Both graphs allow us to look at Sale Price by Above Ground Living Area and Kitchen Quality at the same time. In Figure 4, we overplot these graphs, and use color and shape to identify the Kitchen Quality. Gf_labs(title = "Figure 3: Housing Prices in Ames, Iowa",įigure 3 facets the scatterplot by Kitchen Quality. Gf_point(SalePrice/100000 ~ GrLivArea, data = AmesHousing) %>% # Create distinct scatterplots for each type of kitchen quality Then the result is again passed to the gf_labs() function, which adds titles and labels to the graph. Below we start with a scatterplot and then assign that scatterplot to the gf_facet_grid() function to create distinct panels for each type of kitchen quality. This pipe operator is an easy way to create a chain of processing actions by allowing an intermediate result (left of the %>%) to become the first argument of the next function (right of the %>%). We can use the pipe operator %>% to add a new layer into a graph. In ggformula, this is easily done using the gf_facet_grid() layer. Another useful technique is to use the facet option to render scatterplots for each level of an additional categorical variable, such as kitchen quality. By default, this value is set to 1 (non-transparent), but it can be changed to any number between 0 and 1, where smaller values correspond to more transparency. We can use the alpha argument to adjust the transparency of points so that higher density regions are darker. The scatterplots above suffer from overplotting, that is, many values are being plotted on top of each other many times. Notice that fixed colors are given in quotes while a variable from our data frame is treated as an explanatory variable in our model. Instead of using color = "navy", run the code using color = ~ KithchenQual.What type of shape corresponds to using shape = 1?.In the code above, adjust alpha to any values between 0 and 1.Based upon the data documentation, what are the five different levels for kitchen quality?.Gf_point(log(SalePrice) ~ log(GrLivArea), data = AmesHousing, color = "navy", shape = 15, alpha =. # Create a scatterplot with log transformed variables, coloring by a third variable It is easy to make modifications to the color, shape and transparency of the points in a scatterplot. Making Modifications to Plots with ggformula KitchenQual: The quality rating of the kitchen.Fireplaces: The number of fireplaces in the home.GrLivArea: The above ground living area in the home.To start, we will focus on just a few variables: # The csv file should be imported into RStudio:ĪmesHousing <- read.csv("data/AmesHousing.csv") A full description of this dataset can be found here. The data set contains 2930 observations, and a large number of explanatory variables involved in assessing home values. # This tutorial will use the following packagesĭata: In this tutorial, we will use the AmesHousing data, which provides information on the sales of individual residential properties in Ames, Iowa from 2006 to 2010. While there are numerous ways to create graphs, this tutorial will focus on the R package ggformula, created by Danny Kaplan and Randy Pruim. Many software packages allow the user to make basic plots, but it can be challenging to create plots that are customized to address a specific idea. It is often necessary to create graphs to effectively communicate key patterns within a dataset.
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