Example of a Time Series Graph for Temperature Using R
© 2022 – 2023 https://agronomy4future.com
A Practical Approach to Linear Mixed-Effects Modeling in R
A Linear Mixed-Effects Model (LMM) is a statistical model that combines both fixed effects and random effects to analyze data with repeated measurements or hierarchical structure. Let’s break down the key components and concepts of a Linear Mixed-Effects Model: 1) Fixed Effects: 2) Random Effects: 3) Linear Mixed-Effects Model Equation: The general equation of a Linear Mixed-Effects Model can be written as: Y= Xβ + Zb + ε 4) Estimation: In summary, Linear Mixed-Effects Models are a powerful statistical tool…
Step-by-Step Guide to Calculating and Adding Variable Means in R
Here is one dataset. I want to add the mean of each treatment to a new column, and I am using the following code. However, the code is quite lengthy. Let’s simplify it using tapply() How about there are more variables? Now, I want to add the mean of the combination of treatment and environment. I want to calculate the mean of combination between A and North This value is the same as that in the column. © 2022 –…
Vertical Text Alignment in R
Enhancing Visualizations: Manipulating Color and Shape in R with Two Variables
I have one dataset as below. Now, I’ll create a regression graph between grain number (GN) and average grain weight (AGW). I distinguished genotypes with different colors, and now I want to differentiate resistance (yes and no) using distinct shapes. Therefore, I’ll be changing the shape representation from genotype to resistance. However, the color is not currently applied to the legend. I aim to apply the provided color to the legend, and additionally, assign colors to represent different levels of…
Exploring Best Linear Unbiased Estimators (BLUE) through Practical Examples in R
□ The Best Linear Unbiased Estimator (BLUE): Step-by-Step Guide using R (with AllInOne Package) In my previous post, I explained how to use R to perform the Best Linear Unbiased Estimator (BLUE). Now, this is a practical exercise focusing on BLUE in R. Here is one dataset. I have data on grain number (GN) and average grain weight (AGW) in winter wheat for about five genotypes and one transgenic line. The study examines the response to disease resistance (yes or…
Performing Regression Analysis in R with Variables in the Same Column
When analyzing regression, we typically assume that two continuous variables are situated in separate columns, allowing us to easily designate them as x and y. However, in many cases, data is organized vertically, and variables of interest are found within the same column. This vertical structuring is, in fact, the fundamental data arrangement when conducting data analysis. Now, let’s delve further into the discussion by examining the dataset. Let’s proceed by uploading the dataset.” This data pertains to iron content…
Equivalent Functions: IF function in Excel vs. ifelse() in R
When working with Excel, I believe you use the IF function from time to time, especially when categorizing values. The IF function is particularly useful for this purpose. Here is one example. I want to categorize organic matter (%) by unit 1.0. This process involves converting numeric variables to categorical variables. To achieve this, I have used the IF function as shown above. Then, you can categorize organic matter in the F column as shown above. Now, my next question…
Efficient Multivariate Summary in R: A Guide to Analyzing Multiple Independent Variables
In my previous post, I introduced how to summarize data, such as mean, standard deviation, and standard error. However, at that moment, I demonstrated how to summarize only one variable. □ Streamlined Data Summary in R STUDIO: Enhancing Bar Graphs with Error Bars Now, let’s discuss this further with a dataset. I would like to summarize the Yield data, including the mean, standard deviation, and standard error. I’ll use ddply() Now, I also want to summarize variables GN and AGW….
Converting Rows to Columns in R: A Guide to Transposing Data (feat. pivot_wider and pivot_longer)
When data is arranged, it can be structured either vertically (row-based) or horizontally (column-based). The choice depends on your preference for organizing data. However, when running statistics, data should be arranged row-based, as variables need to be in the same column. On the other hand, when calculating per variable, it is much easier to organize data column-based, allowing for simpler calculations. Regardless of the approach, well-organized data is essential, and the ability to restructure data is a valuable skill. Today,…
A Guide to Normalizing Data for Different Treatments in R
I have data, as shown below, regarding iron contents in soil and the plant uptake of iron at different growth stages in winter wheat. I want to analyze the relationship between the iron content in the soil and the plant uptake of iron at different growth stages in winter wheat. We can simply draw a regression graph. However, before doing that, we need to reshape the data. I’ll transpose the data from rows to columns based on the variables in…
How to delete and change specific texts within a column in R?
When we want to change texts within a columns, you can have several methods which I already introduced before. □ How to Rename Variables within Columns in R? However, changing all texts and specific texts would be different. Let’s upload a data. Now, we can change the variables name as following code: How about changing the text in the ID column? I want to remove ‘Delta_’ and keep only the numbers. Will you change the text one by one as…
How to Upload and Combine Multiple Files In R?
In a folder, I have 5 different .csv files. I want to upload these files to R and combine all of them because the data format (number of columns and structure) is the same. While you can certainly upload them one by one, imagine a scenario where you have 100 datasets. Will you upload all 100 of them individually? No! That would be a waste of time. In such cases, you can use a simple code to upload multiple files…
How to draw boxplot graph using R?
Calculating Predicted Values for Each Group in Basic Modeling
□ The Best Linear Unbiased Estimator (BLUE): Step-by-Step Guide using R (with AllInOne Package) In my previous post, I explained how to estimate dependent values from fitting models. Now I’ll explain how to add this predicted value to the original data using R. First, let’s upload data to R. Now, I’ll predict yield using the model. I believe that ‘row’ represents a random factor for each treatment, so I’d like to adjust the residuals using BLUP (Best Linear Unbiased Predictor),…
How to calculate responsiveness in response to control using R?
In my previous post, I explained how to quantify phenotypic plasticity and introduced the concept of ‘responsiveness.’ □ Quantifying Phenotypic Plasticity of Crops I introduced a formula to calculate responsiveness as (Treatment – Control) / Control. Genotype Control Treatment Responsiveness A 100 90 -10.0% B 120 70 -41.7% C 115 90 -21.7% D 95 85 -10.5% E 110 105 -4.5% However, when analyzing data, the format may not always be the same as above. Mostly, treatments (independent variable) are arranged in…
How to customize the title format in facet_wrap()?
□ Graph Partitioning Using facet_wrap() in R Studio By following my previous post, you can understand how to obtain the figure below. If you copy and paste the code above into your R console, you can obtain the same figure as shown above. Now, I’d like to change the title format by removing the title border. Next, I’d like to draw a line in the title. Please refer to the code below. full code: https://github.com/agronomy4future/r_code/blob/main/How_to_customize_the_title_format_in_facet_wrap().ipynb © 2022 – 2023 https://agronomy4future.com
Variable-Dependent Manipulation of Point and Line Sizes in R
I will randomly create a piece of data and then proceed to plot a line graph with points for this data. I have differentiated point colors and shapes based on the variable “Genotype”. In the above code, the value geom_point(size=5) sets the point size to 5 for both GenotypeA and GenotypeB. However, I would like to increase the point size specifically for GenotypeA. I will change the code from geom_point(size=5) to geom_point(aes(size=Genotype)). This means that I will adjust the point…
Converting an Excel File to an R file: Optimizing File Size
Today, I will introduce a method for converting an Excel file into an R file. I have placed an Excel file in a folder named ‘DataBase’ on the desktop. This file contains wheat grain size data, with 96,320 rows and a size of approximately 15MB. When an Excel file is large, you may experience performance issues, such as Excel slowing down during data operations, especially if your computer has limited memory. It would be more convenient to convert this Excel…
Drawing Lines in ggplot()
When using ggplot() to create multiple graphs, there are times when you might want to add separate lines to the graphs. Today, I’ll be posting about how to draw additional lines on graphs. Let’s start by generating a simple piece of data. Next, I will proceed to draw a regression graph for this data. 1) Drawing a 1:1 ratio line. To examine the slope of the regression line, I would like to draw a 1:1 ratio line. geom_abline (slope=1, linetype…
Two-Way ANOVA Tutorial Using SAS Studio
I will introduce how to perform a Two-Way ANOVA analysis using SAS Studio. Here is the data that you have available: Upload this Excel file to SAS Studio. After uploading the Excel file to SAS Studio, create a data table named “EXP1” in My Libraries. Then, click on the EXP1 data table. Then, select the icon for generating code located at the top. By doing so, a new tab named “Program 1” will be created, allowing you to generate the…
Quantifying Phenotypic Plasticity of Crops
Phenotypic plasticity refers to the ability of an individual organism, in this case, a plant, to display varying phenotypic traits or characteristics in response to different environmental conditions. These traits can include physical features, physiological processes, and behaviors. Phenotypic plasticity is a crucial adaptive mechanism that allows organisms to optimize their survival and reproduction in varying environments. Crops are particularly reliant on phenotypic plasticity to cope with changes in factors such as light, temperature, moisture, nutrient availability, and other environmental…
Statistical Inference on Binomially Distributed Data
The primary purpose of our experiment is to validate hypotheses regarding the population of the subjects under study. As a result, the experimenter must determine whether to accept or reject these hypotheses based on the experiment’s results. In this context, the method of statistical analysis will vary depending on whether the sample data follows a normal distribution or a binomial distribution. Today, we will introduce statistical testing methods for data that conform to a binomial distribution. Let’s delve into an…
Graph Partitioning Using facet_grid() in R Studio
In my previous post, I introduced how to partition graphs using facet_wrap(). Today, I’ll introduce facet_grid(). □ Graph Partitioning Using facet_wrap() in R Studio Actually, the function is the same, but there are very subtle differences between facet_wrap() and facet_grid(). Today, I’ll explain this. Let’s upload one data. I measured chlorophyll contents in leaves for two wheat genotypes under both stress and normal conditions. In this case, there are two factors (stress treatment and genotypes). If you’ve read my previous…
Displaying Both Dates and Numeric Units on the X-Axis in R (feat. patchwork package)
When we create time series graphs in R, it is sometimes necessary to display both dates and numbers on the x-axis. This is because when the x-axis is set to show dates only, it can be challenging to add text or other elements directly onto the graph. However, by using both dates and numbers on the x-axis, we can easily insert texts, lines, and other annotations. Let’s talk with data. and I made a line graph over date. But I…
[데이터 칼럼] 데이터의 시각화에서 데이터 정규화가 필요한 이유는 무엇일까?
데이터의 정규화는 여러 가지 주요 이유로 데이터를 시각화 할 때 필요한데, 가장 중요한 이유는 척도의 균일성 (scale uniformity) 때문입니다. 서로 다른 데이터 변수들은 크게 다른 척도와 단위를 가질 수 있습니다. 예를 들어, 곡물 수확량은 Mg/ha 일 수 있고, 영양소 함량은 일반적으로 % 범위 내에 있을 수 있습니다. 이러한 데이터를 정규화 하면 단위가 다른 여러 개의 변수를 동일한 그래프에서 비교하고 시각화 할 수 있습니다. 또한, 정규화는 데이터의 해석 능력 (visualization interpretability) 을 향상시킵니다. 정규화된 데이터는 패턴에 대한 해석을 더 쉽게 할…
[STAT Article] Easy Guide to Cook’s Distance Calculation Using Excel and R
I have 1,000 data points of measurements of the length (mm) and weight (mg) of wheat grains. With this data, I want to analyze the relationship between the length and weight of the wheat grain to propose an equation model that can predict grain weight. I will draw a graph to visualize the data. If you are new to R, you can copy and paste the following code into your R script window to obtain the same graph as shown…
R-Squared Calculation in Linear Regression with Zero Intercept
Previously, I scanned wheat grains to obtain the area of each grain, and then measured the weight of each grain corresponding to its area in order to develop a model equation. The following regression demonstrates the relationship between grain area and weight. # Data download https://www.kaggle.com/datasets/agronomy4future/wheat-grain-area-vs-weight I obtained the equation y = 3.3333x – 13.7155, where y is the grain weight (mg) and x is the grain area (mm2), using both Excel and R. However, this model predicts negative values…
[STAT article] Two-Way ANOVA: An Essential Tool for Understanding Factorial Experiments
A factorial experiment involves the simultaneous manipulation of multiple factors or independent variables (x) to study their effects on a dependent variable (y). The experiment is called factorial because it involves testing multiple factors simultaneously. In factorial experiments, the combination of the different levels of each factor being tested is called a factorial, and each factorial represents a unique combination of these levels. For instance, N0_Genotyp1, N0_Genotyp2, N1_Genotyp1, N1_Genotyp2, etc. are different factorials used to conduct the experiment and analyze…
[Coding article] A Guide to Analyzing Statistical Tests for Each Level of a Factor in R without Manual Specification
This is my experimental data. There are 10 corn varieties, and I want to analyze the effect of nitrogen treatments (N0, N1) on grain yield for each variety. This is One-Way ANOVA analysis. Let’s assume that there are no blocks for the replicates. Therefore, the statistical model will be a One-Way ANOVA with no blocks. If we run the above analysis, we can observe the overall effect of nitrogen treatments on grain yield across all varieties, as they are pooled…