Binning in R: Create Bins of Continuous Variables
three methods to create bins in R
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dplyr
Correlation in R: Coefficients, Visualizations, & Matrix Analysis
Discover the power of correlation in R as you unravel hidden relationships in your data. From calculating correlation coefficients to visualizing patterns, dive into the world of data analysis and uncover the insights that lie within. Get ready to unlock the secrets of correlation in R.
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How to Randomly Select Rows in R – Sample from Dataframe
How to randomly select rows in R? Learn the sample() and slice_sample() functions to take random samples from dataframes. Explore practical examples and synthetic datasets for hands-on learning. Enhance your data analysis skills and unlock new possibilities with random row selection in R.
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Extract P-Values from lm() in R: Empower Your Data Analysis
In this blog post, we will learn how to extract P-values from the regression models in R. We will explore the process of fitting a regression model, and then dive into the methods of extracting P-values using the lm() function. Additionally, we will demonstrate how to extract P-values from all predictors and leverage the tidy() function for a tidy output. Unlock the power of statistical inference with the ability to extract P-values from lm() in R.
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Wide to Long in R using the pivot_longer & melt functions
In this post, we will learn how to transform data from wide to long in R. Wide-to-long format conversion is often an important data manipulation technique in data analysis. In R, we can use many packages and their functions to transform data from a wide to long format. These functions include the tidyr package’s pivot_longer()
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Countif function in R with Base and dplyr
In this blog post, you will learn how to carry out a countif function using base R and dplyr. Countif is a powerful function that allows you to count the number of times a certain condition is met in a dataset. Countif is particularly useful in cognitive hearing science, where researchers often need to analyze
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Test for Normality in R: Three Different Methods & Interpretation
In this blog post, you will learn how to test for the normality of residuals in R. Testing the normality of residuals is a step in data analysis. It helps determine if the residuals follow a normal distribution, which is important in many fields, including data science and psychology. Approximately normal residuals allow for powerful
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Sum Across Columns in R – dplyr & base
In this blog post, we will learn how to sum across columns in R. Summing over columns can be a useful data analysis technique in various fields, including data science, psychology, and hearing science. Here, we will explore several examples of how to sum across columns in R, including summing across a matrix, multiple columns
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How to Create a Sankey Plot in R: 4 Methods
Sankey plots are an essential tool for data visualization in science and business. Whether you are exploring complex data flows, identifying patterns, or communicating insights, Sankey diagrams make it easy to visualize connections and gain meaningful insights. Discover four methods to create stunning Sankey plots in R and elevate your data analysis game!
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How to Standardize Data in R with scale() & dplyr
This tutorial covers the critical topic of standardizing data in R. Standardizing data involves transforming data such that the mean of the data becomes zero and the standard deviation becomes one. This technique is widely used in data analysis and modeling, including psychology, biology, and finance.
Through this post, you will learn about the circumstances when and why we may want or need to standardize data in R. We will also discuss what it means to standardize variables and which R functions are best suited for standardization. Additionally, the tutorial provides a step-by-step guide on how to standardize data in R. Starting with standardizing a vector, we move on to standardizing a matrix, standardizing data in a dataframe, and finally, standardizing only numeric columns in a dataframe.
Whether you are a seasoned data analyst, a research professional, or a student, this tutorial will equip you with essential skills to standardize data in R. These skills will enable you to understand better and analyze your data, paving the way for better insights and informed decision-making.
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