So only 53.8% is suitable for resistance analysis! We can now filter +
So only 53.6% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
@@ -634,11 +634,11 @@ 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 10,762 isolates for analysis. Now our data looks +
So we end up with 10,714 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,762 -Available: 10,762 (100%, NA: 0 = 0%) +Length: 10,714 +Available: 10,714 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
If you want to get a quick glance of the number of isolates in @@ -1013,50 +1013,50 @@ different bug/drug combinations, you can use the
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5426501
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
@@ -1152,19 +1152,19 @@ own: Hospital A -0.5354760 +0.5537216 Hospital B -0.5500402 +0.5401750 Hospital C -0.5372946 +0.5323097 Hospital D -0.5447796 +0.5546335 @@ -1189,23 +1189,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5354760 -3298 +0.5537216 +3211 Hospital B -0.5500402 -3727 +0.5401750 +3771 Hospital C -0.5372946 -1582 +0.5323097 +1563 Hospital D -0.5447796 -2155 +0.5546335 +2169 @@ -1230,27 +1230,27 @@ therapies very easily: Escherichia -0.7640257 -0.8713062 -0.9781585 +0.7672117 +0.8745697 +0.9776248 Klebsiella -0.8166804 -0.8976053 -0.9785301 +0.8265221 +0.8899083 +0.9799833 Staphylococcus -0.8016679 -0.8836113 -0.9815083 +0.7735166 +0.8932706 +0.9804631 Streptococcus -0.5463966 +0.5344746 0.0000000 -0.5463966 +0.5344746 @@ -1278,23 +1278,23 @@ classes, use a antibiotic class selector such as Hospital A -53.5% -25.9% +55.4% +27.3% Hospital B -55.0% -26.5% +54.0% +26.6% Hospital C -53.7% -27.1% +53.2% +26.2% Hospital D -54.5% -26.2% +55.5% +27.9% @@ -1410,16 +1410,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.025 16 0.001 8 4 >=256 64 0.0625 0.002 0.01 -# [11] 0.002 0.005 8 2 0.0625 32 0.0625 16 0.002 2 -# [21] >=256 4 0.01 0.01 0.005 64 0.125 0.005 0.0625 0.001 -# [31] 0.025 8 32 32 0.025 32 0.25 0.0625 128 0.0625 -# [41] 0.125 1 0.5 1 0.0625 128 0.5 64 32 0.01 -# [51] >=256 4 0.01 32 0.005 4 0.001 128 1 4 -# [61] 1 0.002 0.01 >=256 0.0625 0.0625 0.25 1 1 1 -# [71] 0.01 0.005 0.01 0.0625 16 32 32 0.005 0.01 0.5 -# [81] 128 0.0625 1 0.125 0.125 16 2 0.002 0.001 0.002 -# [91] 0.025 32 32 0.005 0.01 0.0625 0.0625 0.005 16 4
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.025 16 0.001 8 4 >=256 64 0.0625 0.002 0.01 -# [11] 0.002 0.005 8 2 0.0625 32 0.0625 16 0.002 2 -# [21] >=256 4 0.01 0.01 0.005 64 0.125 0.005 0.0625 0.001 -# [31] 0.025 8 32 32 0.025 32 0.25 0.0625 128 0.0625 -# [41] 0.125 1 0.5 1 0.0625 128 0.5 64 32 0.01 -# [51] >=256 4 0.01 32 0.005 4 0.001 128 1 4 -# [61] 1 0.002 0.01 >=256 0.0625 0.0625 0.25 1 1 1 -# [71] 0.01 0.005 0.01 0.0625 16 32 32 0.005 0.01 0.5 -# [81] 128 0.0625 1 0.125 0.125 16 2 0.002 0.001 0.002 -# [91] 0.025 32 32 0.005 0.01 0.0625 0.0625 0.005 16 4
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 19 19 26 23 22 27 29 20 23 27 22 28 26 26 29 24 30 17 27 21 31 30 28 24 31 -# [26] 25 23 19 19 19 21 18 27 31 22 27 30 31 25 30 31 18 20 26 22 19 26 27 24 22 -# [51] 31 18 19 24 18 29 18 25 27 27 18 22 30 29 28 19 26 23 28 31 30 29 20 29 23 -# [76] 29 27 19 27 25 26 29 24 30 21 30 20 31 19 29 18 23 17 19 30 19 20 18 23 31
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")