So only 61.8% is suitable for resistance analysis! We can now filter +
So only 61.5% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
@@ -644,11 +644,11 @@ on it with the data_1st <- data %>% filter_first_isolate()
data_1st <- data %>% filter_first_isolate()
So we end up with 12 360 isolates for analysis. Now our data looks +
So we end up with 12 291 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 12,360 -Available: 12,360 (100%, NA: 0 = 0%) +Length: 12,291 +Available: 12,291 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5843042
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
@@ -1169,19 +1169,19 @@ own: Hospital A -0.5790487 +0.5758659 Hospital B -0.5752233 +0.5828610 Hospital C -0.5984384 +0.5882029 Hospital D -0.5973469 +0.5881384 @@ -1206,23 +1206,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5790487 -3637 +0.5758659 +3638 Hospital B -0.5752233 -4367 +0.5828610 +4411 Hospital C -0.5984384 -1793 +0.5882029 +1814 Hospital D -0.5973469 -2563 +0.5881384 +2428 @@ -1247,27 +1247,27 @@ therapies very easily: Escherichia -0.6646586 -0.6544511 -0.8840361 +0.6655428 +0.6480108 +0.8833446 Klebsiella -0.6521061 -0.6762871 -0.8892356 +0.6599691 +0.6653787 +0.8802164 Staphylococcus -0.6748920 -0.6511413 -0.8834053 +0.6719233 +0.6484230 +0.8862090 Streptococcus -0.4698925 +0.4729656 0.0000000 -0.4698925 +0.4729656 @@ -1295,23 +1295,23 @@ classes, use a antibiotic class selector such as Hospital A -57.9% -36.8% +57.6% +36.5% Hospital B -57.5% -35.8% +58.3% +36.2% Hospital C -59.8% -37.4% +58.8% +36.3% Hospital D -59.7% -35.8% +58.8% +35.7% @@ -1427,18 +1427,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.25 32 0.0625 1 0.125 16 0.0625 16 0.025 -# [10] 0.25 >=256 0.5 1 0.01 >=256 2 0.5 8 -# [19] 64 >=256 1 2 0.005 0.002 0.5 0.01 0.0625 -# [28] 0.125 0.002 0.002 >=256 >=256 0.025 4 1 2 -# [37] 0.01 1 32 128 >=256 <=0.001 0.01 2 0.002 -# [46] 2 4 0.002 0.25 >=256 0.125 0.125 0.25 16 -# [55] 128 0.25 0.0625 8 16 0.002 64 64 >=256 -# [64] 0.5 8 0.5 16 0.002 0.25 <=0.001 >=256 0.002 -# [73] 4 0.005 128 2 8 32 1 1 2 -# [82] 0.025 <=0.001 >=256 0.025 0.5 16 4 2 128 -# [91] 64 0.002 0.002 1 >=256 0.125 0.01 0.01 0.0625 -# [100] 0.0625
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.25 32 0.0625 1 0.125 16 0.0625 16 0.025 -# [10] 0.25 >=256 0.5 1 0.01 >=256 2 0.5 8 -# [19] 64 >=256 1 2 0.005 0.002 0.5 0.01 0.0625 -# [28] 0.125 0.002 0.002 >=256 >=256 0.025 4 1 2 -# [37] 0.01 1 32 128 >=256 <=0.001 0.01 2 0.002 -# [46] 2 4 0.002 0.25 >=256 0.125 0.125 0.25 16 -# [55] 128 0.25 0.0625 8 16 0.002 64 64 >=256 -# [64] 0.5 8 0.5 16 0.002 0.25 <=0.001 >=256 0.002 -# [73] 4 0.005 128 2 8 32 1 1 2 -# [82] 0.025 <=0.001 >=256 0.025 0.5 16 4 2 128 -# [91] 64 0.002 0.002 1 >=256 0.125 0.01 0.01 0.0625 -# [100] 0.0625
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 31 22 24 20 23 20 22 27 19 20 20 23 28 30 23 21 17 17 25 19 21 31 18 31 19 -# [26] 26 28 31 17 21 31 20 30 26 22 24 30 18 31 25 17 29 29 18 26 27 24 21 22 18 -# [51] 17 19 22 27 19 23 24 20 30 30 19 17 31 31 17 25 24 31 22 22 19 27 17 19 19 -# [76] 31 22 22 23 26 29 20 17 29 31 27 30 18 17 31 26 17 27 22 20 19 23 24 28 20
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
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