vignettes/AMR.Rmd
AMR.RmdNote: 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 August 2020.
+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 03 September 2020.
So only 28.3% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
So only 28.6% 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) @@ -532,7 +532,7 @@ Longest: 1First 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 C8, 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 M8, sorted on date:
-
isolate @@ -548,21 +548,21 @@ Longest: 11 -2010-01-13 -C8 +2010-01-28 +M8 B_ESCHR_COLI S S -S +R S TRUE 2 -2010-02-19 -C8 +2010-05-23 +M8 B_ESCHR_COLI -S +R S S S @@ -570,10 +570,10 @@ Longest: 13 -2010-03-12 -C8 +2010-07-11 +M8 B_ESCHR_COLI -R +S S S S @@ -581,30 +581,30 @@ Longest: 14 -2010-03-25 -C8 +2010-10-01 +M8 B_ESCHR_COLI S S R -R +S FALSE 5 -2010-04-28 -C8 +2010-12-02 +M8 B_ESCHR_COLI R S -R +S S FALSE 6 -2010-07-22 -C8 +2010-12-23 +M8 B_ESCHR_COLI S S @@ -614,8 +614,8 @@ Longest: 17 -2010-12-05 -C8 +2011-01-16 +M8 B_ESCHR_COLI S S @@ -625,33 +625,33 @@ Longest: 18 -2011-01-18 -C8 +2011-03-14 +M8 B_ESCHR_COLI -R -R +S +S S S TRUE 9 -2011-05-24 -C8 +2011-04-02 +M8 B_ESCHR_COLI -R -I -R +S +S +S S FALSE 10 -2011-06-24 -C8 +2011-04-05 +M8 B_ESCHR_COLI -S -S +R +R S S FALSE @@ -686,34 +686,34 @@ Longest: 11 -2010-01-13 -C8 +2010-01-28 +M8 B_ESCHR_COLI S S -S +R S TRUE TRUE 2 -2010-02-19 -C8 +2010-05-23 +M8 B_ESCHR_COLI -S +R S S S FALSE -FALSE +TRUE 3 -2010-03-12 -C8 +2010-07-11 +M8 B_ESCHR_COLI -R +S S S S @@ -722,32 +722,32 @@ Longest: 14 -2010-03-25 -C8 +2010-10-01 +M8 B_ESCHR_COLI S S R -R +S FALSE TRUE 5 -2010-04-28 -C8 +2010-12-02 +M8 B_ESCHR_COLI R S -R +S S FALSE TRUE 6 -2010-07-22 -C8 +2010-12-23 +M8 B_ESCHR_COLI S S @@ -758,8 +758,8 @@ Longest: 17 -2010-12-05 -C8 +2011-01-16 +M8 B_ESCHR_COLI S S @@ -770,11 +770,11 @@ Longest: 18 -2011-01-18 -C8 +2011-03-14 +M8 B_ESCHR_COLI -R -R +S +S S S TRUE @@ -782,23 +782,23 @@ Longest: 19 -2011-05-24 -C8 +2011-04-02 +M8 B_ESCHR_COLI -R -I -R +S +S +S S FALSE -TRUE +FALSE 10 -2011-06-24 -C8 +2011-04-05 +M8 B_ESCHR_COLI -S -S +R +R S S FALSE @@ -806,13 +806,13 @@ Longest: 1Instead of 2, now 8 isolates are flagged. In total, 78.4% of all isolates are marked ‘first weighted’ - 50.1% 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 8 isolates are flagged. In total, 79.1% of all isolates are marked ‘first weighted’ - 50.5% 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 15,686 isolates for analysis.
+So we end up with 15,813 isolates for analysis.
We can remove unneeded columns:
data_1st <- data_1st %>% @@ -857,13 +857,77 @@ Longest: 1- +1 -2011-03-20 -W2 +2 +2017-04-19 +S1 +Hospital C +B_STPHY_AURS +S +S +S +S +F +Gram-positive +Staphylococcus +aureus +TRUE ++ +3 +2017-04-21 +L3 +Hospital B +B_ESCHR_COLI +R +S +S +S +M +Gram-negative +Escherichia +coli +TRUE ++ +4 +2011-01-19 +A7 Hospital A +B_STPHY_AURS +S +S +S +S +M +Gram-positive +Staphylococcus +aureus +TRUE ++ +5 +2011-01-24 +H10 +Hospital B +B_ESCHR_COLI +R +S +S +S +M +Gram-negative +Escherichia +coli +TRUE ++ 6 +2011-01-31 +T9 +Hospital C B_STRPT_PNMN -S -S +R +R S R F @@ -873,85 +937,21 @@ Longest: 1TRUE - -3 -2012-11-11 -A5 -Hospital D -B_STPHY_AURS -R -R -S -S -M -Gram-positive -Staphylococcus -aureus -TRUE -- -4 -2012-09-03 -Y3 -Hospital C +7 +2012-12-27 +Q9 +Hospital A B_ESCHR_COLI R +R +R S -R -R F Gram-negative Escherichia coli TRUE - -5 -2012-11-19 -I3 -Hospital C -B_ESCHR_COLI -S -S -S -R -M -Gram-negative -Escherichia -coli -TRUE -- -8 -2013-11-06 -B1 -Hospital B -B_KLBSL_PNMN -R -S -S -S -M -Gram-negative -Klebsiella -pneumoniae -TRUE -- 9 -2017-01-21 -U3 -Hospital D -B_KLBSL_PNMN -R -S -S -S -F -Gram-negative -Klebsiella -pneumoniae -TRUE -Time for the analysis!
@@ -975,8 +975,8 @@ Longest: 1Frequency table
Class: character
-Length: 15,686
-Available: 15,686 (100%, NA: 0 = 0%)
+Length: 15,813
+Available: 15,813 (100%, NA: 0 = 0%)
Unique: 4Shortest: 16
@@ -993,33 +993,33 @@ Longest: 24
Longest: 241 Escherichia coli -7,774 -49.56% -7,774 -49.56% +7,901 +49.97% +7,901 +49.97% 2 Staphylococcus aureus -3,952 -25.19% -11,726 -74.75% +3,946 +24.95% +11,847 +74.92% 3 Streptococcus pneumoniae -2,367 -15.09% -14,093 -89.84% +2,394 +15.14% +14,241 +90.06% 4 Klebsiella pneumoniae -1,593 -10.16% -15,686 +1,572 +9.94% +15,813 100.00%
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:
data_1st %>% resistance(AMX) -# [1] 0.5350631 +# [1] 0.5330424
Or can be used in conjuction with group_by() and summarise(), both from the dplyr package:
@@ -1173,19 +1173,19 @@ Longest: 24Hospital A -0.5338783 +0.5262478 Hospital B -0.5368875 +0.5401022 Hospital C -0.5367068 +0.5257556 @@ -1206,23 +1206,23 @@ Longest: 24 Hospital D -0.5322422 +0.5357938 Hospital A -0.5338783 -4649 +0.5262478 +4648 Hospital B -0.5368875 -5571 +0.5401022 +5673 Hospital C -0.5367068 -2411 +0.5257556 +2349 @@ -1245,27 +1245,27 @@ Longest: 24 Hospital D -0.5322422 -3055 +0.5357938 +3143 Escherichia -0.8205557 -0.8986365 -0.9855930 +0.8220478 +0.8978610 +0.9860777 Klebsiella -0.8323917 -0.8901444 -0.9855618 +0.8276081 +0.9001272 +0.9860051 Staphylococcus -0.8276822 -0.9210526 -0.9868421 +0.8335023 +0.9206792 +0.9878358 diff --git a/docs/articles/AMR_files/figure-html/plot 1-1.png b/docs/articles/AMR_files/figure-html/plot 1-1.png index 7fda4f19a..6fa701d79 100644 Binary files a/docs/articles/AMR_files/figure-html/plot 1-1.png and b/docs/articles/AMR_files/figure-html/plot 1-1.png differ diff --git a/docs/articles/AMR_files/figure-html/plot 3-1.png b/docs/articles/AMR_files/figure-html/plot 3-1.png index ed7101612..cc86c264a 100644 Binary files a/docs/articles/AMR_files/figure-html/plot 3-1.png and b/docs/articles/AMR_files/figure-html/plot 3-1.png differ diff --git a/docs/articles/AMR_files/figure-html/plot 4-1.png b/docs/articles/AMR_files/figure-html/plot 4-1.png index b5de9a4d2..81b411bb0 100644 Binary files a/docs/articles/AMR_files/figure-html/plot 4-1.png and b/docs/articles/AMR_files/figure-html/plot 4-1.png differ diff --git a/docs/articles/AMR_files/figure-html/plot 5-1.png b/docs/articles/AMR_files/figure-html/plot 5-1.png index d2d6fe207..291e600e0 100644 Binary files a/docs/articles/AMR_files/figure-html/plot 5-1.png and b/docs/articles/AMR_files/figure-html/plot 5-1.png differ diff --git a/docs/articles/EUCAST.html b/docs/articles/EUCAST.html index 3ecd97a7a..4a599acc4 100644 --- a/docs/articles/EUCAST.html +++ b/docs/articles/EUCAST.html @@ -39,7 +39,7 @@ Streptococcus -0.5504858 +0.5396825 0.0000000 -0.5504858 +0.5396825
head(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin -# 1 I S S S S R -# 2 S S S R R I -# 3 R S S R S I -# 4 R S S R S S -# 5 S R I R I R -# 6 R R S R R R +# 1 S R S S R S +# 2 S R R S R S +# 3 S R R S S R +# 4 R R S S S R +# 5 I S I S S S +# 6 S S R R S S # kanamycin -# 1 S -# 2 R -# 3 R -# 4 I -# 5 R -# 6 R +# 1 R +# 2 I +# 3 S +# 4 R +# 5 S +# 6 S
We can now add the interpretation of MDR-TB to our data set. You can use:
@@ -368,40 +368,40 @@ Unique: 51 Mono-resistant -3233 -64.66% -3233 -64.66% +3297 +65.94% +3297 +65.94% 2 -Negative -698 -13.96% -3931 -78.62% +Multi-drug-resistant +614 +12.28% +3911 +78.22% 3 -Multi-drug-resistant -556 -11.12% -4487 -89.74% +Negative +603 +12.06% +4514 +90.28% 4 Poly-resistant -300 -6.00% -4787 -95.74% +278 +5.56% +4792 +95.84% diff --git a/docs/articles/PCA.html b/docs/articles/PCA.html index dc573f075..0e768c516 100644 --- a/docs/articles/PCA.html +++ b/docs/articles/PCA.html @@ -39,7 +39,7 @@ 5 Extensively drug-resistant -213 -4.26% +208 +4.16% 5000 100.00%
Now to transform this to a data set with only resistance percentages per taxonomic order and genus:
diff --git a/docs/articles/SPSS.html b/docs/articles/SPSS.html
index a7fa1ef4f..d99fa2f2c 100644
--- a/docs/articles/SPSS.html
+++ b/docs/articles/SPSS.html
@@ -39,7 +39,7 @@
vignettes/SPSS.Rmd
SPSS.Rmd%SI: 78.59%
| diff --git a/docs/articles/benchmarks.html b/docs/articles/benchmarks.html index 90c3a146a..3322982a6 100644 --- a/docs/articles/benchmarks.html +++ b/docs/articles/benchmarks.html @@ -39,7 +39,7 @@ @@ -79,7 +79,7 @@ - Download our reference data sets for own use + Data sets for download / own use |
|---|
