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Data Cleaning and Quality Assurance Tutorials

How to Save and Load RDA Files in R

  • R-PROGRAMMING
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.
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How to Unload a Package in R

  • R-PROGRAMMING
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.
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How to Use rcorr in R to Create a Correlation Matrix

  • R-PROGRAMMING
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.
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Check the Data Type of Each Data Frame Column in R

  • R-PROGRAMMING
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.
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How to Check if a Directory Exists in R

  • R-PROGRAMMING
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:
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How to Check if a Package is Installed in R

  • R-PROGRAMMING
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.
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How to Check if File Exists in R

  • R-PROGRAMMING
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.
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How to Perform Data Cleaning in R

  • R-PROGRAMMING
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(.
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