Lab Data Wrangling

Data Wrangling.R

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+# Lab | Data Wrangling with R
+
+## Objective
+
+Practice data wrangling, cleaning, and manipulation techniques in R using the Sample Super Store dataset. By the end of this activity, you should be able to:
+
+  - Load and explore the dataset.
+- Perform basic and advanced data manipulations.
+- Handle missing data and duplicates.
+- Aggregate and summarize data.
+- Dataset Overview.
+
+The Sample Super Store dataset contains sales, profit, and customer information for a retail store. It includes columns such as:
+
+  - Order ID, Order Date, Ship Date
+- Customer ID, Customer Name, Segment
+- City, State, Region, Category, Sub-Category
+- Sales, Quantity, Discount, Profit
+
+## Instructions
+
+library(dplyr)
+
+### Step 1: Load and Explore the Dataset
+
+1. Load the dataset into R.
+
+superstore <- read.csv("C:\\Users\\fee10\\Ironhack\\Module2\\Labs\\lab-r-dataframes\\dataset\\Sample - Superstore.csv")
+
+2. Explore the dataset using str(), head(), and summary().
+
+str(superstore)
+head(superstore)
+summary(superstore)
+
+3. Identify the number of rows and columns.
+
+dim(superstore)
+
+### Step 2: Basic Data Manipulation
+
+1. Select the following columns: Order ID, Order Date, Customer Name, Sales, Profit.
+
+selected_columns <- select(superstore, Order.ID, Order.Date, Customer.Name, Sales, Profit)
+
+2. Filter the dataset to show only orders with a profit greater than $100.
+
+profit_greater100 <- filter(superstore, Profit>=100)
+
+3. Sort the dataset by Sales in descending order.
+
+sorted_profit_greater100 <- profit_greater100 %>%
+  arrange(desc(Sales))
+
+### Step 3: Handle Missing Data
+
+1. Check for missing values in the dataset.
+
+colSums(is.na(superstore))
+
+
+2. Replace missing values in the Postal Code column with the mode (most frequent value).
+3. Remove rows with missing values in the Customer Name column.
+
+### Step 4: Create and Modify Columns
+
+1. Create a new column Profit_Margin as the ratio of Profit to Sales.
+
+superstore <- superstore %>%
+  mutate(Profit_Margin = (Profit/Sales)*100)
+
+2. Create a new column Order_Year by extracting the year from Order Date.
+
+library(stringr)
+
+superstore <- superstore %>%
+mutate(Order_Year = str_extract(Order.Date, "\\d{4}"))
+str(superstore$Order_Year)
+
+
+3. Convert the Order.Date column to a Date data type.
+
+superstore$Order_Year <- as.Date(superstore$Order.Date, fromat= "%m/%d/%Y")
+
+
+### Step 5: Aggregating and Summarizing Data
+
+1. Calculate the total sales and profit by Category.
+
+Total_Sales_Profit <- superstore %>%
+  group_by(Category) %>%
+  summarize(
+    Total.Sales = sum(Sales),
+    Total.Profit = sum(Profit)
+  )
+
+2. Find the average profit margin by Region.
+
+avg_profit_margin_region <- superstore %>%
+  group_by(Region) %>%
+  summarize(
+    Average_Profit_Margin = mean(Profit_Margin)
+  )
+
+3. Count the number of orders by Customer Segment.
+
+number_orders_by_segment <- superstore %>%
+  group_by(Segment) %>%
+  summarize(
+    Number_Orders = n_distinct(Order.ID)
+  )
+
+
+## Optional
+
+### Extra 1: Advanced Challenges
+
+1. Identify and remove duplicate rows based on Order ID.
+
+duplicated_rows <- superstore[duplicated(superstore$Order.ID),]
+
+superstore <- superstore %>%
+  distinct(Order.ID, .keep_all = TRUE)
+
+
+2. Create a new column Discount_Level that categorizes discounts as "Low" (<10%), "Medium" (10-20%), and "High" (>20%).
+
+Discount_Level <- superstore %>%
+  mutate(Discount_Level = case_when(
+    Discount >= 20 ~ "High",
+    Discount >= 10 & Discount < 20 ~ "Medium",
+    Discount < 10 ~ "Low"
+  ))
+
+3. Merge the dataset with a new dataset containing regional population data (create a dummy dataset for this purpose).
+
+population_data <- data.frame(
+  Region = c("East", "West", "North", "South"),
+  Population = c(500000, 300000, 700000, 400000)
+)
+
+merged_data <- superstore %>%
+  left_join(population_data, by="Region")
+
+
+### Extra 2: Data Visualization
+
+Next class we will talk about data visualization, but lets see if you can pull it off on your own.
+
+1. Create a bar plot of total sales by Category.
+
+library(ggplot2)
+
+ggplot(Total_Sales_Profit, aes(x=Category, y=Total.Sales)) +
+geom_bar(stat = "identity", fill = "skyblue") +  # Erstelle die Balken
+  labs(title = "Total Sales by Category", x = "Category", y = "Total Sales") +
+  theme_minimal()  # Verwende ein einfaches Thema
+
+
+2. Create a scatter plot of Sales vs. Profit with a trend line.
+
+library(ggplot2)
+ggplot(superstore, aes(x = Sales, y=Profit)) +
+  geom_point(aes(y = Profit, x=Sales), stat = "identity", fill = "skyblue") +  
+  geom_smooth(aes(y = Profit), method = "lm", color = "red", size = 1) +  
+  labs(title = "Total Sales by Category with Trendline for Total Profit", 
+       x = "Category", y = "Total Sales / Total Profit") +
+  theme_minimal() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+
+
+3. Create a histogram of Profit_Margin.
+
+ggplot(profit_margin, aes(x = Profit_Margin)) + 
+  geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black", alpha = 0.7) +  # Histogramm
+  labs(title = "Histogram of Profit Margin", x = "Profit Margin", y = "Frequency") +  # Titel und Achsenbeschriftung
+  theme_minimal()
+
+## Deliverables
+
+- Submitted notebook (or file) with your responses to each of the exercises.
+
+## Submission
+
+- Upon completion, add your deliverables to git. 
+- Then commit git and push your branch to the remote.
+- Make a pull request and paste the PR link in the submission field in the Student Portal.
+
+<br>
+
+**Good luck!**