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Small typos in tidy chapter (#821) 

* Fix typo (names_ptypes)

* Replace na.rm with values_drop_na

* Fix typo (be to by)
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Maria Paula Caldas 2020-10-03 16:45:39 +02:00 committed by GitHub
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@ -220,8 +220,8 @@ As you might have guessed from their names, `pivot_wider()` and `pivot_longer()`
(Hint: look at the variable types and think about column _names_.)
`pivot_longer()` has a `names_ptype` argument, e.g.
`names_ptype = list(year = double())`. What does it do?
`pivot_longer()` has a `names_ptypes` argument, e.g.
`names_ptypes = list(year = double())`. What does it do?
1. Why does this code fail?
@ -450,7 +450,7 @@ The best place to start is almost always to gather together the columns that are
in the variable names (e.g. `new_sp_m014`, `new_ep_m014`, `new_ep_f014`)
these are likely to be values, not variables.
So we need to gather together all the columns from `new_sp_m014` to `newrel_f65`. We don't know what those values represent yet, so we'll give them the generic name `"key"`. We know the cells represent the count of cases, so we'll use the variable `cases`. There are a lot of missing values in the current representation, so for now we'll use `na.rm` just so we can focus on the values that are present.
So we need to gather together all the columns from `new_sp_m014` to `newrel_f65`. We don't know what those values represent yet, so we'll give them the generic name `"key"`. We know the cells represent the count of cases, so we'll use the variable `cases`. There are a lot of missing values in the current representation, so for now we'll use `values_drop_na` just so we can focus on the values that are present.
```{r}
who1 <- who %>%
@ -482,7 +482,7 @@ You might be able to parse this out by yourself with a little thought and some e
* `ep` stands for cases of extrapulmonary TB
* `sn` stands for cases of pulmonary TB that could not be diagnosed by
a pulmonary smear (smear negative)
* `sp` stands for cases of pulmonary TB that could be diagnosed be
* `sp` stands for cases of pulmonary TB that could be diagnosed by
a pulmonary smear (smear positive)
3. The sixth letter gives the sex of TB patients. The dataset groups