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(v1.1.0.9014) lose dependencies

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dr. M.S. (Matthijs) Berends
2020-05-19 14:16:45 +02:00
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commit 5216d2b520
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vignettes/AMR.Rmd
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@ -58,17 +58,18 @@ knitr::kable(data.frame(date = Sys.Date(),
```
## Needed R packages
As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the [tidyverse packages](https://www.tidyverse.org) [`dplyr`](https://dplyr.tidyverse.org/) and [`ggplot2`](https://ggplot2.tidyverse.org) by Dr Hadley Wickham. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.
As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the [tidyverse packages](https://www.tidyverse.org) [`dplyr`](https://dplyr.tidyverse.org/) and [`ggplot2`](https://ggplot2.tidyverse.org) by RStudio. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.
Our `AMR` package depends on these packages and even extends their use and functions.
We will also use the `cleaner` package, that can be used for cleaning data and creating frequency tables.
```{r lib packages, message = FALSE, warning = FALSE, results = 'asis'}
library(dplyr)
library(ggplot2)
library(AMR)
library(cleaner)
# (if not yet installed, install with:)
# install.packages(c("dplyr", "ggplot2", "AMR"))
# install.packages(c("dplyr", "ggplot2", "AMR", "cleaner"))
```
# Creation of data
@ -160,12 +161,12 @@ 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 `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.
We also created a package dedicated to data cleaning and checking, called the `cleaner` package. It `freq()` function can be used to create frequency tables.
For example, for the `gender` variable:
```{r freq gender 1, results="asis"}
data %>% freq(gender) # this would be the same: freq(data$gender)
data %>% freq(gender)
```
So, we can draw at least two conclusions immediately. From a data scientists perspective, the data looks clean: only values `M` and `F`. From a researchers perspective: there are slightly more men. Nothing we didn't already know.
@ -218,7 +219,7 @@ data <- data %>%
mutate(first = first_isolate(.))
```
So only `r cleaner::percentage(sum(data$first) / nrow(data))` is suitable for resistance analysis! We can now filter on it with the `filter()` function, also from the `dplyr` package:
So only `r percentage(sum(data$first) / nrow(data))` is suitable for resistance analysis! We can now filter on it with the `filter()` function, also from the `dplyr` package:
```{r 1st isolate filter}
data_1st <- data %>%
@ -238,7 +239,7 @@ data_1st <- data %>%
weighted_df <- data %>%
filter(bacteria == as.mo("E. coli")) %>%
# only most prevalent patient
filter(patient_id == cleaner::top_freq(freq(., patient_id), 1)[1]) %>%
filter(patient_id == top_freq(freq(., patient_id), 1)[1]) %>%
arrange(date) %>%
select(date, patient_id, bacteria, AMX:GEN, first) %>%
# maximum of 10 rows
@ -268,7 +269,7 @@ data <- data %>%
weighted_df2 <- data %>%
filter(bacteria == as.mo("E. coli")) %>%
# only most prevalent patient
filter(patient_id == cleaner::top_freq(freq(., patient_id), 1)[1]) %>%
filter(patient_id == top_freq(freq(., patient_id), 1)[1]) %>%
arrange(date) %>%
select(date, patient_id, bacteria, AMX:GEN, first, first_weighted) %>%
# maximum of 10 rows
@ -280,7 +281,7 @@ weighted_df2 %>%
knitr::kable(align = "c")
```
Instead of `r sum(weighted_df$first)`, now `r sum(weighted_df2$first_weighted)` isolates are flagged. In total, `r cleaner::percentage(sum(data$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.
Instead of `r sum(weighted_df$first)`, now `r sum(weighted_df2$first_weighted)` isolates are flagged. In total, `r percentage(sum(data$first_weighted) / nrow(data))` of all isolates are marked 'first weighted' - `r percentage((sum(data$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}