Agronomy4future

When uploading data to R, we sometimes worry about losing track of the data over time. This is because we save data in different folders according to various projects, and we might forget where we stored it. Additionally, if the file path changes, it can be difficult to upload the data directly and locate its current location. Therefore, a better approach is to save the data as code, allowing us to access it directly when opening the R file where…

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In my previous post, I explained how to calculate interpolation to predict in-between data points. ■ [Data article] Predicting Intermediate Data Points with Linear Interpolation in Excel and R To make interpolation calculations easier, particularly for groups, I recently developed a new R package, interpolate(). First, let’s upload a dataset. This dataset contains chlorophyll content measurements for sorghum and soybean. I measured chlorophyll content every 10 days between 65 and 125 days after sowing, with four replicates at each time…

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■ [Data article] Data Normalization Techniques: Excel and R as the Initial Steps in Machine Learning In my previous post, I explained how to normalize data using various methods and demonstrated how to perform the calculations for each method. To simplify these calculations, I recently developed an R package that easily generates normalized data. 1. Install the normtools() package 2. Basic code format 3. Practice with actual dataset (data upload) 4. Normalize data 4.1. Z-test normalization 4.2. Robust Scaling 4.3….

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■ Python GIS: Interpolating and Plotting Corn Grain Yield Data In my previous post, I explained how to create a GIS map using Python. Today, I’ll introduce how to create the same GIS map using R. First, let’s install all the required packages. and I’ll upload a dataset for practice. Next, I’ll extract columns for latitude, longitude, and y (output) and I’ll interpolate data Finally, I’ll create a GIS map using ggplot(). Full code If you copy and paste the…

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I want to create a normal distribution graph with a specific variance. First, it’s necessary to create the data. I’ll generate data with a mean of 100 and a variance of 100 (which means the standard deviation is 10). However, it’s important to establish a range. To do this, I’ll set up a range of 6σ, and the dataset will contain 1,000 rows. and I’ll create a normal distribution graph. These are graphs with different variances, ranging from 1σ to…

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■ Introduction ■ What is Probability Density Function (PDF) and Cumulative Distribution Function (CDF): How to calculate using Excel and R? In my previous post, I explained what the Probability Density Function (PDF) and the Cumulative Distribution Function (CDF) are. I also explained the formula for the PDF and demonstrated how to manually calculate it in Excel. Additionally, I mentioned the Excel function that performs the same calculation for the PDF, as follows: I then introduced how to create a probability distribution…

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Let’s say we collected relative light intensity data across a wide range of the light spectrum in an LED experiment. and I’d like to create light spectrum curves regarding relative light intensity. First, I’ll define wavelength colors. The color at different ranges of wavelengths is always the same, so if we run this code, we can obtain the same color range at wavelength (which would be the x-axis of the graph). and let’s create curve graph. I’ll highlight the ranges…

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Here is one dataset, and I’ll use facet_wrap() to create bar graphs. First, let’s summarize the data. Then, I’ll create a bar graph using facet_wrap() to divide panels by irrigation. Now, I want to draw a y-axis border for the ‘Irrigation_Yes’ panel. We can achieve this simply by adding scales=”free”. © 2022 – 2023 https://agronomy4future.com

Setting up experimental design according to your experiment goal is the first step to achieve your experiment’s success. In Agronomy studies, experimental design involves the combination of treatments deployed in the field, and these treatments should be randomized. Randomization is important in experimental design as it helps our experiments avoid biases due to physical or biological factors. Of course, there are no specific, unconditional rules for randomization. In a very old-fashioned way, you can write treatment numbers on paper, and…

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Here is one dataset Let’s check the data type of each variable. The time column is in character format. When opening the data in Excel, it is considered text. I wish to create a time series graph, but this cannot be accomplished when the variables are in text format. Therefore, we need to convert the text to a time format. Now we can adjust time using scale_x_datetime() full summary: https://github.com/agronomy4future/r_code/blob/main/How_to_convert_character_to_POSIXct_format_in_R.ipynb © 2022 – 2023 https://agronomy4future.com

full code: https://github.com/agronomy4future/r_code/blob/main/In_R%2C_how_do_I_divide_the_legend_into_two_rows_in_a_graph%3F.ipynb © 2022 – 2023 https://agronomy4future.com

I have two datasets. Now, I want to combine these two datasets, but the row numbers differ between the two datasets. In dataB, the 3rd replicate for Tr1 and the 2nd replicate for Tr3 were deleted due to environmental errors. In this case, simply combining the two datasets is not feasible. One solution is to merge them row-wise using the rbind() function. This way, the two datasets will be combined by row. However, my goal is to combine the two…

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