)     8 (16.0%)    11 (17.2%)   10 (17.9%)
#   AGE                                                                                         
#     Mean         34.12        40.00          32.41          34.62        33.00        30.80   
#   STRATA1                                                                                     
#     A              5            3              3              3            3            5     
#     B              5            4              8              4            5            2     
#     C              7            2              6              1            3            3     
# ——————————————————————————————————————————————————————————————————————————————————————————————
# 
# {1} - hi
# {2} - there
# {3} - These asian women got placebo treatments
# {NA} - this is a placebo
# ——————————————————————————————————————————————————————————————————————————————————————————————