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 28 August 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 29 August 2022.
So only 53.6% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
So only 53.4% 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 10,729 isolates for analysis. Now our data looks like:
So we end up with 10,686 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,729 -Available: 10,729 (100.0%, NA: 0 = 0.0%) +Length: 10,686 +Available: 10,686 (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] 0.5527076
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 -0.5551059 +0.5344992 Hospital B -0.5576464 +0.5487072 Hospital C -0.5445860 +0.5348558 Hospital D -0.5464581 +0.5449219 @@ -1008,23 +1008,23 @@ Longest: 24 Hospital A -0.5551059 -3212 +0.5344992 +3145 Hospital B -0.5576464 -3773 +0.5487072 +3829 Hospital C -0.5445860 -1570 +0.5348558 +1664 Hospital D -0.5464581 -2174 +0.5449219 +2048 @@ -1047,27 +1047,27 @@ Longest: 24 Escherichia -0.7560189 -0.8770421 -0.9804385 +0.7673729 +0.8737288 +0.9779661 Klebsiella -0.8309623 -0.9062762 -0.9866109 +0.8286684 +0.9126378 +0.9838846 Staphylococcus -0.7805057 -0.8809088 -0.9783804 +0.8027757 +0.8867787 +0.9788167 Streptococcus -0.5332095 +0.5451440 0.0000000 -0.5332095 +0.5451440 @@ -1092,23 +1092,23 @@ Longest: 24 Hospital A -55.5% -28.0% +53.4% +25.3% Hospital B -55.8% -27.5% +54.9% +26.0% Hospital C -54.5% -27.8% +53.5% +25.7% Hospital D -54.6% -26.2% +54.5% +27.0% @@ -1206,18 +1206,18 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 2 64 0.25 >=256 4 0.025 128 0.125 2 -# [10] 0.25 32 32 2 0.25 32 0.125 16 32 -# [19] 0.025 1 16 0.01 64 8 32 128 2 -# [28] 1 2 0.002 0.01 2 0.25 0.002 0.01 1 -# [37] 4 8 0.005 0.002 0.005 0.025 8 0.5 8 -# [46] 128 0.25 0.025 0.01 0.01 128 32 1 <=0.001 -# [55] 16 0.002 8 0.0625 0.125 64 0.125 0.0625 0.5 -# [64] 0.025 <=0.001 128 0.01 0.01 1 0.002 >=256 128 -# [73] 0.005 32 0.25 0.002 0.25 0.5 0.5 16 16 -# [82] 2 64 0.005 2 0.025 2 32 0.005 0.0625 -# [91] <=0.001 0.0625 64 0.002 0.025 0.005 64 0.002 1 -# [100] 32
mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 2 64 0.25 >=256 4 0.025 128 0.125 2 -# [10] 0.25 32 32 2 0.25 32 0.125 16 32 -# [19] 0.025 1 16 0.01 64 8 32 128 2 -# [28] 1 2 0.002 0.01 2 0.25 0.002 0.01 1 -# [37] 4 8 0.005 0.002 0.005 0.025 8 0.5 8 -# [46] 128 0.25 0.025 0.01 0.01 128 32 1 <=0.001 -# [55] 16 0.002 8 0.0625 0.125 64 0.125 0.0625 0.5 -# [64] 0.025 <=0.001 128 0.01 0.01 1 0.002 >=256 128 -# [73] 0.005 32 0.25 0.002 0.25 0.5 0.5 16 16 -# [82] 2 64 0.005 2 0.025 2 32 0.005 0.0625 -# [91] <=0.001 0.0625 64 0.002 0.025 0.005 64 0.002 1 -# [100] 32
# base R: plot(mic_values)
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")