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 11 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 14 October 2022.
So only 53.4% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
So only 53.8% 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 10,684 isolates for analysis. Now our data looks like:
So we end up with 10,765 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,684 -Available: 10,684 (100.0%, NA: 0 = 0.0%) +Length: 10,765 +Available: 10,765 (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] 0.5446462
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 -0.5423889 +0.5467334 Hospital B -0.5451578 +0.5472356 Hospital C -0.5370259 +0.5282083 Hospital D -0.5528979 +0.5523125 @@ -1008,23 +1008,23 @@ Longest: 24 Hospital A -0.5423889 -3173 +0.5467334 +3199 Hospital B -0.5451578 -3676 +0.5472356 +3726 Hospital C -0.5370259 -1661 +0.5282083 +1613 Hospital D -0.5528979 -2174 +0.5523125 +2227 @@ -1047,27 +1047,27 @@ Longest: 24 Escherichia -0.7618640 -0.8820009 -0.9764857 +0.7664876 +0.8749731 +0.9735768 Klebsiella -0.8284024 -0.9053254 -0.9788673 +0.8436179 +0.9044801 +0.9873204 Staphylococcus -0.7893186 -0.8861878 -0.9804788 +0.7971851 +0.8812703 +0.9765428 Streptococcus -0.5398482 +0.5343228 0.0000000 -0.5398482 +0.5343228 @@ -1092,23 +1092,23 @@ Longest: 24 Hospital A -54.2% -27.7% +54.7% +27.4% Hospital B -54.5% -25.9% +54.7% +26.0% Hospital C -53.7% -27.5% +52.8% +24.9% Hospital D -55.3% -26.3% +55.2% +26.5% @@ -1206,18 +1206,18 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 8 32 0.125 4 0.0625 32 0.5 0.01 8 -# [10] 0.01 8 1 2 16 0.002 0.25 128 128 -# [19] 256 0.125 <=0.001 0.125 <=0.001 0.125 32 128 16 -# [28] 8 0.125 <=0.001 128 0.005 0.5 1 2 0.25 -# [37] 0.125 0.5 4 0.005 0.5 16 0.002 0.0625 2 -# [46] 0.01 256 <=0.001 64 16 1 2 64 4 -# [55] 0.0625 128 0.002 0.25 8 1 0.005 0.5 16 -# [64] 16 0.25 4 32 2 0.005 32 0.5 0.01 -# [73] 0.01 4 256 256 <=0.001 0.25 32 8 0.01 -# [82] 0.0625 0.01 0.5 0.25 <=0.001 64 1 0.5 16 -# [91] 0.0625 256 4 4 2 2 0.0625 0.125 0.5 -# [100] 4
mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 8 32 0.125 4 0.0625 32 0.5 0.01 8 -# [10] 0.01 8 1 2 16 0.002 0.25 128 128 -# [19] 256 0.125 <=0.001 0.125 <=0.001 0.125 32 128 16 -# [28] 8 0.125 <=0.001 128 0.005 0.5 1 2 0.25 -# [37] 0.125 0.5 4 0.005 0.5 16 0.002 0.0625 2 -# [46] 0.01 256 <=0.001 64 16 1 2 64 4 -# [55] 0.0625 128 0.002 0.25 8 1 0.005 0.5 16 -# [64] 16 0.25 4 32 2 0.005 32 0.5 0.01 -# [73] 0.01 4 256 256 <=0.001 0.25 32 8 0.01 -# [82] 0.0625 0.01 0.5 0.25 <=0.001 64 1 0.5 16 -# [91] 0.0625 256 4 4 2 2 0.0625 0.125 0.5 -# [100] 4
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class <disk> -# [1] 29 24 25 24 31 25 20 22 25 17 19 29 29 27 31 21 23 23 29 17 25 21 25 29 31 -# [26] 20 23 23 20 24 27 22 18 30 21 22 21 28 27 19 24 22 19 30 29 20 17 19 25 19 -# [51] 19 24 26 21 21 27 24 22 21 30 31 29 25 19 24 25 24 25 20 26 28 20 20 19 27 -# [76] 22 22 24 31 24 22 28 22 22 23 26 17 28 18 28 23 22 26 26 21 25 31 22 31 31
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
head(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin -# 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 +# 1 S I I R R S +# 2 R I R I S R +# 3 R S R S I S +# 4 S I I S R I +# 5 S S I I I R +# 6 R I R R I I # kanamycin -# 1 S +# 1 R # 2 S # 3 I -# 4 R -# 5 I +# 4 S +# 5 R # 6 I
We can now add the interpretation of MDR-TB to our data set. You can use:
@@ -357,40 +357,40 @@ Unique: 5 1 Mono-resistant -3261 -65.22% -3261 -65.22% +3186 +63.72% +3186 +63.72% 2 Negative -959 -19.18% -4220 -84.40% +1014 +20.28% +4200 +84.00% 3 Multi-drug-resistant -452 -9.04% -4672 -93.44% +475 +9.50% +4675 +93.50% 4 Poly-resistant -228 -4.56% -4900 -98.00% +233 +4.66% +4908 +98.16% 5 Extensively drug-resistant -100 -2.00% +92 +1.84% 5000 100.00% diff --git a/articles/PCA.html b/articles/PCA.html index 05e15034..e62bfea6 100644 --- a/articles/PCA.html +++ b/articles/PCA.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/articles/SPSS.html b/articles/SPSS.html index 8d9bcb03..ccf8a41a 100644 --- a/articles/SPSS.html +++ b/articles/SPSS.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 @@ -162,7 +162,7 @@ How to import data from SPSS / SAS / Stata Dr. Matthijs Berends - 11 October 2022 + 14 October 2022 Source: vignettes/SPSS.Rmd SPSS.Rmd diff --git a/articles/WHONET.html b/articles/WHONET.html index cc7966dd..fbcd0e2e 100644 --- a/articles/WHONET.html +++ b/articles/WHONET.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/articles/datasets.html b/articles/datasets.html index c1d0151e..ea661e49 100644 --- a/articles/datasets.html +++ b/articles/datasets.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 @@ -161,7 +161,7 @@ Data sets for download / own use - 11 October 2022 + 14 October 2022 Source: vignettes/datasets.Rmd datasets.Rmd @@ -177,7 +177,7 @@ 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. -It was last updated on 11 October 2022 08:55:02 UTC. Find more info about the structure of this data set here. +It was last updated on 14 October 2022 11:03:47 UTC. Find more info about the structure of this data set here. Direct download links: Download as original R Data Structure (RDS) file (1.1 MB) @@ -449,7 +449,7 @@ 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. -It was last updated on 11 October 2022 08:55:02 UTC. Find more info about the structure of this data set here. +It was last updated on 14 October 2022 11:03:47 UTC. Find more info about the structure of this data set here. Direct download links: Download as original R Data Structure (RDS) file (36 kB) @@ -622,7 +622,7 @@ 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. -It was last updated on 11 October 2022 08:55:02 UTC. Find more info about the structure of this data set here. +It was last updated on 14 October 2022 11:03:47 UTC. Find more info about the structure of this data set here. Direct download links: Download as original R Data Structure (RDS) file (4 kB) @@ -754,7 +754,7 @@ 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. -It was last updated on 11 October 2022 08:55:02 UTC. Find more info about the structure of this data set here. +It was last updated on 14 October 2022 11:03:47 UTC. Find more info about the structure of this data set here. Direct download links: Download as original R Data Structure (RDS) file (49 kB) @@ -913,7 +913,7 @@ 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. -It was last updated on 11 October 2022 08:55:02 UTC. Find more info about the structure of this data set here. +It was last updated on 14 October 2022 11:03:47 UTC. Find more info about the structure of this data set here. Direct download links: Download as original R Data Structure (RDS) file (78 kB) @@ -1185,7 +1185,7 @@ 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. -It was last updated on 11 October 2022 08:55:02 UTC. Find more info about the structure of this data set here. +It was last updated on 14 October 2022 11:03:47 UTC. Find more info about the structure of this data set here. Direct download links: Download as original R Data Structure (RDS) file (3 kB) @@ -1302,7 +1302,7 @@ 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. -It was last updated on 11 October 2022 08:55:02 UTC. Find more info about the structure of this data set here. +It was last updated on 14 October 2022 11:03:47 UTC. Find more info about the structure of this data set here. Source @@ -1707,7 +1707,7 @@ 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. -It was last updated on 11 October 2022 08:55:02 UTC. Find more info about the structure of this data set here. +It was last updated on 14 October 2022 11:03:47 UTC. Find more info about the structure of this data set here. Source diff --git a/articles/index.html b/articles/index.html index edab01fb..9f6ae157 100644 --- a/articles/index.html +++ b/articles/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/articles/resistance_predict.html b/articles/resistance_predict.html index 2839b4a3..37e91864 100644 --- a/articles/resistance_predict.html +++ b/articles/resistance_predict.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/articles/welcome_to_AMR.html b/articles/welcome_to_AMR.html index 53858552..93f20e8c 100644 --- a/articles/welcome_to_AMR.html +++ b/articles/welcome_to_AMR.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/authors.html b/authors.html index b00273c7..9faf21e9 100644 --- a/authors.html +++ b/authors.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/index.html b/index.html index bda59d76..0d737709 100644 --- a/index.html +++ b/index.html @@ -42,7 +42,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/news/index.html b/news/index.html index a64dc0bc..6144cd8e 100644 --- a/news/index.html +++ b/news/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 @@ -127,10 +127,10 @@ -AMR 1.8.2.9014 +AMR 1.8.2.9016 This version will eventually become v2.0! We’re happy to reach a new major milestone soon! -Breaking +Breaking Removed all species of the taxonomic kingdom Chromista from the package. This was done for multiple reasons:CRAN allows packages to be around 5 MB maximum, some packages are exempted but this package is not one of them Chromista are not relevant when it comes to antimicrobial resistance, thus lacking the primary scope of this package Chromista are almost never clinically relevant, thus lacking the secondary scope of this package @@ -140,7 +140,7 @@ The mo_matching_score() now count deletions and substitutions as 2 instead of 1, which impacts the outcome of as.mo() and any mo_*() function -New +New EUCAST 2022 and CLSI 2022 guidelines have been added for as.rsi(). EUCAST 2022 is now the new default guideline for all MIC and disks diffusion interpretations. All new algorithm for as.mo() (and thus internally all mo_*() functions) while still following our original set-up as described in our paper (DOI 10.18637/jss.v104.i03).A new argument keep_synonyms allows to not correct for updated taxonomy, in favour of the now deleted argument allow_uncertain @@ -157,7 +157,7 @@ Our data sets are now also continually exported to Apache Feather and Apache Parquet formats. You can find more info in this article on our website. -Changed +Changed Fix for using as.rsi() on certain EUCAST breakpoints for MIC values Fix for using as.rsi() on NA values (e.g. as.rsi(as.disk(NA), ...)) Removed as.integer() for MIC values, since MIC are not integer values and running table() on MIC values consequently failed for not being able to retrieve the level position (as that’s how normally as.integer() on factors work) @@ -183,7 +183,7 @@ Fix for using as.ab() on NA values -Other +Other New website to make use of the new Bootstrap 5 and pkgdown 2.0. The website now contains results for all examples and will be automatically regenerated with every change to our repository, using GitHub Actions Added Peter Dutey-Magni, Dmytro Mykhailenko and Anton Mymrikov as contributors, to thank them for their valuable input All R and Rmd files in this project are now styled using the styler package diff --git a/pkgdown.yml b/pkgdown.yml index 662faecd..cbbbbaec 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -11,7 +11,7 @@ articles: datasets: datasets.html resistance_predict: resistance_predict.html welcome_to_AMR: welcome_to_AMR.html -last_built: 2022-10-11T08:57Z +last_built: 2022-10-14T11:06Z urls: reference: https://msberends.github.io/AMR/reference article: https://msberends.github.io/AMR/articles diff --git a/reference/AMR-deprecated.html b/reference/AMR-deprecated.html index 7028cb35..894eb66b 100644 --- a/reference/AMR-deprecated.html +++ b/reference/AMR-deprecated.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/reference/AMR.html b/reference/AMR.html index d5ffcc40..2835a43f 100644 --- a/reference/AMR.html +++ b/reference/AMR.html @@ -62,7 +62,7 @@ Principal component analysis for AMR AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/reference/Rplot005.png b/reference/Rplot005.png index 55ba022d..4070d81e 100644 Binary files a/reference/Rplot005.png and b/reference/Rplot005.png differ diff --git a/reference/Rplot006.png b/reference/Rplot006.png index d60554b1..3011e27c 100644 Binary files a/reference/Rplot006.png and b/reference/Rplot006.png differ diff --git a/reference/Rplot007.png b/reference/Rplot007.png index 1b4dd383..c6599e4f 100644 Binary files a/reference/Rplot007.png and b/reference/Rplot007.png differ diff --git a/reference/Rplot008.png b/reference/Rplot008.png index b3f2d460..5a4ba843 100644 Binary files a/reference/Rplot008.png and b/reference/Rplot008.png differ diff --git a/reference/Rplot009.png b/reference/Rplot009.png index 2265be79..05c635df 100644 Binary files a/reference/Rplot009.png and b/reference/Rplot009.png differ diff --git a/reference/WHOCC.html b/reference/WHOCC.html index 857d8606..cc07dedb 100644 --- a/reference/WHOCC.html +++ b/reference/WHOCC.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/reference/WHONET.html b/reference/WHONET.html index 9c8be616..0aea57f4 100644 --- a/reference/WHONET.html +++ b/reference/WHONET.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html index 26993bb3..2a222436 100644 --- a/reference/ab_from_text.html +++ b/reference/ab_from_text.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/reference/ab_property.html b/reference/ab_property.html index 89cadd69..b6e2f2a2 100644 --- a/reference/ab_property.html +++ b/reference/ab_property.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html index 11b44b24..3f923710 100644 --- a/reference/add_custom_antimicrobials.html +++ b/reference/add_custom_antimicrobials.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 @@ -253,8 +253,8 @@ # even antibiotic selectors work x <- data.frame(random_column = "test", - coflu = as.rsi("S"), - ampicillin = as.rsi("R")) + coflu = as.rsi("S"), + ampicillin = as.rsi("R")) x #> random_column coflu ampicillin #> 1 test S R diff --git a/reference/age.html b/reference/age.html index 32415169..7cc3a389 100644 --- a/reference/age.html +++ b/reference/age.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 @@ -190,16 +190,16 @@ df #> birth_date age age_exact age_at_y2k -#> 1 1961-11-04 60 60.93425 38 -#> 2 1959-05-25 63 63.38082 40 -#> 3 1932-03-21 90 90.55890 67 -#> 4 1943-08-14 79 79.15890 56 -#> 5 1980-11-06 41 41.92877 19 -#> 6 1962-10-20 59 59.97534 37 -#> 7 1940-09-02 82 82.10685 59 -#> 8 1953-11-03 68 68.93699 46 -#> 9 1965-10-11 57 57.00000 34 -#> 10 1979-06-17 43 43.31781 20 +#> 1 1963-02-15 59 59.66027 36 +#> 2 1979-08-05 43 43.19178 20 +#> 3 1962-10-01 60 60.03562 37 +#> 4 1990-03-08 32 32.60274 9 +#> 5 1982-01-26 40 40.71507 17 +#> 6 1954-02-09 68 68.67671 45 +#> 7 1964-08-19 58 58.15342 35 +#> 8 1938-04-08 84 84.51781 61 +#> 9 1985-07-19 37 37.23836 14 +#> 10 1945-03-20 77 77.56986 54 diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html index 87234024..8f657f3a 100644 --- a/reference/ggplot_pca.html +++ b/reference/ggplot_pca.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/reference/ggplot_rsi.html b/reference/ggplot_rsi.html index 5821c39e..9ae15810 100644 --- a/reference/ggplot_rsi.html +++ b/reference/ggplot_rsi.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html index c8be3cfa..edb0aa25 100644 --- a/reference/guess_ab_col.html +++ b/reference/guess_ab_col.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/reference/index.html b/reference/index.html index 0fc12813..74e303a4 100644 --- a/reference/index.html +++ b/reference/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html index fdc2de14..bdec4242 100644 --- a/reference/intrinsic_resistant.html +++ b/reference/intrinsic_resistant.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html index 918d5413..efe6263b 100644 --- a/reference/italicise_taxonomy.html +++ b/reference/italicise_taxonomy.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/reference/join.html b/reference/join.html index 59437e3e..551da319 100644 --- a/reference/join.html +++ b/reference/join.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html index b072fc49..62f7ffc6 100644 --- a/reference/key_antimicrobials.html +++ b/reference/key_antimicrobials.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 diff --git a/reference/kurtosis.html b/reference/kurtosis.html index 457cc794..d6e757df 100644 --- a/reference/kurtosis.html +++ b/reference/kurtosis.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9014 + 1.8.2.9016 @@ -167,9 +167,9 @@ Examples kurtosis(rnorm(10000)) -#> [1] 3.043154 +#> [1] 2.921036 kurtosis(rnorm(10000), excess = TRUE) -#> [1] 0.0150379 +#> [1] 0.001477015
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 11 October 2022 08:55:02 UTC. Find more info about the structure of this data set here.
It was last updated on 14 October 2022 11:03:47 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
kurtosis(rnorm(10000)) -#> [1] 3.043154 +#> [1] 2.921036 kurtosis(rnorm(10000), excess = TRUE) -#> [1] 0.0150379 +#> [1] 0.001477015