differences for PR #227

Author: actions-user

describe-cases.md aggreagate-visualize.mddescribe-cases.md renamed to aggreagate-visualize.md

@@ -967,36 +967,7 @@ Thanks to the `{numberize}` package, we can convert numbers written in English,
 
 ## Multiple operations at once
 
-Performing data cleaning operations individually can be time-consuming and error-prone.
-The `{cleanepi}` package simplifies this process by offering a convenient wrapper function called `clean_data()`, which allows you to perform multiple operations at once.
-
-When no cleaning operation is specified, the `clean_data()` function automatically applies a series of data cleaning operations to the input dataset.
-Here's an example code chunk illustrating how to use `clean_data()` on a raw simulated Ebola dataset:
-
-
-``` r
-cleaned_data <- cleanepi::clean_data(raw_ebola_data)
-```
-
-``` output
-ℹ Cleaning column names
-```
-
-``` output
-ℹ Removing constant columns and empty rows
-```
-
-``` output
-ℹ Removing duplicated rows
-```
-
-``` output
-! Found 5 duplicated rows in the dataset.
-ℹ Use `print_report(dat, "found_duplicates")` to access them, where "dat" is
-  the object used to store the output from this operation.
-```
-
-Further more, you can combine multiple data cleaning tasks via the base R pipe (`%>%`) or the {magrittr} pipe (`%>%`) operator, as shown in the below code snippet.
+You can combine multiple data cleaning tasks via the base R pipe (`%>%`) or the {magrittr} pipe (`%>%`) operator, as shown in the below code snippet.
 
 
 ``` r
@@ -1030,6 +1001,39 @@ cleaned_data <- raw_ebola_data %>%
 
 
 
+:::::::::::::::::::::::::::::::::::::::: spoiler
+
+Performing data cleaning operations individually can be time-consuming and error-prone.
+The `{cleanepi}` package simplifies this process by offering a convenient wrapper function called `clean_data()`, which allows you to perform multiple operations at once.
+
+When no cleaning operation is specified, the `clean_data()` function automatically applies a series of data cleaning operations to the input dataset.
+Here's an example code chunk illustrating how to use `clean_data()` on a raw simulated Ebola dataset:
+
+
+``` r
+one_step_clean_data <- cleanepi::clean_data(raw_ebola_data)
+```
+
+``` output
+ℹ Cleaning column names
+```
+
+``` output
+ℹ Removing constant columns and empty rows
+```
+
+``` output
+ℹ Removing duplicated rows
+```
+
+``` output
+! Found 5 duplicated rows in the dataset.
+ℹ Use `print_report(dat, "found_duplicates")` to access them, where "dat" is
+  the object used to store the output from this operation.
+```
+
+::::::::::::::::::::::::::::::::::::::::
+
 :::::::::::::: challenge
 
 Have you noticed that `{cleanepi}` contains a set of functions to **diagnose** the cleaning status of the dataset and another set to **perform** cleaning actions on it?

clean-data.md

@@ -59,10 +59,10 @@ contact: 'andree.valle-campos@lshtm.ac.uk'
 
 # Order of episodes in your lesson
 episodes:
-- read-cases.Rmd
+- read-case-data.Rmd
 - clean-data.Rmd
-- validate.Rmd
-- describe-cases.Rmd
+- tag-validate.Rmd
+- aggreagate-visualize.Rmd
 
 # Information for Learners
 learners: