vignettes/AMR.Rmd
AMR.Rmd
So only 53.7% is suitable for resistance analysis! We can now filter +
So only 53.3% 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 10,739 isolates for analysis. Now our data looks +
So we end up with 10,650 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,739 -Available: 10,739 (100%, NA: 0 = 0%) +Length: 10,650 +Available: 10,650 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
If you want to get a quick glance of the number of isolates in @@ -1014,50 +1014,50 @@ different bug/drug combinations, you can use the
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5423224
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
@@ -1154,19 +1154,19 @@ own: Hospital A -0.5322120 +0.5346793 Hospital B -0.5366357 +0.5482421 Hospital C -0.5579757 +0.5480192 Hospital D -0.5560205 +0.5347418 @@ -1191,23 +1191,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5322120 -3151 +0.5346793 +3071 Hospital B -0.5366357 -3876 +0.5482421 +3783 Hospital C -0.5579757 -1561 +0.5480192 +1666 Hospital D -0.5560205 -2151 +0.5347418 +2130 @@ -1232,27 +1232,27 @@ therapies very easily: Escherichia -0.7761634 -0.8715519 -0.9816804 +0.7705656 +0.8819623 +0.9777207 Klebsiella -0.8148457 -0.9082569 -0.9824854 +0.8226478 +0.9159034 +0.9866778 Staphylococcus -0.8017209 -0.8799102 -0.9816685 +0.7979911 +0.8917411 +0.9769345 Streptococcus -0.5325779 +0.5470616 0.0000000 -0.5325779 +0.5470616 @@ -1280,23 +1280,23 @@ classes, use a antibiotic class selector such as Hospital A -53.2% -26.1% +53.5% +25.9% Hospital B -53.7% -25.5% +54.8% +26.6% Hospital C -55.8% -27.9% +54.8% +26.8% Hospital D -55.6% -26.0% +53.5% +24.8% @@ -1412,16 +1412,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.0625 0.005 0.005 0.001 256 128 0.125 0.5 0.125 0.025 -# [11] 8 64 0.0625 0.5 0.001 4 0.01 8 0.0625 4 -# [21] 16 8 0.5 0.0625 0.01 0.001 0.01 8 8 0.01 -# [31] 0.5 1 32 8 4 0.5 0.25 32 0.005 8 -# [41] 128 32 0.001 128 8 0.025 64 8 16 0.0625 -# [51] 0.01 0.5 0.025 16 32 256 0.001 16 8 0.01 -# [61] 0.5 8 8 0.0625 0.01 0.125 128 16 0.002 128 -# [71] 64 128 64 32 32 16 64 0.01 64 2 -# [81] 0.25 0.002 0.025 128 0.002 64 0.001 2 32 16 -# [91] 0.005 8 0.25 0.002 16 32 1 0.25 16 0.005
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.0625 0.005 0.005 0.001 256 128 0.125 0.5 0.125 0.025 -# [11] 8 64 0.0625 0.5 0.001 4 0.01 8 0.0625 4 -# [21] 16 8 0.5 0.0625 0.01 0.001 0.01 8 8 0.01 -# [31] 0.5 1 32 8 4 0.5 0.25 32 0.005 8 -# [41] 128 32 0.001 128 8 0.025 64 8 16 0.0625 -# [51] 0.01 0.5 0.025 16 32 256 0.001 16 8 0.01 -# [61] 0.5 8 8 0.0625 0.01 0.125 128 16 0.002 128 -# [71] 64 128 64 32 32 16 64 0.01 64 2 -# [81] 0.25 0.002 0.025 128 0.002 64 0.001 2 32 16 -# [91] 0.005 8 0.25 0.002 16 32 1 0.25 16 0.005
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 25 31 26 31 24 31 27 17 28 25 21 28 20 20 17 17 26 20 20 29 26 31 30 20 25 -# [26] 25 27 29 29 21 17 27 20 20 17 25 17 20 27 29 19 27 18 28 31 21 23 26 26 27 -# [51] 23 23 21 18 22 23 19 17 22 23 29 28 19 29 20 31 18 25 21 17 18 31 20 21 17 -# [76] 21 28 25 31 24 30 24 17 20 26 31 25 20 30 18 21 29 19 30 20 24 29 23 23 22
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")