Merge pull request #35 from datasciencecampus/dev

merge dev into master ahead of opening up repo

.gitignore

@@ -6,7 +6,16 @@
 .Rhistory
 .Rproj.user
 .RData
+.Rprofile
 
 # Python cache and setup files
 .ipynb_checkpoints
 
+# Quarto
+/.quarto/
+_site/
+_environment.local
+
+# Mac users
+.DS_STORE
+/_*.local

Course_content/chapter_1_getting_started.qmd → CH1_getting_started.qmd

@@ -5,24 +5,10 @@ engine: knitr
 execute:
   echo: true
   eval: false
-format:
-    html: 
-      highlight: null
-      theme: 
-       light: flatly
-       dark: darkly
-      toc: true
-      toc-title: Contents
-      toc-location: right
-      toc-depth: 3
-      number-sections: true
-      link-external-newwindow: true
-      embed-resources: true
+  freeze: auto  # re-render only when source changes
 ---
 
-![](../Images/AF_DSC_banner.png){fig-alt="Data Science Campus and Analysis Function logos."}
-
-> To switch between light and dark modes, use the toggle in the top right
+> To switch between light and dark modes, use the toggle in the top left
 
 # Learning Objectives
 
@@ -80,7 +66,7 @@ R Studio is broken down into four panels.
 
 When you open R Studio for the first time, you see this:
 
-![](../Images/studio2.PNG){fig-alt="R Studio interface with the Code Editor, Environment, Console and Files panes."}
+![](Images/studio2.PNG){fig-alt="R Studio interface with the Code Editor, Environment, Console and Files panes."}
 
 If you don't see the Code Editor pane, go to the tool bar and click **View -\> Panes -\> Show All Panes**.
 
@@ -92,7 +78,7 @@ Upon first opening R Studio, you have the most basic form of the tool that has s
 
 Firstly, navigate to "Tools" and "Global Options", which is where this tweaking takes place.
 
-![](../Images/global_options.png){fig-alt="Global options menu with general, code, appearance and more as options."}
+![](Images/global_options.png){fig-alt="Global options menu with general, code, appearance and more as options."}
 
 You see that R Studio can be heavily customised. You will only scratch the surface here.
 
@@ -131,7 +117,7 @@ The root folder of the R Project (which you choose when you create it) contains
 
 To create an R Project, select **File --\> New Project** and you will be given some examples of where to store the .Rproj file, a.k.a where the working directory will be.
 
-![](../Images/new_r_project.png){fig-alt="A project can be created in a new directory, existing directory or from GitHub."}
+![](Images/new_r_project.png){fig-alt="A project can be created in a new directory, existing directory or from GitHub."}
 
 You can:
 
@@ -147,7 +133,7 @@ Create an R project in an **existing directory**, selecting the **course_content
 
 In your own work, saving it one level higher in the root folder is a better approach. For this course, you must save it where you will save your scripts so the filepaths function correctly.
 
-![](../Images/rproj_folder.png){fig-alt="The root folder showing the .Rproj file alongside the othr folders."}
+![](Images/rproj_folder.png){fig-alt="The root folder showing the .Rproj file alongside the othr folders."}
 
 After creating the R Project, it will open and set your working directory.
 
@@ -166,7 +152,7 @@ Thankfully, you have the project menu in the top right, which allows you to:
 -   Close projects
 -   See recently open projects and jump straight to them
 
-![](../Images/project_menu.png){fig-alt="The top right menu that allows you to interact with projects."}
+![](Images/project_menu.png){fig-alt="The top right menu that allows you to interact with projects."}
 
 From here, assume you create and save your scripts in this project in order for filepaths in Chapter 3 onwards to function.
 
@@ -665,7 +651,7 @@ Notice that even without the digits argument, the round() function works. This i
 
 You can investigate what specific functions do by navigating to the "Help" tab in the bottom right and searching it by name.
 
-![](../Images/round_docstring.png){fig-alt="The document string of the rounding functions in R."}
+![](Images/round_docstring.png){fig-alt="The document string of the rounding functions in R."}
 
 you see:
 
@@ -1061,7 +1047,7 @@ There is a wealth of resource to help you progress in your R journey. Some of th
 
 You can access these by clicking on **Help** tab in R Studio and then **RStudio Cheat Sheets**. They provide an excellent reference point for many common tasks.
 
-![](../Images/data-transformation-cheat-sheet.png){fig-alt="The dplyr cheat sheet for data manipulation."}
+![](Images/data-transformation-cheat-sheet.png){fig-alt="The dplyr cheat sheet for data manipulation."}
 
 ## R Documentation
 

Course_content/chapter_2_data_structures.qmd → CH2_data_structures.qmd

@@ -5,24 +5,10 @@ engine: knitr
 execute:
   echo: true
   eval: true
-format:
-    html: 
-      highlight: null
-      theme: 
-       light: flatly
-       dark: darkly
-      toc: true
-      toc-title: Contents
-      toc-location: right
-      toc-depth: 3
-      number-sections: true
-      link-external-newwindow: true
-      embed-resources: true
+  freeze: auto  # re-render only when source changes
 ---
 
-![](../Images/AF_DSC_banner.png){fig-alt="Data Science Campus and Analysis Function logos."}
-
-> To switch between light and dark modes, use the toggle in the top right
+> To switch between light and dark modes, use the toggle in the top left
 
 # Learning Objectives
 
@@ -129,7 +115,7 @@ There are more types of vectors, but for the purpose of our learning these  are
 
 Visually:
 
-![](../Images/vector.png){fig-alt="Diagram of a vector as a column (collection) of the same red rectangle."}
+![](Images/vector.png){fig-alt="Diagram of a vector as a column (collection) of the same red rectangle."}
 
 ## Creating Vectors 
 
@@ -518,7 +504,7 @@ Unlike with vectors, where combining multiple vectors creates one vector, in lis
 
 Lists enables you to gather a variety of objects with different contents and lengths under one name in an ordered way.
 
-![](../Images/list.png){fig-alt="Visual of a list, with each element being a collection of boxes with different colours."}
+![](Images/list.png){fig-alt="Visual of a list, with each element being a collection of boxes with different colours."}
 
 To create a list we will use the **list()** function.
 
@@ -598,7 +584,7 @@ As a whole, DataFrames have the following features:
 * Rows are observations (i.e. an entry for each variable forms an observation/row).
 * They can hold variables of different types.
 
-![](../Images/dataframe.png){fig-alt="Visual of a dataframe where each column has a name."}
+![](Images/dataframe.png){fig-alt="Visual of a dataframe where each column has a name."}
 
 To create one, we can use the **data.frame()** function on vectors you would like to be your columns, they must be the same length.
 

Course_content/chapter_3_import_export.qmd → CH3_import_export.qmd

@@ -5,24 +5,10 @@ engine: knitr
 execute:
   echo: true
   eval: false
-format:
-    html: 
-      highlight: null
-      theme: 
-       light: flatly
-       dark: darkly
-      toc: true
-      toc-title: Contents
-      toc-location: right
-      toc-depth: 3
-      number-sections: true
-      link-external-newwindow: true
-      embed-resources: true
+  freeze: auto  # re-render only when source changes
 ---
 
-![](../Images/AF_DSC_banner.png){fig-alt="Data Science Campus and Analysis Function logos."}
-
-> To switch between light and dark modes, use the toggle in the top right
+> To switch between light and dark modes, use the toggle in the top left
 
 # Learning Objectives
 
@@ -74,7 +60,7 @@ help(seq)
 ```
 
 
-![](../Images/seq_helpfile.png){fig-alt="Seq() function help file."}
+![](Images/seq_helpfile.png){fig-alt="Seq() function help file."}
 
 
 ### Function help files
@@ -374,7 +360,7 @@ Below is a list of the core packages in tidyverse to provide some awareness into
 * [lubridate](https://lubridate.tidyverse.org/) - For dealing with dates and times - included in tidyverse 2.0.0 onwards. 
 
 
-![](../Images/tidyverse.png){fig-alt="Tidyverse workflow of import, tidy, transform/model/visualise and communicate."}
+![](Images/tidyverse.png){fig-alt="Tidyverse workflow of import, tidy, transform/model/visualise and communicate."}
 
 The first of the core packages we will delve into is **readr**, which deals with reading in data, and by extension **tibbles**, the excellent update to dataframes that the tidyverse provides. 
 
@@ -512,11 +498,11 @@ To do so, we need to know how to go back one folder level, or exit the current d
 
 As such, the relative filepath we need to reach the dataset is
 
->"../data/titanic.csv"
+>"Data/titanic.csv"
 
 Visually, to understand the tree-like folder structure, we have something like the following going on:
 
-![](../Images/folder_structure.jpg){fig-alt="Top level is introduction to R, folders are at level 2, items in the folders are level 3."}
+![](Images/folder_structure.jpg){fig-alt="Top level is introduction to R, folders are at level 2, items in the folders are level 3."}
 
 ### **Loading in the data**{-}
 
@@ -527,7 +513,7 @@ We simply need to go into the **data** folder, then select the **titanic.csv** f
 ```{r}
 # Read in titanic with read_csv()
 
-titanic_data <- read_csv("../data/titanic.csv")
+titanic_data <- read_csv("Data/titanic.csv")
 
 ```
 
@@ -600,7 +586,7 @@ We can easily correct this by adding the **na** paramter to the read_csv() funct
 ```{r}
 # Specifying missing values as a vector to read_csv()
 
-titanic_data <- read_csv("../data/titanic.csv", 
+titanic_data <- read_csv("Data/titanic.csv", 
                            na = c("*", ".", "", "NULL"))
 
 ```
@@ -645,7 +631,7 @@ Let's read in the police dataset.
 ```{r}
 # Reading in excel data using the readxl package
 
-police_data <- read_excel("../data/police_data.xlsx")
+police_data <- read_excel("Data/police_data.xlsx")
 
 View(police_data)
 ```
@@ -655,7 +641,7 @@ We see that this is the **first sheet** in our workbook which is just the "Notes
 ```{r}
 # Observe sheet names in police data
 
-excel_sheets("../data/police_data.xlsx")
+excel_sheets("Data/police_data.xlsx")
 ```
 
 
@@ -677,12 +663,12 @@ You can use the name of the sheet or the number/index.
 
 # Using the sheet parameter in 2 ways
 
-police_data <- read_excel("../data/police_data.xlsx",
+police_data <- read_excel("Data/police_data.xlsx",
                           sheet = 2)
 
 # Alternatively
 
-police_data <- read_excel("../data/police_data.xlsx",
+police_data <- read_excel("Data/police_data.xlsx",
                           sheet = "Table P1")
 
 police_data
@@ -699,7 +685,7 @@ This is better but still not ideal:
 # Using the range parameter to avoid empty rows
 
 
-police_data <- read_excel("../data/police_data.xlsx",
+police_data <- read_excel("Data/police_data.xlsx",
                           sheet = 2,
                           range = "A5:AA48")
 

Course_content/chapter_4_tibbles_dplyr.qmd → CH4_tibbles_dplyr.qmd

@@ -5,41 +5,20 @@ engine: knitr
 execute:
   echo: true
   eval: false
-format:
-    html: 
-      highlight: null
-      theme: 
-       light: flatly
-       dark: darkly
-      toc: true
-      toc-title: Contents
-      toc-location: right
-      toc-depth: 3
-      number-sections: true
-      link-external-newwindow: true
-      embed-resources: true
+  freeze: auto  # re-render only when source changes
 ---
 
-![](../Images/AF_DSC_banner.png){fig-alt="Data Science Campus and Analysis Function logos."}
-
-> To switch between light and dark modes, use the toggle in the top right
+> To switch between light and dark modes, use the toggle in the top left
 
 # Learning Objectives
 
 * Understand the importance of clean variable names.
-
 * Be able to clean column names using the janitor package.
-
 * Understand the use of the pipe operator.
-
 * Be able to sort data with dplyr's **arrange** verb.
-
 * Be able to select data with dplyr's **select** verb.
-
 * Be able to filter data with dplyr's **filter** verb. 
-
 * Be able to transform data with dplyr's **mutate** verb.
-
 * Be able to join datasets together.
 
 
@@ -79,7 +58,7 @@ library(janitor)
 
 # Read in titanic.csv and set null values to be specific symbols
 
-titanic_data <- read_csv("../data/titanic.csv", 
+titanic_data <- read_csv("Data/titanic.csv", 
                            na = c("*", ".", "", "NULL"))
 
 # Have a peak
@@ -693,7 +672,7 @@ This removes the need to type the **.data** argument each time.
 
 * From R 4.1 onwards, the operator comes as standard with base R, as it has become the universal standard for data analysis. It now takes the form **|>**, and this option must be turned on in the Tools --> Global Options tab. 
 
-![](../Images/native_pipe.png){fig-alt="The Code, Editing pane with the native pipe operator tick box."}
+![](Images/native_pipe.png){fig-alt="The Code, Editing pane with the native pipe operator tick box."}
 
 The shortcut for this operator is **CTRL + SHIFT + M** and is one you will use alot from here on. 
 
@@ -1224,7 +1203,7 @@ Of course, if you negate and **and**/**or** relationships:
 
 This comes from logical statements in mathematics, specifically, De Morgan's laws.
 
-![](../Images/demorgans_law.png){fig-alt="Negating a combined statement negates each individual statement, as well as the logical operator combining them."}
+![](Images/demorgans_law.png){fig-alt="Negating a combined statement negates each individual statement, as well as the logical operator combining them."}
 
 Let's take an example we we required those that embarked from Southampton or paid above £100 in fare.
 
@@ -1547,7 +1526,7 @@ A naming convention we must establish here is that of the tibbles themselves, na
 
 Graphically:
 
-![](../Images/sql-joins.png){fig-alt="Venn diagrams for each of the prior examples, with the included data shaded."}
+![](Images/sql-joins.png){fig-alt="Venn diagrams for each of the prior examples, with the included data shaded."}
 
 There are also Semi Joins and Anti Joins for filtering, which you can read about in Hadley Wickhams [R for Data Science Chapter 19](https://r4ds.hadley.nz/joins.html)
 

Course_content/chapter_5_summary_agg.qmd → CH5_summary_agg.qmd

@@ -5,24 +5,10 @@ engine: knitr
 execute:
   echo: true
   eval: false
-format:
-    html: 
-      highlight: null
-      theme: 
-       light: flatly
-       dark: darkly
-      toc: true
-      toc-title: Contents
-      toc-location: right
-      toc-depth: 3
-      number-sections: true
-      link-external-newwindow: true
-      embed-resources: true
+  freeze: auto  # re-render only when source changes
 ---
 
-![](../Images/AF_DSC_banner.png){fig-alt="Data Science Campus and Analysis Function logos."}
-
-> To switch between light and dark modes, use the toggle in the top right
+> To switch between light and dark modes, use the toggle in the top left
 
 # Learning Objectives
 
@@ -48,7 +34,7 @@ library(janitor)
 
 # Prepare the dataset
 
-titanic_data <- read_csv("../data/titanic.csv", 
+titanic_data <- read_csv("Data/titanic.csv", 
                            na = c("*", ".", "", "NULL"))
 
 titanic_data <- clean_names(titanic_data)