vignettes/AMR.Rmd
AMR.Rmd
Now, let’s start the cleaning and the analysis!
So only 61% is suitable for resistance analysis! We can now filter on -it with the filter() function, also from the +
filter()
So only 61.9% is suitable for resistance analysis! We can now filter +on it with the filter() function, also from the dplyr package:
dplyr
data_1st <- data %>% @@ -644,7 +644,7 @@ it with the data_1st <- data %>% filter_first_isolate()
data_1st <- data %>% filter_first_isolate()
So we end up with 12 198 isolates for analysis. Now our data looks +
So we end up with 12 380 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 12,198 -Available: 12,198 (100%, NA: 0 = 0%) +Length: 12,380 +Available: 12,380 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5823086
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
@@ -1169,19 +1169,19 @@ own: Hospital A -0.5925926 +0.5816024 Hospital B -0.5784499 +0.5940042 Hospital C -0.5731311 +0.5499199 Hospital D -0.5802005 +0.5802964 @@ -1206,23 +1206,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5925926 -3726 +0.5816024 +3707 Hospital B -0.5784499 -4232 +0.5940042 +4303 Hospital C -0.5731311 -1846 +0.5499199 +1873 Hospital D -0.5802005 -2394 +0.5802964 +2497 @@ -1247,27 +1247,27 @@ therapies very easily: Escherichia -0.6671217 -0.6498891 -0.8846613 +0.6685241 +0.6546775 +0.8850051 Klebsiella -0.6549080 -0.6533742 -0.8788344 +0.6527571 +0.6766021 +0.8971684 Staphylococcus -0.6830035 -0.6616400 -0.8850141 +0.6686082 +0.6548130 +0.8868792 Streptococcus -0.4837838 +0.4778857 0.0000000 -0.4837838 +0.4778857 @@ -1295,23 +1295,23 @@ classes, use a antibiotic class selector such as Hospital A -59.3% -36.3% +58.2% +35.3% Hospital B -57.8% -35.5% +59.4% +37.5% Hospital C -57.3% -35.6% +55.0% +34.5% Hospital D 58.0% -35.6% +36.5% @@ -1427,18 +1427,16 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.01 >=256 0.5 64 64 >=256 0.005 1 >=256 -# [10] 16 4 32 1 2 64 0.0625 0.0625 8 -# [19] 64 2 0.01 8 1 0.002 0.5 0.5 0.002 -# [28] 0.0625 0.005 32 2 0.01 1 4 64 8 -# [37] 0.125 32 0.125 0.002 >=256 0.005 0.002 2 8 -# [46] >=256 >=256 0.01 32 0.002 64 0.5 8 2 -# [55] 0.25 4 0.002 0.002 0.125 8 0.25 <=0.001 32 -# [64] <=0.001 <=0.001 0.002 >=256 0.002 0.5 0.125 0.01 <=0.001 -# [73] 2 8 0.01 0.25 128 0.025 128 0.25 0.005 -# [82] 0.01 0.002 32 32 1 0.5 >=256 2 0.025 -# [91] 4 8 4 0.25 0.5 0.5 1 0.01 32 -# [100] 0.5
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.01 >=256 0.5 64 64 >=256 0.005 1 >=256 -# [10] 16 4 32 1 2 64 0.0625 0.0625 8 -# [19] 64 2 0.01 8 1 0.002 0.5 0.5 0.002 -# [28] 0.0625 0.005 32 2 0.01 1 4 64 8 -# [37] 0.125 32 0.125 0.002 >=256 0.005 0.002 2 8 -# [46] >=256 >=256 0.01 32 0.002 64 0.5 8 2 -# [55] 0.25 4 0.002 0.002 0.125 8 0.25 <=0.001 32 -# [64] <=0.001 <=0.001 0.002 >=256 0.002 0.5 0.125 0.01 <=0.001 -# [73] 2 8 0.01 0.25 128 0.025 128 0.25 0.005 -# [82] 0.01 0.002 32 32 1 0.5 >=256 2 0.025 -# [91] 4 8 4 0.25 0.5 0.5 1 0.01 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] 24 28 29 21 30 30 22 20 23 25 31 28 24 20 25 18 17 21 28 24 19 31 28 25 17 -# [26] 30 31 25 26 18 29 27 29 24 23 31 18 22 22 31 20 24 27 18 18 29 27 30 25 27 -# [51] 17 29 20 26 24 18 26 28 22 30 29 20 26 28 30 24 19 19 26 17 22 22 17 30 24 -# [76] 17 27 20 23 23 18 30 27 21 24 19 25 23 24 20 18 30 20 23 22 25 18 30 28 21
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")