vignettes/AMR.Rmd
AMR.Rmd
So only 53.1% is suitable for resistance analysis! We can now filter -on it with the filter() function, also from the +
filter()
So only 53% is suitable for resistance analysis! We can now filter on +it with the filter() function, also from the dplyr package:
dplyr
data_1st <- data %>% @@ -629,7 +629,7 @@ 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 10,616 isolates for analysis. Now our data looks +
So we end up with 10,603 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,616 -Available: 10,616 (100%, NA: 0 = 0%) +Length: 10,603 +Available: 10,603 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5417295
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 -0.5468395 +0.5432099 Hospital B -0.5509504 +0.5246981 Hospital C -0.5332103 +0.5336856 Hospital D -0.5243269 +0.5419501 @@ -1186,23 +1186,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5468395 -3085 +0.5432099 +3240 Hospital B -0.5509504 -3788 +0.5246981 +3644 Hospital C -0.5332103 -1626 +0.5336856 +1514 Hospital D -0.5243269 -2117 +0.5419501 +2205 @@ -1227,27 +1227,27 @@ therapies very easily: Escherichia -0.7615681 -0.8776778 -0.9768638 +0.7729741 +0.8646535 +0.9761025 Klebsiella -0.8310811 -0.8910473 -0.9805743 +0.8312236 +0.8945148 +0.9746835 Staphylococcus -0.7897664 -0.8806081 -0.9762699 +0.7958656 +0.8940568 +0.9778516 Streptococcus -0.5365264 +0.5413105 0.0000000 -0.5365264 +0.5413105 @@ -1275,23 +1275,23 @@ classes, use a antibiotic class selector such as Hospital A -54.7% -26.2% +54.3% +26.5% Hospital B -55.1% -27.6% +52.5% +25.0% Hospital C -53.3% -26.8% +53.4% +26.4% Hospital D -52.4% -25.9% +54.2% +27.0% @@ -1407,16 +1407,16 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.125 16 64 0.0625 0.01 4 0.5 1 0.5 1 -# [11] 0.001 16 0.0625 0.025 0.5 0.025 0.025 16 0.5 32 -# [21] 1 0.025 0.5 64 0.25 0.5 0.001 0.5 0.25 0.001 -# [31] 0.0625 2 0.002 0.025 64 1 1 64 0.01 0.001 -# [41] 0.025 0.002 0.01 64 0.01 1 0.005 16 64 0.0625 -# [51] 0.002 128 0.5 32 0.25 0.002 0.01 0.025 8 128 -# [61] 0.025 64 0.25 2 4 0.025 2 16 0.25 0.25 -# [71] 64 32 1 0.125 0.125 32 16 128 2 0.0625 -# [81] 0.01 0.125 0.01 16 32 0.01 2 16 4 0.002 -# [91] 32 8 0.25 0.025 0.005 0.01 8 16 0.005 8
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.125 16 64 0.0625 0.01 4 0.5 1 0.5 1 -# [11] 0.001 16 0.0625 0.025 0.5 0.025 0.025 16 0.5 32 -# [21] 1 0.025 0.5 64 0.25 0.5 0.001 0.5 0.25 0.001 -# [31] 0.0625 2 0.002 0.025 64 1 1 64 0.01 0.001 -# [41] 0.025 0.002 0.01 64 0.01 1 0.005 16 64 0.0625 -# [51] 0.002 128 0.5 32 0.25 0.002 0.01 0.025 8 128 -# [61] 0.025 64 0.25 2 4 0.025 2 16 0.25 0.25 -# [71] 64 32 1 0.125 0.125 32 16 128 2 0.0625 -# [81] 0.01 0.125 0.01 16 32 0.01 2 16 4 0.002 -# [91] 32 8 0.25 0.025 0.005 0.01 8 16 0.005 8
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 28 17 23 21 20 24 23 26 17 17 24 20 29 17 20 24 17 17 27 20 28 21 17 18 30 -# [26] 28 26 20 20 30 26 29 18 28 23 20 23 19 28 22 29 29 19 25 31 26 24 21 23 22 -# [51] 25 25 25 18 28 17 31 29 25 26 27 23 17 24 17 28 25 29 29 24 29 29 18 20 27 -# [76] 26 18 22 25 28 30 31 27 25 21 26 29 18 18 26 22 30 29 29 29 22 24 28 17 27
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
head(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin -# 1 I I R I I S -# 2 S R I R R S -# 3 S R R I S R -# 4 S I R R S I -# 5 I I S I I I -# 6 S I S S I S +# 1 S I S S I R +# 2 I R I S R R +# 3 S I I S R R +# 4 R I S S I I +# 5 I I R I I I +# 6 I I S I S I # kanamycin -# 1 R -# 2 R -# 3 R -# 4 S -# 5 S +# 1 S +# 2 S +# 3 S +# 4 R +# 5 I # 6 S
We can now add the interpretation of MDR-TB to our data set. You can use:
vignettes/SPSS.Rmd
SPSS.Rmd
vignettes/datasets.Rmd
datasets.Rmd
This data set is in R available as microorganisms, after you load the AMR package.
microorganisms
AMR
It was last updated on 31 October 2022 12:26:45 UTC. Find more info +
It was last updated on 5 November 2022 07:20:03 UTC. Find more info about the structure of this data set here.
Direct download links:
This data set is in R available as antibiotics, after you load the AMR package.
antibiotics
This data set is in R available as antivirals, after you load the AMR package.
antivirals
This data set is in R available as rsi_translation, after you load the AMR package.
rsi_translation
This data set is in R available as dosage, after you load the AMR package.
dosage
This data set is in R available as example_isolates, after you load the AMR package.
example_isolates
This data set is in R available as example_isolates_unclean, after you load the AMR package.
example_isolates_unclean
This version will eventually become v2.0! We’re happy to reach a new major milestone soon!
as.rsi()
as.mo()
mo_*()
NA
as.rsi(as.disk(NA), ...)
mo_shortname()
styler
kurtosis(rnorm(10000)) -#> [1] 2.921575 +#> [1] 3.031603 kurtosis(rnorm(10000), excess = TRUE) -#> [1] 0.02400721 +#> [1] 0.02766519