diff --git a/DESCRIPTION b/DESCRIPTION index 38f7b2ae..0b343ec4 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,5 +1,5 @@ Package: AMR -Version: 0.7.1.9024 +Version: 0.7.1.9026 Date: 2019-08-06 Title: Antimicrobial Resistance Analysis Authors@R: c( diff --git a/NAMESPACE b/NAMESPACE index 16953cd9..0e07b823 100755 --- a/NAMESPACE +++ b/NAMESPACE @@ -272,3 +272,4 @@ importFrom(stats,pchisq) importFrom(stats,predict) importFrom(utils,browseURL) importFrom(utils,installed.packages) +importFrom(utils,menu) diff --git a/NEWS.md b/NEWS.md index 22dd0b6f..6ce82d4d 100755 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -# AMR 0.7.1.9024 +# AMR 0.7.1.9026 ### Breaking * Function `freq()` has moved to a new package, [`clean`](https://github.com/msberends/clean) ([CRAN link](https://cran.r-project.org/package=clean)). Creating frequency tables is actually not the scope of this package (never was) and this function has matured a lot over the last two years. Therefore, a new package was created for data cleaning and checking and it perfectly fits the `freq()` function. The [`clean`](https://github.com/msberends/clean) package is available on CRAN and will be installed automatically when updating the `AMR` package, that now imports it. In a later stage, the `skewness()` and `kurtosis()` functions will be moved to the `clean` package too. diff --git a/docs/articles/AMR.html b/docs/articles/AMR.html index bed45232..7a45e304 100644 --- a/docs/articles/AMR.html +++ b/docs/articles/AMR.html @@ -40,7 +40,7 @@ AMR (for R) - 0.7.1.9015 + 0.7.1.9026 @@ -185,7 +185,7 @@

How to conduct AMR analysis

Matthijs S. Berends

-

29 July 2019

+

06 August 2019

@@ -194,7 +194,7 @@ -

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 29 July 2019.

+

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 06 August 2019.

Introduction

@@ -210,21 +210,21 @@ -2019-07-29 +2019-08-06 abcd Escherichia coli S S -2019-07-29 +2019-08-06 abcd Escherichia coli S R -2019-07-29 +2019-08-06 efgh Escherichia coli R @@ -320,68 +320,68 @@ -2017-01-19 -U10 -Hospital A -Klebsiella pneumoniae -S -S -S -S -F - - -2011-09-03 -T7 -Hospital B +2017-03-10 +Q5 +Hospital C Escherichia coli -I S S -R -F - - -2011-12-04 -U5 -Hospital A -Staphylococcus aureus -R S -R S F -2014-06-26 -O1 -Hospital A -Staphylococcus aureus -S -I +2011-12-26 +G6 +Hospital C +Streptococcus pneumoniae S S -F +S +S +M -2012-08-04 -G2 -Hospital B -Streptococcus pneumoniae -R -R +2011-07-01 +L6 +Hospital C +Escherichia coli +S +S S S M -2015-05-10 -W8 +2015-08-22 +A9 Hospital B -Streptococcus pneumoniae +Escherichia coli +R S S S +M + + +2017-03-16 +H1 +Hospital A +Staphylococcus aureus +S +I +S +S +M + + +2017-06-20 +V2 +Hospital D +Escherichia coli +I +S +R S F @@ -406,8 +406,8 @@ # # Item Count Percent Cum. Count Cum. Percent # --- ----- ------- -------- ----------- ------------- -# 1 M 10,424 52.1% 10,424 52.1% -# 2 F 9,576 47.9% 20,000 100.0% +# 1 M 10,366 51.8% 10,366 51.8% +# 2 F 9,634 48.2% 20,000 100.0%

So, we can draw at least two conclusions immediately. From a data scientists perspective, the data looks clean: only values M and F. From a researchers 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 %>%
@@ -437,14 +437,14 @@
 # Pasteurella multocida (no new changes)
 # Staphylococcus (no new changes)
 # Streptococcus groups A, B, C, G (no new changes)
-# Streptococcus pneumoniae (1,485 new changes)
+# Streptococcus pneumoniae (1,439 new changes)
 # Viridans group streptococci (no new changes)
 # 
 # EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)
-# Table 01: Intrinsic resistance in Enterobacteriaceae (1,312 new changes)
+# Table 01: Intrinsic resistance in Enterobacteriaceae (1,329 new changes)
 # Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria (no new changes)
 # Table 03: Intrinsic resistance in other Gram-negative bacteria (no new changes)
-# Table 04: Intrinsic resistance in Gram-positive bacteria (2,746 new changes)
+# Table 04: Intrinsic resistance in Gram-positive bacteria (2,679 new changes)
 # Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci (no new changes)
 # Table 09: Interpretive rules for B-lactam agents and Gram-negative rods (no new changes)
 # Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins (no new changes)
@@ -452,24 +452,24 @@
 # Table 13: Interpretive rules for quinolones (no new changes)
 # 
 # Other rules
-# Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2,284 new changes)
-# Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (116 new changes)
+# Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2,347 new changes)
+# Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (114 new changes)
 # Non-EUCAST: piperacillin = R where piperacillin/tazobactam = R (no new changes)
 # Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no new changes)
 # Non-EUCAST: trimethoprim = R where trimethoprim/sulfa = R (no new changes)
 # Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no new changes)
 # 
 # --------------------------------------------------------------------------
-# EUCAST rules affected 6,581 out of 20,000 rows, making a total of 7,943 edits
+# EUCAST rules affected 6,589 out of 20,000 rows, making a total of 7,908 edits
 # => added 0 test results
 # 
-# => changed 7,943 test results
-#    - 100 test results changed from S to I
-#    - 4,767 test results changed from S to R
-#    - 1,140 test results changed from I to S
-#    - 315 test results changed from I to R
-#    - 1,602 test results changed from R to S
-#    - 19 test results changed from R to I
+# => changed 7,908 test results
+#    - 110 test results changed from S to I
+#    - 4,680 test results changed from S to R
+#    - 1,068 test results changed from I to S
+#    - 326 test results changed from I to R
+#    - 1,711 test results changed from R to S
+#    - 13 test results changed from R to I
 # --------------------------------------------------------------------------
 # 
 # Use verbose = TRUE (on your original data) to get a data.frame with all specified edits instead.
@@ -497,8 +497,8 @@ # NOTE: Using column `bacteria` as input for `col_mo`. # NOTE: Using column `date` as input for `col_date`. # NOTE: Using column `patient_id` as input for `col_patient_id`. -# => Found 5,701 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:

+# => Found 5,681 first isolates (28.4% of total) +

So only 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)

For future use, the above two syntaxes can be shortened with the filter_first_isolate() function:

@@ -508,7 +508,7 @@

First weighted isolates

-

We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient S2, sorted on date:

+

We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient C10, sorted on date:

@@ -524,8 +524,8 @@ - - + + @@ -535,10 +535,10 @@ - - + + - + @@ -546,21 +546,21 @@ - - + + - - + + - - + + - + @@ -568,19 +568,19 @@ - - + + + + + - - - - - + + @@ -590,43 +590,43 @@ - - + + - + - - + + - + - - + + - + - - + + - + @@ -645,7 +645,7 @@ # NOTE: Using column `patient_id` as input for `col_patient_id`.# NOTE: Using column `keyab` as input for `col_keyantibiotics`. Use col_keyantibiotics = FALSE to prevent this.# [Criterion] Inclusion based on key antibiotics, ignoring I. -# => Found 15,136 first weighted isolates (75.7% of total) +# => Found 15,027 first weighted isolates (75.1% of total)
isolate
12010-03-05S22010-07-24C10 B_ESCHR_COL S S
22010-03-09S22010-12-03C10 B_ESCHR_COLSR S S S
32010-08-25S22010-12-19C10 B_ESCHR_COL S SRRSS FALSE
42010-09-20S22011-02-18C10 B_ESCHR_COLSR S S S
52010-10-11S22011-06-26C10 B_ESCHR_COLSSR RSSS FALSE
62011-02-11S22011-06-28C10 B_ESCHR_COL S S
72011-02-24S22011-06-30C10 B_ESCHR_COL S SSR S FALSE
82011-03-12S22011-08-29C10 B_ESCHR_COL S S RSR TRUE
92011-04-21S22011-10-03C10 B_ESCHR_COL S SRS S FALSE
102011-05-04S22011-12-30C10 B_ESCHR_COLSR S S S
@@ -662,8 +662,8 @@ - - + + @@ -674,34 +674,34 @@ - - + + - + - + - - + + - - + + - - + + - + @@ -710,20 +710,20 @@ - - + + + + + - - - - - + + @@ -734,46 +734,46 @@ - - - - - - - - - - - - - - + + + + + + + + + + + + + + - - + + - + - + - - + + - + @@ -782,11 +782,11 @@
isolate
12010-03-05S22010-07-24C10 B_ESCHR_COL S S
22010-03-09S22010-12-03C10 B_ESCHR_COLSR S S S FALSEFALSETRUE
32010-08-25S22010-12-19C10 B_ESCHR_COL S SRRSS FALSE TRUE
42010-09-20S22011-02-18C10 B_ESCHR_COLSR S S S
52010-10-11S22011-06-26C10 B_ESCHR_COLSSR RSSS FALSE TRUE
62011-02-11S22011-06-28C10 B_ESCHR_COL S S
72011-02-24S2B_ESCHR_COLSSSSFALSEFALSE
82011-03-12S22011-06-30C10 B_ESCHR_COL S S R SFALSETRUE
82011-08-29C10B_ESCHR_COLSSRR TRUE TRUE
92011-04-21S22011-10-03C10 B_ESCHR_COL S SRS S FALSEFALSETRUE
102011-05-04S22011-12-30C10 B_ESCHR_COLSR S S S
-

Instead of 2, now 7 isolates are flagged. In total, 75.7% of all isolates are marked ‘first weighted’ - 47.2% 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 10 isolates are flagged. In total, of all isolates are marked ‘first weighted’ - 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:

data_1st <- data %>% 
   filter_first_weighted_isolate()
-

So we end up with 15,136 isolates for analysis.

+

So we end up with 15,027 isolates for analysis.

We can remove unneeded columns:

data_1st <- data_1st %>% 
   select(-c(first, keyab))
@@ -811,15 +811,15 @@ -2 -2011-09-03 -T7 -Hospital B +1 +2017-03-10 +Q5 +Hospital C B_ESCHR_COL -I S S -R +S +S F Gram-negative Escherichia @@ -827,45 +827,13 @@ TRUE -3 -2011-12-04 -U5 -Hospital A -B_STPHY_AUR -R -S -R -S -F -Gram-positive -Staphylococcus -aureus -TRUE - - -4 -2014-06-26 -O1 -Hospital A -B_STPHY_AUR -S -S -S -S -F -Gram-positive -Staphylococcus -aureus -TRUE - - -5 -2012-08-04 -G2 -Hospital B +2 +2011-12-26 +G6 +Hospital C B_STRPT_PNE -R -R +S +S S R M @@ -875,10 +843,10 @@ TRUE -7 -2012-11-12 -G6 -Hospital B +3 +2011-07-01 +L6 +Hospital C B_ESCHR_COL S S @@ -891,19 +859,51 @@ TRUE -8 -2017-07-19 -G4 -Hospital C -B_STPHY_AUR -S -S +6 +2017-06-20 +V2 +Hospital D +B_ESCHR_COL +I +S +R +S +F +Gram-negative +Escherichia +coli +TRUE + + +7 +2012-02-10 +D1 +Hospital A +B_STRPT_PNE S S +R +R M Gram-positive -Staphylococcus -aureus +Streptococcus +pneumoniae +TRUE + + +8 +2010-05-01 +E1 +Hospital A +B_STRPT_PNE +S +S +S +R +M +Gram-positive +Streptococcus +pneumoniae TRUE @@ -925,7 +925,7 @@ 
data_1st %>% freq(genus, species)

Frequency table

Class: character
-Length: 15,136 (of which NA: 0 = 0.00%)
+Length: 15,027 (of which NA: 0 = 0.00%)
Unique: 4

Shortest: 16
Longest: 24

@@ -942,33 +942,33 @@ Longest: 24

1 Escherichia coli -7,458 -49.3% -7,458 -49.3% +7,456 +49.6% +7,456 +49.6% 2 Staphylococcus aureus -3,739 +3,709 24.7% -11,197 -74.0% +11,165 +74.3% 3 Streptococcus pneumoniae -2,362 -15.6% -13,559 -89.6% +2,267 +15.1% +13,432 +89.4% 4 Klebsiella pneumoniae -1,577 -10.4% -15,136 +1,595 +10.6% +15,027 100.0% @@ -979,7 +979,7 @@ Longest: 24

Resistance percentages

The functions portion_S(), portion_SI(), portion_I(), portion_IR() and portion_R() can be used to determine the portion of a specific antimicrobial outcome. As per the EUCAST guideline of 2019, we calculate resistance as the portion of R (portion_R()) and susceptibility as the portion of S and I (portion_SI()). These functions can be used on their own:

data_1st %>% portion_R(AMX)
-# [1] 0.462936
+# [1] 0.4685566

Or can be used in conjuction with group_by() and summarise(), both from the dplyr package:

data_1st %>% 
   group_by(hospital) %>% 
@@ -992,19 +992,19 @@ Longest: 24

 
 
 Hospital A
-0.4701912
+0.4574111
 
 
 Hospital B
-0.4536024
+0.4789054
 
 
 Hospital C
-0.4589011
+0.4639895
 
 
 Hospital D
-0.4714194
+0.4705098
 
 
 
@@ -1022,23 +1022,23 @@ Longest: 24

 
 
 Hospital A
-0.4701912
-4445
+0.4574111
+4473
 
 
 Hospital B
-0.4536024
-5302
+0.4789054
+5262
 
 
 Hospital C
-0.4589011
-2275
+0.4639895
+2291
 
 
 Hospital D
-0.4714194
-3114
+0.4705098
+3001
 
 
 
@@ -1058,27 +1058,27 @@ Longest: 24

 
 
 Escherichia
-0.9249128
-0.8924645
-0.9945025
+0.9244903
+0.8964592
+0.9936964
 
 
 Klebsiella
-0.8338618
-0.8928345
-0.9822448
+0.8244514
+0.9059561
+0.9811912
 
 
 Staphylococcus
-0.9152180
-0.9184274
-0.9946510
+0.9266649
+0.9201941
+0.9935293
 
 
 Streptococcus
-0.6147333
+0.6060873
 0.0000000
-0.6147333
+0.6060873
 
 
 
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diff --git a/docs/articles/MDR.html b/docs/articles/MDR.html
index 968a23dc..c17496e8 100644
--- a/docs/articles/MDR.html
+++ b/docs/articles/MDR.html
@@ -40,7 +40,7 @@
       
       
         AMR (for R)
-        0.7.1.9015
+        0.7.1.9026
@@ -185,7 +185,7 @@

How to determine multi-drug resistance (MDR)

Matthijs S. Berends

-

29 July 2019

+

06 August 2019

@@ -228,16 +228,16 @@

The data set looks like this now:

head(my_TB_data)
 #   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin
-# 1          S         S            S          R            S            S
-# 2          R         S            R          R            I            R
-# 3          S         R            R          R            I            R
-# 4          S         S            R          I            I            R
-# 5          R         S            R          S            S            R
-# 6          R         S            R          R            S            R
+# 1          R         R            S          R            S            S
+# 2          R         R            S          R            S            S
+# 3          R         S            R          S            S            R
+# 4          R         I            S          R            R            R
+# 5          I         S            S          R            I            S
+# 6          R         R            R          S            I            S
 #   kanamycin
 # 1         I
 # 2         S
-# 3         R
+# 3         I
 # 4         S
 # 5         S
 # 6         S
@@ -272,39 +272,39 @@ Unique: 5

1 Mono-resistance -3225 -64.5% -3225 -64.5% +3264 +65.3% +3264 +65.3% 2 Negative -644 -12.9% -3869 -77.4% +627 +12.5% +3891 +77.8% 3 Multidrug resistance -626 -12.5% -4495 -89.9% +607 +12.1% +4498 +90.0% 4 Poly-resistance 288 5.8% -4783 +4786 95.7% 5 Extensive drug resistance -217 +214 4.3% 5000 100.0% diff --git a/docs/articles/WHONET.html b/docs/articles/WHONET.html index 64a1fb84..cf72f871 100644 --- a/docs/articles/WHONET.html +++ b/docs/articles/WHONET.html @@ -40,7 +40,7 @@ AMR (for R) - 0.7.1.9015 + 0.7.1.9026 @@ -185,7 +185,7 @@

How to work with WHONET data

Matthijs S. Berends

-

29 July 2019

+

06 August 2019

@@ -334,7 +334,7 @@ Species: 39

data %>% freq(AMC_ND2)

Frequency table

Class: factor > ordered > rsi (numeric)
-Length: 481 (of which NA: 19 = 3.95%)
+Length: 500 (of which NA: 19 = 3.80%)
Levels: 3: S < I < R
Unique: 3

%SI: 78.6%

diff --git a/docs/articles/index.html b/docs/articles/index.html index 716bf558..f839a116 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -78,7 +78,7 @@ AMR (for R) - 0.7.1.9024 + 0.7.1.9026 diff --git a/docs/authors.html b/docs/authors.html index 56ea8d31..44c9d901 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -78,7 +78,7 @@ AMR (for R) - 0.7.1.9024 + 0.7.1.9026 diff --git a/docs/index.html b/docs/index.html index 2afce086..e5fedb31 100644 --- a/docs/index.html +++ b/docs/index.html @@ -42,7 +42,7 @@ AMR (for R) - 0.7.1.9024 + 0.7.1.9026 diff --git a/docs/news/index.html b/docs/news/index.html index c155f84f..49872dce 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -78,7 +78,7 @@ AMR (for R) - 0.7.1.9024 + 0.7.1.9026 @@ -225,9 +225,9 @@ -
+

-AMR 0.7.1.9024 Unreleased +AMR 0.7.1.9026 Unreleased

@@ -1206,7 +1206,7 @@ Using as.mo(..., allow_uncertain = 3)

Contents

    -
  • 0.7.1.9024
    • +
  • 0.7.1.9026
  • 0.7.1
  • 0.7.0
  • 0.6.1
    • diff --git a/docs/reference/WHOCC.html b/docs/reference/WHOCC.html index 2ef0cefa..cfedd039 100644 --- a/docs/reference/WHOCC.html +++ b/docs/reference/WHOCC.html @@ -80,7 +80,7 @@ AMR (for R) - 0.7.1.9015 + 0.7.1.9026

diff --git a/docs/reference/antibiotics.html b/docs/reference/antibiotics.html index 58a740ce..fbbf2e7f 100644 --- a/docs/reference/antibiotics.html +++ b/docs/reference/antibiotics.html @@ -80,7 +80,7 @@ AMR (for R) - 0.7.1.9015 + 0.7.1.9026
diff --git a/docs/reference/as.ab.html b/docs/reference/as.ab.html index c17cb266..80e2ce26 100644 --- a/docs/reference/as.ab.html +++ b/docs/reference/as.ab.html @@ -80,7 +80,7 @@ AMR (for R) - 0.7.1.9015 + 0.7.1.9026
diff --git a/docs/reference/as.mic.html b/docs/reference/as.mic.html index 0bf7e71e..4764d16b 100644 --- a/docs/reference/as.mic.html +++ b/docs/reference/as.mic.html @@ -80,7 +80,7 @@ AMR (for R) - 0.7.1.9016 + 0.7.1.9026 diff --git a/docs/reference/as.rsi.html b/docs/reference/as.rsi.html index 4127a7f8..1998dea2 100644 --- a/docs/reference/as.rsi.html +++ b/docs/reference/as.rsi.html @@ -80,7 +80,7 @@ AMR (for R) - 0.7.1.9016 + 0.7.1.9026 diff --git a/docs/reference/catalogue_of_life_version.html b/docs/reference/catalogue_of_life_version.html index df53bbd8..477007ac 100644 --- a/docs/reference/catalogue_of_life_version.html +++ b/docs/reference/catalogue_of_life_version.html @@ -80,7 +80,7 @@ AMR (for R) - 0.7.1.9017 + 0.7.1.9026 diff --git a/docs/reference/eucast_rules.html b/docs/reference/eucast_rules.html index 141260a7..39d664d6 100644 --- a/docs/reference/eucast_rules.html +++ b/docs/reference/eucast_rules.html @@ -80,7 +80,7 @@ AMR (for R) - 0.7.1.9018 + 0.7.1.9026 diff --git a/docs/reference/index.html b/docs/reference/index.html index 9d231416..5febcf29 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -78,7 +78,7 @@ AMR (for R) - 0.7.1.9024 + 0.7.1.9026 diff --git a/docs/reference/like.html b/docs/reference/like.html index 11cdfbf6..f83642ef 100644 --- a/docs/reference/like.html +++ b/docs/reference/like.html @@ -80,7 +80,7 @@ AMR (for R) - 0.7.1.9018 + 0.7.1.9026 diff --git a/docs/reference/mdro.html b/docs/reference/mdro.html index 62e484ed..ec9e219b 100644 --- a/docs/reference/mdro.html +++ b/docs/reference/mdro.html @@ -80,7 +80,7 @@ AMR (for R) - 0.7.1.9015 + 0.7.1.9026 diff --git a/docs/reference/microorganisms.html b/docs/reference/microorganisms.html index 445796d3..1ab2dc7f 100644 --- a/docs/reference/microorganisms.html +++ b/docs/reference/microorganisms.html @@ -80,7 +80,7 @@ AMR (for R) - 0.7.1.9024 + 0.7.1.9026 diff --git a/docs/reference/microorganisms.old.html b/docs/reference/microorganisms.old.html index bdea301a..15bf81e6 100644 --- a/docs/reference/microorganisms.old.html +++ b/docs/reference/microorganisms.old.html @@ -80,7 +80,7 @@ AMR (for R) - 0.7.1.9024 + 0.7.1.9026 diff --git a/docs/reference/resistance_predict.html b/docs/reference/resistance_predict.html index c323f78c..048a5e49 100644 --- a/docs/reference/resistance_predict.html +++ b/docs/reference/resistance_predict.html @@ -80,7 +80,7 @@ AMR (for R) - 0.7.1.9021 + 0.7.1.9026