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Fixing typos (#456)

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bahadir cankardes 2016-10-06 19:19:55 +03:00 committed by Hadley Wickham
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EDA.Rmd
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@ -534,7 +534,7 @@ ggplot(data = faithful) +
Patterns provide one of the most useful tools for data scientists because they reveal covariation. If you think of variation as a phenomenon that creates uncertainty, covariation is a phenomenon that reduces it. If two variables covary, you can use the values of one variable to make better predictions about the values of the second. If the covariation is due to a causal relationship (a special case), then you can use the value of one variable to control the value of the second.
Models are a tool for extracting patterns out of data. For example, consider the diamonds data. It's hard to understand the relationship between cut and price, because cut and carat, and carat and price are tightly related. It's possible to use a model to remove the very strong relationship between price and carat so we can explore the subtleties that remain. The following code fits a model that predicts `price` from `carat` and the computes the residuals (the difference between the predicted value and the actual value). The residuals give us a view of the price of the diamond, once the effect of carat has been removed.
Models are a tool for extracting patterns out of data. For example, consider the diamonds data. It's hard to understand the relationship between cut and price, because cut and carat, and carat and price are tightly related. It's possible to use a model to remove the very strong relationship between price and carat so we can explore the subtleties that remain. The following code fits a model that predicts `price` from `carat` and then computes the residuals (the difference between the predicted value and the actual value). The residuals give us a view of the price of the diamond, once the effect of carat has been removed.
```{r, dev = "png"}
library(modelr)
@ -556,7 +556,7 @@ ggplot(data = diamonds2) +
geom_boxplot(mapping = aes(x = cut, y = resid))
```
You'll learn how models, and the modelr package, work in the final part of the book, [model]. We're saving modelling for later because understanding what models are and how they work is easiest once you have tools of data wrangling and programming in hand.
You'll learn how models, and the modelr package, work in the final part of the book, [model](#model-intro). We're saving modelling for later because understanding what models are and how they work is easiest once you have tools of data wrangling and programming in hand.
## ggplot2 calls