So only 53.5% 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
@@ -629,25 +629,25 @@ 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 10,707 isolates for analysis. Now our data looks +
So we end up with 10,720 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: 10,707 -Available: 10,707 (100%, NA: 0 = 0%) +Length: 10,720 +Available: 10,720 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5443168
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 -0.5446860 +0.5351351 Hospital B -0.5468415 +0.5533618 Hospital C -0.5404884 +0.5506899 Hospital D -0.5420827 +0.5601852 @@ -1186,23 +1186,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5446860 -3312 +0.5351351 +3145 Hospital B -0.5468415 -3736 +0.5533618 +3748 Hospital C -0.5404884 -1556 +0.5506899 +1667 Hospital D -0.5420827 -2103 +0.5601852 +2160 @@ -1227,27 +1227,27 @@ therapies very easily: Escherichia -0.7661138 -0.8721394 -0.9777451 +0.7653461 +0.8761428 +0.9775795 Klebsiella -0.8188841 -0.9072961 -0.9785408 +0.8109656 +0.8993453 +0.9836334 Staphylococcus -0.7898065 -0.8846726 -0.9825149 +0.7864565 +0.8799713 +0.9774274 Streptococcus -0.5538020 +0.5428301 0.0000000 -0.5538020 +0.5428301 @@ -1275,23 +1275,23 @@ classes, use a antibiotic class selector such as Hospital A -54.5% -27.2% +53.5% +25.5% Hospital B -54.7% -26.8% +55.3% +26.7% Hospital C -54.0% -25.1% +55.1% +27.5% Hospital D -54.2% -25.3% +56.0% +28.1% @@ -1407,17 +1407,17 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.125 8 2 8 0.0625 16 8 0.01 0.125 -# [10] 0.005 8 64 8 0.002 0.25 0.5 8 0.125 -# [19] 8 <=0.001 0.125 >=256 32 0.025 0.0625 2 4 -# [28] 2 0.125 0.002 16 0.125 4 <=0.001 1 0.125 -# [37] 1 128 16 4 1 16 4 32 <=0.001 -# [46] 2 0.005 0.025 0.005 8 0.25 64 64 8 -# [55] 8 2 2 0.005 4 4 0.5 4 0.01 -# [64] 0.25 0.01 0.0625 128 0.01 0.01 1 4 8 -# [73] >=256 0.002 0.5 0.5 128 0.005 16 4 0.125 -# [82] 8 0.005 0.005 128 0.125 1 0.25 >=256 >=256 -# [91] 0.125 0.25 0.5 0.125 4 32 0.125 0.025 8 +# [1] <=0.001 0.005 16 32 0.002 64 0.125 2 0.005 +# [10] 8 0.002 128 0.025 >=256 1 16 >=256 <=0.001 +# [19] 0.0625 16 0.125 16 4 0.0625 8 64 4 +# [28] >=256 0.025 <=0.001 128 >=256 0.125 0.002 0.125 0.0625 +# [37] 0.005 4 0.5 128 16 0.25 >=256 0.025 >=256 +# [46] 0.5 8 0.01 0.0625 0.125 0.002 2 0.01 1 +# [55] 32 0.002 0.025 0.002 0.025 64 <=0.001 0.25 0.005 +# [64] 0.002 0.005 0.025 128 0.025 8 2 0.5 128 +# [73] 16 0.002 >=256 0.005 2 0.002 0.0625 32 >=256 +# [82] 0.5 128 4 <=0.001 0.0625 16 0.0625 0.01 1 +# [91] 64 >=256 0.125 0.002 0.005 0.002 128 0.025 0.125 # [100] 0.005
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.125 8 2 8 0.0625 16 8 0.01 0.125 -# [10] 0.005 8 64 8 0.002 0.25 0.5 8 0.125 -# [19] 8 <=0.001 0.125 >=256 32 0.025 0.0625 2 4 -# [28] 2 0.125 0.002 16 0.125 4 <=0.001 1 0.125 -# [37] 1 128 16 4 1 16 4 32 <=0.001 -# [46] 2 0.005 0.025 0.005 8 0.25 64 64 8 -# [55] 8 2 2 0.005 4 4 0.5 4 0.01 -# [64] 0.25 0.01 0.0625 128 0.01 0.01 1 4 8 -# [73] >=256 0.002 0.5 0.5 128 0.005 16 4 0.125 -# [82] 8 0.005 0.005 128 0.125 1 0.25 >=256 >=256 -# [91] 0.125 0.25 0.5 0.125 4 32 0.125 0.025 8 +# [1] <=0.001 0.005 16 32 0.002 64 0.125 2 0.005 +# [10] 8 0.002 128 0.025 >=256 1 16 >=256 <=0.001 +# [19] 0.0625 16 0.125 16 4 0.0625 8 64 4 +# [28] >=256 0.025 <=0.001 128 >=256 0.125 0.002 0.125 0.0625 +# [37] 0.005 4 0.5 128 16 0.25 >=256 0.025 >=256 +# [46] 0.5 8 0.01 0.0625 0.125 0.002 2 0.01 1 +# [55] 32 0.002 0.025 0.002 0.025 64 <=0.001 0.25 0.005 +# [64] 0.002 0.005 0.025 128 0.025 8 2 0.5 128 +# [73] 16 0.002 >=256 0.005 2 0.002 0.0625 32 >=256 +# [82] 0.5 128 4 <=0.001 0.0625 16 0.0625 0.01 1 +# [91] 64 >=256 0.125 0.002 0.005 0.002 128 0.025 0.125 # [100] 0.005
# base R: @@ -1452,10 +1452,10 @@ plotting: disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 31 20 26 28 17 30 25 27 30 18 29 25 25 29 18 27 30 19 21 22 26 23 23 21 20 -# [26] 27 20 17 17 30 26 21 17 30 29 19 25 24 23 17 18 18 18 29 26 30 29 20 19 30 -# [51] 30 27 18 18 23 23 25 23 31 19 30 23 27 21 24 19 26 27 21 29 17 25 29 29 26 -# [76] 22 28 19 28 26 23 22 29 28 18 29 17 19 29 29 17 27 20 23 21 24 27 25 20 30
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 31 20 26 28 17 30 25 27 30 18 29 25 25 29 18 27 30 19 21 22 26 23 23 21 20 -# [26] 27 20 17 17 30 26 21 17 30 29 19 25 24 23 17 18 18 18 29 26 30 29 20 19 30 -# [51] 30 27 18 18 23 23 25 23 31 19 30 23 27 21 24 19 26 27 21 29 17 25 29 29 26 -# [76] 22 28 19 28 26 23 22 29 28 18 29 17 19 29 29 17 27 20 23 21 24 27 25 20 30
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")