tests/testthat/_snaps/tm_t_summary_by.md

In teal.modules.clinical: 'teal' Modules for Standard Clinical Outputs

template_summary_by generates correct expressions

Code
  res
Output
  $data
  {
      anl <- adlb %>% df_explicit_na(omit_columns = setdiff(names(adlb), 
          c("AVISIT", "AVAL")), na_level = "<Missing>")
      anl <- anl %>% dplyr::mutate(ARM = droplevels(ARM))
      arm_levels <- levels(anl[["ARM"]])
      adsl <- adsl %>% dplyr::filter(ARM %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ARM = droplevels(ARM))
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Summary Table for AVAL by AVISIT") %>% 
      rtables::split_cols_by("ARM", split_fun = drop_split_levels) %>% 
      rtables::add_overall_col("All Patients") %>% rtables::split_rows_by("AVISIT", 
      split_label = teal.data::col_labels(adlb, fill = FALSE)[["AVISIT"]], 
      split_fun = split_fun, label_pos = "topleft") %>% analyze_vars(vars = "AVAL", 
      na.rm = FALSE, na_str = "<Missing>", denom = "N_col", .stats = c("n", 
          "mean_sd", "mean_ci", "median", "median_ci", "quantiles", 
          "range", "count_fraction"))

  $table
  {
      table <- rtables::build_table(lyt = lyt, df = anl, alt_counts_df = adsl)
  }

template_summary_by generates correct expressions when parallel_vars is true

Code
  res
Output
  $data
  {
      anl <- adlb %>% df_explicit_na(omit_columns = setdiff(names(adlb), 
          c("AVISIT", c("AVAL", "CHG"))), na_level = "<Missing>")
      adsl <- adsl %>% dplyr::mutate(ARM = droplevels(ARM))
      arm_levels <- levels(adsl[["ARM"]])
      anl <- anl %>% dplyr::mutate(ARM = factor(ARM, levels = arm_levels))
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Summary Table for AVAL, CHG by AVISIT") %>% 
      rtables::split_cols_by("ARM") %>% rtables::split_rows_by("AVISIT", 
      split_label = teal.data::col_labels(adlb, fill = FALSE)[["AVISIT"]], 
      split_fun = split_fun, label_pos = "topleft") %>% split_cols_by_multivar(vars = c("AVAL", 
      "CHG")) %>% summarize_colvars(vars = c("AVAL", "CHG"), na.rm = FALSE, 
      denom = "N_col", .stats = c("n", "mean_sd", "mean_ci", "median", 
          "median_ci", "quantiles", "range", "count_fraction"), 
      na_str = "<Missing>")

  $table
  {
      table <- rtables::build_table(lyt = lyt, df = anl, alt_counts_df = adsl)
  }

template_summary_by generates correct expressions when row_groups is true

Code
  res
Output
  $data
  {
      anl <- adsl %>% df_explicit_na(omit_columns = setdiff(names(adsl), 
          c(c("SEX", "COUNTRY"), "AVAL")), na_level = "<Missing>")
      anl <- anl %>% dplyr::mutate(ARM = droplevels(ARM))
      arm_levels <- levels(anl[["ARM"]])
      adsl <- adsl %>% dplyr::filter(ARM %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ARM = droplevels(ARM))
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout_cfun
  cfun_unique <- function(x, labelstr = "", .N_col) {
      y <- length(unique(x))
      rcell(c(y, y/.N_col), label = labelstr)
  }

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Summary Table for AVAL by SEX, COUNTRY") %>% 
      rtables::split_cols_by("ARM", split_fun = drop_split_levels) %>% 
      rtables::split_rows_by("SEX", split_label = teal.data::col_labels(adsl, 
          fill = FALSE)[["SEX"]], split_fun = split_fun, label_pos = "topleft") %>% 
      rtables::summarize_row_groups(var = "USUBJID", cfun = cfun_unique, 
          na_str = "<Missing>") %>% rtables::split_rows_by("COUNTRY", 
      split_label = teal.data::col_labels(adsl, fill = FALSE)[["COUNTRY"]], 
      split_fun = split_fun, label_pos = "topleft") %>% rtables::summarize_row_groups(var = "USUBJID", 
      cfun = cfun_unique, na_str = "<Missing>")

  $table
  {
      table <- rtables::build_table(lyt = lyt, df = anl, alt_counts_df = adsl)
  }

template_summary_by generates correct expressions for customized numeric statistics

Code
  res
Output
  $data
  {
      anl <- adlb %>% df_explicit_na(omit_columns = setdiff(names(adlb), 
          c("AVISIT", "AVAL")), na_level = "<Missing>")
      anl <- anl %>% dplyr::mutate(ARM = droplevels(ARM))
      arm_levels <- levels(anl[["ARM"]])
      adsl <- adsl %>% dplyr::filter(ARM %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ARM = droplevels(ARM))
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Summary Table for AVAL by AVISIT") %>% 
      rtables::split_cols_by("ARM", split_fun = drop_split_levels) %>% 
      rtables::add_overall_col("All Patients") %>% rtables::split_rows_by("AVISIT", 
      split_label = teal.data::col_labels(adlb, fill = FALSE)[["AVISIT"]], 
      split_fun = split_fun, label_pos = "topleft") %>% analyze_vars(vars = "AVAL", 
      na.rm = FALSE, na_str = "<Missing>", denom = "N_col", .stats = c("n", 
          "count_fraction"))

  $table
  {
      table <- rtables::build_table(lyt = lyt, df = anl, alt_counts_df = adsl)
  }

template_summary_by generates correct expressions for drop_zero_levels is true

Code
  res
Output
  $data
  {
      anl <- adlb %>% df_explicit_na(omit_columns = setdiff(names(adlb), 
          c("AVISIT", "AVAL")), na_level = "<Missing>")
      anl <- anl %>% dplyr::mutate(ARM = droplevels(ARM))
      arm_levels <- levels(anl[["ARM"]])
      adsl <- adsl %>% dplyr::filter(ARM %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ARM = droplevels(ARM))
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Summary Table for AVAL by AVISIT") %>% 
      rtables::split_cols_by("ARM", split_fun = drop_split_levels) %>% 
      rtables::add_overall_col("All Patients") %>% rtables::split_rows_by("AVISIT", 
      split_label = teal.data::col_labels(adlb, fill = FALSE)[["AVISIT"]], 
      split_fun = split_fun, label_pos = "topleft") %>% analyze_vars(vars = "AVAL", 
      na.rm = FALSE, na_str = "<Missing>", denom = "N_col", .stats = c("n", 
          "mean_sd", "mean_ci", "median", "median_ci", "quantiles", 
          "range", "count_fraction"))

  $table
  {
      all_zero <- function(tr) {
          if (!inherits(tr, "TableRow") || inherits(tr, "LabelRow")) {
              return(FALSE)
          }
          rvs <- unlist(unname(row_values(tr)))
          isTRUE(all(rvs == 0))
      }
      table <- rtables::build_table(lyt = lyt, df = anl, alt_counts_df = adsl) %>% 
          rtables::trim_rows(criteria = all_zero)
  }

teal.modules.clinical documentation built on April 4, 2025, 12:35 a.m.