So only 52.9% is suitable for resistance analysis! We can now filter +
So only 53.1% 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 10,584 isolates for analysis. Now our data looks +
So we end up with 10,610 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,584 -Available: 10,584 (100%, NA: 0 = 0%) +Length: 10,610 +Available: 10,610 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5409108
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 -0.5492958 +0.5398258 Hospital B -0.5316054 +0.5561224 Hospital C -0.5598456 +0.5287860 Hospital D -0.5306719 +0.5477338 @@ -1189,23 +1189,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5492958 -3195 +0.5398258 +3214 Hospital B -0.5316054 -3781 +0.5561224 +3724 Hospital C -0.5598456 -1554 +0.5287860 +1598 Hospital D -0.5306719 -2054 +0.5477338 +2074 @@ -1230,27 +1230,27 @@ therapies very easily: Escherichia -0.7740052 -0.8762976 -0.9744810 +0.7726687 +0.8785991 +0.9804469 Klebsiella -0.8123909 -0.9013962 -0.9816754 +0.8356282 +0.8958692 +0.9870912 Staphylococcus -0.7840909 -0.8944282 -0.9783724 +0.7967176 +0.8888474 +0.9813502 Streptococcus -0.5421860 +0.5224799 0.0000000 -0.5421860 +0.5224799 @@ -1278,23 +1278,23 @@ classes, use a antibiotic class selector such as Hospital A -54.9% -27.1% +54.0% +26.8% Hospital B -53.2% -26.0% +55.6% +26.8% Hospital C -56.0% -27.3% +52.9% +23.8% Hospital D -53.1% -25.7% +54.8% +27.0% @@ -1410,16 +1410,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 64 0.001 4 64 0.01 0.01 0.0625 0.5 0.001 0.005 -# [11] 0.5 1 64 8 0.01 1 0.001 0.002 0.5 256 -# [21] 0.5 0.025 0.025 128 0.001 4 256 0.005 64 0.125 -# [31] 128 0.005 0.005 8 0.025 2 0.001 256 0.002 256 -# [41] 0.025 0.125 128 128 0.0625 0.25 0.0625 0.025 128 0.5 -# [51] 2 0.005 0.0625 0.0625 0.002 1 1 1 256 64 -# [61] 8 0.001 1 32 0.001 0.125 0.25 32 0.005 32 -# [71] 0.002 128 0.25 16 0.01 0.01 0.0625 64 0.01 16 -# [81] 128 0.005 0.002 4 0.25 0.125 0.001 0.5 0.005 0.125 -# [91] 0.001 0.001 0.01 0.001 0.5 0.125 0.005 256 128 8
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 64 0.001 4 64 0.01 0.01 0.0625 0.5 0.001 0.005 -# [11] 0.5 1 64 8 0.01 1 0.001 0.002 0.5 256 -# [21] 0.5 0.025 0.025 128 0.001 4 256 0.005 64 0.125 -# [31] 128 0.005 0.005 8 0.025 2 0.001 256 0.002 256 -# [41] 0.025 0.125 128 128 0.0625 0.25 0.0625 0.025 128 0.5 -# [51] 2 0.005 0.0625 0.0625 0.002 1 1 1 256 64 -# [61] 8 0.001 1 32 0.001 0.125 0.25 32 0.005 32 -# [71] 0.002 128 0.25 16 0.01 0.01 0.0625 64 0.01 16 -# [81] 128 0.005 0.002 4 0.25 0.125 0.001 0.5 0.005 0.125 -# [91] 0.001 0.001 0.01 0.001 0.5 0.125 0.005 256 128 8
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 20 17 20 22 27 22 22 26 23 30 22 28 20 31 29 21 21 26 23 18 23 23 26 18 17 -# [26] 22 24 27 25 25 31 24 22 29 22 19 24 31 17 27 26 23 21 28 26 22 26 19 25 22 -# [51] 25 29 17 30 30 21 29 23 24 29 29 31 17 19 21 21 23 21 31 18 25 23 31 27 31 -# [76] 19 28 20 29 26 30 29 18 29 31 21 25 27 27 23 22 28 21 24 19 31 20 19 20 26
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")