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
So only 53.1% 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()
So we end up with 10,723 isolates for analysis. Now our data looks like:
So we end up with 10,619 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: 10,723 -Available: 10,723 (100.0%, NA: 0 = 0.0%) +Length: 10,619 +Available: 10,619 (100.0%, NA: 0 = 0.0%) Unique: 4
Shortest: 16 Longest: 24
If you want to get a quick glance of the number of isolates in different bug/drug combinations, you can use the bug_drug_combinations() function:
bug_drug_combinations()
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] 0.5465821
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
data_1st %>% @@ -963,19 +963,19 @@ Longest: 24 Hospital A -0.5332098 +0.5382868 Hospital B -0.5497618 +0.5447543 Hospital C -0.5499058 +0.5595602 Hospital D -0.5588652 +0.5353630 @@ -994,23 +994,23 @@ Longest: 24 Hospital A -0.5332098 -3237 +0.5382868 +3082 Hospital B -0.5497618 -3778 +0.5447543 +3765 Hospital C -0.5499058 -1593 +0.5595602 +1637 Hospital D -0.5588652 -2115 +0.5353630 +2135 @@ -1031,27 +1031,27 @@ Longest: 24 Escherichia -0.7642537 -0.8678198 -0.9750160 +0.7620898 +0.8741364 +0.9771157 Klebsiella -0.8320926 -0.8891646 -0.9743590 +0.8309383 +0.9112426 +0.9805579 Staphylococcus -0.7920792 -0.8877888 -0.9845985 +0.7884756 +0.8862144 +0.9817651 Streptococcus -0.5361217 +0.5354025 0.0000000 -0.5361217 +0.5354025 @@ -1076,23 +1076,23 @@ Longest: 24 Hospital A -53.3% +53.8% 26.2% Hospital B -55.0% +54.5% 27.0% Hospital C -55.0% -25.5% +56.0% +27.9% Hospital D -55.9% -27.3% +53.5% +26.1% @@ -1176,18 +1176,16 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 32 64 128 128 64 8 32 0.002 0.025 -# [10] 0.5 32 <=0.001 2 128 0.01 1 2 0.01 -# [19] 128 0.5 4 0.002 0.125 256 0.0625 4 0.002 -# [28] 0.005 4 0.002 32 0.01 0.0625 0.25 2 64 -# [37] 0.0625 0.5 256 4 1 0.025 128 0.0625 16 -# [46] 0.5 0.005 0.01 1 2 0.01 2 8 0.125 -# [55] 0.25 256 0.0625 0.025 0.002 8 <=0.001 1 0.002 -# [64] 256 8 4 0.005 0.025 4 0.002 <=0.001 2 -# [73] 256 0.005 256 2 4 16 128 256 <=0.001 -# [82] 1 2 1 1 <=0.001 0.5 4 0.25 32 -# [91] 0.002 0.0625 0.01 32 0.005 0.01 2 64 64 -# [100] 2
mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 32 64 128 128 64 8 32 0.002 0.025 -# [10] 0.5 32 <=0.001 2 128 0.01 1 2 0.01 -# [19] 128 0.5 4 0.002 0.125 256 0.0625 4 0.002 -# [28] 0.005 4 0.002 32 0.01 0.0625 0.25 2 64 -# [37] 0.0625 0.5 256 4 1 0.025 128 0.0625 16 -# [46] 0.5 0.005 0.01 1 2 0.01 2 8 0.125 -# [55] 0.25 256 0.0625 0.025 0.002 8 <=0.001 1 0.002 -# [64] 256 8 4 0.005 0.025 4 0.002 <=0.001 2 -# [73] 256 0.005 256 2 4 16 128 256 <=0.001 -# [82] 1 2 1 1 <=0.001 0.5 4 0.25 32 -# [91] 0.002 0.0625 0.01 32 0.005 0.01 2 64 64 -# [100] 2
# base R: plot(mic_values)
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")