From 69dc8795ae63d831bb8c560087e1bdd9a162e6c2 Mon Sep 17 00:00:00 2001 From: github-actions <41898282+github-actions[bot]@users.noreply.github.com> Date: Wed, 22 Feb 2023 13:47:35 +0000 Subject: [PATCH] Built site for AMR: 1.8.2.9138@dad2530 --- 404.html | 2 +- LICENSE-text.html | 2 +- articles/AMR.html | 569 +++++++++--------- .../AMR_files/figure-html/disk_plots-1.png | Bin 38759 -> 39287 bytes .../figure-html/disk_plots_mo_ab-1.png | Bin 56319 -> 55289 bytes .../AMR_files/figure-html/mic_plots-1.png | Bin 28086 -> 27745 bytes .../AMR_files/figure-html/mic_plots-2.png | Bin 39111 -> 36580 bytes .../figure-html/mic_plots_mo_ab-1.png | Bin 38816 -> 38574 bytes .../figure-html/mic_plots_mo_ab-2.png | Bin 50844 -> 49585 bytes articles/AMR_files/figure-html/plot 1-1.png | Bin 42052 -> 41510 bytes articles/AMR_files/figure-html/plot 3-1.png | Bin 28418 -> 28459 bytes articles/AMR_files/figure-html/plot 4-1.png | Bin 78229 -> 78253 bytes articles/AMR_files/figure-html/plot 5-1.png | Bin 49897 -> 49910 bytes articles/EUCAST.html | 2 +- articles/MDR.html | 56 +- articles/PCA.html | 6 +- articles/SPSS.html | 6 +- articles/WHONET.html | 15 +- articles/datasets.html | 20 +- articles/index.html | 2 +- articles/resistance_predict.html | 2 +- articles/welcome_to_AMR.html | 2 +- authors.html | 2 +- index.html | 4 +- news/index.html | 28 +- pkgdown.yml | 2 +- reference/AMR-deprecated.html | 4 +- reference/AMR-options.html | 15 +- reference/AMR.html | 2 +- reference/Rplot005.png | Bin 13278 -> 13184 bytes reference/Rplot006.png | Bin 12816 -> 12708 bytes reference/Rplot007.png | Bin 13683 -> 13657 bytes reference/Rplot008.png | Bin 16702 -> 17950 bytes reference/Rplot009.png | Bin 8406 -> 8082 bytes reference/WHOCC.html | 2 +- reference/WHONET.html | 91 +-- reference/ab_from_text.html | 9 +- reference/ab_property.html | 12 +- reference/add_custom_antimicrobials.html | 22 +- reference/add_custom_microorganisms.html | 6 +- reference/age.html | 24 +- reference/age_groups.html | 4 +- reference/antibiogram-1.png | Bin 26492 -> 15255 bytes reference/antibiogram-2.png | Bin 39648 -> 15454 bytes reference/antibiogram-3.png | Bin 24603 -> 0 bytes reference/antibiogram-4.png | Bin 32347 -> 0 bytes reference/antibiogram.html | 69 +-- reference/antibiotic_class_selectors.html | 86 +-- reference/antibiotics.html | 2 +- reference/as.ab.html | 9 +- reference/as.av.html | 4 +- reference/as.disk.html | 2 +- reference/as.mic.html | 4 +- reference/as.mo.html | 33 +- reference/as.sir.html | 34 +- reference/atc_online.html | 2 +- reference/av_from_text.html | 6 +- reference/av_property.html | 4 +- reference/availability.html | 4 +- reference/bug_drug_combinations.html | 10 +- reference/clinical_breakpoints.html | 2 +- reference/count.html | 8 +- reference/custom_eucast_rules.html | 56 +- reference/dosage.html | 6 +- reference/eucast_rules.html | 22 +- reference/example_isolates.html | 4 +- reference/example_isolates_unclean.html | 2 +- reference/first_isolate.html | 12 +- reference/g.test.html | 2 +- reference/get_episode.html | 110 ++-- reference/ggplot_pca.html | 6 +- reference/ggplot_sir.html | 8 +- reference/guess_ab_col.html | 4 +- reference/index.html | 4 +- reference/intrinsic_resistant.html | 2 +- reference/italicise_taxonomy.html | 2 +- reference/join.html | 2 +- reference/key_antimicrobials.html | 6 +- reference/kurtosis.html | 6 +- reference/like.html | 2 +- reference/mdro.html | 8 +- reference/mean_amr_distance.html | 84 +-- reference/microorganisms.codes.html | 2 +- reference/microorganisms.html | 4 +- reference/mo_matching_score.html | 6 +- reference/mo_property.html | 37 +- reference/mo_source.html | 6 +- reference/pca.html | 6 +- reference/plot-1.png | Bin 27279 -> 27287 bytes reference/plot-2.png | Bin 27156 -> 26491 bytes reference/plot-3.png | Bin 28569 -> 27846 bytes reference/plot-4.png | Bin 38990 -> 38994 bytes reference/plot-5.png | Bin 38827 -> 38171 bytes reference/plot-6.png | Bin 37590 -> 36872 bytes reference/plot-7.png | Bin 38432 -> 38375 bytes reference/plot-8.png | Bin 52990 -> 54792 bytes reference/plot-9.png | Bin 26145 -> 24794 bytes reference/plot.html | 8 +- reference/proportion.html | 10 +- reference/random.html | 37 +- reference/resistance_predict.html | 6 +- reference/skewness.html | 4 +- reference/translate.html | 17 +- search.json | 2 +- 104 files changed, 827 insertions(+), 856 deletions(-) delete mode 100644 reference/antibiogram-3.png delete mode 100644 reference/antibiogram-4.png diff --git a/404.html b/404.html index be9635a7..19574612 100644 --- a/404.html +++ b/404.html @@ -36,7 +36,7 @@ AMR (for R) - 1.8.2.9137 + 1.8.2.9138
+generated on 22 February 2023.So only 61.6% is suitable for resistance analysis! We can now filter +
So only 62.2% is suitable for resistance analysis! We can now filter
on it with the filter()
function, also from the
dplyr
package:
So we end up with 12 322 isolates for analysis. Now our data looks +
So we end up with 12 431 isolates for analysis. Now our data looks like:
head(data_1st)
Time for the analysis!
@@ -808,8 +808,8 @@ readable:data_1st %>% freq(genus, species)
Frequency table
Class: character
-Length: 12,322
-Available: 12,322 (100%, NA: 0 = 0%)
+Length: 12,431
+Available: 12,431 (100%, NA: 0 = 0%)
Unique: 4
Shortest: 16
Longest: 24
If you want to get a quick glance of the number of isolates in @@ -1020,50 +1020,50 @@ different bug/drug combinations, you can use the
proportion_SI()
, equa
own:
data_1st %>% resistance(AMX)
-#> [1] 0.5842396
Or can be used in conjunction with group_by()
and
summarise()
, both from the dplyr
package:
@@ -1159,19 +1159,19 @@ own:Hospital A -0.5827676 +0.5871733 Hospital B -0.5907183 +0.5822430 Hospital C -0.5845813 +0.5788635 @@ -1196,23 +1196,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital D -0.5747605 +0.5717638 Hospital A -0.5827676 -3830 +0.5871733 +3711 Hospital B -0.5907183 -4288 +0.5822430 +4280 Hospital C -0.5845813 -1803 +0.5788635 +1883 @@ -1237,27 +1237,27 @@ therapies very easily: Hospital D -0.5747605 -2401 +0.5717638 +2557 Escherichia -0.6719151 -0.6563409 -0.8921787 +0.6726968 +0.6654941 +0.8934673 Klebsiella -0.6715272 -0.6745971 -0.8879509 +0.6653934 +0.6608098 +0.8800611 Staphylococcus -0.6776137 -0.6595360 -0.8945466 +0.6773613 +0.6587706 +0.8917541 @@ -1285,23 +1285,23 @@ classes, use a antibiotic class selector such as Streptococcus -0.4717286 +0.4777105 0.0000000 -0.4717286 +0.4777105 Hospital A -58.3% -35.1% +58.7% +36.2% Hospital B -59.1% -35.8% +58.2% +35.9% Hospital C -58.5% -37.4% +57.9% +34.9% @@ -1417,16 +1417,16 @@ classes) Hospital D -57.5% -34.9% +57.2% +34.5% <mic>
and<disk>
:mic_values <- random_mic(size = 100) mic_values #> Class 'mic' -#> [1] 0.25 0.5 0.125 0.125 16 32 0.002 >=256 0.025 1 -#> [11] 64 128 32 0.25 0.5 0.0625 128 >=256 8 0.002 -#> [21] 0.005 0.002 4 0.125 0.005 0.125 0.01 4 64 0.005 -#> [31] 32 0.002 0.25 0.002 0.0625 32 16 128 0.001 128 -#> [41] 0.001 0.5 0.025 0.5 128 4 0.005 0.25 0.125 0.002 -#> [51] 16 0.025 0.005 0.0625 4 32 0.005 4 0.025 0.001 -#> [61] 128 0.125 0.025 2 4 0.0625 0.001 8 0.25 0.025 -#> [71] 1 32 >=256 1 16 8 0.0625 0.01 2 0.005 -#> [81] 16 0.025 1 0.002 128 0.002 0.5 8 8 64 -#> [91] 128 0.01 0.005 16 0.002 0.005 >=256 8 0.002 0.25
# base R:
plot(mic_values)
<mic>
and <disk>
:
But we could also be more specific, by generating MICs that are likely to be found in E. coli for ciprofloxacin:
-mic_values <- random_mic(size = 100, mo = "E. coli", ab = "cipro")
mic_values <- random_mic(size = 100, mo = "E. coli", ab = "cipro")
+#> [1] "here"
For the plot()
and autoplot()
function, we
can define the microorganism and an antimicrobial agent the same way.
This will add the interpretation of those values according to a chosen
@@ -1460,10 +1461,10 @@ plotting:
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro")
disk_values
#> Class 'disk'
-#> [1] 28 18 29 27 31 26 27 30 31 18 30 25 17 20 20 31 22 22 30 26 24 26 23 28 28
-#> [26] 24 27 29 20 30 25 25 25 22 23 27 27 23 29 27 23 20 19 23 23 18 29 21 31 29
-#> [51] 19 31 27 27 31 20 22 28 31 19 21 30 28 21 24 21 20 20 19 27 26 30 19 24 29
-#> [76] 17 26 18 27 23 19 29 24 27 27 20 25 28 17 21 29 29 17 22 20 26 24 23 17 25
+#> [1] 27 29 20 31 27 23 30 27 19 24 31 20 19 28 23 25 17 22 20 24 24 28 17 29 26
+#> [26] 29 31 23 23 30 18 29 30 23 21 19 25 24 22 18 28 25 24 18 25 25 27 24 24 27
+#> [51] 27 18 18 25 22 27 30 24 31 26 19 30 21 22 23 19 23 23 22 26 22 29 29 29 29
+#> [76] 26 26 24 30 17 30 17 25 27 17 27 20 23 17 21 17 27 18 23 30 19 22 20 30 17
# base R:
plot(disk_values, mo = "E. coli", ab = "cipro")
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