From 5a59c0b26c6e8dd872327d6855ce4bb68374b36d Mon Sep 17 00:00:00 2001 From: El Potaeto Date: Sun, 8 Mar 2015 00:02:14 +0100 Subject: [PATCH] df spell --- R-package/vignettes/discoverYourData.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R-package/vignettes/discoverYourData.Rmd b/R-package/vignettes/discoverYourData.Rmd index 9f0280ffc..a0e86601d 100644 --- a/R-package/vignettes/discoverYourData.Rmd +++ b/R-package/vignettes/discoverYourData.Rmd @@ -53,7 +53,7 @@ Conversion from categorical to numeric variables ### Looking at the raw data -In this Vignette we will see how to transform a *dense* dataframe (*dense* = few zeroes in the matrix) with *categorical* variables to a very *sparse* matrix (*sparse* = lots of zero in the matrix) of `numeric` features. +In this Vignette we will see how to transform a *dense* `data.frame` (*dense* = few zeroes in the matrix) with *categorical* variables to a very *sparse* matrix (*sparse* = lots of zero in the matrix) of `numeric` features. The method we are going to see is usually called [one-hot encoding](http://en.wikipedia.org/wiki/One-hot). @@ -64,7 +64,7 @@ data(Arthritis) df <- data.table(Arthritis, keep.rownames = F) ``` -> `data.table` is 100% compliant with **R** dataframe but its syntax is very consistent and its performance is really good. +> `data.table` is 100% compliant with **R** `data.frame` but its syntax is very consistent and its performance is really good. The first thing we want to do is to have a look to the first lines of the `data.table`: