This book is written for statisticians, data analysts, programmers, researchers, teachers, students, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using the R programming language. R is an open-source software and object-oriented programming language with a development environment (IDE) called RStudio for computing statistics and graphical displays through data manipulation, modelling, and calculation. R packages and supported libraries provides a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical softwares, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system.For all intents and purposes, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the R users or statisticians. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and non-parametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for reliability and validity of the available datasets. Different research experiments, case scenarios and examples are explained in this book. It is the first book to provide a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as Objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations. Thus, congruence of Statistics and Computer programming for Research.

This module series covers how to import, manipulate, format and plot time series data stored in .csv format in R. Originally designed to teach researchers to use NEON plant phenology and air temperature data; has been used in undergraduate classrooms.

Explanation/Overview:

Corresponding dataset for the analyses and results achieved in the CS Track project in the research line on participation analyses, which is also reported in the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: Collaboration Technologies and Social Computing and published as part of the Lecture Notes in Computer Science book series (LNCS,volume 13632) here. The usernames have been anonymised.

Purpose:

The purpose of this dataset is to provide the basis to reproduce the results reported in the associated deliverable, and in the above-mentioned publication. As such, it does not represent raw data, but rather files that already include certain analysis steps (like calculated degrees or other SNA-related measures), ready for analysis, visualisation and interpretation with R.

Relatedness:

The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.

Content:

In this Zenodo entry, several files can be found. The structure is as follows (files and folders and descriptions).

corresponding_calculations.html

Quarto-notebook to view in browser

corresponding_calculations.qmd

Quarto-notebook to view in RStudio

assets

data

annotations

annotations.csv

List of annotations made per day for each of the analysed projects

comments

comments.csv

Total list of comments with several data fields (i.e., comment id, text, reply_user_id)

rolechanges

478_rolechanges.csv

List of roles per user to determine number of role changes

1104_rolechanges.csv

...

...

totalnetworkdata

Edges

478_edges.csv

Network data (edge set) for the given projects (without time slices)

1104_edges.csv

...

...

Nodes

478_nodes.csv

Network data (node set) for the given projects (without time slices)

1104_nodes.csv

...

...

trajectories

Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021)

478

Edges

edges_4782016_q1.csv

edges_4782016_q2.csv

edges_4782016_q3.csv

edges_4782016_q4.csv

...

Nodes

nodes_4782016_q1.csv

nodes_4782016_q4.csv

nodes_4782016_q3.csv

nodes_4782016_q2.csv

...

1104

Edges

...

Nodes

...

...

scripts

datavizfuncs.R

script for the data visualisation functions, automatically executed from within corresponding_calculations.qmd

import.R

script for the import of data, automatically executed from within corresponding_calculations.qmd

corresponding_calculations_files

files for the html/qmd view in the browser/RStudio

Grouping:

The data is grouped according to given criteria (e.g., project_title or time). Accordingly, the respective files can be found in the data structure