In addition, there are several low-level functions that can add more features to the plot that was created by the high-level plotting function. This first function call takes a blank canvas and creates a complete plot. We can think of this as a new plot on a new “canvas”, like a painter starting a new painting. A call to one of these functions initiates a new plot. There are many functions in base R for making different kinds of plots, including hist(), plot(), boxplot(), dotchart(), barchart(), and mosaicplot() to make, respectively, a histogram, scatter plot, box plot, dot chart, bar chart, and mosaic plot. The graphics functionality in base R creates a statistical graph from a high-level function, such as plot(). We have prepared these data for analysis by, e.g., converting values of 99 and 999 into NAs, formatting variables such as smoking status and education as factors, and collapsing categories with only a few observations. The information available for each baby, is provided in the table below. The babies data frame that we use in this chapter contains a subset of the information collected for 1236 babies–baby boys born during one year of the study who lived at least 28 days and were single births (i.e., not one of a twin or triplet). We use data from the Child Health and Development Studies (CHDS) to demonstrate the plotting features in R (CHDS is described in Section 5.1). We refer you to the many online resources that are available to learn more about the visualization tools in R. R’s plotting tools are flexible, and we barely touch on the multitude of possibilities for customizing and designing your own visualizations.
#Cplot chart in r how to#
In addition to introducing some of the basic plots, we include examples of how to add content and modify features of the graphs.
For all of the examples, we use one set of data that includes a range of variables and data types. This chapter also provides a brief overview of the basic plot types available in R. However, since these two models are quite different, we encourage the beginner to focus on one method at first. In this chapter, we describe and demonstrate each approach through examples. There are two models for creating statistical graphs in R: the painter’s model provided by functions in base R, and the Grammar of Graphics object-based model that has been implemented in the ggplot2 package using the grid graphics approach of the grid package. They are also an essential part of exploratory data analysis. Statistical graphs can offer very effective means for formally presenting the findings from a data analysis or simulation study.
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