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(v0.7.1.9076) mo codes

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dr. M.S. (Matthijs) Berends
2019-09-20 12:33:05 +02:00
parent e2aa4f996b
commit 3596adb295
41 changed files with 465 additions and 520 deletions
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3
vignettes/AMR.Rmd
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@ -121,8 +121,7 @@ data <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
CIP = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.80, 0.00, 0.20)),
GEN = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.92, 0.00, 0.08))
)
prob = c(0.92, 0.00, 0.08)))
```
Using the `left_join()` function from the `dplyr` package, we can 'map' the gender to the patient ID using the `patients_table` object we created earlier:

2
vignettes/benchmarks.Rmd
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@ -95,7 +95,7 @@ In the table above, all measurements are shown in milliseconds (thousands of sec
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_ISLN`), a bug probably never found before in humans:
```{r}
```{r, warning=FALSE}
T.islandicus <- microbenchmark(as.mo("theisl"),
as.mo("THEISL"),
as.mo("T. islandicus"),