Updated installation instructions

Author: DanielThedie

README.md

@@ -13,29 +13,36 @@ This repository provides tools and code to perform social network analysis, gene
 ### 1. Data format
 The input data should be in `.csv`  format, with one column containing author names. This CSV file can be exported from databases such as PubMed, Scopus, Web of Science, or university research databases. 
 
-### 2. Clone the repository
-Clone the entire repository to your computer using the `git clone` command or the `GitHub Desktop` application.
+### 2. Install the package
+Installing this package requires an R version >= 4.4.
 
-### 3. Ruuning the repository 
+Run the following commands in R to install the package:
+```R
+install.packages("pak")
+pak::pkg_install("BioRDM/Network-Analysis")
+```
+
+#### For Linux users
+Additional dependencies might be needed. On Debian-based systems, they can be installed with this command:
+`sudo apt-get install -y --no-install-recommends libcurl4-openssl-dev libharfbuzz-dev libfribidi-dev libtiff5-dev texlive-xetex`
+
+### 3. Generating the report
 
-#### 3.1 intsall Packages: 
-Ensure that the following libraries are installed on your computer:   
-(the list of required packages here.)
+Download the [generate_report.R](https://github.com/BioRDM/Network-Analysis/blob/main/Generate_report.r) script.
 
-#### 3.2 Report generating: 
-The primary file you will need is `Generate_report.R` . You can run this R script in RStudio or Visual Studio Code. Each function and line of code is well-documented, with instructions and explanations on how to modify it to suit your analysis needs.
+You can run this R script in RStudio or Visual Studio Code. Each function and line of code is well-documented, with instructions and explanations on how to modify it to suit your analysis needs.
 The code includes various filter functions that you may adjust depending on your research questions, such as:
 
 - The maximum number of authors per paper in your dataset.
 - The minimum number of papers per author to be included in the analysis.
 - The timeframe (years) you wish to study.
 - Whether to split the analysis into smaller time intervals. 
 
-#### 3.3 output folder:
+#### 3.3 Output folder:
 After running the code, the output (including figures, PDFs, and additional CSV files) will be saved in a folder named `output`. This folder will be created automatically if it does not already exist.
 If you analyse multiple CSV files, a separate subfolder (under the same name of the csv file) will be created within the output folder for each dataset, containing the relevant results.
 
-##### the generated outcomes: 
+##### Generated outcomes: 
       1. `Report_YYYY_YYYY.pdf`  
       This is the main output of the automated code: a PDF report containing the analysis results. It includes a table of contents, detailed interpretations of the findings, and visualisations with captions. 
       The corresponding markdown file, `Report_YYYY_YYYY.md`, is also generated.
@@ -59,19 +66,19 @@ If you analyse multiple CSV files, a separate subfolder (under the same name of
 ## Repository Contents
 
 |---------------- .github             
-            (Automatically generated folder used for GitHub workflows, such as building PDF files.)            
+            (Folder for GitHub workflows, for code testing purposes.)            
 |---------------- Pure Data             
             (Contains datasets extracted from the University of Edinburgh's Research Explorer (Pure))             
 |---------------- R            
             (Includes all R functions used to generate the analysis outcomes and build the PDF report.)                 
 |---------------- Trial Analysis          
             (Contains datasets from the UOE Explorer (Pure) and RMarkdown files used to develop the code.)             
 |---------------- assets         
-            (Stores additional resources, such as the .tiff file for the font type used in the PDF report.)              
+            (Stores additional resources, such as the .ttf file for the font type used in the PDF report.)              
 |---------------- data           
             (Contains datasets from the Centre for Engineering Biology at the University of Edinburgh.)            
 |---------------- tests  
-            (Includes trial datasets and code used during the development and testing of the repository.)  
+            (Contains unit-tests for the package.)  
 |------- .Rbuildignore  
             (Specifies files and folders to ignore when building the R package.)       
 |-------.gitignore     
@@ -94,21 +101,3 @@ If you analyse multiple CSV files, a separate subfolder (under the same name of
             (This file, providing an overview of the repository and instructions for use.)      
 
 ---
-
-
-### Adding new metrics
-To compute new metrics, edit `src/Interactions_class.r` to add a new method (both functions need to be added):
-```r
-get_diameter.Interactions <- function(interactions) {
-  return(diameter(interactions$graph, directed = FALSE, weights = NA))
-}
-
-get_diameter <- function(interactions) {
-  UseMethod("get_diameter", interactions)
-}
-```
-
-The method can then be called in the relevant function in `Report_text.r` to obtain the numbers:
-```r
-diameter <- get_diameter(interactions)
-```