From eab7d77e0fad7fdf1c0f9781ff031c395a93acc4 Mon Sep 17 00:00:00 2001 From: Justinas Petuchovas Date: Sun, 4 Sep 2016 22:39:39 +0300 Subject: [PATCH 1/2] Fix comment mismatch (#406) --- EDA.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/EDA.Rmd b/EDA.Rmd index 24abf27..fa3725c 100644 --- a/EDA.Rmd +++ b/EDA.Rmd @@ -453,7 +453,7 @@ ggplot(data = diamonds) + 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"} ggplot(data = diamonds) + From 397b66b4d4717eb2c523b3ed3ee380bbb838f04b Mon Sep 17 00:00:00 2001 From: Colin Gillespie Date: Thu, 8 Sep 2016 14:10:45 +0100 Subject: [PATCH 2/2] forcats now on CRAN (#415) --- factors.Rmd | 2 -- 1 file changed, 2 deletions(-) diff --git a/factors.Rmd b/factors.Rmd index 81bbec6..2e96b58 100644 --- a/factors.Rmd +++ b/factors.Rmd @@ -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. ```{r setup, message = FALSE} -# devtools::install_github("hadley/forcats") library(forcats) - library(ggplot2) library(dplyr) ```