Course Name Year Term Period Faculty / Graduate School All Instructors Credits
50574:[2025 Spring]Policy Information Processing 2 (P) 2025 Spring Fri3 College　Of　Policy　Science UEHARA TAKURO 2

Campus

OIC

Class Venue

Seminar Room H243

Language

English

Course Outline and Method

This course equips students with the knowledge and skills to analyze quantitative data to elicit policy implications. It focuses on R, a free software platform used for data processing, data analysis, and graphics. R provides an environment for implementing almost any data analysis method, including basic statistical analysis, factor analysis, structural equation modeling, econometric analysis, and content analysis, thoroughly preparing students to conduct data analyses when writing a graduation thesis. Students also study how to use a generative AI to enhance their knowledge and skills in R.

Student Attainment Objectives

Students will become familiar with the use of R and RStudio.
Students will become familiar with basic statistical analyses using R.
Students will be able to analyze data for their own research using R.
Students will gain a foundation to develop skills for advanced analyses using R in the future.

Recommended Preparatory Course

Statistics

Course Schedule

Lecture/Instructor（When there are multiple instructors) Theme
Keyword, References and Supplementary Information
1

What is R?

【+R-Jugyo】Information for “+R-Jugyo” will be posted on manaba+R.
The R environment; things we can do with R; installing and running R packages

2

Data frames and variable types

Understanding types of variables; changing variable types; managing data frames

3-4

Basic mathematical operations

Basic mathematical operations; vector operations (finding means, medians, and standard deviations); solving mathematical problems

5

Data management

Reading data; convert variable types; cleaning data

6

Variables and manipulation

Recording variables; transforming variables; creating variables; creating dummy variables

7

Univariate and descriptive statistics

Measures of central tendency; measures of dispersion

8

Visualizing data

Visualizing data using ggplot2

9-10

Hypothesis testing

t-test; nonparametric differences test

11-12

Bivariate analysis

Crosstabulations; correlation; Chi-squared test; Fisher’s exact test

14

Presentations

Students present their projects using R.

Class Format

Face-to-face

Recommendations for Private Study

Students will be directed to the website and the data sets by the textbook publisher, which can be used for individual practice. Homework assignments will help students practice the skills learned in class.

Grade Evaluation Method

Kind Percentage Grading Criteria etc.
Final Examination (Written)

Report Examination
(A report to be submitted by the unified deadline)

Exams and/or Reports other than those stated above, and Continuous Assessment　
(Evaluation of Everyday Performance in Class)		 100

short quizzes and a presentation.

Grade Evaluation Method （Note）

Advice to Students on Study and Research Methods

Students are encouraged to apply R to their research to reinforce their R knowledge.

Textbooks

Title Author Publisher ISBN Code Comment
Quantitative Social Science Data with R: An Introduction Brian J. Fogarty SAGE Publications 9781529790467 2nd edition

Textbooks （Frequency of Use, Note）

Reference Books

Reference Books （Frequency of Use, Note）

Web Pages for Reference

How to Communicate with the Instructor In and Out of Class(Including Instructor Contact Information)

Talk with Students

Other Comments

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URL:https://secure.ritsumei.ac.jp/students/pathways-future/course/curriculum.html/