Merge pull request #83 from OxfordIHTM/dev

add analytics id; spell check; fix #80; fix #82

01-installing-software.qmd

@@ -4,7 +4,7 @@ output-file: installing-software.html
 
 # Installing and setting up tools {#sec-install-tools}
 
-Following are the steps to installing R, RStudio, and Git depending on your operationg system.
+Following are the steps to installing R, RStudio, and Git depending on your operating system.
 
 ## Installing R, RStudio, and git {#sec-install-r}
 
@@ -32,15 +32,15 @@ Once downloaded, double-click on `.exe` file downloaded to your **Downloads** fo
 
 ### Step 3: Download and install `Rtools` {#sec-install-rtools .unnumbered}
 
-For the things that you will be taught in the **Open and Reproducibile Science** sub-module, you we will need to expand the installation of R by installing the `Rtools` software.
+For the things that you will be taught in the **Open and Reproducible Science** sub-module, you we will need to expand the installation of R by installing the `Rtools` software.
 
 Go to [https://cran.r-project.org/bin/windows/Rtools/](https://cran.r-project.org/bin/windows/Rtools/) and choose to download the latest version of the installer (which is the Rtools version compatible with the R version you have installed in Step 1).
 
 Once you have downloaded the `.exe` file, double-click on the `.exe` file and follow all install prompts. Choose all the recommended options.
 
 ### Step 4: Download and install Git for Windows {#sec-install-git-windows .unnumbered}
 
-For the things that you will be taught in the **Open and Reproducibile Science** sub-module, you we will need to install **Git for Windows**.
+For the things that you will be taught in the **Open and Reproducible Science** sub-module, you we will need to install **Git for Windows**.
 
 Go to this link - [https://github.com/git-for-windows/git/releases/latest](https://github.com/git-for-windows/git/releases/latest) - to download the latest version of git. Make sure to select the version compatible with your Windows machine (64-bit or 32-bit).
 
@@ -66,7 +66,7 @@ Once downloaded, double-click on `.dmg` file downloaded to your `Downloads` fold
 
 ### Install git for macOS {#sec-install-git-macos}
 
-For the things that you will be taught in the **Open and Reproducibile Science** sub-module, you we will need to install **git for macOS**. Apple machines are already pre-installed with `git` but it is usually an Apple specific version of git and tends to be older and not configured in the way we need it. So we need to install another version of it that comes with Apple's `Xcode command line tools`.
+For the things that you will be taught in the **Open and Reproducible Science** sub-module, you we will need to install **git for macOS**. Apple machines are already pre-installed with `git` but it is usually an Apple specific version of git and tends to be older and not configured in the way we need it. So we need to install another version of it that comes with Apple's `Xcode command line tools`.
 
 To install, go to the macOS terminal and type the following command:
 

05-writing-functions.qmd

@@ -137,7 +137,7 @@ calculate_bmi <- function(weight, height) {
 }
 ```
 
-We now have a function for calculating and outputing BMI values.
+We now have a function for calculating and producing BMI values.
 
 Let us now test it with our 3 sets of data:
 

09-initiating-rstudio-project.qmd

@@ -65,7 +65,7 @@ If you need your existing RStudio session and the project within it to remain op
 
 Once you click on ***Create New Project***, you will now see the new project open in RStudio.
 
-You will also see something like below witin the file explorer pane of RStudio.
+You will also see something like below within the file explorer pane of RStudio.
 
 ![](images/new_project6.png)
 
@@ -101,4 +101,4 @@ This will include creating bespoke R functions (as required) and creating an Rsc
 
 ## Next steps
 
-The next steps will depend on whether you will use git and GitHub for versioning your project and whether or not you will work on your project as a solo scientiest or work and collaborate with other scientists.
+The next steps will depend on whether you will use git and GitHub for versioning your project and whether or not you will work on your project as a solo scientist or work and collaborate with other scientists.

10-creating-portable-reproducible-workflows.qmd

@@ -56,7 +56,7 @@ In general, an R script should reflect the different steps outlined above. Hence
 
 ```
 
-In this chapter, we will go through a step-by-step walkthrough of how to build a robust scientific workflow in R. A robust worklow is one that is ***portable*** i.e., not dependent on hardware and software and instead can be run on almost any machine with very minimal, if any, additional setup or configuration required, and one that is ***reproducible*** i.e., can be run over and over again without issues, providing the expected results with the same data or providing updated results with new and/or updated data.
+In this chapter, we will go through a step-by-step walkthrough of how to build a robust scientific workflow in R. A robust workflow is one that is ***portable*** i.e., not dependent on hardware and software and instead can be run on almost any machine with very minimal, if any, additional setup or configuration required, and one that is ***reproducible*** i.e., can be run over and over again without issues, providing the expected results with the same data or providing updated results with new and/or updated data.
 
 ## Create a new RStudio project {#sec-rstudio-project}
 
@@ -140,11 +140,11 @@ library(ggplot2)
 Create placeholder directories for different components of the workflow.These placeholder directories will provide an organising structure to the project and remind you of where to save/store specific files and outputs.
 
 ::: {.callout-tip}
-Following are steps on how to create placeholder directores:
+Following are steps on how to create placeholder directories:
 
 * In the lower right pane of RStudio (the file manager pane), find the menu button labelled ***Folder***.
 
-* Give this new folder the label of ***R***. This filder will hold all bespoke functions that we will create to use for this project workflow; 
+* Give this new folder the label of ***R***. This folder will hold all bespoke functions that we will create to use for this project workflow; 
 
 * Repeat these steps to create new folders with the following labels:
 

11-contributing-to-codehub-projects.qmd

@@ -60,7 +60,7 @@ This communication is important as this will trigger the developer/maintainer to
 
 Once you have been added to the project repository as a collaborator, you can now `clone` the repository to your local machine and then start making your contribution.
 
-If for some reason you haven't been added to the project repository as a collaborator yet but you are itching to contribute, you can still start contributing but instead of cloning, you will need to `fork` the repository. This is similar to cloning but your `fork` is identified as being from someone outside the organisation. This means you will need to make your own GitHub repository under your own username of the project as a fork of the original, write your code contribution and commit to your repository and then make a pull requeset to the original repository. 
+If for some reason you haven't been added to the project repository as a collaborator yet but you are itching to contribute, you can still start contributing but instead of cloning, you will need to `fork` the repository. This is similar to cloning but your `fork` is identified as being from someone outside the organisation. This means you will need to make your own GitHub repository under your own username of the project as a fork of the original, write your code contribution and commit to your repository and then make a pull request to the original repository. 
 
 These forking steps are described in all the CONTRIBUTING notes/guidance in each project as we open contributions from anyone (not just CodeHub members) but not all are eligible to be internal collaborators.
 

_quarto.yml

@@ -3,7 +3,7 @@ project:
   output-dir: _site
 
 book:
-  title: Open and Reprodubile Science in R
+  title: Open and Reproducible Science in R
   subtitle: Technical Handbook
   author:
     - Ernest Guevarra
@@ -37,6 +37,9 @@ book:
   repo-actions: [edit]
   downloads: [pdf, epub]
   sharing: [twitter, facebook]
+  google-analytics: 
+    tracking-id: "G-06FXWQBZHC"
+    anonymize-ip: true
 
 bibliography: references.bib
 

index.qmd

@@ -2,15 +2,15 @@
 
 The Open and Reproducible Science in R sub-module of the [MSc in International Health and Tropical Medicine](https://www.tropicalmedicine.ox.ac.uk/study-with-us/msc-ihtm) is designed to equip students with the knowledge and skills necessary to conduct both **academic research** and more importantly **real-world data analysis** that is transparent, reproducible, and in line with the principles of open science.
 
-This tecnical handbook serves as the *go-to* guide for MSc IHTM students to the various tools, technologies, and processes that they will be learning and using within the module.
+This technical handbook serves as the *go-to* guide for MSc IHTM students to the various tools, technologies, and processes that they will be learning and using within the module.
 
 ## Organisation
 
 This handbook is divided into three sections:
 
 ### 1. Tools
 
-This section cover topics on the various tools and technologies that are to be used and/or introduced in the module. The [R](https://r-project.org) *language and environment for statistical computing and graphics* is primary of these as the module is specific to R. All other tools and tecnologies are either built specific for use with R (e.g. [RStudio](https://posit.co/products/open-source/rstudio/) which is the IDE of choice for the module and for this handbook) or are general tools that enhance the *userR* experience and/or supports known and accepted best practices for open and reproducible science using R.
+This section cover topics on the various tools and technologies that are to be used and/or introduced in the module. The [R](https://r-project.org) *language and environment for statistical computing and graphics* is primary of these as the module is specific to R. All other tools and technologies are either built specific for use with R (e.g. [RStudio](https://posit.co/products/open-source/rstudio/) which is the IDE of choice for the module and for this handbook) or are general tools that enhance the *userR* experience and/or supports known and accepted best practices for open and reproducible science using R.
 
 ### 2. Practices