vignettes/AMR.Rmd
AMR.Rmd
So only 52.9% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the @@ -634,7 +634,7 @@ on it with the data_1st <- data %>% filter_first_isolate() # Including isolates from ICU. -
filter()
data_1st <- data %>% filter_first_isolate() # Including isolates from ICU.
So we end up with 10,571 isolates for analysis. Now our data looks +
So we end up with 10,582 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,571 -Available: 10,571 (100%, NA: 0 = 0%) +Length: 10,582 +Available: 10,582 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5429004
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
dplyr
@@ -1152,19 +1152,19 @@ own: Hospital A -0.5397836 +0.5490875 Hospital B -0.5503731 +0.5438596 Hospital C -0.5403882 +0.5260078 Hospital D -0.5360577 +0.5427757 @@ -1189,23 +1189,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5397836 -3142 +0.5490875 +3178 Hospital B -0.5503731 -3752 +0.5438596 +3762 Hospital C -0.5403882 -1597 +0.5260078 +1538 Hospital D -0.5360577 -2080 +0.5427757 +2104 @@ -1230,27 +1230,27 @@ therapies very easily: Escherichia -0.7722404 -0.8716940 -0.9768306 +0.7719983 +0.8764629 +0.9809276 Klebsiella -0.8239316 -0.9017094 -0.9769231 +0.8260504 +0.8949580 +0.9798319 Staphylococcus -0.8006620 -0.8852519 -0.9764619 +0.8025926 +0.8862963 +0.9796296 Streptococcus -0.5372568 +0.5442733 0.0000000 -0.5372568 +0.5442733 @@ -1278,22 +1278,22 @@ classes, use a antibiotic class selector such as Hospital A -54.0% -26.8% +54.9% +27.4% Hospital B -55.0% -26.8% +54.4% +25.6% Hospital C -54.0% -24.9% +52.6% +24.8% Hospital D -53.6% +54.3% 24.9% @@ -1410,16 +1410,16 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.005 0.0625 0.001 128 32 4 8 32 0.125 64 -# [11] 0.5 16 0.125 8 0.25 0.5 16 0.5 1 0.005 -# [21] 0.025 16 0.005 0.01 2 2 0.002 0.025 0.005 64 -# [31] 128 4 0.0625 2 128 128 32 2 0.125 2 -# [41] 0.0625 0.25 1 2 0.125 0.5 >=256 0.005 0.001 8 -# [51] 16 16 0.0625 8 >=256 128 1 >=256 4 4 -# [61] 128 0.002 0.25 0.005 0.005 4 8 0.002 0.001 >=256 -# [71] 0.125 0.0625 0.001 0.25 128 64 0.125 32 16 128 -# [81] 0.002 4 0.001 0.001 0.005 0.025 0.001 >=256 0.0625 >=256 -# [91] 2 0.5 0.0625 8 1 0.5 0.01 0.001 8 0.5
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.005 0.0625 0.001 128 32 4 8 32 0.125 64 -# [11] 0.5 16 0.125 8 0.25 0.5 16 0.5 1 0.005 -# [21] 0.025 16 0.005 0.01 2 2 0.002 0.025 0.005 64 -# [31] 128 4 0.0625 2 128 128 32 2 0.125 2 -# [41] 0.0625 0.25 1 2 0.125 0.5 >=256 0.005 0.001 8 -# [51] 16 16 0.0625 8 >=256 128 1 >=256 4 4 -# [61] 128 0.002 0.25 0.005 0.005 4 8 0.002 0.001 >=256 -# [71] 0.125 0.0625 0.001 0.25 128 64 0.125 32 16 128 -# [81] 0.002 4 0.001 0.001 0.005 0.025 0.001 >=256 0.0625 >=256 -# [91] 2 0.5 0.0625 8 1 0.5 0.01 0.001 8 0.5
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 28 22 28 23 22 31 27 19 23 22 18 17 29 23 28 20 21 31 28 17 22 30 31 24 29 -# [26] 24 23 19 17 21 26 30 26 29 25 23 31 29 20 31 26 26 19 26 22 24 28 28 28 27 -# [51] 28 18 17 19 22 19 25 20 19 17 24 20 26 22 26 18 18 27 24 30 27 24 24 26 30 -# [76] 26 21 22 27 22 24 27 24 28 20 29 17 28 26 20 21 29 18 24 20 22 28 22 29 21
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")