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 21 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 22 October 2022.
Now, let’s start the cleaning and the analysis!
So only 53.7% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
So only 53.6% 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 10,731 isolates for analysis. Now our data looks like:
So we end up with 10,729 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,731 -Available: 10,731 (100.0%, NA: 0 = 0.0%) +Length: 10,729 +Available: 10,729 (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] 0.5461746
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 -0.5456783 +0.5464199 Hospital B -0.5524140 +0.5451604 Hospital C -0.5469820 +0.5317705 Hospital D -0.5353107 +0.5496909 @@ -1008,23 +1008,23 @@ Longest: 24 Hospital A -0.5456783 -3251 +0.5464199 +3296 Hospital B -0.5524140 -3749 +0.5451604 +3709 Hospital C -0.5469820 -1607 +0.5317705 +1621 Hospital D -0.5353107 -2124 +0.5496909 +2103 @@ -1047,27 +1047,27 @@ Longest: 24 Escherichia -0.7644169 -0.8727040 -0.9769329 +0.7740125 +0.8728686 +0.9769048 Klebsiella -0.8386824 -0.9146959 -0.9831081 +0.8237317 +0.8899398 +0.9724850 Staphylococcus -0.7969265 -0.8975485 -0.9849982 +0.7914878 +0.8884120 +0.9846209 Streptococcus -0.5323640 +0.5297146 0.0000000 -0.5323640 +0.5297146 @@ -1093,22 +1093,22 @@ Longest: 24 Hospital A 54.6% -25.6% +27.2% Hospital B -55.2% -27.5% +54.5% +26.0% Hospital C -54.7% -26.1% +53.2% +25.6% Hospital D -53.5% -26.5% +55.0% +26.9% @@ -1206,16 +1206,18 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.0625 0.0625 0.25 0.025 16 256 8 4 16 0.125 -# [11] 16 0.0625 128 64 0.025 2 2 0.0625 0.25 0.025 -# [21] 256 0.125 0.125 4 8 16 4 1 0.002 4 -# [31] 1 8 1 0.001 16 0.002 64 16 32 0.002 -# [41] 0.002 0.25 0.25 1 0.5 0.005 0.125 0.0625 4 8 -# [51] 0.002 0.025 16 2 4 0.025 0.0625 256 64 4 -# [61] 0.25 0.5 64 128 16 2 0.025 0.002 64 0.0625 -# [71] 64 0.5 0.001 0.025 2 0.5 8 64 0.25 256 -# [81] 0.025 0.001 0.25 0.125 64 256 256 0.5 0.002 8 -# [91] 64 64 0.5 4 0.5 0.125 2 32 0.01 0.25
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.0625 0.0625 0.25 0.025 16 256 8 4 16 0.125 -# [11] 16 0.0625 128 64 0.025 2 2 0.0625 0.25 0.025 -# [21] 256 0.125 0.125 4 8 16 4 1 0.002 4 -# [31] 1 8 1 0.001 16 0.002 64 16 32 0.002 -# [41] 0.002 0.25 0.25 1 0.5 0.005 0.125 0.0625 4 8 -# [51] 0.002 0.025 16 2 4 0.025 0.0625 256 64 4 -# [61] 0.25 0.5 64 128 16 2 0.025 0.002 64 0.0625 -# [71] 64 0.5 0.001 0.025 2 0.5 8 64 0.25 256 -# [81] 0.025 0.001 0.25 0.125 64 256 256 0.5 0.002 8 -# [91] 64 64 0.5 4 0.5 0.125 2 32 0.01 0.25
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 30 27 25 26 26 30 31 29 25 26 25 22 17 25 18 20 18 30 31 28 22 29 26 17 21 -# [26] 27 30 26 29 28 22 21 26 20 24 26 30 27 25 26 30 17 20 18 20 21 24 20 18 21 -# [51] 27 23 23 26 24 20 29 21 22 26 31 28 26 25 18 26 21 19 21 18 23 30 19 27 25 -# [76] 31 31 21 30 24 28 28 26 29 25 23 19 20 29 20 18 17 28 21 28 27 17 21 18 25
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
head(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin -# 1 S R I R S S -# 2 I R S I S S -# 3 I I I I I I -# 4 S R I R I R -# 5 S I I I S S -# 6 I I R R S I +# 1 S R I S S S +# 2 I S R R R S +# 3 S R S S I S +# 4 I R S S R S +# 5 I S I S S R +# 6 R I I S S I # kanamycin -# 1 S -# 2 R -# 3 S +# 1 I +# 2 S +# 3 I # 4 S -# 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 21 October 2022 19:58:04 UTC. Find more info about the structure of this data set here.
It was last updated on 22 October 2022 08:21:06 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!
combine_IR
count_df()
proportion_df()
rsi_df()
combine_SI
as.rsi()
as.mo()
mo_*()
keep_synonyms
allow_uncertain
NA
as.rsi(as.disk(NA), ...)
as.integer()
table()
factor
as.ab()
styler
kurtosis(rnorm(10000)) -#> [1] 2.90906 +#> [1] 3.041992 kurtosis(rnorm(10000), excess = TRUE) -#> [1] -0.02213884 +#> [1] 0.01855637