Instructor:

Taro Mieno:

  • Email: tmieno2@unl.edu
  • Office: 209 Filley Hall

Schedule

  • Lectures: MW 8:50 - 10:20 AM
  • Office Hours: by appointment

Course Description:

The goal of this course is to prepare students for jobs that require quantitative skills beyond Microsoft Excel and graduate programs. The R software is used throughout the course. In order to achieve the goal, students will be introduced to the basics of programming and how to apply it to real world issues in the field of agricultural (agricultural economics, agronomy, etc) and environmental sciences. By completing the course, students will know data wrangling (e.g., merging, transforming datasets), data visualization, and exploratory data analysis, spatial data management.

Reading Materials

  • Recommended:

    • Grolemund, Garrett. and Wickham, Hadley. 2019
    • Lovelace, Robin., Nowosad, Jakub., and Muenchow, Jannes. 2019

  • Prerequisites:

    • Introductory statistics (STAT 218) or equivalent

Grading

title

score

Assignments (2 assignments)

60%

Final Paper

40%

Total

100%

  • Assignments: There will be 2 assignments. Late submissions will have 1/3 of a letter grade deducted from the grade for that submission, increasing by an additional 1/3 grade for each 24 hours beyond the deadline.

  • Final Paper: In this assignment, you write a short paper with a particular emphasis on programming using real-world data sets. You must identify a topic that would involve collecting datasets from multiple different data sources. The topic has to be approved by me to avoid a final project without significant programming tasks by . The proposal of your final project detailing what datasets to use, where you collect them, and how you use them have to be submitted by .

Important Deadlines:

  • Final project topic approved by the instructor: October 30
  • Final project proposal: November 13
  • Final project submission: December 20

Topics Covered

  • Introduction to R
  • Quarto
  • Data Wrangling
  • Merge and reshape datasets
  • Data visualization
  • Miscellaneous data manipulations
  • How to write and organize codes
  • Research flow illustration
  • Writing your own function
  • Looping
  • Parallel computing
  • Create tables