vignettes/AMR.Rmd
AMR.Rmd
So only 61.6% is suitable for resistance analysis! We can now filter +
So only 60.9% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
@@ -640,11 +640,11 @@ 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 12,316 isolates for analysis. Now our data looks +
So we end up with 12,181 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: 12,316 -Available: 12,316 (100%, NA: 0 = 0%) +Length: 12,181 +Available: 12,181 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
If you want to get a quick glance of the number of isolates in @@ -1019,50 +1019,50 @@ different bug/drug combinations, you can use the
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5802209
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
@@ -1158,19 +1158,19 @@ own: Hospital A -0.5650667 +0.5991561 Hospital B -0.5838028 +0.5865273 Hospital C -0.5864369 +0.5910560 Hospital D -0.5923414 +0.5797160 @@ -1195,23 +1195,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5650667 -3750 +0.5991561 +3555 Hospital B -0.5838028 -4260 +0.5865273 +4305 Hospital C -0.5864369 -1799 +0.5910560 +1856 Hospital D -0.5923414 -2507 +0.5797160 +2465 @@ -1236,27 +1236,27 @@ therapies very easily: Escherichia -0.6598423 -0.6524585 -0.8848800 +0.6646027 +0.6444788 +0.8778810 Klebsiella -0.6648308 -0.6396538 -0.8686074 +0.6520400 +0.6574288 +0.8760585 Staphylococcus -0.6738263 -0.6759742 -0.8922983 +0.6681008 +0.6653350 +0.8899816 Streptococcus -0.4564860 +0.4751929 0.0000000 -0.4564860 +0.4751929 @@ -1284,23 +1284,23 @@ classes, use a antibiotic class selector such as Hospital A -56.5% -34.9% +59.9% +35.9% Hospital B -58.4% -37.2% +58.7% +36.4% Hospital C -58.6% +59.1% 37.4% Hospital D -59.2% -37.7% +58.0% +36.4% @@ -1416,18 +1416,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] <=0.001 0.025 128 0.125 0.0625 8 0.025 32 >=256 -# [10] 4 32 0.005 4 2 32 0.5 0.0625 128 -# [19] 0.01 16 1 0.01 64 0.0625 8 >=256 0.005 -# [28] 128 <=0.001 8 1 0.025 1 64 128 0.0625 -# [37] 2 0.125 4 4 4 16 0.0625 16 0.0625 -# [46] 0.5 0.5 <=0.001 64 8 128 1 64 2 -# [55] 1 >=256 <=0.001 0.002 0.5 4 4 32 <=0.001 -# [64] 16 0.002 128 128 4 0.002 8 8 0.0625 -# [73] 0.002 0.0625 8 16 0.125 0.005 8 >=256 8 -# [82] 0.01 0.025 0.005 64 1 0.025 0.002 16 32 -# [91] 0.005 32 0.002 <=0.001 0.25 4 <=0.001 0.25 16 -# [100] 0.025
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] <=0.001 0.025 128 0.125 0.0625 8 0.025 32 >=256 -# [10] 4 32 0.005 4 2 32 0.5 0.0625 128 -# [19] 0.01 16 1 0.01 64 0.0625 8 >=256 0.005 -# [28] 128 <=0.001 8 1 0.025 1 64 128 0.0625 -# [37] 2 0.125 4 4 4 16 0.0625 16 0.0625 -# [46] 0.5 0.5 <=0.001 64 8 128 1 64 2 -# [55] 1 >=256 <=0.001 0.002 0.5 4 4 32 <=0.001 -# [64] 16 0.002 128 128 4 0.002 8 8 0.0625 -# [73] 0.002 0.0625 8 16 0.125 0.005 8 >=256 8 -# [82] 0.01 0.025 0.005 64 1 0.025 0.002 16 32 -# [91] 0.005 32 0.002 <=0.001 0.25 4 <=0.001 0.25 16 -# [100] 0.025
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 29 26 20 20 26 31 29 17 28 21 27 17 30 23 21 19 23 23 20 23 19 27 23 20 23 -# [26] 25 22 25 28 31 19 31 20 31 19 19 28 31 20 29 23 25 28 27 18 24 26 21 30 23 -# [51] 22 31 27 26 27 26 23 22 17 19 18 24 31 25 29 21 29 19 27 20 31 23 23 25 18 -# [76] 31 18 27 31 20 21 23 25 22 31 26 28 21 30 17 31 29 19 25 24 17 19 19 21 21
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")