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 15 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 18 October 2022.
So only 53.2% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
So only 53.5% 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 10,641 isolates for analysis. Now our data looks like:
So we end up with 10,701 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: 10,641 -Available: 10,641 (100.0%, NA: 0 = 0.0%) +Length: 10,701 +Available: 10,701 (100.0%, NA: 0 = 0.0%) Unique: 4
Shortest: 16 Longest: 24
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] 0.5466591
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 -0.5511962 +0.5472908 Hospital B -0.5518367 +0.5424089 Hospital C -0.5463475 +0.5286935 Hospital D -0.5317893 +0.5414567 @@ -1008,23 +1008,23 @@ Longest: 24 Hospital A -0.5511962 -3135 +0.5472908 +3119 Hospital B -0.5518367 -3675 +0.5424089 +3761 Hospital C -0.5463475 -1629 +0.5286935 +1638 Hospital D -0.5317893 -2202 +0.5414567 +2183 @@ -1047,27 +1047,27 @@ Longest: 24 Escherichia -0.7737634 -0.8752688 -0.9750538 +0.7729049 +0.8753799 +0.9780721 Klebsiella -0.8398230 -0.8964602 -0.9787611 +0.8208829 +0.8989813 +0.9813243 Staphylococcus -0.7914244 -0.8902616 -0.9792878 +0.7931661 +0.8876772 +0.9756452 Streptococcus -0.5400664 +0.5420129 0.0000000 -0.5400664 +0.5420129 @@ -1092,23 +1092,23 @@ Longest: 24 Hospital A -55.1% -26.5% +54.7% +26.4% Hospital B -55.2% -26.7% +54.2% +26.5% Hospital C -54.6% -25.0% +52.9% +25.6% Hospital D -53.2% -25.3% +54.1% +26.4% @@ -1206,16 +1206,18 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 128 64 128 0.001 4 0.25 64 0.5 32 0.25 -# [11] 0.025 0.002 1 16 0.005 0.025 0.25 0.005 0.001 64 -# [21] 0.025 1 16 >=256 16 1 16 0.001 0.125 64 -# [31] 8 0.002 0.5 0.005 0.005 4 8 0.002 0.025 32 -# [41] 0.0625 0.001 0.125 2 8 16 0.25 1 128 4 -# [51] 0.001 0.125 0.005 0.25 0.5 0.0625 0.0625 0.005 0.5 1 -# [61] 0.005 16 4 0.025 32 16 128 0.5 16 4 -# [71] 0.002 1 0.5 0.5 0.025 8 32 16 64 32 -# [81] 4 32 2 >=256 0.125 0.025 >=256 16 2 0.01 -# [91] 0.5 128 0.01 0.0625 128 32 0.125 1 0.125 0.025
mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 128 64 128 0.001 4 0.25 64 0.5 32 0.25 -# [11] 0.025 0.002 1 16 0.005 0.025 0.25 0.005 0.001 64 -# [21] 0.025 1 16 >=256 16 1 16 0.001 0.125 64 -# [31] 8 0.002 0.5 0.005 0.005 4 8 0.002 0.025 32 -# [41] 0.0625 0.001 0.125 2 8 16 0.25 1 128 4 -# [51] 0.001 0.125 0.005 0.25 0.5 0.0625 0.0625 0.005 0.5 1 -# [61] 0.005 16 4 0.025 32 16 128 0.5 16 4 -# [71] 0.002 1 0.5 0.5 0.025 8 32 16 64 32 -# [81] 4 32 2 >=256 0.125 0.025 >=256 16 2 0.01 -# [91] 0.5 128 0.01 0.0625 128 32 0.125 1 0.125 0.025
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class <disk> -# [1] 28 21 23 24 17 19 17 17 29 20 28 20 31 17 24 25 21 29 19 18 30 21 22 29 19 -# [26] 24 27 18 19 28 20 19 21 30 17 29 28 26 23 19 20 30 25 19 19 28 19 25 23 19 -# [51] 20 18 23 27 28 28 17 30 22 24 30 27 18 24 25 22 30 21 29 30 29 31 23 28 31 -# [76] 19 17 22 25 25 21 17 31 31 24 30 30 26 20 19 20 25 25 23 17 24 21 20 18 18
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
j7bQNF`wG$GhF;uP9l*}h7WbJ&-5^?u;SQusA!^2Tmt z4kW6HZd_{c1MXdH8yX|XbhbbaA+0ClK#eCrT