So only 53.8% 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 10,750 isolates for analysis. Now our data looks +
So we end up with 10,571 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,750 -Available: 10,750 (100%, NA: 0 = 0%) +Length: 10,571 +Available: 10,571 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.543814
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 -0.5540994 +0.5397836 Hospital B -0.5473965 +0.5503731 Hospital C -0.5031606 +0.5403882 Hospital D -0.5518207 +0.5360577 @@ -1189,23 +1189,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5540994 -3281 +0.5397836 +3142 Hospital B -0.5473965 -3745 +0.5503731 +3752 Hospital C -0.5031606 -1582 +0.5403882 +1597 Hospital D -0.5518207 -2142 +0.5360577 +2080 @@ -1230,27 +1230,27 @@ therapies very easily: Escherichia -0.7671492 -0.8686805 -0.9758758 +0.7722404 +0.8716940 +0.9768306 Klebsiella -0.8237774 -0.9038786 -0.9806071 +0.8239316 +0.9017094 +0.9769231 Staphylococcus -0.7899963 -0.8859875 -0.9834498 +0.8006620 +0.8852519 +0.9764619 Streptococcus -0.5303176 +0.5372568 0.0000000 -0.5303176 +0.5372568 @@ -1278,23 +1278,23 @@ classes, use a antibiotic class selector such as Hospital A -55.4% -26.9% +54.0% +26.8% Hospital B -54.7% -27.3% +55.0% +26.8% Hospital C -50.3% -25.5% +54.0% +24.9% Hospital D -55.2% -25.9% +53.6% +24.9% @@ -1410,16 +1410,16 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 8 0.01 0.01 0.025 128 64 2 0.025 0.0625 0.005 -# [11] 0.001 0.0625 1 0.002 0.5 0.25 64 0.025 256 128 -# [21] 0.001 0.005 1 0.125 256 8 0.001 0.25 0.0625 1 -# [31] 4 0.005 0.5 0.0625 0.002 2 0.0625 0.005 0.005 0.25 -# [41] 32 0.01 0.01 2 128 16 0.25 8 4 0.025 -# [51] 32 256 0.0625 128 128 0.001 0.5 0.5 0.125 64 -# [61] 0.25 8 0.0625 0.001 1 2 256 0.005 4 16 -# [71] 0.125 0.002 256 64 0.01 8 0.5 0.5 64 1 -# [81] 0.5 32 4 1 2 0.025 0.0625 0.25 0.005 16 -# [91] 0.0625 0.002 8 0.001 0.125 0.125 128 64 0.125 64
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 8 0.01 0.01 0.025 128 64 2 0.025 0.0625 0.005 -# [11] 0.001 0.0625 1 0.002 0.5 0.25 64 0.025 256 128 -# [21] 0.001 0.005 1 0.125 256 8 0.001 0.25 0.0625 1 -# [31] 4 0.005 0.5 0.0625 0.002 2 0.0625 0.005 0.005 0.25 -# [41] 32 0.01 0.01 2 128 16 0.25 8 4 0.025 -# [51] 32 256 0.0625 128 128 0.001 0.5 0.5 0.125 64 -# [61] 0.25 8 0.0625 0.001 1 2 256 0.005 4 16 -# [71] 0.125 0.002 256 64 0.01 8 0.5 0.5 64 1 -# [81] 0.5 32 4 1 2 0.025 0.0625 0.25 0.005 16 -# [91] 0.0625 0.002 8 0.001 0.125 0.125 128 64 0.125 64
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 26 30 22 29 29 24 23 31 22 19 29 24 22 30 20 18 26 25 18 23 23 25 31 24 25 -# [26] 18 30 22 31 24 22 22 25 22 17 22 24 25 28 19 21 21 21 22 18 31 25 22 25 30 -# [51] 25 31 23 21 17 19 31 23 19 20 28 22 26 25 25 24 20 24 25 19 27 28 21 26 23 -# [76] 17 22 23 30 20 20 18 18 22 19 29 31 18 26 20 28 27 24 27 21 17 25 30 22 22
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")