vignettes/AMR.Rmd
AMR.Rmd
Now, let’s start the cleaning and the analysis!
So only 52.9% is suitable for resistance analysis! We can now filter +
So only 53.9% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
@@ -634,7 +634,7 @@ on it with the data_1st <- data %>% filter_first_isolate() # Including isolates from ICU.
data_1st <- data %>% filter_first_isolate() # Including isolates from ICU.
So we end up with 10,583 isolates for analysis. Now our data looks +
So we end up with 10,772 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: 10,583 -Available: 10,583 (100%, NA: 0 = 0%) +Length: 10,772 +Available: 10,772 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5408674
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
@@ -1152,19 +1152,19 @@ own: Hospital A -0.5445669 +0.5404491 Hospital B -0.5366764 +0.5344360 Hospital C -0.5427167 +0.5435294 Hospital D -0.5413386 +0.5391467 @@ -1189,23 +1189,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5445669 -3175 +0.5404491 +3251 Hospital B -0.5366764 -3749 +0.5344360 +3688 Hospital C -0.5427167 -1627 +0.5435294 +1700 Hospital D -0.5413386 -2032 +0.5391467 +2133 @@ -1230,27 +1230,27 @@ therapies very easily: Escherichia -0.7651007 -0.8809266 -0.9742368 +0.7727176 +0.8703980 +0.9765908 Klebsiella -0.8231707 -0.9128920 -0.9799652 +0.8240271 +0.9060914 +0.9780034 Staphylococcus -0.7911677 -0.8910928 -0.9805389 +0.7986823 +0.8868960 +0.9795022 Streptococcus -0.5270522 +0.5368226 0.0000000 -0.5270522 +0.5368226 @@ -1278,23 +1278,23 @@ classes, use a antibiotic class selector such as Hospital A -54.5% -27.0% +54.0% +25.9% Hospital B -53.7% -26.7% +53.4% +25.6% Hospital C -54.3% -27.8% +54.4% +28.5% Hospital D -54.1% -27.0% +53.9% +26.0% @@ -1410,18 +1410,16 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 32 8 128 256 0.5 32 0.025 0.005 0.005 -# [10] 64 0.002 16 0.5 0.002 1 <=0.001 256 0.125 -# [19] 0.25 256 0.025 0.0625 8 0.0625 256 0.125 128 -# [28] 0.005 256 64 0.025 4 8 2 0.0625 8 -# [37] 32 1 16 0.5 0.01 16 8 0.025 16 -# [46] 0.005 <=0.001 1 1 64 0.25 16 <=0.001 0.025 -# [55] 256 0.002 0.25 0.01 2 1 0.5 0.5 4 -# [64] 0.01 0.5 2 0.5 0.0625 16 16 0.025 64 -# [73] 256 0.5 0.5 0.125 16 16 0.125 0.002 128 -# [82] <=0.001 64 64 64 0.005 0.25 64 0.5 256 -# [91] 0.025 16 4 0.025 0.0625 <=0.001 64 128 128 -# [100] 8
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 32 8 128 256 0.5 32 0.025 0.005 0.005 -# [10] 64 0.002 16 0.5 0.002 1 <=0.001 256 0.125 -# [19] 0.25 256 0.025 0.0625 8 0.0625 256 0.125 128 -# [28] 0.005 256 64 0.025 4 8 2 0.0625 8 -# [37] 32 1 16 0.5 0.01 16 8 0.025 16 -# [46] 0.005 <=0.001 1 1 64 0.25 16 <=0.001 0.025 -# [55] 256 0.002 0.25 0.01 2 1 0.5 0.5 4 -# [64] 0.01 0.5 2 0.5 0.0625 16 16 0.025 64 -# [73] 256 0.5 0.5 0.125 16 16 0.125 0.002 128 -# [82] <=0.001 64 64 64 0.005 0.25 64 0.5 256 -# [91] 0.025 16 4 0.025 0.0625 <=0.001 64 128 128 -# [100] 8
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 24 19 26 26 21 31 28 19 25 29 20 26 22 18 20 29 18 18 20 18 26 20 17 19 25 -# [26] 25 24 23 28 22 21 23 21 29 19 22 31 30 25 26 31 20 21 21 27 26 20 30 22 20 -# [51] 25 17 23 24 17 19 26 25 30 20 20 20 26 26 29 17 29 24 22 18 26 25 28 23 20 -# [76] 23 31 17 17 18 24 19 27 30 22 17 23 22 17 21 18 29 19 26 31 27 26 22 22 25
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")