vignettes/AMR.Rmd
AMR.Rmd
Note: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 10 October 2022.
Note: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 11 October 2022.
Now, let’s start the cleaning and the analysis!
So only 53.1% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
So only 53.4% 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 10,620 isolates for analysis. Now our data looks like:
So we end up with 10,684 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: 10,620 -Available: 10,620 (100.0%, NA: 0 = 0.0%) +Length: 10,684 +Available: 10,684 (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] 0.5410546
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 -0.5427928 +0.5423889 Hospital B -0.5446957 +0.5451578 Hospital C -0.5358423 +0.5370259 Hospital D -0.5362524 +0.5528979 @@ -1008,23 +1008,23 @@ Longest: 24 Hospital A -0.5427928 -3108 +0.5423889 +3173 Hospital B -0.5446957 -3714 +0.5451578 +3676 Hospital C -0.5358423 -1674 +0.5370259 +1661 Hospital D -0.5362524 -2124 +0.5528979 +2174 @@ -1047,27 +1047,27 @@ Longest: 24 Escherichia -0.7762419 -0.8645788 -0.9792657 +0.7618640 +0.8820009 +0.9764857 Klebsiella -0.8307692 -0.9042735 -0.9854701 +0.8284024 +0.9053254 +0.9788673 Staphylococcus -0.7940850 -0.8913124 -0.9818854 +0.7893186 +0.8861878 +0.9804788 Streptococcus -0.5390071 +0.5398482 0.0000000 -0.5390071 +0.5398482 @@ -1092,23 +1092,23 @@ Longest: 24 Hospital A -54.3% -26.1% +54.2% +27.7% Hospital B 54.5% -26.4% +25.9% Hospital C -53.6% -25.1% +53.7% +27.5% Hospital D -53.6% -26.0% +55.3% +26.3% @@ -1206,18 +1206,18 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 0.005 2 1 0.0625 128 0.5 1 >=256 64 -# [10] 0.5 32 16 0.002 0.01 0.025 32 >=256 2 -# [19] <=0.001 0.01 2 0.25 0.5 0.5 32 0.005 0.5 -# [28] 0.125 2 1 0.025 128 0.125 0.002 0.125 16 -# [37] <=0.001 2 0.0625 8 1 0.125 0.002 >=256 0.0625 -# [46] 4 16 4 0.005 0.0625 4 0.002 0.0625 16 -# [55] 2 4 1 0.5 16 0.5 64 128 0.005 -# [64] 128 0.125 0.01 2 0.25 0.5 <=0.001 16 32 -# [73] 2 8 >=256 64 2 0.002 0.002 32 4 -# [82] 0.005 64 32 32 128 0.002 0.002 0.025 >=256 -# [91] 4 4 0.5 0.002 0.25 0.5 32 0.002 4 -# [100] 8
mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 0.005 2 1 0.0625 128 0.5 1 >=256 64 -# [10] 0.5 32 16 0.002 0.01 0.025 32 >=256 2 -# [19] <=0.001 0.01 2 0.25 0.5 0.5 32 0.005 0.5 -# [28] 0.125 2 1 0.025 128 0.125 0.002 0.125 16 -# [37] <=0.001 2 0.0625 8 1 0.125 0.002 >=256 0.0625 -# [46] 4 16 4 0.005 0.0625 4 0.002 0.0625 16 -# [55] 2 4 1 0.5 16 0.5 64 128 0.005 -# [64] 128 0.125 0.01 2 0.25 0.5 <=0.001 16 32 -# [73] 2 8 >=256 64 2 0.002 0.002 32 4 -# [82] 0.005 64 32 32 128 0.002 0.002 0.025 >=256 -# [91] 4 4 0.5 0.002 0.25 0.5 32 0.002 4 -# [100] 8
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class <disk> -# [1] 19 23 20 29 19 24 18 31 30 31 19 19 28 25 19 20 27 20 20 30 22 30 25 27 24 -# [26] 23 29 17 19 23 29 24 26 24 28 17 24 23 22 19 31 26 25 19 23 21 24 28 20 20 -# [51] 26 27 30 27 18 28 27 17 24 31 27 28 24 23 31 26 17 30 24 23 22 24 29 31 30 -# [76] 31 19 18 29 27 28 18 17 30 31 25 24 19 31 27 18 24 20 26 19 17 31 27 29 31
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
head(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin -# 1 I S R I R I -# 2 S S S I R S -# 3 S S I R I I -# 4 I I R R R S -# 5 S S R I I R -# 6 S S R I S R +# 1 S I I R S R +# 2 R S S R S I +# 3 I I R R S R +# 4 S I R I I I +# 5 R R I I R I +# 6 R R R I S S # kanamycin # 1 S -# 2 I -# 3 R +# 2 S +# 3 I # 4 R -# 5 S -# 6 S
We can now add the interpretation of MDR-TB to our data set. You can use:
mdro(my_TB_data, guideline = "TB")
vignettes/SPSS.Rmd
SPSS.Rmd
vignettes/datasets.Rmd
datasets.Rmd
A data set with 48,787 rows and 22 columns, containing the following column names:mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, source, lpsn, lpsn_parent, lpsn_renamed_to, gbif, gbif_parent, gbif_renamed_to, prevalence and snomed.
This data set is in R available as microorganisms, after you load the AMR package.
microorganisms
AMR
It was last updated on 10 October 2022 19:40:20 UTC. Find more info about the structure of this data set here.
It was last updated on 11 October 2022 08:55:02 UTC. Find more info about the structure of this data set here.
Direct download links:
A data set with 464 rows and 14 columns, containing the following column names:ab, cid, name, group, atc, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units and loinc.
This data set is in R available as antibiotics, after you load the AMR package.
antibiotics
A data set with 102 rows and 9 columns, containing the following column names:atc, cid, name, atc_group, synonyms, oral_ddd, oral_units, iv_ddd and iv_units.
This data set is in R available as antivirals, after you load the AMR package.
antivirals
A data set with 20,369 rows and 11 columns, containing the following column names:guideline, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R and uti.
This data set is in R available as rsi_translation, after you load the AMR package.
rsi_translation
A data set with 134,659 rows and 2 columns, containing the following column names:mo and ab.
This data set is in R available as intrinsic_resistant, after you load the AMR package.
intrinsic_resistant
A data set with 169 rows and 9 columns, containing the following column names:ab, name, type, dose, dose_times, administration, notes, original_txt and eucast_version.
This data set is in R available as dosage, after you load the AMR package.
dosage
A data set with 2,000 rows and 46 columns, containing the following column names:date, patient, age, gender, ward, mo, PEN, OXA, FLC, AMX, AMC, AMP, TZP, CZO, FEP, CXM, FOX, CTX, CAZ, CRO, GEN, TOB, AMK, KAN, TMP, SXT, NIT, FOS, LNZ, CIP, MFX, VAN, TEC, TCY, TGC, DOX, ERY, CLI, AZM, IPM, MEM, MTR, CHL, COL, MUP and RIF.
This data set is in R available as example_isolates, after you load the AMR package.
example_isolates
A data set with 3,000 rows and 8 columns, containing the following column names:patient_id, hospital, date, bacteria, AMX, AMC, CIP and GEN.
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!
mo_matching_score()
as.mo()
mo_*()
as.rsi()
keep_synonyms
allow_uncertain
NA
as.rsi(as.disk(NA), ...)
as.integer()
table()
factor
as.ab()
styler