The qplot function produces insightful visualizations even with limited data and arguments. I’ll demonstrate some basic graphs in this introduction post to R programming with qplot in ggplot2.

The `ggplot2`

library is required to use the `qplot()`

function, t. Use the `install.packages`

and `library`

functions to make sure it is installed:

I’ll use the `ChickWeight`

data set for this demonstration. This is loaded by running `data("ChickWeight")`

.You can find the docs online.

#### Absolute Basics qplot Chart

A useful visualization is to see the weight distribution for all of the chickens on a given diet. To get that for diet 1, I will:

- Build the filter
- Call qplot

*filter <- ChickWeight$Diet == 1qplot(data=ChickWeight[filter,], x=Chick,y=weight, size=I(4), color=I(“blue”))*

#### Generating a Box Plot with qplot

For a great in-depth reference on box plot, see this post.

A box plot can be generated by adding one more argument to the call above.

That is, the `geom`

parameter

*qplot(data=ChickWeight[filter,], x=Chick,y=weight, size=I(2), color=I(“blue”),geom=”boxplot”)*

Check out the following to see the actual numbers for Chick ‘7’:

Above, I linked to a blog post which contains a very clear definition of box plot, “A boxplot is a standardized way of displaying the distribution of data based on a five number summary (“minimum”, first quartile (Q1), median, third quartile (Q3), and “maximum”)”.

From the `summary`

function run above, you can see the values for Chick ‘7` are:

- Min: 41.0
- First Quartile: 67.5
- Median: 150.0
- Third Quartile: 226.0
- Max: 305

This matches the chart.

That concludes this introduction to `qplot`

in the ggplot2 library. Insights and distributions can be visualized with a basic filter and minimal arguments.

Categories: R Programming

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