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 15 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 18 October 2022.
So only 53.2% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
So only 53.5% 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 10,641 isolates for analysis. Now our data looks like:
So we end up with 10,701 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: 10,641 -Available: 10,641 (100.0%, NA: 0 = 0.0%) +Length: 10,701 +Available: 10,701 (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] 0.5466591
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 -0.5511962 +0.5472908 Hospital B -0.5518367 +0.5424089 Hospital C -0.5463475 +0.5286935 Hospital D -0.5317893 +0.5414567 @@ -1008,23 +1008,23 @@ Longest: 24 Hospital A -0.5511962 -3135 +0.5472908 +3119 Hospital B -0.5518367 -3675 +0.5424089 +3761 Hospital C -0.5463475 -1629 +0.5286935 +1638 Hospital D -0.5317893 -2202 +0.5414567 +2183 @@ -1047,27 +1047,27 @@ Longest: 24 Escherichia -0.7737634 -0.8752688 -0.9750538 +0.7729049 +0.8753799 +0.9780721 Klebsiella -0.8398230 -0.8964602 -0.9787611 +0.8208829 +0.8989813 +0.9813243 Staphylococcus -0.7914244 -0.8902616 -0.9792878 +0.7931661 +0.8876772 +0.9756452 Streptococcus -0.5400664 +0.5420129 0.0000000 -0.5400664 +0.5420129 @@ -1092,23 +1092,23 @@ Longest: 24 Hospital A -55.1% -26.5% +54.7% +26.4% Hospital B -55.2% -26.7% +54.2% +26.5% Hospital C -54.6% -25.0% +52.9% +25.6% Hospital D -53.2% -25.3% +54.1% +26.4% @@ -1206,16 +1206,18 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 128 64 128 0.001 4 0.25 64 0.5 32 0.25 -# [11] 0.025 0.002 1 16 0.005 0.025 0.25 0.005 0.001 64 -# [21] 0.025 1 16 >=256 16 1 16 0.001 0.125 64 -# [31] 8 0.002 0.5 0.005 0.005 4 8 0.002 0.025 32 -# [41] 0.0625 0.001 0.125 2 8 16 0.25 1 128 4 -# [51] 0.001 0.125 0.005 0.25 0.5 0.0625 0.0625 0.005 0.5 1 -# [61] 0.005 16 4 0.025 32 16 128 0.5 16 4 -# [71] 0.002 1 0.5 0.5 0.025 8 32 16 64 32 -# [81] 4 32 2 >=256 0.125 0.025 >=256 16 2 0.01 -# [91] 0.5 128 0.01 0.0625 128 32 0.125 1 0.125 0.025
mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 128 64 128 0.001 4 0.25 64 0.5 32 0.25 -# [11] 0.025 0.002 1 16 0.005 0.025 0.25 0.005 0.001 64 -# [21] 0.025 1 16 >=256 16 1 16 0.001 0.125 64 -# [31] 8 0.002 0.5 0.005 0.005 4 8 0.002 0.025 32 -# [41] 0.0625 0.001 0.125 2 8 16 0.25 1 128 4 -# [51] 0.001 0.125 0.005 0.25 0.5 0.0625 0.0625 0.005 0.5 1 -# [61] 0.005 16 4 0.025 32 16 128 0.5 16 4 -# [71] 0.002 1 0.5 0.5 0.025 8 32 16 64 32 -# [81] 4 32 2 >=256 0.125 0.025 >=256 16 2 0.01 -# [91] 0.5 128 0.01 0.0625 128 32 0.125 1 0.125 0.025
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class <disk> -# [1] 28 21 23 24 17 19 17 17 29 20 28 20 31 17 24 25 21 29 19 18 30 21 22 29 19 -# [26] 24 27 18 19 28 20 19 21 30 17 29 28 26 23 19 20 30 25 19 19 28 19 25 23 19 -# [51] 20 18 23 27 28 28 17 30 22 24 30 27 18 24 25 22 30 21 29 30 29 31 23 28 31 -# [76] 19 17 22 25 25 21 17 31 31 24 30 30 26 20 19 20 25 25 23 17 24 21 20 18 18
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
head(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin -# 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 +# 1 I R I I R S +# 2 R R I S S R +# 3 R R R R I R +# 4 R R I S R S +# 5 R R I R S I +# 6 S S I S S I # kanamycin -# 1 S -# 2 I +# 1 I +# 2 R # 3 R -# 4 R +# 4 S # 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
R has a huge community.
Many R users just ask questions on websites like StackOverflow.com, the largest online community for programmers. At the time of writing, 466,054 R-related questions have already been asked on this platform (that covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.
Many R users just ask questions on websites like StackOverflow.com, the largest online community for programmers. At the time of writing, 466,988 R-related questions have already been asked on this platform (that covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.
R understands any data type, including SPSS/SAS/Stata.
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 15 October 2022 13:21:31 UTC. Find more info about the structure of this data set here.
It was last updated on 18 October 2022 20:55:18 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] 3.023296 +#> [1] 2.997868 kurtosis(rnorm(10000), excess = TRUE) -#> [1] 0.1095747 +#> [1] -0.06572697