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 14 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 15 October 2022.
So only 53.3% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
So only 53.2% 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 10,660 isolates for analysis. Now our data looks like:
So we end up with 10,641 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: 10,660 -Available: 10,660 (100.0%, NA: 0 = 0.0%) +Length: 10,641 +Available: 10,641 (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] 0.5409006
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 -0.5371953 +0.5511962 Hospital B -0.5490456 +0.5518367 Hospital C -0.5335844 +0.5463475 Hospital D -0.5374532 +0.5317893 @@ -1008,23 +1008,23 @@ Longest: 24 Hospital A -0.5371953 -3159 +0.5511962 +3135 Hospital B -0.5490456 -3772 +0.5518367 +3675 Hospital C -0.5335844 -1593 +0.5463475 +1629 Hospital D -0.5374532 -2136 +0.5317893 +2202 @@ -1047,27 +1047,27 @@ Longest: 24 Escherichia -0.7642520 -0.8737677 -0.9747107 +0.7737634 +0.8752688 +0.9750538 Klebsiella -0.8163265 -0.8886054 -0.9855442 +0.8398230 +0.8964602 +0.9787611 Staphylococcus -0.7935223 -0.8914244 -0.9775488 +0.7914244 +0.8902616 +0.9792878 Streptococcus -0.5316516 +0.5400664 0.0000000 -0.5316516 +0.5400664 @@ -1092,23 +1092,23 @@ Longest: 24 Hospital A -53.7% -27.5% +55.1% +26.5% Hospital B -54.9% -27.3% +55.2% +26.7% Hospital C -53.4% -26.0% +54.6% +25.0% Hospital D -53.7% -25.7% +53.2% +25.3% @@ -1206,18 +1206,16 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 64 4 0.25 64 256 4 128 0.125 64 -# [10] 64 4 4 8 16 32 2 0.005 0.002 -# [19] 4 256 0.25 0.5 2 <=0.001 0.025 0.0625 32 -# [28] 1 128 0.005 0.0625 0.25 2 0.002 8 1 -# [37] 16 0.125 8 1 32 16 1 16 8 -# [46] 16 0.005 0.5 128 <=0.001 32 2 0.0625 0.5 -# [55] 2 64 128 4 0.5 16 0.0625 0.0625 0.002 -# [64] 0.25 0.025 4 16 32 0.0625 0.0625 0.5 0.5 -# [73] 0.002 32 16 0.005 1 0.25 256 0.25 0.025 -# [82] 128 8 0.25 0.0625 1 256 16 0.5 0.125 -# [91] 0.005 2 0.002 128 0.5 32 0.0625 0.025 0.125 -# [100] 256
mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 64 4 0.25 64 256 4 128 0.125 64 -# [10] 64 4 4 8 16 32 2 0.005 0.002 -# [19] 4 256 0.25 0.5 2 <=0.001 0.025 0.0625 32 -# [28] 1 128 0.005 0.0625 0.25 2 0.002 8 1 -# [37] 16 0.125 8 1 32 16 1 16 8 -# [46] 16 0.005 0.5 128 <=0.001 32 2 0.0625 0.5 -# [55] 2 64 128 4 0.5 16 0.0625 0.0625 0.002 -# [64] 0.25 0.025 4 16 32 0.0625 0.0625 0.5 0.5 -# [73] 0.002 32 16 0.005 1 0.25 256 0.25 0.025 -# [82] 128 8 0.25 0.0625 1 256 16 0.5 0.125 -# [91] 0.005 2 0.002 128 0.5 32 0.0625 0.025 0.125 -# [100] 256
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class <disk> -# [1] 23 29 23 29 18 26 17 23 28 31 21 24 18 17 25 26 24 18 25 18 28 18 30 20 28 -# [26] 24 28 21 31 25 29 21 19 18 18 20 31 18 28 31 17 24 21 18 31 21 26 22 27 18 -# [51] 29 29 19 31 17 20 30 21 21 26 30 25 31 26 26 29 29 29 20 21 23 18 27 25 28 -# [76] 29 28 20 20 26 17 20 24 21 28 28 28 31 21 20 21 17 28 24 23 29 22 23 30 29
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
head(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin -# 1 R R I R R S -# 2 I I I I S R -# 3 S R R S S I -# 4 I S I R R S -# 5 R R R S S S -# 6 I S S I I I +# 1 R S R S I R +# 2 I S R R R S +# 3 S I I S I I +# 4 I I S S R R +# 5 R I R S I I +# 6 I I S S I S # kanamycin # 1 S # 2 I -# 3 I +# 3 R # 4 R -# 5 R -# 6 R
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 14 October 2022 13:45:54 UTC. Find more info about the structure of this data set here.
It was last updated on 15 October 2022 13:21:31 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
# returns NA and throws a warning (which is now suppressed): + # \donttest{ + +# returns NA and throws a warning (which is now suppressed): suppressWarnings( as.ab("test") ) @@ -263,6 +265,7 @@ #> 'ampicillin' #> coflu ampicillin #> 1 S R +# }
# \donttest{ + +# returns NA and throws a warning (which is now suppressed): suppressWarnings( as.ab("test") ) @@ -263,6 +265,7 @@ #> 'ampicillin' #> coflu ampicillin #> 1 S R +# }
kurtosis(rnorm(10000)) -#> [1] 2.946943 +#> [1] 3.023296 kurtosis(rnorm(10000), excess = TRUE) -#> [1] 0.02192351 +#> [1] 0.1095747