Now, let’s start the cleaning and the analysis!
So only 52.9% 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 10,582 isolates for analysis. Now our data looks +
So we end up with 10,702 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: 10,582 -Available: 10,582 (100%, NA: 0 = 0%) +Length: 10,702 +Available: 10,702 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5426195
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 -0.5490875 +0.5506410 Hospital B -0.5438596 +0.5437995 Hospital C -0.5260078 +0.5497288 Hospital D -0.5427757 +0.5503985 @@ -1189,23 +1189,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5490875 -3178 +0.5506410 +3120 Hospital B -0.5438596 -3762 +0.5437995 +3790 Hospital C -0.5260078 -1538 +0.5497288 +1659 Hospital D -0.5427757 -2104 +0.5503985 +2133 @@ -1230,27 +1230,27 @@ therapies very easily: Escherichia -0.7719983 -0.8764629 -0.9809276 +0.7724538 +0.8747314 +0.9774388 Klebsiella -0.8260504 -0.8949580 -0.9798319 +0.8107230 +0.8952071 +0.9764419 Staphylococcus -0.8025926 -0.8862963 -0.9796296 +0.7896085 +0.8818149 +0.9806074 Streptococcus -0.5442733 +0.5465451 0.0000000 -0.5442733 +0.5465451 @@ -1278,23 +1278,23 @@ classes, use a antibiotic class selector such as Hospital A -54.9% -27.4% +55.1% +25.8% Hospital B 54.4% -25.6% +26.2% Hospital C -52.6% -24.8% +55.0% +26.4% Hospital D -54.3% -24.9% +55.0% +27.0% @@ -1410,16 +1410,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.001 0.125 0.01 0.002 32 0.01 8 64 0.0625 0.002 -# [11] 0.025 0.25 128 0.125 4 8 0.125 4 0.025 32 -# [21] 64 0.125 8 0.005 0.5 0.002 32 0.5 0.25 32 -# [31] 0.025 128 0.25 0.001 0.01 256 16 0.002 16 0.0625 -# [41] 0.125 0.001 0.025 8 4 4 0.01 0.5 2 64 -# [51] 2 256 0.5 2 0.001 16 16 0.125 64 4 -# [61] 8 16 0.5 0.125 0.001 0.5 0.001 2 32 8 -# [71] 0.125 8 0.01 0.5 2 0.01 32 1 0.002 0.01 -# [81] 0.5 8 0.5 0.5 0.25 0.005 0.001 16 32 0.005 -# [91] 64 0.125 16 1 0.01 0.002 0.5 16 8 0.005
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.001 0.125 0.01 0.002 32 0.01 8 64 0.0625 0.002 -# [11] 0.025 0.25 128 0.125 4 8 0.125 4 0.025 32 -# [21] 64 0.125 8 0.005 0.5 0.002 32 0.5 0.25 32 -# [31] 0.025 128 0.25 0.001 0.01 256 16 0.002 16 0.0625 -# [41] 0.125 0.001 0.025 8 4 4 0.01 0.5 2 64 -# [51] 2 256 0.5 2 0.001 16 16 0.125 64 4 -# [61] 8 16 0.5 0.125 0.001 0.5 0.001 2 32 8 -# [71] 0.125 8 0.01 0.5 2 0.01 32 1 0.002 0.01 -# [81] 0.5 8 0.5 0.5 0.25 0.005 0.001 16 32 0.005 -# [91] 64 0.125 16 1 0.01 0.002 0.5 16 8 0.005
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 19 25 22 29 26 26 28 19 29 20 24 24 17 31 24 25 27 27 22 21 31 27 23 18 30 -# [26] 21 26 29 17 29 31 21 24 31 19 24 27 31 27 29 23 20 31 18 29 25 30 21 25 30 -# [51] 18 23 22 23 19 19 23 24 23 26 17 21 24 27 29 29 27 26 29 17 22 17 29 21 31 -# [76] 21 22 17 19 28 29 17 27 17 25 18 25 26 28 31 30 24 29 21 31 26 31 23 24 28
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")