So only 52.8% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
So only 53.7% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
data_1st <- data %>% filter(first == TRUE)
data_1st <- data %>% filter_first_isolate() # Including isolates from ICU.
So we end up with 10,557 isolates for analysis. Now our data looks like:
So we end up with 10,731 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: 10,557 -Available: 10,557 (100.0%, NA: 0 = 0.0%) +Length: 10,731 +Available: 10,731 (100.0%, NA: 0 = 0.0%) Unique: 4
Shortest: 16 Longest: 24
As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (proportion_R(), equal to resistance()) and susceptibility as the proportion of S and I (proportion_SI(), equal to susceptibility()). These functions can be used on their own:
proportion_R()
resistance()
proportion_SI()
susceptibility()
data_1st %>% resistance(AMX) -# [1] 0.5391683
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
data_1st %>% @@ -975,19 +975,19 @@ Longest: 24 Hospital A -0.5290698 +0.5456783 Hospital B -0.5434023 +0.5524140 Hospital C -0.5325670 +0.5469820 Hospital D -0.5510580 +0.5353107 @@ -1008,23 +1008,23 @@ Longest: 24 Hospital A -0.5290698 -3096 +0.5456783 +3251 Hospital B -0.5434023 -3721 +0.5524140 +3749 Hospital C -0.5325670 -1566 +0.5469820 +1607 Hospital D -0.5510580 -2174 +0.5353107 +2124 @@ -1047,27 +1047,27 @@ Longest: 24 Escherichia -0.7751648 -0.8714286 -0.9795604 +0.7644169 +0.8727040 +0.9769329 Klebsiella -0.8316327 -0.8954082 -0.9838435 +0.8386824 +0.9146959 +0.9831081 Staphylococcus -0.7993967 -0.8906486 -0.9792609 +0.7969265 +0.8975485 +0.9849982 Streptococcus -0.5392382 +0.5323640 0.0000000 -0.5392382 +0.5323640 @@ -1092,23 +1092,23 @@ Longest: 24 Hospital A -52.9% -24.5% +54.6% +25.6% Hospital B -54.3% +55.2% 27.5% Hospital C -53.3% -26.4% +54.7% +26.1% Hospital D -55.1% -25.8% +53.5% +26.5% @@ -1206,16 +1206,16 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.002 0.01 0.25 2 >=256 8 0.002 0.025 0.25 0.125 -# [11] 32 0.005 0.005 4 32 128 16 0.01 2 4 -# [21] 0.005 0.002 1 0.25 0.001 8 0.025 64 0.01 >=256 -# [31] 32 0.025 2 0.025 0.002 0.005 8 1 0.25 16 -# [41] 0.001 64 2 0.002 0.005 0.002 >=256 0.5 0.002 0.002 -# [51] 0.125 64 0.01 1 0.025 0.0625 0.002 32 32 2 -# [61] 0.002 64 64 0.01 0.001 2 16 16 0.125 >=256 -# [71] 32 >=256 16 0.005 32 0.25 0.005 1 0.25 0.125 -# [81] 0.005 0.0625 2 8 >=256 0.25 0.01 0.025 32 0.0625 -# [91] >=256 0.25 >=256 0.001 128 1 0.125 0.125 0.5 0.125
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.002 0.01 0.25 2 >=256 8 0.002 0.025 0.25 0.125 -# [11] 32 0.005 0.005 4 32 128 16 0.01 2 4 -# [21] 0.005 0.002 1 0.25 0.001 8 0.025 64 0.01 >=256 -# [31] 32 0.025 2 0.025 0.002 0.005 8 1 0.25 16 -# [41] 0.001 64 2 0.002 0.005 0.002 >=256 0.5 0.002 0.002 -# [51] 0.125 64 0.01 1 0.025 0.0625 0.002 32 32 2 -# [61] 0.002 64 64 0.01 0.001 2 16 16 0.125 >=256 -# [71] 32 >=256 16 0.005 32 0.25 0.005 1 0.25 0.125 -# [81] 0.005 0.0625 2 8 >=256 0.25 0.01 0.025 32 0.0625 -# [91] >=256 0.25 >=256 0.001 128 1 0.125 0.125 0.5 0.125
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 30 25 27 27 22 25 26 31 26 18 18 28 23 28 22 31 21 22 28 22 21 30 31 29 31 -# [26] 19 27 22 31 25 17 25 24 27 27 27 23 21 25 20 30 30 19 30 20 29 28 29 26 27 -# [51] 17 31 31 17 21 18 25 19 23 23 19 20 24 26 26 24 30 17 25 18 24 30 29 29 21 -# [76] 29 22 23 22 24 28 30 23 17 20 31 27 17 27 19 21 31 22 30 23 25 29 21 17 20
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")