Introduction to getTBinR

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# Introduction to getTBinR
## Explore WHO Tuberculosis data using R
### Sam Abbott
### 2018/03/06

---

# Aims

1. Introduce [getTBinR](https://www.samabbott.co.uk/getTBinR/)

1. Explore package functionality

1. Look at package structure

1. Outreach

1. Planned functionality

1. Wrap up and analysis ideas

---
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# Background

---

# Motivation

- Attended Epidemics at which there were many great R packages being presented, this made me very jealous!

- All my analytical work is wrapped in R packages so this gave me the tools I needed to quickly make a package.

- Working on the WHO TB data for several projects I found myself constantly downloading it and cleaning it.

- Started developing some tools to make this process quicker, along the way I realised more people might find the package useful.

- getTBinR is born!

---

# World Health Organisation TB data

The WHO provide multiple cleaned datasets via their website. They also have a backend API through which other datasets can be accessed, although these tend to be far from tidy. All analysis ready datasets come with a complete data dictionary.

![](img/who-data.png)

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# Set-up

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# Install the package

CRAN version:

```r
install.packages("getTBinR")
```

Alternatively install the GitHub development version:

```r
install.packages("devtools")
devtools::install_github("seabbs/getTBinR")
```

---

# Packages for this presentation

This presentation makes use of the [tidyverse](https://www.tidyverse.org/), and [knitr](https://yihui.name/knitr/). It was built using [xaringan](https://github.com/yihui/xaringan).

```r
library(getTBinR)
#install.packages("tidyverse")
library(tidyverse)
#install.packages("knitr")
library(knitr)
```

---

# Getting the WHO TB burden data

Get the WHO TB burden data.

```r
tb_burden <- getTBinR::get_tb_burden()
```

```
## Downloading data from: https://extranet.who.int/tme/generateCSV.asp?ds=estimates
```

```
## Saving data to: /tmp/RtmpJgWBcF/TB_burden.rds
```

Inspect the dimensions.

```r
dim(tb_burden)
```

```
## [1] 3651   71
```

---

# Getting the WHO TB burden data

Inspect variables.

```r
names(tb_burden)
```

```
##  [1] "country"                  "iso2"                    
##  [3] "iso3"                     "iso_numeric"             
##  [5] "g_whoregion"              "year"                    
##  [7] "e_pop_num"                "e_inc_100k"              
##  [9] "e_inc_100k_lo"            "e_inc_100k_hi"           
## [11] "e_inc_num"                "e_inc_num_lo"            
## [13] "e_inc_num_hi"             "e_inc_num_f014"          
## [15] "e_inc_num_f014_lo"        "e_inc_num_f014_hi"       
## [17] "e_inc_num_f15plus"        "e_inc_num_f15plus_lo"    
## [19] "e_inc_num_f15plus_hi"     "e_inc_num_f"             
## [21] "e_inc_num_f_lo"           "e_inc_num_f_hi"          
## [23] "e_inc_num_m014"           "e_inc_num_m014_lo"       
## [25] "e_inc_num_m014_hi"        "e_inc_num_m15plus"       
## [27] "e_inc_num_m15plus_lo"     "e_inc_num_m15plus_hi"    
## [29] "e_inc_num_m"              "e_inc_num_m_lo"          
## [31] "e_inc_num_m_hi"           "e_inc_num_014"           
## [33] "e_inc_num_014_lo"         "e_inc_num_014_hi"        
## [35] "e_inc_num_15plus"         "e_inc_num_15plus_lo"     
## [37] "e_inc_num_15plus_hi"      "e_tbhiv_prct"            
## [39] "e_tbhiv_prct_lo"          "e_tbhiv_prct_hi"         
## [41] "e_inc_tbhiv_100k"         "e_inc_tbhiv_100k_lo"     
## [43] "e_inc_tbhiv_100k_hi"      "e_inc_tbhiv_num"         
## [45] "e_inc_tbhiv_num_lo"       "e_inc_tbhiv_num_hi"      
## [47] "e_mort_exc_tbhiv_100k"    "e_mort_exc_tbhiv_100k_lo"
## [49] "e_mort_exc_tbhiv_100k_hi" "e_mort_exc_tbhiv_num"    
## [51] "e_mort_exc_tbhiv_num_lo"  "e_mort_exc_tbhiv_num_hi" 
## [53] "e_mort_tbhiv_100k"        "e_mort_tbhiv_100k_lo"    
## [55] "e_mort_tbhiv_100k_hi"     "e_mort_tbhiv_num"        
## [57] "e_mort_tbhiv_num_lo"      "e_mort_tbhiv_num_hi"     
## [59] "e_mort_100k"              "e_mort_100k_lo"          
## [61] "e_mort_100k_hi"           "e_mort_num"              
## [63] "e_mort_num_lo"            "e_mort_num_hi"           
## [65] "cfr"                      "cfr_lo"                  
## [67] "cfr_hi"                   "c_newinc_100k"           
## [69] "c_cdr"                    "c_cdr_lo"                
## [71] "c_cdr_hi"
```

---

# Getting the WHO TB data dictionary

Get the WHO TB data dictionary.

```r
tb_dict <- getTBinR::get_data_dict()
```

```
## Downloading data from: https://extranet.who.int/tme/generateCSV.asp?ds=dictionary
```

```
## Saving data to: /tmp/RtmpJgWBcF/TB_data_dict.rds
```

Inspect the dimensions.

```r
dim(tb_dict)
```

```
## [1] 402   4
```

Inspect variables.

```r
names(tb_dict)
```

```
## [1] "variable_name" "dataset"       "code_list"     "definition"
```

---
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# Exploring getTBinR functionality

---

# Search for a variable

```r
vars_of_interest <- getTBinR::search_data_dict(var = c("e_inc_100k"),
                                               def = "incidence")
```

```
## Loading data from: /tmp/RtmpJgWBcF/TB_data_dict.rds
```

```
## 1 results found for your variable search for e_inc_100k
```

```
## 12 results found for your definition search for incidence
```

---

# Search Result

```r
kable(vars_of_interest, "html")
```

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> variable_name </th>
   <th style="text-align:left;"> dataset </th>
   <th style="text-align:left;"> code_list </th>
   <th style="text-align:left;"> definition </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> e_inc_100k </td>
   <td style="text-align:left;"> Estimates </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> Estimated incidence (all forms) per 100 000 population </td>
  </tr>
  <tr>
   <td style="text-align:left;"> e_inc_100k_hi </td>
   <td style="text-align:left;"> Estimates </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> Estimated incidence (all forms) per 100 000 population, high bound </td>
  </tr>
  <tr>
   <td style="text-align:left;"> e_inc_100k_lo </td>
   <td style="text-align:left;"> Estimates </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> Estimated incidence (all forms) per 100 000 population, low bound </td>
  </tr>
  <tr>
   <td style="text-align:left;"> e_inc_rr_num </td>
   <td style="text-align:left;"> Estimates </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> Estimated incidence of rifampicin resistant TB (absolute number) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> e_inc_rr_num_hi </td>
   <td style="text-align:left;"> Estimates </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> Estimated incidence of rifampicin resistant TB (absolute number): high bound </td>
  </tr>
  <tr>
   <td style="text-align:left;"> e_inc_rr_num_lo </td>
   <td style="text-align:left;"> Estimates </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> Estimated incidence of rifampicin resistant TB (absolute number): low bound </td>
  </tr>
  <tr>
   <td style="text-align:left;"> e_inc_tbhiv_100k </td>
   <td style="text-align:left;"> Estimates </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> Estimated incidence of TB cases who are HIV-positive per 100 000 population </td>
  </tr>
  <tr>
   <td style="text-align:left;"> e_inc_tbhiv_100k_hi </td>
   <td style="text-align:left;"> Estimates </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> Estimated incidence of TB cases who are HIV-positive per 100 000 population, high bound </td>
  </tr>
  <tr>
   <td style="text-align:left;"> e_inc_tbhiv_100k_lo </td>
   <td style="text-align:left;"> Estimates </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> Estimated incidence of TB cases who are HIV-positive per 100 000 population, low bound </td>
  </tr>
  <tr>
   <td style="text-align:left;"> e_inc_tbhiv_num </td>
   <td style="text-align:left;"> Estimates </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> Estimated incidence of TB cases who are HIV-positive </td>
  </tr>
  <tr>
   <td style="text-align:left;"> e_inc_tbhiv_num_hi </td>
   <td style="text-align:left;"> Estimates </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> Estimated incidence of TB cases who are HIV-positive, high bound </td>
  </tr>
  <tr>
   <td style="text-align:left;"> e_inc_tbhiv_num_lo </td>
   <td style="text-align:left;"> Estimates </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> Estimated incidence of TB cases who are HIV-positive, low bound </td>
  </tr>
</tbody>
</table>

---

# Map Global Incidence Rates

```r
getTBinR::map_tb_burden(metric = "e_inc_100k", verbose = FALSE, interactive = FALSE)
```

![](presentation_files/figure-html/map-global-inc-1.svg)

---

# Get 10 countries with highest incidence rates in 2016

```r
high_inc_countries <- tb_burden %>% 
  filter(year == 2016) %>% 
  group_by(country) %>% 
  summarise(e_inc_100k = max(e_inc_100k)) %>% 
  ungroup %>% 
  arrange(desc(e_inc_100k)) %>% 
  slice(1:10) %>% 
  pull(country) %>% 
  unique
```

---

# 10 countries with highest incidence rates in 2016

```r
high_inc_countries
```

```
##  [1] "South Africa"                         
##  [2] "Lesotho"                              
##  [3] "Kiribati"                             
##  [4] "Philippines"                          
##  [5] "Mozambique"                           
##  [6] "Democratic People's Republic of Korea"
##  [7] "Timor-Leste"                          
##  [8] "Gabon"                                
##  [9] "Namibia"                              
## [10] "Papua New Guinea"
```

---

# Overview of TB incidence rates

For the countries with the 10 highest incidence rates

```r
getTBinR::plot_tb_burden_overview(metric = "e_inc_100k", countries = high_inc_countries,
                                  verbose = FALSE)
```

![](presentation_files/figure-html/plot-tb-inc-overview-1.svg)
---

# Trends over time in TB incidence rates

For the countries with the 10 highest incidence rates

```r
plot_tb_burden(metric = "e_inc_100k",  
               countries = high_inc_countries,
               facet = "country", scales = "free_y",
               verbose = FALSE)
```

![](presentation_files/figure-html/plot-inc--by-country-1.svg)

---

# For non R users

getTBinR has a built in `shiny` dashboard which can be run locally using `getTBinR::run_tb_dashboard()`. Alternatively there is a [hosted version](http://www.seabbs.co.uk/shiny/ExploreGlobalTB/).

![](img/ExploreGlobalTB.png)
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# Package Structure

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# Documentation

Documentation is built using `roxygen`, this is then used to generate a package website using `pkgdown`. See [here](https://github.com/seabbs/getTBinR/blob/master/_pkgdown.yml) for the yaml and [here](https://www.samabbott.co.uk/getTBinR/index.html) for the site.

![](img/pkgdown_site.png)

---

# Testing

- Unit tests are implemented using the `testthat` package.

- The package is then tested on Linux and Mac using [Travis](https://travis-ci.com/), and on Windows using [AppVeyor](https://ci.appveyor.com/login). See [here](https://github.com/seabbs/getTBinR/tree/master/tests/testthat) for tests, [here](https://github.com/seabbs/getTBinR/blob/master/.travis.yml) for travis set-up , and [here] for [here](https://github.com/seabbs/getTBinR/blob/master/appveyor.yml) Appveyor set-up.

- Code coverage is then checked using [Codecov](https://codecov.io/github/seabbs/getTBinR?branch=master).

Example test:

```r
test_that("TB burden data is the same when downloaded using utils::read.csv", {
  tb_data_utils <- get_tb_burden(download_data = TRUE,
                                 use_utils = TRUE,
                                 burden_save_name = "TB_with_utils")
  
  skip_on_cran()
  expect_equal(tb_data, tb_data_utils)
})
```

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# Outreach

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# Blog posts

I have written several blog posts which have then be editted into vignettes, see [here](https://www.samabbott.co.uk/tags/who/)

![](img/blog.png)

---

# Gists and Tweets

Gists [here](https://gist.github.com/seabbs) and twitter [here](https://twitter.com/seabbs).

![](img/gists.png)

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# Planned Funtionality

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# Issues

Currently planned functionality has been outlined in the GitHub issues, found [here](https://github.com/seabbs/getTBinR/issues).

![](img/issues.png)

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# Wrap up

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# Wrap up

Hopefully you enjoyed your whistle stop tour of `getTBinR` and R package construction. I'm interested in your ideas of what can be done with this data. An example;

- Multilevel model to explore country level contributions to incidence rates.
---