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Update EDA.Rmd (#196) 

typo
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S'busiso Mkhondwane 2016-07-26 14:41:44 +02:00 committed by Hadley Wickham
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@ -163,7 +163,7 @@ Clusters of similar values suggest that subgroups exist in your data. To underst
* Why might the appearance of clusters be misleading? * Why might the appearance of clusters be misleading?
The histogram shows the length (in minutes) of 272 eruptions of the Old Faithful Geyser in Yellowstone National Park. Eruption times appear to be clustered in to two groups: there are short eruptions (of around 2 minutes) and long eruption (4-5 minutes), but little in between. The histogram shows the length (in minutes) of 272 eruptions of the Old Faithful Geyser in Yellowstone National Park. Eruption times appear to be clustered into two groups: there are short eruptions (of around 2 minutes) and long eruptions (4-5 minutes), but little in between.
```{r} ```{r}
ggplot(data = faithful, mapping = aes(x = eruptions)) + ggplot(data = faithful, mapping = aes(x = eruptions)) +
@ -174,7 +174,7 @@ Many of the questions above will prompt you to explore a relationship *between*
### Unusual values ### Unusual values
Outliers are observations that are unusual; data points that are don't seem to fit the pattern. Sometimes outliers are data entry errors; other times outliers suggest important new science. When you have a lot of data, outliers are sometimes difficult to see in a histogram. For example, take the distribution of the `x` variable from the diamonds dataset. The only evidence of outliers is the unusually wide limits on the x-axis. Outliers are observations that are unusual; data points that don't seem to fit the pattern. Sometimes outliers are data entry errors; other times outliers suggest important new science. When you have a lot of data, outliers are sometimes difficult to see in a histogram. For example, take the distribution of the `x` variable from the diamonds dataset. The only evidence of outliers is the unusually wide limits on the x-axis.
```{r} ```{r}
ggplot(diamonds) + ggplot(diamonds) +