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Fixes in tidy (#210) 

* Fixed URL to WHO data

The link was not rendered as missing the protocol.

* Typos
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Christian Mongeau 2016-07-31 18:33:58 +02:00 committed by Hadley Wickham
parent fe73722b0a
commit 3eb371e111
1 changed files with 3 additions and 3 deletions
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tidy.Rmd
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@ -171,7 +171,7 @@ Spreading is the opposite of gathering. You use it when an observation is scatte
table2
```
To tidy this up, we first analysis the representation in similar way to `gather()`. This time, however, we only need two parameters:
To tidy this up, we first analyse the representation in similar way to `gather()`. This time, however, we only need two parameters:
* The column that contains variable names, the `key` column. Here, it's
`type`.
@ -380,7 +380,7 @@ stocks %>%
`complete()` takes a set of columns, and finds all unique combinations. It then ensures the original dataset contains all those values, filling in explicit `NA`s where necessary.
There's one other important tool that you should know for working with missing values. Sometimes when a data source has primarily been used for data entry, missing values indicate the the previous value should be carried forward:
There's one other important tool that you should know for working with missing values. Sometimes when a data source has primarily been used for data entry, missing values indicate that the previous value should be carried forward:
```{r}
treatment <- frame_data(
@ -407,7 +407,7 @@ treatment %>%
## Case Study
To finish off the chapter, let's pull together everything you've learned to tackle a realistic data tidying problem. The `tidyr::who` dataset contains reporter tuberculosis (TB) cases broken down by year, country, age, gender, and diagnosis method. The data comes from the *2014 World Health Organization Global Tuberculosis Report*, available for download at <www.who.int/tb/country/data/download/en/>.
To finish off the chapter, let's pull together everything you've learned to tackle a realistic data tidying problem. The `tidyr::who` dataset contains reporter tuberculosis (TB) cases broken down by year, country, age, gender, and diagnosis method. The data comes from the *2014 World Health Organization Global Tuberculosis Report*, available for download at <http://www.who.int/tb/country/data/download/en/>.
There's a wealth of epidemiological information in this dataset, but it's challenging to work with the data in the form that it's provided: