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articles/WHONET.md
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@@ -11,6 +11,7 @@ date fields are imported correctly.
An example syntax could look like this:
``` r
library(readxl)
data <- read_excel(path = "path/to/your/file.xlsx")
```
@@ -27,6 +28,7 @@ I suggest you read about it on their website:
<https://www.tidyverse.org/>.
``` r
library(dplyr) # part of tidyverse
library(ggplot2) # part of tidyverse
library(AMR) # this package
@@ -50,6 +52,7 @@ analysis:
for.
``` r
# transform variables
data <- WHONET %>%
# get microbial ID based on given organism
@@ -68,6 +71,7 @@ function can be used to create frequency tables.
So let’s check our data, with a couple of frequency tables:
``` r
# our newly created `mo` variable, put in the mo_name() function
data %>% freq(mo_name(mo), nmax = 10)
```
@@ -82,22 +86,23 @@ Unique: 38
Shortest: 11
Longest: 40
| | Item | Count | Percent | Cum. Count | Cum. Percent |
|:----|:-----------------------------------------|------:|--------:|-----------:|-------------:|
| 1 | Escherichia coli | 245 | 49.0% | 245 | 49.0% |
| 2 | Coagulase-negative Staphylococcus (CoNS) | 74 | 14.8% | 319 | 63.8% |
| 3 | Staphylococcus epidermidis | 38 | 7.6% | 357 | 71.4% |
| 4 | Streptococcus pneumoniae | 31 | 6.2% | 388 | 77.6% |
| 5 | Staphylococcus hominis | 21 | 4.2% | 409 | 81.8% |
| 6 | Proteus mirabilis | 9 | 1.8% | 418 | 83.6% |
| 7 | Enterococcus faecium | 8 | 1.6% | 426 | 85.2% |
| 8 | Staphylococcus capitis urealyticus | 8 | 1.6% | 434 | 86.8% |
| 9 | Enterobacter cloacae | 5 | 1.0% | 439 | 87.8% |
| 10 | Enterococcus columbae | 4 | 0.8% | 443 | 88.6% |
| | Item | Count | Percent | Cum. Count | Cum. Percent |
|:---|:---|---:|---:|---:|---:|
| 1 | Escherichia coli | 245 | 49.0% | 245 | 49.0% |
| 2 | Coagulase-negative Staphylococcus (CoNS) | 74 | 14.8% | 319 | 63.8% |
| 3 | Staphylococcus epidermidis | 38 | 7.6% | 357 | 71.4% |
| 4 | Streptococcus pneumoniae | 31 | 6.2% | 388 | 77.6% |
| 5 | Staphylococcus hominis | 21 | 4.2% | 409 | 81.8% |
| 6 | Proteus mirabilis | 9 | 1.8% | 418 | 83.6% |
| 7 | Enterococcus faecium | 8 | 1.6% | 426 | 85.2% |
| 8 | Staphylococcus capitis urealyticus | 8 | 1.6% | 434 | 86.8% |
| 9 | Enterobacter cloacae | 5 | 1.0% | 439 | 87.8% |
| 10 | Enterococcus columbae | 4 | 0.8% | 443 | 88.6% |
(omitted 28 entries, n = 57 \[11.4%\])
``` r
# our transformed antibiotic columns
# amoxicillin/clavulanic acid (J01CR02) as an example
data %>% freq(AMC_ND2)
@@ -132,6 +137,7 @@ included [`ggplot_sir()`](https://amr-for-r.org/reference/ggplot_sir.md)
function:
``` r
data %>%
group_by(Country) %>%
select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%