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(v0.9.0) v0.9.0

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dr. M.S. (Matthijs) Berends
2019-11-29 19:43:23 +01:00
parent 9feef53bde
commit bb2ed55842
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26
vignettes/WHONET.Rmd
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@ -67,9 +67,10 @@ We also created a package dedicated to data cleaning and checking, called the `c
So let's check our data, with a couple of frequency tables:
```{r, results = 'asis'}
# our newly created `mo` variable
data %>% freq(mo, nmax = 10)
# our newly created `mo` variable, put in the mo_name() function
data %>% freq(mo_name(mo), nmax = 10)
```
```{r, results = 'asis'}
# our transformed antibiotic columns
# amoxicillin/clavulanic acid (J01CR02) as an example
data %>% freq(AMC_ND2)
@ -77,8 +78,21 @@ data %>% freq(AMC_ND2)
### A first glimpse at results
An easy ggplot will already give a lot of information, using the included `ggplot_rsi()` function:
An easy `ggplot` will already give a lot of information, using the included `ggplot_rsi()` function:
```{r}
ggplot_rsi(data, translate_ab = 'ab')
```{r, eval = FALSE}
data %>%
group_by(Country) %>%
select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%
ggplot_rsi(translate_ab = 'ab', facet = "Country", datalabels = FALSE)
```
```{r, echo = FALSE}
# on very old and some new releases of R, this may lead to an error
tryCatch(data %>%
group_by(Country) %>%
select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%
ggplot_rsi(translate_ab = 'ab', facet = "Country", datalabels = FALSE) %>%
print(),
error = function(e) base::invisible())
```