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typo #4

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4 changes: 2 additions & 2 deletions exploratorygraphs.Rmd
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Expand Up @@ -113,7 +113,7 @@ with(filter(pollution, pm25 > 15), points(longitude, latitude))

At this point, you might decide to follow up on these counties, or ignore them if you are interested in other features. Since these counties appear to have very high levels, relative to the distribution of levels in the other counties in the U.S., they may be worth following up on if you are interested in describing counties that are potentially in violation of the standards.

Note that the plot/map above is not very pretty, but it was made quickly and it gave us a sense of where these outlying counties were located and conveyed enough information to help decide if we an to follow up or not.
Note that the plot/map above is not very pretty, but it was made quickly and it gave us a sense of where these outlying counties were located and conveyed enough information to help decide if we want to follow up or not.


## Histogram
Expand Down Expand Up @@ -184,7 +184,7 @@ We can see quite clearly that there are many more counties in the eastern U.S. i

So far we've covered some of the main tools used to summarize one dimensional data. For investigating data in two dimensions and beyond, there is an array of additional tools. Some of the key approaches are

* **Multiple or overlayed 1-D plots** (Lattice/ggplot2): Using multiple boxplots or multiple histograms can be useful for seeing the relationship between two variables, especially when on is naturally categorical.
* **Multiple or overlayed 1-D plots** (Lattice/ggplot2): Using multiple boxplots or multiple histograms can be useful for seeing the relationship between two variables, especially when one is naturally categorical.

* **Scatterplots**: Scatterplots are the natural tool for visualizing two continuous variables. Transformations of the variables (e.g. log or square-root transformation) may be necessary for effective visualization.

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