Aggregate antibiotic usage data by AWaRe group

awr_aggregate(
  df,
  atc = atc,
  ddd = ddd,
  ...,
  tall = FALSE,
  method = getOption("abxaware.method", "dk"),
  ignore.other = FALSE,
  silent = FALSE
)

Arguments

df	Data frame.
atc	ATC code.
ddd	Amount (usually Defined Daily Doses).
...	Grouping variables.
tall	If TRUE (default) outputs data in tall format.
method	'dk' (default), uk or 'who' indicating the AWaRe classification to be used. The default may be changed within an R session using `options(abxaware.method = 'who')`.
ignore.other	If TRUE, ignores drugs that have no AWaRe class.
silent	If TRUE, prints method.

Value

A data frame.

Examples

awr_aggregate(abx_sales)
#> Aggregating data using the "dk" AWaRe classification
#> # A tibble: 1 × 5
#>       total reserve     watch    access other
#>       <dbl>   <dbl>     <dbl>     <dbl> <dbl>
#> 1 25286569. 997134. 10693833. 13592934. 2668.
awr_aggregate(abx_sales, atc, ddd)
#> Aggregating data using the "dk" AWaRe classification
#> # A tibble: 1 × 5
#>       total reserve     watch    access other
#>       <dbl>   <dbl>     <dbl>     <dbl> <dbl>
#> 1 25286569. 997134. 10693833. 13592934. 2668.
awr_aggregate(abx_sales, atc, ddd, region)
#> Aggregating data using the "dk" AWaRe classification
#> # A tibble: 5 × 6
#>   region         total reserve    watch   access other
#>   <chr>          <dbl>   <dbl>    <dbl>    <dbl> <dbl>
#> 1 Hovedstaden 9964511. 572993. 3764616. 5624234. 2668.
#> 2 Midtjylland 4692156. 132510. 2405504. 2154141.    0 
#> 3 Nordjylland 2278616.  36727. 1030179. 1211710.    0 
#> 4 Sjælland    3369530.  71470. 1479130. 1818930.    0 
#> 5 Syddanmark  4981757. 183433. 2014403. 2783921.    0 
awr_aggregate(abx_sales, atc, ddd, region, tall = TRUE)
#> Aggregating data using the "dk" AWaRe classification
#> # A tibble: 16 × 5
#>    aware   region           ddd    total        p
#>    <fct>   <chr>          <dbl>    <dbl>    <dbl>
#>  1 reserve Hovedstaden  572993. 9964511. 0.0575  
#>  2 reserve Midtjylland  132510. 4692156. 0.0282  
#>  3 reserve Nordjylland   36727. 2278616. 0.0161  
#>  4 reserve Sjælland      71470. 3369530. 0.0212  
#>  5 reserve Syddanmark   183433. 4981757. 0.0368  
#>  6 watch   Hovedstaden 3764616. 9964511. 0.378   
#>  7 watch   Midtjylland 2405504. 4692156. 0.513   
#>  8 watch   Nordjylland 1030179. 2278616. 0.452   
#>  9 watch   Sjælland    1479130. 3369530. 0.439   
#> 10 watch   Syddanmark  2014403. 4981757. 0.404   
#> 11 access  Hovedstaden 5624234. 9964511. 0.564   
#> 12 access  Midtjylland 2154141. 4692156. 0.459   
#> 13 access  Nordjylland 1211710. 2278616. 0.532   
#> 14 access  Sjælland    1818930. 3369530. 0.540   
#> 15 access  Syddanmark  2783921. 4981757. 0.559   
#> 16 other   Hovedstaden    2668. 9964511. 0.000268
awr_aggregate(abx_sales, atc, ddd, month)
#> Aggregating data using the "dk" AWaRe classification
#> # A tibble: 79 × 6
#>    month        total reserve   watch  access other
#>    <date>       <dbl>   <dbl>   <dbl>   <dbl> <dbl>
#>  1 2015-01-01 276997.  15936. 125162. 135863.    35
#>  2 2015-02-01 288550.  16781. 132353. 139401.    15
#>  3 2015-03-01 396840.  19708. 181769. 195318.    45
#>  4 2015-04-01 260539.  15093. 118676. 126705.    65
#>  5 2015-05-01 275710.  16959. 123166. 135570.    15
#>  6 2015-06-01 312190.  17329. 133961. 160845.    55
#>  7 2015-07-01 293852.  16140. 124887. 152765.    60
#>  8 2015-08-01 281087.  17058. 119319. 144706.     5
#>  9 2015-09-01 304548.  12741. 131995. 159767.    45
#> 10 2015-10-01 307043.   9933. 138503. 158571.    35
#> # … with 69 more rows
awr_aggregate(abx_sales, atc, ddd, month, region)
#> Aggregating data using the "dk" AWaRe classification
#> # A tibble: 395 × 7
#>    month      region        total reserve  watch access other
#>    <date>     <chr>         <dbl>   <dbl>  <dbl>  <dbl> <dbl>
#>  1 2015-01-01 Hovedstaden  98537.   9311. 39442. 49749     35
#>  2 2015-01-01 Midtjylland  56220.   1874. 29831. 24515      0
#>  3 2015-01-01 Nordjylland  27845.    704. 13895. 13246.     0
#>  4 2015-01-01 Sjælland     37140.    944. 16308. 19887.     0
#>  5 2015-01-01 Syddanmark   57255.   3104. 25686. 28466.     0
#>  6 2015-02-01 Hovedstaden 104359.   9838. 43469. 51037.    15
#>  7 2015-02-01 Midtjylland  57580.   1767. 31091. 24722.     0
#>  8 2015-02-01 Nordjylland  26508.    526. 12927. 13054.     0
#>  9 2015-02-01 Sjælland     37975.   1074. 17294. 19607.     0
#> 10 2015-02-01 Syddanmark   62129.   3576. 27572. 30981.     0
#> # … with 385 more rows
awr_aggregate(abx_sales, atc, ddd, month, region, hospital)
#> Aggregating data using the "dk" AWaRe classification
#> # A tibble: 1,580 × 8
#>    month      region      hospital             total reserve  watch access other
#>    <date>     <chr>       <chr>                <dbl>   <dbl>  <dbl>  <dbl> <dbl>
#>  1 2015-01-01 Hovedstaden Amager og Hvidovre…  9079.    273.  4241.  4565.     0
#>  2 2015-01-01 Hovedstaden Bispebjerg og Fred… 14395.    858.  6235.  7303.     0
#>  3 2015-01-01 Hovedstaden Bornholms Hospital   2152.    147.  1068.   937.     0
#>  4 2015-01-01 Hovedstaden Herlev og Gentofte… 21331.    855.  6715. 13762.     0
#>  5 2015-01-01 Hovedstaden Hospitalerne i Nor… 15470.    849.  6407.  8214.     0
#>  6 2015-01-01 Hovedstaden Rigshospitalet      36109.   6329. 14777. 14968.    35
#>  7 2015-01-01 Midtjylland Aarhus Universitet… 25140.   1229. 12239. 11672.     0
#>  8 2015-01-01 Midtjylland Hospitalsenhed Midt  8762.    170   4589.  4003.     0
#>  9 2015-01-01 Midtjylland Hospitalsenheden V… 10881.    358.  6026.  4497.     0
#> 10 2015-01-01 Midtjylland Regionshospitalet …  6134.     50   3480.  2603.     0
#> # … with 1,570 more rows