1. Line
chart –
A line chart is a graph that
connects a series of points by drawing line segments between them. These points
are ordered in one of their coordinate (usually the x-coordinate) value. Line
charts are usually used in identifying the trends in data.
The plot() function in R is used to create the line graph.
Syntax
The basic syntax to create a line chart in R is –
plot(v,type,col,xlab,ylab)
Following is the description of
the parameters used −
v is a vector containing the numeric values.
type takes the value "p" to draw only the points, "l" to draw only the lines and "o" to draw both points and lines.
xlab is the label for x axis.
ylab is the label for y axis.
main is the Title of the chart.
col is used to give colors to both the points and lines.
Example
A simple line chart is created
using the input vector and the type parameter as "O". The below
script will create and save a line chart in the current R working directory.
# Create the data for the chart. v <-
c(7,12,28,3,41)
# Give the chart file a name. png(file =
"line_chart.jpg")
# Plot the bar chart.
plot(v,type = "o")
# Save the file.
dev.off()
When we execute the above code, it produces the following result −
Line Chart Title,
Color and Labels
The features of the line chart
can be expanded by using additional parameters. We add color to the points and
lines, give a title to the chart and add labels to the axes.
Example
# Create the data for the chart. v <-
c(7,12,28,3,41)
# Give the chart file a name. png(file =
"line_chart_label_colored.jpg")
# Plot the bar chart. plot(v,type = "o",
col = "red", xlab = "Month", ylab = "Rain fall", main = "Rain fall chart")
# Save the file.
dev.off()
When we execute the above code, it produces the following result –
Multiple Lines in
a Line Chart
More than one line can be drawn
on the same chart by using the lines()function.
After the first line is plotted, the lines() function can use an additional vector as input to draw the second line in the chart,
# Create the data for the chart.
v <- c(7,12,28,3,41)
t <- c(14,7,6,19,3)
# Give the chart file a name.
png(file =
"line_chart_2_lines.jpg")
# Plot the bar chart.
plot(v,type = "o",col = "red",
xlab = "Month", ylab = "Rain fall", main = "Rain fall chart")
lines(t, type = "o", col = "blue")
# Save the file.
dev.off()
When we execute the above code,
it produces the following result –
2. Scatterplots
–
Scatterplots show many points
plotted in the Cartesian plane. Each point represents the values of two
variables. One variable is chosen in the horizontal axis and another in the
vertical axis.
The simple scatterplot is created using the plot() function.
Syntax
The basic syntax for creating scatterplot in R is −
plot(x, y, main, xlab, ylab, xlim, ylim, axes)
Following is the description of the parameters used −
x is the data set whose values are the horizontal coordinates.
y is the data set whose values are the vertical coordinates.
main is the tile of the graph.
xlab is the label in the horizontal axis.
ylab is the label in the vertical axis.
xlim is the limits of the values of x used for plotting.
ylim is the limits of the values of y used for plotting.
axes indicates whether both axes should be drawn on the plot.
Example
We use the data set
"mtcars" available in the R environment to create a basic
scatterplot. Let's use the columns "wt" and "mpg" in
mtcars.
input <- mtcars[,c('wt','mpg')] print(head(input))
When we execute the above code,
it produces the following result −
wt mpg
Mazda RX4 2.620 21.0
Mazda RX4 Wag 2.875
21.0
Datsun 710 2.320 22.8
Hornet 4 Drive 3.215
21.4
Hornet Sportabout 3.440
18.7
Valiant 3.460 18.1
Creating the
Scatterplot
The below script will create a
scatterplot graph for the relation between wt(weight) and mpg(miles per
gallon).
# Get the input values.
input <- mtcars[,c('wt','mpg')]
# Give the chart file a name.
png(file = "scatterplot.png")
# Plot the chart for cars with
weight between 2.5 to 5 and mileage between 15 and 30.
plot(x = input$wt,y = input$mpg, xlab = "Weight", ylab = "Milage",
xlim = c(2.5,5),
ylim = c(15,30),
main = "Weight vs Milage")
# Save the file. dev.off()
When we execute the above code,
it produces the following result −
Scatterplot
Matrices
When we have more than two
variables and we want to find the correlation between one variable versus the
remaining ones we use scatterplot matrix. We use pairs() function to create
matrices of scatterplots.
Syntax
The basic syntax for creating
scatterplot matrices in R is −
pairs(formula, data)
Following is the description of the parameters used −
formula represents the series of variables used in pairs.
data represents the data set from which the variables will be taken.
Example
Each variable is paired up with
each of the remaining variable. A scatterplot is plotted for each pair.
# Give the chart file a name. png(file =
"scatterplot_matrices.png")
# Plot the matrices between 4
variables giving 12 plots.
# One variable with 3 others and
total 4 variables.
pairs(~wt+mpg+disp+cyl,data = mtcars, main = "Scatterplot Matrix")
# Save the file.
dev.off()
When the above code is executed we get the following output.
3. Histograms
–
A histogram represents the
frequencies of values of a variable bucketed into ranges. Histogram is similar
to bar chat but the difference is it groups the values into continuous ranges.
Each bar in histogram represents the height of the number of values present in
that range.
R creates histogram using hist()
function. This function takes a vector as an input and uses some more
parameters to plot histograms.
Syntax
The basic syntax for creating a
histogram using R is −
hist(v,main,xlab,xlim,ylim,breaks,col,border)
Following is the description of the parameters used −
v is a vector containing numeric
values used in histogram.
main indicates title of the chart.
col is used to set color of the bars.
border is used to set border color of each bar.
xlab is used to give description of x-axis.
xlim is used to specify the range of values on the x-axis.
ylim is used to specify the range of values on the y-axis.
breaks is used to mention the width of each bar.
Example
A simple histogram is created
using input vector, label, col and border parameters.
The script given below will create and save the histogram in the current R working directory.
# Create data for the graph.
v <- c(9,13,21,8,36,22,12,41,31,33,19)
# Give the chart file a name. png(file =
"histogram.png")
# Create the histogram. hist(v,xlab =
"Weight",col = "yellow",border = "blue")
# Save the file.
dev.off()
When we execute the above code,
it produces the following result –
Range of X and Y
values
To specify the range of values
allowed in X axis and Y axis, we can use the xlim and ylim parameters.
The width of each of the bar can be decided by using breaks.
# Create data for the graph.
v <-
c(9,13,21,8,36,22,12,41,31,33,19)
# Give the chart file a name.
png(file =
"histogram_lim_breaks.png")
# Create the histogram.
hist(v,xlab = "Weight",col =
"green",border = "red", xlim = c(0,40), ylim = c(0,5), breaks = 5)
# Save the file.
dev.off()
When we execute the above code,
it produces the following result –
4. Bar
charts –
A bar chart represents data in
rectangular bars with length of the bar proportional to the value of the
variable. R uses the function barplot() to create bar charts. R can draw both
vertical and Horizontal bars in the bar chart. In bar chart each of the bars
can be given different colors.
Syntax
The basic syntax to create a
bar-chart in R is −
barplot(H,xlab,ylab,main, names.arg,col)
Following is the description of the parameters used −
H is a vector or matrix containing numeric values used in bar chart. xlab is the label for x axis. ylab is the label for y axis.
main is the title of the bar chart.
names.arg is a vector of names appearing under each bar.
col is used to give colors to
the bars in the graph.
Example
A simple bar chart is created
using just the input vector and the name of each bar.
The below script will create and save the bar chart in the current R working directory.
# Create the data for the chart
H <- c(7,12,28,3,41)
# Give the chart file a name
png(file =
"barchart.png")
# Plot the bar chart
barplot(H)
# Save the file dev.off()
When we execute above code, it
produces following result –
Bar Chart Labels,
Title and Colors
The features of the bar chart
can be expanded by adding more parameters. The main parameter is used to add
title. The col parameter is used to add colors to the bars. The args.name is a
vector having same number of values as the input vector to describe the meaning
of each bar.
Example
The below script will create and
save the bar chart in the current R working directory.
# Create the data for the chart
H <- c(7,12,28,3,41)
M <-
c("Mar","Apr","May","Jun","Jul")
# Give the chart file a name
png(file =
"barchart_months_revenue.png")
# Plot the bar chart
barplot(H,names.arg=M,xlab="Month",ylab="Revenue",col="blue",
main="Revenue chart",border="red")
# Save the file dev.off()
When we execute above code, it
produces following result –
Group Bar Chart
and Stacked Bar Chart
We can create bar chart with
groups of bars and stacks in each bar by using a matrix as input values.
More than two variables are
represented as a matrix which is used to create the group bar chart and stacked
bar chart.
# Create the input vectors.
colors = c("green","orange","brown")
months <-
c("Mar","Apr","May","Jun","Jul")
regions <- c("East","West","North")
# Create the matrix of the
values.
Values <-
matrix(c(2,9,3,11,9,4,8,7,3,12,5,2,8,10,11), nrow = 3, ncol = 5, byrow = TRUE)
# Give the chart file a name
png(file =
"barchart_stacked.png")
# Create the bar chart
barplot(Values, main =
"total revenue", names.arg = months, xlab = "month", ylab =
"revenue", col = colors)
# Add the legend to the chart
legend("topleft",
regions, cex = 1.3, fill = colors)
# Save the file dev.off()
5. Pie
charts –
Pie chart is created using the
pie() function which takes positive numbers as a vector input. The additional
parameters are used to control labels, color, title etc.
Syntax
The basic syntax for creating a
pie-chart using the R is −
pie(x, labels, radius, main, col, clockwise)
Following is the description of the parameters used −
x is a vector containing the numeric values used in the pie chart.
labels is used to give description to the slices.
radius indicates the radius of the circle of the pie chart.(value between −1 and +1).
main indicates the title of the chart.
col indicates the color palette.
clockwise is a logical value indicating if the slices are drawn clockwise or anti clockwise.
Example
A very simple pie-chart is
created using just the input vector and labels. The below script will create
and save the pie chart in the current R working directory.
# Create data for the graph.
x <- c(21, 62, 10, 53)
labels <-
c("London", "New York", "Singapore",
"Mumbai")
# Give the chart file a name.
png(file = "city.png")
# Plot the chart. pie(x,labels)
# Save the file.
dev.off()
When we execute the above code,
it produces the following result –
Pie Chart Title
and Colors
We can expand the features of
the chart by adding more parameters to the function. We will use parameter main
to add a title to the chart and another parameter is col which will make use of
rainbow colour pallet while drawing the chart. The length of the pallet should
be same as the number of values we have for the chart. Hence we use length(x).
Example
The below script will create and
save the pie chart in the current R working directory.
# Create data for the graph.
x <- c(21, 62, 10, 53)
labels <-
c("London", "New York", "Singapore",
"Mumbai")
# Give the chart file a name.
png(file =
"city_title_colours.jpg")
# Plot the chart with title and
rainbow color pallet.
pie(x, labels, main = "City
pie chart", col = rainbow(length(x)))
# Save the file.
dev.off()
When we execute the above code,
it produces the following result –
Slice Percentages
and Chart Legend
We can add slice percentage and
a chart legend by creating additional chart variables.
# Create data for the graph.
x <- c(21, 62, 10,53)
labels <- c("London","New
York","Singapore","Mumbai")
piepercent<-
round(100*x/sum(x), 1)
# Give the chart file a name. png(file =
"city_percentage_legends.jpg")
# Plot the chart. pie(x, labels = piepercent, main =
"City pie chart",col = rainbow(length(x)))
legend("topright", c("London","New
York","Singapore","Mumbai"), cex = 0.8, fill = rainbow(length(x)))
# Save the file.
dev.off()
When we execute the above code,
it produces the following result –
3D Pie Chart
A pie chart with 3 dimensions
can be drawn using additional packages. The package plotrix has a function
called pie3D() that is used for this.
# Get the library. library(plotrix)
# Create data for the graph.
x <- c(21, 62, 10,53)
lbl <- c("London","New
York","Singapore","Mumbai")
# Give the chart file a name. png(file =
"3d_pie_chart.jpg")
# Plot the chart.
pie3D(x,labels = lbl,explode =
0.1, main = "Pie Chart of Countries ")
# Save the file.
dev.off()
When we execute the above code,
it produces the following result –
6. Box
plot –
Boxplots are a measure of how
well distributed is the data in a data set. It divides the data set into three
quartiles. This graph represents the minimum, maximum, median, first quartile
and third quartile in the data set. It is also useful in comparing the
distribution of data across data sets by drawing boxplots for each of them.
Boxplots are created in R by
using the boxplot() function.
Syntax
The basic syntax to create a
boxplot in R is −
boxplot(x, data, notch, varwidth, names, main)
Following is the description of the parameters used −
x is a vector or a formula.
data is the data frame.
notch is a logical value. Set as TRUE to draw a notch.
varwidth is a logical value. Set as true to draw width of the box proportionate to the sample size.
names are the group labels which will be printed under each boxplot.
main is used to give a title to the graph.
Example
We use the data set "mtcars"
available in the R environment to create a basic boxplot. Let's look at the
columns "mpg" and "cyl" in mtcars.
input <- mtcars[,c('mpg','cyl')]
print(head(input))
When we execute above code, it
produces following result −
mpg cyl
Mazda RX4 21.0
6
Mazda RX4 Wag 21.0
6
Datsun 710 22.8
4
Hornet 4 Drive 21.4
6
Hornet Sportabout 18.7 8
Valiant 18.1 6
Creating the
Boxplot
The below script will create a
boxplot graph for the relation between mpg (miles per gallon) and cyl (number
of cylinders).
# Give the chart file a name. png(file =
"boxplot.png")
# Plot the chart.
boxplot(mpg ~ cyl, data = mtcars, xlab = "Number
of Cylinders", ylab = "Miles
Per Gallon", main = "Mileage Data")
# Save the file. dev.off()
When we execute the above code,
it produces the following result −
Boxplot with
Notch
We can draw boxplot with notch
to find out how the medians of different data groups match with each other.
The below script will create a boxplot graph with
notch for each of the data group # Give the chart file a name.
png(file =
"boxplot_with_notch.png")
# Plot the chart.
boxplot(mpg ~ cyl, data = mtcars, xlab = "Number of
Cylinders", ylab = "Miles
Per Gallon", main = "Mileage
Data", notch = TRUE, varwidth = TRUE, col =
c("green","yellow","purple"),
names = c("High","Medium","Low")
)
# Save the file.
dev.off()
When we execute the above code,
it produces the following result –
