官术网_书友最值得收藏!

Creating pairs plots

A pairs plot is a matrix of scatter plots and is a very handy visualization to quickly scan the correlations between many variables in a dataset.

How to do it...

We will use the built-in iris dataset, which gives the measurements in centimeters of the sepal length and sepal width, and petal length and petal width variables, respectively, for 50 flowers from each of three species of iris:

pairs(iris[,1:4])

How it works...

As you can see in the preceding figure, the pairs() command makes a matrix of scatter plots, where all the variables in the specified dataset are plotted against each other. The variable names, displayed in the diagonal running across from the top-left corner to the bottom-right corner, are the key to reading the graph. For example, the scatter plot in the first row and second column shows the relationship between Sepal Length on the y axis and Sepal Width on the x axis.

There's more...

Here's a fun fact: we can produce the preceding graph using the plot() function instead of pairs() in exactly the same manner:

plot(iris[,1:4],
main="Relationships between characteristics of iris flowers",
pch=19,
col="blue",
cex=0.9)

So, if you pass a data frame with more than two variables to the plot() function, it creates a scatter plot matrix by default. We've also added a plot title and modified the plotting symbol style, color, and size using the pch, col, and cex arguments, respectively. We delve into the details of these settings in Chapter 3, Beyond the Basics – Adjusting Key Parameters.

See also

We cover some more interesting recipes in Chapter 4, Creating Scatter Plots, building upon the things you learned in Chapter 3, Beyond the Basics – Adjusting Key Parameters.

主站蜘蛛池模板: 东乌珠穆沁旗| 东莞市| 岳阳县| 治多县| 鄂尔多斯市| 大石桥市| 桦甸市| 汉中市| 宜章县| 阳东县| 台安县| 福泉市| 台江县| 内黄县| 闵行区| 万山特区| 彩票| 龙口市| 西贡区| 台州市| 平罗县| 诸城市| 冀州市| 信宜市| 陵川县| 饶平县| 谢通门县| 枣庄市| 博白县| 岗巴县| 通州市| 山西省| 凉城县| 沙洋县| 桂东县| 巩义市| 娱乐| 香河县| 榆树市| 区。| 永靖县|