Line plots (geom_line) in R Programming - Mini Project: Build & Apply

Line plots (geom_line)

📖 Scenario: You are a data analyst working with monthly sales data for a small store. You want to create a simple line plot to see how sales change over the months.

🎯 Goal: Build a line plot using geom_line() in R with ggplot2 to visualize monthly sales data.

📋 What You'll Learn

Create a data frame called sales_data with two columns: month and sales with exact values.

Create a variable called plot_title with the exact string 'Monthly Sales Over Time'.

Use ggplot() with geom_line() to create a line plot using sales_data.

Add the title stored in plot_title to the plot using ggtitle().

Print the plot.

💡 Why This Matters

🌍 Real World

Line plots are used to show trends over time, like sales, temperature, or stock prices.

💼 Career

Data analysts and scientists use line plots to communicate changes and patterns clearly to others.

1

Create the sales data frame

Create a data frame called sales_data with two columns: month and sales. Use these exact values: month as c('Jan', 'Feb', 'Mar', 'Apr', 'May') and sales as c(150, 200, 180, 220, 210).

R Programming

# Create the sales_data data frame with month and sales columns
# Your code here

Hint

Use data.frame() to create the data frame with the exact column names and values.

2

Create the plot title variable

Create a variable called plot_title and assign it the string 'Monthly Sales Over Time'.

R Programming

sales_data <- data.frame(
  month = c('Jan', 'Feb', 'Mar', 'Apr', 'May'),
  sales = c(150, 200, 180, 220, 210)
)

# Create the plot_title variable
# Your code here

Hint

Assign the exact string to plot_title using the assignment operator <-.

3

Create the line plot with ggplot2

Use ggplot() with sales_data as data. Map month to the x-axis and sales to the y-axis. Add geom_line() to create the line plot. Store the plot in a variable called sales_plot. Then add the title stored in plot_title using ggtitle().

R Programming

sales_data <- data.frame(
  month = c('Jan', 'Feb', 'Mar', 'Apr', 'May'),
  sales = c(150, 200, 180, 220, 210)
)

plot_title <- 'Monthly Sales Over Time'

# Create the sales_plot using ggplot, geom_line, and ggtitle
# Your code here

Hint

Remember to load ggplot2 with library(ggplot2) before creating the plot.

4

Display the line plot

Print the plot stored in sales_plot to display the line plot.

R Programming

library(ggplot2)

sales_data <- data.frame(
  month = c('Jan', 'Feb', 'Mar', 'Apr', 'May'),
  sales = c(150, 200, 180, 220, 210)
)

plot_title <- 'Monthly Sales Over Time'

sales_plot <- ggplot(sales_data, aes(x = month, y = sales)) +
  geom_line() +
  ggtitle(plot_title)

# Print the sales_plot to display the line plot
# Your code here

Hint

Use print() to show the plot in the R console or script.

Practice

1. What is the main purpose of using geom_line() in an R ggplot?

easy

A. To connect data points with lines to show trends or changes over time

B. To create bar charts showing counts of categories

C. To add labels to points on a scatter plot

D. To fill areas under curves with color

Solution

1. Step 1: Understand the role of geom_line()

   geom_line() is used in ggplot2 to draw lines connecting data points, which helps visualize trends or changes over time.

2. Step 2: Compare with other geoms

   Other geoms like geom_bar() create bar charts, and geom_text() adds labels, so they do not connect points with lines.

3. Final Answer:

   To connect data points with lines to show trends or changes over time -> Option A

4. Quick Check:

   Line plots connect points = A [OK]

Hint: Remember: geom_line connects points in order [OK]

Common Mistakes:

• Confusing geom_line with geom_bar
• Thinking geom_line adds labels
• Using geom_line to fill areas

2. Which of the following is the correct syntax to add a line plot layer to a ggplot object named p?

easy

A. p + geom_line()

B. p + geom_point()

C. p + geom_bar()

D. p + geom_text()

Solution

1. Step 1: Identify the correct geom for line plots

   geom_line() is the function used to add line plots in ggplot2.

2. Step 2: Check the syntax for adding layers

   In ggplot2, layers are added using the + operator, so p + geom_line() correctly adds a line plot layer.

3. Final Answer:

   p + geom_line() -> Option A

4. Quick Check:

   Adding line plot layer = p + geom_line() [OK]

Hint: Add layers with + and use geom_line for lines [OK]

Common Mistakes:

• Using geom_point instead of geom_line for lines
• Forgetting the + operator
• Using geom_bar for line plots

3. What will be the output of the following R code?

library(ggplot2)
data <- data.frame(x = 1:3, y = c(2, 5, 3))
ggplot(data, aes(x = x, y = y)) + geom_line()

medium

A. A scatter plot with points at (1,2), (2,5), (3,3)

B. A bar chart with bars at x=1,2,3 with heights 2,5,3

C. An error because geom_line requires grouping

D. A line plot connecting points (1,2), (2,5), and (3,3)

Solution

1. Step 1: Understand the data and mapping

   The data frame has x values 1, 2, 3 and y values 2, 5, 3. The aes maps x and y for plotting.

2. Step 2: What does geom_line() do here?

   geom_line() connects the points in order of x, so it draws a line through (1,2), (2,5), and (3,3).

3. Final Answer:

   A line plot connecting points (1,2), (2,5), and (3,3) -> Option D

4. Quick Check:

   geom_line connects points = C [OK]

Hint: geom_line connects points in order of x [OK]

Common Mistakes:

• Thinking geom_line creates scatter plot
• Expecting bar chart output
• Assuming error without grouping for simple data

4. Consider this code snippet:

library(ggplot2)
data <- data.frame(time = c(1, 2, 1, 2), value = c(3, 4, 5, 6))
ggplot(data, aes(x = time, y = value)) + geom_line()

What is the main issue with this plot?

medium

A. The plot will be empty because data is invalid

B. The plot will show two lines overlapping because grouping is missing

C. The plot will show a single line connecting all points correctly

D. The code will produce a syntax error

Solution

1. Step 1: Analyze the data structure

   The data has repeated time values (1 and 2) with different values, indicating multiple groups but no grouping variable specified.

2. Step 2: Effect of missing group aesthetic

   Without specifying a group, geom_line() connects points in the order they appear, causing lines to overlap or connect incorrectly.

3. Final Answer:

   The plot will show two lines overlapping because grouping is missing -> Option B

4. Quick Check:

   Missing group causes overlapping lines = D [OK]

Hint: Add group aesthetic to separate lines [OK]

Common Mistakes:

• Assuming syntax error instead of grouping issue
• Expecting correct single line without group
• Thinking data is invalid or empty

5. You have a dataset with daily sales for two stores over a week:

sales <- data.frame(
  day = rep(1:7, 2),
  store = rep(c('A', 'B'), each = 7),
  sales = c(10, 12, 15, 14, 13, 16, 18, 8, 9, 11, 10, 12, 13, 15)
)

How do you create a line plot showing sales trends for both stores with different colored lines?

hard

A. ggplot(sales, aes(x = day, y = sales)) + geom_line()

B. ggplot(sales, aes(x = day, y = sales, group = store)) + geom_line()

C. ggplot(sales, aes(x = day, y = sales, color = store)) + geom_line()

D. ggplot(sales, aes(x = day, y = sales, fill = store)) + geom_line()

Solution

1. Step 1: Identify the need for separate lines by store

   To show sales trends for each store, lines must be grouped by store and visually distinct.

2. Step 2: Use color aesthetic to differentiate stores

   Mapping color = store inside aes() creates separate colored lines for each store automatically grouping data.

3. Final Answer:

   ggplot(sales, aes(x = day, y = sales, color = store)) + geom_line() -> Option C

4. Quick Check:

   Color by store for separate lines = B [OK]

Hint: Use color aesthetic to separate lines by group [OK]

Common Mistakes:

• Omitting color causes single line for all data
• Using fill aesthetic instead of color for lines
• Not grouping lines by store