Agronomy4future

In my previous post, I explained how to calculate Cook’s Distance step by step, and noted that in R you can simply use the function cooks.distance(). However, this simple function only provides the Cook’s Distance values. In my previous post, I explained the formula for Cook’s Distance step by step, including how to compute residuals, leverage, and internal studentized residuals. To make it easier to calculate these values, I recently developed an R package called datacooks(). □ datacooks() Before installing…

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When analyzing yield, we usually use bar or box plots based on the mean. However, sometimes we need to visualize the spatial variation of yield. In those cases, GIS maps are commonly used, often created with powerful software such as ArcGIS, which I have used and found to be excellent. The drawback is that ArcGIS is not free. If you want to create a simple yield map using R, it is possible, although the map quality may be slightly lower….

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In my previous post, I introduced the R code I developed to capture green color. This updated R function, colorcapture(), replaces the earlier greencapture(). 1. Install the pacakge Before installing colorcapture(), please download Rtools (https://cran.r-project.org/bin/windows/Rtools), and install the following package. □ colorcapture() 2. Basic code If you want to change other criteria, type ?colorcapture to see detailed information about the colorcapture() function. The default code is set to capture the yellow color. If you run the code, it will detect the yellow surface area….

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When measuring leaf area or fruit surface area, these have usually been measured manually, which is time-consuming and often inaccurate, especially when the shape is not perpendicular. For more reliable measurement, image analysis provides a good alternative. To facilitate this process, I developed an R function, greencapture() that captures the green area of leaves or fruits in the lab using a fixed frame. 1. Install the greencapture() package 2. Set up folders for input and output images I will save…

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When analyzing data, we sometimes need to analyze cumulative data. When calculating cumulative data, grouping is important, and it takes time to perform the grouping and calculations. To simplify this process, I developed an R package called datacume(). Let’s upload the dataset. This dataset contains biomass measurements across different treatments over time, recorded for various plants and their branches. I want to calculate the cumulative biomass over time. To do this accurately, I’ll first calculate the average biomass of the…

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https://github.com/agronomy4future/r_code/blob/main/geom_mark_ellipse.ipynb We aim to develop open-source code for agronomy ([email protected]) © 2022 – 2025 https://agronomy4future.com – All Rights Reserved. Last Updated: 19/07/2025

This post is part of a series introducing R packages that present methods for calculating phenotypic plasticity. □ [R package] Finlay-Wilkinson Regression model (feat. fwrmodel)□ [R package] Calculate the responsiveness of each treatment relative to a control (Feat. deltactrl)□ [R package] Quantifying Reaction Norm Plasticity from Slopes to Individual Responses (Feat. nrmodel) In my previous post, I explained how to quantify phenotypic plasticity in crops and described three different approaches: Responsiveness, Reaction Norm, and the Finlay-Wilkinson Regression Model. □ Quantifying…

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■ [R package] Embedding Key Descriptive Statistics within Original Data (Feat. descriptivestat) ■ [R package] Calculate the responsiveness of each treatment relative to a control (Feat. deltactrl) In my previous posts, I introduced two R packages. The first package, descriptivestat(), displays raw data along with mean values and additional descriptive statistics. The second package, deltactrl(), calculates the responsiveness of dependent variables in response to the control. Today, in this article, I will demonstrate how combining these two R packages allows…

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■ Quantifying Phenotypic Plasticity of Crops In my previous posts, I explained how to quantify phenotypic plasticity of crops in response to environmental factors, and introduced the concept of responsiveness, calculated using the formula: (Treatment – Control) / Control, as shown in the table below. Genotype Control Treatment Responsiveness A 100 90 -10.0% = (90-100)/100 B 120 70 -41.7% C 115 90 -21.7% D 95 85 -10.5% E 110 105 -4.5% While calculating responsiveness for a single variable is relatively…

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When analyzing data, we often need to examine descriptive statistics such as standard deviation, standard error, or the coefficient of variation (CV). These are typically summarized in a short table, but in some cases, it may be necessary to include such statistics directly in the original dataset for further analysis. Let’s upload a dataset to begin. I would like to calculate the mean, variance, standard deviation, standard error, 95% confidence interval, coefficient of variation, and the 25th percentile of yield…

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