授業科目名 年度 学期 開講曜日・時限 学部・研究科 全担当教員 単位数
50574:[2025 春セメスター]Policy Information Processing 2(P) 2025 春セメスター 金3 政策科学部 上原 拓郎 2

キャンパス

OIC

授業施設

H243セミナールーム

授業で利用する言語

English

授業の概要と方法

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.

受講生の到達目標

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.

事前に履修しておくことが望まれる科目

Statistics

授業スケジュール

授業回数/
担当教員（複数担当の場合） 		テーマ
キーワード・文献・補足事項等
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.

授業実施形態

Face-to-face

授業外学習の指示

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.

成績評価方法

種別 割合(%) 評価基準等
定期試験（筆記）

レポート試験
（統一締切日を締切とするレポート）

上記以外の試験・レポート、平常点評価
（日常的な授業における取組状況の評価）		 100

short quizzes and a presentation.

成績評価方法(備考)

受講および研究に関するアドバイス

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

教科書

書名 著者 出版社 ISBNコード 備考
Quantitative Social Science Data with R: An Introduction Brian J. Fogarty SAGE Publications 9781529790467 2nd edition

教科書(使用頻度、その他補足)

参考書

参考書(使用頻度、その他補足)

参考になるwwwページ

授業内外における学生・教員間のコミュニケーションの方法

学生との直接対話

備考

【科目ナンバリング・カリキュラムマップはこちらから/Click here to see the Curriculum-Map and Course-Numbering】
URL:https://secure.ritsumei.ac.jp/students/pathways-future/course/curriculum.html/