So only 52.7% 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 10,548 isolates for analysis. Now our data looks like:
So we end up with 10,708 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,548 -Available: 10,548 (100.0%, NA: 0 = 0.0%) +Length: 10,708 +Available: 10,708 (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] 0.544653
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 -0.5401018 +0.5446373 Hospital B -0.5452092 +0.5459846 Hospital C -0.5366162 +0.5303218 Hospital D -0.5564478 +0.5389393 @@ -1008,23 +1008,23 @@ Longest: 24 Hospital A -0.5401018 -3142 +0.5446373 +3226 Hospital B -0.5452092 -3705 +0.5459846 +3773 Hospital C -0.5366162 -1584 +0.5303218 +1616 Hospital D -0.5564478 -2117 +0.5389393 +2093 @@ -1047,27 +1047,27 @@ Longest: 24 Escherichia -0.7694656 -0.8774264 -0.9790622 +0.7708784 +0.8760277 +0.9757681 Klebsiella -0.8264059 -0.8956805 -0.9812551 +0.8184902 +0.9007634 +0.9813401 Staphylococcus -0.8060377 -0.8743396 -0.9784906 +0.8030248 +0.8797263 +0.9773136 Streptococcus -0.5359540 +0.5384977 0.0000000 -0.5359540 +0.5384977 @@ -1092,23 +1092,23 @@ Longest: 24 Hospital A -54.0% -27.0% +54.5% +26.0% Hospital B -54.5% -25.4% +54.6% +26.5% Hospital C -53.7% -24.9% +53.0% +26.8% Hospital D -55.6% -26.8% +53.9% +25.4% @@ -1206,18 +1206,16 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 0.125 2 32 0.025 4 0.01 0.025 4 8 -# [10] 2 1 2 0.25 2 16 8 0.0625 1 -# [19] 0.5 0.01 0.125 0.25 0.0625 0.01 0.025 8 8 -# [28] 0.002 128 0.0625 128 1 4 0.025 0.5 4 -# [37] 0.125 0.0625 0.5 2 0.002 8 0.125 >=256 0.01 -# [46] >=256 0.01 2 32 64 4 16 <=0.001 0.25 -# [55] 16 128 128 8 0.25 <=0.001 0.5 >=256 32 -# [64] 0.025 0.25 16 32 0.002 128 8 8 128 -# [73] 0.25 128 0.005 128 <=0.001 0.002 0.002 0.5 0.0625 -# [82] 4 0.125 0.002 0.25 0.025 <=0.001 2 0.005 0.0625 -# [91] 32 8 0.005 0.0625 >=256 0.5 0.125 0.25 4 -# [100] 0.5
mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 0.125 2 32 0.025 4 0.01 0.025 4 8 -# [10] 2 1 2 0.25 2 16 8 0.0625 1 -# [19] 0.5 0.01 0.125 0.25 0.0625 0.01 0.025 8 8 -# [28] 0.002 128 0.0625 128 1 4 0.025 0.5 4 -# [37] 0.125 0.0625 0.5 2 0.002 8 0.125 >=256 0.01 -# [46] >=256 0.01 2 32 64 4 16 <=0.001 0.25 -# [55] 16 128 128 8 0.25 <=0.001 0.5 >=256 32 -# [64] 0.025 0.25 16 32 0.002 128 8 8 128 -# [73] 0.25 128 0.005 128 <=0.001 0.002 0.002 0.5 0.0625 -# [82] 4 0.125 0.002 0.25 0.025 <=0.001 2 0.005 0.0625 -# [91] 32 8 0.005 0.0625 >=256 0.5 0.125 0.25 4 -# [100] 0.5
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class <disk> -# [1] 17 29 17 23 23 22 26 31 29 30 23 23 24 22 23 24 23 22 18 25 25 26 30 23 23 -# [26] 18 24 22 26 19 22 22 25 17 29 30 28 27 17 31 30 17 28 29 31 27 19 18 21 23 -# [51] 18 22 24 28 20 27 21 23 18 18 28 31 27 21 21 24 29 18 18 18 26 21 18 23 22 -# [76] 29 19 19 27 22 21 25 18 21 19 22 28 25 18 27 25 19 17 19 29 19 23 25 26 21
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")