Label x and y arguments in aes(), closes #1159

EDA.qmd

@@ -471,7 +471,7 @@ You can do that by exchanging the x and y aesthetic mappings.
 #|   on the y-axis and ordered by increasing median highway mileage.
 
 ggplot(mpg,
-       aes(y = fct_reorder(class, hwy, median), x = hwy)) +
+       aes(x = hwy, y = fct_reorder(class, hwy, median))) +
   geom_boxplot()
 ```
 

factors.qmd

@@ -256,7 +256,8 @@ It takes a factor, `f`, and then any number of levels that you want to move to t
 #|   bottom of the y-axis. Generally there is a positive association
 #|   between income and age, and the income band with the highest average
 #|   age is "Not applicable".
-ggplot(rincome_summary, aes(age, fct_relevel(rincome, "Not applicable"))) +
+
+ggplot(rincome_summary, aes(x = age, y = fct_relevel(rincome, "Not applicable"))) +
   geom_point()
 ```
 
@@ -291,7 +292,7 @@ by_age <- gss_cat |>
     prop = n / sum(n)
   )
 
-ggplot(by_age, aes(age, prop, color = marital)) +
+ggplot(by_age, aes(x = age, y = prop, color = marital)) +
   geom_line(na.rm = TRUE)
 
 ggplot(by_age, aes(x = age, y = prop, color = fct_reorder2(marital, age, prop))) +

functions.qmd

@@ -666,11 +666,11 @@ For example, imagine that you're making a lot of histograms:
 ```{r}
 #| fig-show: hide
 diamonds |> 
-  ggplot(aes(carat)) +
+  ggplot(aes(x = carat)) +
   geom_histogram(binwidth = 0.1)
 
 diamonds |> 
-  ggplot(aes(carat)) +
+  ggplot(aes(x = carat)) +
   geom_histogram(binwidth = 0.05)
 ```
 
@@ -680,7 +680,7 @@ This is easy as pie once you know that `aes()` is a data-masking function and yo
 ```{r}
 histogram <- function(df, var, binwidth = NULL) {
   df |> 
-    ggplot(aes({{ var }})) + 
+    ggplot(aes(x = {{ var }})) + 
     geom_histogram(binwidth = binwidth)
 }
 
@@ -705,7 +705,7 @@ For example, maybe you want an easy way to eyeball whether or not a data set is
 # https://twitter.com/tyler_js_smith/status/1574377116988104704
 linearity_check <- function(df, x, y) {
   df |>
-    ggplot(aes({{ x }}, {{ y }})) +
+    ggplot(aes(x = {{ x }}, y = {{ y }})) +
     geom_point() +
     geom_smooth(method = "loess", color = "red", se = FALSE) +
     geom_smooth(method = "lm", color = "blue", se = FALSE) 
@@ -722,7 +722,7 @@ Or maybe you want an alternative to colored scatterplots for very large datasets
 # https://twitter.com/ppaxisa/status/1574398423175921665
 hex_plot <- function(df, x, y, z, bins = 20, fun = "mean") {
   df |> 
-    ggplot(aes({{ x }}, {{ y }}, z = {{ z }})) + 
+    ggplot(aes(x = {{ x }}, y = {{ y }}, z = {{ z }})) + 
     stat_summary_hex(
       aes(color = after_scale(fill)), # make border same color as fill
       bins = bins, 
@@ -760,7 +760,7 @@ Or maybe you want to make it easy to draw a bar plot just for a subset of the da
 conditional_bars <- function(df, condition, var) {
   df |> 
     filter({{ condition }}) |> 
-    ggplot(aes({{ var }})) + 
+    ggplot(aes(x = {{ var }})) + 
     geom_bar()
 }
 
@@ -779,7 +779,7 @@ fancy_ts <- function(df, val, group) {
     summarize(breaks = max({{ val }}))
   
   df |> 
-    ggplot(aes(date, {{ val }}, group = {{ group }}, color = {{ group }})) +
+    ggplot(aes(x = date, y = {{ val }}, group = {{ group }}, color = {{ group }})) +
     geom_path() +
     scale_y_continuous(
       breaks = labs$breaks, 
@@ -813,7 +813,7 @@ The only advantage of this syntax is that `vars()` uses tidy evaluation so you c
 ```{r}
 # https://twitter.com/sharoz/status/1574376332821204999
 foo <- function(x) {
-  ggplot(mtcars, aes(mpg, disp)) +
+  ggplot(mtcars, aes(x = mpg, y = disp)) +
     geom_point() +
     facet_wrap(vars({{ x }}))
 }
@@ -828,7 +828,7 @@ For example, the following function makes it particularly easy to interactively
 # https://twitter.com/yutannihilat_en/status/1574387230025875457
 density <- function(color, facets, binwidth = 0.1) {
   diamonds |> 
-    ggplot(aes(carat, after_stat(density), color = {{ color }})) +
+    ggplot(aes(x = carat, y = after_stat(density), color = {{ color }})) +
     geom_freqpoly(binwidth = binwidth) +
     facet_wrap(vars({{ facets }}))
 }
@@ -845,7 +845,7 @@ Remember the histogram function we showed you earlier?
 ```{r}
 histogram <- function(df, var, binwidth = NULL) {
   df |> 
-    ggplot(aes({{ var }})) + 
+    ggplot(aes(x = {{ var }})) + 
     geom_histogram(binwidth = binwidth)
 }
 ```
@@ -863,7 +863,7 @@ histogram <- function(df, var, binwidth) {
   label <- rlang::englue("A histogram of {{var}} with binwidth {binwidth}")
   
   df |> 
-    ggplot(aes({{ var }})) + 
+    ggplot(aes(x = {{ var }})) + 
     geom_histogram(binwidth = binwidth) + 
     labs(title = label)
 }
@@ -917,7 +917,7 @@ This makes it easier to see the hierarchy in your code by skimming the left-hand
 # missing extra two spaces
 density <- function(color, facets, binwidth = 0.1) {
 diamonds |> 
-  ggplot(aes(carat, after_stat(density), color = {{ color }})) +
+  ggplot(aes(x = carat, y = after_stat(density), color = {{ color }})) +
   geom_freqpoly(binwidth = binwidth) +
   facet_wrap(vars({{ facets }}))
 }
@@ -925,7 +925,7 @@ diamonds |>
 # Pipe indented incorrectly
 density <- function(color, facets, binwidth = 0.1) {
   diamonds |> 
-  ggplot(aes(carat, after_stat(density), color = {{ color }})) +
+  ggplot(aes(x = carat, y = after_stat(density), color = {{ color }})) +
   geom_freqpoly(binwidth = binwidth) +
   facet_wrap(vars({{ facets }}))
 }

layers.qmd

@@ -949,10 +949,10 @@ There are two other coordinate systems that are occasionally helpful.
 
     nz <- map_data("nz")
 
-    ggplot(nz, aes(long, lat, group = group)) +
+    ggplot(nz, aes(x = long, y = lat, group = group)) +
       geom_polygon(fill = "white", color = "black")
 
-    ggplot(nz, aes(long, lat, group = group)) +
+    ggplot(nz, aes(x = long, y = lat, group = group)) +
       geom_polygon(fill = "white", color = "black") +
       coord_quickmap()
     ```

logicals.qmd

@@ -365,7 +365,7 @@ flights |>
     prop_delayed = mean(arr_delay > 0, na.rm = TRUE),
     .groups = "drop"
   ) |> 
-  ggplot(aes(prop_delayed)) + 
+  ggplot(aes(x = prop_delayed)) + 
   geom_histogram(binwidth = 0.05)
 ```
 

numbers.qmd

@@ -436,7 +436,7 @@ slide_vec(x, sum, .before = 2, .after = 2, .complete = TRUE)
     ```{r}
     flights |> 
       filter(month == 1, day == 1) |> 
-      ggplot(aes(sched_dep_time, dep_delay)) +
+      ggplot(aes(x = sched_dep_time, y = dep_delay)) +
       geom_point()
     ```
 
@@ -649,7 +649,7 @@ flights |>
     n = n(),
     .groups = "drop"
   ) |> 
-  ggplot(aes(mean, median)) + 
+  ggplot(aes(x = mean, y = median)) + 
   geom_abline(slope = 1, intercept = 0, color = "white", size = 2) +
   geom_point()
 ```
@@ -731,12 +731,12 @@ This suggests that the mean is unlikely to be a good summary and we might prefer
 #| fig-height: 2
 
 flights |>
-  ggplot(aes(dep_delay)) + 
+  ggplot(aes(x = dep_delay)) + 
   geom_histogram(binwidth = 15)
 
 flights |>
   filter(dep_delay < 120) |> 
-  ggplot(aes(dep_delay)) + 
+  ggplot(aes(x = dep_delay)) + 
   geom_histogram(binwidth = 5)
 ```
 
@@ -756,7 +756,7 @@ The distributions seem to follow a common pattern, suggesting it's fine to use t
 #|   overlapping forming a thick black bland.
 flights |>
   filter(dep_delay < 120) |> 
-  ggplot(aes(dep_delay, group = interaction(day, month))) + 
+  ggplot(aes(x = dep_delay, group = interaction(day, month))) + 
   geom_freqpoly(binwidth = 5, alpha = 1/5)
 ```
 

quarto/diamond-sizes.qmd

@@ -23,6 +23,6 @@ The distribution of the remainder is shown below:
 #| echo: false
 
 smaller |> 
-  ggplot(aes(carat)) + 
+  ggplot(aes(x = carat)) + 
   geom_freqpoly(binwidth = 0.01)
 ```

regexps.qmd

@@ -166,7 +166,7 @@ It looks like they've radically increased in popularity lately!
 babynames |> 
   group_by(year) |> 
   summarize(prop_x = mean(str_detect(name, "x"))) |> 
-  ggplot(aes(year, prop_x)) + 
+  ggplot(aes(x = year, y = prop_x)) + 
   geom_line()
 ```
 

workflow-pipes.qmd

@@ -140,7 +140,7 @@ We wish this transition wasn't necessary but unfortunately ggplot2 was created b
 
 diamonds |> 
   count(cut, clarity) |> 
-  ggplot(aes(clarity, cut, fill = n)) + 
+  ggplot(aes(x = clarity, y = cut, fill = n)) + 
   geom_tile()
 ```
 

workflow-scripts.qmd

@@ -298,7 +298,7 @@ Don't worry about the details, you'll learn them later in the book.
 
 library(tidyverse)
 
-ggplot(diamonds, aes(carat, price)) + 
+ggplot(diamonds, aes(x = carat, y = price)) + 
   geom_hex()
 ggsave("diamonds.pdf")
 

workflow-style.qmd

@@ -212,7 +212,7 @@ flights |>
   summarize(
     delay = mean(arr_delay, na.rm = TRUE)
   ) |> 
-  ggplot(aes(month, delay)) +
+  ggplot(aes(x = month, y = delay)) +
   geom_point() + 
   geom_line()
 ```
@@ -228,7 +228,7 @@ flights |>
     distance = mean(distance),
     speed = mean(air_time / distance, na.rm = TRUE)
   ) |> 
-  ggplot(aes(distance, speed)) +
+  ggplot(aes(x = distance, y = speed)) +
   geom_smooth(
     method = "loess",
     span = 0.5,