across

04-descriptives.Rmd

@@ -1687,35 +1687,12 @@ what I mean). It is also possible to summarise every column at once:
 gasoline %>%
   select(-year) %>%
   group_by(country) %>%
-  summarise(across(everything, tibble::lst(mean, sd, min, max), .names = "{fn}_{col}"))
+  summarise(across(everything(), tibble::lst(mean, sd, min, max), .names = "{fn}_{col}"))
 ```
 
 I removed the `year` variable because it's not a variable for which we want to have descriptive
 statistics.
 
-### mutate_*()
-
-TBD
-
-Of course, `mutate()` and `transmute()` also come with scoped versions:
-
-```{r}
-gasoline %>%
-  mutate_if(is.double, exp)
-```
-
-```{r}
-gasoline %>%
-  mutate_at(vars(starts_with("l")), exp)
-```
-
-```{r}
-gasoline %>%
-  mutate_all(as.character)
-```
-
-I think that by now, you are able to understand the above lines quite easily. If not, try some other
-functions, or mutating other variables and you'll see that it is not that complicated!
 
 ## Other useful `{tidyverse}` functions
 
@@ -2897,6 +2874,29 @@ are the central objects and where the analyst operates over them with functional
 
 ### Exercise 1 {-}
 
+* Combine `mutate()` with `across()` to exponentiate every column of type `double`.
+
+```{r, eval = FALSE, include = FALSE}
+gasoline %>%
+  mutate(across(is.double, exp))
+```
+
+* Exponeniate columns starting with the character `"l"`.
+
+```{r, eval = FALSE, include = FALSE}
+gasoline %>%
+  mutate(across(starts_with("l"), exp))
+```
+
+* Convert all columns' classes into the character class.
+
+```{r, eval = FALSE, include = FALSE}
+gasoline %>%
+  mutate(across(everything(), as.character))
+```
+
+### Exercise 2 {-}
+
 Load the `LaborSupply` dataset from the `Ecdat` package and answer the following questions:
 
 * Compute the average annual hours worked by year (plus standard deviation)
@@ -2949,31 +2949,28 @@ LaborSupply %>%
   filter(year == 1980) %>%
   group_by(no_kids) %>%
   summarise(mean(lnwg), sd(lnwg), n())
-
 ```
 
-### Exercise 2 {-}
+### Exercise 3 {-}
 
 * What does the following code do? Copy and paste it in an R interpreter to find out!
 
 ```{r, eval=FALSE}
 LaborSupply %>%
   group_by(id) %>%
-  mutate_at(vars(starts_with("l")), funs(lag, lead))
+  mutate(across(starts_with("l"), tibble::lst(lag, lead)))
 ```
 
-`mutate_at()` is a scoped version of `mutate()` which allows you to specify a number of columns and
-functions in one go. This also exists for `summarise()`.
 
-* Using `summarise_at()`, compute the mean, standard deviation and number of individuals of `lnhr` and `lnwg` for each individual.
+* Using `summarise()` and `across()`, compute the mean, standard deviation and number of individuals of `lnhr` and `lnwg` for each individual.
 
 ```{r, eval=FALSE, include = FALSE}
 LaborSupply %>%
   group_by(id) %>%
-  summarise_at(vars(starts_with("l"), funs(mean, sd)))
+  summarise(across(starts_with("l"), tibble::lst(mean, sd, n)))
 ```
 
-### Exercise 3 {-}
+### Exercise 4 {-}
 
 * In the dataset folder you downloaded at the beginning of the chapter, there is a folder called
 "unemployment". I used the data in the section about working with lists of datasets. Using

05-graphs.Rmd

@@ -127,31 +127,31 @@ ggplot(starwars, aes(height)) +
   geom_density()
 ```
 
-Let's go into more detail now; what if you would like to plot the densities for females and males
+Let's go into more detail now; what if you would like to plot the densities for feminines and masculines
 only (removing the droids from the data first)? This can be done by first filtering the data using
 `dplyr` and then separating the dataset by gender:
 
 
 ```{r, eval=FALSE}
 starwars %>%
-  filter(gender %in% c("female", "male"))
+  filter(gender %in% c("feminine", "masculine"))
 ```
 
-The above lines do the filtering; only keep gender if gender is in the vector `"female", "male"`.
-This is much easier than having to write `gender == "female" | gender == "male"`. Then, we pipe
+The above lines do the filtering; only keep gender if gender is in the vector `"feminine", "masculine"`.
+This is much easier than having to write `gender == "feminine" | gender == "masculine"`. Then, we pipe
 this dataset to `ggplot`:
 
 ```{r}
 starwars %>%
-  filter(gender %in% c("female", "male")) %>%
+  filter(gender %in% c("feminine", "masculine")) %>%
   ggplot(aes(height, fill = gender)) +
   geom_density()
 ```
 
 Let's take a closer look to the `aes()` function: I've added `fill = gender`. This means that
 there will be one density plot for each gender in the data, and each will be coloured accordingly.
 This is where `{ggplot2}` might be confusing; there is no need to write explicitly (even if it is
-possible) that you want the *female* density to be red and the *male* density to be blue. You just
+possible) that you want the *feminine* density to be red and the *masculine* density to be blue. You just
 map the variable `gender` to this particular aesthetic. You conclude the plot by adding
 `geom_density()` which is this case is the plot you want. We will see later how to change the
 colours of your plot.
@@ -162,7 +162,7 @@ the aesthetics inside the `geom_density()` function:
 
 ```{r}
 filtered_data <- starwars %>%
-  filter(gender %in% c("female", "male"))
+  filter(gender %in% c("feminine", "masculine"))
 
 ggplot(filtered_data) +
   geom_density(aes(height, fill = gender))
@@ -225,7 +225,7 @@ symbol in the code below:
 starwars %>%
   mutate(human = case_when(species == "Human" ~ "Human",
                            species != "Human" ~ "Not Human")) %>%
-  filter(gender %in% c("female", "male"), !is.na(human)) %>%
+  filter(gender %in% c("feminine", "masculine"), !is.na(human)) %>%
   ggplot(aes(height, fill = gender)) +
   facet_grid(. ~ human) + #<--- this is a formula
   geom_density()
@@ -238,7 +238,7 @@ use it for facetting. By changing the formula, you change how the facetting is d
 starwars %>%
   mutate(human = case_when(species == "Human" ~ "Human",
                            species != "Human" ~ "Not Human")) %>%
-  filter(gender %in% c("female", "male"), !is.na(human)) %>%
+  filter(gender %in% c("feminine", "masculine"), !is.na(human)) %>%
   ggplot(aes(height, fill = gender)) +
   facet_grid(human ~ .) +
   geom_density()
@@ -255,7 +255,7 @@ starwars %>%
                             n_films != 1 ~ "More than 1 movie")) %>%
   mutate(human = case_when(species == "Human" ~ "Human",
                            species != "Human" ~ "Not Human")) %>%
-  filter(gender %in% c("female", "male"), !is.na(human)) %>%
+  filter(gender %in% c("feminine", "masculine"), !is.na(human)) %>%
   ggplot(aes(height, fill = gender)) +
   facet_grid(human ~ more_1) +
   geom_density()
@@ -466,7 +466,7 @@ starwars %>%
     geom_smooth(aes(height, mass, colour = gender))
 ```
 
-Because there are only a few observations for females and `NA`s the regression lines are not very informative,
+Because there are only a few observations for feminines and `NA`s the regression lines are not very informative,
 but this was only an example to show you some options of `geom_smooth()`.
 
 Let's go back to the unemployment line plots. For now, let's keep the base `{ggplot2}` theme, but

08-functional_programming.Rmd

@@ -1,4 +1,4 @@
-# Functional programming
+ %>% # Functional programming
 
 Functional programming is a paradigm that I find very suitable for data science. In functional
 programming, your code is organised into functions that perform the operations you need. Your scripts
@@ -1349,7 +1349,7 @@ effects:
 
 ```{r}
 simulation_results <- results %>% 
-  mutate(difference = map2(.x = lpm, .y = true_effect, bind_cols)) %>% 
+  mutate(difference = map2(.x = lpm, .y = true_effect, full_join)) %>%  
   mutate(difference = map(difference, ~mutate(., difference = true_effect - estimate))) %>% 
   mutate(difference = map(difference, ~select(., term, difference))) %>% 
   pull(difference) %>% 
@@ -1383,7 +1383,7 @@ monte_carlo <- function(coefs, sample_size, repeats){
     mutate(true_effect = map(true_effect, bind_rows))
 
   simulation_results <- results %>% 
-    mutate(difference = map2(.x = lpm, .y = true_effect, bind_cols)) %>% 
+    mutate(difference = map2(.x = lpm, .y = true_effect, full_join)) %>% 
     mutate(difference = map(difference, ~mutate(., difference = true_effect - estimate))) %>% 
     mutate(difference = map(difference, ~select(., term, difference))) %>% 
     pull(difference) %>%