When uploading data to R, we first need to check the data structure before analyzing it. Here are some tips for checking the data structure in R. First, I’ll upload a dataset from my GitHub. In this dataset, let’s check the structure of the data. ■ Code to display the first or last certain rows When we examine the data, we can simply run the variable df or use print(df) to display it. However, if we want to quickly understand…
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…
In my previous post, I introduced the necessity of data normalization in visualizing data. By following that post, you may gain an understanding of how we can organize data according to our preferences. □ Why is data normalization necessary when visualizing data? Today, I’ll introduce various methods for data normalization, utilizing the biomass with N and P uptake data available on my GitHub. R coding Python coding I also aim to create regression graphs illustrating the relationship between biomass and…
Data normalization is necessary when visualizing data for several key reasons, and I believe the most important reason is for scale uniformity. Different data variables can have vastly different scales and units. For example, grain yield might be in Mg/ha, while nutrient contents might typically range from %. Normalizing these data to a common scale (like 0 to 1) allows them to be compared and visualized on the same axis without one overshadowing the other due to its scale. Additionally,…
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…
□ How to convert character to POSIXct format in R? In my previous post, I created a curve graph like the one shown below. The curve on the graph appears to be not very smooth, and I want to make it smoother. Therefore, I will add geom_smooth(), but the method will be method=”gam” code summary: https://github.com/agronomy4future/r_code/blob/main/Achieving_Smooth_Curve_Graphs_with_R.ipynb © 2022 – 2023 https://agronomy4future.com
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
Here is one dataset. With this data, I’ll create a line graph to show the change in day length over time. First, let’s transpose the columns to rows using pivot_longer(). I’ll sort the data by Day and Month, but since the month column is in text format, sorting it from January to December directly isn’t feasible. Therefore, I’ll add a number corresponding to each month for sorting purposes. Now, I can sort by ‘month1’ and ‘Day’ from January 1 to…
First, let’s create a dataset. and observe the different data formats of each value. I have two sets of yield data: one in character format (yield column) and the other in numeric format (yield1 column). How to convert missing value to 0 when data is numeric? When data is numeric (yield1 column), and if there are missing values, how can we replace it to 0? or you can also use the following code. How to convert missing values to 0…
□ Graph Partitioning Using facet_wrap() in R Studio□ How to customize the title format in facet_wrap()? In my previous posts, I introduced how to divide panels in one figure using facet_wrap(). Today, I’ll introduce how to add separate text to panels. First, let’s make sure we have the required packages installed. I’ll create a dataset as shown below: Next, I’ll reshape the dataset into columns to facilitate data analysis. And then, I’ll summarize this data using descriptive statistics. Finally, I’ll…
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
Data created Cumulative Sum per group © 2022 – 2023 https://agronomy4future.com
In R, I want to draw a line in a graph, and first, I’ll create the data. Next, I’ll create a bar graph. In this graph, I want to draw a horizontal line. The code to draw lines is introduced in the post below. □ Drawing Lines in ggplot() I added a horizontal line to represent the mean yield of all cultivars. Next, I would like to draw a horizontal line starting from Cultivar B. How can this be achieved?…
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…
Google Colab is essentially a Jupyter notebook environment, which means that typically only Python code works. However, it is also possible to use R code in Google Colab. If you’re unfamiliar with Google Colab, please read the post below to grasp its general concept. □ How to use Google Colab for Python (power tool to analyze data)? When opening a new Google Colab window, navigate to Runtime in the menu, choose Change runtime type, and a new window will appear,…
Let’s say I uploaded a dataset to R. Now, I want to save this data as code so that I can store it in my web note. This is because it would be difficult to find the original dataset after a long time. Therefore, I want to save it as text code in a list on my web note. 1) using dput() First, we can use dput() function. 2) using datapasta() Second, we can use datapasta() function 3) using constructive()…
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…
I have a dataset as below. Now, I want to create a bar graph about this data. First, let’s summarize the data. Then, let’s create a bar graph. Now, to save space, I’d like to split the x-axis text into two rows using the following code. When you run the same code to create a bar graph, the resulting graph is shown below. Code summary https://github.com/agronomy4future/r_code/blob/main/Customizing_R_Graphs_Splitting_Text_into_Two_Rows.ipynb © 2022 – 2023 https://agronomy4future.com
© 2022 – 2023 https://agronomy4future.com
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…
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 –…
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…
□ 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…
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…
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…
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….
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,…
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…