So only 61.8% is suitable for resistance analysis! We can now filter +
So only 61.7% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
@@ -641,11 +641,11 @@ on it with the data_1st <- data %>% filter_first_isolate()
data_1st <- data %>% filter_first_isolate()
So we end up with 12 354 isolates for analysis. Now our data looks +
So we end up with 12 333 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: 12,354 -Available: 12,354 (100%, NA: 0 = 0%) +Length: 12,333 +Available: 12,333 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -#> [1] 0.5781124
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
@@ -1159,19 +1159,19 @@ own: Hospital A -0.5903060 +0.5860452 Hospital B -0.5759064 +0.5836969 Hospital C -0.5773087 +0.5804851 Hospital D -0.5644320 +0.5831363 @@ -1196,23 +1196,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5903060 -3693 +0.5860452 +3626 Hospital B -0.5759064 -4275 +0.5836969 +4355 Hospital C -0.5773087 -1895 +0.5804851 +1814 Hospital D -0.5644320 -2491 +0.5831363 +2538 @@ -1237,27 +1237,27 @@ therapies very easily: Escherichia -0.6764509 -0.6476015 -0.8901375 +0.6682083 +0.6425146 +0.8795820 Klebsiella -0.6516330 -0.6531882 -0.8833593 +0.6477795 +0.6569678 +0.8843798 Staphylococcus -0.6591388 -0.6561277 -0.8858777 +0.6564195 +0.6467752 +0.8758288 Streptococcus -0.4829132 +0.4820951 0.0000000 -0.4829132 +0.4820951 @@ -1285,23 +1285,23 @@ classes, use a antibiotic class selector such as Hospital A -59.0% -36.6% +58.6% +36.4% Hospital B -57.6% -35.7% +58.4% +37.2% Hospital C -57.7% -36.6% +58.0% +35.9% Hospital D -56.4% -34.5% +58.3% +36.0% @@ -1417,16 +1417,16 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values #> Class 'mic' -#> [1] 0.0625 0.001 0.01 0.125 0.01 0.01 0.025 32 0.25 0.025 -#> [11] 0.25 4 0.01 0.001 0.005 16 2 0.01 0.025 0.025 -#> [21] 0.01 0.005 0.0625 0.01 0.005 0.001 0.01 0.002 0.01 >=256 -#> [31] 0.025 0.025 0.005 4 0.005 0.001 0.5 64 0.025 4 -#> [41] 0.0625 >=256 1 0.125 16 32 0.01 0.01 64 64 -#> [51] 1 0.25 0.25 0.125 128 0.002 64 0.125 16 128 -#> [61] 0.01 0.01 0.01 128 0.0625 8 0.01 0.5 16 1 -#> [71] 0.005 128 0.002 0.125 2 16 0.01 0.25 1 2 -#> [81] 0.001 4 0.002 8 0.25 8 0.002 16 0.01 128 -#> [91] 1 128 0.125 0.5 0.01 0.025 0.5 32 0.0625 0.002
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values #> Class 'mic' -#> [1] 0.0625 0.001 0.01 0.125 0.01 0.01 0.025 32 0.25 0.025 -#> [11] 0.25 4 0.01 0.001 0.005 16 2 0.01 0.025 0.025 -#> [21] 0.01 0.005 0.0625 0.01 0.005 0.001 0.01 0.002 0.01 >=256 -#> [31] 0.025 0.025 0.005 4 0.005 0.001 0.5 64 0.025 4 -#> [41] 0.0625 >=256 1 0.125 16 32 0.01 0.01 64 64 -#> [51] 1 0.25 0.25 0.125 128 0.002 64 0.125 16 128 -#> [61] 0.01 0.01 0.01 128 0.0625 8 0.01 0.5 16 1 -#> [71] 0.005 128 0.002 0.125 2 16 0.01 0.25 1 2 -#> [81] 0.001 4 0.002 8 0.25 8 0.002 16 0.01 128 -#> [91] 1 128 0.125 0.5 0.01 0.025 0.5 32 0.0625 0.002
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values #> Class 'disk' -#> [1] 27 26 31 31 27 28 21 17 30 29 22 24 19 27 22 31 19 17 23 27 18 29 24 26 19 -#> [26] 30 28 22 30 24 19 27 27 20 25 22 19 19 26 17 27 29 27 26 27 27 30 19 27 21 -#> [51] 24 24 19 23 21 18 30 22 28 22 26 26 21 19 31 19 17 24 30 28 25 22 23 30 24 -#> [76] 23 21 26 30 23 28 24 27 22 28 31 21 30 23 30 26 17 28 17 29 19 20 28 28 27
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")