Now, let’s start the cleaning and the analysis!
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
@@ -634,11 +634,11 @@ 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 10,641 isolates for analysis. Now our data looks +
So we end up with 10,584 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: 10,641 -Available: 10,641 (100%, NA: 0 = 0%) +Length: 10,584 +Available: 10,584 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5409266
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 -0.5447103 +0.5492958 Hospital B -0.5458963 +0.5316054 Hospital C -0.5324921 +0.5598456 Hospital D -0.5330882 +0.5306719 @@ -1189,23 +1189,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5447103 -3176 +0.5492958 +3195 Hospital B -0.5458963 -3704 +0.5316054 +3781 Hospital C -0.5324921 -1585 +0.5598456 +1554 Hospital D -0.5330882 -2176 +0.5306719 +2054 @@ -1230,27 +1230,27 @@ therapies very easily: Escherichia -0.7681096 -0.8736132 -0.9769415 +0.7740052 +0.8762976 +0.9744810 Klebsiella -0.8434856 -0.9069374 -0.9780034 +0.8123909 +0.9013962 +0.9816754 Staphylococcus -0.8043478 -0.8909359 -0.9837878 +0.7840909 +0.8944282 +0.9783724 Streptococcus -0.5474860 +0.5421860 0.0000000 -0.5474860 +0.5421860 @@ -1278,23 +1278,23 @@ classes, use a antibiotic class selector such as Hospital A -54.5% -25.0% +54.9% +27.1% Hospital B -54.6% -26.7% +53.2% +26.0% Hospital C -53.2% -26.2% +56.0% +27.3% Hospital D -53.3% -25.5% +53.1% +25.7% @@ -1410,18 +1410,16 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] >=256 <=0.001 16 0.25 4 0.025 128 16 >=256 -# [10] 0.005 0.25 1 1 64 8 0.002 0.5 64 -# [19] >=256 0.25 0.5 0.005 0.025 0.25 8 2 0.01 -# [28] 0.002 0.025 0.5 64 4 2 64 2 1 -# [37] <=0.001 128 0.25 0.0625 64 8 1 64 64 -# [46] 0.5 >=256 0.01 2 16 4 0.002 0.005 32 -# [55] 16 32 0.0625 4 8 0.125 8 0.01 64 -# [64] 8 0.005 0.025 0.5 2 16 0.25 1 <=0.001 -# [73] 0.005 0.0625 0.01 0.0625 0.25 32 0.5 0.005 0.5 -# [82] 0.005 4 0.002 4 <=0.001 8 <=0.001 0.01 0.125 -# [91] 0.005 0.25 16 0.01 0.01 >=256 0.01 0.025 0.5 -# [100] <=0.001
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] >=256 <=0.001 16 0.25 4 0.025 128 16 >=256 -# [10] 0.005 0.25 1 1 64 8 0.002 0.5 64 -# [19] >=256 0.25 0.5 0.005 0.025 0.25 8 2 0.01 -# [28] 0.002 0.025 0.5 64 4 2 64 2 1 -# [37] <=0.001 128 0.25 0.0625 64 8 1 64 64 -# [46] 0.5 >=256 0.01 2 16 4 0.002 0.005 32 -# [55] 16 32 0.0625 4 8 0.125 8 0.01 64 -# [64] 8 0.005 0.025 0.5 2 16 0.25 1 <=0.001 -# [73] 0.005 0.0625 0.01 0.0625 0.25 32 0.5 0.005 0.5 -# [82] 0.005 4 0.002 4 <=0.001 8 <=0.001 0.01 0.125 -# [91] 0.005 0.25 16 0.01 0.01 >=256 0.01 0.025 0.5 -# [100] <=0.001
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 30 28 18 29 30 23 29 25 25 24 19 22 18 23 27 21 27 21 25 30 23 31 25 27 26 -# [26] 26 29 23 22 24 18 29 27 24 17 25 23 22 20 28 27 21 31 21 25 25 27 23 23 18 -# [51] 31 31 31 31 24 27 26 31 26 27 27 26 20 26 26 28 24 26 19 25 21 27 19 31 21 -# [76] 18 26 30 23 28 28 27 31 21 17 28 28 22 21 26 23 26 28 27 31 29 30 28 23 28
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")