Now, let’s start the cleaning and the analysis!
So only 53.5% is suitable for resistance analysis! We can now filter +
So only 52.8% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
@@ -634,11 +634,11 @@ 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 10,690 isolates for analysis. Now our data looks +
So we end up with 10,551 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,690 -Available: 10,690 (100%, NA: 0 = 0%) +Length: 10,551 +Available: 10,551 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5403181
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 -0.5356587 +0.5512346 Hospital B -0.5376112 +0.5463693 Hospital C -0.5159194 +0.5256817 Hospital D -0.5683550 +0.5464268 @@ -1189,23 +1189,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5356587 -3211 +0.5512346 +3240 Hospital B -0.5376112 -3709 +0.5463693 +3677 Hospital C -0.5159194 -1539 +0.5256817 +1577 Hospital D -0.5683550 -2231 +0.5464268 +2057 @@ -1230,27 +1230,27 @@ therapies very easily: Escherichia -0.7696617 -0.8733032 -0.9790993 +0.7705999 +0.8828829 +0.9817623 Klebsiella -0.8361630 -0.8998302 -0.9830221 +0.8172589 +0.9077834 +0.9856176 Staphylococcus -0.7949002 -0.8869180 -0.9837398 +0.7912979 +0.8879056 +0.9782448 Streptococcus -0.5376443 +0.5251662 0.0000000 -0.5376443 +0.5251662 @@ -1278,23 +1278,23 @@ classes, use a antibiotic class selector such as Hospital A -53.6% -26.2% +55.1% +27.0% Hospital B -53.8% -25.6% +54.6% +27.7% Hospital C -51.6% -26.0% +52.6% +24.6% Hospital D -56.8% -28.1% +54.6% +26.5% @@ -1410,16 +1410,16 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.01 0.5 >=256 64 128 0.01 16 0.0625 8 >=256 -# [11] 64 0.01 128 0.125 4 0.025 0.025 0.01 0.125 2 -# [21] 32 128 >=256 0.125 >=256 64 0.01 0.25 0.5 4 -# [31] 2 0.025 0.025 1 0.0625 0.001 8 >=256 8 0.5 -# [41] 4 32 1 0.125 4 0.0625 0.001 8 0.001 0.001 -# [51] 2 0.01 1 64 0.002 8 1 64 0.005 128 -# [61] 0.002 1 0.025 0.25 0.001 1 0.125 0.001 1 0.025 -# [71] 0.001 0.25 16 128 2 16 4 0.5 >=256 0.125 -# [81] 0.125 2 0.001 0.25 0.01 4 128 >=256 0.001 4 -# [91] 2 64 8 64 0.0625 0.001 0.0625 16 8 8
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.01 0.5 >=256 64 128 0.01 16 0.0625 8 >=256 -# [11] 64 0.01 128 0.125 4 0.025 0.025 0.01 0.125 2 -# [21] 32 128 >=256 0.125 >=256 64 0.01 0.25 0.5 4 -# [31] 2 0.025 0.025 1 0.0625 0.001 8 >=256 8 0.5 -# [41] 4 32 1 0.125 4 0.0625 0.001 8 0.001 0.001 -# [51] 2 0.01 1 64 0.002 8 1 64 0.005 128 -# [61] 0.002 1 0.025 0.25 0.001 1 0.125 0.001 1 0.025 -# [71] 0.001 0.25 16 128 2 16 4 0.5 >=256 0.125 -# [81] 0.125 2 0.001 0.25 0.01 4 128 >=256 0.001 4 -# [91] 2 64 8 64 0.0625 0.001 0.0625 16 8 8
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 30 19 17 17 24 26 28 18 19 24 22 26 24 29 24 21 31 27 28 19 29 17 27 30 24 -# [26] 17 19 19 24 24 31 23 30 31 31 19 25 24 20 28 31 30 20 25 19 25 28 25 31 25 -# [51] 17 18 23 23 30 25 24 22 21 24 17 19 28 31 26 23 26 21 21 21 24 20 31 26 28 -# [76] 20 25 25 30 21 29 31 29 22 25 30 18 26 19 17 27 18 20 31 26 28 22 26 22 25
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")