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 10,635 isolates for analysis. Now our data looks like:
So we end up with 10,694 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: 10,635 -Available: 10,635 (100.0%, NA: 0 = 0.0%) +Length: 10,694 +Available: 10,694 (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] 0.5456512
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 -0.5437558 +0.5417823 Hospital B -0.5492089 +0.5486465 Hospital C -0.5465995 +0.5634971 Hospital D -0.5415282 +0.5485369 @@ -1008,23 +1008,23 @@ Longest: 24 Hospital A -0.5437558 -3211 +0.5417823 +3243 Hospital B -0.5492089 -3729 +0.5486465 +3731 Hospital C -0.5465995 -1588 +0.5634971 +1567 Hospital D -0.5415282 -2107 +0.5485369 +2153 @@ -1047,27 +1047,27 @@ Longest: 24 Escherichia -0.7712661 -0.8777608 -0.9748524 +0.7708831 +0.8791495 +0.9791712 Klebsiella -0.8178808 -0.9097682 -0.9850993 +0.8193604 +0.9109767 +0.9783924 Staphylococcus -0.7931785 -0.8809869 -0.9785922 +0.7871953 +0.8795926 +0.9770826 Streptococcus -0.5371783 +0.5268472 0.0000000 -0.5371783 +0.5268472 @@ -1092,23 +1092,23 @@ Longest: 24 Hospital A -54.4% +54.2% 26.2% Hospital B 54.9% -26.7% +27.0% Hospital C -54.7% -26.6% +56.3% +28.1% Hospital D -54.2% -25.9% +54.9% +27.1% @@ -1206,18 +1206,16 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 0.005 0.0625 <=0.001 0.0625 64 0.25 >=256 0.125 0.0625 -# [10] 0.01 4 2 32 0.25 0.0625 2 0.025 4 -# [19] 0.0625 >=256 0.125 0.5 64 0.0625 0.005 0.25 8 -# [28] 2 2 64 0.005 0.125 8 0.01 16 0.0625 -# [37] <=0.001 0.01 16 32 32 0.0625 0.5 32 64 -# [46] 0.025 4 2 1 <=0.001 128 32 0.002 <=0.001 -# [55] 0.5 128 128 <=0.001 >=256 32 0.5 0.01 64 -# [64] 1 64 0.0625 0.025 0.002 1 0.01 0.005 16 -# [73] 1 0.0625 0.125 1 <=0.001 0.0625 64 128 0.01 -# [82] 0.5 0.025 <=0.001 0.0625 0.005 64 1 0.25 32 -# [91] 0.025 16 0.005 128 0.01 0.005 0.005 64 32 -# [100] 0.5
mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 0.005 0.0625 <=0.001 0.0625 64 0.25 >=256 0.125 0.0625 -# [10] 0.01 4 2 32 0.25 0.0625 2 0.025 4 -# [19] 0.0625 >=256 0.125 0.5 64 0.0625 0.005 0.25 8 -# [28] 2 2 64 0.005 0.125 8 0.01 16 0.0625 -# [37] <=0.001 0.01 16 32 32 0.0625 0.5 32 64 -# [46] 0.025 4 2 1 <=0.001 128 32 0.002 <=0.001 -# [55] 0.5 128 128 <=0.001 >=256 32 0.5 0.01 64 -# [64] 1 64 0.0625 0.025 0.002 1 0.01 0.005 16 -# [73] 1 0.0625 0.125 1 <=0.001 0.0625 64 128 0.01 -# [82] 0.5 0.025 <=0.001 0.0625 0.005 64 1 0.25 32 -# [91] 0.025 16 0.005 128 0.01 0.005 0.005 64 32 -# [100] 0.5
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class <disk> -# [1] 18 18 28 20 25 20 24 24 29 19 20 19 21 28 25 18 28 27 20 29 18 30 28 23 26 -# [26] 19 19 19 23 25 21 22 19 22 25 30 27 28 26 25 30 26 30 25 31 18 21 30 27 17 -# [51] 20 21 17 26 22 21 17 22 27 24 17 29 23 18 18 22 21 29 30 30 26 21 26 24 25 -# [76] 26 17 31 22 18 31 23 20 29 18 26 31 23 21 18 31 18 30 17 31 29 30 17 22 21
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
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