Ggplot Error a Continuous Variable Can Not Be Mapped to Shape

Introduction

The ggplot2 package is a relatively novel approach to generating highly informative publication-quality graphics. The "gg" stands for "Grammar of Graphics". In short, instead of thinking about a single function that produces a plot, ggplot2 uses a "grammar" approach, akin to building more and more complex sentences to layer on more information or nuance.

Data Model

The ggplot2 package assumes that data are in the form of a data.frame. In some cases, the data will need to be manipulated into a form that matches assumptions that ggplot2 uses. In particular, if one has a matrix of numbers associated with different subjects (samples, people, etc.), the data will usually need to be transformed into a "long" data frame.

Getting started

To use the ggplot2 package, it must be installed and loaded. Assuming that installation has been done already, we can load the package directly:

            library(ggplot2)          

Playing with ggplot2

mtcars data

We are going to use the mtcars dataset, included with R, to experiment with ggplot2.

              data(mtcars)            

• Exercise: Explore the mtcars dataset using View, summary, dim, class, etc.

We can also take a quick look at the relationships between the variables using the pairs plotting function.

              pairs(mtcars)            

That is a useful view of the data. We want to use ggplot2 to make an informative plot, so let's approach this in a piecewise fashion. We first need to decide what type of plot to produce and what our basic variables will be. In this case, we have a number of choices.

              ggplot(mtcars,aes(x=disp,y=hp))            

First, a little explanation is necessary. The ggplot function takes as its first argument a data.frame. The second argument is the "aesthetic", aes. The x and y take column names from the mtcars data.frame and will form the basis of our scatter plot.

But why did we get that "Error: No layers in plot"? Remember that ggplot2 is a "grammar of graphics". We supplied a subject, but no verb (called a layer by ggplot2). So, to generate a plot, we need to supply a verb. There are many possibilities. Each "verb" or layer typically starts with "geom" and then a descriptor. An example is necessary.

              ggplot(mtcars,aes(x=disp,y=hp)) + geom_point()            

We finally produced a plot. The power of ggplot2, though, is the ability to make very rich plots by adding "grammar" to the "plot sentence". We have a number of other variables in our mtcars data.frame. How can we add another value to a two-dimensional plot?

              ggplot(mtcars,aes(x=disp,y=hp,color=cyl)) + geom_point()            

The color of the points is a based on the numeric variable wt, the weight of the car. Can we do more? We can change the size of the points, also.

              ggplot(mtcars,aes(x=disp,y=hp,color=wt,size=mpg)) + geom_point()            

So, on our 2D plot, we are now plotting four variables. Can we do more? We can manipulate the shape of the points in addition to the color and the size.

              ggplot(mtcars,aes(x=disp,y=hp)) + geom_point(aes(size=mpg,color=wt,shape=cyl))            

Why did we get that error? Ggplot2 is trying to be helpful by telling us that a "continuous varialbe cannot be mapped to 'shape'". Well, in our mtcars data.frame, we can look at cyl in detail.

              class(mtcars$cyl)            

              ## [1] "numeric"            

              summary(mtcars$cyl)            

              ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  ##   4.000   4.000   6.000   6.188   8.000   8.000            

              table(mtcars$cyl)            

              ##  ##  4  6  8  ## 11  7 14            

The cyl variable is "kinda" continuous in that it is numeric, but it could also be thought of as a "category" of engines. R has a specific data type for "category" data, called a factor. We can easily convert the cyl column to a factor like so:

              mtcars$cyl = as.factor(mtcars$cyl)            

Now, we can go ahead with our previous approach to make a 2-dimensional plot that displays the relationships between five variables.

              ggplot(mtcars,aes(x=disp,y=hp)) + geom_point(aes(size=mpg,color=wt,shape=cyl))            

NYC Flight data

I leave this section open-ended for you to explore further options with the ggplot2 package. The data represent the on-time data for all flights that departed New York City in 2013.

              library(nycflights13) head(flights)            

              ## # A tibble: 6 × 19 ##    year month   day dep_time sched_dep_time dep_delay arr_time ##   <int> <int> <int>    <int>          <int>     <dbl>    <int> ## 1  2013     1     1      517            515         2      830 ## 2  2013     1     1      533            529         4      850 ## 3  2013     1     1      542            540         2      923 ## 4  2013     1     1      544            545        -1     1004 ## 5  2013     1     1      554            600        -6      812 ## 6  2013     1     1      554            558        -4      740 ## # ... with 12 more variables: sched_arr_time <int>, arr_delay <dbl>, ## #   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>, ## #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, ## #   time_hour <dttm>            

Feel free to explore. Consider using other "geoms" during your exploration.

Session Info

            sessionInfo()          

            ## R Under development (unstable) (2016-10-26 r71594) ## Platform: x86_64-apple-darwin13.4.0 (64-bit) ## Running under: macOS Sierra 10.12.1 ##  ## locale: ## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 ##  ## attached base packages: ## [1] stats     graphics  grDevices utils     datasets  base      ##  ## other attached packages: ## [1] nycflights13_0.2.0 ggplot2_2.2.0      BiocStyle_2.3.15   ## [4] knitr_1.15.1       ##  ## loaded via a namespace (and not attached): ##  [1] Rcpp_0.12.8.2      codetools_0.2-15   assertthat_0.1     ##  [4] digest_0.6.10      rprojroot_1.1      plyr_1.8.4         ##  [7] grid_3.4.0         gtable_0.2.0       backports_1.0.4    ## [10] magrittr_1.5       evaluate_0.10      scales_0.4.1       ## [13] stringi_1.1.2      lazyeval_0.2.0     rmarkdown_1.2.9000 ## [16] labeling_0.3       tools_3.4.0        stringr_1.1.0      ## [19] munsell_0.4.3      yaml_2.1.14        colorspace_1.3-1   ## [22] htmltools_0.3.5    tibble_1.2         methods_3.4.0          

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Source: https://seandavi.github.io/hour_of_code/ggplot2.html

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