fix yaml

_episodes_rmd/03-dplyr-tidyr.Rmd

@@ -3,31 +3,31 @@ title: "Introducing dplyr and tidyr"
 teaching: 45
 exercises: 20
 questions:
-- "How can I select specific rows and/or columns from a data frame?"
-- "How can I combine multiple commands into a single command?"
-- "How can create new columns or remove existing columns from a data frame?"
-- "How can I reformat a dataframe to meet my needs?"
+  - "How can I select specific rows and/or columns from a data frame?"
+  - "How can I combine multiple commands into a single command?"
+  - "How can create new columns or remove existing columns from a data frame?"
+  - "How can I reformat a dataframe to meet my needs?"
 objectives:
 - "Describe the purpose of the **`dplyr`** and **`tidyr`** packages."
 - "Select certain columns in a data frame with the **`dplyr`** function `select`."
-- "Select certain rows in a data frame according to filtering conditions with the **`dplyr`** function `filter` ."
+- "Select certain rows in a data frame according to filtering conditions with the **`dplyr`** function `filter`."
 - "Link the output of one **`dplyr`** function to the input of another function with the 'pipe' operator `%>%`."
 - "Add new columns to a data frame that are functions of existing columns with `mutate`."
 - "Use the split-apply-combine concept for data analysis."
 - "Use `summarize`, `group_by`, and `count` to split a data frame into groups of observations, apply a summary statistics for each group, and then combine the results."
 - "Describe the concept of a wide and a long table format and for which purpose those formats are useful."
 - "Describe what key-value pairs are."
-- "Reshape a data frame from long to wide format and back with the `spread` and `gather` commands from the **`tidyr`** package.
-- "Export a data frame to a .csv file."
+- "Reshape a data frame from long to wide format and back with the `spread` and `gather` commands from the **`tidyr`** package."
+- "Export a data frame to a csv file."
 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 `gather()` to go from wide to long format."
-- "Use `spread()` to go from long to wide format."
+  - "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 `gather()` to go from wide to long format."
+  - "Use `spread()` to go from long to wide format."
 ---
 
 

_episodes_rmd/04-ggplot2.Rmd

@@ -3,23 +3,23 @@ title: "Data visualisation with ggplot2"
 teaching: 45
 exercises: 20
 questions:
-- "What are the components of a ggplot?"
-- "How do I create scatterplots, boxplots, and barplots?"
-- "How can I change the aesthetics (ex. colour, transparency) of my plot?"
-- "How can I create multiple plots at once?"
+  - "What are the components of a ggplot?"
+  - "How do I create scatterplots, boxplots, and barplots?"
+  - "How can I change the aesthetics (ex. colour, transparency) of my plot?"
+  - "How can I create multiple plots at once?"
 objectives:
-- "Produce scatter plots, boxplots, and time series plots using ggplot.
-- "Set universal plot settings."
-- "Describe what faceting is and apply faceting in ggplot."
-- "Modify the aesthetics of an existing ggplot plot (including axis labels and color)."
-- "Build complex and customized plots from data in a data frame."
+   - "Produce scatter plots, boxplots, and time series plots using ggplot."
+   - "Set universal plot settings."
+   - "Describe what faceting is and apply faceting in ggplot."
+   - "Modify the aesthetics of an existing ggplot plot (including axis labels and color)."
+   - "Build complex and customized plots from data in a data frame."
 keypoints:
-- "`ggplot2` is a flexible and useful tool for creating plots in R."
-- "The data set and coordinate system can be defined using the `ggplot` function."
-- "Additional layers, including geoms, are added using the `+` operator."
-- "Boxplots are useful for visualizing the distribuion of a continuous variable."
-- "Barplot are useful for visualizing categorical data."
-- "Facetting allows you to generate multiple plots based on a categorical variable."
+   - "`ggplot2` is a flexible and useful tool for creating plots in R."
+   - "The data set and coordinate system can be defined using the `ggplot` function."
+   - "Additional layers, including geoms, are added using the `+` operator."
+   - "Boxplots are useful for visualizing the distribuion of a continuous variable."
+   - "Barplot are useful for visualizing categorical data."
+   - "Facetting allows you to generate multiple plots based on a categorical variable."
 ---
 
 ```{r, include=FALSE}