ggplot2 scatter plot with multiple dataframes r

#GGPLOT2 SCATTER PLOT WITH MULTIPLE DATAFRAMES R HOW TO#
#GGPLOT2 SCATTER PLOT WITH MULTIPLE DATAFRAMES R CODE#
#GGPLOT2 SCATTER PLOT WITH MULTIPLE DATAFRAMES R DOWNLOAD#

So, if you want to divide the figure along the y axis, you put variable in the data that you want to use to decide which plot data goes into as the first entry in the formula. We can do this using facet_grid and a formula syntax, y ~ x. Since the resulting three plots that we want will all share an x axis (Date), we can imagine slicing up the figure in the vertical direction so that the x axis remains in-tact but we end up with three different y axes.

#GGPLOT2 SCATTER PLOT WITH MULTIPLE DATAFRAMES R CODE#

We could have written code to filter the data frame to the appropriate values and make a plot for each of them, but we can also take advantage of facet_grid. Now, we know that we can’t keep these different parameters on the same plot. # Now, we can look at the plot and see how it looks before we facet # Obviously, the scales are off because we are plotting flow with concentrations p # Setup plot without facets p <- ggplot(data = wi_daily_wq, aes(x = Date, y = value)) + geom_point( aes(color = site_no)) + theme_bw()

# Get the data by giving site numbers and parameter codes # 00060 = stream flow, 00530 = total suspended solids, 00631 = concentration of inorganic nitrogen wi_daily_wq % select( - ends_with( '_cd')) %>% gather(key = 'parameter', value = 'value', -site_no, -Date) Library(dplyr) # for `rename` & `select` library(tidyr) # for `gather` library(ggplot2) Three USGS gage sites in Wisconsin were chosen because they have data for all three water quality parameters (flow, total suspended solids, and inorganic nitrogen) we are using in this example.

#GGPLOT2 SCATTER PLOT WITH MULTIPLE DATAFRAMES R DOWNLOAD#

We will download USGS water data for use in this example from the USGS National Water Information System (NWIS) using the dataRetrieval package (you can learn more about dataRetrieval in this curriculum). First, setup your ggplot code as if you aren’t faceting. Sounds like a lot, but facets can make this very simple. You want three different plots in the same figure - a timeseries for each of the parameters with different colored symbols for the different sites.

You have a ame with four columns: Date, site_no, parameter, and value. Let’s start by considering a set of graphs with a common x axis.

#GGPLOT2 SCATTER PLOT WITH MULTIPLE DATAFRAMES R HOW TO#

You write your ggplot2 code as if you were putting all of the data onto one plot, and then you use one of the faceting functions to specify how to slice up the graph. When you are creating multiple plots and they share axes, you should consider using facet functions from ggplot2 ( facet_grid, facet_wrap). Multiple plots in one figure using ggplot2 and facets However, there are other methods to do this that are optimized for ggplot2 plots. You may have already heard of ways to put multiple R plots into a single figure - specifying mfrow or mfcol arguments to par, split.screen, and layout are all ways to do this. While ggplot2 has many useful features, this post will explore how to create figures with multiple ggplot2 plots.

In the Introduction to R class, we have switched to teaching ggplot2 because it works nicely with other tidyverse packages (dplyr, tidyr), and can create interesting and powerful graphics with little code.

Website with everything you want to know about ggplot2 by Selva Prabhakaran.

Intermediate plots (error bars, density plots, bar charts, multiple windows, saving to a file, etc) from University of Georgia.

Basic plots (histograms, boxplots, scatter plots, QQ plots) from University of Georgia.

Another blog breaking down basic plotting from FlowingData.

Breakdown of how to create a plot from R-bloggers.

The Introduction to R curriculum summarizes some of the most used plots, but cannot begin to expose people to the breadth of plot options that exist.There are existing resources that are great references for plotting in R: Some clarification about setting group=1: I think that I found an explanation in Hadley Wickham's book ' ggplot2: Elegant Graphics for Data Analysis.R can create almost any plot imaginable and as with most things in R if you don’t know where to start, try Google. Stat_summary(fun=mean, geom='line', aes(group=1)) + Is that what you are looking for? library(ggplot2)

Ggplot2 scatter plot with multiple dataframes r