6.7 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 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 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:

#> # A tibble: 7,240 x 60
#>   country iso2  iso3   year new_sp_m014 new_sp_m1524 new_sp_m2534 new_sp_m3544
#>   <chr>   <chr> <chr> <int>       <int>        <int>        <int>        <int>
#> 1 Afghan~ AF    AFG    1980          NA           NA           NA           NA
#> 2 Afghan~ AF    AFG    1981          NA           NA           NA           NA
#> 3 Afghan~ AF    AFG    1982          NA           NA           NA           NA
#> 4 Afghan~ AF    AFG    1983          NA           NA           NA           NA
#> 5 Afghan~ AF    AFG    1984          NA           NA           NA           NA
#> 6 Afghan~ AF    AFG    1985          NA           NA           NA           NA
#> # ... with 7,234 more rows, and 52 more variables: new_sp_m4554 <int>,
#> #   new_sp_m5564 <int>, new_sp_m65 <int>, new_sp_f014 <int>,
#> #   new_sp_f1524 <int>, new_sp_f2534 <int>, new_sp_f3544 <int>,
#> #   new_sp_f4554 <int>, new_sp_f5564 <int>, new_sp_f65 <int>,
#> #   new_sn_m014 <int>, new_sn_m1524 <int>, new_sn_m2534 <int>,
#> #   new_sn_m3544 <int>, new_sn_m4554 <int>, new_sn_m5564 <int>,
#> #   new_sn_m65 <int>, new_sn_f014 <int>, new_sn_f1524 <int>,
#> #   new_sn_f2534 <int>, new_sn_f3544 <int>, new_sn_f4554 <int>,
#> #   new_sn_f5564 <int>, new_sn_f65 <int>, new_ep_m014 <int>,
#> #   new_ep_m1524 <int>, new_ep_m2534 <int>, new_ep_m3544 <int>,
#> #   new_ep_m4554 <int>, new_ep_m5564 <int>, new_ep_m65 <int>,
#> #   new_ep_f014 <int>, new_ep_f1524 <int>, new_ep_f2534 <int>,
#> #   new_ep_f3544 <int>, new_ep_f4554 <int>, new_ep_f5564 <int>,
#> #   new_ep_f65 <int>, newrel_m014 <int>, newrel_m1524 <int>,
#> #   newrel_m2534 <int>, newrel_m3544 <int>, newrel_m4554 <int>,
#> #   newrel_m5564 <int>, newrel_m65 <int>, newrel_f014 <int>,
#> #   newrel_f1524 <int>, newrel_f2534 <int>, newrel_f3544 <int>,
#> #   newrel_f4554 <int>, newrel_f5564 <int>, newrel_f65 <int>

This is a very typical real-life example dataset. It contains redundant columns, odd variable codes, and many missing values. In short, who is messy, and we’ll need multiple steps to tidy it. Like dplyr, tidyr is designed so that each function does one thing well. That means in real-life situations you’ll usually need to string together multiple verbs into a pipeline. The best place to start is almost always to gather together the columns that are not variables. Let’s have a look at what we’ve got:
* It looks like country, iso2, and iso3 are three variables that redundantly specify the country.
* year is also a variable
* We don’t know what all the other columns are yet, but given the structure 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 pivot 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 "name". 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 = TRUE just so we can focus on the values that are present.

We can get some hint of the structure of the values in the new name column by counting them:

You might be able to parse this out by yourself with a little thought and some experimentation, but luckily we have the data dictionary handy. It tells us:
1. The first three letters of each column denote whether the column contains new or old cases of TB. In this dataset, each column contains new cases.
2. The next two letters describe the type of TB: * rel stands for cases of relapse
* 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 a pulmonary smear (smear positive)
3. The sixth letter gives the sex of TB patients. The dataset groups cases by males (m) and females (f). 4. The remaining numbers gives the age group. The dataset groups cases into seven age groups:
* 014 = 0 – 14 years old
* 1524 = 15 – 24 years old
* 2534 = 25 – 34 years old
* 3544 = 35 – 44 years old
* 4554 = 45 – 54 years old
* 5564 = 55 – 64 years old
* 65 = 65 or older

We need to make a minor fix to the format of the column names: unfortunately the names are slightly inconsistent because instead of new_rel we have newrel (it’s hard to spot this here but if you don’t fix it we’ll get errors in subsequent steps). You’ll learn about str_replace() in strings, but the basic idea is pretty simple: replace the characters “newrel” with “new_rel”. This makes all variable names consistent.

We can separate the values in each code with two passes of extracct()(This can also be done in the previous pivot_longer() step by passing a vector to names_to).

Then, let’s also drop iso2 and iso3 since they’re redundant (see Exercise 6.6 below for proof).

Now who4 is a tidy version of tidyr::who!