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(v1.1.0.9019) mo_source fix

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dr. M.S. (Matthijs) Berends
2020-05-25 01:01:14 +02:00
parent f5ff2e6634
commit ae1969b941
73 changed files with 619 additions and 571 deletions
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5
vignettes/AMR.Rmd
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@ -477,8 +477,7 @@ The next example uses the `example_isolates` data set. This is a data set includ
We will compare the resistance to fosfomycin (column `FOS`) in hospital A and D. The input for the `fisher.test()` can be retrieved with a transformation like this:
```{r, results = 'markup'}
# use package 'tidyr' to pivot data;
# it gets installed with this 'AMR' package
# use package 'tidyr' to pivot data:
library(tidyr)
check_FOS <- example_isolates %>%
@ -501,4 +500,4 @@ We can apply the test now with:
fisher.test(check_FOS)
```
As can be seen, the p value is `r round(fisher.test(check_FOS)$p.value, 3)`, which means that the fosfomycin resistance found in hospital A and D are really different.
As can be seen, the p value is `r round(fisher.test(check_FOS)$p.value, 3)`, which means that the fosfomycin resistance found in isolates from patients in hospital A and D are really different.

1
vignettes/MDR.Rmd
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@ -65,7 +65,6 @@ example_isolates %>%
freq() # show frequency table of the result
```
```{r, echo = FALSE, results = 'asis', message = FALSE, warning = FALSE}
library(dplyr)
example_isolates %>%
mdro(info = FALSE) %>%
freq() # show frequency table of the result

1
vignettes/PCA.Rmd
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@ -86,7 +86,6 @@ ggplot_pca(pca_result)
You can also print an ellipse per group, and edit the appearance:
```{r}
ggplot_pca(pca_result, ellipse = TRUE) +
ggplot2::labs(title = "An AMR/PCA biplot!")
```

3
vignettes/benchmarks.Rmd
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@ -122,9 +122,6 @@ Uncommon microorganisms take some more time than common microorganisms. To furth
Repetitive results are unique values that are present more than once. Unique values will only be calculated once by `as.mo()`. We will use `mo_name()` for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses `as.mo()` internally.
```{r, message = FALSE, eval = FALSE}
library(dplyr)
```
```{r, message = FALSE}
# take all MO codes from the example_isolates data set
x <- example_isolates$mo %>%

4
vignettes/resistance_predict.Rmd
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@ -89,7 +89,7 @@ ggplot_rsi_predict(predict_TZP, ribbon = FALSE)
### Choosing the right model
Resistance is not easily predicted; if we look at vancomycin resistance in Gram positives, the spread (i.e. standard error) is enormous:
Resistance is not easily predicted; if we look at vancomycin resistance in Gram-positive bacteria, the spread (i.e. standard error) is enormous:
```{r}
example_isolates %>%
@ -110,7 +110,7 @@ Valid values are:
| `"loglin"` or `"poisson"` | `glm(..., family = poisson)` | Generalised linear model with poisson distribution |
| `"lin"` or `"linear"` | `lm()` | Linear model |
For the vancomycin resistance in Gram positive bacteria, a linear model might be more appropriate since no (left half of a) binomial distribution is to be expected based on the observed years:
For the vancomycin resistance in Gram-positive bacteria, a linear model might be more appropriate since no binomial distribution is to be expected based on the observed years:
```{r}
example_isolates %>%