![]() Note: All the line graphs plotted above were through the function plot(). Title="Event types", text.font=3, bg='lightblue') One can also customize legend, see below: If some doesn’t want to deal with coordinates, one specify legend position in terms of keywords like: “bottom”,”bottomright”, “bottomleft”, “left”, “topleft”, “top”, “right”, “topright” and “center”. The first two parameters in the legend function show the x and y-axis where legend needs are placed. However, from a readability perspective, it could be placed as per one’s own comfortability. The legend is usually placed on the top right-hand side corner. Legend plays a crucial factor there in order to understand plotted data in a lucid way. When there are more than two lines in the same line graph, it becomes clumsy to read. We saw how to plot multiple lines in a single line chart. Plot(events1,type = "o",col = "red", xlab = "Month", ylab = "Event Count", In order to plot multiple lines in a single line chart, below is the R code for that: In a real-world scenario, there is always a comparison between various line charts. See the location, and you will find “Line_chart.png” will be created. Function: getwd() and setwd() can help you do so. Here the png file will be saved in your current working directory, which you always check and change as per your requirement. However, there come to the cases when you need to save it in the local system in the form of png files. The line graph drawn till now is in Rstudio pane. Plot(Vec,type = "o",xlab = "Month", ylab = "Event Count", main = "Event Count by Month")įig 3: Vector plot with customized labels 2. Here you will notice x label, y label has not been assigned, so the default names as came. Plot(Vec,type = "o") # Plot the bar chart. Simple Line Graph in R code (with Plot function): ![]() ![]() However, for ggplot, the library “ggplot2” needs to be installed and read that library like: “library(ggplot2)” in the R environment.įor installation in RStudio. For plot(), one need not install any library. (It looks fine on my Mac using recent versions of Chrome and Firefox.The first function we will learn is plot() and another one would be ggplot. By the way, if you only see one image above it's because your browser doesn't support SVG. But now, most modern browsers can render high-quality SVG images using the standard tag, and R can easily generate SVG files with the svg device driver.įor example, here's a c alendar heat map of Apple stock since the beginning of 2010 rendered as a SVG:įor comparison, here's the same image rendered as a PNG:Īs you can see, the SVG file has much finer detail this will become even more apparent if you right-click on each image to view them in their own tab, or download AAPL.svg and view it directly. The SVG version (rendered at 14in wide) will show much more detail in larger format. The problem until recently is that not many browsers have had native support for vector images, limiting their usefulness on the web. Vector-based formats like PostScript and SVG always look their best regardless of the resolution, because they're rendered on demand from the component lines, symbols, and other elements of your graphic: it's a bit like using a laser printer instead of a dot-matrix printer, with a corresponding increase in quality. But if you're publishing on the Web, you're probably limited in resolution (here on the blog, I'm limited to 500px in the horizontal direction, for example). Common formats like GIF and JPG are raster-based: the image is composed of pixels, and if you don't choose a high enough resolution, you're likely to lose fine details and/or the image will look blocky. If you want the graphics you create with R to look their best, in general it's best to go for a vector-based graphics format instead of a raster-based format.
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