R Programming Training

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R Programming TrainingAn Introduction for Data Analysis and GraphicsUF INFORMATION TECHNOLOGYSpring 2021Authored by: Jose Lorenzo Silva-Lugo, Ph.D.

R Programming TrainingAn Introduction for Data Analysis and GraphicsInstructorDr. Jose Lorenzo Silva-Lugo, HUB 285, Phone: (352) 273-1579. Email: joselugo@ufl.edu.Office Hours: TBA.Textbooks Field, A, J. Miles, and Z. Field. 2012. Discovering Statistics using R. Sage Publications Ltd,London.Michael, W. Trosset. 2009. An Introduction to Statistical Inference and its Application withR. A Chapman & Hall Book, CRC Press. Boca Raton, Florida.Muenchen, R. A. 2011. R for SAS and SPSS Users. 2nd Edition. Springers, New York.William N. Venables, D. M. Smith, and the R Development Core Team. 2009. AnIntroduction to R. 2nd edition. Network Theory, Ltd.Training ScheduleFrom February 22nd (8:00 am) to April 16th (5:00 pm), 2021Location: online (self-paced)Goal and ObjectivesR Programming Training Spring 2021These training series are an introduction to the program language R specifically designed forfaculty, staff, postdocs, graduate students, and teaching assistants. The purpose is to get familiarwith the R commands in such a way that participants will feel self-prepared to continue by theirown. After completing these trainings, participants will be able to:1 Install R, install and load packets and entering dataManage the data set and prepare it for analysisExplore the data set with descriptive statistics, normality test and graphicsPerform elementary statistical analyses such as two or more mean comparison, one-way andtwo-way ANOVA, correlation, bivariate and multiple linear regression, proportioncomparisons, and the logistic regression.Training Series DescriptionThis is an introductory R training for participants with basic knowledge in statistics at thegraduate level. It is oriented toward the Natural and Social Science. Participants should befamiliar with all basic concepts and the statistical procedures presented in the objectives.Therefore, participants should have taken at least a graduate course in statistics before

enrolling because these trainings summarize the most important statistical analyses included inthree graduate courses in the Statistics Department: STA 6166 Statistical Methods in Research I,STA 6126 Statistics Methods in Social Research I and STA 6127 Statistic Methods in SocialResearch II. However, it is important to highlight that although these training series coverseveral topics in statistics, it is not a substitute of any of the courses mentioned above. R is just astatistical program language, and the training focuses on explaining how to use this language tocarry out the statistical analysis.The first training introduces data input with the programming language called ‘S’. Then, it willdescribe how to format different types of data and how to summarize them, which they areessential steps before the actual analysis. The ability of R to simulate different types of randomdata allows you to carry out simulations in order to find out what type of distribution your datamight have. During this process, graphic representation of the distribution of your data set iscritical, and R is particularly very handy providing a variety of charts. Then, statistical inferencecomes into place by formulating hypotheses for testing means and medians. In this regard, thetwo-sample t-test and ANOVA are very useful and common statistical analyses. In addition,regression and correlation are other important components in any classical statistical analysis.After that, cross-tabulation will allow us to do another type of analysis: goodness of fit andindependence. Finally, the logistic regression closes the training series with a sophisticated wayto carry out the analysis. Therefore, these training series present you a free program language tocarry out the most common statistical analyses.Participants can register in any of the eight training sessions. However, if you are a beginner, it isstrongly recommended to attend the first three trainings because the skill learned in thesetrainings will be used later in other trainings.Training Content1. Introduction to R: What is R? Installing R, R Studio and R Commander Installing and loading packages Loading data sets from a computer Mathematical operations with objects Working with vectors Data structures Working with functionsR Programming Training Spring 2021The teaching technique will follow the classical training session composes of lecturing andactivities, in which students take the dynamic role of watching videos, asking questions, andworking on hands-on activities. Lectures and activities will always be together to provideknowledge and to practice the skill. Class is 100% online and self-paced.2

2.3.R Programming Training Spring 20214.35. Working with data sets Working with graphics Getting help with RData Preparation: Detecting anomalies Working with missing values Removing duplicate cases Dealing with outliers Aggregating cases Normalizing variables Merging filesExploratory data analysis: Performing exploratory data analysis for one or more variables Creating charts for one variable Transforming data Computing a new variable:* SqRt, Log10/Ln, and 1/X* Mathematical Operations ( , -, *, /)* Raising to the power* Trigonometric transformations Transforming scale data into an ordinal/binary one Making decisions about the statistical analysis to follow based on the exploratory dataanalysis.Mean and Median Comparison: Making inferential statistics about the mean and the median Appling the parametric t-test:* One-sample t-test* Two-independent samples t-test* Paired difference t-test Applying non-parametric t-test:* Sign test* Mann-Whitney U Test* Wilcoxon Signed Rank Test Build charts for two mean comparison Carry out t-test involving violations of normalityANOVA, Correlation, and Bivariate Linear Regression: Making inferential statistics by using one-way ANOVA and two-way ANOVA Executing the Kruskal-Wallis test (non-parametric of one-way ANOVA) Performing bivariate correlation parametric and non-parametric

Carry out bivariate linear regression parametric and non-parametric6. Multiple Linear Regression: Making EDA and charts for more than 2 variables Multiple linear regression and correlation Model building and selection Quantile regression7. Proportion Comparison: Two proportions comparison Chi-squared goodness of fit tests Chi-squared tests of independence8. Logistic Regression: Demonstrating the general procedure to carry out the logistic regression with scale,binary and categorical predictor variables:* Fit the model to the data* Estimate and interpret regression coefficients, the odds ratio, and confidence intervals* Estimate the probability of occurrence Carrying out exercises with the combination of binary, categorical, and scale predictorvariables. Testing the linearity assumptionGENERAL NOTICE TO STUDENTSTraining Policies You are responsible for following the workflow during training and for studying all materialsand resources posted in Canvas You are responsible for submitting all activities during the training sessions If you have questions, please contact me at joselugo@ufl.eduSoftware UseAll faculty, staff, and students of the University are required to obey the laws and legalagreements regarding software use. It is Illegal to copy licensed and/or copy written materials.This is a third-degree felony under Florida law. Failure to do so can lead to monetary damagesand/or criminal penalties for the individual violator. Because such violations are also againstUniversity policies and rules, disciplinary action will be taken as appropriate. The Office ofR Programming Training Spring 2021Students with DisabilitiesIf you need classroom accommodation because of a disability, you must register with the Deanof Students Office (http://dso.ufl.edu/drc). This office will provide you several forms, and one ofthem must be turned in to the instructor. Since some of these accommodations require time to bein place, I will appreciate that the form is giving to me with two weeks in advanced.4

Academic Technology and the members of the University of Florida community, pledge to holdour peers and ourselves to the highest standards of honesty and integrity.UF Counseling ServicesResources are available on campus for students having personal problems or lacking clear careerand academic goals that interfere with their academic performance. These resources include: R Programming Training Spring 2021 5U Matter We Care, 352-294-2273 umatter@ufl.edu, help for students in distressCounseling and Wellness Center, 3190 Radio Road, 392-1575, personal, sexual assault, andcareer counselingCareer Resources Center, Reitz Union, 392-1601, career development assistance andcounseling

Manage the data set and prepare it for analysis Explore the data set with descriptive statistics, normality test and graphics Perform elementary statistical analyses such as two or more mean comparison, one-way and two-way ANOVA, correlation, bivariate and multiple linear regression, proportion

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