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typos (#1403)

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Mitsuo Shiota 2023-04-08 23:36:31 +09:00 committed by GitHub
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@ -291,7 +291,7 @@ df |>
How does the reshaping work?
It's easier to see if we think about it column by column.
As shown in @fig-pivot-variables, the values in column that was already a variable in the original dataset (`var`) need to be repeated, once for each column that is pivoted.
As shown in @fig-pivot-variables, the values in column that was already a variable in the original dataset (`id`) need to be repeated, once for each column that is pivoted.
```{r}
#| label: fig-pivot-variables
@ -360,7 +360,7 @@ There are two columns that are already variables and are easy to interpret: `cou
They are followed by 56 columns like `sp_m_014`, `ep_m_4554`, and `rel_m_3544`.
If you stare at these columns for long enough, you'll notice there's a pattern.
Each column name is made up of three pieces separated by `_`.
The first piece, `sp`/`rel`/`ep`, describes the method used for the diagnosis, the second piece, `m`/`f` is the `gender` (coded as a binary variable in this dataset), and the third piece, `014`/`1524`/`2535`/`3544`/`4554`/`65` is the `age` range (`014` represents 0-14, for example).
The first piece, `sp`/`rel`/`ep`, describes the method used for the diagnosis, the second piece, `m`/`f` is the `gender` (coded as a binary variable in this dataset), and the third piece, `014`/`1524`/`2534`/`3544`/`4554`/`5564/``65` is the `age` range (`014` represents 0-14, for example).
So in this case we have six pieces of information recorded in `who2`: the country and the year (already columns); the method of diagnosis, the gender category, and the age range category (contained in the other column names); and the count of patients in that category (cell values).
To organize these six pieces of information in six separate columns, we use `pivot_longer()` with a vector of column names for `names_to` and instructors for splitting the original variable names into pieces for `names_sep` as well as a column name for `values_to`: