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(v0.9.0.9026) update documentation

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dr. M.S. (Matthijs) Berends
2020-02-17 14:38:01 +01:00
parent 11f00f8a0a
commit 5e2b294351
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8
vignettes/AMR.Rmd
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@ -145,7 +145,7 @@ Using the `left_join()` function from the `dplyr` package, we can 'map' the gend
data <- data %>% left_join(patients_table)
```
The resulting data set contains `r format(nrow(data), big.mark = ",")` blood culture isolates. With the `head()` function we can preview the first 6 values of this data set:
The resulting data set contains `r format(nrow(data), big.mark = ",")` blood culture isolates. With the `head()` function we can preview the first 6 rows of this data set:
```{r preview data set 1, eval = FALSE}
head(data)
@ -163,14 +163,10 @@ We also created a package dedicated to data cleaning and checking, called the `c
For example, for the `gender` variable:
```{r freq gender 1, eval = FALSE}
```{r freq gender 1, results="asis"}
data %>% freq(gender) # this would be the same: freq(data$gender)
```
```{r freq gender 2, echo = FALSE, results = 'markup'}
data %>% freq(gender, markdown = FALSE, header = TRUE)
```
So, we can draw at least two conclusions immediately. From a data scientists perspective, the data looks clean: only values `M` and `F`. From a researchers 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: