parent
0ea0ce5e14
commit
1b782799b0
|
@ -61,7 +61,7 @@ x <- c(1, 4, 5, 7, NA)
|
||||||
coalesce(x, 0)
|
coalesce(x, 0)
|
||||||
```
|
```
|
||||||
|
|
||||||
You could use `mutate()` together with `across()` to apply to every this treatment to (say) every numeric column in a data frame:
|
You could use `mutate()` together with `across()` to apply this treatment to (say) every numeric column in a data frame:
|
||||||
|
|
||||||
```{r, eval = FALSE}
|
```{r, eval = FALSE}
|
||||||
df |>
|
df |>
|
||||||
|
@ -127,9 +127,9 @@ stocks <- tibble(
|
||||||
|
|
||||||
This dataset has two missing observations:
|
This dataset has two missing observations:
|
||||||
|
|
||||||
- The `price` in the fourth quarter of 2021 is explicitly missing, because its value is `NA`.
|
- The `price` in the fourth quarter of 2020 is explicitly missing, because its value is `NA`.
|
||||||
|
|
||||||
- The `price` for the first quarter of 2022 is implicitly missing, because it simply does not appear in the dataset.
|
- The `price` for the first quarter of 2021 is implicitly missing, because it simply does not appear in the dataset.
|
||||||
|
|
||||||
One way to think about the difference is with this Zen-like koan:
|
One way to think about the difference is with this Zen-like koan:
|
||||||
|
|
||||||
|
@ -257,7 +257,7 @@ ggplot(health, aes(smoker)) +
|
||||||
```
|
```
|
||||||
|
|
||||||
The same problem comes up more generally with `dplyr::group_by()`.
|
The same problem comes up more generally with `dplyr::group_by()`.
|
||||||
You can request that all factor levels be preserved with `.drop = TRUE`:
|
And again you can use `.drop = FALSE` to preserve all factor levels:
|
||||||
|
|
||||||
```{r}
|
```{r}
|
||||||
health |>
|
health |>
|
||||||
|
|
Loading…
Reference in New Issue