vignettes/AMR.Rmd
AMR.Rmd
Now, let’s start the cleaning and the analysis!
So only 53.3% is suitable for resistance analysis! We can now filter +
So only 53.1% 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 10,650 isolates for analysis. Now our data looks +
So we end up with 10,612 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,650 -Available: 10,650 (100%, NA: 0 = 0%) +Length: 10,612 +Available: 10,612 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
If you want to get a quick glance of the number of isolates in @@ -1014,50 +1014,50 @@ different bug/drug combinations, you can use the
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5415962
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
@@ -1154,19 +1154,19 @@ own: Hospital A -0.5346793 +0.5427245 Hospital B -0.5482421 +0.5589124 Hospital C -0.5480192 +0.5351942 Hospital D -0.5347418 +0.5241280 @@ -1191,23 +1191,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5346793 -3071 +0.5427245 +3230 Hospital B -0.5482421 -3783 +0.5589124 +3641 Hospital C -0.5480192 -1666 +0.5351942 +1648 Hospital D -0.5347418 -2130 +0.5241280 +2093 @@ -1232,27 +1232,27 @@ therapies very easily: Escherichia -0.7705656 -0.8819623 -0.9777207 +0.7791784 +0.8739404 +0.9819604 Klebsiella -0.8226478 -0.9159034 -0.9866778 +0.8033058 +0.9074380 +0.9826446 Staphylococcus -0.7979911 -0.8917411 -0.9769345 +0.7915115 +0.8804914 +0.9791512 Streptococcus -0.5470616 +0.5295508 0.0000000 -0.5470616 +0.5295508 @@ -1280,23 +1280,23 @@ classes, use a antibiotic class selector such as Hospital A -53.5% -25.9% +54.3% +26.9% Hospital B -54.8% -26.6% +55.9% +26.8% Hospital C -54.8% -26.8% +53.5% +25.5% Hospital D -53.5% -24.8% +52.4% +25.9% @@ -1412,18 +1412,16 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.125 2 0.01 <=0.002 >=256 16 16 1 0.01 -# [10] 0.25 0.01 64 <=0.002 16 128 0.125 0.125 0.0625 -# [19] 0.005 1 0.125 >=256 16 0.01 0.125 2 0.25 -# [28] 0.025 >=256 0.01 0.0625 0.125 0.025 64 0.125 4 -# [37] 32 64 >=256 32 128 32 64 32 0.01 -# [46] 0.005 0.0625 16 32 4 4 0.0625 0.01 2 -# [55] 8 64 4 >=256 4 >=256 1 0.005 <=0.002 -# [64] 0.25 4 0.125 1 128 0.125 128 0.25 1 -# [73] >=256 8 0.025 64 4 2 16 16 32 -# [82] 0.25 8 4 16 0.005 64 0.0625 0.25 0.125 -# [91] 0.25 0.0625 0.01 0.125 0.125 0.01 64 0.125 0.005 -# [100] 32
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.125 2 0.01 <=0.002 >=256 16 16 1 0.01 -# [10] 0.25 0.01 64 <=0.002 16 128 0.125 0.125 0.0625 -# [19] 0.005 1 0.125 >=256 16 0.01 0.125 2 0.25 -# [28] 0.025 >=256 0.01 0.0625 0.125 0.025 64 0.125 4 -# [37] 32 64 >=256 32 128 32 64 32 0.01 -# [46] 0.005 0.0625 16 32 4 4 0.0625 0.01 2 -# [55] 8 64 4 >=256 4 >=256 1 0.005 <=0.002 -# [64] 0.25 4 0.125 1 128 0.125 128 0.25 1 -# [73] >=256 8 0.025 64 4 2 16 16 32 -# [82] 0.25 8 4 16 0.005 64 0.0625 0.25 0.125 -# [91] 0.25 0.0625 0.01 0.125 0.125 0.01 64 0.125 0.005 -# [100] 32
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 22 22 17 17 30 20 21 18 30 22 28 21 18 25 28 19 30 22 22 19 22 29 27 18 31 -# [26] 17 17 27 26 30 23 21 17 23 31 22 27 31 20 18 20 22 23 17 18 20 24 26 21 29 -# [51] 28 31 21 31 23 24 28 31 28 28 23 27 31 18 31 23 22 21 19 21 21 19 31 27 29 -# [76] 20 22 31 17 24 23 27 21 28 21 28 17 31 23 17 25 25 21 31 23 31 18 23 18 19
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")