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Merged
avallecam merged 6 commits into from
Jun 10, 2026
Merged

fix workflow runs #231

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5383769
update renv lockfile and pin activate.R to renv 1.1.7 for R 4.5.x com…
avallecam Jun 6, 2026
815bc21
fix: update mpox dataset download URL in pipeline learner
avallecam Jun 6, 2026
f414325
update lesson-requirements lockfile: cli 3.6.6 for R 4.6.0 compat, re…
avallecam Jun 6, 2026
f42ee37
add install file to add missing dependencies by lintr
avallecam Jun 9, 2026
fa10c27
fix lintr warning
avallecam Jun 10, 2026
90a22b5
remove cache test
avallecam Jun 10, 2026
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11 changes: 11 additions & 0 deletions episodes/install.R
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Original file line number Diff line number Diff line change
@@ -0,0 +1,11 @@
pak::pak("knitr")
pak::pak("xml2")
pak::pak("lintr")
library(knitr)
opts_chunk$set(comment = "")
library(xml2)
x <- read_xml("<foo> <bar> text <baz/> </bar> </foo>")
x
xml_name(x)
library(lintr)
available_tags(packages = "lintr")
8 changes: 4 additions & 4 deletions learners/mpox_data_cleaning_pipeline.Rmd
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Expand Up @@ -12,7 +12,7 @@
In this document, we use the [{cleanepi}](https://epiverse-trace.github.io/cleanepi/)
package to clean and standardize a messy mpox (formerly known as Monkeypox)
dataset obtained from the [global.health](https://global.health/) platform.
The dataset is in a `csv` format available on this [link](https://mpox-2024.s3.eu-central-1.amazonaws.com/latest.csv).
The dataset is in a `csv` format available on this [link](https://ivcjkmyexc.execute-api.eu-central-1.amazonaws.com/web/url?folder=&file_name=latest.csv).

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 [uninformative link text]: [link](https://ivcjkmyexc.execute-api.eu-central-1.amazonaws.com/web/url?folder=&file_name=latest.csv)

We begin by importing the data into R and then utilize {cleanepi}
functionalities to perform the following operations in a streamlined manner:
Expand All @@ -28,7 +28,7 @@
```{r echo=TRUE, eval=FALSE}
cleaned_data <-
data.table::fread(
"https://mpox-2024.s3.eu-central-1.amazonaws.com/latest.csv") |>
"https://ivcjkmyexc.execute-api.eu-central-1.amazonaws.com/web/url?folder=&file_name=latest.csv") |>
cleanepi::replace_missing_values(na_strings = "") |>
cleanepi::remove_constants() |>
cleanepi::standardize_dates(error_tolerance = 1) |>
Expand Down Expand Up @@ -70,7 +70,7 @@

## Data laoding and inspection

::: {.callout-important}

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 [unknown div] callout-important
## Data download

The data file is quite large ($\sim 17$ MB) and may fail to download using
Expand All @@ -82,10 +82,10 @@
with both `View()` and `wakefield::table_heat()` functions to understand the
distribution of missing values within the dataset.

```{r echo=TRUE, cache=TRUE, eval=TRUE}
```{r echo=TRUE, eval=TRUE}
# import the data
data_in <- data.table::fread(
"https://mpox-2024.s3.eu-central-1.amazonaws.com/latest.csv"
"https://ivcjkmyexc.execute-api.eu-central-1.amazonaws.com/web/url?folder=&file_name=latest.csv"
)

# Visualise the distribution of the different types as well as missing data
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