![]() Using the theme function to change the font family of the text. p1 + labs(title = "Venezuela's collapsing car sales", x = "Year", y = "Thousands of units", caption = "Data: ")Īdd the following line to add a new label for every 2 years: scale_x_continuous(breaks=seq(2002,2016,2)) Change the font To add a title, caption and change the name of the axes, use the labs function and the self-explanatory names title, x, y, and caption. p1 <- ggplot() + geom_line(aes(y = units, x = year), Next, tell it where to find the data by using the venezuela.data variable.įinally, write another p1 to visualize the plot. Make sure these are the same as the names of your data columns. Pass the name of your y axis and x axis inside the aes function, which stands for aesthetic mappings. ![]() To create a line chart, we'll use ggplot's geom_line and aes functions. That will open up the csv's data in a separate tab on the top-left pane. You can also click the chart icon on the top-right environment pane. Use the view function, see below, to open a preview of your data. venezuela.data <- read.csv(file="venezuela.csv") Preview the csv The following line reads the csv and stores it as the variable venezuela.data. Open the csv and change the column names to year and units. Quartz open-sources the data behind its graphics on The Atlas. We’ll be looking at collapsing car sales in Venezuela, replicating a line chart that Quartz published last week. ![]() You should see the package downloading and installing in the console pane. To install it in R Studio, open a new R script in “File” > “New File” > “R Script.” Type install.packages(“ggplot2”) on line 1 of the top-left pane. We’ll need ggplot2, a graphing package, to plot our data. New to R? Storybench has published an introductory tutorial on R and R Studio as well as a tutorial in R for geocoding addresses in a csv. The following tutorial will get you started using R’s ggplot2 package to make a simple line chart from a csv of data. Hope it helps!Ĭongratulations, you can now add the regression line equation and several measures to your ggplot2 visualizations.R can be used to explore, clean, analyze and visualize data. If you simply need an introduction into R, and less into the Data Science part, I can absolutely recommend this book by Richard Cotton. rr.label.)) +īy the way, if you’re having trouble understanding some of the code and concepts, I can highly recommend “An Introduction to Statistical Learning: with Applications in R”, which is the must-have data science bible. Stat_regline_equation(label.y = 350, aes(label =. Stat_regline_equation(label.y = 400, aes(label =. For every subset of your data, there is a different regression line equation and accompanying measures. ![]() BIC.label.: BIC for the fitted model.īy the way, you can easily use the measures from ggpubr in facets using facet_wrap() or facet_grid(). adj.rr.label.: Adjusted R2 of the fitted model as a character string to be parsed rr.label.: R2 of the fitted model as a character string to be parsed eq.label.: equation for the fitted polynomial as a character string to be parsed Here are the other measures you can access:
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