[Tutorial] Statistical analysis and drawing of biological community (ecology) data in R language

View the original text>>>R language biological community (ecology) data statistical analysis and drawing practice

It also integrates "R Language Basics", "Tidyverse Data Cleaning", "Multivariate Statistical Analysis", "Random Forest Model", "Regression and Mixed Effects Model", "Structural Equation Model", and "Statistical Results Mapping" seven-in-one version plan

The open source, free, and free features of R language make it widely used in statistical analysis of biological community data. Biome data are diverse and complex, involving numerous statistical analysis methods. This course focuses on the most commonly used statistical methods in biological community data analysis, such as regression and mixed effects models, multivariate statistical analysis techniques, structural equations and other quantitative analysis methods. Through multiple examples from classic research, the R of each method is explained in detail. Language implementation approach (see teaching content for details). The main feature is to focus on the field of ecological research, from R language basic operations and drawing, data preparation and organization, to the application scenario analysis of various quantitative analysis methods, to achieve a complete scientific research data analysis process from data organization to analysis results display, and "R Language Basics", "Tidyverse Data Cleaning", "Multivariate Statistical Analysis", "Random Forest Model", "Regression and Mixed Effects Model", "Structural Equation Model" and "Statistical Results Mapping" were combined (7 combined 1). This article is not only suitable for beginners of R language and statistical analysis of biological community (ecological) data, but also for graduate students and scientific researchers with advanced application needs. Through a large number of examples, everyone can deal with complex data situations in scientific research work, choose appropriate models, and improve data analysis capabilities.

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Note: Please prepare your own computer and install the required software in advance.

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