Count isolates

These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in dplyrs summarise and support grouped variables, see Examples.

count_R and count_IR can be used to count resistant isolates, count_S and count_SI can be used to count susceptible isolates.

count_R(..., also_single_tested = FALSE)

count_IR(..., also_single_tested = FALSE)

count_I(..., also_single_tested = FALSE)

count_SI(..., also_single_tested = FALSE)

count_S(..., also_single_tested = FALSE)

count_all(...)

n_rsi(...)

count_df(data, translate_ab = getOption("get_antibiotic_names",
  "official"), combine_IR = FALSE)

Arguments

...	one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with as.rsi if needed.
also_single_tested	a logical to indicate whether (in combination therapies) also observations should be included where not all antibiotics were tested, but at least one of the tested antibiotics contains a target interpretation (e.g. S in case of portion_S and R in case of portion_R). This would lead to selection bias in almost all cases.
data	a data.frame containing columns with class rsi (see as.rsi)
translate_ab	a column name of the antibiotics data set to translate the antibiotic abbreviations to, using abname. This can be set with getOption("get_antibiotic_names").
combine_IR	a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. IR (susceptible vs. non-susceptible)

Source

Wickham H. Tidy Data. The Journal of Statistical Software, vol. 59, 2014. http://vita.had.co.nz/papers/tidy-data.html

Value

Integer

Details

These functions are meant to count isolates. Use the portion_* functions to calculate microbial resistance.

n_rsi is an alias of count_all. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to n_distinct. Their function is equal to count_S(...) + count_IR(...).

count_df takes any variable from data that has an "rsi" class (created with as.rsi) and counts the amounts of R, I and S. The resulting tidy data (see Source) data.frame will have three rows (S/I/R) and a column for each variable with class "rsi".

See also

portion_* to calculate microbial resistance and susceptibility.

Examples

# septic_patients is a data set available in the AMR package. It is true, genuine data.
?septic_patients
#> Rendering development documentation for 'septic_patients'

# Count resistant isolates
count_R(septic_patients$amox)
#> [1] 683
count_IR(septic_patients$amox)
#> [1] 686

# Or susceptible isolates
count_S(septic_patients$amox)
#> [1] 486
count_SI(septic_patients$amox)
#> [1] 489

# Count all available isolates
count_all(septic_patients$amox)
#> [1] 1172
n_rsi(septic_patients$amox)
#> [1] 1172

# Since n_rsi counts available isolates, you can
# calculate back to count e.g. non-susceptible isolates.
# This results in the same:
count_IR(septic_patients$amox)
#> [1] 686
portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
#> [1] 686

library(dplyr)
septic_patients %>%
  group_by(hospital_id) %>%
  summarise(R  = count_R(cipr),
            I  = count_I(cipr),
            S  = count_S(cipr),
            n1 = count_all(cipr), # the actual total; sum of all three
            n2 = n_rsi(cipr),     # same - analogous to n_distinct
            total = n())          # NOT the amount of tested isolates!
#> # A tibble: 4 x 7
#>   hospital_id     R     I     S    n1    n2 total
#>   <fct>       <int> <int> <int> <int> <int> <int>
#> 1 A              26     3   178   207   207   321
#> 2 B              85    35   379   499   499   663
#> 3 C              41    22   139   202   202   254
#> 4 D              76     9   416   501   501   762

# Count co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy.
# Please mind that `portion_S` calculates percentages right away instead.
count_S(septic_patients$amcl)   # S = 1057 (67.1%)
#> [1] 1291
count_all(septic_patients$amcl) # n = 1576
#> [1] 1828

count_S(septic_patients$gent)   # S = 1372 (74.0%)
#> [1] 1372
count_all(septic_patients$gent) # n = 1855
#> [1] 1855

with(septic_patients,
     count_S(amcl, gent))       # S = 1396 (92.0%)
#> [1] 1609
with(septic_patients,           # n = 1517
     n_rsi(amcl, gent))
#> [1] 1747

# Get portions S/I/R immediately of all rsi columns
septic_patients %>%
  select(amox, cipr) %>%
  count_df(translate = FALSE)
#>   Interpretation Antibiotic Value
#> 1              S       amox   486
#> 2              I       amox     3
#> 3              R       amox   683
#> 4              S       cipr  1112
#> 5              I       cipr    69
#> 6              R       cipr   228

# It also supports grouping variables
septic_patients %>%
  select(hospital_id, amox, cipr) %>%
  group_by(hospital_id) %>%
  count_df(translate = FALSE)
#> # A tibble: 24 x 4
#>    Interpretation hospital_id Antibiotic Value
#>    <ord>          <fct>       <chr>      <int>
#>  1 S              A           amox          63
#>  2 S              B           amox         141
#>  3 S              C           amox          59
#>  4 S              D           amox         223
#>  5 I              A           amox           0
#>  6 I              B           amox           1
#>  7 I              C           amox           0
#>  8 I              D           amox           2
#>  9 R              A           amox          87
#> 10 R              B           amox         231
#> # ... with 14 more rows