Histogram and density plots in R Programming - Mini Project: Build & Apply

Histogram and Density Plots

📖 Scenario: You are a data analyst working with a small dataset of daily temperatures. You want to understand how the temperatures are distributed by creating a histogram and a density plot.

🎯 Goal: Build an R script that creates a vector of temperature data, sets a bin width for the histogram, generates both a histogram and a density plot using base R plotting functions, and then displays the plots.

📋 What You'll Learn

Create a numeric vector called temperatures with the exact values: 22, 25, 19, 24, 30, 28, 21, 23, 27, 26

Create a numeric variable called bin_width and set it to 3

Use the hist() function with breaks=seq() to create a histogram of temperatures with bins of width bin_width

Use the plot() function with density() to create a density plot of temperatures

Print both plots so they are visible

💡 Why This Matters

🌍 Real World

Histograms and density plots help you understand how data points are spread out, which is useful in weather analysis, sales data, or any measurements.

💼 Career

Data analysts and scientists use these plots to explore data distributions before making decisions or building models.

1

Create the temperature data vector

Create a numeric vector called temperatures with these exact values: 22, 25, 19, 24, 30, 28, 21, 23, 27, 26

R Programming

# Create the temperatures vector with the exact values
# Your code here

Hint

Use the c() function to combine numbers into a vector.

2

Set the bin width for the histogram

Create a numeric variable called bin_width and set it to 3

R Programming

temperatures <- c(22, 25, 19, 24, 30, 28, 21, 23, 27, 26)
# Create bin_width variable and set it to 3
# Your code here

Hint

Just assign the number 3 to the variable bin_width.

3

Create the histogram and density plot

Use the hist() function with breaks=seq() to create a histogram of temperatures with bins of width bin_width. Then use the plot() function with density() to create a density plot of temperatures.

R Programming

temperatures <- c(22, 25, 19, 24, 30, 28, 21, 23, 27, 26)
bin_width <- 3
# Create histogram with breaks using seq from min to max with bin_width
# Create density plot using plot and density functions
# Your code here

Hint

Use seq(min(temperatures), max(temperatures) + bin_width, by=bin_width) to create breaks for the histogram bins.

Use density() to calculate density and plot() to draw it.

4

Display the plots

Run the code so that both the histogram and the density plot are displayed in the R plotting window.

R Programming

temperatures <- c(22, 25, 19, 24, 30, 28, 21, 23, 27, 26)
bin_width <- 3
hist(temperatures, breaks=seq(min(temperatures), max(temperatures) + bin_width, by=bin_width), main="Histogram of Temperatures", xlab="Temperature", col="lightblue")
density_temp <- density(temperatures)
plot(density_temp, main="Density Plot of Temperatures", xlab="Temperature", col="red")
# Run the code to display the plots
# Your code here

Hint

Just run the full code to see both plots in the R plotting window.

Practice

1. What is the main difference between a histogram and a density plot in R?

easy

A. Histogram shows exact values; density plot shows counts only.

B. Histogram uses bars; density plot uses a smooth curve.

C. Histogram is for categorical data; density plot is for numeric data.

D. Histogram requires ggplot2; density plot does not.

Solution

1. Step 1: Understand histogram representation

   A histogram displays data grouped into bins using bars to show frequency counts.

2. Step 2: Understand density plot representation

   A density plot shows a smooth curve estimating the data distribution, not bars.

3. Final Answer:

   Histogram uses bars; density plot uses a smooth curve. -> Option B

4. Quick Check:

   Histogram = bars, Density plot = smooth curve [OK]

Hint: Bars mean histogram; smooth curve means density plot [OK]

Common Mistakes:

• Thinking histogram shows exact values instead of grouped counts
• Confusing density plot with bar charts
• Assuming histograms only work with categorical data

2. Which of the following is the correct R code to create a histogram of the numeric vector data?

easy

A. hist(data)

B. plot.histogram(data)

C. histogram(data)

D. density(data)

Solution

1. Step 1: Recall the base R function for histograms

   The correct function to create a histogram in base R is hist().

2. Step 2: Check the options

   Only hist(data) is valid syntax; others are either incorrect function names or for density plots.

3. Final Answer:

   hist(data) -> Option A

4. Quick Check:

   hist() creates histograms in R [OK]

Hint: Use hist() for histograms in base R [OK]

Common Mistakes:

• Using non-existent function histogram()
• Confusing density() with hist()
• Trying plot.histogram() which is invalid

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

set.seed(1)
data <- rnorm(100)
hist(data, breaks=5, col='blue')

medium

A. An error because breaks cannot be numeric

B. A density plot with 5 blue lines

C. A histogram with 5 blue bars showing data distribution

D. A scatter plot of 100 points

Solution

1. Step 1: Understand hist() with breaks parameter

   The breaks=5 argument divides data into 5 bins for the histogram.

2. Step 2: Check color and plot type

   The col='blue' colors the bars blue; hist() creates a bar plot, not a density or scatter plot.

3. Final Answer:

   A histogram with 5 blue bars showing data distribution -> Option C

4. Quick Check:

   hist() + breaks=5 + col='blue' = blue bars histogram [OK]

Hint: breaks sets bar count; col sets bar color [OK]

Common Mistakes:

• Thinking breaks=5 creates density plot
• Assuming breaks cannot be numeric
• Confusing hist() with scatter plot

4. Identify the error in this R code for plotting a density plot:

data <- rnorm(50)
density(data, col='red')

medium

A. density() does not accept a col argument directly

B. rnorm() cannot generate 50 values

C. Missing parentheses after density

D. data must be a data frame, not a vector

Solution

1. Step 1: Check density() function usage

   The density() function returns density data but does not plot directly or accept col.

2. Step 2: Correct plotting method

   To plot with color, use plot(density(data), col='red') instead.

3. Final Answer:

   density() does not accept a col argument directly -> Option A

4. Quick Check:

   density() returns data; plot() colors it [OK]

Hint: Use plot(density()) to color density plots [OK]

Common Mistakes:

• Passing col directly to density()
• Thinking density() plots automatically
• Assuming rnorm() can't generate 50 values

5. You want to compare the distribution of two numeric vectors x and y in R using a single plot. Which code correctly overlays their density plots with different colors?

hard

A. plot(density(x, col='blue'), density(y, col='red'))

B. hist(x, col='blue'); hist(y, col='red')

C. density(x, col='blue'); density(y, col='red')

D. plot(density(x), col='blue'); lines(density(y), col='red')

Solution

1. Step 1: Plot first density with plot()

   Use plot(density(x), col='blue') to draw the first density curve in blue.

2. Step 2: Add second density with lines()

   Use lines(density(y), col='red') to overlay the second density curve in red.

3. Final Answer:

   plot(density(x), col='blue'); lines(density(y), col='red') -> Option D

4. Quick Check:

   plot() then lines() overlays densities with colors [OK]

Hint: plot() first density, lines() add second [OK]

Common Mistakes:

• Using hist() which creates separate plots
• Passing col to density() instead of plot() or lines()
• Trying to plot both densities in one plot() call