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 10 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 11 October 2022.
Now, let’s start the cleaning and the analysis!
So only 53.1% 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 10,620 isolates for analysis. Now our data looks like:
So we end up with 10,684 isolates for analysis. Now our data looks like:
head(data_1st)
Time for the analysis!
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,620 -Available: 10,620 (100.0%, NA: 0 = 0.0%) +Length: 10,684 +Available: 10,684 (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] 0.5410546
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 -0.5427928 +0.5423889 Hospital B -0.5446957 +0.5451578 Hospital C -0.5358423 +0.5370259 Hospital D -0.5362524 +0.5528979 @@ -1008,23 +1008,23 @@ Longest: 24 Hospital A -0.5427928 -3108 +0.5423889 +3173 Hospital B -0.5446957 -3714 +0.5451578 +3676 Hospital C -0.5358423 -1674 +0.5370259 +1661 Hospital D -0.5362524 -2124 +0.5528979 +2174 @@ -1047,27 +1047,27 @@ Longest: 24 Escherichia -0.7762419 -0.8645788 -0.9792657 +0.7618640 +0.8820009 +0.9764857 Klebsiella -0.8307692 -0.9042735 -0.9854701 +0.8284024 +0.9053254 +0.9788673 Staphylococcus -0.7940850 -0.8913124 -0.9818854 +0.7893186 +0.8861878 +0.9804788 Streptococcus -0.5390071 +0.5398482 0.0000000 -0.5390071 +0.5398482 @@ -1092,23 +1092,23 @@ Longest: 24 Hospital A -54.3% -26.1% +54.2% +27.7% Hospital B 54.5% -26.4% +25.9% Hospital C -53.6% -25.1% +53.7% +27.5% Hospital D -53.6% -26.0% +55.3% +26.3% @@ -1206,18 +1206,18 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 0.005 2 1 0.0625 128 0.5 1 >=256 64 -# [10] 0.5 32 16 0.002 0.01 0.025 32 >=256 2 -# [19] <=0.001 0.01 2 0.25 0.5 0.5 32 0.005 0.5 -# [28] 0.125 2 1 0.025 128 0.125 0.002 0.125 16 -# [37] <=0.001 2 0.0625 8 1 0.125 0.002 >=256 0.0625 -# [46] 4 16 4 0.005 0.0625 4 0.002 0.0625 16 -# [55] 2 4 1 0.5 16 0.5 64 128 0.005 -# [64] 128 0.125 0.01 2 0.25 0.5 <=0.001 16 32 -# [73] 2 8 >=256 64 2 0.002 0.002 32 4 -# [82] 0.005 64 32 32 128 0.002 0.002 0.025 >=256 -# [91] 4 4 0.5 0.002 0.25 0.5 32 0.002 4 -# [100] 8
mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 0.005 2 1 0.0625 128 0.5 1 >=256 64 -# [10] 0.5 32 16 0.002 0.01 0.025 32 >=256 2 -# [19] <=0.001 0.01 2 0.25 0.5 0.5 32 0.005 0.5 -# [28] 0.125 2 1 0.025 128 0.125 0.002 0.125 16 -# [37] <=0.001 2 0.0625 8 1 0.125 0.002 >=256 0.0625 -# [46] 4 16 4 0.005 0.0625 4 0.002 0.0625 16 -# [55] 2 4 1 0.5 16 0.5 64 128 0.005 -# [64] 128 0.125 0.01 2 0.25 0.5 <=0.001 16 32 -# [73] 2 8 >=256 64 2 0.002 0.002 32 4 -# [82] 0.005 64 32 32 128 0.002 0.002 0.025 >=256 -# [91] 4 4 0.5 0.002 0.25 0.5 32 0.002 4 -# [100] 8
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class <disk> -# [1] 19 23 20 29 19 24 18 31 30 31 19 19 28 25 19 20 27 20 20 30 22 30 25 27 24 -# [26] 23 29 17 19 23 29 24 26 24 28 17 24 23 22 19 31 26 25 19 23 21 24 28 20 20 -# [51] 26 27 30 27 18 28 27 17 24 31 27 28 24 23 31 26 17 30 24 23 22 24 29 31 30 -# [76] 31 19 18 29 27 28 18 17 30 31 25 24 19 31 27 18 24 20 26 19 17 31 27 29 31
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
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