Summarise a TB Metric - A Quick Look Summary

This function provides a curated list of summary measures for a given TB metric in countries of interest. It can been used to facilitate reporting and is used extensively in the TB report included in the package (see render_country_report). It outputs the most recent year of data in the target country for a given metric, along with the year this data was recorded, the regional and global rank and the average change in the last decade. For a more customisable metric summary see summarise_tb_burden as a starting point.

summarise_metric(df = NULL, metric = NULL, countries = NULL,
  conf = c("_lo", "_hi"), download_data = TRUE, save = TRUE,
  verbose = TRUE, ...)

Arguments

df	Dataframe of TB burden data, as sourced by get_tb_burden. If not specified then will source the WHO TB burden data, either locally if available or directly from the WHO (if download_data = TRUE).
metric	Character string specifying the metric to plot
countries	A character string specifying the countries to target.
conf	Character vector specifying the name variations to use to specify the upper and lower confidence intervals. Defaults to c("_lo", "_hi"), if set to NULL then no confidence intervals are shown.
download_data	Logical, defaults to TRUE. If not found locally should the data be downloaded from the specified URL?
save	Logical, should the data be saved for reuse during the current R session. Defaults to TRUE. If TRUE then the data is saved to the temporary directory specified by tempdir.
verbose	Logical, defaults to TRUE. Should additional status and progress messages be displayed.
...	Additional arguments to pass to get_tb_burden.

Value

A tibble containing the name of the target country, the year of the most recent data available, the most recent value for the metric, the regional rank, the global rank and the average change in the previous decade.

Examples

## Get a summary of TB incidence rates for the united kingdom and germany
summarise_metric(metric = "e_inc_100k", countries = c("United Kingdom", "Germany"))
#> Loading data from: /tmp/RtmpA3cD0E/tb_burden.rds
#> Loading data from: /tmp/RtmpA3cD0E/mdr_tb.rds
#> Joining TB burden data and MDR TB data.
#> # A tibble: 2 x 6
#>   country         year metric          world_rank region_rank avg_change
#>   <chr>          <int> <chr>                <int>       <int> <chr>     
#> 1 United Kingdom  2018 8 (7.2 - 8.8)          165          33 -5.9%     
#> 2 Germany         2018 7.3 (6.2 - 8.4)        166          34 2.6%      

## Get a summary of case detection rates in France
summarise_metric(metric = "c_cdr", countries = "France")
#> Loading data from: /tmp/RtmpA3cD0E/tb_burden.rds
#> Loading data from: /tmp/RtmpA3cD0E/mdr_tb.rds
#> Joining TB burden data and MDR TB data.
#> # A tibble: 1 x 6
#>   country  year metric       world_rank region_rank avg_change
#>   <chr>   <int> <chr>             <int>       <int> <chr>     
#> 1 France   2018 83 (73 - 94)         93          37 0%        

## Get a summary of case detection rates in France - without confidence intervals
summarise_metric(metric = "c_cdr", countries = "France", conf = NULL)
#> Loading data from: /tmp/RtmpA3cD0E/tb_burden.rds
#> Loading data from: /tmp/RtmpA3cD0E/mdr_tb.rds
#> Joining TB burden data and MDR TB data.
#> # A tibble: 1 x 6
#>   country  year metric world_rank region_rank avg_change
#>   <chr>   <int>  <dbl>      <int>       <int> <chr>     
#> 1 France   2018     83         93          37 0%        

## Provide a dataset and get summary measures from it. 
tb <- get_tb_burden()
#> Loading data from: /tmp/RtmpA3cD0E/tb_burden.rds
#> Loading data from: /tmp/RtmpA3cD0E/mdr_tb.rds
#> Joining TB burden data and MDR TB data.
summarise_metric(df = tb, metric = "c_cdr", countries = "France")
#> # A tibble: 1 x 6
#>   country  year metric       world_rank region_rank avg_change
#>   <chr>   <int> <chr>             <int>       <int> <chr>     
#> 1 France   2018 83 (73 - 94)         93          37 0%