Make fixes #193

Karim-Mane · 2025-06-16T10:56:14Z

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github-actions · 2025-06-16T10:56:24Z

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Degoot-AM

Thanks @Karim-Mane for the suggestions, which improve the overall readability of this tutorials.

Degoot-AM · 2025-06-26T12:43:27Z

episodes/clean-data.Rmd

+In the process of analyzing outbreak data, it's essential to ensure that the dataset is clean, curated, standardized, and validated. This will increase the accuracy (i.e. you are analysing what you think you are analysing) and reproducibility (i.e. if someone wants to go back and repeat your analysis steps with your code, you can be confident they will get the same results) of the
+downstream analyses. This episode focuses on cleaning epidemics and outbreaks data using the [{cleanepi}](https://epiverse-trace.github.io/cleanepi/) package.
+To demonstrate the functionalities of this package, we'll apply them on a simulated dataset of Ebola cases.


Suggested change

In the process of analyzing outbreak data, it's essential to ensure that the dataset is clean, curated, standardized, and validated. This will increase the accuracy (i.e. you are analysing what you think you are analysing) and reproducibility (i.e. if someone wants to go back and repeat your analysis steps with your code, you can be confident they will get the same results) of the

downstream analyses. This episode focuses on cleaning epidemics and outbreaks data using the [{cleanepi}](https://epiverse-trace.github.io/cleanepi/) package.

To demonstrate the functionalities of this package, we'll apply them on a simulated dataset of Ebola cases.

In the process of analyzing outbreak data, it's essential to ensure that the dataset is clean, curated, standardized, and validated. This will increase the accuracy (i.e. you are analysing what you think you are analysing) and reproducibility (i.e. if someone wants to go back and repeat your analysis steps with your code, you can be confident they will get the same results) of the downstream analyses. This episode focuses on cleaning epidemics and outbreaks data using the [{cleanepi}](https://epiverse-trace.github.io/cleanepi/) package.

To demonstrate the functionalities of this package, we'll apply them on a simulated dataset of Ebola cases.

text alignment

Degoot-AM · 2025-06-26T12:43:46Z

episodes/clean-data.Rmd

+In the process of analyzing outbreak data, it's essential to ensure that the dataset is clean, curated, standardized, and validated. This will increase the accuracy (i.e. you are analysing what you think you are analysing) and reproducibility (i.e. if someone wants to go back and repeat your analysis steps with your code, you can be confident they will get the same results) of the
+downstream analyses. This episode focuses on cleaning epidemics and outbreaks data using the [{cleanepi}](https://epiverse-trace.github.io/cleanepi/) package.
+To demonstrate the functionalities of this package, we'll apply them on a simulated dataset of Ebola cases.


text alignment

Degoot-AM · 2025-06-26T12:44:57Z

episodes/clean-data.Rmd

+The `::` in R lets you access functions or objects from a specific package without attaching the entire package to the search path. It offers several important
+advantages including the followings:


Suggested change

The `::` in R lets you access functions or objects from a specific package without attaching the entire package to the search path. It offers several important

advantages including the followings:

The `::` in R lets you access functions or objects from a specific package without attaching the entire package to the search path. It offers several important advantages including the followings:

text alignment

Degoot-AM · 2025-06-26T12:48:25Z

episodes/clean-data.Rmd

+You can get the number and location of the duplicated rows that where found. Run `cleanepi::print_report()`, wait for the report to open in your browser, and
+find the "Duplicates" tab.


Suggested change

You can get the number and location of the duplicated rows that where found. Run `cleanepi::print_report()`, wait for the report to open in your browser, and

find the "Duplicates" tab.

You can get the number and location of the duplicated rows that where found. Run `cleanepi::print_report()`, wait for the report to open in your browser, and find the "Duplicates" tab.

Degoot-AM · 2025-06-26T12:50:05Z

episodes/clean-data.Rmd

+Each entry in the dataset represents a subject (e.g. a disease case or study participant) and should be distinguishable by a specific ID formatted in a 
+particular way. These IDs can contain numbers falling within a specific range, a prefix and/or suffix, and might be written such that they contain a specific number of characters. The `{cleanepi}` package offers the function `check_subject_ids()` designed precisely for this task as shown in the below code chunk. This function checks whether the IDs are unique and meet the required criteria specified by the user.


Suggested change

Each entry in the dataset represents a subject (e.g. a disease case or study participant) and should be distinguishable by a specific ID formatted in a

particular way. These IDs can contain numbers falling within a specific range, a prefix and/or suffix, and might be written such that they contain a specific number of characters. The `{cleanepi}` package offers the function `check_subject_ids()` designed precisely for this task as shown in the below code chunk. This function checks whether the IDs are unique and meet the required criteria specified by the user.

Each entry in the dataset represents a subject (e.g. a disease case or study participant) and should be distinguishable by a specific ID formatted in a particular way. These IDs can contain numbers falling within a specific range, a prefix and/or suffix, and might be written such that they contain a specific number of characters. The `{cleanepi}` package offers the function `check_subject_ids()` designed precisely for this task as shown in the below code chunk. This function checks whether the IDs are unique and meet the required criteria specified by the user.

Degoot-AM · 2025-06-26T15:45:03Z

episodes/read-cases.Rmd

@@ -180,7 +184,7 @@ db_connection <- DBI::dbConnect(

 A real-life connection to an external SQLite database would look like this:

-```r
+```{r}


Suggested change

```{r}

```{r, eval=FALSE}

Suggested change

```{r}

```{r}

Do not run this code because it not excuetable

avallecam · 2025-07-03T11:42:26Z

Hi @Karim-Mane, let me know when this is ready to review. I'll be able to review and assess merging tomorrow, ready for the start of next week's training.

I can deal with solving some of the remining gh-action issues

Karim-Mane requested a review from avallecam June 16, 2025 10:56

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differences for PR #193

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Karim-Mane and others added 11 commits July 3, 2025 11:17

update introduction

91e3a5f

update setup section

ad25138

update the :: operator section

b70387a

update data cleaning episode

fa76bd7

add project file

18fb8da

edit the simple-analysis episode

d3c0887

edit the read-cases episode

d3a54e6

update the describe-cases episode

bc32abc

resolve conflicting issues

d7b60be

minor changes in validatation episodes

d5c4de9

update data validation episode

a53d9f3

Karim-Mane force-pushed the make_fixes branch from 38837d3 to a53d9f3 Compare July 3, 2025 11:19

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		The `::` in R lets you access functions or objects from a specific package without attaching the entire package to the search path. It offers several important
		advantages including the followings:

		You can get the number and location of the duplicated rows that where found. Run `cleanepi::print_report()`, wait for the report to open in your browser, and
		find the "Duplicates" tab.

		Each entry in the dataset represents a subject (e.g. a disease case or study participant) and should be distinguishable by a specific ID formatted in a
		particular way. These IDs can contain numbers falling within a specific range, a prefix and/or suffix, and might be written such that they contain a specific number of characters. The `{cleanepi}` package offers the function `check_subject_ids()` designed precisely for this task as shown in the below code chunk. This function checks whether the IDs are unique and meet the required criteria specified by the user.

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