Agronomy4future
  • CURRICULUM VITAE
  • BASIC STAT
  • STATISTICAL MODEL
    • ANOVA
    • REGRESSION
    • SPLIT-PLOT
    • GLM
    • Mixed Model
    • REML
    • NONLINEAR
  • CODING
    • R coding
    • SAS
    • PYTHON
    • Machine Learning
  • DATABASE
    • Structured query language (SQL)
    • EXCEL/VBA
    • Power Query
    • Data Science

Agronomy4future

Stories about cereals and statistics (plus coding). We aim to develop open-source code for agronomy.

ANOVA

ANOVA

■ ANOVA basic interpretation

  • Two-Way ANOVA: An Essential Tool for Understanding Factorial Experiments
  • Easy-to-Understand Guide to Factorial Experiments and Two-Way ANOVA

■ R2 interpretation

  • How to calculate and interpret R-squared in ANOVA?

■ F-Ratio interpretation

  • What is the F-ratio in statistics?

■ Residual interpretation

  • How to calculate pooled variance when blocks are included in the experimental design?
  • Understanding Mean Absolute Error (MAE) in ANOVA: A Step-by-Step Guide to Calculation in Excel

JK Kim 
([email protected])
"We aim to develop open-source code for agronomy"

• GitHub: github.com/agronomy4future
• YouTube:
@agronomy4future

annotate() aov () arrange() c(rep() case_when() colnames() data.frame() ddply () dplyr facet_grid() facet_wrap() filter() fwrmodel() gdds() geom_abline () geom_bar () geom_vline() github HSD.test() if() ifelse() intercept0() kimindex() mutate() nls() nlsLM() normtools() pivot_longer() poly() predict() probdistz() rbind() reshape2::melt() residuals() sapply() scale_fill_manual () spread() str_replace_all () subset() table() tdist() theme_grey() windows () write_xlsx() xtabs()

Local SQL servers limit data access. Cloud-based SQL offers better efficiency. I’ve simplified importing data to Cloud-based SQL with Python via Command Prompt. For big data, Cloud-based SQL servers are key.

* Full article: https://t.co/GSOscCcqY7 pic.twitter.com/5XMdHLgm8J

— J.K Kim (@agronomy4future) October 5, 2025

[R package] greencapture(): Segment and Measure Green Objects in Images. This function provides tools for capturing, and quantifying green plant tissue area from digital images. Designed for high-throughput in vitro or greenhouse image analysis (https://t.co/nVpyRZTAgh) pic.twitter.com/asilupDIut

— J.K Kim (@agronomy4future) September 29, 2025

[R package] datacume()
• Compute Cumulative Summaries of Grouped Data

Just released a new R function. This function helps you easily calculate cumulative values over time (or trials), grouped by category (also supports averaging).

□ code explained: https://t.co/7Az1kmXsEl pic.twitter.com/Vdjt41xwAD

— J.K Kim (@agronomy4future) September 7, 2025

Just released a new R function: #datacooks(). It adds diagnostic stats (residuals, leverage, studentized residuals, Cook’s distance, etc.) directly to your model dataset — then automatically spots and flags potential outliers.

□ code explained: https://t.co/nbwk2dwZtY pic.twitter.com/ZInoHebVAi

— J.K Kim (@agronomy4future) August 17, 2025

[R package] #rnmodel()
The concept behind this code is that plasticity can be quantified by estimating slopes for individual genotypes across environments. It offers an easy way to obtain these slopes for each specific environment.

□ Code explained: https://t.co/2KsHyADjWO pic.twitter.com/RDrPnsJXEF

— J.K Kim (@agronomy4future) June 23, 2025

Previously, I developed two R packages, descriptivestat() and deltactrl(). By combining these packages, it becomes easy to create a responsiveness graph.

[Data article] Visualizing Responsiveness: Integrating Raw Data for a Holistic Dataset View (https://t.co/Yz3I6bbMsF) pic.twitter.com/eBesZBJUY9

— J.K Kim (@agronomy4future) June 9, 2025

□ New R package: #deltactrl(); delta control

This package is designed to easily calculate the responsiveness of each treatment relative to a control.

□ Github: https://t.co/BYCSf7KU9v
□ Code explained: https://t.co/GlXvnhh4mV pic.twitter.com/iIhWHLDAAH

— J.K Kim (@agronomy4future) June 9, 2025

We’re working on measuring crop canopy with image analysis and comparing it to actual leaf area. If the model fits well, this could be a fast way to estimate canopy size. Currently setting up the frame and code. pic.twitter.com/GE33yujdKi

— J.K Kim (@agronomy4future) May 21, 2025

New R package: #descriptivestat()
This package automatically embeds descriptive statistics into the dataset, allowing for clear visualization alongside the raw data.

□ Github: https://t.co/tYJPmzvCzJ
□ Code explained: https://t.co/sPOqQwIDgu pic.twitter.com/EL330dIH21

— J.K Kim (@agronomy4future) May 18, 2025

When the intercept is forced to 0 in a simple linear regression, most software programs report an incorrect R². Therefore, I developed a new R package, #intercept0(), which provides the correct R².

□ Github: https://t.co/In3GS6szFI
□ Code explained: https://t.co/EAL3Rbeo6U pic.twitter.com/LJ2eoUlLeT

— J.K Kim (@agronomy4future) May 11, 2025
https://twitter.com/agronomy4future/status/1903192051631481171

This is a new R package I developed, #datazip(), which enables the simple conversion of data into a single line of code, making it easy to save as a script. Save your data as code for easy access.

□ Github: https://t.co/mNmhyldp98
□ Code explained: https://t.co/8zEgJe0pEF pic.twitter.com/SrkuzVRK7N

— J.K Kim (@agronomy4future) March 7, 2025

I developed an R package, #interpolate() to facilitate data interpolation, particularly by grouping. With this R package, you can easily predict intermediate data points based on actual data points.
□ Github: https://t.co/ClJuZPAMfH
□ Code explained: https://t.co/RRoaphRSi8 pic.twitter.com/u6SVdY1tBO

— J.K Kim (@agronomy4future) March 3, 2025

I will continue my #Agrivoltaics study at Cornell University. Over the past 1.5 years, I have conducted research on the source-sink strength of crops in response to shading at the University of Illinois Urbana-Champaign, and I look forward to gaining further insights at Cornell. pic.twitter.com/o1kxJLR32e

— J.K Kim (@agronomy4future) January 13, 2025

At the 2024 ASA meeting in San Antonio, I presented my current #agrivoltaics study and proposed distinct farming strategies for sorghum and soybean at pre- and post-anthesis, respectively, focusing on yield components (grain number and weight) in terms of source-sink strength. pic.twitter.com/QdV2kEP9zQ

— J.K Kim (@agronomy4future) November 19, 2024
https://twitter.com/agronomy4future/status/1855022899519692972
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https://twitter.com/agronomy4future/status/1689110911028473856
  • Agricultural Institute
  • AI
  • Article
  • Canada
  • Cereals
  • Conference
  • Crop breeding
  • Crop physiology
  • Data Science
  • España
  • Excel
  • Fertilizer
  • GHGs
  • Google Colab
  • hemp (Cannabis sativa)
  • JK column
  • JMP
  • Machine learning
  • Maize
  • Nederland
  • Principle-Centered Leadership
  • Python programming
  • R programming
  • SAS
  • Soil
  • Sorghum
  • source-sink dynamics
  • Soybean
  • SQL
  • Statistics
  • Tool
  • Uncategorized
  • United States
  • VBA
  • Wheat
  • How to Import Data to a Cloud MySQL Server (DigitalOcean) Using Python from Command Prompt
  • [R package] Segment and Measure Green Objects in Images (Feat. greencapture)
  • [R package] Compute Cumulative Summaries of Grouped Data (Feat. datacume)
  • [R package] Cook’s Distance Diagnostics and Outlier Detection (Feat. datacooks)
  • [R package] Streamlined Mixed-Effects Analysis for Agrivoltaics Experiments (Feat. agrivoltaics)
  • geom_mark_ellipse
  • [STAT Article] Statistical Models in Agrivoltaics: Linear Mixed Models Across Different Field Layouts
  • [R package] Quantifying Reaction Norm Plasticity from Slopes to Individual Responses (Feat. nrmodel)
  • [STAT Article] How to calculate reaction norm in crop physiology?
  • [Data article] Visualizing Responsiveness: Integrating Raw Data for a Holistic Dataset View
  • [R package] Calculate the responsiveness of each treatment relative to a control (Feat. deltactrl)
  • Amblyseius swirskii – Predatory Mite
  • The New York Night scene from the Airplane
  • pepper flowering initiated
  • [R package] Embedding Key Descriptive Statistics within Original Data (Feat. descriptivestat)
  • How to upload data from Google Drive to Google Colab in an R environment?
  • [R package] R-Squared Calculation in Simple Linear Regression with Zero Intercept (Feat. Intercept0)
  • How to download data from R environment in Google Colab?
  • Urea (46-0-0) application at the agrivoltaics field in 2024 and 2025
  • How to Upload Data from GitHub Using R and Python?
  • [Data article] Simulating Crop Growth Over Time Using a Sigmoid Growth Model
  • [STAT Article] RMSE Calculation with Excel and R: A Comprehensive Guide
  • What is split-plot design in agronomy research?
  • [R Package] Convert Data into Code Instantly – Save as a Script with One Line
  • [R package] An easy way to use interpolation code to predict in-between data points
  • [Data article] Predicting Intermediate Data Points with Linear Interpolation in Excel and R
  • How to Combine Files and Create a New Data Table in MySQL
  • How to Rename Variables within Columns in R (feat. case_when() code)?
  • How to convert to a .json file using Python?
  • How to Use Temporary Tables for Quick Calculations in MySQL?



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