These arguments accept vectors in the form c(lower_limit, upper_limit, number_of_intervals). We can do this using the xaxp and yaxp arguments. The last aspect of the axes that you might want to change are the axis tick marks. Nice, our plot looks a little less crowded. So if we wanted the X axis to go from 1 to 5, we would say xlim = c(1, 5). These arguments accept vectors of the form c(lower_limit, upper_limit). We can do this using the xlim and ylim arguments. We can also make the graph a little more spacious by editing the limits of the axes. This just keeps the code cleaner and more readable, but you could have also written it all in one long line. Notice that I also pressed the “Enter” or “Return” key after each comma in the plot() function. xlab refers to the label on the X axis, while ylab refers to the label on the Y axis. We can modify the xlab and ylab arguments within the plot() function. To help with that, I’m going to show you some different customizations that allow you to modify several of the plot elements. Now we have a basic scatterplot, but it doesn’t look all that great aesthetically. # Plot plant weight versus water received Now, using the plot() function, let’s create a plot of plant weight versus the amount of water that the plant received. If you’re following along in RStudio (which you should be! 😄), then you can just copy and paste the code below to add the new column. We’re going to add another column to this data set called “water”, which will describe the amount of water that each plant has received throughout its life, in liters. The second column, “group”, lists the experimental treatment that each plant was given. The first column, “weight”, represents the dry biomass of each plant in grams. This data set has 30 rows of data and two columns. We’re going to create the same scatterplot that we did in the other lesson by loading up the data set PlantGrowth. Scatterplots are one of the most common types of plots in ecology, where they show the relationship (or lack thereof) between two continuous variables. You can also watch this blog post as a video by clicking on the image below. If you want to take this even a step further, check out my step-by-step tutorial introduction to publication-quality scatterplots. Now that you’ve learned the very basics of plotting from our earlier tutorial on making your very first plot in R, this blog post will teach you how to customize your scatterplots to make them look better.

Scatter plot rstudio