Line Graph in R

 

Line Graph in R

Introduction

A line graph is used to display the relationship between two variables by connecting data points with lines. It is particularly useful for showing:

  • Trends over time
  • Growth or decline
  • Continuous data
  • Comparisons between multiple datasets

Common applications include:

  • Stock prices
  • Monthly sales
  • Population growth
  • Temperature variation
  • Experimental observations

In R, line graphs are created using the plot() function with the parameter:

type="l"

where "l" stands for line.

Syntax

plot(x, y,
     type="l",
     main,
     xlab,
     ylab,
     col,
     lwd)

where

  • x : x-axis values
  • y : y-axis values
  • type="l" : line graph
  • main : title
  • xlab : x-axis label
  • ylab : y-axis label
  • col : line color
  • lwd : line width

Example 1: Simple Line Graph

Suppose the sales of a company over five years are:

Year    Sales
2020    15
2021    18
2022    22
2023    25
2024    30
year <- c(2020,2021,2022,2023,2024)
sales <- c(15,18,22,25,30)

plot(year,
     sales,
     type="l")

This produces a simple line graph.



Example 2: Adding Title and Labels

plot(year,
     sales,
     type="l",
     main="Annual Sales",
     xlab="Year",
     ylab="Sales")



Example 3: Changing Line Color

plot(year,
     sales,
     type="l",
     col="blue")


Available colors:

  • red
  • blue
  • green
  • black
  • orange
  • purple

Example 4: Changing Line Width

plot(year,
     sales,
     type="l",
     col="red",
     lwd=3)

lwd=3 produces a thicker line.



Example 5: Plotting Points and Lines

plot(year,
     sales,
     type="b",
     pch=16,
     col="blue")

Here

  • b = both points and lines
  • pch=16 = filled circles


Example 6: Different Line Styles

plot(year,
     sales,
     type="l",
     lty=2)

Common values of lty:

ltyStyle
1Solid
2Dashed
3Dotted
4Dot-Dash
5Long Dash

Example:

plot(year,
     sales,
     type="l",
     col="green",
     lwd=3,
     lty=2)


Example 7: Multiple Line Graphs

Suppose sales of two products are:

year <- c(2020,2021,2022,2023,2024)

productA <- c(10,15,20,25,30)
productB <- c(12,18,24,28,35)
plot(year,
     productA,
     type="l",
     col="blue",
     ylim=c(0,40),
     xlab="Year",
     ylab="Sales",
     main="Sales Comparison")

lines(year,
      productB,
      col="red")

legend("topleft",
       legend=c("Product A","Product B"),
       col=c("blue","red"),
       lty=1)



Example 8: Monthly Temperature Variation

month <- 1:12

temp <- c(28,29,31,34,35,33,
          32,31,30,29,28,27)

plot(month,
     temp,
     type="l",
     col="red",
     lwd=2,
     xlab="Month",
     ylab="Temperature",
     main="Monthly Temperature")
grid()


Example 9: Plotting Mathematical Functions

Plot y = x²

x <- seq(-5,5,0.1)

y <- x^2

plot(x,
     y,
     type="l",
     col="blue",
     main="y=x^2")



Plot y = sin(x)

x <- seq(0,2*pi,0.1)
y <- sin(x)


plot(x,
     y,
     type="l",
     col="red")
grid()


Example 10: Adding Grid

plot(year,
     sales,
     type="l",
     lwd=3)

grid()

The grid improves readability.

Example 11: Adding Points

year <- c(2020,2021,2022,2023,2024)
sales <- c(15,18,22,25,30)
plot(year,
     sales,
     type="l")

points(year,
       sales,
       pch=19,
       col="red")


Example 12: Adding Text Labels

plot(year,
     sales,
     type="b")

text(year,
     sales,
     labels=sales,
     pos=3)

This displays sales values above each point.



Example 13: Customized Graph

year <- c(2020,2021,2022,2023,2024)

sales <- c(12,16,20,28,35)

plot(year,
     sales,
     type="b",
     col="darkgreen",
     pch=18,
     lwd=3,
     main="Company Sales",
     xlab="Year",
     ylab="Sales")

grid()


Important Parameters

ParameterPurpose
type="l"Line graph
colColor
lwdWidth of line
ltyStyle of line
mainTitle
xlabX-axis label
ylabY-axis label
pchPoint symbol
ylimY-axis limits
xlimX-axis limits
grid()Add grid
points()Add points
lines()Add additional lines
text()Add labels
legend()Add legend

Applications of Line Graphs

  • Population growth
  • Stock market trends
  • Monthly sales analysis
  • Temperature monitoring
  • Experimental data
  • Machine learning loss curves
  • Accuracy curves
  • Sensor measurements
  • Time-series analysis

Conclusion

A line graph is one of the most commonly used plots in R for representing trends and continuous data. Using the plot() function with type="l", along with lines(), points(), legend(), and customization parameters such as col, lwd, and lty, highly informative graphs can be created for scientific and statistical analysis.

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