So only 53.6% is suitable for resistance analysis! We can now filter +
So only 53.5% 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 10,719 isolates for analysis. Now our data looks +
So we end up with 10,701 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: 10,719 -Available: 10,719 (100%, NA: 0 = 0%) +Length: 10,701 +Available: 10,701 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5435209
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 -0.5481644 +0.5509074 Hospital B -0.5404344 +0.5428951 Hospital C -0.5331288 +0.5330813 Hospital D -0.5502162 +0.5495710 @@ -1189,23 +1189,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5481644 -3187 +0.5509074 +3251 Hospital B -0.5404344 -3821 +0.5428951 +3765 Hospital C -0.5331288 -1630 +0.5330813 +1587 Hospital D -0.5502162 -2081 +0.5495710 +2098 @@ -1230,27 +1230,27 @@ therapies very easily: Escherichia -0.7708688 -0.8711670 -0.9795571 +0.7659983 +0.8741468 +0.9718430 Klebsiella -0.8333333 -0.9025000 -0.9900000 +0.8243819 +0.9028133 +0.9838022 Staphylococcus -0.7976753 -0.8899382 -0.9771159 +0.7977901 +0.8968692 +0.9830571 Streptococcus -0.5521739 +0.5303529 0.0000000 -0.5521739 +0.5303529 @@ -1278,23 +1278,23 @@ classes, use a antibiotic class selector such as Hospital A -54.8% -26.2% +55.1% +27.1% Hospital B -54.0% -24.9% +54.3% +26.5% Hospital C 53.3% -26.4% +27.3% Hospital D 55.0% -26.0% +25.5% @@ -1410,16 +1410,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.25 0.002 0.001 1 0.5 0.025 0.25 0.005 1 0.5 -# [11] 8 0.025 0.025 0.01 0.005 2 2 8 0.5 1 -# [21] 0.01 0.5 0.001 128 0.005 0.025 128 4 0.25 16 -# [31] 16 0.25 0.005 0.01 4 0.25 8 0.01 0.125 256 -# [41] 0.002 2 0.5 16 0.125 1 0.0625 0.0625 0.025 2 -# [51] 0.01 2 32 0.005 0.025 0.025 4 128 0.001 4 -# [61] 0.01 4 64 0.125 0.0625 0.0625 2 256 0.125 0.025 -# [71] 8 0.5 64 0.0625 8 0.005 0.25 0.001 256 16 -# [81] 128 64 0.125 0.025 0.5 2 0.005 0.0625 32 16 -# [91] 1 8 128 0.25 0.0625 64 0.002 4 0.025 64
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.25 0.002 0.001 1 0.5 0.025 0.25 0.005 1 0.5 -# [11] 8 0.025 0.025 0.01 0.005 2 2 8 0.5 1 -# [21] 0.01 0.5 0.001 128 0.005 0.025 128 4 0.25 16 -# [31] 16 0.25 0.005 0.01 4 0.25 8 0.01 0.125 256 -# [41] 0.002 2 0.5 16 0.125 1 0.0625 0.0625 0.025 2 -# [51] 0.01 2 32 0.005 0.025 0.025 4 128 0.001 4 -# [61] 0.01 4 64 0.125 0.0625 0.0625 2 256 0.125 0.025 -# [71] 8 0.5 64 0.0625 8 0.005 0.25 0.001 256 16 -# [81] 128 64 0.125 0.025 0.5 2 0.005 0.0625 32 16 -# [91] 1 8 128 0.25 0.0625 64 0.002 4 0.025 64
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 24 18 27 18 24 19 22 17 18 31 24 25 28 21 25 22 21 21 20 25 23 28 31 24 24 -# [26] 23 22 18 17 23 30 26 20 23 19 20 17 19 29 24 20 31 29 29 30 20 30 20 30 25 -# [51] 19 27 22 21 29 29 23 28 21 31 29 20 20 18 17 22 23 29 25 23 23 30 28 30 30 -# [76] 28 19 20 29 30 30 20 20 19 22 20 24 25 26 31 23 23 20 24 17 28 24 28 19 26
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")