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Merge branch 'master' of github.com:hadley/r4ds

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hadley 2016-09-16 14:02:44 -05:00
parent 55f0e1b0e5 397b66b4d4
commit 40eb4b4be9
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2
EDA.Rmd
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@ -453,7 +453,7 @@ ggplot(data = diamonds) +
geom_point(mapping = aes(x = carat, y = price)) geom_point(mapping = aes(x = carat, y = price))
``` ```
Scatterplots become less useful as the size of your dataset grows, because points begin to overplot, and pile up into areas of uniform black (as above). This problem is similar to showing the distribution of price by color using a scatterplot: Scatterplots become less useful as the size of your dataset grows, because points begin to overplot, and pile up into areas of uniform black (as above). This problem is similar to showing the distribution of price by cut using a scatterplot:
```{r, dev = "png"} ```{r, dev = "png"}
ggplot(data = diamonds) + ggplot(data = diamonds) +

2
factors.Rmd
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@ -14,9 +14,7 @@ For more historical context on factors, I recommend [_stringsAsFactors: An unaut
To work with factors, we'll use the __forcats__ package, which provides tools for dealing with **cat**egorical variables (and it's an anagram of factors!). It provides a wide range of helpers for working with factors. We'll also need dplyr for some data manipulation, and ggplot2 for visualisation. To work with factors, we'll use the __forcats__ package, which provides tools for dealing with **cat**egorical variables (and it's an anagram of factors!). It provides a wide range of helpers for working with factors. We'll also need dplyr for some data manipulation, and ggplot2 for visualisation.
```{r setup, message = FALSE} ```{r setup, message = FALSE}
# devtools::install_github("hadley/forcats")
library(forcats) library(forcats)
library(ggplot2) library(ggplot2)
library(dplyr) library(dplyr)
``` ```