Test table variations"

In volker: High-Level Functions for Tabulating, Charting and Reporting Survey Data

knitr::opts_chunk$set(echo = FALSE, warning = TRUE)

library(dplyr)
library(ggplot2)
library(volker)

theme_set(theme_bw())

data <- volker::chatgpt

Categorical variables

tab_counts_one

tab_counts(data, sd_gender)

tab_counts_one_grouped

tab_counts(data, adopter, sd_gender)

tab_counts_one_cor

tab_counts(data, adopter, sd_age, metric = TRUE)

tab_counts_items

tab_counts(data, starts_with("cg_adoption_"))

tab_counts_items_grouped

tab_counts(data, starts_with("cg_adoption_"), sd_gender)

tab_counts_items_cor

tab_counts(data, starts_with("cg_adoption_"), sd_age, metric = TRUE)

Metric variables

tab_metrics_one

tab_metrics(data, sd_age)

tab_metrics_one_grouped

tab_metrics(data, sd_age, sd_gender)

tab_metrics_one_cor

tab_metrics(data, sd_age, use_private, metric = TRUE)

tab_metrics_items

tab_metrics(data, starts_with("cg_adoption_"))

tab_metrics_items_grouped

tab_metrics(data, starts_with("cg_adoption_"), sd_gender)

tab_metrics_items_cor

tab_metrics(data, starts_with("cg_adoption_adv"), sd_age, metric = TRUE)

Scales

# Add a single index
data %>%
  add_index(starts_with("cg_adoption_")) %>%
  tab_metrics_one(idx_cg_adoption)

# Compare the index values by group
data %>%
  add_index(starts_with("cg_adoption_")) %>%
  tab_metrics_one_grouped(idx_cg_adoption, adopter)

# Add multiple indizes and summarize them
data %>%
  add_index(starts_with("cg_adoption_")) %>%
  add_index(starts_with("cg_adoption_advantage")) %>%
  add_index(starts_with("cg_adoption_fearofuse")) %>%
  add_index(starts_with("cg_adoption_social")) %>%
  tab_metrics_items(starts_with("idx_cg_adoption"))

Labels

Set custom item labels

newlabels <- tribble(
  ~item_name, ~item_label,
  "cg_adoption_advantage_01", "Allgemeine Vorteile",
  "cg_adoption_advantage_02", "Finanzielle Vorteile",
  "cg_adoption_advantage_03", "Vorteile bei der Arbeit",
  "cg_adoption_advantage_04", "Macht mehr Spaß"
)


data %>%
  labs_apply(newlabels) %>%

  labs_store() %>% 
  labs_clear() %>% 
  labs_restore() %>% 

  tab_counts_items(starts_with("cg_adoption_advantage_"))

Remove labels

data %>%
  labs_clear(everything()) %>%
  tab_counts_items(starts_with("cg_adoption_advantage_"))

Apply value labels

data %>%
  filter(sd_gender != "diverse") %>% 
  labs_apply(
    cols = sd_gender,
    values = list(
      "female" = "Weiblich",
      "male" = "Maennlich"
    )
  ) %>%
  tab_metrics(sd_age, sd_gender)

volker documentation built on April 12, 2025, 9:16 a.m.