Merge pull request #52 from UtrechtUniversity/dorien-changes

Small fixes, move R project slide to morning session

Author: DorienHuijser

book/index.qmd

@@ -17,4 +17,4 @@ your work.
 
 Our workshop material is licensed under a Creative Commons Attribution 4.0 
 International License. You can view the license 
-[in our GitHub repository](https://github.com/UtrechtUniversity/workshop-introduction-to-R-and-data/blob/main/LICENSE.md).
+[in our GitHub repository](https://github.com/UtrechtUniversity/workshop-introduction-to-R-and-data/blob/main/LICENSE).

book/slides/slides_introduction.qmd

@@ -92,6 +92,25 @@ Part 1: Basics of R
 
 5. From the ‘Files’ menu (bottom right), click **baseR_exercises.Rmd**.
 
+## R project files
+
+When you start programming for yourself:
+
+- Create a folder dedicated to your project
+- Start a new R project: **File > New Project > Existing Directory**
+- An `.RProj` file will be created
+
+. . .
+
+Advantages:
+
+- Automatically set your working directory to that folder
+- Automatically retrieve only the history and objects from that R project
+- More reproducible (relative vs. absolute paths)
+
+```r
+getwd()
+```
 
 ## Running R code
 

book/slides/slides_tidyverse.qmd

@@ -56,43 +56,6 @@ dplyr::rename(iris, petal_length = Petal.Length)
 
 # Go to Exercise 0 in `datascience_exercises.Rmd`
 
-# Some good coding practices {background-color=#FFCD00}
-
-## R projects and working directories
-
-When you start programming for yourself:
-
-- Create a folder dedicated to your project
-- Start a new R project: **File > New Project > Existing Directory**
-- An `.RProj` file will be created
-
-. . .
-
-Advantages:
-
-- Automatically set your working directory to that folder
-- Automatically retrieve only the history and objects from that R project
-- More reproducible (relative vs. absolute paths)
-
-```r
-getwd()
-```
-
-## Comments
-
-If a line of R code starts with `#`, it is ignored.
-
-- Help your future you and collaborators/supervisors understand what your code does
-- Explain (the purpose of) code that is not self-explanatory
-- Comment and un-comment to debug code without having to delete it[^1]
-
-```{r}
-# Print a very important line
-print("A very important line")
-```
-
-[^1]: You should delete non-working code when done debugging though.
-
 ## The Data Science workflow
 
 ![Source: [R 4 Data Science](https://r4ds.had.co.nz/introduction.html)](https://d33wubrfki0l68.cloudfront.net/571b056757d68e6df81a3e3853f54d3c76ad6efc/32d37/diagrams/data-science.png "The data science workflow."){fig-align="center"}

course-materials/datascience_exercises.Rmd

@@ -77,7 +77,7 @@ We are going to clean up our data by selecting/filtering only the relevant infor
 
 ## Exercise 4
 
-We are going to select columns with relevant data, for our upcoming analysis.
+We are going to select columns from the `penguins` dataframe with relevant data, for our upcoming analysis.
 The research we do is on penguin bill sizes (described in our data with the word "Culmen").
 
 We want to include a few relevant variables so we can analyse them later:
@@ -90,8 +90,6 @@ We want to include a few relevant variables so we can analyse them later:
 
 Use the function `select()` to include the right columns in our data subset. 
 
-**Note** that in order to work with the subset from Exercise 4, we need to use that data frame (`penguin_subset`) as our data input!
-
 ```{r}
 penguins_subset <- ???
 ```