vignettes/AMR.Rmd
AMR.Rmd
Now, let’s start the cleaning and the analysis!
So only 53% is suitable for resistance analysis! We can now filter on -it with the filter() function, also from the +
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:
dplyr
data_1st <- data %>% @@ -634,7 +634,7 @@ 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 10,607 isolates for analysis. Now our data looks +
So we end up with 10,556 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,607 -Available: 10,607 (100%, NA: 0 = 0%) +Length: 10,556 +Available: 10,556 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5453946
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 -0.5539478 +0.5429724 Hospital B -0.5484475 +0.5454295 Hospital C -0.5440806 +0.5458488 Hospital D -0.5280418 +0.5428845 @@ -1189,23 +1189,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5539478 -3179 +0.5429724 +3223 Hospital B -0.5484475 -3736 +0.5454295 +3632 Hospital C -0.5440806 -1588 +0.5458488 +1614 Hospital D -0.5280418 -2104 +0.5428845 +2087 @@ -1230,27 +1230,27 @@ therapies very easily: Escherichia -0.7746142 -0.8772006 -0.9780483 +0.7735184 +0.8766248 +0.9806125 Klebsiella -0.8227425 -0.8963211 -0.9790970 +0.8250853 +0.9052901 +0.9880546 Staphylococcus -0.7789356 -0.9036100 -0.9810197 +0.7991924 +0.8883994 +0.9801762 Streptococcus -0.5336788 +0.5299387 0.0000000 -0.5336788 +0.5299387 @@ -1278,23 +1278,23 @@ classes, use a antibiotic class selector such as Hospital A -55.4% -27.0% +54.3% +25.1% Hospital B -54.8% -26.5% +54.5% +26.6% Hospital C -54.4% -26.2% +54.6% +26.7% Hospital D -52.8% -27.1% +54.3% +27.4% @@ -1410,16 +1410,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 8 16 16 0.01 8 0.01 0.001 0.01 0.125 32 -# [11] 2 4 0.002 0.002 0.002 16 0.0625 0.005 0.005 8 -# [21] 0.001 64 2 0.125 16 >=256 0.005 0.001 4 0.005 -# [31] 2 0.005 2 32 2 4 16 1 0.01 0.025 -# [41] 0.0625 0.01 8 128 0.25 0.01 0.01 128 0.0625 0.25 -# [51] 2 8 0.0625 32 0.01 32 32 0.01 8 0.002 -# [61] 0.25 4 2 0.25 16 64 4 >=256 64 64 -# [71] 64 32 32 0.25 >=256 0.125 0.01 0.0625 128 4 -# [81] 0.01 0.25 0.001 2 16 0.002 0.01 0.002 >=256 0.025 -# [91] 8 >=256 16 0.002 2 0.125 0.0625 0.002 >=256 0.025
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 8 16 16 0.01 8 0.01 0.001 0.01 0.125 32 -# [11] 2 4 0.002 0.002 0.002 16 0.0625 0.005 0.005 8 -# [21] 0.001 64 2 0.125 16 >=256 0.005 0.001 4 0.005 -# [31] 2 0.005 2 32 2 4 16 1 0.01 0.025 -# [41] 0.0625 0.01 8 128 0.25 0.01 0.01 128 0.0625 0.25 -# [51] 2 8 0.0625 32 0.01 32 32 0.01 8 0.002 -# [61] 0.25 4 2 0.25 16 64 4 >=256 64 64 -# [71] 64 32 32 0.25 >=256 0.125 0.01 0.0625 128 4 -# [81] 0.01 0.25 0.001 2 16 0.002 0.01 0.002 >=256 0.025 -# [91] 8 >=256 16 0.002 2 0.125 0.0625 0.002 >=256 0.025
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 17 22 17 18 28 30 30 31 20 20 17 23 18 30 27 28 18 27 25 25 25 25 17 19 20 -# [26] 24 19 25 20 23 30 25 23 18 20 23 24 29 19 27 29 21 17 21 25 25 17 29 24 20 -# [51] 24 20 17 30 23 22 25 17 17 21 19 20 27 29 26 25 22 21 29 31 19 21 29 24 29 -# [76] 31 24 22 20 22 28 18 28 28 17 20 23 31 19 23 26 31 19 30 29 17 26 28 29 30
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")