비 CS 전공자로서 코딩 공부는 늘 한계를 느끼곤 합니다. 혼자 코딩을 독학하며 현업에 필요한 프로그래밍 코드를 사용하고 있지만 가끔 “이 코드는 왜 이렇게 작동되는 것일까?” 에 대한 궁금증은 늘 가지고 있습니다. 그래서 여러 교육 플랫폼에서 온라인 강의를 들어봐도 대부분의 시각은 전공자에게 맞춰져 있기 때문에 저 같은 비 전공자가 따라 가기에는 종종 한계를 느끼곤 합니다. 최근 저 같은 비 전공자에게 아주 유익한 코딩 교육 플랫폼을 찾았습니다. 이름은 코드트리 (Code Tree) 입니다. 오늘은 이 코딩 교육 플랫폼에 대해 소개해 볼까 합니다. 참고로…
I have corn yield data (Mg/ha) that I want to visualize. First, let’s upload the data. First, I’ll create yield distribution data. Now, we can see that the general grain yield varies from 10 to 30 Mg/ha, with some outliers. I’ll create a yield map to visualize this variation. https://github.com/agronomy4future/python_code/blob/main/Python_GIS_Interpolating_and_Plotting_Corn_Grain_Yield_Data.ipynb If you have the ArcGIS program, you can create a more detailed GIS map.
In my previous post, I introduced stepwise regression to select the best model. I suggested that grain yield = -4616.47 + 10.53 * stem biomass + 41.03 * height, indicating that stem biomass and height are the most important variables affecting grain yield. ■ Stepwise Regression: A Practical Approach for Model Selection using R Now, I’ll find the best model using machine learning. This is a small dataset, which might not be suitable for machine learning, but it serves as…
SCOPUS is one of the largest abstract and citation databases, providing access to a wide range of peer-reviewed literature across various disciplines. It ensures researchers have access to high-quality, up-to-date academic papers, conference proceedings, and other scholarly materials. Pybliometrics is a Python library that streamlines the retrieval of bibliometric data from SCOPUS. It simplifies accessing and manipulating large datasets, saving researchers time and effort compared to manual data collection. Using Pybliometrics to mine academic papers from SCOPUS enables efficient data…
Today, I’ll introduce one method for data normalization, utilizing the biomass with N and P uptake data available on my GitHub. I also aim to create regression graphs illustrating the relationship between biomass and either nitrogen or phosphorus. First, I’ll generate a regression graph for biomass with either nitrogen or phosphorus to observe the data patterns. I notice a clear pattern between biomass and nitrogen. However, when combining nitrogen and phosphorus in the same panel due to their different data…
When creating a data table in R, it seems simple. How about using Python? © 2022 – 2023 https://agronomy4future.com
Kaggle is a popular online platform for data science and machine learning competitions, datasets, and tutorials. You can find high-quality data on Kaggle to practice data analysis. I have uploaded some of my data on Kaggle to share it with others. Recently, I’ve begun learning machine learning, and one of the most fundamental datasets for this purpose is the Titanic dataset. By visiting the website below, you can download the Titanic survivor data and practice machine learning with this foundational…
I have soybean yield data that I want to upload to Github and access from R. First, let’s upload the data to Github. The data should be in .csv format. Click Add file, choose Upload files, and, after uploading, select the Raw button to view the data in .csv format as text. and you can find the address for this data, starting with https://raw.githubusercontent.com/… Let’s copy this address. Next, I’ll bring this data into R from Github. Before that, let’s…
Before diving into in-depth data analysis, a crucial step is data preprocessing. This essential process not only ensures better data quality but also significantly improves the efficiency of your analysis. In this guide, I will introduce a range of powerful Python methods for data preprocessing, equipping you with the tools to optimize your data for more accurate and insightful analysis. I use Goolge Colab when using Python because it’s more user friendly. Please refer how to setup Google Colab in…
□ How to use Google Colab for Python (power tool to analyze data)? Last time, I introduced how to mount your Google Drive in Google Colab. This time, I will explain how to set a folder path in Google Colab. First, let’s establish the connection between Google Colab and your Google Drive. The code is as follows: Then, the folders from your Google Drive will appear in the left sidebar of Google Colab. I created a folder called “coding_practice” in…
In this session, using Python, I will draw a normal distribution graph using the actual data I collected. I measured individual grain areas for two wheat genotypes. For Cultivar_A, I measured the area for 1,225 grains and for Cultivar_B, I measured the area for 1,841 grains. Therefore, the total number of grain area data is 3,066. This is natural data collected by me in the actual cultivation field, and it would be interesting to see if it also follows a…
Today, I will explain the meaning of the F-value in testing for significance through statistical processing. Let me give you an example. Suppose we want to determine whether there are differences in the yield according to the varieties (A, B, C). The total experimental unit is 12 (3 varieties x 4 replicates). What would happen if there is a significant difference in yield among varieties A and C? If there is a large difference in yield between these varieties, the…
Google Colaboratory (aka. Colab) is a cloud-based platform that provides a Jupyter notebook environment and therefore users can write and run Python code. You don’t have to install Anaconda to use Jupyter notebook. If you have a google account, simply you can analyze data. Google Colab is a powerful tool for collaborative coding and data analysis, providing users with an easy-to-use platform with a wide range of features and resource. I introduce how to set up Google Colab. Step 1)…