Key PointsLabels Matter! Axis labels are like signposts on your graph – they tell the reader what they're looking at. Without them, it's just pretty colours and confusion.The Trouble with Defaults: ggplot2 is awesome, but sometimes those labels automat...

Are you a data analyst who works with spatial data in R? Do you want to learn how to convert data frames to rasters in R and why it is useful and important for spatial data analysis and visualization? If yes, then this article is for you.# Convert the ...

Have you ever considered how we train machine learning models that could mirror our biases? As a seasoned data analyst, I frequently confront this problem. We desire objective systems yet inadvertently impose our preconceived notions of what patterns o...

Are you looking for a simple, robust, and efficient method to help you with classification and dimensionality reduction? Do you want to learn how to implement and evaluate LDA in R programming, one of the most popular and powerful techniques for these ...

Key Points Linear discriminant analysis (LDA) is a supervised learning technique that can be used for classification, dimensionality reduction, feature extraction, clustering, or visualization.It assumes that the data follow a multivariate normal distribution with class-specific parameters and estimates the prior probabilities, the means, and the covariance matrices of each ...

Key TakeawaysThe mutate function from the dplyr package allows you to create new variables or modify existing variables in a data frame or a tibble in R.The variants of mutate, such as mutate_all, mutate_at, mutate_if, and mutate_across, allow you to a...

Key takeawaysMultiple methods exist for creating new variables in R, each with advantages and limitations. Understanding these options empowers you to choose the best tool for your needs and data context.Best practices prioritize clarity and efficiency...

The repeated measures ANCOVA in R tests whether the average values of one or more variables measured repeatedly on the same subjects differ significantly after adjusting for a covariate.The code for performing a one-way repeated measures ANOVA in R is:# Fit the repeated measures ANOVA model model General Linear ...

ANCOVA is short for analysis of covariance, a statistical method that lets you compare the means of an outcome variable among two or more groups while accounting for (or correcting for) the variability of other variables, known as covariates. Cova...

Key Pointsscale_color_manual is a function in R that allows you to specify the colors of your plots manually.You can use color names, hex codes, or RGB values to choose the colors for scale_color_manual.You can create different plots using different color palettes and scales, such ...

Key pointsScale_fill_gradient function allows us to create a continuous color gradient based on a numeric variable for our graphs.We can use the scale_fill_gradient function with the ggplot2 package, a powerful and popular R data visualization tool.We ...

Key pointsThe scale_fill_gradientn is a powerful function of ggplot2 for data visualization customization.It is a function in the ggplot2 package that allows you to create an n-color gradient scale for fill aesthetics by specifying a vector of colors a...

KeyPointsscale_fill_gradient2 is a function in the ggplot2 package that creates a diverging color gradient for fill aesthetics.fill aesthetic is an aesthetic that controls the color of the interior of a geometric object, such as a bar, a point, or a polygon.diverging color gradient is a color ...

Key Pointsggplot shapes represent the points in a scatter plot, and they can be used to distinguish different groups of data, highlight outliers, or add more information to the plot.We can use 25 different ggplot shapes, ranging from simple circles and squares to more complex shapes like stars and triangles. ...

Key PointsData visualization is the art and science of presenting data visually, making it easy to understand and explore.R is a powerful and popular programming language for data analysis and visualization, and ggplot2 is one of the most popular and powerful packages for data visualization in R.ggplot2 is ...

Key pointsExploratory data analysis (EDA) is crucial in any data analysis project. It involves exploring, summarizing, and visualizing your data to gain insights, identify patterns, and detect outliers.EDA can also help you formulate hypotheses, choose...

Key pointsA contingency table is a way to show how often different categories of two or more variables occur together. You can make a two-way contingency table in R with the table() function. You can also add the totals and percentages of each cat...

Key Points R date format is a skill that can help you handle one of the most common and important data types in any data analysis project.R has a special class for representing dates called the date class. It stores dates as the number of days sin...

Key points Confidence intervals are a way of expressing the uncertainty associated with a point estimate. They provide a range of values likely to contain the true population parameter with a certain confidence level. R has several built-in functions that can calculate confidence intervals for different types of data and ...

Key points A word count is the number of words in a text or a collection of texts. It can be useful for many purposes, such as measuring the length and complexity of a text by comparing texts from different sources.In R, there are many ways to count words ...