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.

The scripts in this folder weer used to combine all call statistic files per day into one file, resulting in nine files containing all call statistics per data. The script ‘merging_dataset.R’ was used to combine all days worth of call statistics and create subsets of two frequency ranges (18-32 and 32-96). The script ‘camera_data’ was used to combine all camera and observation data.

I have done Data Analysis Project on Movies Dataset using codes in Rstudio. I have used various data cleaning functions and used multiple data visualization codes to demonstrate various key findings on this projects. Dataset is related to a category #Entertainment.

Load and view a real-world dataset in RStudio

• Calculate “Measure of Frequency” metrics

• Calculate “Measure of Central Tendency” metrics

• Calculate “Measure of Dispersion” metrics

• Use R’s in-built functions for additional data quality metrics

• Create a custom R function to calculate descriptive statistics on any given dataset

Learn how to create professional data visualizations using R and ggplot2. A step-by-step guide for startup founders and analysts to build publication-quality charts.

ABSTRACT Meta-analysis is an adequate statistical technique to combine results from different studies, and its use has been growing in the medical field. Thus, not only knowing how to interpret meta-analysis, but also knowing how to perform one, is fundamental today. Therefore, the objective of this article is to present the basic concepts and serve as a guide for conducting a meta-analysis using R and RStudio software. For this, the reader has access to the basic commands in the R and RStudio software, necessary for conducting a meta-analysis. The advantage of R is that it is a free software. For a better understanding of the commands, two examples were presented in a practical way, in addition to revising some basic concepts of this statistical technique. It is assumed that the data necessary for the meta-analysis has already been collected, that is, the description of methodologies for systematic review is not a discussed subject. Finally, it is worth remembering that there are many other techniques used in meta-analyses that were not addressed in this work. However, with the two examples used, the article already enables the reader to proceed with good and robust meta-analyses. Level of Evidence V, Expert Opinion.

This is a repository for codes and datasets for the open-access paper in Linguistik Indonesia, the flagship journal for the Linguistic Society of Indonesia (Masyarakat Linguistik Indonesia [MLI]) (cf. the link in the references below).To cite the paper (in APA 6th style):Rajeg, G. P. W., Denistia, K., & Rajeg, I. M. (2018). Working with a linguistic corpus using R: An introductory note with Indonesian negating construction. Linguistik Indonesia, 36(1), 1–36. doi: 10.26499/li.v36i1.71To cite this repository:Click on the Cite (dark-pink button on the top-left) and select the citation style through the dropdown button (default style is Datacite option (right-hand side)This repository consists of the following files:1. Source R Markdown Notebook (.Rmd file) used to write the paper and containing the R codes to generate the analyses in the paper.2. Tutorial to download the Leipzig Corpus file used in the paper. It is freely available on the Leipzig Corpora Collection Download page.3. Accompanying datasets as images and .rds format so that all code-chunks in the R Markdown file can be run.4. BibLaTeX and .csl files for the referencing and bibliography (with APA 6th style). 5. A snippet of the R session info after running all codes in the R Markdown file.6. RStudio project file (.Rproj). Double click on this file to open an RStudio session associated with the content of this repository. See here and here for details on Project-based workflow in RStudio.7. A .docx template file following the basic stylesheet for Linguistik IndonesiaPut all these files in the same folder (including the downloaded Leipzig corpus file)!To render the R Markdown into MS Word document, we use the bookdown R package (Xie, 2018). Make sure this package is installed in R.Yihui Xie (2018). bookdown: Authoring Books and Technical Documents with R Markdown. R package version 0.6.

Dataset

This dataset was created by Dishani Mishra

Contents

Students learn about the importance of good data management and begin to explore QGIS and RStudio for spatial analysis purposes. Students will explore National Land Cover Database raster data and made-up vector point data on both platforms.

In a world of increasing crime, many organizations are interested in examining incident details to learn from and prevent future crime. Our client, based in Los Angeles County, was interested in this exact thing. They asked us to examine the data to answer several questions; among them, what was the rate of increase or decrease in crime from 2020 to 2023, and which ethnicity or group of people were targeted the most.

Our data was collected from Kaggle.com at the following link:

https://www.kaggle.com/datasets/nathaniellybrand/los-angeles-crime-dataset-2020-present

It was cleaned, examined for further errors, and the analysis performed using RStudio. The results of this analysis are in the attached PDF entitled: "crime_data_analysis_report." Please feel free to review the results as well as follow along with the dataset on your own machine.

R scripts to re-analyze data published in Melo et al., 2020. We recommend using the R studio interface to run the code in these scripts. The script already contains the data table; therefore, there is no need to set the data tables in the script. Data table files are only available for better visualization between wide and long formats. Reference: Melo, M. B.; Favaro, V. M.; Oliveira, M. G. M (2020). The dorsal subiculum is required for the contextual fear consolidation in rats. Behavioural Brain Research. 390:112661. Doi: https://doi.org/10.1016/j.bbr.2020.112661.

This study investigated predictive processing during both silent and oral reading, revealing a more pronounced predictability effect in the context of oral reading.

• Automated parametric analysis workflow built using R Studio.
• Demonstrates core statistical analysis methods on numerical datasets.
• Includes step-by-step R scripts for performing t-tests, ANOVA, and summary statistics.
• Provides visual outputs such as boxplots and distribution plots for better interpretation.
• Designed for students, researchers, and data analysts learning statistical automation in R.
• Useful for understanding reproducible research workflows in data analysis.
• Dataset helps in teaching how to automate statistical pipelines using R programming.

Self Organizing Map Syntax and Data

This file compiles the different datasets used and analysis made in the paper "Visual Continuous Time Preferences". Both RStudio and Stata were used for the analysis. The first was used for descriptive statistics and graphs, the second for regressions. We join the datasets for both analysis.

"Analysis VCTP - RStudio.R" is the RStudio analysis. "Analysis VCTP - Stata.do" is the Stata analysis.

The RStudio datasets are: "data_Seville.xlsx" is the dataset of observations. "FormularioEng.xlsx" is the dataset of control variables.

The Stata datasets are: "data_Seville_Stata.dta" is the dataset of observations. "FormularioEng.dta" is the dataset of control variables

Additionally, the experimental instructions of the six experimental conditions are also available: "Hypothetical MPL-VCTP.pdf" is the instructions and task for hypothetical payment and MPL answered before VCTP. "Hypothetical VCTP-MPL.pdf" is the instructions and task for hypothetical payment and VCTP answered before MPL. "OneTenth MPL-VCTP.pdf" is the instructions and task for BRIS payment and MPL answered before VCTP. "OneTenth VCTP-MPL.pdf" is the instructions and task for BRIS payment and VCTP answered before MPL. "Real MPL-VCTP.pdf" is the instructions and task for real payment and VCTP answered before MPL. "Real VCTP-MPL.pdf" is the instructions and task for real payment and VCTP answered before MPL.

R/R-Studio software code for waste survey data analysis of Jos Plateau state, Nigeria. This details the software code used in R-Studio (RStudio 2023.06.2+561 "Mountain Hydrangea" ) and R (R version 4.3.1) for analyzing the study on waste characterization survey/waste audit in Jos, Plateau state, Nigeria. The code is provided in .R, .docx, and .pdf., it is accessible directly using RStudio for .docx and .pdf. It includes codes for generating plots used in paper publication. Note: that you require the dataset used in the study which is provided in steps to reproduce.

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

This dataset contains original quantitative datafiles, analysis data, a codebook, R scripts, syntax for replication, the original output from R studio and figures from a statistical program. The analyses can be found in Chapter 5 of my PhD dissertation, i.e., ‘Political Factors Affecting the EU Legislative Decision-Making Speed’. The data supporting the findings of this study are accessible and replicable. Restrictions apply to the availability of these data, which were used under license for this study. The datafiles include: File name of R script: Chapter 5 script.R File name of syntax: Syntax for replication 5.0.docx File name of the original output from R studio: The original output 5.0.pdf File name of code book: Codebook 5.0.txt File name of the analysis data: data5.0.xlsx File name of the dataset: Original quantitative data for Chapter 5.xlsx File name of the dataset: Codebook of policy responsiveness.pdf File name of figures: Chapter 5 Figures.zip Data analysis software: R studio R version 4.1.0 (2021-05-18) -- "Camp Pontanezen" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin17.0 (64-bit)

This repository contains the data and code necessary to reproduce the analysis and figures in the manuscript "Individual foraging site fidelity persists within and across stopover seasons in a migratory shorebird, Numenius phaeopus (Whimbrel)" by Handmaker et al. (2024).To run the analysis:Download this repository; all code and data is available in this repositoryIf necessary, extract the zipped repository to the desired location on your local machine.Open the whimbrel_site_fidelity.Rproj file in RStudio on a computer with internet access; RStudio requires R to be installed. This opens the RStudio project associated with the data, code, and analysis.All of the code necessary to process, analyze, and generate figures is located in the Scripts directory. Open and begin by running the code within 00_core_stopover_filter.R. Proceed sequentially through other similarly-named files. If interested in just one component of the analysis, all necessary data are loaded at the top of each script, so that each script can be run without running all previous files. If interested only in the figures, these are provided as a convenience in the Figures directory (and each can be recreated by running the code within 11_make_figures.R.)

In this study, we analyzed the efficiency of different adeno-associated viral (AAV) injections in transfecting neurons in the salamander Pleurodeles waltl. To query sources of variation in AAV injection outcomes, we analyzed metadata and outcomes for an additional 68 intraparenchymal injections performed 39 in post-metamorphic Pleurodeles salamanders. This dataset included both quantitative variables (age, weight, viral genomes (v.g.) injected, and a cell transduction score ranging from 0 to 4, see STAR Methods) and categorical variables (here referred to as qualitative variables: serotype, promoter, reporter, single vs. dual injection, manufacturer, and injection site). To assess the associations between these variables, we performed a Factor Analysis for Mixed Data (FAMD), a principal component method that is designed to determine significant sources of variability within datasets that contain both quantitative and qualitative data types. This dataset contains the injection outcomes and metadata for AAV injections administered in the salamander Pleurodeles waltl. The RactoMineR package (https://CRAN.R-project.org/package=FactoMineR) was used to determine significant contributions of a number of variables contributing to injection outcomes. Analysis of these data revealed that, among other factors, age co-varies with injection score. Therefore, we conclude that increased animal age decreases the efficacy of this tool.