{"id":49,"date":"2026-03-03T09:41:57","date_gmt":"2026-03-03T09:41:57","guid":{"rendered":"https:\/\/gigz.pk\/r\/?post_type=lesson&p=49"},"modified":"2026-04-01T11:54:43","modified_gmt":"2026-04-01T11:54:43","slug":"cleaning-and-transforming-data","status":"publish","type":"lesson","link":"https:\/\/gigz.pk\/r\/lesson\/cleaning-and-transforming-data\/","title":{"rendered":"Cleaning and Transforming Data"},"content":{"rendered":"\n

Data cleaning and transformation are essential steps in data analysis. Raw data often contains missing values, inconsistencies, or unstructured formats. R provides powerful tools and packages like dplyr<\/code> and tidyr<\/code> to clean, transform, and prepare data for analysis.<\/p>\n\n\n\n

1. Inspecting Data<\/strong><\/h1>\n\n\n\n

Before cleaning, inspect the dataset to identify issues:<\/p>\n\n\n\n

# Load data
data <- read.csv(\"data.csv\")# View first few rows
head(data)# Check structure and types
str(data)# Summary statistics
summary(data)<\/pre>\n\n\n\n
2. Handling Missing Values<\/strong><\/h1>\n\n\n\n
a) Identifying Missing Values<\/strong><\/h3>\n\n\n\n
is.na(data)            # Logical matrix of missing values
colSums(is.na(data))   # Count missing per column<\/pre>\n\n\n\n
b) Removing Missing Values<\/strong><\/h3>\n\n\n\n
data_clean <- na.omit(data)  # Removes rows with any missing values<\/pre>\n\n\n\n
c) Replacing Missing Values<\/strong><\/h3>\n\n\n\n
data$Age[is.na(data$Age)] <- mean(data$Age, na.rm = TRUE)  # Replace with mean<\/pre>\n\n\n\n
3. Removing Duplicates<\/strong><\/h1>\n\n\n\n
data <- data[!duplicated(data), ]<\/pre>\n\n\n\n
4. Renaming Columns<\/strong><\/h1>\n\n\n\n
library(dplyr)data <- data %>%
  rename(
    CustomerID = ID,
    PurchaseAmount = Amount
  )<\/pre>\n\n\n\n
5. Filtering and Selecting Data<\/strong><\/h1>\n\n\n\n
# Filter rows where Age > 30
data_filtered <- data %>% filter(Age > 30)# Select specific columns
data_selected <- data %>% select(Name, Age, PurchaseAmount)<\/pre>\n\n\n\n
6. Creating New Variables<\/strong><\/h1>\n\n\n\n
# Add a new column based on existing data
data <- data %>%
  mutate(
    DiscountedAmount = PurchaseAmount * 0.9,  # Apply 10% discount
    AgeGroup = ifelse(Age < 30, \"Young\", \"Adult\")
  )<\/pre>\n\n\n\n
7. Reshaping Data<\/strong><\/h1>\n\n\n\n
a) Wide to Long<\/strong><\/h3>\n\n\n\n
library(tidyr)
data_long <- pivot_longer(data, cols = starts_with(\"Month\"), names_to = \"Month\", values_to = \"Sales\")<\/pre>\n\n\n\n
b) Long to Wide<\/strong><\/h3>\n\n\n\n
data_wide <- pivot_wider(data_long, names_from = \"Month\", values_from = \"Sales\")<\/pre>\n\n\n\n
8. Sorting Data<\/strong><\/h1>\n\n\n\n
# Sort by PurchaseAmount descending
data <- data %>% arrange(desc(PurchaseAmount))<\/pre>\n\n\n\n
9. Advantages of Data Cleaning and Transformation<\/strong><\/h1>\n\n\n\n