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
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