vignettes/AMR.Rmd
AMR.Rmd
# [1] "AMX" "AMC" "CIP" "GEN" data <- data %>% - mutate(across(where(is_sir_eligible), as.sir))
Finally, we will apply EUCAST rules on our antimicrobial results. In Europe, most medical microbiological laboratories already apply these rules. Our package @@ -627,9 +632,9 @@ takes into account the antimicrobial susceptibility test results using # Basing inclusion on all antimicrobial results, using a points threshold of # 2 # Including isolates from ICU. -# => Found 12,429 'phenotype-based' first isolates (62.1% of total where a +# => Found 20,000 'phenotype-based' first isolates (100.0% of total where a # microbial ID was available) -
So only 62.1% is suitable for resistance analysis! We can now filter +
So only 100% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
@@ -638,15 +643,13 @@ on it with the For future use, the above two syntaxes can be shortened: data_1st <- data %>% - filter_first_isolate() -# Including isolates from ICU. -So we end up with 12,429 isolates for analysis. Now our data looks + filter_first_isolate()
For future use, the above two syntaxes can be shortened: data_1st <- data %>% - filter_first_isolate() -# Including isolates from ICU. -So we end up with 12,429 isolates for analysis. Now our data looks + filter_first_isolate()
data_1st <- data %>% - filter_first_isolate() -# Including isolates from ICU.
So we end up with 12,429 isolates for analysis. Now our data looks + filter_first_isolate()
So we end up with 20,000 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: 12,429 -Available: 12,429 (100%, NA: 0 = 0%) +Length: 20,000 +Available: 20,000 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
If you want to get a quick glance of the number of isolates in @@ -1018,51 +1014,51 @@ different bug/drug combinations, you can use the
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5882211
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
@@ -1158,19 +1154,19 @@ own: Hospital A -0.5883152 +0.6017567 Hospital B -0.5836154 +0.6139130 Hospital C -0.5717427 +0.6064538 Hospital D -0.6082433 +0.5972906 @@ -1195,23 +1191,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5883152 -3680 +0.6017567 +6034 Hospital B -0.5836154 -4431 +0.6139130 +6900 Hospital C -0.5717427 -1819 +0.6064538 +3006 Hospital D -0.6082433 -2499 +0.5972906 +4060 @@ -1236,27 +1232,27 @@ therapies very easily: Escherichia -0.6621849 -0.6502521 -0.8878992 +0.6691442 +0.6721575 +0.8938329 Klebsiella -0.6415797 -0.6684054 -0.8941878 +0.6503906 +0.6533203 +0.8803711 Staphylococcus -0.6676683 -0.6529447 -0.8846154 +0.6654655 +0.6648649 +0.8852853 Streptococcus -0.4693201 +0.4325225 0.0000000 -0.4693201 +0.4325225 @@ -1284,23 +1280,23 @@ classes, use a antibiotic class selector such as Hospital A -58.8% -36.2% +60.2% +36.8% Hospital B -58.4% -36.9% +61.4% +37.8% Hospital C -57.2% -36.3% +60.6% +36.6% Hospital D -60.8% -37.1% +59.7% +35.9% @@ -1416,16 +1412,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 256 64 0.001 0.01 8 8 1 0.5 0.5 16 -# [11] 0.125 256 0.5 0.002 2 0.005 1 1 256 0.002 -# [21] 0.002 0.01 0.25 2 256 64 0.125 128 0.001 4 -# [31] 0.125 0.0625 1 64 256 0.25 128 0.5 0.5 32 -# [41] 16 2 0.125 0.005 8 0.0625 0.01 0.25 0.125 0.025 -# [51] 0.0625 4 0.125 8 0.002 0.5 0.01 0.025 0.125 0.025 -# [61] 0.001 16 1 0.25 0.5 2 8 0.01 8 0.0625 -# [71] 2 0.025 8 0.005 0.025 8 0.002 0.002 256 2 -# [81] 0.01 0.002 16 0.0625 64 8 0.005 32 0.125 0.001 -# [91] 0.002 2 0.125 8 1 128 0.01 1 0.001 0.025
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 256 64 0.001 0.01 8 8 1 0.5 0.5 16 -# [11] 0.125 256 0.5 0.002 2 0.005 1 1 256 0.002 -# [21] 0.002 0.01 0.25 2 256 64 0.125 128 0.001 4 -# [31] 0.125 0.0625 1 64 256 0.25 128 0.5 0.5 32 -# [41] 16 2 0.125 0.005 8 0.0625 0.01 0.25 0.125 0.025 -# [51] 0.0625 4 0.125 8 0.002 0.5 0.01 0.025 0.125 0.025 -# [61] 0.001 16 1 0.25 0.5 2 8 0.01 8 0.0625 -# [71] 2 0.025 8 0.005 0.025 8 0.002 0.002 256 2 -# [81] 0.01 0.002 16 0.0625 64 8 0.005 32 0.125 0.001 -# [91] 0.002 2 0.125 8 1 128 0.01 1 0.001 0.025
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 25 20 25 25 26 18 20 24 23 28 30 18 27 27 25 20 28 21 19 23 27 28 28 24 28 -# [26] 17 25 19 23 30 22 17 31 23 31 24 28 25 28 28 27 31 20 24 18 18 24 29 17 31 -# [51] 17 28 23 26 19 25 18 22 24 18 17 27 26 26 25 18 23 21 21 28 17 25 30 29 29 -# [76] 18 31 20 22 28 23 26 24 27 28 20 25 19 19 17 24 24 17 18 17 30 24 21 29 27
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")