The ggtitle() command is another alternative to the labs() functionДҐ. Changing the titles and axis labels of the plot ggplot(midwest, aes(x=area,y=poptotal))+geom_point()+geom_smooth(method = 'lm')+xlim(c(0,0.1))+ylim(c(0,1000000))+ labs(title='Area Vs Population', subtitle='From midwest dataset', y='Population', x='Area', caption='Midwest Demographics') The xlim() and ylim() commands limit the range of the X and Y axis, which produces a zooming effect.Д¤. Adjusting the X and Y axis limit in the plot ggplot(midwest, aes(x=area,y=poptotal))+geom_point()+geom_smooth(method = 'lm')+xlim(c(0,0.1))+ylim(c(0,1000000)) The geom_smooth() layer adds a smoothing layer to the plotДЈ. Adding a smoothing layer to the plot ggplot(midwest, aes(x=area,y=poptotal))+geom_point()+geom_smooth(method = 'lm') Adding a layer enhances the graph in some ways.Дў. The ggplot2 package has many such geom layers.

The geom_point() command is called a layer. Ggplot(midwest, aes(x=area,y=poptotal))+geom_point() A simple scatter plot with the ggplot2 package library(ggplot2) The syntax of the ggplot2 package is slightly different from the base graphics package. It offers a lot of customization for the plots. Ggplot2 is probably the most powerful graphics package in R. In R, the coplot() function can be used to render conditional plots. Main = 'Anderson's Iris Data - 3 species',Д¬onditional plots or coplots are plots of two variables conditional to a third variable. This function renders multiple scatter plots of every possible pair of the input variables. It is very useful when the number of variables is more than two. The pairs() function returns a matrix of multiple scatter plots.

boxplot(airquality,main='Air quality in NY city') We can make a box plot by using the boxplot() function of R. They show important milestones of the data such as the minimum value, the first quartile, the median, the third quartile, and the maximum value. plot(rnorm(50),type='s')Д«ox plots show groups of numerical data through quartiles. We can make a stepped line graph in R using the plot() function and setting the type argument as s. These are very useful to show changes in a measure at irregular intervals. The stepped line graph is like a line graph with steps between data points. Time-series graphs can be rendered in R using the ts.plot() function. Time-series graphs are line graphs that show repeated measurements taken over time. We can make pie charts in R by using the pie() function. Pie charts show the percentage distribution of a single variable in the form of parts of a circle. They are the default for the plot() function. Scatterplots usually show two variables in a 2D cartesian plane. The barplot() function can be used to plot a bar-chart in R. plot(airquality$Ozone, type=вЂќhвЂќ)Д«ar charts show categorical data in the form of rectangular bars. This gives us a histogram like a high-density vertical line plot. We can also use the plot() function to make a histogram by setting the type argument to h.

We can plot a histogram in R by using the hist() function.

It shows the spread and shape of continuous data. Histograms are a means to show frequency distribution graphically. Let us take a look at the various types of graphs one-by-one: 1. They also include the incredible plot() function that can render multiple kinds of graphs depending on the input arguments. R base packages come with functions like the hist() function, the boxplot() function, the barplot() function, etc. R is believed to be the best at data visualization for good reason. Keeping you updated with latest technology trends, Join TechVidvan on Telegram Graphical Data Analysis in R

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