R Programming: Using R For Agriculture Data Analytics

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Mawunyo Simon Pierre KITEGI

2:16:19

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1. R Programming The first skills you need to become a master in R Programming

1. R Programming Install R and Rstudio.html

2. R Programming Create a new project in RStudio.mp4

03:16

3. R programming Generate reports, notebooks, pdf, html and word document form R.mp4

07:21

2. R Programming The basics

1. R Programming Master the Basics of R.mp4

17:35

2.1 chap1.pdf

2. R Programming All the basic codes you need.html

3. R programing Beginning PRATICE.html

3. R Programing Descriptive Statistics on Agricultural Experimental Data

1. R Programming Let's understand together the data we will use for this module.mp4

06:33

2.1 chap2.pdf

2. R Programming Compute group means.mp4

07:34

3. R programming Compute other group statistics.mp4

10:52

4. R Programming Statistics Test.html

5. R Programming Lets understand this second data.mp4

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6. R programming Summarize agricultural data using dplyr package.mp4

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7. R Programing Statistics Test Two Summarize data.html

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

1. R Programming View the data and the metadata for the study in R.mp4

04:55

2.1 chap3.pdf

2. R Programming Simple Correlation [Response of winter wheat to saflufenacil].mp4

04:11

3. Simple correlation practice in R.html

5. R Programming Compare yields with Student t-test !

1. R programing One-sample t-test in R.mp4

04:52

2.1 chap4.pdf

2. R Programming Two-sample t-Test in R.mp4

05:47

3. R Programming Paired t-Test in R.mp4

03:46

4. R Programming Student t-test practice. write your own code R code.html

6. R Programming Analyse of Variance (ANOVA)

1. R programming One-Way Analysis of Variance in R.mp4

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2. R Programming Analyse of Variance in R.html

3. R Programming Multi-Factor ANOVA in R.mp4

03:48

4. R Programming Multi-Factor ANOVA in R.html

7. R Programming Linear Regression in R

1. R Programming Linear Regression in agriculture using R.mp4

08:06

2. R programming Linear regression.html

3. R Programming Multilinear regression in agriculture using R.mp4

05:14

4. Multilinear regression using R.html

8. R Programming Analysis of Covariance using R

1. R Programming Mechanical Weed Control analysis using R.mp4

06:27

Description

Master Agriculture Data Analysis with R: Tune In Now for Live Coding Exercises!

What You'll Learn?

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

Who is this for?

anyone interested by agriculture data analysis

What You Need to Know?

None

More details

Description
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.
Who this course is for:
anyone interested by agriculture data analysis

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R experts are skilled programmers who are knowledgeable in using R for statistical computing and data analysis.Run by Mawunyo Simon Pierre KITEGI, skilled R programmer with expertise in data science, particularly in the fields of agriculture, climate, and finance. With his extensive knowledge and experience in R programming, he has been able to develop reproducible analytics that have revolutionized the way data is analyzed and utilized in these fields.His background in agriculture and climate change has given him a unique perspective on how data can be leveraged to make better decisions and improve outcomes in these industries. He has worked with large datasets to uncover insights and patterns that would have been impossible to discover using traditional methods. He has also created innovative visualizations and interactive dashboards that allow stakeholders to quickly and easily understand complex data.One of the key benefits of his expertise in R programming is the ability to create reproducible analytics. By using R, he can write scripts and code that can be easily shared and replicated, ensuring that others can verify and build upon his work. This is especially important in fields like agriculture, climate, and finance, where the stakes are high, and the accuracy of data analysis is critical to success.As a bilingual R programmer, he is able to communicate his findings and insights effectively in both French and English. This has allowed him to collaborate with colleagues and stakeholders from diverse backgrounds and ensure that his work is accessible to a wider audience.His expertise in R programming and data science has been essential in advancing the fields of agriculture, climate, and finance. His ability to create reproducible analytics has allowed for better decision-making and improved outcomes, while his bilingualism has enabled effective communication and collaboration with colleagues and stakeholders.---------------------Communicateur et designer, coach en design de présentation. Il dispose de plus de 6 ans d’expérience en design de présentation.Spécialiste en communication, marketing et analyse des données, il est un passionné de la communication d'impact. Pour lui dans sa carrière d'entrepreneur chercheur la communication est la fondation de tout succès.Il faut parler pour convaincre, pour faire passer un message, pour retenir des investisseur, la liste est longue…Entrepreneur et chercheur il dispose de beaucoup de compétences en communication. Il décide de partager ses acquis…

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