Add x and y to aes(), addresses #1159

EDA.qmd

@@ -341,7 +341,7 @@ nycflights13::flights |>
     sched_min = sched_dep_time %% 100,
     sched_dep_time = sched_hour + (sched_min / 60)
   ) |> 
-  ggplot(aes(sched_dep_time)) + 
+  ggplot(aes(x = sched_dep_time)) + 
   geom_freqpoly(aes(color = cancelled), binwidth = 1/4)
 ```
 

communication.qmd

@@ -583,7 +583,7 @@ For example, take this plot that shows when each US president started and ended
 
 presidential |>
   mutate(id = 33 + row_number()) |>
-  ggplot(aes(start, id)) +
+  ggplot(aes(x = start, y = id)) +
   geom_point() +
   geom_segment(aes(xend = end, yend = id)) +
   scale_x_date(name = NULL, breaks = presidential$start, date_labels = "'%y")
@@ -759,7 +759,7 @@ For example, if we map presidential party to color, we want to use the standard
 
 presidential |>
   mutate(id = 33 + row_number()) |>
-  ggplot(aes(start, id, color = party)) +
+  ggplot(aes(x = start, y = id, color = party)) +
   geom_point() +
   geom_segment(aes(xend = end, yend = id)) +
   scale_color_manual(values = c(Republican = "red", Democratic = "blue"))
@@ -852,10 +852,10 @@ For example, if we extract two classes of cars and plot them separately, it's di
 suv <- mpg |> filter(class == "suv")
 compact <- mpg |> filter(class == "compact")
 
-ggplot(suv, aes(displ, hwy, color = drv)) +
+ggplot(suv, aes(x = displ, y = hwy, color = drv)) +
   geom_point()
 
-ggplot(compact, aes(displ, hwy, color = drv)) +
+ggplot(compact, aes(x = displ, y = hwy, color = drv)) +
   geom_point()
 ```
 

data-tidy.qmd

@@ -121,7 +121,7 @@ table1 |>
   count(year, wt = cases)
 
 # Visualise changes over time
-ggplot(table1, aes(year, cases)) +
+ggplot(table1, aes(x = year, y = cases)) +
   geom_line(aes(group = country), color = "grey50") +
   geom_point(aes(color = country, shape = country)) +
   scale_x_continuous(breaks = c(1999, 2000))
@@ -249,7 +249,7 @@ The code is shown below and the result is @fig-billboard-ranks.
 #|   >50.
 
 billboard_tidy |> 
-  ggplot(aes(week, rank, group = track)) + 
+  ggplot(aes(x = week, y = rank, group = track)) + 
   geom_line(alpha = 1/3) + 
   scale_y_reverse()
 ```
@@ -722,7 +722,7 @@ Depending on what you want to do next, you may find any of the following three s
 
     cms_patient_care |> 
       filter(type == "observed") |> 
-      ggplot(aes(score)) + 
+      ggplot(aes(x = score)) + 
       geom_histogram(binwidth = 2) + 
       facet_wrap(vars(measure_abbr))
     ```
@@ -739,7 +739,7 @@ Depending on what you want to do next, you may find any of the following three s
         names_from = measure_abbr,
         values_from = score
       ) |> 
-      ggplot(aes(dyspnea_screening, dyspena_treatment)) + 
+      ggplot(aes(x = dyspnea_screening, y = dyspena_treatment)) + 
       geom_point() + 
       coord_equal()
     ```

data-transform.qmd

@@ -390,9 +390,9 @@ flights <- flights |> mutate(
   dep_sched = dep_time + dep_delay
 )
 
-ggplot(flights, aes(dep_sched)) + geom_histogram(binwidth = 60)
-ggplot(flights, aes(dep_sched %% 60)) + geom_histogram(binwidth = 1)
-ggplot(flights, aes(air_time - airtime2)) + geom_histogram()
+ggplot(flights, aes(x = dep_sched)) + geom_histogram(binwidth = 60)
+ggplot(flights, aes(x = dep_sched %% 60)) + geom_histogram(binwidth = 1)
+ggplot(flights, aes(x = air_time - airtime2)) + geom_histogram()
 ```
 
 1.  Compare `air_time` with `arr_time - dep_time`.
@@ -601,7 +601,7 @@ delays <- flights |>
     n = n()
   )
 
-ggplot(delays, aes(delay)) + 
+ggplot(delays, aes(x = delay)) + 
   geom_freqpoly(binwidth = 10)
 ```
 
@@ -615,7 +615,7 @@ That seems pretty surprising, so lets draw a scatterplot of number of flights vs
 #|   (from -50 to ~300), but the variability rapidly decreases as the 
 #|   number of flights increases.
 
-ggplot(delays, aes(n, delay)) + 
+ggplot(delays, aes(x = n, y = delay)) + 
   geom_point(alpha = 1/10)
 ```
 
@@ -638,7 +638,7 @@ When looking at this sort of plot, it's often useful to filter out the groups wi
 
 delays |>  
   filter(n > 25) |> 
-  ggplot(aes(n, delay)) + 
+  ggplot(aes(x = n, y = delay)) + 
   geom_point(alpha = 1/10) + 
   geom_smooth(se = FALSE)
 ```
@@ -676,7 +676,7 @@ When we plot the skill of the batter (measured by the batting average, `ba`) aga
 
 batters |> 
   filter(n > 100) |> 
-  ggplot(aes(n, perf)) +
+  ggplot(aes(x = n, y = perf)) +
     geom_point(alpha = 1 / 10) + 
     geom_smooth(se = FALSE)
 ```

datetimes.qmd

@@ -228,7 +228,7 @@ With this data, we can visualize the distribution of departure times across the
 #|   few flights in early Februrary, early July, late November, and late
 #|   December.
 flights_dt |> 
-  ggplot(aes(dep_time)) + 
+  ggplot(aes(x = dep_time)) + 
   geom_freqpoly(binwidth = 86400) # 86400 seconds = 1 day
 ```
 
@@ -243,7 +243,7 @@ Or within a single day:
 #|   before 6am and after 8pm.
 flights_dt |> 
   filter(dep_time < ymd(20130102)) |> 
-  ggplot(aes(dep_time)) + 
+  ggplot(aes(x = dep_time)) + 
   geom_freqpoly(binwidth = 600) # 600 s = 10 minutes
 ```
 
@@ -355,7 +355,7 @@ flights_dt |>
     avg_delay = mean(dep_delay, na.rm = TRUE),
     n = n()
   ) |> 
-  ggplot(aes(minute, avg_delay)) +
+  ggplot(aes(x = minute, y = avg_delay)) +
   geom_line()
 ```
 
@@ -375,7 +375,7 @@ sched_dep <- flights_dt |>
     n = n()
   )
 
-ggplot(sched_dep, aes(minute, avg_delay)) +
+ggplot(sched_dep, aes(x = minute, y = avg_delay)) +
   geom_line()
 ```
 
@@ -396,7 +396,7 @@ Always be alert for this sort of pattern whenever you work with data that involv
 #|   all most all flights are scheduled to depart on multiples of five, 
 #|   with a few extra at 15, 45, and 55 minutes.
 #| echo: false
-ggplot(sched_dep, aes(minute, n)) +
+ggplot(sched_dep, aes(x = minute, y = n)) +
   geom_line()
 ```
 
@@ -415,7 +415,7 @@ This, for example, allows us to plot the number of flights per week:
 #|   weeks of the year (approximately 2,500 flights).
 flights_dt |> 
   count(week = floor_date(dep_time, "week")) |> 
-  ggplot(aes(week, n)) +
+  ggplot(aes(x = week, y = n)) +
   geom_line() + 
   geom_point()
 ```
@@ -428,7 +428,7 @@ You can use rounding to show the distribution of flights across the course of a
 #|   since midnight so it's hard to interpret.
 flights_dt |> 
   mutate(dep_hour = dep_time - floor_date(dep_time, "day")) |> 
-  ggplot(aes(dep_hour)) +
+  ggplot(aes(x = dep_hour)) +
   geom_freqpoly(binwidth = 60 * 30)
 ```
 
@@ -445,7 +445,7 @@ We can convert that to an `hms` object to get a more useful x-axis:
 #|   around 12,000 per hour until 8pm, when they rapidly drop again. 
 flights_dt |> 
   mutate(dep_hour = hms::as_hms(dep_time - floor_date(dep_time, "day"))) |> 
-  ggplot(aes(dep_hour)) +
+  ggplot(aes(x = dep_hour)) +
   geom_freqpoly(binwidth = 60 * 30)
 ```
 

factors.qmd

@@ -147,7 +147,7 @@ Or with a bar chart:
 #|   A bar chart showing the distribution of race. There are ~2000
 #|   records with race "Other", 3000 with race "Black", and other
 #|   15,000 with race "White".
-ggplot(gss_cat, aes(race)) +
+ggplot(gss_cat, aes(x = race)) +
   geom_bar()
 ```
 
@@ -185,7 +185,7 @@ relig_summary <- gss_cat |>
     n = n()
   )
 
-ggplot(relig_summary, aes(tvhours, relig)) + 
+ggplot(relig_summary, aes(x = tvhours, x = relig)) + 
   geom_point()
 ```
 
@@ -202,7 +202,7 @@ We can improve it by reordering the levels of `relig` using `fct_reorder()`.
 #|   The same scatterplot as above, but now the religion is displayed in
 #|   increasing order of tvhours. "Other eastern" has the fewest tvhours
 #|   under 2, and "Don't know" has the highest (over 5).
-ggplot(relig_summary, aes(tvhours, fct_reorder(relig, tvhours))) +
+ggplot(relig_summary, aes(x = tvhours, y = fct_reorder(relig, tvhours))) +
   geom_point()
 ```
 
@@ -218,7 +218,7 @@ relig_summary |>
   mutate(
     relig = fct_reorder(relig, tvhours)
   ) |>
-  ggplot(aes(tvhours, relig)) +
+  ggplot(aes(x = tvhours, y = relig)) +
   geom_point()
 ```
 
@@ -238,7 +238,7 @@ rincome_summary <- gss_cat |>
     n = n()
   )
 
-ggplot(rincome_summary, aes(age, fct_reorder(rincome, age))) + 
+ggplot(rincome_summary, aes(x = age, y = fct_reorder(rincome, age))) + 
   geom_point()
 ```
 
@@ -294,7 +294,7 @@ by_age <- gss_cat |>
 ggplot(by_age, aes(age, prop, color = marital)) +
   geom_line(na.rm = TRUE)
 
-ggplot(by_age, aes(age, prop, color = fct_reorder2(marital, age, prop))) +
+ggplot(by_age, aes(x = age, y = prop, color = fct_reorder2(marital, age, prop))) +
   geom_line() +
   labs(color = "marital")
 ```
@@ -309,7 +309,7 @@ Combine it with `fct_rev()` if you want them in increasing frequency so that in
 #|   (~3,000), never married (~5,000), married (~10,000).
 gss_cat |>
   mutate(marital = marital |> fct_infreq() |> fct_rev()) |>
-  ggplot(aes(marital)) +
+  ggplot(aes(x = marital)) +
   geom_bar()
 ```
 

iteration.qmd

@@ -972,7 +972,7 @@ Let's first make a function that draws the plot we want:
 
 ```{r}
 carat_histogram <- function(df) {
-  ggplot(df, aes(carat)) + geom_histogram(binwidth = 0.1)  
+  ggplot(df, aes(x = carat)) + geom_histogram(binwidth = 0.1)  
 }
 
 carat_histogram(by_clarity$data[[1]])

joins.qmd

@@ -373,7 +373,7 @@ flights2 |>
 
     airports |>
       semi_join(flights, join_by(faa == dest)) |>
-      ggplot(aes(lon, lat)) +
+      ggplot(aes(x = lon, y = lat)) +
         borders("state") +
         geom_point() +
         coord_quickmap()
@@ -394,7 +394,7 @@ flights2 |>
       summarize(delay = mean(arr_delay), n = n()) |>
       filter(n > 5) |>
       inner_join(airports, by = c("dest" = "faa")) |>
-      ggplot(aes(lon, lat)) +
+      ggplot(aes(x = lon, y = lat)) +
         borders("state") +
         geom_point(aes(size = n, color = delay)) +
         coord_quickmap()

missing-values.qmd

@@ -235,11 +235,11 @@ You can force them to display by supplying `drop = FALSE` to the appropriate dis
 #| 
 #|   The same bar chart as the last plot, but now with two values on
 #|   the x-axis, "yes" and "no". There is no bar for the "yes" category.
-ggplot(health, aes(smoker)) +
+ggplot(health, aes(x = smoker)) +
   geom_bar() +
   scale_x_discrete()
 
-ggplot(health, aes(smoker)) +
+ggplot(health, aes(x = smoker)) +
   geom_bar() +
   scale_x_discrete(drop = FALSE)
 ```

quarto.qmd

@@ -412,7 +412,7 @@ To illustrate the principle, the following three plots have `fig-width` of 4, 6,
 ```{r}
 #| include: false
 
-plot <- ggplot(mpg, aes(displ, hwy)) + geom_point()
+plot <- ggplot(mpg, aes(x = displ, y = hwy)) + geom_point()
 ```
 
 ```{r}

quarto/fuel-economy.qmd

@@ -18,7 +18,7 @@ class <- mpg |> filter(class == params$my_class)
 ```{r}
 #| message: false
 
-ggplot(class, aes(displ, hwy)) + 
+ggplot(class, aes(x = displ, y = hwy)) + 
   geom_point() + 
   geom_smooth(se = FALSE)
 ```