오늘은 데이터 사이에 있는 값을 예측하기 위한 선형 보간법 (Linear Interpolation) 에 대해 설명하겠습니다. 예를 들어, 현장에서 데이터를 수집할 때 매일 데이터를 수집할 수는 없을 것입니다. 그래서 우리는 일정한 간격 (매주, 격주, etc.,) 으로 데이터를 수집합니다. 그러나 데이터를 제시할 때는 일별로 표시해야 할 경우가 발생 합니다. 예를 들어, 질소 비료 시비량이 0kg/ha, 30kg/ha, 60kg/ha, 120kg/ha 일 때 반응하는 작물의 수확량 차이를 조사한다고 가정해 보겠습니다. 0부터 120까지의 각 질소 비료량에서 수확량 차이를 나타내야 한다면 어떻게 데이터를 추정할 수 있을까요? 이런 상황에서…
Today, I’ll explain the interpolation technique used to predict in-between data points. For example, when collecting field data, we might not be able to gather information every day, so we establish our own interval (e.g., weekly or bi-weekly). However, when presenting the data, it might be necessary to show it on a daily basis. As another example, consider investigating yield differences in response to varying continuous variables, such as nitrogen at levels of 0, 30, 60, 120. What if we…
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…
All of a sudden, I became curious about this question, How many time do we need to compare each other according to group numbers?” and searched for the answer on a website, but I couldn’t find a clear answer. Therefore, I calculated it myself. For example, when there are two groups, we will compare them only once. When there are three groups, we need to compare each group with every other group, resulting in three comparisons. With four groups, we…
Sometimes, I see many people managing their data as columns like the example below. It seems convenient because we can see our data all at once. However, this data format is problematic for data analysis, which fundamentally relies on variables, namely independent and dependent variables. Download data file (.csv) https://github.com/agronomy4future/raw_data_practice/blob/main/yield_per_location.csv In the given data format, each level for the independent variable (i.e., location) was not combined in one column, and therefore we need to rearrange the data format. If the…