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 52.8% 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 10,687 isolates for analysis. Now our data looks like:
So we end up with 10,557 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,687 -Available: 10,687 (100.0%, NA: 0 = 0.0%) +Length: 10,557 +Available: 10,557 (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] 0.540844
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 -0.5347460 +0.5290698 Hospital B -0.5486298 +0.5434023 Hospital C -0.5512224 +0.5325670 Hospital D -0.5274779 +0.5510580 @@ -1008,23 +1008,23 @@ Longest: 24 Hospital A -0.5347460 -3209 +0.5290698 +3096 Hospital B -0.5486298 -3722 +0.5434023 +3721 Hospital C -0.5512224 -1718 +0.5325670 +1566 Hospital D -0.5274779 -2038 +0.5510580 +2174 @@ -1047,27 +1047,27 @@ Longest: 24 Escherichia -0.7625853 -0.8683874 -0.9769625 +0.7751648 +0.8714286 +0.9795604 Klebsiella -0.8266200 -0.9054291 -0.9824869 +0.8316327 +0.8954082 +0.9838435 Staphylococcus -0.7997775 -0.8846867 -0.9818317 +0.7993967 +0.8906486 +0.9792609 Streptococcus -0.5462963 +0.5392382 0.0000000 -0.5462963 +0.5392382 @@ -1092,23 +1092,23 @@ Longest: 24 Hospital A -53.5% -26.7% +52.9% +24.5% Hospital B -54.9% -27.3% +54.3% +27.5% Hospital C -55.1% -24.3% +53.3% +26.4% Hospital D -52.7% -26.4% +55.1% +25.8% @@ -1206,18 +1206,16 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.125 0.005 128 0.25 16 0.5 0.002 4 256 -# [10] 0.01 32 0.0625 64 0.5 0.005 0.125 0.01 4 -# [19] 16 2 2 256 4 16 4 0.025 0.125 -# [28] 2 0.0625 16 0.5 1 0.025 <=0.001 0.5 0.5 -# [37] 0.025 2 0.01 0.0625 16 8 <=0.001 0.01 8 -# [46] 0.005 0.125 0.002 0.0625 16 0.25 64 128 0.5 -# [55] 64 2 0.125 0.0625 0.01 16 0.005 1 1 -# [64] 64 0.002 4 16 64 0.125 0.01 0.01 8 -# [73] 0.5 0.5 <=0.001 256 256 64 0.01 4 32 -# [82] 0.01 8 0.25 <=0.001 <=0.001 64 0.01 0.005 0.002 -# [91] 2 0.01 0.002 0.5 2 0.125 1 0.5 0.0625 -# [100] 0.005
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.125 0.005 128 0.25 16 0.5 0.002 4 256 -# [10] 0.01 32 0.0625 64 0.5 0.005 0.125 0.01 4 -# [19] 16 2 2 256 4 16 4 0.025 0.125 -# [28] 2 0.0625 16 0.5 1 0.025 <=0.001 0.5 0.5 -# [37] 0.025 2 0.01 0.0625 16 8 <=0.001 0.01 8 -# [46] 0.005 0.125 0.002 0.0625 16 0.25 64 128 0.5 -# [55] 64 2 0.125 0.0625 0.01 16 0.005 1 1 -# [64] 64 0.002 4 16 64 0.125 0.01 0.01 8 -# [73] 0.5 0.5 <=0.001 256 256 64 0.01 4 32 -# [82] 0.01 8 0.25 <=0.001 <=0.001 64 0.01 0.005 0.002 -# [91] 2 0.01 0.002 0.5 2 0.125 1 0.5 0.0625 -# [100] 0.005
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 24 18 17 20 25 19 31 25 27 19 20 19 31 19 21 20 27 21 21 29 22 17 26 23 20 -# [26] 25 22 21 20 24 28 17 17 18 31 21 26 17 30 17 23 22 26 30 18 29 20 27 22 28 -# [51] 17 23 27 23 27 23 25 22 22 31 18 20 17 25 20 24 27 17 29 25 25 28 25 24 20 -# [76] 30 20 17 29 28 31 26 18 21 26 26 27 28 21 18 19 27 31 26 24 21 23 22 29 25
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")