京都大学 京都大学

Programming Practice (R)-E2 :For managing and analysing data

Numbering Code U-LAS30 20038 SE10 Year/Term 2022 ・ Second semester
Number of Credits 2 Course Type Seminar
Target Year Mainly 1st & 2nd year students Target Student For all majors
Language English Day/Period Mon.5
Instructor name Daniel Epron (Graduate School of Agriculture Professor)
Outline and Purpose of the Course R is a programming language whose purpose is to be able to process and organize data sets, and to represent these data graphically. Since the two last decades, R is widely used by scientists worldwide for data management and statistical analyses. This course aims to get students to start using R for analysing data and interpreting the output of basic statistical tests. Classes are taught in the form of practical exercises on computers.
Course Goals Upon successful completion of this course students will be able (i) to design and statistically analyse a simple experimental plan using R, (ii) to find and perform by themselves an accurate test for solving their scientific question, even if it has not been specifically addressed during the course and (iii) to produce smart graphics for the presentation of analysed data.
Schedule and Contents The course will simultaneously address how to use the R language to manage data, to implement relevant statistical tests and to generate graphical output
Course schedule:
1. Introduction
2. object in R: vectors, matrix, functions
3. data frame -importing data
4. Descriptive statistics
5. Programming with R and random numbers
6. Study of the distribution of quantitative variables
7. Importing, managing and analysing data (1)
8. Importing, managing and analysing data (2)
9. Linear model: linear regression
10. Importing, managing and analysing data (3)
11. Linear model: analysis of variance
12. Improving the quality of graphics for a presentation or report
13. Analysing a dataset: building the script and writing a report (1)
14. Analysing a dataset: building the script and writing a report (2)
15. Feedback
Evaluation Methods and Policy Grading: Homework (three to five, 50%), script and report based on the final exercise (50%).
In no case will English language proficiency be a criterion for evaluating students.
Class attendance is expected: students who are absent more than three times without sound reasons (documented unavoidable absence) will not be credited.
Course Requirements All students are welcome
Students will have to bring their own laptops to use in class that they will also use for homework. Students have to download and install R software and R-studio software before starting the course.
Study outside of Class (preparation and review) Work not finished in class time should be finished at home. Self-training is recommended: exercises will be provided.
Textbooks Textbooks/References Lecture notes will be provided before the class and R scripts will be provided after each class (uploaded on PandA).
References, etc. An Introduction to R (https://cran.r-project.org/manuals.html)
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