final spread/gather -> pivots

06_working_continuous.Rmd

@@ -594,10 +594,11 @@ africa2002 <- gapdata %>%              # save as africa2002
 head(africa2002)                       # inspect
 
 africa2002 %>% 
-  gather(key, lifeExp, -country) %>%   # gather values to same column
-  ggplot(aes(x = lifeExp)) +             
+  # pivot lifeExp and lifeExp_log values to same column (for easy plotting):
+  pivot_longer(contains("lifeExp")) %>% 
+  ggplot(aes(x = value)) +             
   geom_histogram(bins = 15) +          # make histogram
-  facet_wrap(~key, scales = "free")    # facet with axes free to vary
+  facet_wrap(~name, scales = "free")    # facet with axes free to vary
 
 ```
 

09_logistic_regression.Rmd

@@ -422,7 +422,7 @@ melanoma %>%
     mort_5yr.num = as.numeric(mort_5yr) - 1
   ) %>% 
   select(mort_5yr.num, age, year) %>% 
-  gather(key = "predictors", value = "value", -mort_5yr.num) %>% 
+  pivot_longer(all_of(c("age", "year")), names_to = "predictors") %>% 
   ggplot(aes(x = value, y = mort_5yr.num)) + 
   geom_point(size = 0.5, alpha = 0.5) +
   geom_smooth(method = "loess") + 
@@ -509,11 +509,11 @@ select_explanatory <- c("ulcer.factor", "sex.factor", "t_stage.factor")
 
 melanoma %>% 
   select(one_of(select_explanatory)) %>% 
-  gather(key, value, -sex.factor) %>% 
+  pivot_longer(-sex.factor) %>% 
   ggplot(aes(value, fill = sex.factor)) + 
   geom_bar(position = "fill") +
   ylab("proportion") +
-  facet_wrap(~key, scale = "free", ncol = 2) +
+  facet_wrap(~name, scale = "free", ncol = 2) +
   coord_flip()
 ```
 

data/gbd_process.R

@@ -28,7 +28,7 @@ gbd_short %>%
 gbd_long %>%
   write_csv(path = "data/global_burden_disease_cause-year-sex-income.csv")
 
-#Chapter 3 - spread() and gather()
+#Chapter 3 - pivot_wider() and pivot_longer()
 gbd_long_example = gbd_long %>%
   filter(year %in% c(1990, 2017)) %>%
   group_by(cause, sex, year) %>%