From a925a5d2f8f0d14bdd02bc67bf4c444e87b7c810 Mon Sep 17 00:00:00 2001 From: "Matthijs S. Berends" Date: Fri, 17 May 2019 13:01:17 +0200 Subject: [PATCH] add Disqus to website --- .gitlab-ci.yml | 1 - docs/articles/AMR.html | 436 +++++++++--------- .../AMR_files/figure-html/plot 1-1.png | Bin 35645 -> 35603 bytes .../AMR_files/figure-html/plot 3-1.png | Bin 19269 -> 19293 bytes .../AMR_files/figure-html/plot 4-1.png | Bin 69011 -> 69011 bytes .../AMR_files/figure-html/plot 5-1.png | Bin 50774 -> 50771 bytes docs/articles/EUCAST.html | 2 +- docs/articles/G_test.html | 2 +- docs/articles/SPSS.html | 2 +- docs/articles/WHONET.html | 2 +- docs/articles/ab_property.html | 2 +- docs/articles/benchmarks.html | 76 +-- .../figure-html/unnamed-chunk-5-1.png | Bin 28513 -> 28040 bytes docs/articles/freq.html | 2 +- docs/articles/mo_property.html | 2 +- docs/articles/resistance_predict.html | 2 +- docs/extra.css | 6 + docs/extra.js | 45 +- pkgdown/extra.css | 6 + pkgdown/extra.js | 45 +- 20 files changed, 338 insertions(+), 293 deletions(-) diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 936bf5f6..ef213d01 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -82,7 +82,6 @@ R-devel: - echo 'LANGUAGE="en_US.utf8"' > .Renviron - echo 'LANG="en_US.utf8"' >> .Renviron - echo 'LANGUAGE="en_US.utf8"' > ~/.Renviron - - rm -rf installed_deps - Rscriptdevel -e 'sessionInfo()' # install missing and outdated packages - Rscriptdevel -e 'source(".gitlab-ci.R"); gl_update_pkg_all(repos = "https://cran.rstudio.com", quiet = TRUE, install_pkgdown = TRUE)' diff --git a/docs/articles/AMR.html b/docs/articles/AMR.html index 9afd5d2f..f1fd8915 100644 --- a/docs/articles/AMR.html +++ b/docs/articles/AMR.html @@ -192,7 +192,7 @@

How to conduct AMR analysis

Matthijs S. Berends

-

16 May 2019

+

17 May 2019

@@ -201,7 +201,7 @@ -

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 16 May 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 17 May 2019.

Introduction

@@ -217,21 +217,21 @@ -2019-05-16 +2019-05-17 abcd Escherichia coli S S -2019-05-16 +2019-05-17 abcd Escherichia coli S R -2019-05-16 +2019-05-17 efgh Escherichia coli R @@ -327,69 +327,69 @@ -2013-05-13 -X4 -Hospital D -Staphylococcus aureus +2014-11-13 +S2 +Hospital B +Escherichia coli +S +S +S +S +F + + +2013-01-01 +V1 +Hospital C +Streptococcus pneumoniae +S +S +S +S +F + + +2016-11-12 +G5 +Hospital A +Klebsiella pneumoniae +S +S +S +S +M + + +2011-09-23 +U6 +Hospital A +Klebsiella pneumoniae +R I -S -S +R S F - -2016-09-19 -G9 -Hospital A -Escherichia coli -R -R -S -S -M - -2015-09-22 -L6 +2010-04-06 +C9 Hospital D -Escherichia coli -R +Streptococcus pneumoniae +S S S S M -2017-09-13 -N3 -Hospital D -Escherichia coli -S -S -S -S -M - - -2015-12-01 -Z3 -Hospital A -Escherichia coli -S -R -S -S -F - - -2011-02-25 -F10 +2012-04-11 +D2 Hospital B Staphylococcus aureus +R +S S S -R -R M @@ -411,8 +411,8 @@ #> #> Item Count Percent Cum. Count Cum. Percent #> --- ----- ------- -------- ----------- ------------- -#> 1 M 10,410 52.0% 10,410 52.0% -#> 2 F 9,590 48.0% 20,000 100.0% +#> 1 M 10,325 51.6% 10,325 51.6% +#> 2 F 9,675 48.4% 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 %>%
@@ -442,14 +442,14 @@
 #> Pasteurella multocida (no new changes)
 #> Staphylococcus (no new changes)
 #> Streptococcus groups A, B, C, G (no new changes)
-#> Streptococcus pneumoniae (1532 new changes)
+#> Streptococcus pneumoniae (1508 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 (1341 new changes)
+#> Table 01: Intrinsic resistance in Enterobacteriaceae (1335 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 (2834 new changes)
+#> Table 04: Intrinsic resistance in Gram-positive bacteria (2792 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)
@@ -457,24 +457,24 @@
 #> Table 13: Interpretive rules for quinolones (no new changes)
 #> 
 #> Other rules
-#> Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2225 new changes)
-#> Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (109 new changes)
+#> Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2206 new changes)
+#> Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (141 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 6,633 out of 20,000 rows, making a total of 8,041 edits
+#> EUCAST rules affected 6,587 out of 20,000 rows, making a total of 7,982 edits
 #> => added 0 test results
 #> 
-#> => changed 8,041 test results
-#>    - 118 test results changed from S to I
-#>    - 4,906 test results changed from S to R
-#>    - 1,094 test results changed from I to S
-#>    - 326 test results changed from I to R
-#>    - 1,577 test results changed from R to S
-#>    - 20 test results changed from R to I
+#> => changed 7,982 test results
+#>    - 102 test results changed from S to I
+#>    - 4,844 test results changed from S to R
+#>    - 1,047 test results changed from I to S
+#>    - 345 test results changed from I to R
+#>    - 1,615 test results changed from R to S
+#>    - 29 test results changed from R to I
 #> --------------------------------------------------------------------------
 #> 
 #> Use verbose = TRUE to get a data.frame with all specified edits instead.
@@ -502,8 +502,8 @@ #> 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 5,706 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 5,711 first isolates (28.6% of total) +

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)

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

@@ -529,19 +529,19 @@ 1 -2010-01-31 -O3 +2010-02-03 +Q6 B_ESCHR_COL S S -R +S S TRUE 2 -2010-04-24 -O3 +2010-03-18 +Q6 B_ESCHR_COL S S @@ -551,10 +551,10 @@ 3 -2010-04-26 -O3 +2010-04-04 +Q6 B_ESCHR_COL -R +S S S S @@ -562,8 +562,8 @@ 4 -2010-05-07 -O3 +2010-04-12 +Q6 B_ESCHR_COL S S @@ -573,10 +573,10 @@ 5 -2010-07-07 -O3 +2010-05-08 +Q6 B_ESCHR_COL -R +S S S S @@ -584,10 +584,10 @@ 6 -2010-07-28 -O3 +2010-08-03 +Q6 B_ESCHR_COL -S +R S S S @@ -595,30 +595,30 @@ 7 -2010-08-30 -O3 +2010-08-04 +Q6 B_ESCHR_COL -R -R +S +S S S FALSE 8 -2010-09-08 -O3 +2010-11-02 +Q6 B_ESCHR_COL R -S R S +S FALSE 9 -2010-10-11 -O3 +2010-11-09 +Q6 B_ESCHR_COL R S @@ -628,11 +628,11 @@ 10 -2010-11-11 -O3 +2010-12-18 +Q6 B_ESCHR_COL -S -S +R +R S S FALSE @@ -650,7 +650,7 @@ #> 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 15,027 first weighted isolates (75.1% of total) +#> => Found 15,123 first weighted isolates (75.6% of total) @@ -667,70 +667,70 @@ - - + + - + - - + + - + - - + + - + - + - - + + - + - - + + - + - + - - + + - + @@ -739,11 +739,11 @@ - - + + - - + + @@ -751,20 +751,20 @@ - - + + - + - - + + @@ -775,11 +775,11 @@ - - + + - - + + @@ -787,11 +787,11 @@
isolate
12010-01-31O32010-02-03Q6 B_ESCHR_COL S SRS S TRUE TRUE
22010-04-24O32010-03-18Q6 B_ESCHR_COL S S S S FALSETRUEFALSE
32010-04-26O32010-04-04Q6 B_ESCHR_COLRS S S S FALSETRUEFALSE
42010-05-07O32010-04-12Q6 B_ESCHR_COL S S S S FALSETRUEFALSE
52010-07-07O32010-05-08Q6 B_ESCHR_COLRS S S S FALSETRUEFALSE
62010-07-28O32010-08-03Q6 B_ESCHR_COLSR S S S
72010-08-30O32010-08-04Q6 B_ESCHR_COLRRSS S S FALSE
82010-09-08O32010-11-02Q6 B_ESCHR_COL RS R SS FALSE TRUE
92010-10-11O32010-11-09Q6 B_ESCHR_COL R S
102010-11-11O32010-12-18Q6 B_ESCHR_COLSSRR S S FALSE
-

Instead of 1, now 10 isolates are flagged. In total, 75.1% of all isolates are marked ‘first weighted’ - 46.6% 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 1, now 6 isolates are flagged. In total, 75.6% of all isolates are marked ‘first weighted’ - 47.1% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.

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 15,027 isolates for analysis.

+

So we end up with 15,123 isolates for analysis.

We can remove unneeded columns:

data_1st <- data_1st %>% 
   select(-c(first, keyab))
@@ -817,78 +817,78 @@ 1 -2013-05-13 -X4 -Hospital D -B_STPHY_AUR -I +2014-11-13 +S2 +Hospital B +B_ESCHR_COL +S S S S F -Gram positive -Staphylococcus -aureus +Gram negative +Escherichia +coli TRUE -2 -2016-09-19 -G9 +3 +2016-11-12 +G5 Hospital A -B_ESCHR_COL -R +B_KLBSL_PNE R S S +S M Gram negative -Escherichia -coli +Klebsiella +pneumoniae TRUE -3 -2015-09-22 -L6 -Hospital D -B_ESCHR_COL +4 +2011-09-23 +U6 +Hospital A +B_KLBSL_PNE +R +I R S -S -S -M +F Gram negative -Escherichia -coli +Klebsiella +pneumoniae TRUE 5 -2015-12-01 -Z3 -Hospital A -B_ESCHR_COL +2010-04-06 +C9 +Hospital D +B_STRPT_PNE S S S -S -F -Gram negative -Escherichia -coli +R +M +Gram positive +Streptococcus +pneumoniae TRUE 6 -2011-02-25 -F10 +2012-04-11 +D2 Hospital B B_STPHY_AUR +R +S S S -R -R M Gram positive Staphylococcus @@ -896,16 +896,16 @@ TRUE -7 -2010-08-13 -G10 +8 +2012-08-17 +Z4 Hospital C B_ESCHR_COL +R S +R S -S -S -M +F Gram negative Escherichia coli @@ -928,9 +928,9 @@
freq(paste(data_1st$genus, data_1st$species))

Or can be used like the dplyr way, which is easier readable:

data_1st %>% freq(genus, species)
-

Frequency table of genus and species from a data.frame (15,027 x 13)

+

Frequency table of genus and species from a data.frame (15,123 x 13)

Columns: 2
-Length: 15,027 (of which NA: 0 = 0.00%)
+Length: 15,123 (of which NA: 0 = 0.00%)
Unique: 4

Shortest: 16
Longest: 24

@@ -947,33 +947,33 @@ Longest: 24

1 Escherichia coli -7,318 -48.7% -7,318 -48.7% +7,466 +49.4% +7,466 +49.4% 2 Staphylococcus aureus -3,729 -24.8% -11,047 +3,649 +24.1% +11,115 73.5% 3 Streptococcus pneumoniae -2,370 -15.8% -13,417 -89.3% +2,381 +15.7% +13,496 +89.2% 4 Klebsiella pneumoniae -1,610 -10.7% -15,027 +1,627 +10.8% +15,123 100.0% @@ -984,7 +984,7 @@ 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. They can be used on their own:

data_1st %>% portion_IR(AMX)
-#> [1] 0.4973714
+#> [1] 0.5012233

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

data_1st %>% 
   group_by(hospital) %>% 
@@ -997,19 +997,19 @@ Longest: 24

Hospital A -0.4993375 +0.5100641 Hospital B -0.4931245 +0.4945632 Hospital C -0.5068182 +0.4960457 Hospital D -0.4949396 +0.5036765 @@ -1027,23 +1027,23 @@ Longest: 24

Hospital A -0.4993375 -4528 +0.5100641 +4521 Hospital B -0.4931245 -5236 +0.4945632 +5334 Hospital C -0.5068182 -2200 +0.4960457 +2276 Hospital D -0.4949396 -3063 +0.5036765 +2992 @@ -1063,27 +1063,27 @@ Longest: 24

Escherichia -0.8800219 -0.8938234 -0.9885215 +0.8775784 +0.8984731 +0.9910260 Klebsiella -0.7136646 -0.9012422 -0.9732919 +0.7221881 +0.9053473 +0.9778734 Staphylococcus -0.8806651 -0.9115044 -0.9895414 +0.8766785 +0.9188819 +0.9887640 Streptococcus -0.5751055 +0.5732885 0.0000000 -0.5751055 +0.5732885 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 21c067cde84e4068446aafec89f2f665837c5224..2ee99121e0b559bb74f4c02000c227fefd98a657 100644 GIT binary patch literal 35603 zcmeEvc{tYV+O}4!Xu#7Tg;*+?X&@45p@ht1#wKORoP^L?Jtb61hRj7Wg$6QHN{SGX zkTEiodG>tgt+n61kNxev-uHWl?~nJ`j$~D33`-esmF>}GEG@&CM;bkus&aB*4n*FASM6W(%KO{ z;i=?IR-24cjb(~Dj}4z_Xe1YnEB_|2Wo4<*Nx5HMDK#^9HH+CX{Pw=S&2@j|qx=cB zih7Qs==@JNSy)(>VK|rgRFe9Z;2-F8f-Wt^uWQmv=<$o<`V$j={W9`12YwysSRsjD zm)@?k$1l3uyshNd7WEh8lL}G)fe)%A_AL=RweNgCpMXG8pXPVj6Hh+xEa6xu$H&L_ zIp5vIgqgnd)@|~$M(%U2i=C;dDfy!h4^_h{a?+eF8P@tH{>w@=<_?Ah1qFqN8`ZsP 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How to apply EUCAST rules

Matthijs S. Berends

-

16 May 2019

+

17 May 2019

diff --git a/docs/articles/G_test.html b/docs/articles/G_test.html index fbb905f1..929930c9 100644 --- a/docs/articles/G_test.html +++ b/docs/articles/G_test.html @@ -192,7 +192,7 @@

How to use the G-test

Matthijs S. Berends

-

16 May 2019

+

17 May 2019

diff --git a/docs/articles/SPSS.html b/docs/articles/SPSS.html index f726b5d4..286b2ef5 100644 --- a/docs/articles/SPSS.html +++ b/docs/articles/SPSS.html @@ -192,7 +192,7 @@

How to import data from SPSS / SAS / Stata

Matthijs S. Berends

-

16 May 2019

+

17 May 2019

diff --git a/docs/articles/WHONET.html b/docs/articles/WHONET.html index 3286f33b..21f15619 100644 --- a/docs/articles/WHONET.html +++ b/docs/articles/WHONET.html @@ -192,7 +192,7 @@

How to work with WHONET data

Matthijs S. Berends

-

16 May 2019

+

17 May 2019

diff --git a/docs/articles/ab_property.html b/docs/articles/ab_property.html index 1364f68f..386a05a7 100644 --- a/docs/articles/ab_property.html +++ b/docs/articles/ab_property.html @@ -192,7 +192,7 @@

How to get properties of an antibiotic

Matthijs S. Berends

-

16 May 2019

+

17 May 2019

diff --git a/docs/articles/benchmarks.html b/docs/articles/benchmarks.html index 6031f919..0733bfee 100644 --- a/docs/articles/benchmarks.html +++ b/docs/articles/benchmarks.html @@ -192,7 +192,7 @@

Benchmarks

Matthijs S. Berends

-

16 May 2019

+

17 May 2019

@@ -217,14 +217,14 @@
times = 10) print(S.aureus, unit = "ms", signif = 2) #> Unit: milliseconds -#> expr min lq mean median uq max neval -#> as.mo("sau") 17.0 18 26 18.0 18.0 65 10 -#> as.mo("stau") 47.0 47 52 48.0 49.0 92 10 -#> as.mo("staaur") 17.0 18 22 18.0 18.0 62 10 -#> as.mo("STAAUR") 18.0 18 24 18.0 18.0 78 10 -#> as.mo("S. aureus") 28.0 28 37 28.0 30.0 73 10 -#> as.mo("S. aureus") 28.0 28 46 28.0 72.0 120 10 -#> as.mo("Staphylococcus aureus") 7.9 8 13 8.3 8.5 52 10
+#> expr min lq mean median uq max neval +#> as.mo("sau") 17.0 18 27 18 18 65.0 10 +#> as.mo("stau") 47.0 48 57 48 48 92.0 10 +#> as.mo("staaur") 17.0 18 24 18 18 81.0 10 +#> as.mo("STAAUR") 18.0 18 31 18 62 62.0 10 +#> as.mo("S. aureus") 28.0 28 28 28 29 29.0 10 +#> as.mo("S. aureus") 28.0 28 42 28 31 110.0 10 +#> as.mo("Staphylococcus aureus") 7.9 8 8 8 8 8.6 10

In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second. The second input is the only one that has to be looked up thoroughly. All the others are known codes (the first one is a WHONET code) or common laboratory codes, or common full organism names like the last one. Full organism names are always preferred.

To achieve this speed, the as.mo function also takes into account the prevalence of human pathogenic microorganisms. The downside is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of Thermus islandicus (B_THERMS_ISL), a bug probably never found before in humans:

-

That takes 8 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like Thermus islandicus) are almost fast - these are the most probable input from most data sets.

+#> as.mo("theisl") 470 470 500 500 520 520 10 +#> as.mo("THEISL") 470 470 500 470 520 580 10 +#> as.mo("T. islandicus") 76 76 86 76 77 130 10 +#> as.mo("T. islandicus") 75 76 82 76 78 120 10 +#> as.mo("Thermus islandicus") 74 74 110 120 120 150 10 +

That takes 8.2 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like Thermus islandicus) are almost fast - these are the most probable input from most data sets.

In the figure below, we compare Escherichia coli (which is very common) with Prevotella brevis (which is moderately common) and with Thermus islandicus (which is very uncommon):

par(mar = c(5, 16, 4, 2)) # set more space for left margin text (16)
 
@@ -286,9 +286,9 @@
                          times = 10)
 print(run_it, unit = "ms", signif = 3)
 #> Unit: milliseconds
-#>            expr min  lq mean median  uq max neval
-#>  mo_fullname(x) 732 811  816    817 819 941    10
-

So transforming 500,000 values (!!) of 50 unique values only takes 0.82 seconds (817 ms). You only lose time on your unique input values.

+#> expr min lq mean median uq max neval +#> mo_fullname(x) 823 826 864 833 869 1080 10 +

So transforming 500,000 values (!!) of 50 unique values only takes 0.83 seconds (832 ms). You only lose time on your unique input values.

-

So going from mo_fullname("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0017 seconds - it doesn’t even start calculating if the result would be the same as the expected resulting value. That goes for all helper functions:

+#> expr min lq mean median uq max neval +#> A 12.90 13.30 13.7 13.70 14.10 14.5 10 +#> B 25.10 25.70 26.2 25.90 26.60 27.8 10 +#> C 1.46 1.68 6.2 1.84 1.89 46.1 10 +

So going from mo_fullname("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0018 seconds - it doesn’t even start calculating if the result would be the same as the expected resulting value. That goes for all helper functions:

+#> A 0.354 0.404 0.470 0.486 0.527 0.534 10 +#> B 0.413 0.529 0.581 0.538 0.642 0.917 10 +#> C 1.510 1.720 1.780 1.810 1.870 1.900 10 +#> D 0.443 0.522 0.547 0.538 0.595 0.664 10 +#> E 0.371 0.431 0.474 0.467 0.515 0.588 10 +#> F 0.372 0.427 0.453 0.453 0.494 0.519 10 +#> G 0.400 0.430 0.456 0.446 0.488 0.522 10 +#> H 0.194 0.276 0.301 0.301 0.332 0.395 10

Of course, when running mo_phylum("Firmicutes") the function has zero knowledge about the actual microorganism, namely S. aureus. But since the result would be "Firmicutes" too, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.

+#> en 18.18 18.30 18.50 18.46 18.63 18.88 10 +#> de 23.02 23.10 23.38 23.27 23.57 24.23 10 +#> nl 36.73 36.79 37.05 36.95 37.26 37.74 10 +#> es 22.97 23.14 23.35 23.22 23.68 23.79 10 +#> it 22.99 23.05 23.17 23.13 23.22 23.55 10 +#> fr 23.12 23.14 32.29 23.25 23.90 68.31 10 +#> pt 23.10 23.12 32.39 23.56 23.79 68.65 10

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How to create frequency tables

Matthijs S. Berends

-

16 May 2019

+

17 May 2019

diff --git a/docs/articles/mo_property.html b/docs/articles/mo_property.html index 4aee7ecb..96483d4a 100644 --- a/docs/articles/mo_property.html +++ b/docs/articles/mo_property.html @@ -192,7 +192,7 @@

How to get properties of a microorganism

Matthijs S. Berends

-

16 May 2019

+

17 May 2019

diff --git a/docs/articles/resistance_predict.html b/docs/articles/resistance_predict.html index 7d748d0a..a1e69a2e 100644 --- a/docs/articles/resistance_predict.html +++ b/docs/articles/resistance_predict.html @@ -192,7 +192,7 @@

How to predict antimicrobial resistance

Matthijs S. Berends

-

16 May 2019

+

17 May 2019

diff --git a/docs/extra.css b/docs/extra.css index 2a1d3b1d..f6f60742 100644 --- a/docs/extra.css +++ b/docs/extra.css @@ -203,3 +203,9 @@ table a:not(.btn):hover, .table a:not(.btn):hover { color: black; font-weight: bold; } + +#collapseDisqus { + padding: 2%; + border: 2px #2c3e50 dashed; + margin-top: 1%; +} diff --git a/docs/extra.js b/docs/extra.js index 4737e496..4d579b9d 100644 --- a/docs/extra.js +++ b/docs/extra.js @@ -23,6 +23,8 @@ // Add updated Font Awesome 5.6.3 library $('head').append(''); +// Add Disqus +$('head').append('