Creating a Shift Table in R

Here is a workflow to create a Shift Table in R, using the {tidyverse} suite for data processing, and {gt} to build the desired table layout.

Required Packages

Code

suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(tidyr))
suppressPackageStartupMessages(library(rlang))
suppressPackageStartupMessages(library(purrr))
suppressPackageStartupMessages(library(stringr))
suppressPackageStartupMessages(library(gt))
suppressPackageStartupMessages(library(here))

# list all files
files <- list.files(here("R"), pattern = ".R", full.names = TRUE)
# Read all files
walk(files, source)

Data used for Analysis

We will make use of the adsl and adlb test ADaM datasets from the {pharmaverseadam} R package for analysis.

ADSL
ADLB

ADSL is the subject level analysis dataset

Code

adsl <- pharmaverseadam::adsl

ADLB is the analysis dataset for Laboratory Records

Code

adlb <- pharmaverseadam::adlb

Variables used for Analysis

USUBJID - Unique Subject Identifier
SAFFL - Safety Population Flag
TRT01A - Actual Treatment Arm for Period 01
PARAM - Parameter
PARAMCD - Parameter Code
AVISIT - Analysis Visit
AVISITN - Analysis Visit (Numeric)
AVAL - Analysis Value
ANL01FL - Analysis Flag 01
BNRIND - Baseline Reference Range Indicator
ANRIND - Analysis Reference Range Indicator

Programming Flow

1. Calculating BIG N

Keep only safety subjects (SAFFL == 'Y') in adsl
Count number of subjects in the full safety analysis set within each treatment arm (TRT01A)

Code

adsl_bign <- adsl |>
  na_to_missing() |>
  filter(.data$SAFFL == "Y") |>
  select(all_of(c("USUBJID", "TRT01A"))) |>
  add_count(.data$TRT01A, name = "TRT_N")

2. Preprocessing Lab Records

Merge adsl_bign to adlb to add TRT_N
Filter out missing values in Baseline Reference Range Indicator (BNRIND), Analysis Reference Range Indicator (ANRIND) and Analysis Value (AVAL)
Subset the resulting data for subjects with post-does records where analysis flag (ANL01FL) is equal to 'Y'
Subset data to keep records within the time period (eg. Week 2, Week 4, Week 6) we want to see the shifts in Laboratory Tests
Add BIG N to treatment labels by concatenating TRT_N with TRT01A

Code

adlb_prep <- adlb |>
  na_to_missing() |>
  mutate(across(all_of(c("BNRIND", "ANRIND")), str_to_title)) |>
  left_join(adsl_bign, by = c("USUBJID", "TRT01A")) |>
  filter(
    .data$BNRIND != "<Missing>",
    .data$ANRIND != "<Missing>",
    !is.na(.data$AVAL),
    .data$ANL01FL == "Y",
    .data$AVISIT %in% c("Week 2", "Week 4", "Week 6")
  ) |>
  mutate(TRT_VAR = paste0(.data$TRT01A, "<br>(N=", .data$TRT_N, ")")) |>
  select(-TRT_N)

Subset adlb_prep to keep only Hemoglobin records

Code

adlb_hgb <- adlb_prep |>
  filter(.data$PARAMCD == "HGB")

3. Get all combinations of Range Indicator values

Create a dummy dataset that contains all possible combination of BNRIND and ANRIND values by Treatment and Visit.

Code

comb_base_pbase <- expand_grid(
  TRT_VAR = unique(adlb_hgb[["TRT_VAR"]]),
  AVISIT = unique(adlb_hgb[["AVISIT"]]),
  BNRIND = c("Low", "Normal", "High", "Total")
) |>
  cross_join(tibble(ANRIND = c("Low", "Normal", "High")))

4. Performing Counts by Analysis Visit

Code

shift_counts <- adlb_hgb |>
  bind_rows(mutate(adlb_hgb, BNRIND = "Total")) |>
  group_by(!!!syms(c("TRT_VAR", "AVISITN", "ANRIND", "BNRIND"))) |>
  count(.data[["AVISIT"]], name = "CNT") |>
  ungroup() |>
  # merge dummy dataset to get all combinations of `ANRIND` and `BNRIND` values
  full_join(comb_base_pbase, by = c("TRT_VAR", "AVISIT", "BNRIND", "ANRIND")) |>
  mutate(across("CNT", ~ replace_na(.x, 0))) |>
  arrange(
    .data[["TRT_VAR"]],
    factor(.data[["BNRIND"]], levels = c("Low", "Normal", "High", "Total"))
  )

5. Reshaping Data

Reshaping data to wide format to get the final Shift Table layout
Adding Post-Baseline Grade Totals

Code

shift_wide <- shift_counts |>
  pivot_wider(
    id_cols = all_of(c("AVISIT", "ANRIND")),
    names_from = all_of(c("TRT_VAR", "BNRIND")),
    values_from = "CNT",
    names_sep = "^"
  )

post_base_grade_totals <- shift_wide |>
  summarize(across(where(is.numeric), sum), .by = all_of("AVISIT")) |>
  mutate(ANRIND = "Total")

visit_levels <-
  arrange(filter(shift_counts, !is.na(.data$AVISITN)), by = .data$AVISITN) |>
  pull(.data$AVISIT) |>
  unique()

shift_final <- shift_wide |>
  bind_rows(post_base_grade_totals) |>
  arrange(
    factor(.data$AVISIT, levels = visit_levels),
    factor(.data$ANRIND, levels = c("Low", "Normal", "High", "Total"))
  )

An alternate and tidier approach would be to create a function say count_shifts_by_visit() to cover Steps 3-5

Code

shift_final <-
  count_shifts_by_visit(
    bds_dataset = adlb_hgb,
    trt_var = exprs(TRT_VAR),
    analysis_grade_var = exprs(ANRIND),
    base_grade_var = exprs(BNRIND),
    grade_var_order = exprs(Low, Normal, High),
    visit_var = exprs(AVISIT, AVISITN)
  )

6. Adding Percentages

Code

trt_bign <-
  map(
    set_names(unique(adsl_bign[["TRT01A"]])),
    \(trt_val) get_trt_total(adsl_bign, exprs(TRT01A, TRT_N), trt_val)
  )

shift_final <- shift_final |>
  add_pct2cols(
    exclude_cols = exprs(AVISIT, ANRIND),
    trt_bign = trt_bign
  )

7. Displaying the Final Table with `{gt}`

Single Parameter (Hemoglobin)
Multiple Parameters

Code

out <-
  shift_final |>
  gt(groupname_col = "AVISIT", row_group_as_column = TRUE) |>
  cols_label_with(
    columns = contains("ANRIND"), \(x) md("Reference<br>Range")
  ) |>
  tab_spanner_delim(delim = "^") |>
  text_transform(
    fn = \(x) map(x, \(y) md(paste0(y, "<br>Baseline<br>n (%)"))),
    locations = cells_column_spanners()
  ) |>
  # headers and footers
  tab_stubhead(md("Analysis Visit")) |>
  tab_footnote(footnote = md("N: Number of subjects in the full safety analysis set, within each treatment group<br>n: Subjects with at least one baseline and post-baseline records")) |>
  tab_header(
    preheader = c("Protocol: CDISCPILOT01", "Cutoff date: DDMMYYYY"), # for rtf
    title = md(
      "Table x.x<br>Shift Table of Lab Hematology<br>(Full Safety Analysis Set)"
    ),
    subtitle = paste0("Parameter = ", unique(pull(adlb_hgb, "PARAM")))
  ) |>
  tab_source_note(
    "Source: ADLB DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY"
  ) |>
  # cell styling
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_body(columns = 2)
  ) |>
  tab_style(
    style = cell_text(align = "center"),
    locations = cells_body(columns = -c(1, 2))
  ) |>
  tab_style(
    style = cell_text(align = "center"),
    locations = cells_column_labels(columns = -c(1, 2))
  ) |>
  # other options
  tab_options(
    # rtf options
    page.orientation = "landscape",
    page.numbering = TRUE,
    page.header.use_tbl_headings = TRUE,
    page.footer.use_tbl_notes = TRUE,
    # page.height = "18in", uncomment to modify page dimensions while saving as rtf
    # other styling
    table.background.color = "white",
    table.font.names = "monospace-slab-serif",
    row_group.font.weight = "bold",
    column_labels.font.weight = "bold",
    heading.title.font.weight = "bold",
    heading.title.font.size = "20px",
    heading.padding = "10px",
    heading.subtitle.font.size = "14px"
  ) |>
  opt_css(
    css = "
    .gt_heading {
      border-top-style: hidden !important;
    }
    .gt_sourcenote {
      border-bottom-style: hidden !important;
    }
    .gt_table {
      width: max-content !important;
    }
    .gt_subtitle, .gt_footnotes, .gt_sourcenote {
      text-align: left !important;
      font-weight: bold !important;
      color: gray !important;
    }
    "
  )

Analysis Visit	Reference Range	Placebo (N=86) Baseline n (%)				Xanomeline High Dose (N=72) Baseline n (%)				Xanomeline Low Dose (N=96) Baseline n (%)
Table x.x Shift Table of Lab Hematology (Full Safety Analysis Set)
Parameter = Hemoglobin (mmol/L)
Analysis Visit	Reference Range	Low	Normal	High	Total	Low	Normal	High	Total	Low	Normal	High	Total
Week 2	Low	4 (4.7%)	2 (2.3%)	0	6 (7%)	1 (1.4%)	3 (4.2%)	0	4 (5.6%)	4 (4.2%)	0	0	4 (4.2%)
	Normal	3 (3.5%)	73 (84.9%)	0	76 (88.4%)	0	65 (90.3%)	2 (2.8%)	67 (93.1%)	1 (1%)	67 (69.8%)	1 (1%)	69 (71.9%)
	High	0	0	0	0	0	0	0	0	0	0	0	0
	Total	7 (8.1%)	75 (87.2%)	0	82 (95.3%)	1 (1.4%)	68 (94.4%)	2 (2.8%)	71 (98.6%)	5 (5.2%)	67 (69.8%)	1 (1%)	73 (76%)
Week 4	Low	4 (4.7%)	2 (2.3%)	0	6 (7%)	1 (1.4%)	0	0	1 (1.4%)	4 (4.2%)	2 (2.1%)	0	6 (6.2%)
	Normal	1 (1.2%)	68 (79.1%)	0	69 (80.2%)	0	63 (87.5%)	1 (1.4%)	64 (88.9%)	1 (1%)	57 (59.4%)	0	58 (60.4%)
	High	0	0	0	0	0	1 (1.4%)	0	1 (1.4%)	0	0	0	0
	Total	5 (5.8%)	70 (81.4%)	0	75 (87.2%)	1 (1.4%)	64 (88.9%)	1 (1.4%)	66 (91.7%)	5 (5.2%)	59 (61.5%)	0	64 (66.7%)
Week 6	Low	3 (3.5%)	1 (1.2%)	0	4 (4.7%)	0	1 (1.4%)	0	1 (1.4%)	4 (4.2%)	3 (3.1%)	0	7 (7.3%)
	Normal	2 (2.3%)	64 (74.4%)	0	66 (76.7%)	0	51 (70.8%)	1 (1.4%)	52 (72.2%)	1 (1%)	50 (52.1%)	0	51 (53.1%)
	High	0	1 (1.2%)	0	1 (1.2%)	0	1 (1.4%)	0	1 (1.4%)	0	0	0	0
	Total	5 (5.8%)	66 (76.7%)	0	71 (82.6%)	0	53 (73.6%)	1 (1.4%)	54 (75%)	5 (5.2%)	53 (55.2%)	0	58 (60.4%)
Source: ADLB DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY
N: Number of subjects in the full safety analysis set, within each treatment group n: Subjects with at least one baseline and post-baseline records

Split adlb_prep by multiple parameters.
Map over count_shifts_by_visit() on the data split by parameters
Add percentages to numeric columns within each resulting data.frame from count_shifts_by_visit()
Create a function std_shift_display() to combine the {gt} table display steps and map it over on the list output retrieved from the previous step

Code

adlb_multi <- adlb_prep |>
  filter(toupper(.data$PARAMCD) %in% c("PLAT", "HCT", "MCH")) |>
  group_nest(.data$PARAM)

shift_out <- map(adlb_multi$data, \(x) {
  count_shifts_by_visit(
    bds_dataset = x,
    trt_var = exprs(TRT_VAR),
    analysis_grade_var = exprs(ANRIND),
    base_grade_var = exprs(BNRIND),
    grade_var_order = exprs(Low, Normal, High),
    visit_var = exprs(AVISIT, AVISITN)
  )
}) |>
  set_names(adlb_multi$PARAM)

# add percentages
shift_out <- map(shift_out, \(df) {
  df |>
    add_pct2cols(
      exclude_cols = exprs(AVISIT, ANRIND),
      trt_bign = trt_bign
    )
})

list_out <-
  map(names(shift_out), \(x) {
    shift_out[[x]] |>
      std_shift_display(
        param = x,
        group_col = "AVISIT",
        stub_header = "Analysis Visit",
        rtf_preheader = "Protocol: CDISCPILOT01",
        title = "Table x.x<br>Shift Table of Lab
        Hematology<br>(Full Safety Analysis Set)",
        footnote = "N: Number of subjects in the full safety analysis set, within each treatment group<br>n: Subjects with at least one baseline and post-baseline records",
        sourcenote =
          "Source: ADLB DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY"
      )
  })

gt_group(.list = list_out)

Analysis Visit	Reference Range	Placebo (N=86) Baseline n (%)				Xanomeline High Dose (N=72) Baseline n (%)				Xanomeline Low Dose (N=96) Baseline n (%)
Table x.x Shift Table of Lab Hematology (Full Safety Analysis Set)
Parameter = Ery. Mean Corpuscular Hemoglobin (fmol(Fe))
Analysis Visit	Reference Range	Low	Normal	High	Total	Low	Normal	High	Total	Low	Normal	High	Total
Week 2	Low	0	0	0	0	1 (1.4%)	0	0	1 (1.4%)	0	0	0	0
	Normal	0	78 (90.7%)	2 (2.3%)	80 (93%)	0	63 (87.5%)	2 (2.8%)	65 (90.3%)	0	68 (70.8%)	2 (2.1%)	70 (72.9%)
	High	0	0	2 (2.3%)	2 (2.3%)	0	0	5 (6.9%)	5 (6.9%)	0	2 (2.1%)	1 (1%)	3 (3.1%)
	Total	0	78 (90.7%)	4 (4.7%)	82 (95.3%)	1 (1.4%)	63 (87.5%)	7 (9.7%)	71 (98.6%)	0	70 (72.9%)	3 (3.1%)	73 (76%)
Week 4	Low	0	0	0	0	1 (1.4%)	0	0	1 (1.4%)	0	0	0	0
	Normal	0	70 (81.4%)	1 (1.2%)	71 (82.6%)	0	58 (80.6%)	1 (1.4%)	59 (81.9%)	0	60 (62.5%)	2 (2.1%)	62 (64.6%)
	High	0	2 (2.3%)	2 (2.3%)	4 (4.7%)	0	0	6 (8.3%)	6 (8.3%)	0	1 (1%)	1 (1%)	2 (2.1%)
	Total	0	72 (83.7%)	3 (3.5%)	75 (87.2%)	1 (1.4%)	58 (80.6%)	7 (9.7%)	66 (91.7%)	0	61 (63.5%)	3 (3.1%)	64 (66.7%)
Week 6	Low	0	0	0	0	0	0	0	0	0	0	0	0
	Normal	0	65 (75.6%)	2 (2.3%)	67 (77.9%)	0	47 (65.3%)	1 (1.4%)	48 (66.7%)	0	55 (57.3%)	2 (2.1%)	57 (59.4%)
	High	0	2 (2.3%)	2 (2.3%)	4 (4.7%)	0	0	6 (8.3%)	6 (8.3%)	0	0	1 (1%)	1 (1%)
	Total	0	67 (77.9%)	4 (4.7%)	71 (82.6%)	0	47 (65.3%)	7 (9.7%)	54 (75%)	0	55 (57.3%)	3 (3.1%)	58 (60.4%)
Source: ADLB DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY
N: Number of subjects in the full safety analysis set, within each treatment group n: Subjects with at least one baseline and post-baseline records

Analysis Visit	Reference Range	Placebo (N=86) Baseline n (%)				Xanomeline High Dose (N=72) Baseline n (%)				Xanomeline Low Dose (N=96) Baseline n (%)
Table x.x Shift Table of Lab Hematology (Full Safety Analysis Set)
Parameter = Hematocrit (1)
Analysis Visit	Reference Range	Low	Normal	High	Total	Low	Normal	High	Total	Low	Normal	High	Total
Week 2	Low	0	0	0	0	1 (1.4%)	0	0	1 (1.4%)	1 (1%)	2 (2.1%)	0	3 (3.1%)
	Normal	3 (3.5%)	79 (91.9%)	0	82 (95.3%)	0	66 (91.7%)	2 (2.8%)	68 (94.4%)	0	67 (69.8%)	1 (1%)	68 (70.8%)
	High	0	0	0	0	0	1 (1.4%)	0	1 (1.4%)	0	0	1 (1%)	1 (1%)
	Total	3 (3.5%)	79 (91.9%)	0	82 (95.3%)	1 (1.4%)	67 (93.1%)	2 (2.8%)	70 (97.2%)	1 (1%)	69 (71.9%)	2 (2.1%)	72 (75%)
Week 4	Low	3 (3.5%)	0	0	3 (3.5%)	0	0	0	0	1 (1%)	2 (2.1%)	0	3 (3.1%)
	Normal	0	71 (82.6%)	0	71 (82.6%)	1 (1.4%)	61 (84.7%)	2 (2.8%)	64 (88.9%)	1 (1%)	58 (60.4%)	1 (1%)	60 (62.5%)
	High	0	0	0	0	0	1 (1.4%)	0	1 (1.4%)	0	0	0	0
	Total	3 (3.5%)	71 (82.6%)	0	74 (86%)	1 (1.4%)	62 (86.1%)	2 (2.8%)	65 (90.3%)	2 (2.1%)	60 (62.5%)	1 (1%)	63 (65.6%)
Week 6	Low	2 (2.3%)	1 (1.2%)	0	3 (3.5%)	1 (1.4%)	0	0	1 (1.4%)	1 (1%)	1 (1%)	0	2 (2.1%)
	Normal	0	66 (76.7%)	0	66 (76.7%)	0	50 (69.4%)	2 (2.8%)	52 (72.2%)	1 (1%)	52 (54.2%)	1 (1%)	54 (56.2%)
	High	0	1 (1.2%)	0	1 (1.2%)	0	1 (1.4%)	0	1 (1.4%)	0	0	0	0
	Total	2 (2.3%)	68 (79.1%)	0	70 (81.4%)	1 (1.4%)	51 (70.8%)	2 (2.8%)	54 (75%)	2 (2.1%)	53 (55.2%)	1 (1%)	56 (58.3%)
Source: ADLB DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY
N: Number of subjects in the full safety analysis set, within each treatment group n: Subjects with at least one baseline and post-baseline records

Analysis Visit	Reference Range	Placebo (N=86) Baseline n (%)				Xanomeline High Dose (N=72) Baseline n (%)				Xanomeline Low Dose (N=96) Baseline n (%)
Table x.x Shift Table of Lab Hematology (Full Safety Analysis Set)
Parameter = Platelet (10^9/L)
Analysis Visit	Reference Range	Low	Normal	High	Total	Low	Normal	High	Total	Low	Normal	High	Total
Week 2	Low	0	0	0	0	1 (1.4%)	0	0	1 (1.4%)	0	0	0	0
	Normal	0	80 (93%)	1 (1.2%)	81 (94.2%)	0	70 (97.2%)	0	70 (97.2%)	1 (1%)	66 (68.8%)	1 (1%)	68 (70.8%)
	High	0	0	1 (1.2%)	1 (1.2%)	0	0	0	0	0	2 (2.1%)	0	2 (2.1%)
	Total	0	80 (93%)	2 (2.3%)	82 (95.3%)	1 (1.4%)	70 (97.2%)	0	71 (98.6%)	1 (1%)	68 (70.8%)	1 (1%)	70 (72.9%)
Week 4	Low	0	0	0	0	0	0	0	0	1 (1%)	0	0	1 (1%)
	Normal	0	72 (83.7%)	0	72 (83.7%)	0	65 (90.3%)	0	65 (90.3%)	0	61 (63.5%)	1 (1%)	62 (64.6%)
	High	0	0	2 (2.3%)	2 (2.3%)	0	0	0	0	0	0	0	0
	Total	0	72 (83.7%)	2 (2.3%)	74 (86%)	0	65 (90.3%)	0	65 (90.3%)	1 (1%)	61 (63.5%)	1 (1%)	63 (65.6%)
Week 6	Low	0	0	0	0	0	0	0	0	1 (1%)	0	0	1 (1%)
	Normal	0	69 (80.2%)	1 (1.2%)	70 (81.4%)	0	53 (73.6%)	0	53 (73.6%)	0	56 (58.3%)	1 (1%)	57 (59.4%)
	High	0	0	1 (1.2%)	1 (1.2%)	0	0	0	0	0	0	0	0
	Total	0	69 (80.2%)	2 (2.3%)	71 (82.6%)	0	53 (73.6%)	0	53 (73.6%)	1 (1%)	56 (58.3%)	1 (1%)	58 (60.4%)
Source: ADLB DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY
N: Number of subjects in the full safety analysis set, within each treatment group n: Subjects with at least one baseline and post-baseline records

Colorize cells (Optional)

Suppose we want to highlight values which are Normal in Baseline but Low or High in post-baseline

Code

out |>
  data_color(
    columns = contains("Normal"),
    rows = ANRIND %in% c("High", "Low"),
    palette = c("white", "lightpink")
  )

Analysis Visit	Reference Range	Placebo (N=86) Baseline n (%)				Xanomeline High Dose (N=72) Baseline n (%)				Xanomeline Low Dose (N=96) Baseline n (%)
Table x.x Shift Table of Lab Hematology (Full Safety Analysis Set)
Parameter = Hemoglobin (mmol/L)
Analysis Visit	Reference Range	Low	Normal	High	Total	Low	Normal	High	Total	Low	Normal	High	Total
Week 2	Low	4 (4.7%)	2 (2.3%)	0	6 (7%)	1 (1.4%)	3 (4.2%)	0	4 (5.6%)	4 (4.2%)	0	0	4 (4.2%)
	Normal	3 (3.5%)	73 (84.9%)	0	76 (88.4%)	0	65 (90.3%)	2 (2.8%)	67 (93.1%)	1 (1%)	67 (69.8%)	1 (1%)	69 (71.9%)
	High	0	0	0	0	0	0	0	0	0	0	0	0
	Total	7 (8.1%)	75 (87.2%)	0	82 (95.3%)	1 (1.4%)	68 (94.4%)	2 (2.8%)	71 (98.6%)	5 (5.2%)	67 (69.8%)	1 (1%)	73 (76%)
Week 4	Low	4 (4.7%)	2 (2.3%)	0	6 (7%)	1 (1.4%)	0	0	1 (1.4%)	4 (4.2%)	2 (2.1%)	0	6 (6.2%)
	Normal	1 (1.2%)	68 (79.1%)	0	69 (80.2%)	0	63 (87.5%)	1 (1.4%)	64 (88.9%)	1 (1%)	57 (59.4%)	0	58 (60.4%)
	High	0	0	0	0	0	1 (1.4%)	0	1 (1.4%)	0	0	0	0
	Total	5 (5.8%)	70 (81.4%)	0	75 (87.2%)	1 (1.4%)	64 (88.9%)	1 (1.4%)	66 (91.7%)	5 (5.2%)	59 (61.5%)	0	64 (66.7%)
Week 6	Low	3 (3.5%)	1 (1.2%)	0	4 (4.7%)	0	1 (1.4%)	0	1 (1.4%)	4 (4.2%)	3 (3.1%)	0	7 (7.3%)
	Normal	2 (2.3%)	64 (74.4%)	0	66 (76.7%)	0	51 (70.8%)	1 (1.4%)	52 (72.2%)	1 (1%)	50 (52.1%)	0	51 (53.1%)
	High	0	1 (1.2%)	0	1 (1.2%)	0	1 (1.4%)	0	1 (1.4%)	0	0	0	0
	Total	5 (5.8%)	66 (76.7%)	0	71 (82.6%)	0	53 (73.6%)	1 (1.4%)	54 (75%)	5 (5.2%)	53 (55.2%)	0	58 (60.4%)
Source: ADLB DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY
N: Number of subjects in the full safety analysis set, within each treatment group n: Subjects with at least one baseline and post-baseline records

Saving the Table

Code

# as rtf
gtsave(out, "adlb_rxxxx_20240428.rtf", "path to the output directory")
# as pdf
gtsave(out, "adlb_rxxxx_20240428.pdf", "path to the output directory")
# as word
gtsave(out, "adlb_rxxxx_20240428.docx", "path to the output directory")
# as html
gtsave(out, "adlb_rxxxx_20240428.html", "path to the output directory")

Required Packages

Data used for Analysis

Variables used for Analysis

Programming Flow

1. Calculating BIG N

2. Preprocessing Lab Records

3. Get all combinations of Range Indicator values

4. Performing Counts by Analysis Visit

5. Reshaping Data

6. Adding Percentages

7. Displaying the Final Table with {gt}

Colorize cells (Optional)

Saving the Table

7. Displaying the Final Table with `{gt}`