How To Plot Bar Graph in R Programming

📘 Full Explanation of the R Code

🧩 Line 1: Define subject names

subjects <- c("Mathematics", "Physics", "Chemistry")

What it does:

• The **c()** function in R combines values into a vector.
• Here, we are creating a **character vector** containing the names of the three subjects.
• This vector will be used later to **label the bars** in the bar plot.

Explanation of components:

• subjects → variable name (vector of subject labels)
• <- → assignment operator in R (assigns the value on the right to the variable on the left)
• "Mathematics", "Physics", "Chemistry" → strings representing subject names

So after this line,

subjects = ["Mathematics", "Physics", "Chemistry"].

📊 Line 2–10: Create the bar plot

barplot(
  mean_marks1,
  names.arg = subjects,
  col = c("skyblue", "lightgreen", "lightcoral"),
  main = "Average Marks by Subject",
  xlab = "Subjects",
  ylab = "Mean Marks",
  ylim = c(0, 100)
)

Function Overview:

barplot() is a **base R function** that creates a bar chart (vertical or horizontal) from numeric data.

Argument-by-argument breakdown:

1. mean_marks1

   • The main numeric vector that you want to plot.
   • Each value in this vector represents the **mean marks** for one subject.
   • Example:

     mean_marks1 <- c(80.16667, 73.5, 81.33333)

2. names.arg = subjects

   • This argument tells R what names to put **under each bar**.
   • It uses the previously defined subjects vector.
   • So each bar will be labeled as *Mathematics*, *Physics*, and *Chemistry*.

3. col = c("skyblue", "lightgreen", "lightcoral")

   • Specifies colors for the bars.
   • Each color corresponds to a bar (in order).
   • You can use named colors (like "red", "blue") or hexadecimal color codes (like "#FF5733").

4. main = "Average Marks by Subject"

   • Sets the **title** at the top of the plot.
   • It helps describe what the graph represents.

5. xlab = "Subjects"

   • Adds a **label on the x-axis** (horizontal axis).

6. ylab = "Mean Marks"

   • Adds a **label on the y-axis** (vertical axis).

7. ylim = c(0, 100)

   • Defines the **range of the y-axis**.
   • Here it starts at 0 and ends at 100, ensuring all marks fit within the visible range.
   • Helps keep the scale consistent (since marks are usually out of 100).

🏷️ Line 11–17: Add labels above the bars

text(
  x = seq_along(mean_marks1),
  y = mean_marks1,
  label = round(mean_marks1, 1),
  pos = 3,
  cex = 0.8
)

Function Overview:

text() adds **text annotations** to a plot — in this case, to display the exact mean values above each bar.

Argument breakdown:

1. x = seq_along(mean_marks1)

   • seq_along() generates a sequence of numbers from 1 to the length of mean_marks1.
   • Example: if there are 3 means, it returns 1, 2, 3.
   • These are the **x-coordinates** (horizontal positions) where each text label will appear — directly above each bar.

2. y = mean_marks1

   • These are the **y-coordinates** (vertical positions) for the text labels.
   • Each label will be placed above the corresponding bar height.

3. label = round(mean_marks1, 1)

   • Specifies the text to display.
   • round(mean_marks1, 1) rounds each mean value to 1 decimal place for cleaner display (e.g., 81.3 instead of 81.33333).

4. pos = 3

   • Determines where the text is placed relative to the coordinates.
   • pos = 3 means **above** the specified (x, y) point.
   • Other possible values:
     • 1: below
     • 2: left
     • 3: above
     • 4: right

5. cex = 0.8

   • Controls the **size of the text**.
   • 1 is the default size; smaller values like 0.8 make the text slightly smaller and less cluttered.

✅ Final Output

When you run the entire block:

1. Three bars appear — one each for Mathematics, Physics, and Chemistry.
2. Each bar has a different color.
3. The y-axis shows mean marks from 0 to 100.
4. The top of each bar shows the numeric mean value (rounded to one decimal).

💡 Summary Table