vignettes/AMR.Rmd
AMR.Rmd
So only 53.2% is suitable for resistance analysis! We can now filter +
So only 52.9% 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 10,640 isolates for analysis. Now our data looks +
So we end up with 10,583 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,640 -Available: 10,640 (100%, NA: 0 = 0%) +Length: 10,583 +Available: 10,583 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
If you want to get a quick glance of the number of isolates in @@ -1013,50 +1013,50 @@ different bug/drug combinations, you can use the
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5412594
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 -0.5416017 +0.5445669 Hospital B -0.5477551 +0.5366764 Hospital C -0.5425532 +0.5427167 Hospital D -0.5287303 +0.5413386 @@ -1189,23 +1189,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5416017 -3209 +0.5445669 +3175 Hospital B -0.5477551 -3675 +0.5366764 +3749 Hospital C -0.5425532 -1598 +0.5427167 +1627 Hospital D -0.5287303 -2158 +0.5413386 +2032 @@ -1230,27 +1230,27 @@ therapies very easily: Escherichia -0.7714411 -0.8712437 -0.9749945 +0.7651007 +0.8809266 +0.9742368 Klebsiella -0.8312129 -0.8956743 -0.9847328 +0.8231707 +0.9128920 +0.9799652 Staphylococcus -0.7827338 -0.8780576 -0.9748201 +0.7911677 +0.8910928 +0.9805389 Streptococcus -0.5494816 +0.5270522 0.0000000 -0.5494816 +0.5270522 @@ -1278,23 +1278,23 @@ classes, use a antibiotic class selector such as Hospital A -54.2% -26.5% +54.5% +27.0% Hospital B -54.8% +53.7% 26.7% Hospital C 54.3% -25.7% +27.8% Hospital D -52.9% -25.9% +54.1% +27.0% @@ -1410,16 +1410,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 32 0.01 4 2 0.25 32 0.025 0.001 0.0625 4 -# [11] 128 64 0.005 0.01 0.025 0.25 0.025 0.125 256 0.0625 -# [21] 0.001 0.125 0.25 0.01 8 0.0625 1 0.125 16 0.5 -# [31] 0.125 16 0.25 128 0.25 2 256 32 0.005 0.002 -# [41] 16 0.005 0.001 256 0.025 4 128 256 0.005 32 -# [51] 1 0.125 16 0.25 0.01 0.5 0.5 0.125 0.0625 64 -# [61] 0.005 128 0.001 0.005 16 0.0625 32 0.01 8 256 -# [71] 0.001 0.0625 1 0.01 32 128 0.01 16 2 0.001 -# [81] 0.01 0.0625 0.0625 16 2 0.001 0.125 0.001 4 0.0625 -# [91] 0.5 1 0.125 0.005 8 4 1 0.025 0.5 32
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 32 0.01 4 2 0.25 32 0.025 0.001 0.0625 4 -# [11] 128 64 0.005 0.01 0.025 0.25 0.025 0.125 256 0.0625 -# [21] 0.001 0.125 0.25 0.01 8 0.0625 1 0.125 16 0.5 -# [31] 0.125 16 0.25 128 0.25 2 256 32 0.005 0.002 -# [41] 16 0.005 0.001 256 0.025 4 128 256 0.005 32 -# [51] 1 0.125 16 0.25 0.01 0.5 0.5 0.125 0.0625 64 -# [61] 0.005 128 0.001 0.005 16 0.0625 32 0.01 8 256 -# [71] 0.001 0.0625 1 0.01 32 128 0.01 16 2 0.001 -# [81] 0.01 0.0625 0.0625 16 2 0.001 0.125 0.001 4 0.0625 -# [91] 0.5 1 0.125 0.005 8 4 1 0.025 0.5 32
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 29 23 28 30 26 21 27 30 21 31 23 25 22 30 18 24 24 24 29 28 31 28 25 31 27 -# [26] 26 30 21 25 30 31 24 24 30 17 30 27 20 31 26 20 23 17 31 31 28 28 26 20 19 -# [51] 24 30 26 24 18 18 17 24 28 24 19 27 31 20 18 23 27 31 19 19 17 27 23 25 19 -# [76] 18 20 26 18 19 26 21 17 28 22 26 24 28 27 22 17 22 28 20 19 17 17 20 19 24
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")