vignettes/AMR.Rmd
AMR.Rmd
So only 61.5% is suitable for resistance analysis! We can now filter -on it with the filter() function, also from the +
filter()
So only 61% is suitable for resistance analysis! We can now filter on +it with the filter() function, also from the dplyr package:
dplyr
data_1st <- data %>% @@ -644,7 +644,7 @@ on it with the data_1st <- data %>% filter_first_isolate()
data_1st <- data %>% filter_first_isolate()
So we end up with 12 299 isolates for analysis. Now our data looks +
So we end up with 12 198 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: 12,299 -Available: 12,299 (100%, NA: 0 = 0%) +Length: 12,198 +Available: 12,198 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5841125
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
@@ -1169,19 +1169,19 @@ own: Hospital A -0.5842217 +0.5925926 Hospital B -0.5908142 +0.5784499 Hospital C -0.5761879 +0.5731311 Hospital D -0.5779626 +0.5802005 @@ -1206,23 +1206,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5842217 -3752 +0.5925926 +3726 Hospital B -0.5908142 -4311 +0.5784499 +4232 Hospital C -0.5761879 -1831 +0.5731311 +1846 Hospital D -0.5779626 -2405 +0.5802005 +2394 @@ -1247,27 +1247,27 @@ therapies very easily: Escherichia -0.6666099 -0.6517811 -0.8825635 +0.6671217 +0.6498891 +0.8846613 Klebsiella -0.6570156 -0.6466221 -0.8619154 +0.6549080 +0.6533742 +0.8788344 Staphylococcus -0.6667698 -0.6574876 -0.8932550 +0.6830035 +0.6616400 +0.8850141 Streptococcus -0.4700486 +0.4837838 0.0000000 -0.4700486 +0.4837838 @@ -1295,23 +1295,23 @@ classes, use a antibiotic class selector such as Hospital A -58.4% -37.3% +59.3% +36.3% Hospital B -59.1% -37.2% +57.8% +35.5% Hospital C -57.6% -35.2% +57.3% +35.6% Hospital D -57.8% -34.3% +58.0% +35.6% @@ -1427,18 +1427,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.5 0.25 4 0.002 <=0.001 0.005 0.25 0.005 32 -# [10] 0.025 64 0.025 0.5 0.125 16 0.25 0.01 32 -# [19] <=0.001 0.125 0.125 64 2 0.125 16 128 0.002 -# [28] 0.002 1 0.0625 0.125 8 64 128 0.0625 64 -# [37] 128 0.005 0.125 0.005 32 64 64 32 256 -# [46] 128 0.0625 <=0.001 <=0.001 256 8 4 2 1 -# [55] 16 0.002 1 1 <=0.001 1 8 0.5 2 -# [64] 2 2 1 <=0.001 2 32 16 0.01 0.002 -# [73] 16 0.025 0.002 0.01 1 0.25 0.25 64 1 -# [82] 0.125 <=0.001 16 0.25 8 4 0.25 0.002 0.0625 -# [91] 128 0.005 128 256 64 2 256 0.025 0.25 -# [100] 4
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.5 0.25 4 0.002 <=0.001 0.005 0.25 0.005 32 -# [10] 0.025 64 0.025 0.5 0.125 16 0.25 0.01 32 -# [19] <=0.001 0.125 0.125 64 2 0.125 16 128 0.002 -# [28] 0.002 1 0.0625 0.125 8 64 128 0.0625 64 -# [37] 128 0.005 0.125 0.005 32 64 64 32 256 -# [46] 128 0.0625 <=0.001 <=0.001 256 8 4 2 1 -# [55] 16 0.002 1 1 <=0.001 1 8 0.5 2 -# [64] 2 2 1 <=0.001 2 32 16 0.01 0.002 -# [73] 16 0.025 0.002 0.01 1 0.25 0.25 64 1 -# [82] 0.125 <=0.001 16 0.25 8 4 0.25 0.002 0.0625 -# [91] 128 0.005 128 256 64 2 256 0.025 0.25 -# [100] 4
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 18 29 29 26 17 27 24 26 25 24 23 20 19 25 22 24 20 19 24 17 23 21 19 21 20 -# [26] 26 18 17 26 22 25 19 25 26 17 26 19 29 20 27 19 22 31 25 19 28 25 17 29 31 -# [51] 26 31 29 21 19 28 30 30 31 28 30 22 22 22 17 31 22 19 21 17 28 29 17 28 26 -# [76] 20 20 20 19 25 17 28 28 31 24 28 21 28 20 25 21 28 27 23 27 22 31 27 27 18
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")