vignettes/AMR.Rmd
AMR.Rmd
Note: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 11 October 2022.
Note: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 14 October 2022.
So only 53.4% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
So only 53.8% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
data_1st <- data %>% filter(first == TRUE)
data_1st <- data %>% filter_first_isolate() # Including isolates from ICU.
So we end up with 10,684 isolates for analysis. Now our data looks like:
So we end up with 10,765 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,684 -Available: 10,684 (100.0%, NA: 0 = 0.0%) +Length: 10,765 +Available: 10,765 (100.0%, NA: 0 = 0.0%) Unique: 4
Shortest: 16 Longest: 24
If you want to get a quick glance of the number of isolates in different bug/drug combinations, you can use the bug_drug_combinations() function:
bug_drug_combinations()
As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (proportion_R(), equal to resistance()) and susceptibility as the proportion of S and I (proportion_SI(), equal to susceptibility()). These functions can be used on their own:
proportion_R()
resistance()
proportion_SI()
susceptibility()
data_1st %>% resistance(AMX) -# [1] 0.5446462
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
data_1st %>% @@ -975,19 +975,19 @@ Longest: 24 Hospital A -0.5423889 +0.5467334 Hospital B -0.5451578 +0.5472356 Hospital C -0.5370259 +0.5282083 Hospital D -0.5528979 +0.5523125 @@ -1008,23 +1008,23 @@ Longest: 24 Hospital A -0.5423889 -3173 +0.5467334 +3199 Hospital B -0.5451578 -3676 +0.5472356 +3726 Hospital C -0.5370259 -1661 +0.5282083 +1613 Hospital D -0.5528979 -2174 +0.5523125 +2227 @@ -1047,27 +1047,27 @@ Longest: 24 Escherichia -0.7618640 -0.8820009 -0.9764857 +0.7664876 +0.8749731 +0.9735768 Klebsiella -0.8284024 -0.9053254 -0.9788673 +0.8436179 +0.9044801 +0.9873204 Staphylococcus -0.7893186 -0.8861878 -0.9804788 +0.7971851 +0.8812703 +0.9765428 Streptococcus -0.5398482 +0.5343228 0.0000000 -0.5398482 +0.5343228 @@ -1092,23 +1092,23 @@ Longest: 24 Hospital A -54.2% -27.7% +54.7% +27.4% Hospital B -54.5% -25.9% +54.7% +26.0% Hospital C -53.7% -27.5% +52.8% +24.9% Hospital D -55.3% -26.3% +55.2% +26.5% @@ -1206,18 +1206,18 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 8 32 0.125 4 0.0625 32 0.5 0.01 8 -# [10] 0.01 8 1 2 16 0.002 0.25 128 128 -# [19] 256 0.125 <=0.001 0.125 <=0.001 0.125 32 128 16 -# [28] 8 0.125 <=0.001 128 0.005 0.5 1 2 0.25 -# [37] 0.125 0.5 4 0.005 0.5 16 0.002 0.0625 2 -# [46] 0.01 256 <=0.001 64 16 1 2 64 4 -# [55] 0.0625 128 0.002 0.25 8 1 0.005 0.5 16 -# [64] 16 0.25 4 32 2 0.005 32 0.5 0.01 -# [73] 0.01 4 256 256 <=0.001 0.25 32 8 0.01 -# [82] 0.0625 0.01 0.5 0.25 <=0.001 64 1 0.5 16 -# [91] 0.0625 256 4 4 2 2 0.0625 0.125 0.5 -# [100] 4
mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 8 32 0.125 4 0.0625 32 0.5 0.01 8 -# [10] 0.01 8 1 2 16 0.002 0.25 128 128 -# [19] 256 0.125 <=0.001 0.125 <=0.001 0.125 32 128 16 -# [28] 8 0.125 <=0.001 128 0.005 0.5 1 2 0.25 -# [37] 0.125 0.5 4 0.005 0.5 16 0.002 0.0625 2 -# [46] 0.01 256 <=0.001 64 16 1 2 64 4 -# [55] 0.0625 128 0.002 0.25 8 1 0.005 0.5 16 -# [64] 16 0.25 4 32 2 0.005 32 0.5 0.01 -# [73] 0.01 4 256 256 <=0.001 0.25 32 8 0.01 -# [82] 0.0625 0.01 0.5 0.25 <=0.001 64 1 0.5 16 -# [91] 0.0625 256 4 4 2 2 0.0625 0.125 0.5 -# [100] 4
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class <disk> -# [1] 29 24 25 24 31 25 20 22 25 17 19 29 29 27 31 21 23 23 29 17 25 21 25 29 31 -# [26] 20 23 23 20 24 27 22 18 30 21 22 21 28 27 19 24 22 19 30 29 20 17 19 25 19 -# [51] 19 24 26 21 21 27 24 22 21 30 31 29 25 19 24 25 24 25 20 26 28 20 20 19 27 -# [76] 22 22 24 31 24 22 28 22 22 23 26 17 28 18 28 23 22 26 26 21 25 31 22 31 31
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")