From 642f6571fea42f06cdbeada2f7bbaa38d0a6e9f6 Mon Sep 17 00:00:00 2001
From: "Matthijs S. Berends"
Date: Wed, 27 Feb 2019 14:22:07 +0100
Subject: [PATCH] as.mo fix
---
NAMESPACE | 1 +
R/freq.R | 2 +-
R/mo.R | 64 ++-
R/zzz.R | 2 +
docs/articles/AMR.html | 421 +++++++++---------
.../AMR_files/figure-html/plot 1-1.png | Bin 33737 -> 33622 bytes
.../AMR_files/figure-html/plot 3-1.png | Bin 21114 -> 21129 bytes
.../AMR_files/figure-html/plot 4-1.png | Bin 69623 -> 69623 bytes
.../AMR_files/figure-html/plot 5-1.png | Bin 51367 -> 51371 bytes
docs/articles/benchmarks.html | 74 +--
.../figure-html/unnamed-chunk-5-1.png | Bin 29788 -> 27642 bytes
11 files changed, 291 insertions(+), 273 deletions(-)
diff --git a/NAMESPACE b/NAMESPACE
index a4708cc3..15abcb3d 100755
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -217,6 +217,7 @@ importFrom(dplyr,bind_cols)
importFrom(dplyr,bind_rows)
importFrom(dplyr,case_when)
importFrom(dplyr,desc)
+importFrom(dplyr,distinct)
importFrom(dplyr,everything)
importFrom(dplyr,filter)
importFrom(dplyr,filter_all)
diff --git a/R/freq.R b/R/freq.R
index 1f8f5aac..d498932b 100755
--- a/R/freq.R
+++ b/R/freq.R
@@ -682,7 +682,7 @@ format_header <- function(x, markdown = FALSE, decimal.mark = ".", big.mark = ",
# numeric values
if (has_length == TRUE & any(x_class %in% c("double", "integer", "numeric", "raw", "single"))) {
header$sd <- paste0(header$sd, " (CV: ", header$cv, ", MAD: ", header$mad, ")")
- header$fivenum <- paste0(paste(header$fivenum, collapse = " | "), " (IQR: ", header$IQR, ", CQV: ", header$cqv, ")")
+ header$fivenum <- paste0(paste(trimws(header$fivenum), collapse = " | "), " (IQR: ", header$IQR, ", CQV: ", header$cqv, ")")
header$outliers_total <- paste0(header$outliers_total, " (unique count: ", header$outliers_unique, ")")
header <- header[!names(header) %in% c("cv", "mad", "IQR", "cqv", "outliers_unique")]
}
diff --git a/R/mo.R b/R/mo.R
index df08ec54..66deefa2 100755
--- a/R/mo.R
+++ b/R/mo.R
@@ -165,30 +165,44 @@
#' mutate(mo = as.mo(paste(genus, species)))
#' }
as.mo <- function(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain = TRUE, reference_df = get_mo_source()) {
+ if (!"AMR" %in% base::.packages()) {
+ library("AMR")
+ # check onLoad() in R/zzz.R: data tables are created there.
+ }
+
if (all(x %in% AMR::microorganisms$mo)
& isFALSE(Becker)
& isFALSE(Lancefield)
& is.null(reference_df)) {
y <- x
- } else if (all(x %in% AMR::microorganisms$fullname)
- & isFALSE(Becker)
- & isFALSE(Lancefield)
- & is.null(reference_df)) {
- # we need special treatment for very prevalent full names, they are likely!
+
+ } else if (all(tolower(x) %in% microorganismsDT$fullname_lower)
+ & isFALSE(Becker)
+ & isFALSE(Lancefield)
+ & is.null(reference_df)) {
+ # we need special treatment for very prevalent full names, they are likely! (case insensitive)
# e.g. as.mo("Staphylococcus aureus")
- y <- microorganismsDT[prevalence == 1][data.table(fullname = x), on = "fullname", "mo"][[1]]
+ y <- microorganismsDT[prevalence == 1][data.table(fullname_lower = tolower(x)),
+ on = "fullname_lower",
+ "mo"][[1]]
if (any(is.na(y))) {
- y[is.na(y)] <- microorganismsDT[prevalence == 2][data.table(fullname = x[is.na(y)]), on = "fullname", "mo"][[1]]
+ y[is.na(y)] <- microorganismsDT[prevalence == 2][data.table(fullname_lower = tolower(x[is.na(y)])),
+ on = "fullname_lower",
+ "mo"][[1]]
}
if (any(is.na(y))) {
- y[is.na(y)] <- microorganismsDT[prevalence == 3][data.table(fullname = x[is.na(y)]), on = "fullname", "mo"][[1]]
+ y[is.na(y)] <- microorganismsDT[prevalence == 3][data.table(fullname_lower = tolower(x[is.na(y)])),
+ on = "fullname_lower",
+ "mo"][[1]]
}
+
} else {
# will be checked for mo class in validation and uses exec_as.mo internally if necessary
y <- mo_validate(x = x, property = "mo",
Becker = Becker, Lancefield = Lancefield,
allow_uncertain = allow_uncertain, reference_df = reference_df)
}
+
structure(.Data = y, class = "mo")
}
@@ -198,7 +212,7 @@ is.mo <- function(x) {
identical(class(x), "mo")
}
-#' @importFrom dplyr %>% pull left_join n_distinct progress_estimated filter
+#' @importFrom dplyr %>% pull left_join n_distinct progress_estimated filter distinct
#' @importFrom data.table data.table as.data.table setkey
#' @importFrom crayon magenta red blue silver italic has_color
exec_as.mo <- function(x, Becker = FALSE, Lancefield = FALSE,
@@ -298,22 +312,30 @@ exec_as.mo <- function(x, Becker = FALSE, Lancefield = FALSE,
# existing mo codes when not looking for property "mo", like mo_genus("B_ESCHR_COL")
y <- microorganismsDT[prevalence == 1][data.table(mo = x), on = "mo", ..property][[1]]
if (any(is.na(y))) {
- y[is.na(y)] <- microorganismsDT[prevalence == 2][data.table(mo = x[is.na(y)]), on = "mo", ..property][[1]]
+ y[is.na(y)] <- microorganismsDT[prevalence == 2][data.table(mo = x[is.na(y)]),
+ on = "mo",
+ ..property][[1]]
}
if (any(is.na(y))) {
- y[is.na(y)] <- microorganismsDT[prevalence == 3][data.table(mo = x[is.na(y)]), on = "mo", ..property][[1]]
+ y[is.na(y)] <- microorganismsDT[prevalence == 3][data.table(mo = x[is.na(y)]),
+ on = "mo",
+ ..property][[1]]
}
x <- y
- } else if (all(x %in% AMR::microorganisms$fullname)) {
+ } else if (all(tolower(x) %in% microorganismsDT$fullname_lower)) {
# we need special treatment for very prevalent full names, they are likely!
# e.g. as.mo("Staphylococcus aureus")
- y <- microorganismsDT[prevalence == 1][data.table(fullname = x), on = "fullname", ..property][[1]]
+ y <- microorganismsDT[prevalence == 1][data.table(fullname_lower = tolower(x)), on = "fullname_lower", ..property][[1]]
if (any(is.na(y))) {
- y[is.na(y)] <- microorganismsDT[prevalence == 2][data.table(fullname = x[is.na(y)]), on = "fullname", ..property][[1]]
+ y[is.na(y)] <- microorganismsDT[prevalence == 2][data.table(fullname_lower = tolower(x[is.na(y)])),
+ on = "fullname_lower",
+ ..property][[1]]
}
if (any(is.na(y))) {
- y[is.na(y)] <- microorganismsDT[prevalence == 3][data.table(fullname = x[is.na(y)]), on = "fullname", ..property][[1]]
+ y[is.na(y)] <- microorganismsDT[prevalence == 3][data.table(fullname_lower = tolower(x[is.na(y)])),
+ on = "fullname_lower",
+ ..property][[1]]
}
x <- y
@@ -521,13 +543,13 @@ exec_as.mo <- function(x, Becker = FALSE, Lancefield = FALSE,
# FIRST TRY FULLNAMES AND CODES
# if only genus is available, return only genus
if (all(!c(x[i], x_trimmed[i]) %like% " ")) {
- found <- microorganismsDT[tolower(fullname) %in% tolower(c(x_species[i], x_trimmed_species[i])), ..property][[1]]
+ found <- microorganismsDT[fullname_lower %in% tolower(c(x_species[i], x_trimmed_species[i])), ..property][[1]]
if (length(found) > 0) {
x[i] <- found[1L]
next
}
if (nchar(x_trimmed[i]) >= 6) {
- found <- microorganismsDT[tolower(fullname) %like% paste0(x_withspaces_start_only[i], "[a-z]+ species"), ..property][[1]]
+ found <- microorganismsDT[fullname_lower %like% paste0(x_withspaces_start_only[i], "[a-z]+ species"), ..property][[1]]
if (length(found) > 0) {
x[i] <- found[1L]
next
@@ -564,13 +586,13 @@ exec_as.mo <- function(x, Becker = FALSE, Lancefield = FALSE,
e.x_withspaces_start_only,
f.x_withspaces_end_only) {
- found <- data_to_check[tolower(fullname) %in% tolower(c(a.x_backup, b.x_trimmed)), ..property][[1]]
+ found <- data_to_check[fullname_lower %in% tolower(c(a.x_backup, b.x_trimmed)), ..property][[1]]
# most probable: is exact match in fullname
if (length(found) > 0) {
return(found[1L])
}
- found <- data_to_check[tolower(fullname) == tolower(c.x_trimmed_without_group), ..property][[1]]
+ found <- data_to_check[fullname_lower == tolower(c.x_trimmed_without_group), ..property][[1]]
if (length(found) > 0) {
return(found[1L])
}
@@ -664,7 +686,7 @@ exec_as.mo <- function(x, Becker = FALSE, Lancefield = FALSE,
# MISCELLANEOUS ----
# look for old taxonomic names ----
- found <- microorganisms.oldDT[tolower(fullname) == tolower(x_backup[i])
+ found <- microorganisms.oldDT[fullname_lower == tolower(x_backup[i])
| fullname %like% x_withspaces_start_end[i],]
if (NROW(found) > 0) {
col_id_new <- found[1, col_id_new]
@@ -693,7 +715,7 @@ exec_as.mo <- function(x, Becker = FALSE, Lancefield = FALSE,
if (nchar(b.x_trimmed) > 4 & !b.x_trimmed %like% " ") {
if (!grepl("^[A-Z][a-z]+", b.x_trimmed, ignore.case = FALSE)) {
# not when input is like Genustext, because then Neospora would lead to Actinokineospora
- found <- microorganismsDT[tolower(fullname) %like% paste(b.x_trimmed, "species"), ..property][[1]]
+ found <- microorganismsDT[fullname_lower %like% paste(b.x_trimmed, "species"), ..property][[1]]
if (length(found) > 0) {
x[i] <- found[1L]
uncertainties <<- rbind(uncertainties,
diff --git a/R/zzz.R b/R/zzz.R
index 5403922f..1bd04758 100755
--- a/R/zzz.R
+++ b/R/zzz.R
@@ -28,6 +28,7 @@
if (!all(c("microorganismsDT", "microorganisms.oldDT") %in% ls(envir = asNamespace("AMR")))) {
microorganisms.oldDT <- as.data.table(AMR::microorganisms.old)
+ microorganisms.oldDT$fullname_lower <- tolower(microorganisms.oldDT$fullname)
setkey(microorganisms.oldDT, col_id, fullname)
assign(x = "microorganisms",
@@ -84,6 +85,7 @@ make <- function() {
#' @importFrom data.table as.data.table setkey
make_DT <- function() {
microorganismsDT <- as.data.table(make())
+ microorganismsDT$fullname_lower <- tolower(microorganismsDT$fullname)
setkey(microorganismsDT,
kingdom,
prevalence,
diff --git a/docs/articles/AMR.html b/docs/articles/AMR.html
index 915b8c7d..3e234c50 100644
--- a/docs/articles/AMR.html
+++ b/docs/articles/AMR.html
@@ -327,68 +327,68 @@
-2015-01-18 |
-F9 |
-Hospital B |
-Escherichia coli |
+2011-10-07 |
+O2 |
+Hospital C |
+Streptococcus pneumoniae |
R |
S |
-R |
S |
-M |
+S |
+F |
-2017-12-07 |
-H7 |
+2013-10-20 |
+C10 |
Hospital A |
-Klebsiella pneumoniae |
-R |
+Escherichia coli |
S |
+R |
S |
S |
M |
-2016-02-14 |
-J4 |
+2014-08-25 |
+O4 |
Hospital A |
Escherichia coli |
-R |
-I |
-S |
-S |
-M |
-
-
-2010-12-25 |
-P2 |
-Hospital B |
-Streptococcus pneumoniae |
S |
S |
S |
S |
F |
-
-2016-12-26 |
-S8 |
-Hospital A |
+
+2011-10-28 |
+E3 |
+Hospital B |
Streptococcus pneumoniae |
S |
-I |
S |
S |
-F |
+S |
+M |
+
+
+2010-08-31 |
+H1 |
+Hospital A |
+Klebsiella pneumoniae |
+R |
+S |
+R |
+S |
+M |
-2010-03-27 |
-R7 |
-Hospital D |
-Klebsiella pneumoniae |
-S |
+2014-11-03 |
+U7 |
+Hospital A |
+Staphylococcus aureus |
S |
S |
+R |
S |
F |
@@ -411,8 +411,8 @@
#>
#> Item Count Percent Cum. Count Cum. Percent
#> --- ----- ------- -------- ----------- -------------
-#> 1 M 10,386 51.9% 10,386 51.9%
-#> 2 F 9,614 48.1% 20,000 100.0%
+#> 1 M 10,303 51.5% 10,303 51.5%
+#> 2 F 9,697 48.5% 20,000 100.0%
So, we can draw at least two conclusions immediately. From a data scientist perspective, the data looks clean: only values M
and F
. From a researcher perspective: there are slightly more men. Nothing we didn’t already know.
The data is already quite clean, but we still need to transform some variables. The bacteria
column now consists of text, and we want to add more variables based on microbial IDs later on. So, we will transform this column to valid IDs. The mutate()
function of the dplyr
package makes this really easy:
data <- data %>%
@@ -443,10 +443,10 @@
#> Kingella kingae (no changes)
#>
#> EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)
-#> Table 1: Intrinsic resistance in Enterobacteriaceae (1291 changes)
+#> Table 1: Intrinsic resistance in Enterobacteriaceae (1256 changes)
#> Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria (no changes)
#> Table 3: Intrinsic resistance in other Gram-negative bacteria (no changes)
-#> Table 4: Intrinsic resistance in Gram-positive bacteria (2787 changes)
+#> Table 4: Intrinsic resistance in Gram-positive bacteria (2821 changes)
#> Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci (no changes)
#> Table 9: Interpretive rules for B-lactam agents and Gram-negative rods (no changes)
#> Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria (no changes)
@@ -462,9 +462,9 @@
#> Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no changes)
#> Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no changes)
#>
-#> => EUCAST rules affected 7,442 out of 20,000 rows
+#> => EUCAST rules affected 7,457 out of 20,000 rows
#> -> added 0 test results
-#> -> changed 4,078 test results (0 to S; 0 to I; 4,078 to R)
+#> -> changed 4,077 test results (0 to S; 0 to I; 4,077 to R)
+#> => Found 5,704 first isolates (28.5% of total)
So only 28.5% is suitable for resistance analysis! We can now filter on it with the filter()
function, also from the dplyr
package:
@@ -516,96 +516,96 @@
1 |
-2010-02-12 |
-I9 |
+2010-01-18 |
+I8 |
B_ESCHR_COL |
+I |
S |
S |
S |
-R |
TRUE |
2 |
-2010-02-12 |
-I9 |
+2010-03-30 |
+I8 |
B_ESCHR_COL |
-R |
S |
+I |
S |
S |
FALSE |
3 |
-2010-02-22 |
-I9 |
+2010-10-05 |
+I8 |
B_ESCHR_COL |
R |
-S |
R |
S |
+S |
FALSE |
4 |
-2010-03-05 |
-I9 |
+2011-01-26 |
+I8 |
B_ESCHR_COL |
S |
-S |
+R |
R |
S |
-FALSE |
+TRUE |
5 |
-2010-03-08 |
-I9 |
+2011-02-01 |
+I8 |
B_ESCHR_COL |
S |
S |
-R |
-R |
+S |
+S |
FALSE |
6 |
-2010-03-17 |
-I9 |
+2011-03-08 |
+I8 |
B_ESCHR_COL |
+R |
S |
-S |
-S |
+R |
S |
FALSE |
7 |
-2010-05-03 |
-I9 |
+2011-04-11 |
+I8 |
B_ESCHR_COL |
S |
-S |
+R |
S |
S |
FALSE |
8 |
-2010-07-03 |
-I9 |
+2011-04-23 |
+I8 |
B_ESCHR_COL |
S |
S |
-S |
+R |
S |
FALSE |
9 |
-2010-09-11 |
-I9 |
+2011-06-21 |
+I8 |
B_ESCHR_COL |
R |
S |
@@ -615,18 +615,18 @@
10 |
-2010-09-24 |
-I9 |
+2011-07-06 |
+I8 |
B_ESCHR_COL |
S |
-R |
+S |
S |
S |
FALSE |
-Only 1 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The key_antibiotics()
function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.
+Only 2 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The key_antibiotics()
function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.
If a column exists with a name like ‘key(…)ab’ the first_isolate()
function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output:
+#> => Found 15,909 first weighted isolates (79.5% of total)
1 |
-2010-02-12 |
-I9 |
+2010-01-18 |
+I8 |
B_ESCHR_COL |
+I |
S |
S |
S |
-R |
TRUE |
TRUE |
2 |
-2010-02-12 |
-I9 |
+2010-03-30 |
+I8 |
B_ESCHR_COL |
-R |
S |
+I |
S |
S |
FALSE |
-TRUE |
+FALSE |
3 |
-2010-02-22 |
-I9 |
+2010-10-05 |
+I8 |
B_ESCHR_COL |
R |
-S |
R |
S |
+S |
FALSE |
TRUE |
4 |
-2010-03-05 |
-I9 |
+2011-01-26 |
+I8 |
B_ESCHR_COL |
S |
-S |
+R |
R |
S |
-FALSE |
+TRUE |
TRUE |
5 |
-2010-03-08 |
-I9 |
+2011-02-01 |
+I8 |
B_ESCHR_COL |
S |
S |
-R |
-R |
+S |
+S |
FALSE |
TRUE |
6 |
-2010-03-17 |
-I9 |
+2011-03-08 |
+I8 |
B_ESCHR_COL |
+R |
S |
-S |
-S |
+R |
S |
FALSE |
TRUE |
7 |
-2010-05-03 |
-I9 |
+2011-04-11 |
+I8 |
B_ESCHR_COL |
S |
-S |
+R |
S |
S |
FALSE |
-FALSE |
+TRUE |
8 |
-2010-07-03 |
-I9 |
+2011-04-23 |
+I8 |
B_ESCHR_COL |
S |
S |
-S |
+R |
S |
FALSE |
-FALSE |
+TRUE |
9 |
-2010-09-11 |
-I9 |
+2011-06-21 |
+I8 |
B_ESCHR_COL |
R |
S |
@@ -762,11 +762,11 @@
10 |
-2010-09-24 |
-I9 |
+2011-07-06 |
+I8 |
B_ESCHR_COL |
S |
-R |
+S |
S |
S |
FALSE |
@@ -774,11 +774,11 @@
-Instead of 1, now 8 isolates are flagged. In total, 79.3% of all isolates are marked ‘first weighted’ - 50.8% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
+Instead of 2, now 9 isolates are flagged. In total, 79.5% of all isolates are marked ‘first weighted’ - 51% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
As with filter_first_isolate()
, there’s a shortcut for this new algorithm too:
-So we end up with 15,861 isolates for analysis.
+So we end up with 15,909 isolates for analysis.
We can remove unneeded columns:
@@ -786,7 +786,6 @@
-1 |
-2015-01-18 |
-F9 |
-Hospital B |
-B_ESCHR_COL |
-R |
-S |
-R |
-S |
-M |
-Gram negative |
-Escherichia |
-coli |
-TRUE |
-
-
-3 |
-2016-02-14 |
-J4 |
-Hospital A |
-B_ESCHR_COL |
-R |
-I |
-S |
-S |
-M |
-Gram negative |
-Escherichia |
-coli |
-TRUE |
-
-
-4 |
-2010-12-25 |
-P2 |
-Hospital B |
+2011-10-07 |
+O2 |
+Hospital C |
B_STRPT_PNE |
-S |
+R |
S |
S |
R |
@@ -851,51 +817,78 @@
TRUE |
-5 |
-2016-12-26 |
-S8 |
+2013-10-20 |
+C10 |
Hospital A |
-B_STRPT_PNE |
-S |
-I |
+B_ESCHR_COL |
S |
R |
+S |
+S |
+M |
+Gram negative |
+Escherichia |
+coli |
+TRUE |
+
+
+2014-08-25 |
+O4 |
+Hospital A |
+B_ESCHR_COL |
+S |
+S |
+S |
+S |
F |
+Gram negative |
+Escherichia |
+coli |
+TRUE |
+
+
+2011-10-28 |
+E3 |
+Hospital B |
+B_STRPT_PNE |
+S |
+S |
+S |
+R |
+M |
Gram positive |
Streptococcus |
pneumoniae |
TRUE |
-6 |
-2010-03-27 |
-R7 |
-Hospital D |
+2010-08-31 |
+H1 |
+Hospital A |
B_KLBSL_PNE |
R |
S |
+R |
S |
-S |
-F |
+M |
Gram negative |
Klebsiella |
pneumoniae |
TRUE |
-8 |
-2016-08-08 |
-K8 |
-Hospital B |
-B_KLBSL_PNE |
+2014-11-03 |
+U7 |
+Hospital A |
+B_STPHY_AUR |
+S |
+S |
R |
-I |
S |
-S |
-M |
-Gram negative |
-Klebsiella |
-pneumoniae |
+F |
+Gram positive |
+Staphylococcus |
+aureus |
TRUE |
@@ -915,9 +908,9 @@
Or can be used like the dplyr
way, which is easier readable:
-Frequency table of genus
and species
from a data.frame
(15,861 x 13)
+Frequency table of genus
and species
from a data.frame
(15,909 x 13)
Columns: 2
-Length: 15,861 (of which NA: 0 = 0.00%)
+Length: 15,909 (of which NA: 0 = 0.00%)
Unique: 4
Shortest: 16
Longest: 24
@@ -934,33 +927,33 @@ Longest: 24
1 |
Escherichia coli |
-7,879 |
-49.7% |
-7,879 |
-49.7% |
+7,837 |
+49.3% |
+7,837 |
+49.3% |
2 |
Staphylococcus aureus |
-3,915 |
-24.7% |
-11,794 |
-74.4% |
+3,940 |
+24.8% |
+11,777 |
+74.0% |
3 |
Streptococcus pneumoniae |
-2,482 |
-15.6% |
-14,276 |
-90.0% |
+2,554 |
+16.1% |
+14,331 |
+90.1% |
4 |
Klebsiella pneumoniae |
-1,585 |
-10.0% |
-15,861 |
+1,578 |
+9.9% |
+15,909 |
100.0% |
@@ -971,7 +964,7 @@ Longest: 24
Resistance percentages
The functions portion_R
, portion_RI
, portion_I
, portion_IS
and portion_S
can be used to determine the portion of a specific antimicrobial outcome. They can be used on their own:
+#> [1] 0.4787856
Or can be used in conjuction with group_by()
and summarise()
, both from the dplyr
package:
@@ -1014,23 +1007,23 @@ Longest: 24
Hospital A |
-0.4759916 |
-4790 |
+0.4717819 |
+4731 |
Hospital B |
-0.4808997 |
-5602 |
+0.4802632 |
+5624 |
Hospital C |
-0.4682779 |
-2317 |
+0.4870184 |
+2388 |
Hospital D |
-0.4651015 |
-3152 |
+0.4804169 |
+3166 |
@@ -1050,27 +1043,27 @@ Longest: 24
Escherichia |
-0.7292804 |
-0.8975758 |
-0.9772814 |
+0.7256603 |
+0.8983029 |
+0.9729488 |
Klebsiella |
-0.7438486 |
-0.9015773 |
-0.9741325 |
+0.7338403 |
+0.8935361 |
+0.9689480 |
Staphylococcus |
-0.7315453 |
-0.9154534 |
-0.9793103 |
+0.7309645 |
+0.9233503 |
+0.9794416 |
Streptococcus |
-0.7352941 |
+0.7372749 |
0.0000000 |
-0.7352941 |
+0.7372749 |
diff --git a/docs/articles/AMR_files/figure-html/plot 1-1.png b/docs/articles/AMR_files/figure-html/plot 1-1.png
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