So only 53% is suitable for resistance analysis! We can now filter on -it with the filter() function, also from the +
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:
dplyr
data_1st <- data %>% @@ -629,7 +629,7 @@ 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 10,603 isolates for analysis. Now our data looks +
So we end up with 10,707 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,603 -Available: 10,603 (100%, NA: 0 = 0%) +Length: 10,707 +Available: 10,707 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
If you want to get a quick glance of the number of isolates in @@ -1009,50 +1009,50 @@ different bug/drug combinations, you can use the
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5352259
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
@@ -1149,19 +1149,19 @@ own: Hospital A -0.5432099 +0.5446860 Hospital B -0.5246981 +0.5468415 Hospital C -0.5336856 +0.5404884 Hospital D -0.5419501 +0.5420827 @@ -1186,23 +1186,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5432099 -3240 +0.5446860 +3312 Hospital B -0.5246981 -3644 +0.5468415 +3736 Hospital C -0.5336856 -1514 +0.5404884 +1556 Hospital D -0.5419501 -2205 +0.5420827 +2103 @@ -1227,27 +1227,27 @@ therapies very easily: Escherichia -0.7729741 -0.8646535 -0.9761025 +0.7661138 +0.8721394 +0.9777451 Klebsiella -0.8312236 -0.8945148 -0.9746835 +0.8188841 +0.9072961 +0.9785408 Staphylococcus -0.7958656 -0.8940568 -0.9778516 +0.7898065 +0.8846726 +0.9825149 Streptococcus -0.5413105 +0.5538020 0.0000000 -0.5413105 +0.5538020 @@ -1275,23 +1275,23 @@ classes, use a antibiotic class selector such as Hospital A -54.3% -26.5% +54.5% +27.2% Hospital B -52.5% -25.0% +54.7% +26.8% Hospital C -53.4% -26.4% +54.0% +25.1% Hospital D 54.2% -27.0% +25.3% @@ -1407,16 +1407,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.125 64 32 0.01 0.25 0.01 1 0.005 4 8 -# [11] 64 0.001 8 0.005 4 0.5 0.005 16 0.001 128 -# [21] 64 16 64 8 2 2 0.25 128 0.001 >=256 -# [31] 4 4 8 2 4 16 0.5 2 0.25 0.025 -# [41] 2 8 8 0.005 128 2 0.01 16 1 1 -# [51] 0.002 64 16 32 1 0.025 0.0625 16 64 0.001 -# [61] 0.001 0.002 2 0.002 0.025 0.01 0.025 0.01 16 16 -# [71] >=256 64 16 0.25 0.01 64 0.25 64 0.001 0.001 -# [81] 64 16 >=256 4 0.25 0.002 0.5 1 128 0.002 -# [91] 0.025 0.125 0.025 0.025 0.002 0.25 0.025 64 4 8
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.125 64 32 0.01 0.25 0.01 1 0.005 4 8 -# [11] 64 0.001 8 0.005 4 0.5 0.005 16 0.001 128 -# [21] 64 16 64 8 2 2 0.25 128 0.001 >=256 -# [31] 4 4 8 2 4 16 0.5 2 0.25 0.025 -# [41] 2 8 8 0.005 128 2 0.01 16 1 1 -# [51] 0.002 64 16 32 1 0.025 0.0625 16 64 0.001 -# [61] 0.001 0.002 2 0.002 0.025 0.01 0.025 0.01 16 16 -# [71] >=256 64 16 0.25 0.01 64 0.25 64 0.001 0.001 -# [81] 64 16 >=256 4 0.25 0.002 0.5 1 128 0.002 -# [91] 0.025 0.125 0.025 0.025 0.002 0.25 0.025 64 4 8
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 21 22 20 24 22 19 26 17 18 29 25 23 23 30 27 29 23 27 17 26 28 17 23 22 24 -# [26] 19 17 18 21 18 19 26 23 28 20 26 29 20 29 21 30 23 31 22 31 22 27 20 25 22 -# [51] 19 24 26 29 21 21 23 22 24 31 24 20 17 18 20 21 29 20 18 23 18 17 30 21 18 -# [76] 18 17 23 21 26 29 25 26 30 30 29 29 18 18 23 26 19 29 28 24 28 24 23 17 20
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")