Use the data.frame() function in R to create a data frame from vectors. The data frame in R is a two-dimensional array or table where each row contains the value of each column and the column contains the value for each variable. It can contain heterogeneous data. Vectors in R contain data of the same data type. In this example, we will discuss how to create a data frame from vectors, add a new column vector to a data frame.

Using the square bracket with conditional selection in the data frame, you can subset the data frame in r and extract the observations you would like to keep or exclude. You can also subsetting the data frame in r using the subset() function in R. In this tutorial, we will discuss how to subset rows of data from the data frame using the logical expression in R. Subset Rows with Equal To condition Using the equal to (==) operator, you can subset the data frame.

A Data frame is a primary data structure for handling tabular data sets like a spreadsheet. R Data frame is a two-dimensional data structure and displays data in the format of a table. Data frames are an atomic data structure in R. R Data frames are like matrices except that the columns are allowed to be of different types. R data frames stores heterogeneous data types, which means it stores different types of data in it.

Using the write.csv function in base R, it can export a data frame to a CSV file. The write.csv function takes data frame and path of export to CSV file as the input parameters. Use the following syntax to write a data frame to the CSV file. write.csv(dataframe,"path of export data frame to csv file", row.names = FALSE) R language provides other methods like the write_csv function from the readr package and fwrite function of data.

is.data.frame() function in R is used to check if the object is a data frame or not. is.data.frame() function in R returns TRUE if the object is data frame else returns FALSE. Data frame in R is very powerful to store data of different data types, data manipulation. data.frame() function is used to create a data frame in R. In this tutorial, we will discuss how to use is.data.frame() function in R to check the object is data frame in R or not.

All the data frames have row names. Using the row.names() or row.names.data.frame() function in R, we can get the data frame row names. row.names() or row.names.data.frame() function gets the character vectors of the length of the rows in the data frame. In this tutorial, we will discuss about how to get data frame row names in R using row.names() and row.names.data.frame() function. Get R Data Frame Row Names using row.names Use the row.

mean() function and colMeans() function of the R base package is used to get the mean of columns in the data frame. It accepts the data frame as input argument and column name to find the mean of the column in R. The function calculates the mean of a column by calculating the sum of the values and dividing by the number of rows in it. In this tutorial, we will discuss how to get the mean of columns in a data frame in R using mean(), colMeans(), and summarise() function of the dplyr package.

Data Frame in R is a two-dimensional array or table. Rows in the data frame are observations and columns in the data frame are variables. Using the colnames() and names() function in R,we can get the columns of data frame. colnames() function in R is used to get the columns from the matrix like ie.. data frame. names() function in R gets the name of R object in our case it is a data frame.

Merge data frames are quite useful when data is available in different data stores. The combining of data frames in R gives more insights to perform analysis of the dataset. Using merge() , rbind() functions, we can merge data frames in R. merge() is a built-in R function that merges data frames by one or more common column names. It merges the two data frames horizontally. rbind() function in R combines data frames vertically.

Using the logical condition and operator in a data frame, we can remove rows in r based on conditions. In this tutorial, we will discuss how to remove a row from a data frame in R, remove multiple rows in R and how to remove rows from a data frame in R based on condition. How to remove a row from a data frame in R Using the logical condition in the data frame using the square bracket notation, we can remove the row from a data frame in R.

rowsums() function along with is.na() and ncol() is used to remove all na rows in R. filter function of dplyr is also used to remove rows with na values. A data frame is passed as an argument to filter Function. Using the na.omit() function and is.na(), we can remove all rows with na in R. Data frame is passed as an argument, and these functions check for missing values of na values in rows of a data frame and remove na from a data frame.

Using the negative indexing into the data frame, we can remove the first row of a data frame in R. Syntax to remove the first row of the data frame is given below: df[-1,] where df = data frame Use negative indexing -1 to drop In this tutorial, we will discuss how to remove the first row of a data frame in R using negative indexing. Remove First Row of Data Frame using Negative Indexing Use the negative index into the data frame to delete the row of df.

Reorder columns of a data frame in R is quite important when you want to deal with large data set and have large columns. Reordering columns of a data frame is helpful to keep important and useful columns at the beginning for data analysis. Using the select() function in R and relocate() function available in the dplyr package of tidverse helps to change the column position of a data frame in R.

Selecting columns from the data frame in R is very important when we have to analyze large data set. Using the dplyr package select() function, we can select specific columns by column name or column index. Using the base R, we can select specific columns from the data frame using their column name or column index as given below: # Select column 1 and column 2 from data frame by column name df[c('col1','col2')] # Select column 2 and column 3 from data frame by column index df[c(2,3)] In this tutorial, we will discuss different ways of selecting columns in R from the data frame.

Using the subset() function in R, we can select rows by conditions specified in the square bracket notation for the data frame. We can specify the logical condition with operators to select rows by the condition in R. The Subsetting data frame returns a subset of the data frame which meets the condition. subset syntax: subset(x, ...) where x = data frame object to be subsetted. In this tutorial, we will discuss how to select rows by the condition in R, subset rows with filter in R.

Using the order() function in R, sort data frame by column. order() function as default sorts the data in ascending order. However using the minus (-) sign before the column, we can sort data frame by column in descending order. We can use the order() function to sort a data frame by a single column or multiple columns. In this tutorial, we will discuss in R programming, how to sort data frame by column.

subset() function in R returns the subset of the data frame which meets the condition. Subsetting data frame in R extracts the variables and observations from the data frame which meets the specified condition. subset() function accepts three parameters as data frame to be subsetted, logical expression to keep row or elements to keep and select indicates columns to select from the data frame. subset(x, subset, select, drop = FALSE, …) In this R subset data frame by column value tutorial, we will discuss how to subset data frame by column value in R with examples.

Indexing in R starts at 1, which means the first element is at index 1. In R indexing data frame can be achieved using the brackets notation to access the elements. Data frame is like a two-dimensional array or table where rows are the observations and the columns are the variable. If you want to access the element of the 2nd row and 1 column, use [2,1] indexing in the data frame to access the value of the element.

The function to calculate z-score in Excel is STANDARDIZE(). This function accepts input parameters like data set, mean, and standard deviation. To calculate the mean and standard deviation, use the AVERAGE(x1 : xn ) and STDEV.S(x1 : xn ) functions in Excel. The Z-score is a measure of how many standard deviations an observation or data point is from the mean. It’s important for statistical analysis and research because it tells us whether a result is significant or not at a certain level of confidence.

Yes, Z-Score can be negative. The negative z score value represents the raw score value that is either left or below the mean of the standard normal distribution. The Z-score value can be negative, positive, or 0. If the z score value is negative, use the negative z-score table chart to find the area. It shows the area under the curve of normal distribution. The Z-score can be calculated using the either z-score formula or the z-score calculator.

In this article, we will discuss what is the critical value of z and when to use the critical value of z. We will discuss critical value z for the left-tailed test, a right-tailed test, and a two-tailed test on TI 84 with examples. What is critical value or Significant Value? The value of the test statistic which separates the critical (or rejection) region and the acceptance region is called the critical value or significant value.

The area between two negative scores can be found by finding the area between two z-score and subtracting the small area from the larger area. The Z-Score table represents the area under the normal curve for a normal distribution. In this article, we will discuss how to find the area between two negative z scores on one side of the mean with an example. The area between two negative Z Scores To find the area between two negative z-scores, let’s understand with an example.

In this article, we will discuss step by step guide on how to find the area between two z scores on both sides of the mean. Find the area between two z scores Step to find the area between two z scores: Step 1: Find the area for the two z-scores using the z-score table. Step 2: Subtract the smaller area from the greater area. the area between the two z = larger area – smaller area (calculated)

The normal distribution is a continuous probability distribution. The total area under the standard normal curve is always 1. The normal distribution looks like a bell-shaped curve. In this article, we will discuss a step-by-step guide on finding the area under the standard normal curve to the left of the z score. The area under the Standard Normal Curve to the left of z Steps to find the area under the standard normal curve to the left of z

In this article, we will discuss step by step guide on how to find the area under the standard normal curve to the right of the z score. The area under the Standard Normal Curve to the right of Z Step to find the area under the standard normal curve to the right of z Step 1: Find the area to the left of z. Step 2: As the total area under the bell curve is 1.

In this blog post, we will discuss how to find the z-score for having an area 0.09 to the left of the standard normal curve. To find the z-score for a given area, check if the area is left to the mean or right to the mean. Based on that, we will use either a negative z-table or a positive z-table. How to Find the z score for having an area 0.

Z Score for the top 5 percentile of a normal distribution is 1.645. To find the top 5th percentile of a normal distribution, look at the z table. Check the probability closest to 0.05 in the z table. Sometimes the exact values do not exist, in that case, we will consider the best closest value. In this blog post, we will discuss how to find the top 5 percentile of a standard normal distribution using the z table.

The Normal Cumulative Distribution Function normalcdf() is used to find percentiles from the z score on the TI-84 calculator. normalcdf (lower bound, z-score, μ, σ) where: lower bound: lower bound for the given z-score. We use infinity as a lower bound. Z-score: given z-score μ: population mean σ: population standard deviation How to find percentile from z score using TI 84 The following steps will guide us on how to calculate the Percentiles from Z-Score using TI-84 Calculator:

The Inverse Normal Probability Distribution Function invNorm() is used to find z score from the percentile on TI-84 plus calculator. invNorm(probability, μ, σ) where: probability: the given percentile μ: population mean σ: population standard deviation Note: Finding the Z-score indicates that the given distribution is a standard normal distribution with a mean of 0 and a standard deviation as 1. Using the invNorm function on TI 84 plus The following steps will guide us on how to calculate the Z-Score from Percentile on TI-84 plus Calculator:

The Inverse Normal Probability Distribution Function invNorm() is used to find the z score on TI-NSpire. invNorm(area, μ, σ) where: area: the given area to the left. μ: population mean σ: population standard deviation Note: Finding the Z-score indicates that the given distribution is a standard normal distribution with a mean of 0 and a standard deviation of 1. In this blog post, we will discuss with step by step guide on how to find the z score on TI-NSpire and examples.

To transform the set of raw scores into a set of z-scores in order, you will need to convert them into a standard normal distribution with a mean of 0 and a standard deviation of 1. Transforming raw scores into standard distribution helps us to compare the two raw scores from two different distributions. In this blog post, we will discuss, why would you want to transform a set of raw scores into a set of z-scores with examples as given below.

A negative Z score value indicates the raw score value is either left or below the mean of the standard normal distribution. The negative z score in the z-table represents the area under the bell curve to the left of z. If the z score value is negative, use the negative z table to find an area on the left of the mean of the distribution. You can refer Z score calculator article which explains how to calculate the Z score using the z score formula below.

The one-sample proportion Z-test is a statistical tool used to conduct a hypothesis test about population proportions(p). It is used to estimate the difference between the proportion of responses(or number of successes) in sample data and the actual proportion in the population data from which we draw the sample. What are the conditions required for conducting a one-sample proportion z test? Assumptions for the one sample proportion z-test are as follows:-

The one-sample Z-test is a statistical hypothesis test used to determine whether the mean of a single sample is significantly different from a known population mean. It is used to draw conclusions about a population based on sample data. In this article, we will discuss the concept of a one-sample z-test in R with practical examples. Why Use the One-Sample Z-Test The one-sample z-test statistical tool can be used: Hypothesis Testing: It helps you to assess whether your sample data provides enough evidence to support or reject a hypothesis about a population parameter.

Z score allows a comparison between two scores that are from different normal distributions of raw scores to standardized scores. Z Score Formula If the population mean and population standard deviation are known, the Z score is calculated using the below formula z = (x -μ )/σ Where: μ = is the population mean for the unstandardized value σ = is the population standard deviation for the unstandardized value x = is the raw score value z = is the calculated z-score value.

Z table or the Z-score table also called the standard normal table is a mathematical table used to find the probability that the z-score value is below, above, or between the values on the standard normal distribution. Standard scores are most commonly called z-scores. In this post, equivalent terms like z-values, standardized variables, and normal scores are used. The standard normal distribution table is the normal distribution having a mean value of 0 and a standard deviation of 1.

What is a Z-Score? Z-score is the distance of the raw score value from the mean in terms of standard deviation. Raw scores above the mean have a positive Z-score value, while a raw score below the mean has a negative z-score value. Z-Score is also known as the standard score because it allows the comparison of scores on different kinds of data by standardizing the distribution. Z-Score value can be positive, negative, or zero.

Using the $ operator or square bracket, we can add a column to a data frame. We can also create vector data and add it as a column to a data frame. Use the add_column function in the tibble package to add a new column or append vector to a data frame. In this tutorial, we will discuss how to add a column to a data frame in R using the $ operator and square bracket and add_column to append a column to a data frame.

cbind() function and data.frame() function in the base R package is used to combine two vectors in R. Vectors in R hold the multiple values of the same data type. Functions take vectors as input parameters and combine two vectors to data frame or matrix in R. cbind() function of base R package takes the sequence of vectors and combines them by columns. cbind() syntax: cbind(..., deparse.level = 1) Where, .

Using the data.table package setDT() function in R, we can convert data frame to a data table. The data.table package in R provides an enhanced version of a data frame. setDT() function converts the data frame to the data table. setDT() syntax: setDT(x, keep.rownames=FALSE, key=NULL, check.names=FALSE) where, x = data frame, data table or named or unnamed list In this tutorial, we will discuss how to convert a data frame to a data table using the setDT() function and as.

data.matrix() and as.matrix() functions in R is used to convert a data frame to matrix. The data.matrix() function converts all data frame variables to numeric and displays them as columns of the matrix. The as.matrix() is a generic function and returns a character matrix. In this tutorial, we will discuss how to convert a data frame to a matrix using the data.matrix and as.matrix method. Convert a Data Frame to Matrix using as.

Data frame in R stores heterogeneous data types in the format of tabular forms. It is a list of vectors of equal length. Converting a list to a data frame provides more insight to analyze the tabular format of data. The as.data.frame() function in R is used to create a data frame in R. It can create a data frame from the list of vectors by specifying the list parameter in data.

The write_xlsx() from writexl library used to export data frame to excel file. The following method shows how you can do it with syntax. Method: Use write_xlsx() From writexl Package library("writexl") write_xlsx(dataframe,"file_path") The following example shows how to use write_xlsx() function in R. Export Data Frame to Excel File Using write_xlsx() Function in R To use write_xlsx() function you need to follow below steps: Step 1: Create a Data Frame Let’s first start with creating data frame:

There are multiple function are available in R to export data. The following methods show how you can do it with syntax. Method 1: Exporting as a Text File write.table(data,"file.txt",sep="\t") Method 2: Exporting as a Excel File library("writexl") write_xlsx(df,"file.xlsx") Method 3: Exporting as a CSV File write.csv(data,"file.csv") Method 4: Exporting as a RDA File save(data,file= "file.Rda") The following examples show how to export data in R, depending on file format.

There are multiple ways to filter rows based on condition in R data frame. The following methods show how you can do it with syntax. Method 1: Use filter() Function library(dplyr) df %>% filter(column > n) Method 2: Use Logical Indexing df[df$column > n, ] Method 3: Use subset() Function subset(df, column > n) The following examples show how to filter rows based on condition in a dataframe. Using filter() Function Let’s see how we can use filter() function from dplyr package:

To add count column to data frame in R, you can use add_count() function from dplyr package. The following method shows how you can do it with syntax. Method 1: Use add_count() Function library(dplyr) df %>% add_count(column1, column2, name = "count") The following example shows how to add count column to dataframe in R. Using add_count() Function Let’s see how we can add count column to dataframe using add_count() function:

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 percentage by group in R, you can use group_by() and mutate() function from dplyr package. The following method shows how you can do it with syntax. Method: Use dplyr Package library(dplyr) dataframe %>% group_by(column1) %>% mutate(percent = column2/sum(column2)) The following example shows how to calculate percentage by group in R. Use dplyr Package to Calculate Percentage by Group Let’s see how we can calculate percentage by group in R:

To calculate quantiles by group in R, you can use quantile() function from dplyr package. The following method shows how you can do it with syntax. Method: Use quantile() Function library(dplyr) df %>% group_by(column1) %>% summarize(quant25 = quantile(column2, 0.25, quant50 = quantile(column2, 0.50), quant75 = quantile(column2, 0.75), quant100 = quantile(column2, 1.0)) The following example shows how to calculate quantile values by group in R. Using quantile() Function Let’s see how we can calculate quantile values of column of of data frame:

To calculate rolling correlation in R, you can use rollapply() function from zoo package. The Rolling correlation is basically Rolling correlations are correlations between two time series on a rolling window. The following method shows how you can do it with syntax. Method: Use rollapply() Function library(zoo) rollapply(df, width=3, function(x) cor(x[,2],x[,3]), by.column=FALSE) The following example shows how to calculate rolling correlation in R. Use rollapply() to Calculate rolling correlation Let’s see how we can calculate rolling correlation between column of data frame:

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 caclulate standard error of the mean in R, you can use std.error() function from plotrix package or you can create function to create standard error of the mean. The following methods show how you can do it with syntax. Method 1: Use std.error() Function library(plotrix) std.error(data) Method 2: Create Function function(data) sd(data)/sqrt(length(data)) The following examples show how to calculate standard error of the mean in R. Use std.error() to Calculate Standard Error of Mean Let’s see how we can use std.

To calculate standardized regression coefficient in R, first you need to scale data using scale() function and then calculate linear regression using lm() function. The following method shows how you can do it with syntax. Method 1: Use scale() and lm() Function scaled_data <- scale(data) lm(scaled_data) The following example shows how to calculate regression coefficient in R. Using scale() and lm() Function Let’s see how we can calculate standard regression coefficient in R for dataframe:

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 ckeck which package version is loaded in R, you can use packageVersion() function. The following method shows how you can do it with syntax. Method: Use packageVersion() Function packageVersion("Package_name") The following example shows how to use packageVersion() function in R. Using packageVersion() Function Let’s see how we can use packageVersion() function to check version of package: # Get version package packageVersion("ggplot2") Output: [1] ‘3.4.4’ Here the output shows package version of ggplot2 package.

To count TRUE values in logical vector in R, you can use sum() function. The following method shows how you can do it with syntax. Method: Use sum() Function sum(vector) The following example shows how to count TRUE valeus from logical vector in R. Using sum() Function Let’s see how we can use sum() function to count TRUE values in logical vector: # Define vector Status <- c(TRUE,TRUE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE) # Count TRUE value s <- sum(Status) # Print count print(s) Output:

To count unique values by group in R, you can use two different function one way is group_by() and summarise() function from dplyr package. The other way to use unique() and length() function data.table package. The following methods show how you can do it with syntax. Method 1: Use dplyr Package library(dplyr) df %>% group_by(group_columm) %>% summarise(count = n_distinct(values_column)) Method 2: Use data.table Package library(data.table) df[, .(count = length(unique(values_column))), by = group_column] The following examples show how to count uniqque values by group in R.

To count unique values from column of data frame in R, there are two different methods one is length() function with unique() function and other is summarise() function from dplyr package. The following methods show how you can do it with syntax. Method 1: Use length() and unique() Functions length(unique(df$column_name)) Method 2: Use summarise() Function library(dplyr) df %>% summarise(count=n_distinct(df$column_name)) The following examples show how to count unique values from column of data frame in R.

To count the value in column with condition in R, you can use two different ways one is using nrow() function and other is using summarise() function from dplyr package. The following methods show how you can do it with syntax. Method 1: Use nrow() Function nrow(df[df$column_name == value, ]) Method 2: Use summarise() Function df %>% filter(column_name == value) %>% summarise(count = n()) The following examples show how to count value in column with condition in R.

To create a frequency table of multiple variables in R, you can use table() function and summarise() function from dplyr package. The following methods show how you can do it with syntax. Method 1: Use table() Function table(df$column1, df$column2) Method 2: Use dplyr Package library(dplyr) df %>% group_by(df$column1, df$column2) %>% summarise(count = n()) The following examples show how to create frequency table of multiple variables in R. Create Frequency Table of Multiple Variables Using table() Function in R Let’s see how we can use table() function in R:

To create a multiple line comment in R, you can use two different ways. The following methods show how you can do it with syntax. Method 1: Use # Symbol # Method 2: Use Shortcuts You can add multi line comment in by just pressing Ctrl + Shift + C from keyboard. Let see how can create multi line comment in R. Create Multi-Line Comments Using # Symbol Let’s see how we can add multiline comment in R.

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 create relative frequency tables in R, you can use two different ways prop.table() function and dplyr package. The following methods show how you can do it with syntax. Method 1: Use prop.table() Function frequency_table <- table(data) prop.table(frequency_table) Method 2: Use dplyr Package library(dplyr) df %>% group_by(data) %>% summarise(count = n()) %>% mutate(relative_frequency = count / sum(count)) The following examples show how to create relative frequency table in R. Using prop.

To delete file in R, you can use file.remove() or unlink() function. The following methods show how you can do it with syntax. Method 1: Use file.remove() Function file.remove("file_path") Method 2: Use unlink() Function unlink("file_path") The following examples show how we remove file in R using two different functions. Using file.remove() Function Let’s see how we can use file.remove() function to delete file in R: # Define file to delete file1 <- "sample.

To export data frame to CSV file in R, you can use write.csv() function. The following method shows how you can do it with syntax. Method 1: Use write.csv() Function write.csv(df,file_path) The following example shows how to export data frame to csv file using write.csv() function. Using write.csv() Function Let’s see how we can use write.csv() function in R to export data frame to CSV file: # Create dataframe df<-data.frame(Date=c("21-10-2023","22-10-2023","23-10-2023","24-10-2023","01-11-2023","02-11-2023","03-11-2023","04-11-2023"), Machine_type=c("Mach_1","Mach_2","Mach_3","Mach_4","Mach_A","Mach_B","Mach_C","Mach_D"), Status=c(TRUE,TRUE,FALSE,TRUE,FALSE,TRUE,FALSE,FALSE), Pressure=c(11,12,10,11,89,85,87,90), Temperature=c(45.

To export multiple dataframes to multiple excel sheets, you can use write.xlsx() function from openxlsx library. The following method shows how you can do it with syntax. Method: Use write.xlsx() Function library(openxlsx) dataset_ <- list('Sheet1' = df1, 'Sheet2' = df2, 'Sheet3' = df3,...) # Export each data frame to separate sheets in same Excel file openxlsx::write.xlsx(dataset_, file = 'file.xlsx') The following example shows how export dataframes to multiple excel sheets in R.

To export list to a file in R, you can use sink() function. Using this function we can create connection with file then using print() you can write list to file. The following method shows how you can do it with syntax. Method: Use sink() Function sink('sample.txt') print(list_) sink() The following example shows how to export list to file in R using sink() function. Using sink() Function Let’s see how we can use sink() function to export list to file in R:

To export multiple plots to PDF in R, you can use pdf() function and dev.off() function. The pdf() function opens a graphics device to save plots to a PDF file, while dev.off() closes the graphics device and saves the file. The following method shows how you can do it with syntax. Method 1: Use pdf() and dev.off() Function pdf(file) for (i in data) { x=rnorm(i) y=rnorm(i) plot(x, y) } dev.off() The following example shows how to export multiple plots to pdf in R using pdf() and dev.

To find all unique combinations of two vectors in R, you can use two different functions expand.grid() and crossing() function from tidyr package. The following methods show how you can do it with syntax. Method 1: Use expand.grid() Function expand.grid(vector1, vector2) Method 2: Use crossing() Function library(tidyr) crossing(Temperature, Humidity) The following examples show how to find all unique combination of two vector using expand.grid() and crossing() function in R. Using expand.

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.

To find the range in R, you can use two different function range() function or difference between minimum and maximum value of dataset. The following methods show how you can do it with syntax. Method 1: Use range() Function range(data) Method 2: Use min() and max() Function max(data) - min(data) The following examples show to find range in R using different functions. Using range() Function Let’s see how we can use range() function to find range in R:

To find the size of data frame in R, you can use different function depending on you requirement. The following methods show how you can do it with syntax. Method 1: Use nrow() Function nrow(df) Method 2: Use ncol() Function ncol(df) Method 3: Use dim() Function dim(df) Method 4: Use object.size() Function object.size(df) The following examples show how to find size of data frame using different function in R. Using nrow() Function Let’s see how we can use nrow() function to find number of rows in data frame:

To find the unique values from column of dataframe, you can use unique() function. The following method shows how you can do it with syntax. Method 1: Use unique() Function unique(df$column_name) The following example shows how to find unique values from column of dataframe in R. Using unique() Function Let’s see how we can use unique() function in R: # Create data frame df <- data.frame(Machine_name=c("A","D","B","C","B","D","C","A"), Pressure=c(78.2, 81.21, 71.7, 80.21, 71.

To group by and count with a condition in R, you can use functions from dplyr package. The following method shows how you can do it with syntax. Method: Use dplyr Package df %>% group_by(column1) %>% summarise(count = sum(condition on column2)) The following example shows how to group by and count with condition in R using dplyr package. Using dplyr Package Let’s see how we can use dplyr package in R:

To import TSV file in R, you can use read_table() or read_tsv() function from readr package. The following methods show how you can do it with syntax. Method 1: Use read_table() Function library(readr) read.table("path of file") Method 2: Use read_tsv() Function library(readr) read_tsv('path of file') The following examples show how to import TSV file in R using different function. Use read_table() Function Let’s see how we can use read_table() function from readr package in R:

To merge two dataframes in R, you can use merge() function or *_join() function from dplyr package. The following methods show how you can do it with syntax. Method 1: Use merge() Function merge(df1,df2,by="common_column") Method 2: Use join() Function library(dplyr) *_join(df1, df2, by = "common_column") Using join() function you can perform inner_join, right_join, left_join and outer_join operations on dataframe. The following examples show how to merge two dataframes using merge and join function in R.

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:

To peform element-wise multiplication in R, you can use “*” operator. The following method shows how you can do it with syntax. Method 1: Use * Operator vector1*vector2 The following example shows how to perform element-wise multiplication in R. Using * Operator Suppose we have two vectors and want to perform multiplication between them: # Declare two vectors x <- c(78,89,85,74,56) y <- c(12,13,14,56.21,85) # Perform element wise multiplication m <- x*y # Print multiplication output print(m) Output:

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:

To perform matrix multiplication in R, you can use %*% operator. The following method shows how you can do it with syntax. Method: Using %*% Operator matrix1 %*% matrix2 The following example shows how perform multiplication of matrices in R. Let’s see how we can multiply matrices in R: # Define matrix A <- matrix(c(7,8,9,4),ncol=2) B <- matrix(c(1,2,3,4),ncol=2) # Multiply matrix m <- A%*%B # Print matrix print(m) Output: [,1] [,2] [1,] 25 57 [2,] 16 40 Here the output shows multiplication of matrices using %*% operator.

Univariate analysis involves summarizing and visualizing a single variable in a dataset. This involves calculating statistical values, calculate frequency table and plotting charts. The following method shows how you can do it with syntax. Method 1: Calculate Statistical Values # Calculate mean mean(df$column) # Calculate median median(df$column) # Calculate difference between max and min value max(df$column)-min(df$column) # Calculate IQR IQR(df$column) # Calculate standard deviation sd(df$column) Method: Create Frequency Table table(df$column) Method: Plotting Chart

To plot equation in R, you can use curve() function or ggplot2 package. The following methods show how you can do it with syntax. Method 1: Use curve() Function curve(equation, from=-10, to=10, , xlab="x", ylab="y") Method 2: Use ggplot2 Package library(ggplot2) ggplot(data.frame(m=c(-10, 10)), aes(m=m)) + stat_function(fun=equation) The following examples show how to plot equation in R using two different ways. Using curve() Function Let’s see how we can use curve() function in R:

To create pivot tables in R, you can use group_by() and summarise() function from dplyr package. The following method shows how you can do it with syntax. Method 1: Use dplyr Package df %>% group_by(group1,group2) %>% summarize(sum = sum(values), mean = mean(values)) The following example shows how to create pivot table in R using dplyr package. Using dplyr Package Let’s see how we can use group_by() and summarise() function from dplyr package in R:

There are two different methods in R to read specific rows from CSV file into R. The following methods show how you can do it with syntax. Method 1: Using read.csv() with skip Argument df <- read.csv("data.csv",skip=n) Method 2: Select Rows Based on Condition library(sqldf) df <- read.csv.sql("data.csv", sql = "condition", eol = "\n") In above syntax read.csv.sql method is used from sqldf package.It import rows from CSV file based on provided sql condition.

You can read zip files in R using unzip() function from readr package. Method: Use unzip() function library(readr) df <- read_csv(unzip("file.zip","data.csv")) Let’s understand this function using example. Using unzip() function Let’s assume I have product.zip file in my working directory. Follow below syntax to show all files in ZIP: # Get list of all files in zip folder files = unzip("product.zip", list = TRUE) # Print all files in zip folder print(files) Output :

To rename files in R, you can use file.rename() function. The following method shows how you can do it with syntax. Method 1: Use file.rename() Function file.rename(from='old_file_name.csv', to='new_file_name.csv') The following example shows how to renam file in R using file.rename() function. Using file.rename() Function Let’s see how we can use file.rename() function in R: # Rename one file file.rename(from='Machine1.csv', to='Data.csv') Output: [1] TRUE Here the output shows TRUE which means we successfully rename the file name.

To reshape data between wide and long format in R, you can use pivot_wider() and pivot_longer() function from tidyr library. The following methods show how you can do it with syntax. Method 1: Use pivot_longer() Function library(tidyr) pivot_longer(df, cols = everything()) Method 2: Use pivot_wider() Function library(tidyr) pivot_wider(df, names_from=group, values_from=c(column1, column2)) The following examples show how to reshape data between wide and long format in R. Using pivot_longer() Function Let’s see how we ca reshape dataframe to long format using pivot_longer() function in R:

To save and load RDA files in R, yo can use save() and load() functions respectively. The following methods show how you can do it with syntax. Method 1: Use save() Function save(object,"file.Rda") Method 2: Use load() Function load(file.Rda) # Create object print(object) The following examples show how to save and load rda files in R. Using save() Function Let’s see how we can use save() function in R: # Create data frame df <- data.

To save R output to text file, you can use sink() function or cat() function in R. The following methods show how you can do it with syntax. Method 1: Use sink() Function # Rredirecting output to a file sink("file.txt") # Save output in file print("output") # Close the file sink() Method 2: Use cat() Function cat("output",file = "file.txt") The following examples show how to save R output in text file using two different methods.

To solve a equation in R, you can use solve() function in R. This function takes two main arguments: the coefficients of the equations and the right-hand side values. The following method shows how you can do it with syntax. Method 1: Use solve() Function solve(coefficients, RHS_values) The following example shows how to use solve() function to solve equation in R. Using solve() Function Let’s see how we can use solve() function in R:

To sort a dataframe in R, you can use order() or arrange() function from dplyr package. The following methods show how you can do it with syntax. Method 1: Use order() Function # Sort dataframe in ascending order df[order(df$column1), ] # Sort dataframe in descending order df[order(-df$column1), ] Method 2: Use arrange() Function library(dplyr) # Sort in ascending order df %>% arrange(column1) # Sort in descending order df %>% arrange(desc(column1)) The following examples show how to sort dataframe in R using two different approach.

To sort table in R there are two methods in R. The following methods show how you can do it with syntax. Method 1: Use order() Function # Sort in ascending order order(x) # Sort in descending order order(x,decreasing=TRUE) Method 2: Use sort() Function # Sort in ascending order sort(x) # Sort in descending order sort(x,decreasing=TRUE) The following examples show how to use these function in R. Use order() Function to Sort Table in Ascending Order Let’s see how we can use order() function in R:

To calculate sum of specific column of dataframe you can use rowSums() function in R. The following method shows how you can do it with syntax. Method 1: Use rowSums() Function rowSums(df[ , c(columns to sum)]) The following example shows how to use rowSums() function in R. Use rowSums() to Calculate Sum of Specific Columns Let’s see how we can calculate sum of specific column of data frame: # Create data frame df <- data.

You can transpose dataframe in R using two different methods. The following methods show how you can do it with syntax. Method 1: Use t() Function t(df) Method 2: Use transpose() Function from data.table Package library(data.table) transpose(df) The following examples show how to use these function in R. Use t() Function Let’s see how we can use t() function to transpose the dataframe: # Create dataframe df <- data.frame(Start_date=as.Date(c("2000-05-21","1998-03-28","2001-01-19","2003-04-27","2004-11-26","2008-11-25")), Machine_name = c("Machine1","Machine2","Machine3","Machine4","Machine5","Machine6"), Value = c(108,120,135,95,98,105),Reading= c(110,97,91,89,80,85)) # Transpose dataframe transpose_df <- t(df) # Print transposed dataframe print(transpose_df) Output:

To unload the package in R, you can use unloadNamespace() function. The following method shows how you can do it with syntax. Method 1: Use unloadNamespace() Function unloadNamespace("Package_name") The following example shows how to unload package in R using unloadNamespace() function. Using unloadNamespace() Function Let’s see how we can use unloadNamespace() function in R: # Unload package unloadNamespace("tidyr") Here in the above code we unload the tidyr package using unloadNamespace() function.

The corrplot() function is used to plot correlation matrix in R. The following method shows how you can do it with syntax. Method 1: Use corrplot() Function library(corrplot) corrplot(correlation_matrix) The following example shows how to use corrplot() function to plot correlation matrix in R. Using corrplot() Function Suppose we want to plot correlation matrix created from dataframe then we first need to use cor() function and after this you plot correlation matrix:

The fread() function from data.table package used to read large data files in R. The following method shows how you can do it with syntax. Method: Use fread() Function library(data.table) fread("file_name") The following example shows how to use fread() function in R. Use fread() Function From data.table Package Let’s see how we can use fread() function to read data from CSV file: # Import library library(data.table) # Import CSV file data <- fread("product.

The list.files() function in R used to list out every file in specific folder. The following methods show how you can do it with syntax. Method 1: List All Files Using list.files() Function list.files(path_of_folder) Method 2: List Files With Specific Extension list.files(file_path,pattern="csv") Method 3: List First N Files in Folder list.files(file_path)[1:n] The following examples show how to use list.files() function in R. List All Files in Folder If I want to list all the files in the CSV_data folder, we can achieve this using the list.

The min() and max() function is used to find minimum and maximum values from list, data frame or vector in R. The following method shows how you can do it with syntax. Method: Use min() and max() Function min(x) max(x) The following examples shows how to use min() and max() in R. Use min() and max() Function on Vector Let’s see how we can find minimum and maximum value from numeric vector:

The mtext() function in R is used to write text in the margins of a plot. The following method shows how you can do it with syntax. Method 1: Use mtext() Function mtext("Text", side = x, line = y) The following example shows how to use mtext() function in R to write text in margins of plot. Using mtext() Function Let’s see how we can use mtext() function in R:

The ncol function is used to get number of columns in data frame,matrix or table like structure in R. The following method shows how you can do it with syntax. Method: Use ncol() Function ncol(df) The following examples show how to use ncol() function in R. Use ncol() Function to Get Number of Columns of Data Frame Let’s see how to find number of columns of data frame using ncol() function:

The pivot_longer() function from tidyr package is used to pivot dataframe from wide format to a long format. The following method shows how you can do it with syntax. Method 1: Use pivot_longer() Function pivot_longer(df, cols = everything()) The following example shows how to use pivot_longer() function in R to create dataframe from wide format to long format. Using pivot_longer() Function Let’s see how we can use pivot_longer() Function in R:

The pivot_wider() function from reshape2 package is used to reshape dataframe from longer to wider format. When you want to pivot multiple columns, you can specify the columns you want to pivot using the values_from argument. The following method shows how you can do it with syntax. Method 1: Use pivot_wider() Function library(tidyr) pivot_wider(df, names_from=group, values_from=c(column1, column2)) The following example shows how to use pivot_wider() function to reshape data frame from longer to wider format.

The rcorr() function from Hmisc package is used to create correlation matrix in R. The following method shows how you can do it with syntax. Method 1: Use rcorr() Function library(Hmisc) rcorr(as.matrix(df)) The following example shows how to use rcorr() function in R to create correlation matrix. Using rcorr() Function Let’s see how we can use rcorr() function in R: # Import library library(Hmisc) # Create data frame df <- data.

The read.delim() function in R is used to read data from a delimited text file into a data frame. The following method shows how you can do it with syntax. Method 1: Use read.delim() Function read.delim(file, header=TRUE, sep='\t') The following example shows how to use read.delim() function in R to read data from text file. Using read.delim() Function Let’s see how to use read.delim() function in R: # Read in tab-delimited text file df_ <- read.

The read.table() function used to read data from file or text into data frame. The following method shows how you can do it with syntax. Method 1: Use read.table() Function read.table(file_path) The following example shows how to use read.table() function in R. Use read.table() Function to Read Text Data from File Suppose we want to read text data from sample.txt file which stored in current working directory: # Read file into data frame df <- read.

The readlines() function used to read text from a file or connection. The following method shows how you can do it with syntax. Method: Use readLines() Function readLines(con,n=-1L) con : A connection string or file path n : Number of lines to read. By default it read all lines. The following examples show how to use readLines() function in R. Use readLines() to Read All Lines from Text File Suppose we have sample.

The seq() function used to generate sequence of numbers with specified increment or decrement between values. The following method shows how you can do it with syntax. Method 1: Use seq() Function seq(from=x, to=y, by=n, length.out=a) from: Used to specify the starting point of sequence to: Used to specify the ending point of sequence by: Used to add increment or decrement between values length.out: Used to specify length of sequence

The sign() function used to get sign of numeric value or numeric vector in R. It returns -1 for negative numbers, 0 for zero and 1 for positive number. The following method shows how you can do it with syntax. Method 1: Use sign() Function sign(x) x: Number ot numeric vector The follwing example shows how to use sign() function. Use sign() Function on Numeric Vector Let’s apply sign() function on numeric value:

The sprintf() function is used to print formatted strings in R. The following method shows how you can do it with syntax. Method 1: Use sprintf() Function sprintf(format,string) The following example show how to use sprintf() method in R: Use sprintf() Function to Print Formatted String Let’s see how we can use sprintf() function in R to print formatted string: # Declare string str <- "I am %s. I am %d years old and having experience of %0.

The str() function in R is used to display internal structure of an R object. The following method shows how you can do it with syntax. Method 1: Use str() Function str(object) The following example shows how to use str() function in R. Apply str() Function on Vector Let’s see how we can check internal structure of an array using str() function: # Create vector x=c(78,89,85,84,81,79,77,85,77,78,75,74,71,79,76,78) # Show vector print(x) # Display internal structure of vector str(x) Output:

There is strsplit() function in R used to split element in string with specified delimiter. The following method shows how you can do it with syntax. Method 1: Use strsplit() Function strsplit(string, pattern) The following example shows how to use strsplit() function in R: Apply strsplit() Function on Character Vector Let’s see how we can split character vector using strsplit() function based on comma: # Split string based on spaces str <- "R, Python, Java, C++, Ruby" # Split string split_str <- strsplit(str,",") # Show output print(split_str) # Check class of string class(split_str) Output:

The substring() function is used to extract substrings in a character vector. The following method shows how you can do it with syntax. Method 1: Use substring() Function substring(data,first=x,last=y) data : A character vector x : Starting position of the substring y: Ending position of the substring The following example shows how to use substring() method. Get Substring Using substring() Function We can use following example to get substring from character vector using substring() function:

The sum() function used to calculate sum of a numeric vector, list or data frame. The following method shows how you can do it with syntax. Method 1: Use sum() Function sum(vector.na.rm=TRUE) The following example shows how to use sum() function in R. Use sum() Function to Sum Values in Vector Let’s see how we can calculate sum of values in vector: # Define vector vector1 <-c(20,25,27,29,30,32,39,40) # Find sum of vector values sum(vector1) Output:

The text() function is used to add text to plot in R. The following method shows how you can do it with syntax. Method 1: Use text() Function text(x=x, y=y, labels="Text") The following example shows how to use text() function in R to add text to plot. Using text() Function Let’s see how to use text() function in R: # Create dataframe df <- data.frame(Machine_name=c("A","B","C","D","E","F","G","H"), Temperature=c(12,9,14,13,18,28,22,23), Pressure=c(20,25,27,29,30,32,39,40)) # Create scatterplot plot(df$Temperature,df$Pressure) # Add text in plot text(x=20,y=35,"This is scatter plot") Here the above snippet shows scatter chart along with text added to it.

The cast() function from reshape2 package is used to reshape dataframes. This cast() function convert dataframe from long format to wide format. The wide format having values that does not repeat in first column and long format contains values that contain repeat values in first column. The following method shows how you can do it with syntax. Method 1: Use cast() Function library(reshape2) cast(df, column1 ~ column2) The following example shows how to use cast() function in R.

The dim() function is used to get dimension of data frame, matrix or array in R. The following method shows how you can do it with syntax. Method: Use dim() Function dim(dataframe) The following example shows how you can use the dim() function in R. Use dim() Function to Get Dimension of Data Frame Let’s apply dim() function on data frame: # Create data frame df <- data.frame(Machine_name=c("A","B","C","D","E","F"), Pressure=c(78.2, 71.7, 80.

The dist() function is used to calculate distance matrix which shows distance between rows of matrix or dataframe. The following method shows how you can do it with syntax. Method 1: Use dist() Function dist(matrix) The following example shows how to use dist() function in R. Using dist() Function Let’s see how we can use dist() function to calculate distance matrix in R: # Define vectors a <-c(78,85,89,96,74,75) b <-c(65,66,64,69,70,61) c <-c(23,25,26,19,21,18) d <-c(45,41,35,36,39,48) # Define matrix matrix1 <- rbind(a,b,c,d) # Show matrix print(matrix1) # Calculate distance matrix dist(matrix1) [,1] [,2] [,3] [,4] [,5] [,6] a 78 85 89 96 74 75 b 65 66 64 69 70 61 c 23 25 26 19 21 18 d 45 41 35 36 39 48 a b c b 45.

You can use get() function to get named objects. The following methods show how you can do it with syntax. Method 1: Use get() Function get("object_name") Method 2: Use get0() Function The get0() function is used to get named object if not found then show custom message. get0("object_name",ifnotfound="custom message") Method 3: Use mget() Function The mget() function to get multiple objects. mget(c("object1","object2","object3",..)) Here is few examples to understand this function in detailed:

The scan() function is used to read data from file in vector or list format in R. The following method shows how you can do it with syntax. Method 1: Use scan() Function scan(file="name_of_file",what=type_of_data_to_read) The following example shows how to use scan() function in R. Use scan() function Suppose I have Machine_data.csv file in my working directory and want to read data from file in list format: # Read from CSV file into list format data <- scan("Machine_data.

In R language you can export string values or dataframes in text and CSV file. For this you can use sink() function to create external connection. The following method shows how you can do it with syntax. Method: Use sink() Function sink("file_name.txt") sink() The following example shows how to use this function: Use sink() Function to Export String to Text File Let’s see how we can use sink() function to export string to text file:

In R you can use unlist() function for convert list to a vector. The following method shows how you can do it with syntax. Method 1: Use unlist() Function unlist(list_name) Let’s first create list with numeic values: # Create list lst <- list(A=c(12.39,11.25,12.15,13.48,13.78),B=c(78,89,85,84,81),C=c(5,7,1,2,7)) # Print list print(lst) Output: $A [1] 12.39 11.25 12.15 13.48 13.78 $B [1] 78 89 85 84 81 $C [1] 5 7 1 2 7 The output shows list with numeric values.

In R we use with() and within() function to evaluate expression based on dataframe. The following methods show how you can do it with syntax. Method 1: Use with() Function with(df,expression) Method 2: Use within() Function within(df,expression) expression : expression to evaluate The with() evaluates the expression without modifying the original data frame. The within() evaluates the expression and creates a copy of the original data frame. Let’s first create data frame to apply expression:

The write.table() function is used to export data to text file in R. The following method shows how you can do it with syntax. Method 1: Use write.table() Function write.table(dataframe,"file_name.txt") You can provide separator in values of exported file but by default it seperated by single space. For separator you can use sep argument. For example, you could choose tab as a separator: write.table(dataframe,"file_name.txt",sep='\t') The following example shows how to use write.

Suppose you want to import CSV file and keep the spaces in the column names, you can use the check.names=FALSE argument. The following method shows how you can do it with syntax. Method 1: Import CSV file with Column Names Contains Spaces df <- read.csv("data.csv",check.names=FALSE) The following example shows how to import CSV with column names that contain spaces in R. Import CSV file with Column Names Contains Spaces Let’s first import csv file in R without using check.

To remove column from dataframe in R, you can use four different ways: The following methods show how you can do it with syntax. Method 1: Use subset() with Negative Index subset(df, select = -n) Method 2: Use subset() with Column Name subset(df, select = -column_name) Method 3: Use Indexing Operator df[, !names(df) %in% c('column_name')] Method 4: Use select() Function library(dplyr) df %>% select(-column_name) The following examples show how to remove column from dataframe in R.

The iris dataset is built-in datset in R, it has data on 150 iris flowers, with measurements for four features: sepal length, sepal width, petal length, and petal width. In this article we see how to load, explore, summarize and visualize iris dataset in R. Load the Iris Dataset To load the iris dataset we use data() function: # Load the iris dataset data(iris) Let see how we can get first six rows from iris dataset:

The mtcars dataset is a built-in dataset in R that contains data on the design and performance of various car models. In this article explains how to load, explore, summarize and visualize the mtcars dataset in R. Load the mtcars Dataset To load the mtcars dataset we use data() function: # Load dataset data(mtcars) Let see how we can get first six rows from mtcars dataset: # Get first six rows head(mtcars) The below output shows first six rows from mtcars dataset.

To add new column to data frame in R, you can use two different methods. The following methods show how you can do it with syntax. Method 1: Use $ Operator df$new_column <- c(value1,value2,value3,....) Method 2: Use cbind() Function df <- cbind(df,new_column = c(value1,value2,value3,....)) Let’s first create data frame using data.frame() function: # Create data frame df <- data.frame(Start_date=as.Date(c("2000-05-21","1998-03-28","2001-01-19","2003-04-27","2004-11-26","2008-11-25")), Machine_name = c("Machine1","Machine2","Machine3","Machine4","Machine5","Machine6"), Value = c(108,120,135,95,98,105),Reading= c(110,97,91,89,80,85)) # Print data frame print(df) Output:

To check data type of each column of data frame, you can use class function or str() function. You can use class() function with lapply() or sapply() function. The following methods show how you can do it with syntax. Method 1: Use class() With lapply() Function lapply(df, class) Method 2: Use class() With sapply() Function sapply(df, class) Method 3: Use str() Function str(df) The following examples show how to check data type of each column of data frame in R.

To convert numeric to character column of data frame in R, you can use as.character() function. The following method shows how you can do it with syntax. Method: Use as.character() Function as.character(df$column_name) Let’s first create data frame and check data type of each column data frame: # Create data frame df <- data.frame(Start_date=as.Date(c("2000-05-21","2000-05-22","2000-05-23","2000-05-24","2000-05-25","2000-05-26")), Machine_name = c("Machine1","Machine2","Machine1","Machine3","Machine2","Machine3"), Value = c(108,120,135,95,98,105),Reading= c(110,97,91,89,80,85)) # Print data frame print(df) # print datatype print(str(df)) Output:

You can use write.table() function to export data frame to a text file in R. The following method shows how you can do it with syntax. Method: Use write.table() Function write.table(dataframe, path = "C:\\Users\\admin\\data.txt") The following example shows how to export data frame to text file in R. Using write.table() Function To use write.table() function you need to follow below steps: Step 1: Create a Data Frame Let first create data frame in R:

To pass data frame to csv file you can use write.csv() function in R. The following method shows how you can do it with syntax. Method: Use write.csv() Function write.csv(dataframe,path_of_csv_file) The following examples shows to export data frame to csv in R. Using write.csv() Function To use write.csv() function you need to follow below steps: Step 1: Create a Data Frame First let create data frame using data.frame() function: # Create data frame df <- data.

You can use aggregate() function for multiple column of data frame in R.The aggregate() function is basically used calculate statistical summary of multiple columns of data frame based on group. The following method shows how you can do it with syntax. Method: Use aggregate() Function aggregate(column1 ~ column2, data = data frame, FUN = mean) column1: Column used for applying aggregate() function. column2: Column used for applying group_by function. The following examples show how to use aggregate() function in R.

To calculate euclidean distance in R, you can declare function manually. The following method shows how you can do it with syntax. Method: Declare Function euclidean <- function(x1, x2) { sqrt(sum((x1 - x2)^2)) } The following example shows how to calculate euclidean distance in R. Calculate Euclidean Distance Let’s see how to calculate euclidean distance between two vector in R: # Declare euclidean function euclidean <- function(x1, x2) { sqrt(sum((x1 - x2)^2)) } # Declare vector a <-c(78,85,89,96,74,75) b <-c(65,66,64,69,70,61) # Calculate euclidean distance d <- euclidean(a,b) # Show euclidean distance print(d) Output:

To calculate expected value in R, there two different ways are available in R. The following methods show how you can do it with syntax. Method 1: Use sum() Function sum(data*probability) Method 2: Use mean() Function mean(data) The following example shows how to calculate expected value in R. Using sum() Function Let’s see how to use sum() function to calculate expected value when we have sample data and probability values:

To calculate five number summary in R, you can use fivenum() function. This function gives minimum, maximum, median, 1st quartile and 3rd quartile value of dataset. The following method shows how you can do it with syntax. Method: Use fivenum() Function fivenum(data) The following example shows how to use fivenum() function in R. Use fivenum() to Calculate Five Number Summary Let’s see how we can calculate five number summary of data frame:

To calculate hamming distance in R, you can use sum() function along with inequality operator !=. The following method shows how you can do it with syntax. Method: Use sum() Function and inequality operator sum(vector1!=vector2) The following example shows how to calculate hamming distance in R. Calculate Hamming Distance Between Two Vectors Let’s see how we can calculate hamming distance between two vectors: # Define binary vector x <- c(0,1,1,0,1,0) y <- c(0,0,1,1,1,0) # Calculate hamming distance sum(x!

To calculate the interquartile range in R, you can use IQR() function.This interquartile range gives range between first quartile and third quartile of a dataset. The following method shows how you can do it with syntax. Method: Use IQR() Function IQR(data) The following example shows how to calculate interquartile range using IQR() in R. Use IQR() to Calculate Interquartile Range for Dataset Let’s see how we can use IQR() function to calculate interquartile range:

To calculate levenshtein distance in R, you can use stringdist() function from stringdist package.The Levenshtein distance is distance between two strings is the minimum number of single-character edits required to turn one word into the other. The following example shows how you can do it with syntax. Method: Use stringdist() Function library(stringdist) stringdist(string1, string2, method = "lv") The following example shows how to calculate levenshtein distance in R. Use stringdist() Function to Calculate Levenshtein Distance Let’s see how we can calculate levenshtein distance between strings.

To calculate mahalanobis distance in R, you can use mahalanobis() function from stats package. The Mahalanobis distance is the distance between two points in a multivariate space. The following method shows how you can do it with syntax. Method: Use mahalanobis() Function mahalanobis(df, colMeans(df), cov(df)) colMeans(df): Mean vector cov(df): Covariance matrix The following example shows how to calculate mahalanobis distance in R. Use mahalanobis() to Calculate Mahalanobis Distance Let’s see how to calculate mahalanobis distance of data frame:

To calculate manhattan distance in R, you can use dist() function with method argument or create function manually. The Manhattan distance is the sum of absolute differences in each dimension of the data points. The following method shows how you can do it with syntax. Method 1: Use dist() Function dist(matrix,method="manhattan") Method 2: Create Function Manually manhattan_dist <- function(x, y){ dist <- abs(x-y) dist <- sum(dist) return(dist) } The following example shows how to calculate manhattan distance in R.

To calculate minkowski distance in R, you can use dist() function along with method argument. Minkowski distance is a generalized metric that includes Euclidean distance (p = 2) and Manhattan distance (p = 1) as special cases. The following method shows how you can do it with syntax. Method: Use dist() Function dist(matrix, method="minkowski", p) The following example shows how to use dist() function in R. Use dist() Function to Calculate Minkowski Distance Let’s see how we can calculate minkowski distance between matrices using dist() function.

To calculate partial correlation in R, you can use pcor() function from ppcor package. The partial correlation is used to calculate correlation between two columns of data frame while controlling third column of data frame. The following method shows how you can do it with syntax. Method: Use pcor() Function library(ppcor) p <- pcor(df) The following example shows how to calculate partial corrlation in R. Use pcor() to Calculate Partial Correlation Let’s see how we can calculate partial correlation of data frame:

To calculate point-biserial correlattion in R, you can use cor.test() function. The point-biserial correlation coefficient measures the strength and direction of the association between a continuous variable and a binary variable. The following method shows how you can do it with syntax. Method: Use cor.test() Function cor.test(vector1,vector2) The following example shows how to calculate point-biserial correlation in R. Using cor.test() Function Let’s see how we can calculate point-biserial correlation using cor.

To calculate polychoric correlation in R, you can use polycor() function from polycor package. The polychoric correlation measures the association between two ordinal variables by estimating the correlation between the underlying continuous variables. The following method shows how you can do it with syntax. Method: Use polycor() Function library(polycor) polychor(vector1,vector2) There is value for polychoric correlation ranges from -1 to 1. -1 :-indicates a perfect negative correlation. 0:- indicates no correlation.

There are two different ways to calculate ratios in R. The following methods show how you can do it with syntax. Method 1: Use Base R ratio <- column1/column2 Method 2: Use dplyr Package library(dplyr) df %>% mutate(ratio = column1/column2) The following examples show how to calculate ratio in R. Calculate Ratios in R Using Base R Let’s see how we can calculate ratio in R: # Create data frame df <- data.

You can check if directory exists or not using dir.exist() function in R. This function takes directory path and return output in logicaal value. The following method shows how you can do it with syntax. Method: Use dir.exist() Function dir.exist(path_of_directory) The following example shows how to check if directory exists or not. Using dir.exist() Function Suppose we have /Documents/R Studio patb of directory and want to check is this directory exist or not:

To check if package is installed in R, you can use the installed.packages(). The following method shows how you can do it with syntax. Method: Use installed.packages() Function package_name %in% rownames(installed.packages()) The following example shows how to check if package installed or not in R. Using installed.packages() Function Let’s check if data.table package is installed or not in R: # Declare package package <- "data.table" # Check package installed or not package %in% rownames(installed.

To check if file exists or not, you can use file.exists() function in R. The following method shows how you can do it with syntax. Method: Use file.exists() Function file.exists(file) The following example shows how to use file.exists() function in R. Using file.exists() Function Let’s see how we can check ot file exist using file.exists() function in R: data <- 'data.csv' file.exists(data) Output: [1] FALSE As the output shows FALSE means data.

To count the number of elements in list in R, you can use length() function or length() function. The length() function gives count of number of elements in list and lengths() function gives count of number of elements in each component of list. The following methods show how you can do it with syntax. Method 1: Use length() Function length(list) Method 2: Use lengths() Function lengths(list) The following examples show how to count number of elements in list.

To count number of occurences in columns of data frame in R, you can use table() function or count() function from dplyr package. The following method shows how you can do it with syntax. Method 1: Use table() Function table(df) Method 2: Use count() Function library(dplyr) count(df) The following examples show how to count occurences in column of data frame in R. Count Occurrences in Columns in R Using table() Function Let’s see how we can use table() function to count number occurences in columns of data frame:

To count number of rows in R data frame, you can use nrow() function. The following method shows how you can do it with syntax. Method: Use nrow() Function nrow(df) The following example shows how to use nrow() function to count number of rows in data frame. Using nrow() Function Let’s see how to use nrow() function in R: # Create dafaframe df <- data.frame(Machine_name=c("A","B","C","D","E","F","G","H","A","B","A","C","D","B","E","H"), Pressure=c(12.39,11.25,12.15,13.48,13.78,12.89,12.21,12.58,9.6,8.85,7.89,10.24,12.36,11.45,9.47,8.12), Status=c("OK","Suspect","OK","OK","Suspect","Suspect","Suspect","OK","OK","OK","OK","OK","Suspect","Suspect","Suspect","OK")) # Count total rows in data frame r <- nrow(df) # Print count of rows print(r) Output:

To create line chart in R, you can use plot() function or ggplot() function from ggplot2 package. The following methods show how you can do it with syntax. Method 1: Use plot() Function plot(x, y, type = "l", col = "blue", lwd = 2, xlab = "X-axis label", ylab = "Y-axis label", main = "My Line Chart") Method 2: Use ggplot() Function ggplot(df, aes(x, Vy))+geom_line()+xlab(label='x')+ylab(label='y') The following examples show to create line chart in R.

To create a pie chart in R, you can use three different ways using base R, using ggplot2 package or using plotrix library. The following methods show how you can do it with syntax. Method 1: Use pie() Function pie(values, labels = labels, main = "Pie Chart") Method 2: Use ggplot() Function library(ggplot2) ggplot(df, aes(x = "", fill = category, weight = value)) + geom_bar(width = 1) + coord_polar("y", start = 0) + labs(x = NULL, y = NULL, fill = NULL, title = "Pie Chart") Method 3: Use pie3D() Function

To create frequency table in R, you can use two different ways one is using table() function and other is using dplyr package. The followings methods show how you can do it with syntax. Method 1: Use table() Function table(vector) Method 2: Use dplyr Package library(dplyr) result <- df %>% group_by(data) %>% summarise(count = n()) The following examples show how to create frequency table in R. Create Frequency Table Using table() Function Let’s see how we can use table() function to create frequency table in R:

To create identity matrix in R, you can use diag() function. The following method shows how you can do it with syntax. Method: Use diag() Function diag(n) The following example shows how to create identity matrix in R using diag() function. Using diag() Function Let’s see how use diag() function to create identity matrix: # Create identity matrix ident <- diag(5) # Print identity matrix print(ident) Output: [,1] [,2] [,3] [,4] [,5] [1,] 1 0 0 0 0 [2,] 0 1 0 0 0 [3,] 0 0 1 0 0 [4,] 0 0 0 1 0 [5,] 0 0 0 0 1 Here the output shows identity matrix of 5X5 whicj created using diag() function.

To export data frame to an excel file in R, you can use write_xlsx() function from writexl package. The following method shows how you can do it with syntax. Method: Use write.xlsx() Function library(writexl) write_xlsx(df,file_path) The following example shows how to export data frame to an excel using write_xlsx() function in R. Using write_xlsx() Function Let’s see how we can use write_xlsx() function to export a data frame to an excel in R:

To find and and visualize quartile values in R, you can use quantile() function boxplot() function respectively. The following methods show how you can do it with syntax. Method: Use quantile() Function quantile(data) Method: Use boxplot() Function boxplot(data, ylab = "y", xlab = "x", main = "Boxplot with Quartiles") The following examples show how to find and visualize quartile values in R using quantile() and boxplot() function. Using quantile() Function Let’s see how we can use quantile() function in R:

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. The following method shows how you can do it with syntax. Method: Use IQR() Function q1 <- quantile(df$column, 0.25) # Compute the first quartile (25th percentile) q3 <- quantile(df$column, 0.

There are three different methods available to import CSV file in R. The following methods show how you can do it with syntax. Method 1: Use read.csv() Function data <- read.csv("path_of_file",header=TRUE) Method 2: Use read_csv() from readr Package library(readr) data <- read_csv("path_of_file") Method 3: Use fread from data.table Package library(data.table) data <- fread("path_of_file") Let’s see example for each method how we can achieve importing CSV in R. Import CSV file Using read.

In R, you can manually enter raw data in using c() function to create vectors. The following methods shows how to do it with syntax. Method 1: Enter Raw Data Using Vector x <- c(value1,value2,...) Method 2: Enter Raw Data to a Data Frame data.frame(vector1, vector2, vector3) Method 3 : Enter Raw Data in Matrix cbind(vector1,vector2,...) The following examples shows how to enter raw data in R. Create Vector to Enter Raw Data Let’s see example to create vector:

The cor() function is used to calculate correlation between two variables in R. This function returns value between -1 to 1. The following method shows how you can do it with syntax. Method: Use cor() Function cor(x,y) The following example shows how to use cor() function in R. Use cor() to Calculate Correlation Between Two Vectors Let’s see how we can calculate correlation between two vectors using cor() function: # Create vector x <- c(77,85,89,85,82,88,81,89) y <- c(77,78,79,81,84,85,85,89) # Find correlation coefficient cor(x,y) Output:

The fwrite() function from data.table package is used to write data frame or data table in a file. The following method shows how you can do it with syntax. Method: Use fwrite() Function library(data.table) fwrite(df,"File name") The following example shows how to use fwrite() function in R. Use fwrite() Function to Write Data Frame in File Let’s see how we can write data frame in CSV file. # Import library library(data.

The length() function is used to find the length of list,vector or data frame. The following method shows how you can do it with syntax. Method: Use length() Function length(x) The following examples show how to use length() function in R. Use length() Function to Find Length of Vector Suppose we want to find length of vector using length() function: # Define vector vector1 <-c(45,85,89,78,12,36,68,32,12) # Find length len <- length(vector1) # Show length print(len) Output:

The lines() function is used to add lines to an existing plot in R. The following method shows how you can do it with syntax. Method: Use lines() Function lines(x, y, col, lwd, lty) x: Vector of x-coordinates y: Vector of y-coordinates col: Color of line lwd: Line width lty: Type of line The following example shows how to use lines() function in R: Use lines() Function to Add Line in Plot Let’s see how we can add line to plot using lines() function:

The match() function used to find first position of element in a vector,list or data frame in R. The following method shows how you can do it with syntax. Method: Use match() Function match(x,list) x: The element whose position want to find. The following example shows how to use match() function in R: Use match() Function to Find Position of Element from Vector Let’s see how we can find element position from vector using match() function:

The mean() function is used to calculate mean value of numeric vector,dataframe or list in R. The following method shows how you can do it with syntax. Method: Use mean() Function mean(x) The following examples show how to use mean() function in R. Use mean() Function to Calculate Mean of Vector Let’s see how we can calculate mean of numeric vector using mean() function: # Declare vector x <- c(2.39,11.25,12.15,13.48,13.78,12.89,12.21,12.58) # Calculate mean of vector mean_x = mean(x) # Show mean print(mean_x) Output:

The nrow() function is used to get number of rows in data frame, matrix or table like structure. The following method shows how you can do it with syntax. Method: Use nrow() Function nrow(df) Let’s see example using nrow() function. Use nrow() Function to Get Number of Rows of Data Frame Suppose we have data frame and want to find number of rows of data frame: # Create data frame df <- data.

The cat() function to concatenate or merge objects in R. The following method shows how you can do it with syntax. Method: Use cat() Function cat(value1,value2,..., file = "file_name", sep = " ", append = FALSE) Here we pass values,file name in which store values,seperated by symbol and append is used to whether to append output to existing file or create new file. The following examples show how to use cat() function in R.

The duplicated() function in R is used to get duplicate values in vector or data frame. The following method shows how to use duplicated function in R with syntax. Method: Use duplicated() Function dataframe[duplicated(dataframe$column name), ] The following example shows how to use this method with example. First create data frame which having duplicate rows in it: # Define data frame df <- data.frame(Machine_name=c("A","B","C","D","A","A","G","B"), Temperature=c(12,9,14,13,18,28,22,23), Pressure=c(20,25,27,29,30,32,39,40)) # Print data frame print(df) Output:

The identical() function is used compare two object and check whether they are identical or not.This function gives output in boolean value means it gives TRUE when two objects are identical and it gives FALSE when two objects are not identical. The following method show how you can do it with syntax. Method: Use identical() Function identical(object1,object2) The following example shows how to use identical() function in R. Use identical() to Check if Two Strings are Identical or Not Let’s see how we can comapre two string and check they are equal or not:

To fnd common elements between two vector or object, you can use intersect() function in R. The following method shows how you can do it with syntax. Method: Use intersect() Function intersect(object1,object2) Let’s understood this syntax using example: Use intersect() Function For Vectors Let’s create two vectors and apply intersect() function on them: # Define vector Temp1=c(77,"A",89,78,"C",84,81,74,"B") Temp2=c(77,85,"A",77,78,"D",75,74,"B") # Use intersect function i <- intersect(Temp1,Temp2) # Show common values print(i) Output:

The melt() function from reshape package is used to convert data frame into wide format to long format in R. A long format data frame repeat values from first column and in wide format data frame does not repeat values from first column. The following method shows how you can do it with syntax. Method: Use melt() Function library(reshape) melt(dataframe,id="column_name") The following example shows how to use melt() function in R.

The quantile() function in R used to calculate sample quantiles values from dataset.The quantile are values that divide dataset into equal groups. The following method shows how you can do it with syntax. Method: Use quantile() Function quantile(x, probs = seq(0, 1, 0.25),na.rm=FALSE) x : name of dataset probs : numeric dataset of probabilities na.rm : used for whether to remove NA values. The following examples show how to use quantile() function in R.

To import data from text file into R you can use read.csv() function. In this you need to pass text file path to this function. The following method shows how you can do it with syntax. Method: Use read.csv() Function read.csv(path of text file\\file_name.txt) The following example shows how to use read.csv() function in R. Using read.csv() Function to Import Text File Suppose I want to read machine.txt file then use read.

To import excel file, you can use read_excel() function from readxl package. The following method shows how you can do it with syntax. Method: Use read.excel() function library("readxl") df = read_excel("file.xlsx") The following example shows how to use read_excel() function in R. Import Excel File Using read_excel() Function Let’s see example how to import excel file in R: # Install package install.packages("readxl") # Import library library("readxl") # Read data from excel df=read_excel("cylinder.

Boston dataset from MASS package in R is widely used in statistics and machine-learning. It contains information about housing price in various suburbs in Boston, Massachusetts. This article explains how to load,summarize and visualize Boston dataset. Load the Boston Dataset Before working on Boston dataset, we need to load the MASS package: # Load library to import dataset library(MASS) Now we can use head() function to get the first six rows from dataset:

The diamonds dataset is built-in dataset in ggplot2 package in R. It contains information on 53,940 round-cut diamonds, providing measurements for 10 different variables. Let’s see how to load,explore,visualize and summarize this dataset in R. Load Diamond Dataset The diamond dataset is a built-in ggplot2 package, first we must to install ggplot2 package # Install ggplot2 if not already installed install.packages('ggplot2') # Load ggplot2 library(ggplot2) After installing ggplot2 you can load diamond dataset using data() function:

The c() function in R is used for combining values to vector or list. The c stands for combine or concatenate. The following method shows how you can do it with syntax. Method: Use c() Function c(value1,value2,value3,...) The following example shows how to use c() function in R. Use c() Function Let’s create vector using c() function in R: # Create vector vect <- c(12,23.34,45,56,67,78,89,90) # Show vector print(vect) Output:

To concatenate column values into new column in R, you can use two different function i.e. paste() function and unite() function. The following methods show how you can use it with syntax. Method 1: Use paste() Function paste(df$column1, df$column2, df$column3,..... , sep = "_") Method 2: Use unite() Function unite(df, new_column, column1, column2, column3,...., sep = "_") Let’s create data frame first to apply this function on it: # Create data frame df <- data.

To convert character to integer column in R, you can use as.integer() or as.numeric() function. The following methods show how you can do it with syntax. Method 1: Use as.intger() Function as.integer(df$column_name) Method 2: Use as.numeric() Function as.numeric(df$column_name) The following examples show how you can use these methods. Let’s first create data frame and also check datatype of each column of data frame. # Create data frame df <- data.frame(Start_date=as.Date(c("2000-05-21","2000-05-22","2000-05-23","2000-05-24","2000-05-25","2000-05-26")), Machine_name = c("Machine1","Machine2","Machine1","Machine3","Machine2","Machine3"), Value = c("108","120","135","95","98","105"),Reading= c(110,97,91,89,80,85)) # Print data frame print(df) # print datatype print(str(df)) Output:

To create data frame in R langauge, you can use data.frame() function. The following method shows how you can do it with syntax. Method: Use data.frame() Function data.frame(column_name = c(value1,value2,...)) Here c represents column. The following examples shows how to create data frame in R. Create Data Frame Using data.frame() Function in R Here we see example to create data frame which having machine data: # Create data frame df <- data.

In R, you can use the following methods to add multiple columns to a data frame in R. Method 1: Add Multiple Columns to data.frame Object df[c('new_col1', 'new_col2', 'new_col3',..)] <- NA Method 2: Add Multiple Column to data.table object library(data.table) df[ , ':='(new_col1 = new_col1, new_col2 = new_col2, new_col3 = new_col3,...)] The following examples shows how to add multiple columns to data frame. How to Add Multiple Columns to data.frame Object Suppose you have data frame already created and you want to add multiple columns to it.

To calculate cross product in R, you can use cross() function from pracma library. The cross product is a binary operation on two vectors in three-dimensional space (R^3) and results in another vector that is perpendicular to the plane containing the input vectors. The following method shows how you can do it with syntax. Method: Use cross() Function library(pracma) cross(a,b) The following example shows how to calculate cross product in R.

In R, you can calculate the autocorrelation using acf() function from tseries package. Autocorrelation measures the degree of similarity between a time series and a lagged version of itself over successive time intervals. The following method shows how you can use it with syntax. Method: Use acf() Function library(tseries) acf(data) The following example shows how to calculate autocorrelation in R. Calculate AutoCorrelation Using acf() Function in R Let’s see how we can calculate autocorrelation for numeric vector in R:

To calculate combination use choose() function and to calculate permutation use factorial() function in R. Combinations and permutations are used to count the number of ways in which objects can be selected from a set or arranged in a specific order. The following methods show how you can use it with syntax. Method 1: Use choose() Function choose(x,y) x: The total number of items y: The number of items to choose

To calculate correlation between multiple variables in R, you can use cor() function. This function computes the correlation coefficient between variables in a data frame or matrix. The following method shows how you can do it with syntax. Method: Use cor() Function cor(x,y) The following examples shows how to use cor() function in R. Use cor() Function to Calculate Correlation Between Two Variables Let’s see how we can use cor() function to calculate correlation between two variables of data frame:

To calculate correlation by group in R, you can use functions from dplyr package.This provides functions to manipulate and summarize data by groups. You can use group_by() function to create group based on categorical variable. The summarize() function to apply the cor() function to the variables. The following method shows how you can do it with syntax. Method: Calculate Corrlation by Group df %>% group_by(column1) %>% summarize(cor=cor(column2, column3)) The following example shows how we can calculate corrlation by group in R.

To calculate cosine similarity in R, you can use the cosine() function from lsa package. The following method shows how you can do it with syntax. Method: Use cosine() Function library(lsa) cosine(a,b) a, b: Vector or matrices The following example shows how to calculate cosine similarity using cosine() function in R. Use cosine() to Calculate Cosine Similarity Between Vectors Let’s see how to calculate cosine similarity using cosine() function in R.

To calculate deciles in R, you can use quantile() function with sequence of probability for each decile. The following method shows how to do it with syntax. Method: Use quantile() Function quantile(data, probs = seq(0.1, 0.9, by = 0.1)) seq(0.1, 0.9, by = 0.1) : It generate sequence of numbers from 0.1 to 0.9, incrementing by 0.1. The following example shows how to calculate deciles in R. Use quantile() to Calculate Deciles Values of Vector Let’s see how we can calculate deciles values of vector using quantile() function in R:

To calculate descriptive statistics in R, you can use summary() function or describeBy() function from psych package. The following methods show how you can do it with syntax. Method 1: Use summary() Function summary(df) Method 2: Use describeBy() Function library(psych) describeBy(df, group) The following examples shows how to calculate descriptive statistics in R. Use summary() Function to Calculate Descriptive Statistics Let see how we can use summary() function to calculate statistical values of data frame:

To calculate the dot product in R, you can use three different methods. The dot product is basically multiplication of vectors. The following methods show how you can do it with syntax. Method 1: Use %*% Operator a %*% b Method 2: Use sum() and * operator sum (a * b) Method 3: Use dot() Function from pracma Package library(pracma) dot(a, b) The following examples shows how to use all this methods to calculate dot product in R.

To combine data frames vertically in R, you can use rbind() function or bind_rows() function from dplyr package in R. The following methods show how you can use it with syntax. Method 1: Use rbind() Function rbind(df1,df2,df3,...) Method 2: Use bind_rows() Function library(dplyr) bind_rows(df1,df2,df3,...) The following example shows how to combine data frames vertically in R. Combine Data Frame Vertically Using rbind() Function in R Let’s see how we can use rbind() function in R:

To convert character to date column in R, you can use as.Date() function. The following method shows how you can use it with syntax. Method: Use as.Date() Function as.Date(data) Let’s first create data frame which having character to be converted to date. # Create data frame df <- data.frame(Start_date=c("2000-05-21","2000-05-22","2000-05-23","2000-05-24","2000-05-25","2000-05-26"), Machine_name = c("Machine1","Machine2","Machine1","Machine3","Machine2","Machine3"), Value = c(108,120,135,95,98,105),Reading= c(110,97,91,89,80,85)) # Print data frame print(df) Output: Start_date Machine_name Value Reading 1 2000-05-21 Machine1 108 110 2 2000-05-22 Machine2 120 97 3 2000-05-23 Machine1 135 91 4 2000-05-24 Machine3 95 89 5 2000-05-25 Machine2 98 80 6 2000-05-26 Machine3 105 85 The following example shows how to convert character to date column of data frame in R.

To character or string type data into numeric data you can use as.numeric() function in R. The following method shows how you can use it with syntax. Method: Use as.numeric() Function as.numeric(df$column_name) Suppose we have data frame df which having multiple columns in it: # Create data frame df <- data.frame(Start_date=as.Date(c("2000-05-21","2000-05-22","2000-05-23","2000-05-24","2000-05-25","2000-05-26")), Machine_name = c("Machine1","Machine2","Machine1","Machine3","Machine2","Machine3"), Value = c("108","120","135","95","98","105"),Reading= c(110,97,91,89,80,85)) # Print data frame print(df) # print datatype print(str(df)) Output: Start_date Machine_name Value Reading 1 2000-05-21 Machine1 108 110 2 2000-05-22 Machine2 120 97 3 2000-05-23 Machine1 135 91 4 2000-05-24 Machine3 95 89 5 2000-05-25 Machine2 98 80 6 2000-05-26 Machine3 105 85 'data.

To create contingency table in R there are two different ways to do it. The following methods show how you can do it with syntax. Method 1: Use table() Function table(df$column1, df$column2) Method 2: Use tapply() Function tapply(df$column1, df$column2, table) The following example shows how to create a contigency table in R using two different approaches. Create a Contigency Table Using table() Function in R Let’s see how we can use table() function to create contingency table in R:

To create a correlation matrix for data frame in R, you can use cor() function. The following method shows how you can do it with syntax. Method: Use cor() Function cor(df) The following example shows how create correlation matrix in R using cor() function. Using cor() Function Let’s see how we can use cor() function to create correlation matrix: # Create data frame df <- data.frame(Value = c(108,120,135,95,98,105),Reading= c(110,97,91,89,80,85)) # Correlation matrix matrix <- cor(df) # Print correlation matrix print(matrix) Output:

To create covariance matrix for data frame in R, you can use cov() function. The following method shows how you can do it with syntax. Method: Use cov() Function cov(df) The following example shows how to create covariance matrix in R using cov() function. Create Covariance Matrix for R Data Frame Using cov() Function Let’s see how we can use create covariance matrix in R: # Create data frame df <- data.

To plot graph in R, you can use plot() function. The following method shows how you can do it with syntax. Method: Use plot() Function plot(x, y) The following example shows how to plot graph in R. Create a Graph Using plot() Function Let’s plot graph for two columns of data frame using plot() function: # Create data frame df <- data.frame(Start_date=as.Date(c("2000-05-21","1998-03-28","2001-01-19","2003-04-27","2004-11-26","2008-11-25")), Machine_name = c("Machine1","Machine2","Machine3","Machine4","Machine5","Machine6"), Value = c(108,99,135,95,98,105),Reading= c(110,97,91,89,80,85)) # Plot graph plot(df$Start_date, df$Reading) # Give title to graph title("Graph of date vs Reading") Output:

The antilog of a number is the process of finding the original value from its logarithmic representation. To find the antilog of values in R, you can use base of the logarithm and the exponentiation operator (^). Method 1: Use base 10 log_value <- log10(value) 10^log_value Method 2: Use base e exp(value) The following examples shows how to find antilog of values in R. Calcualte Antilog of a Value Using Base 10 Let’s see how we can use base 10 to find antilog of value:

If your dataset has missing values, you can handle them by either removing or replacing them using different methods in R. The following methods show how you can do it with syntax. Method 1: Use na.omit() Function From dplyr Package library(dplyr) new_df <- df %>% na.omit() Method 2: Replace Missing Values With Other Values library(dplyr) library(tidyr) df %>% mutate(across(where(is.numeric), ~replace_na(., mean(., na.rm=TRUE)))) Method 3: Remove Duplicate Rows library(dplyr) df %>% distinct(.

The diff() function is used to calculate difference between consecutive elements of numeric list, vector or data frame in R. This function returns vector of differences between consecutive elements. The following method shows how you can do it with syntax. Method: Use diff() Function diff(vector) The following example shows how to use diff() function in R. Use diff() to Find Difference Between Consecutive Elements of Vector Let’s apply diff() function on numeric vector:

The assign() function is used to assign value to variable in R. The following method in R shows how you can do it with syntax. Method: Use assign() Function assign(variable,value) The following examples shows how to use assign() function in R. Use assign() Function to Single Variable Let’s see how we can use assign value to variable using assign() function: # Assign value to variable assign("var1",52) # Show variable print(var1) Output: