So only 61.8% is suitable for resistance analysis! We can now filter +
So only 61.3% 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 12 352 isolates for analysis. Now our data looks +
So we end up with 12 263 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: 12,352 -Available: 12,352 (100%, NA: 0 = 0%) +Length: 12,263 +Available: 12,263 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
If you want to get a quick glance of the number of isolates in @@ -1020,50 +1020,50 @@ different bug/drug combinations, you can use the
proportion_SI()
data_1st %>% resistance(AMX) -#> [1] 0.5845207
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 -0.5857797 +0.5897998 Hospital B -0.5846154 +0.5851407 Hospital C -0.5920836 +0.5656620 Hospital D -0.5768763 +0.5919967 @@ -1196,23 +1196,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5857797 -3713 +0.5897998 +3647 Hospital B -0.5846154 -4355 +0.5851407 +4334 Hospital C -0.5920836 -1819 +0.5656620 +1858 Hospital D -0.5768763 -2465 +0.5919967 +2424 @@ -1237,27 +1237,27 @@ therapies very easily: Escherichia -0.6661088 -0.6510460 -0.8816736 +0.6784922 +0.6477912 +0.8882824 Klebsiella -0.6430769 -0.6546154 -0.8792308 +0.6192661 +0.6605505 +0.8631498 Staphylococcus -0.6634496 -0.6534869 -0.8826276 +0.6616869 +0.6657330 +0.8882664 Streptococcus -0.4734584 +0.4827036 0.0000000 -0.4734584 +0.4827036 @@ -1285,23 +1285,23 @@ classes, use a antibiotic class selector such as Hospital A -58.6% -37.9% +59.0% +36.9% Hospital B 58.5% -34.9% +35.8% Hospital C -59.2% -38.1% +56.6% +34.6% Hospital D -57.7% -36.7% +59.2% +37.3% @@ -1417,18 +1417,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values #> Class 'mic' -#> [1] 4 0.002 16 1 0.125 <=0.001 64 0.5 2 -#> [10] <=0.001 0.0625 1 2 0.5 0.01 0.002 0.0625 >=256 -#> [19] >=256 0.5 <=0.001 8 64 0.025 0.005 0.025 >=256 -#> [28] 128 0.125 0.125 0.25 64 32 128 0.01 0.0625 -#> [37] 64 0.5 >=256 0.025 0.0625 >=256 0.25 0.0625 8 -#> [46] <=0.001 16 0.125 <=0.001 <=0.001 <=0.001 <=0.001 2 0.5 -#> [55] 0.005 <=0.001 >=256 0.0625 2 0.0625 0.025 64 <=0.001 -#> [64] 0.025 128 0.25 2 64 128 >=256 0.025 2 -#> [73] 0.005 0.5 <=0.001 4 16 1 <=0.001 0.005 0.125 -#> [82] 0.005 1 0.125 32 32 0.25 8 16 0.025 -#> [91] 0.005 0.005 0.002 0.005 128 0.5 0.002 32 4 -#> [100] 8
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values #> Class 'mic' -#> [1] 4 0.002 16 1 0.125 <=0.001 64 0.5 2 -#> [10] <=0.001 0.0625 1 2 0.5 0.01 0.002 0.0625 >=256 -#> [19] >=256 0.5 <=0.001 8 64 0.025 0.005 0.025 >=256 -#> [28] 128 0.125 0.125 0.25 64 32 128 0.01 0.0625 -#> [37] 64 0.5 >=256 0.025 0.0625 >=256 0.25 0.0625 8 -#> [46] <=0.001 16 0.125 <=0.001 <=0.001 <=0.001 <=0.001 2 0.5 -#> [55] 0.005 <=0.001 >=256 0.0625 2 0.0625 0.025 64 <=0.001 -#> [64] 0.025 128 0.25 2 64 128 >=256 0.025 2 -#> [73] 0.005 0.5 <=0.001 4 16 1 <=0.001 0.005 0.125 -#> [82] 0.005 1 0.125 32 32 0.25 8 16 0.025 -#> [91] 0.005 0.005 0.002 0.005 128 0.5 0.002 32 4 -#> [100] 8
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values #> Class 'disk' -#> [1] 21 17 22 25 18 19 27 23 26 29 23 21 31 23 18 23 19 21 28 24 22 19 19 28 17 -#> [26] 18 29 29 21 26 25 29 17 26 31 28 22 17 31 28 21 21 30 25 20 30 27 19 21 18 -#> [51] 23 18 27 23 18 19 24 27 21 20 25 27 18 17 23 23 31 23 22 26 20 29 28 21 26 -#> [76] 17 18 18 19 19 17 21 20 18 25 21 23 28 25 18 18 28 18 27 18 22 23 18 19 24
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")