Creating a Line Chart in R
In R, we can create a line plot using the same
plot() function by adding a plot type of "l". This will plot the (x,y) paired observations and connect them with lines.
Let's generate our own data for this lesson. We will use the
rnorm() function to generate a set of 100 random numbers that follow a normal distribution. These random numbers will be plotted on y-axis. x-axis will be a sequence of numbers from 1 to 100.
> x <- c(1:100) > rand_data <- rnorm(100, mean = 0) > data <- data.frame(x, rand_data) > plot(data$x, data$rand_data, type = "l", xlab="x", ylab = "Data")
The resulting line plot is displayed below:
You can change the appearance of the line by using the
- lty="solid" or lty=1 (default)
- lty="dashed" or lty=2
- lty="dotted" or lty=3
- lty="dotdash" or lty=4
- lty="longdash" or lty=5
- lty="twodash" or lty=6
- lty="blank" or lty=0
The line width can be changed using the
lwd parameter. The default width is 1. So, you can plot a thicker line using a higher number.
The line color can be changed using the
Below we replot the line plot with a dotted line, thickness of 2, and in orange color.
> plot(data$x, data$rand_data, type = "l", xlab="x", ylab = "Data",lty=3,lwd=2,col="orange")
We know that numerical data generally conforms to a normal probability distribution characterized by a bell curve. In this exercise you are asked to create a bell curve using the normal data.
- Use the
rnorm()function to generate random numbers that follow a normal distribution (mean 0 and standard deviation of 1). Generate upto 5000 numbers. Store these numbers in a variable x.
- Plot the density of these random numbers using
plot(density(x))This will plot the bell curve.
- Try different values for mean and standard deviation and observe how the graph changes.
Note: You can also compute the Gaussian density using the
dnorm() function, for example
dnorm(x, mean = 0, sd = 1).
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- Overview of Data Visualization
- When to Use Bar Chart, Column Chart, and Area Chart
- What is Line Chart and When to Use It
- What are Pie Chart and Donut Chart and When to Use Them
- How to Read Scatter Chart and Bubble Chart
- What is a Box Plot and How to Read It
- Understanding Japanese Candlestick Charts and OHLC Charts
- Understanding Treemap, Heatmap and Other Map Charts
- Visualization in Data Science
- Graphic Systems in R
- Accessing Built-in Datasets in R
- How to Create a Scatter Plot in R
- Create a Scatter Plot in R with Multiple Groups
- Creating a Bar Chart in R
- Creating a Line Chart in R
- Plotting Multiple Datasets on One Chart in R
- Adding Details and Features to R Plots
- Introduction to ggplot2
- Grammar of Graphics in ggplot
- Data Import and Basic Manipulation in R - German Credit Dataset
- Create ggplot Graph with German Credit Data in R
- Splitting Plots with Facets in ggplots
- ggplot2 - Chart Aesthetics and Position Adjustments in R
- Creating a Line Chart in ggplot 2 in R
- Add a Statistical Layer on Line Chart in ggplot2
- stat_summary for Statistical Summary in ggplot2 R
- Facets for ggplot2 Charts in R (Faceting Layer)
- Coordinates in ggplot2 in R
- Changing Themes (Look and Feel) in ggplot2 in R