Data Analysis Guides - Multidisciplinary
Data Analysis with SPSS for Survey-Based Research by
Call Number: HA32 .R66 2021 (4th floor)
Written for research students and early-career researchers to quickly and easily learn how to analyse data using SPSS. Rather than discuss fundamentals of statistics, this book focuses directly on the technical aspects of using SPSS to analyse data.
Moving from IBM® SPSS® to R and RStudio® by
Call Number: HA32 .T65 2022 (4th floor)
Been wanting to learn R and RStudio®, but you don′t know where to begin? Want to perform all the same functions you use in IBM® SPSS® in R? This is a concise guide for users who want to know learn how to perform statistical calculations in R. Chapters walk through differences between SPSS and R, in terms of data files, concepts, and structure. Detailed examples provide walk-throughs for different types of data conversions and transformations and their equivalent in R. Appendices provide tables of each statistical transformation in R with its equivalent in SPSS.
Data Analysis in R - Discipline Specific
Geographic Data Science with R: Visualizing and Analyzing Environmental Change by
Publication Date: 2023
Provides a series of tutorials aimed at teaching good practices for using time series and geospatial data to address topics related to environmental change It is based on the R language and environment, which currently provides the best option for working with diverse sources of spatial and non-spatial data using a single platform. Assumes that readers are familiar with basic geospatial data structures, such as vector and raster data, along with basic cartographic concepts such as projections and coordinate systems.
A Criminologist's Guide to R: Crime by the numbers by
Call Number: Read Online (one reader at a time)
Publication Date: 2022
Introduces the programming language R and covers the necessary skills to conduct quantitative research in criminology. By the end of this book, a person without any prior programming experience can take raw crime data, be able to clean it, visualize the data, present it using R Markdown, and change it to a format ready for analysis. Focuses on skills specifically for criminology such as spatial joins, mapping, and scraping data from PDFs, however any social scientist looking for an introduction to R for data analysis will find this useful.