Data Analysis and Visualization Tutorials

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.

To calculate jaccard similarity in R, you can define function for Jaccard Similarity. The Jaccard similarity index measures the similarity between two sets of data. It can range from 0 to 1. The higher the number, the more similar the two sets of data.The Jaccard Similarity calculated as divide number of observations in both sets to number in either set. The following method shows how you can do it with syntax.

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:

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.

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.

To calculate summary statistics in R, you can use two different function in R. The following methods show how you can do it with syntax. Method 1: Use summary() Function summary(data) Method 2: Use summarize() Function from dplyr Package library(dplyr) summary <- df %>% summarize( Mean = mean(colum1), Median = median(colum1), Min = min(colum1), Max = max(colum1), StdDev = sd(colum1), Variance = var(colum1), ) The following examples show how use this methods to calculate summary statistics in R.

To calculate the Z-score in R, you can use scale() function or calculate manually using mean() and sd() function. The following methods show how you can do it with syntax. Method 1: Use scale() Function scale(data) Method 2: Use mean() and sd() Function (data - mean(data))/sd(data) The following examples show how to calculate Z-score in R. Use scale() Function Let see how we can calculate Z-score for one of the column of dataframe using scale() function:

To create a three-way table in R, you can use ftable() function along with xtabs() function. The following method shows how you can do it with syntax. Method 1: Use ftable() and xtabs() Function tab <- xtabs(~ column1 + column2 + column3, data=df) ftable(tab) The following example shows how to create three-way table using ftable() and xtabs() function in R. Using ftable() and xtabs() Function Let’s see how we can use ftable() and xtabs() function in R:

To create two-way table in R, you can use table() function. The following method shows how you can do it with syntax. Method 1: Use table() Function table(vector1,vector2) The following example shows how to create two-way table in R. Using table() Function Let’s see how we can use table() function to create two-way table in R: # Create matrix data=matrix(c(78,89,85,84,81,79,77,85),ncol=2) # Create table Machine_info <- table(data) # Show table print(Machine_info) Output:

To find the P-value of correlation coefficient in R, you can use cor.test() function in R. The following method shows how you can do it with syntax. Method 1: Use cor.test() Function cor.test(df$column1,df$column2) The following example shows how to find P-value of correlation coefficient in R using cor.test() function. Using cor.test() Function Let’s see how we can use cor.test() function in R: # Create data frame df <- data.frame(Pressure1=c(78.2, 88.2, 71.

Bivariate analysis in R involves analyzing the relationship between two variables. To perform Bivariate analysis you can perform operation like finding corrlation coefficient and perform regression, visualizations like scatter plots. To perform bivariate analysis you need to follow below steps: Step 1: Load required libraries library(tidyverse) Here we load tidyverse library which is required for further use. Step 2: Load dataset You can load in-built dataset or create dataframe to perform bivariate analysis on it:

Exploratory Data Analysis is an essential step in understanding your dataset’s structure, relationships, and potential issues.To perform Exploratory Data Analysis you can use dplyr, ggplot2 and summarytools package. Follow below steps to perform exploratory data analysis in R. Step 1: Load dataset First we need to import necessary packages and load dataset: library(tidyverse) # Load diamonds dataset data(diamonds) Here we load diamond dataset from tidyverse package. Step 2: Summarize the data Use summary() function to summarize the data: