vignettes/AMR.Rmd
AMR.Rmd
So only 53.3% is suitable for resistance analysis! We can now filter +
So only 53.6% 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 10,653 isolates for analysis. Now our data looks +
So we end up with 10,715 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: 10,653 -Available: 10,653 (100%, NA: 0 = 0%) +Length: 10,715 +Available: 10,715 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5518633
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 -0.5650169 +0.5543512 Hospital B -0.5469568 +0.5427041 Hospital C -0.5635802 +0.5265432 Hospital D -0.5314100 +0.5499765 @@ -1189,23 +1189,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5650169 -3253 +0.5543512 +3229 Hospital B -0.5469568 -3631 +0.5427041 +3735 Hospital C -0.5635802 +0.5265432 1620 Hospital D -0.5314100 -2149 +0.5499765 +2131 @@ -1230,27 +1230,27 @@ therapies very easily: Escherichia -0.7669797 -0.8698406 -0.9744486 +0.7695287 +0.8734668 +0.9771896 Klebsiella -0.8150685 -0.9126712 -0.9845890 +0.8260511 +0.9051937 +0.9785655 Staphylococcus -0.7865088 -0.8855400 -0.9788303 +0.7865209 +0.8841530 +0.9759563 Streptococcus -0.5304389 +0.5341232 0.0000000 -0.5304389 +0.5341232 @@ -1278,23 +1278,23 @@ classes, use a antibiotic class selector such as Hospital A -56.5% -27.3% +55.4% +26.4% Hospital B -54.7% +54.3% 27.0% Hospital C -56.4% -28.1% +52.7% +27.0% Hospital D -53.1% -25.5% +55.0% +26.1% @@ -1410,16 +1410,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 128 32 0.002 0.005 0.01 32 0.25 128 32 >=256 -# [11] 0.005 0.125 16 2 0.01 4 >=256 4 0.01 0.001 -# [21] 16 >=256 32 0.25 16 0.001 128 0.001 8 0.0625 -# [31] 16 16 1 16 0.125 16 8 2 >=256 2 -# [41] 0.002 0.01 0.5 16 1 0.0625 0.01 0.025 0.002 0.0625 -# [51] 0.25 64 0.01 0.125 0.0625 0.005 0.0625 >=256 0.0625 32 -# [61] 1 0.002 0.125 32 0.002 128 0.01 8 0.25 8 -# [71] 0.005 >=256 2 0.01 32 4 >=256 2 0.002 0.01 -# [81] 1 16 0.025 16 4 0.5 32 32 64 0.25 -# [91] 0.01 >=256 0.01 16 0.025 16 1 0.005 0.25 0.025
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 128 32 0.002 0.005 0.01 32 0.25 128 32 >=256 -# [11] 0.005 0.125 16 2 0.01 4 >=256 4 0.01 0.001 -# [21] 16 >=256 32 0.25 16 0.001 128 0.001 8 0.0625 -# [31] 16 16 1 16 0.125 16 8 2 >=256 2 -# [41] 0.002 0.01 0.5 16 1 0.0625 0.01 0.025 0.002 0.0625 -# [51] 0.25 64 0.01 0.125 0.0625 0.005 0.0625 >=256 0.0625 32 -# [61] 1 0.002 0.125 32 0.002 128 0.01 8 0.25 8 -# [71] 0.005 >=256 2 0.01 32 4 >=256 2 0.002 0.01 -# [81] 1 16 0.025 16 4 0.5 32 32 64 0.25 -# [91] 0.01 >=256 0.01 16 0.025 16 1 0.005 0.25 0.025
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 18 29 28 22 20 28 25 20 28 23 23 31 24 26 31 31 30 20 29 20 31 29 28 29 25 -# [26] 28 26 23 27 22 21 23 17 25 30 30 29 28 25 28 19 17 28 18 31 20 29 29 23 23 -# [51] 19 25 29 20 27 29 19 24 22 24 31 22 25 29 18 31 17 23 26 19 24 31 29 31 28 -# [76] 31 22 27 30 27 30 22 22 27 23 24 28 25 26 24 29 20 26 20 29 29 22 19 25 26
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")