Title

Title

tabyl_pivot_longer(tabyl, col_var = NULL, list_var = NULL)

Arguments

tabyl

Value

Examples

datasets::Titanic %>%
  data.frame() %>%
  janitor::clean_names() %>%
  tidyr::uncount(weights = freq) %>%
  janitor::tabyl(survived, sex) %>%
  janitor::adorn_totals(c("row", "col")) %>%
  janitor::adorn_percentages(denominator = "col") %>%
  janitor::adorn_pct_formatting() %>%
  janitor::adorn_ns(position = "front") %>%
  tabyl_pivot_longer(col_var = "sex")
#> Warning: `as_data_frame()` was deprecated in tibble 2.0.0.
#> Please use `as_tibble()` instead.
#> The signature and semantics have changed, see `?as_tibble`.
#> # A tibble: 9 × 3
#>   survived sex    value          
#>   <fct>    <fct>  <chr>          
#> 1 No       Male   "1364  (78.8%)"
#> 2 No       Female "126  (26.8%)" 
#> 3 No       Total  "1490  (67.7%)"
#> 4 Yes      Male   " 367  (21.2%)"
#> 5 Yes      Female "344  (73.2%)" 
#> 6 Yes      Total  " 711  (32.3%)"
#> 7 Total    Male   "1731 (100.0%)"
#> 8 Total    Female "470 (100.0%)" 
#> 9 Total    Total  "2201 (100.0%)"

 datasets::Titanic %>%
  data.frame() %>%
  janitor::clean_names() %>%
  tidyr::uncount(weights = freq) %>%
  janitor::tabyl(survived, sex,class) %>%
  janitor::adorn_totals(c("row", "col")) %>%
  janitor::adorn_percentages(denominator = "col") %>%
  janitor::adorn_pct_formatting() %>%
  janitor::adorn_ns(position = "front") %>%
  tabyl_pivot_longer(col_var = "sex", list_var = "class")
#> # A tibble: 36 × 4
#>    class survived sex    value         
#>    <fct> <fct>    <fct>  <chr>         
#>  1 1st   No       Male   "118  (65.6%)"
#>  2 1st   No       Female "  4   (2.8%)"
#>  3 1st   No       Total  "122  (37.5%)"
#>  4 1st   Yes      Male   " 62  (34.4%)"
#>  5 1st   Yes      Female "141  (97.2%)"
#>  6 1st   Yes      Total  "203  (62.5%)"
#>  7 1st   Total    Male   "180 (100.0%)"
#>  8 1st   Total    Female "145 (100.0%)"
#>  9 1st   Total    Total  "325 (100.0%)"
#> 10 2nd   No       Male   "154  (86.0%)"
#> # … with 26 more rows

library(ggplot2)
#> Warning: package ‘ggplot2’ was built under R version 3.6.2
 datasets::Titanic %>%
  data.frame() %>%
  janitor::clean_names() %>%
  tidyr::uncount(weights = freq) %>%
  janitor::tabyl(survived, sex, class)  %>%
  tabyl_pivot_longer(col_var = "sex", list_var = "class") %>%
  ggplot() +
  aes(x = sex, y = survived) +
  facet_wrap(~class) +
  geom_tile() +
  aes(fill = value) +
  geom_text(aes(label = value), color = "white")

 datasets::Titanic %>%
  data.frame() %>%
  janitor::clean_names() %>%
  tidyr::uncount(weights = freq) %>%
  janitor::tabyl(survived, sex,class) %>%
  janitor::adorn_totals(c("row", "col")) %>%
  janitor::adorn_percentages(denominator = "col") ->
  percentages_prep

  percentages_prep %>%
  janitor::adorn_pct_formatting() %>%
  janitor::adorn_ns(position = "front") %>%
  tabyl_pivot_longer(col_var = "sex", list_var = "class") %>%
  ggplot() +
  aes(x = sex, y = survived %>% forcats::fct_rev()) +
  facet_wrap( ~ class) +
  geom_tile(data = percentages_prep %>%
  tabyl_pivot_longer(col_var = "sex", list_var = "class"),
  aes(fill = value)) +
  geom_text(aes(label = value), color = "white") +
  scale_fill_viridis_c(limits = 0:1)