Merge pull request #8 from chrbknudsen/main

questions, objectives og keypoints

episodes/03-dplyr-tidyr.Rmd

@@ -1,15 +1,23 @@
 ---
 title: "Data Wrangling with dplyr and tidyr"
-keypoints:
-- Use the `dplyr` package to manipulate dataframes.
-- Use `select()` to choose variables from a dataframe.
-- Use `filter()` to choose data based on values.
-- Use `group_by()` and `summarize()` to work with subsets of data.
-- Use `mutate()` to create new variables.
-- Use the `tidyr` package to change the layout of dataframes.
-- Use `pivot_wider()` to go from long to wide format.
-- Use `pivot_longer()` to go from wide to long format.
-objectives:
+
+teaching: 20
+exercises: 10
+
+---
+
+:::: questions
+
+- How can I select specific rows and/or columns from a dataframe?
+- How can I combine multiple commands into a single command?
+- How can I create new columns or remove existing columns from a dataframe?
+- How can I reformat a dataframe to meet my needs?
+
+::::
+
+
+:::: objectives
+
 - Describe the purpose of an R package and the **`dplyr`** and **`tidyr`** packages.
 - Select certain columns in a dataframe with the **`dplyr`** function `select`.
 - Select certain rows in a dataframe according to filtering conditions with the **`dplyr`**
@@ -27,16 +35,8 @@ objectives:
 - Reshape a dataframe from long to wide format and back with the `pivot_wider` and
   `pivot_longer` commands from the **`tidyr`** package.
 - Export a dataframe to a csv file.
-questions:
-- How can I select specific rows and/or columns from a dataframe?
-- How can I combine multiple commands into a single command?
-- How can I create new columns or remove existing columns from a dataframe?
-- How can I reformat a dataframe to meet my needs?
-teaching: 20
-exercises: 10
-source: Rmd
----
 
+::::
 
 ```{r, include = FALSE}
 library(dplyr)
@@ -236,22 +236,28 @@ interviews_ch
 Note that the final dataframe (`interviews_ch`) is the leftmost part of this
 expression.
 
-> ## Exercise
->
->  Using pipes, subset the `interviews` data to include interviews
-> where respondents were members of an irrigation association
-> (`memb_assoc`) and retain only the columns `affect_conflicts`,
-> `liv_count`, and `no_meals`.
->
-> > ## Solution
-> >
-> > ```{r}
-> > interviews %>%
-> >     filter(memb_assoc == "yes") %>%
-> >     select(affect_conflicts, liv_count, no_meals)
-> > ```
-> {: .solution}
-{: .challenge}
+
+:::: challenge
+
+
+## Exercise
+
+  Using pipes, subset the `interviews` data to include interviews
+ where respondents were members of an irrigation association
+ (`memb_assoc`) and retain only the columns `affect_conflicts`,
+ `liv_count`, and `no_meals`.
+
+:::: solution
+ ## Solution
+
+ ```{r}
+ interviews %>%
+     filter(memb_assoc == "yes") %>%
+     select(affect_conflicts, liv_count, no_meals)
+ ```
+
+::::
+
 
 ### Mutate
 
@@ -270,29 +276,32 @@ interviews %>%
 
 
 
+:::: challenge 
 
-> ## Exercise
->
->  Create a new dataframe from the `interviews` data that meets the following
->  criteria: contains only the `village` column and a new column called
->  `total_meals` containing a value that is equal to the total number of meals
->  served in the household per day on average (`no_membrs` times `no_meals`).
->  Only the rows where `total_meals` is greater than 20 should be shown in the
->  final dataframe.
->
->  **Hint**: think about how the commands should be ordered to produce this data
->  frame!
->
-> > ## Solution
-> >
-> > ``` {r}
-> > interviews_total_meals <- interviews %>%
-> >     mutate(total_meals = no_membrs * no_meals) %>%
-> >     filter(total_meals > 20) %>%
-> >     select(village, total_meals)
-> > ```
-> {: .solution}
-{: .challenge}
+## Exercise
+
+Create a new dataframe from the `interviews` data that meets the following
+criteria: contains only the `village` column and a new column called
+`total_meals` containing a value that is equal to the total number of meals
+served in the household per day on average (`no_membrs` times `no_meals`).
+Only the rows where `total_meals` is greater than 20 should be shown in the
+final dataframe.
+
+**Hint**: think about how the commands should be ordered to produce this data
+frame!
+
+:::: solution
+
+## Solution
+
+ ``` {r}
+ interviews_total_meals <- interviews %>%
+     mutate(total_meals = no_membrs * no_meals) %>%
+     filter(total_meals > 20) %>%
+     select(village, total_meals)
+ ```
+
+::::
 
 ### Split-apply-combine data analysis and the summarize() function
 
@@ -395,6 +404,10 @@ interviews %>%
     count(village, sort = TRUE)
 ```
 
+
+
+
+
 > ## Exercise
 >
 > How many households in the survey have an average of
@@ -476,4 +489,15 @@ if (!dir.exists("../data_output")) dir.create("../data_output")
 write_csv(interviews, "../data_output/interviews_plotting.csv")
 ```
 
-{% include links.md %}
+:::: keypoints
+
+- Use the `dplyr` package to manipulate dataframes.
+- Use `select()` to choose variables from a dataframe.
+- Use `filter()` to choose data based on values.
+- Use `group_by()` and `summarize()` to work with subsets of data.
+- Use `mutate()` to create new variables.
+- Use the `tidyr` package to change the layout of dataframes.
+- Use `pivot_wider()` to go from long to wide format.
+- Use `pivot_longer()` to go from wide to long format.
+
+::::

episodes/04-functions-plots.Rmd

@@ -2,27 +2,24 @@
 title: "A couple of plots. And making our own functions"
 teaching: 80
 exercises: 35
-questions:
-  - "How do I create scatterplots, boxplots, and barplots?"
-  - "How can I define my own functions?"
-
-objectives:
-   - "Produce scatter plots and boxplots using Base R."
-   - "Write your own function"
-   - "Write loops to repeat calculations"
-   - "Use logical tests in loops"
-
-keypoints:
-   - "Boxplots are useful for visualizing the distribution of a continuous variable."
-   - "Barplots are useful for visualizing categorical data."
-   - "Functions allows you to repeat the same set of operations again and again."
-   - "Loops allows you to apply the same function to lots of data."
-   - "Logical tests allow you to apply different calculations on different sets of data."
-
-source: Rmd
 ---
 
+:::: questions:
 
+- "How do I create scatterplots, boxplots, and barplots?"
+- "How can I define my own functions?"
+
+::::  
+
+
+:::: objectives:
+
+- "Produce scatter plots and boxplots using Base R."
+- "Write your own function"
+- "Write loops to repeat calculations"
+- "Use logical tests in loops"
+
+::::
 
 We start by loading the **`tidyverse`** package.
 
@@ -317,4 +314,13 @@ interviews_plotting %>%
 It looks different, and we get a warning about `binwidth`. geom_histogram automatically
 chooses 30 bins for us, and that is normally not the right number.
 
-{% include links.md %}
+:::: keypoints
+
+- "Boxplots are useful for visualizing the distribution of a continuous variable."
+- "Barplots are useful for visualizing categorical data."
+- "Functions allows you to repeat the same set of operations again and again."
+- "Loops allows you to apply the same function to lots of data."
+- "Logical tests allow you to apply different calculations on different sets of data."
+
+::::
+

episodes/05-whats-next.Rmd

@@ -2,24 +2,22 @@
 title: "What is the next step?"
 teaching: 10
 exercises: 0
-questions:
-  - "What do I do now?"
-  - "What is the next step?"
-
-objectives:
-   - "Present suggestions for further reading,"
-   - "Tips on problems to work on to practice,"
-
-keypoints:
-   - "Practice is important!"
-   - "Working on data that YOU find interesting is a really good idea,"
-   - "The amount of ressources online is immense."
-   - "KUB Datalab is there for your."
-
-source: Rmd
 ---
 
 
+:::: questions
+
+- "What do I do now?"
+- "What is the next step?"
+
+::::
+
+:::: objectives
+
+- "Present suggestions for further reading,"
+- "Tips on problems to work on to practice,"
+
+::::
 
 
 ## Great sites
@@ -57,4 +55,11 @@ Our mail: [email protected]
 
 
 
-{% include links.md %}
+:::: keypoints:
+   - "Practice is important!"
+   - "Working on data that YOU find interesting is a really good idea,"
+   - "The amount of ressources online is immense."
+   - "KUB Datalab is there for your."
+
+::::
+