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Data Analysis and Visualization Tutorials

How to Create a Covariance Matrix for R Data Frame

  • R-PROGRAMMING
To create a covariance matrix for a data frame in R, you can use the cov() function. A covariance matrix is a table that shows the covariance between pairs of variables in a data set. In this article, we will explore how to create a covariance matrix for an R data frame using the cov() function. Method: Use cov() Function The cov() function in R is used to calculate the covariance matrix for a data frame.
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How to Create a Line Chart in R

  • R-PROGRAMMING
To create a line chart in R, you can use the plot() function or the ggplot() function from the ggplot2 package. In this article, we will explore how to create a line chart in R using these methods with examples. Method 1: Use plot() Function The plot() function in R is used to create various types of plots, including line charts. Here’s the syntax: plot(x, y, type = "l", col = "blue", lwd = 2, xlab = "X-axis label", ylab = "Y-axis label", main = "My Line Chart") The following example shows how to create a line chart using the plot() function:
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How to Create Frequency Tables in R

  • R-PROGRAMMING
To create a frequency table in R, you can use two different methods: the table() function and the dplyr package. In this article, we will explore how to create frequency tables in R using these methods with examples. Method 1: Use table() Function The table() function in R is used to create a frequency table. Here’s the syntax: table(vector) The following example shows how to create a frequency table using the table() function:
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How to Find Outliers in R

  • R-PROGRAMMING
There are multiple methods to find outliers in R, but a common method is using the Interquartile Range (IQR). The values that fall outside 1.5 times the interquartile range (IQR) below the first quartile (Q1) or above the third quartile (Q3) are identified as outliers. In this article, we will explore how to find outliers in R using the IQR method with examples. Method: Use IQR() Function The IQR() function in R is used to calculate the interquartile range.
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How to Perform Bivariate Analysis in R

  • R-PROGRAMMING
Bivariate analysis in R involves analyzing the relationship between two variables. To perform bivariate analysis, you can perform operations like finding the correlation coefficient, performing regression, and creating visualizations like scatter plots. In this article, we will explore how to perform bivariate analysis in R. Step 1: Load Required Libraries To begin, load the necessary libraries. Here, we use the tidyverse library, which is required for further use. library(tidyverse) Step 2: Load Dataset You can load an in-built dataset or create a dataframe to perform bivariate analysis on it.
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How to Use corrplot in R to Create a Correlation Matrix

  • R-PROGRAMMING
The corrplot() function is used to plot a correlation matrix in R. This function provides a visual representation of the correlation matrix, making it easier to understand the relationships between variables. In this article, we will explore how to use the corrplot() function in R to create a correlation matrix with examples. Method: Use corrplot() Function To plot a correlation matrix, we need to load the corrplot package and use the corrplot() function.
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How to Calculate a Trimmed Mean in R

  • R-PROGRAMMING
To calculate trimmed mean you can use mean() function with trim argument. A trimmed mean is the mean of a dataset that has been calculated after removing a specific percentage of the smallest and largest values from the dataset. The following method shows how you can do it with syntax. Method 1: Use mean() Function mean(data,trim) The following example shows how to calculate trimmed mean in R. Use mean() To Calculate Trimmed Mean Let’s see how we can calculate trimmed mean for one of the column of dataframe.
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How to Calculate Sample & Population Variance in R

  • R-PROGRAMMING
To calculate sample and population variance in R, you can use var() function and sd() function. The following methods show how you can do it with syntax. Method 1: Use var() Function var(data) Method 2: Use var() and length() Function var(data) * (length(data) - 1) / length(data) The following examples show how to calculate sample and population variance in R. Using var() Function Let’s see how to calculate sample variance using var() function:
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How to Calculate Spearman Rank Correlation in R

  • R-PROGRAMMING
To calculate spearman rank correlation in R, you can use cor.test() function with method argument. The following method shows how you can do it with syntax. Method 1: Use cor.test() Function cor.test(vector1,vector2,method="spearman") The following example shows how to calculate spearman rank correlation in R. Using cor.test() Function Let’s see how we can calculate spearman rank correlation in R using cor.test() function: # Create data frame df <- data.frame(Machine_name=c("A","B","C","D","E","F","G","H"), Pressure=c(12.39,11.25,12.15,13.48,13.78,12.89,12.21,12.58), Temperature=c(78,89,85,84,81,79,77,85), Status=c(TRUE,TRUE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE)) # Calculate Spearman Rank Correlation x <- cor.
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How to Calculate Summary Statistics by Group in R

  • R-PROGRAMMING
To calculate summary statistics by group in R, you can use tapply() function or create function manually using group_by() summarise() function from dplyr package. The following methods show how you can do it with syntax. Method 1: Use tapply() Function tapply(data, summary) Method 2: Create Function Manually library(dplyr) d <- df %>% group_by(column1) %>% summarize(min = min(column2), q1 = quantile(column2, 0.25), median = median(column2), mean = mean(column2), q3 = quantile(column2, 0.
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