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(v0.8.0.9021) update vignettes

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dr. M.S. (Matthijs) Berends
2019-11-09 11:33:22 +01:00
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vignettes/AMR.Rmd
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@ -144,13 +144,9 @@ Now, let's start the cleaning and the analysis!
# Cleaning the data
We also created a package dedicated to data cleaning and checking, called the `clean` package. It gets automatically installed with the `AMR` package, so we only have to load it:
We also created a package dedicated to data cleaning and checking, called the `cleaner` package. It gets automatically installed with the `AMR` package. For its `freq()` function to create frequency tables, you don't even need to load it yourself as it is available through the `AMR` package as well.
```{r lib clean, message = FALSE}
library(clean)
```
Use the frequency table function `freq()` from this `clean` package to look specifically for unique values in any variable. For example, for the `gender` variable:
For example, for the `gender` variable:
```{r freq gender 1, eval = FALSE}
data %>% freq(gender) # this would be the same: freq(data$gender)
@ -210,7 +206,7 @@ data <- data %>%
mutate(first = first_isolate(.))
```
So only `r AMR:::percentage(sum(data$first) / nrow(data))` is suitable for resistance analysis! We can now filter on it with the `filter()` function, also from the `dplyr` package:
So only `r cleaner::percentage(sum(data$first) / nrow(data))` is suitable for resistance analysis! We can now filter on it with the `filter()` function, also from the `dplyr` package:
```{r 1st isolate filter}
data_1st <- data %>%
@ -230,7 +226,7 @@ data_1st <- data %>%
weighted_df <- data %>%
filter(bacteria == as.mo("E. coli")) %>%
# only most prevalent patient
filter(patient_id == top_freq(freq(., patient_id), 1)[1]) %>%
filter(patient_id == cleaner::top_freq(freq(., patient_id), 1)[1]) %>%
arrange(date) %>%
select(date, patient_id, bacteria, AMX:GEN, first) %>%
# maximum of 10 rows
@ -260,7 +256,7 @@ data <- data %>%
weighted_df2 <- data %>%
filter(bacteria == as.mo("E. coli")) %>%
# only most prevalent patient
filter(patient_id == top_freq(freq(., patient_id), 1)[1]) %>%
filter(patient_id == cleaner::top_freq(freq(., patient_id), 1)[1]) %>%
arrange(date) %>%
select(date, patient_id, bacteria, AMX:GEN, first, first_weighted) %>%
# maximum of 10 rows
@ -272,7 +268,7 @@ weighted_df2 %>%
knitr::kable(align = "c")
```
Instead of `r sum(weighted_df$first)`, now `r sum(weighted_df2$first_weighted)` isolates are flagged. In total, `r AMR:::percentage(sum(data$first_weighted) / nrow(data))` of all isolates are marked 'first weighted' - `r AMR:::percentage((sum(data$first_weighted) / nrow(data)) - (sum(data$first) / nrow(data)))` more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
Instead of `r sum(weighted_df$first)`, now `r sum(weighted_df2$first_weighted)` isolates are flagged. In total, `r cleaner::percentage(sum(data$first_weighted) / nrow(data))` of all isolates are marked 'first weighted' - `r cleaner::percentage((sum(data$first_weighted) / nrow(data)) - (sum(data$first) / nrow(data)))` more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
As with `filter_first_isolate()`, there's a shortcut for this new algorithm too:
```{r 1st isolate filter 3, results = 'hide', message = FALSE, warning = FALSE}