![]() In the next edition of this blog we will look at how to use the ggplot2 package to add colour and a wide range of other features to our graphs. Of course, we have not yet even drawn upon any of R's custom plotting packages. Moreover, the code would not increase even if we were plotting 100 charts. But note that once we have created the plotM function, we only have to write 3 lines of code to make 4 separate charts. Okay, so the graph does not look like it came from NASA – or to be honest with NASA's ailing reputation maybe it does. Hence we can iterate over a vector containing the values of a, calling plotM each time. Whatever plots we now create will be placed sequentially into this grid. Using the par(mfrow=c(2,2)) we create a 2x2 grid in which to place the next four plots. ![]() Finally, we use the abline function to plot a linear fit to highlight the trend. The next line simply creates a standard R plot of y versus z. The lineįilters the data frame to include only those rows where the variable a=l. This function takes an argument "l" that determines which value of the variable we are plotting. The relevant code to create this plot starts with the function " plotM", standing for plot Multiple. It seems sensible to plot z against y for each of these different a values. A preliminary inspection shows that there are only 4 observed values of a. Suppose that we are analysing this data set, unaware of the relationship between the x,y and z variables. Three of these variables are randomly generated, with the z variable dependent upon the other 3. The first few lines of code create a data frame with 4 variables: x,y,z,a. # Loop through each value of "a" and call the plotM function # Create a grid to plot the different values of "a" Main=paste("Value of key variable: ",toString(l)))Ībline(lm(df.temp$z~df.temp$y),col="red") Plot(df.temp$y,df.temp$z,xlab="Y Value",ylab="Z Value", # Create a function that plots the value of "z" against the "y" value We start with a simple script that allows us to plot several graphs at the same time, each with a different value of a key variable. Instead we will progress one layer at a time, adding additional levels of complexity and functionality. Of course, Rome was not built in a day, and a thorough knowledge of R plotting cannot be built in one. In the next few editions of this blog we will build up a basic repertoire of plotting techniques, focusing on graphics that are sickeningly impressive. I have noticed that the most common reason that people avoid R is that they cannot rapidly make graphs that meet the high standards of their clients. Here at least is my contribution to the collective intelligence of biologicals. No longer can a lack of computing power or software be blamed for a lack of productivity growth: rate-limiting factors are now exclusively human. ![]() The level of computational analysis that can be conducted in a few hours with nothing more than a desktop PC, a broadband connection, and copious amounts of caffeine is phenomenal. Although the majority of the time taken consisted of collecting the data and making various adjustments, it took a not inconsiderable amount of work to write the code.Īs I was cursing the apply function – not for the last time I am sure – I suddenly realised the insane level of productivity that I have come to see as “par for the course”. Below is the syntax for mfrow.Today, in one of my more productive days, I managed to create a sleek R script that plotted several histograms in a lattice, allowing for easy identification of the underlying trend. Mfrow combines plots filled by rows i.e it takes two arguments, the number of rows and number of columns and then starts filling the plots by row. Let us combine plots using both the above parameters. These two parameters create a matrix of plots filled by rows and columns respectively. For combining multiple plots, we can use the graphical parameters mfrow and mfcol. Par() allows us to customize the graphical parameters(title, axis, font, color, size) for a particular session. You can specify the behaviour - na.omit and na.exclude: returns the object with observations removed if they contain any missing values differences between omitting and excluding NAs can be seen in some prediction and residual functions - na.pass: returns the object unchanged - na. layout() serves the same purpose but offers more flexibility by allowing us to modify the height and width of rows and columns. par() can be used to set graphical parameters regarding plot layout using the mfcol and mfrow arguments. The Graphics package offers two methods to combine multiple plots. Often, it is useful to have multiple plots in the same frame as it allows us to get a comprehensive view of a particular variable or compare among different variables. In this post, we will learn how to combine multiple plots. In the previous post, we learnt how to add text annotations to plots. This is the tenth post in the series Data Visualization With R. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |