R Programming: Using R For Agriculture Data Analytics

R Programming: Using R For Agriculture Data Analytics, available at $34.99, has an average rating of 4.05, with 44 lectures, 9 quizzes, based on 13 reviews, and has 98 subscribers.

You will learn about R Programming Agriculture statistics using R Reporting using R Data vizualisation in R This course is ideal for individuals who are anyone interested by agriculture data analysis It is particularly useful for anyone interested by agriculture data analysis.

Enroll now: R Programming: Using R For Agriculture Data Analytics

Summary

Title: R Programming: Using R For Agriculture Data Analytics

Price: $34.99

Average Rating: 4.05

Number of Lectures: 44

Number of Quizzes: 9

Number of Published Lectures: 44

Number of Published Quizzes: 9

Number of Curriculum Items: 53

Number of Published Curriculum Objects: 53

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

R Programming
Agriculture statistics using R
Reporting using R
Data vizualisation in R

Who Should Attend

anyone interested by agriculture data analysis

Target Audiences

anyone interested by agriculture data analysis

Get The Secrets to use R and RStudio to Analyse your Agriculture Data with Practical Coding Exercises

The course is designed to provide students with a comprehensive introduction to data collection, analysis, and reproducible report preparation using R and R studio. The course focuses on the context of environmental and agricultural science, as well as environmental and agricultural economics, to provide relevant examples to students.

Throughout the course, students learn to identify the appropriate statistical techniques for different types of data and how to obtain and interpret results using the R software platform. The course covers various statistical methods such as ANOVA, linear regression, generalized linear regression, and non-parametric methods. Online lectures are used to explain and illustrate these methods, and practical computer-based exercises are provided to help students develop their knowledge and understanding of each approach.

In addition to statistical methods, the course also introduces basic programming concepts that allow R to be used for automating repetitive data management and analysis tasks. Students are also exposed to the advanced graphics capacity of R and learn about the workflow for reproducible report generation.

Upon completion of the course, students will have the knowledge and skills necessary to undertake data analysis at a standard that meets most workplace demands using R. This course provides a strong foundation for further study and application of data analysis techniques, making it an essential course for students pursuing careers in environmental and agricultural sciences or related fields.

Overall, the course aims to equip students with practical skills and knowledge for data analysis and report generation in the context of environmental and agricultural sciences, which will help them become better-prepared professionals in their future careers.

Course Curriculum

Chapter 1: Using R for Agricultural Data: First important skills

Lecture 1: R Programming: Install R and Rstudio

Lecture 2: R Programming: Create a new project in RStudio

Lecture 3: R programming: Generate reports, notebooks, pdf, html and word document form R

Chapter 2: R Programming: The basics

Lecture 1: R Programming: Master the Basics of R

Lecture 2: R Programming: All the basic codes you need

Chapter 3: R Programing: Descriptive Statistics on Agricultural Experimental Data

Lecture 1: R Programming: Let's understand together the data we will use for this module

Lecture 2: R Programming: Compute group means

Lecture 3: R programming: Compute other group statistics

Lecture 4: R Programming: Let's understand this second data

Lecture 5: R programming: Summarize agricultural data using dplyr package

Chapter 4: R Programming: Simple Correlation [Response of winter wheat to saflufenacil]

Lecture 1: R Programming : View the data and the metadata for the study in R

Lecture 2: R Programming: Simple Correlation [Response of winter wheat to saflufenacil]

Chapter 5: R Programming: Compare yields with Student t-test !

Lecture 1: Interpreting P Value

Lecture 2: R programing: One-sample t-test in R

Lecture 3: R Programming : Two-sample t-Test in R

Lecture 4: R Programming: Paired t-Test in R

Chapter 6: R Programming: Analyse of Variance (ANOVA)

Lecture 1: R programming: One-Way Analysis of Variance in R

Lecture 2: R Programming: Multi-Factor ANOVA in R

Chapter 7: R Programming: Linear Regression in R

Lecture 1: R Programming: Linear Regression in agriculture using R

Lecture 2: R Programming: Multilinear regression in agriculture using R

Chapter 8: R Programming: Analysis of Covariance using R

Lecture 1: R Programming: Mechanical Weed Control analysis using R

Chapter 9: Summary

Lecture 1: Mind map

Chapter 10: R Programming: R packages for Agricultural Research

Lecture 1: Agricultural & land use databases

Lecture 2: Agricultural data sets

Lecture 3: General analytical packages supporting agricultural research

Lecture 4: R Packages for Agricultural economics

Lecture 5: Agrometeorology

Lecture 6: Agronomic trials

Lecture 7: Agronomic trials: High throughput phenotyping (HTP)

Lecture 8: Trial analysis

Lecture 9: Animal science

Lecture 10: Breeding & quantitative genetics

Lecture 11: Linkage mapping & QTL analysis

Lecture 12: GWAS

Lecture 13: Genomic prediction

Lecture 14: Crop growth models & crop modelling

Lecture 15: Entomology

Lecture 16: Food science

Lecture 17: Genotype-by-environment interactions

Lecture 18: Plant pathology

Lecture 19: Rural sociology

Lecture 20: Soil science and precision agriculture

Lecture 21: Weed science

Lecture 22: Additional links

Instructors

Mawunyo Simon Pierre KITEGI
Researcher and Entrepreneur

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 4 votes
4 stars: 3 votes
5 stars: 5 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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