## ----setup-------------------------------------------------------------------- library(summarytabl) ## ----------------------------------------------------------------------------- cat_tbl(data = nlsy, var = "race") ## ----------------------------------------------------------------------------- cat_tbl(data = nlsy, var = "race", ignore = "Black", na.rm = TRUE) ## ----------------------------------------------------------------------------- # Default: counts and percentages cat_group_tbl(data = nlsy, row_var = "race", col_var = "bthwht", na.rm.row_var = TRUE) # Counts only cat_tbl(data = nlsy, var = "race", ignore = "Black", na.rm = TRUE, only = "count") # Percents only cat_group_tbl(data = nlsy, row_var = "race", col_var = "bthwht", na.rm.row_var = TRUE, only = "percent") ## ----------------------------------------------------------------------------- cat_group_tbl(data = nlsy, row_var = "gender", col_var = "bthwht") ## ----------------------------------------------------------------------------- cat_group_tbl(data = nlsy, row_var = "race", col_var = "bthwht", na.rm.row_var = TRUE, ignore = c(race = "Non-Black,Non-Hispanic"), pivot = "wider") ## ----------------------------------------------------------------------------- cat_group_tbl(data = nlsy, row_var = "race", col_var = "bthwht", na.rm.row_var = TRUE, ignore = list(race = c("Non-Black,Non-Hispanic", "Hispanic")), pivot = "wider") ## ----------------------------------------------------------------------------- # Default: counts and percentages cat_group_tbl(data = nlsy, row_var = "race", col_var = "bthwht", na.rm.row_var = TRUE) # Counts only cat_group_tbl(data = nlsy, row_var = "race", col_var = "bthwht", na.rm.row_var = TRUE, only = "count") # Percents only cat_group_tbl(data = nlsy, row_var = "race", col_var = "bthwht", na.rm.row_var = TRUE, only = "percent") ## ----------------------------------------------------------------------------- names(depressive) ## ----------------------------------------------------------------------------- select_tbl(data = depressive, var_stem = "dep") ## ----------------------------------------------------------------------------- # Default listwise removal, value '3' removed from data select_tbl(data = depressive, var_stem = "dep", ignore = 3) # Pairwise removal, value '3' removed from data select_tbl(data = depressive, var_stem = "dep", ignore = 3, na_removal = "pairwise") ## ----------------------------------------------------------------------------- # Default longer format select_tbl(data = depressive, var_stem = "dep") # Wider format select_tbl(data = depressive, var_stem = "dep", pivot = "wider") ## ----------------------------------------------------------------------------- select_tbl(data = depressive, var_stem = "dep", pivot = "wider", var_labels = c( dep_1="how often child feels sad and blue", dep_2="how often child feels nervous, tense, or on edge", dep_3="how often child feels happy", dep_4="how often child feels bored", dep_5="how often child feels lonely", dep_6="how often child feels tired or worn out", dep_7="how often child feels excited about something", dep_8="how often child feels too busy to get everything" ) ) ## ----------------------------------------------------------------------------- # Default: counts and percentages select_tbl(data = depressive, var_stem = "dep", pivot = "wider") # Counts only select_tbl(data = depressive, var_stem = "dep", pivot = "wider", only = "count") # Percents only select_tbl(data = depressive, var_stem = "dep", pivot = "wider", only = "percent") ## ----------------------------------------------------------------------------- dep_recoded <- depressive |> dplyr::mutate( race = dplyr::case_match(.x = race, 1 ~ "Hispanic", 2 ~ "Black", 3 ~ "Non-Black/Non-Hispanic", .default = NA) ) |> dplyr::mutate( dplyr::across( .cols = dplyr::starts_with("dep"), .fns = ~ dplyr::case_when(.x == 1 ~ "often", .x == 2 ~ "sometimes", .x == 3 ~ "hardly ever") ) ) # longer format select_group_tbl(data = dep_recoded, var_stem = "dep", group = "race", pivot = "longer") # wider format select_group_tbl(data = dep_recoded, var_stem = "dep", group = "race", pivot = "wider") ## ----------------------------------------------------------------------------- # Default listwise removal: 'often' value removed from all # dep_ variables, and 'Non-Black/Non-Hispanic' value removed # from race variable select_group_tbl(data = dep_recoded, var_stem = "dep", group = "race", pivot = "longer", ignore = c(dep = "often", race = "Non-Black/Non-Hispanic")) # Pairwise removal: 'often' value removed from all # dep_ variables, and 'Non-Black/Non-Hispanic' value removed # from race variable select_group_tbl(data = dep_recoded, var_stem = "dep", group = "race", pivot = "longer", ignore = c(dep = "often", race = "Non-Black/Non-Hispanic"), na_removal = "pairwise") ## ----------------------------------------------------------------------------- select_group_tbl(data = dep_recoded, var_stem = "dep", group = "race", pivot = "longer", ignore = list(race = c("Hispanic", "Non-Black/Non-Hispanic"))) ## ----------------------------------------------------------------------------- select_group_tbl(data = stem_social_psych, var_stem = "belong_belong", group = "_w\\d", group_type = "pattern", pivot = "longer") ## ----------------------------------------------------------------------------- select_group_tbl(data = stem_social_psych, var_stem = "belong_belong", group = "_w\\d", group_type = "pattern", group_name = "wave", pivot = "longer") ## ----------------------------------------------------------------------------- select_group_tbl(data = stem_social_psych, var_stem = "belong_belong", group = "_w\\d", group_type = "pattern", group_name = "wave", pivot = "longer", var_labels = c( belong_belongStem_w1 = "I feel like I belong in STEM (wave 1)", belong_belongStem_w2 = "I feel like I belong in STEM (wave 2)" )) ## ----------------------------------------------------------------------------- # Default: counts and percentages select_group_tbl(data = stem_social_psych, var_stem = "belong_belong", group = "_w\\d", group_type = "pattern", group_name = "wave", pivot = "longer", only = "count") # Counts only select_group_tbl(data = stem_social_psych, var_stem = "belong_belong", group = "_w\\d", group_type = "pattern", group_name = "wave", pivot = "longer", only = "count") # Percents only select_group_tbl(data = stem_social_psych, var_stem = "belong_belong", group = "_w\\d", group_type = "pattern", group_name = "wave", pivot = "longer", only = "percent") ## ----------------------------------------------------------------------------- mean_tbl(data = social_psy_data, var_stem = "belong") ## ----------------------------------------------------------------------------- mean_tbl(data = social_psy_data, var_stem = "belong", ignore = 5) ## ----------------------------------------------------------------------------- # Default listwise removal mean_tbl(data = social_psy_data, var_stem = "belong", ignore = 5) # Pairwise removal mean_tbl(data = social_psy_data, var_stem = "belong", na_removal = "pairwise", ignore = 5) ## ----------------------------------------------------------------------------- mean_tbl(data = social_psy_data, var_stem = "belong", na_removal = "pairwise", var_labels = c( belong_1 = "I feel like I belong at this institution", belong_2 = "I feel like part of the community", belong_3 = "I feel valued by this institution") ) ## ----------------------------------------------------------------------------- # Default: all summary statistics returned # (mean, sd, min, max, nobs) mean_tbl(data = social_psy_data, var_stem = "belong", na_removal = "pairwise") # Means and non-missing observations returned mean_tbl(data = social_psy_data, var_stem = "belong", na_removal = "pairwise", only = c("mean", "nobs")) # Means and standard deviations returned mean_tbl(data = social_psy_data, var_stem = "belong", na_removal = "pairwise", only = c("mean", "sd")) ## ----------------------------------------------------------------------------- mean_group_tbl(data = stem_social_psych, var_stem = "belong_belong", group = "urm", group_type = "variable") ## ----------------------------------------------------------------------------- # Default listwise removal mean_group_tbl(data = stem_social_psych, var_stem = "belong_belong", group = "urm", ignore = c(belong_belong = 5, urm = 0) ) # Pairwise removal mean_group_tbl(data = stem_social_psych, var_stem = "belong_belong", group = "urm", na_removal = "pairwise", ignore = c(belong_belong = 5, urm = 0) ) ## ----------------------------------------------------------------------------- mean_group_tbl(data = stem_social_psych, var_stem = "belong_belong", group = "urm", ignore = list(belong_belong = c(4,5), urm = 0) ) ## ----------------------------------------------------------------------------- mean_group_tbl(data = stem_social_psych, var_stem = "belong_belong", group = "_w\\d", group_type = "pattern") ## ----------------------------------------------------------------------------- mean_group_tbl(data = stem_social_psych, var_stem = "belong_belong", group = "_w\\d", group_type = "pattern", group_name = "wave", var_labels = c( belong_belongStem_w1 = "I feel like I belong in computing", belong_belongStem_w2 = "I feel like I belong in computing") ) ## ----------------------------------------------------------------------------- # Default: all summary statistics returned # (mean, sd, min, max, nobs) mean_group_tbl(data = stem_social_psych, var_stem = "belong_belong", group = "_w\\d", group_type = "pattern", group_name = "wave", var_labels = c( belong_belongStem_w1 = "I feel like I belong in computing", belong_belongStem_w2 = "I feel like I belong in computing") ) # Means and non-missing observations only mean_group_tbl(data = stem_social_psych, var_stem = "belong_belong", group = "_w\\d", group_type = "pattern", group_name = "wave", var_labels = c( belong_belongStem_w1 = "I feel like I belong in computing", belong_belongStem_w2 = "I feel like I belong in computing"), only = c("mean", "nobs") ) # Means and standard deviations only mean_group_tbl(data = stem_social_psych, var_stem = "belong_belong", group = "_w\\d", group_type = "pattern", group_name = "wave", var_labels = c( belong_belongStem_w1 = "I feel like I belong in computing", belong_belongStem_w2 = "I feel like I belong in computing"), only = c("mean", "sd") )