How to Calculate Mean; Standard Deviation; Frequencies in R (Descriptive Statistics R Tutorial 2.7) Video Lecture | Mastering R Programming: For Data Science and Analytics - Database Management

Ans. To calculate the mean in R, you can use the `mean()` function. This function takes a vector as input and returns the average value of the elements in the vector. Here's an example: ```R # Create a vector data <- c(4, 6, 8, 10, 12) # Calculate the mean mean_value <- mean(data) # Print the result print(mean_value) ``` This will give you the mean value of the vector `data`, which in this case is 8.

Ans. Calculating the standard deviation in R can be done using the `sd()` function. This function takes a vector as input and returns the standard deviation of the elements in the vector. Here's an example: ```R # Create a vector data <- c(4, 6, 8, 10, 12) # Calculate the standard deviation sd_value <- sd(data) # Print the result print(sd_value) ``` This will give you the standard deviation of the vector `data`, which in this case is approximately 3.16.

Ans. To calculate the frequencies of values in R, you can use the `table()` function. This function takes a vector as input and returns a table of the frequencies of each unique value in the vector. Here's an example: ```R # Create a vector data <- c(1, 2, 2, 3, 3, 3) # Calculate the frequencies freq_table <- table(data) # Print the result print(freq_table) ``` This will give you a table showing the frequencies of each unique value in the vector `data`. In this case, the output will be: ``` data 1 2 3 1 2 3 ``` This means that the value 1 appears once, the value 2 appears twice, and the value 3 appears three times in the vector.

Ans. Yes, you can calculate the mean, standard deviation, and frequencies for a specific variable within a larger dataset in R. Here's how: 1. Load your dataset into R using a function like `read.csv()` or `read.table()`. 2. Access the specific variable of interest within your dataset using the `$` operator or indexing. 3. Use the `mean()`, `sd()`, or `table()` function on the extracted variable to calculate the desired statistics. Here's an example using a dataset named `mydata` with a variable named `height`: ```R # Load the dataset mydata <- read.csv("dataset.csv") # Access the height variable height <- mydata$height # Calculate the mean mean_value <- mean(height) # Calculate the standard deviation sd_value <- sd(height) # Calculate the frequencies freq_table <- table(height) # Print the results print(mean_value) print(sd_value) print(freq_table) ``` This will give you the mean, standard deviation, and frequencies for the `height` variable within your dataset.

Ans. Yes, it is possible to calculate the mean, standard deviation, and frequencies for multiple variables simultaneously in R. Here's how: 1. Load your dataset into R using a function like `read.csv()` or `read.table()`. 2. Access the multiple variables of interest within your dataset using the `$` operator or indexing. 3. Use the `mean()`, `sd()`, or `table()` function on the extracted variables to calculate the desired statistics. Here's an example using a dataset named `mydata` with variables named `height`, `weight`, and `age`: ```R # Load the dataset mydata <- read.csv("dataset.csv") # Access the variables height <- mydata$height weight <- mydata$weight age <- mydata$age # Calculate the means mean_height <- mean(height) mean_weight <- mean(weight) mean_age <- mean(age) # Calculate the standard deviations sd_height <- sd(height) sd_weight <- sd(weight) sd_age <- sd(age) # Calculate the frequencies freq_table_height <- table(height) freq_table_weight <- table(weight) freq_table_age <- table(age) # Print the results print(mean_height) print(mean_weight) print(mean_age) print(sd_height) print(sd_weight) print(sd_age) print(freq_table_height) print(freq_table_weight) print(freq_table_age) ``` This will give you the mean, standard deviation, and frequencies for the multiple variables (`height`, `weight`, and `age`) within your dataset.