So only 61.1% 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
@@ -644,7 +644,7 @@ on it with the data_1st <- data %>% filter_first_isolate()
data_1st <- data %>% filter_first_isolate()
So we end up with 12 223 isolates for analysis. Now our data looks +
So we end up with 12 299 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 12,223 -Available: 12,223 (100%, NA: 0 = 0%) +Length: 12,299 +Available: 12,299 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
If you want to get a quick glance of the number of isolates in @@ -1025,46 +1025,46 @@ different bug/drug combinations, you can use the
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5847173
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 -0.5854654 +0.5842217 Hospital B -0.5881089 +0.5908142 Hospital C -0.5802920 +0.5761879 Hospital D -0.5812526 +0.5779626 @@ -1206,23 +1206,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5854654 -3674 +0.5842217 +3752 Hospital B -0.5881089 -4188 +0.5908142 +4311 Hospital C -0.5802920 -1918 +0.5761879 +1831 Hospital D -0.5812526 -2443 +0.5779626 +2405 @@ -1247,27 +1247,27 @@ therapies very easily: Escherichia -0.6638438 -0.6527826 -0.8840304 +0.6666099 +0.6517811 +0.8825635 Klebsiella -0.6716196 -0.6619614 -0.8893016 +0.6570156 +0.6466221 +0.8619154 Staphylococcus -0.6843066 -0.6508516 -0.8886861 +0.6667698 +0.6574876 +0.8932550 Streptococcus -0.4608985 +0.4700486 0.0000000 -0.4608985 +0.4700486 @@ -1295,23 +1295,23 @@ classes, use a antibiotic class selector such as Hospital A -58.5% -35.8% +58.4% +37.3% Hospital B -58.8% -35.8% +59.1% +37.2% Hospital C -58.0% -36.7% +57.6% +35.2% Hospital D -58.1% -36.0% +57.8% +34.3% @@ -1427,16 +1427,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] >=256 0.0625 0.0625 0.01 0.25 0.002 8 0.25 128 1 -# [11] 4 1 0.01 2 0.002 8 1 0.001 128 0.002 -# [21] 8 0.002 0.005 0.001 128 0.01 16 8 0.125 1 -# [31] 0.25 16 0.002 0.025 128 0.0625 64 0.5 >=256 2 -# [41] 4 8 0.25 64 0.25 128 0.005 4 0.005 2 -# [51] >=256 >=256 >=256 32 0.25 0.0625 64 32 >=256 4 -# [61] 128 0.025 8 0.5 16 128 128 0.125 0.005 1 -# [71] >=256 64 0.025 0.001 128 0.005 8 64 0.002 8 -# [81] 0.005 32 1 0.005 64 16 32 0.5 0.01 0.002 -# [91] 0.25 2 0.5 32 4 0.005 0.0625 8 0.002 0.005
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] >=256 0.0625 0.0625 0.01 0.25 0.002 8 0.25 128 1 -# [11] 4 1 0.01 2 0.002 8 1 0.001 128 0.002 -# [21] 8 0.002 0.005 0.001 128 0.01 16 8 0.125 1 -# [31] 0.25 16 0.002 0.025 128 0.0625 64 0.5 >=256 2 -# [41] 4 8 0.25 64 0.25 128 0.005 4 0.005 2 -# [51] >=256 >=256 >=256 32 0.25 0.0625 64 32 >=256 4 -# [61] 128 0.025 8 0.5 16 128 128 0.125 0.005 1 -# [71] >=256 64 0.025 0.001 128 0.005 8 64 0.002 8 -# [81] 0.005 32 1 0.005 64 16 32 0.5 0.01 0.002 -# [91] 0.25 2 0.5 32 4 0.005 0.0625 8 0.002 0.005
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 28 20 23 25 19 22 25 19 24 22 23 24 27 30 18 31 19 28 28 26 21 20 21 30 30 -# [26] 27 30 22 27 30 23 27 27 27 30 18 28 21 20 26 21 19 24 21 19 28 19 18 29 27 -# [51] 18 26 18 28 17 21 30 21 21 20 21 22 30 27 28 31 24 29 27 18 27 25 30 27 25 -# [76] 24 28 24 29 27 17 30 25 24 21 17 26 22 23 24 26 18 26 20 23 24 28 17 25 22
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")