From 1bddb9c56183bfa43190fcb27bb2337c1d564381 Mon Sep 17 00:00:00 2001 From: hadley Date: Mon, 11 Jul 2016 08:35:07 -0500 Subject: [PATCH] Eliminate use of DSR --- DESCRIPTION | 2 -- tidy.Rmd | 7 +++---- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 7ddf5a9..80c2c6c 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -11,7 +11,6 @@ Imports: bookdown, broom, dplyr, - DSR, gapminder, ggplot2, hexbin, @@ -36,7 +35,6 @@ Imports: tibble, tidyr Remotes: - garrettgman/DSR, hadley/modelr, hadley/readr, hadley/stringr, diff --git a/tidy.Rmd b/tidy.Rmd index 6405cef..ab7a887 100644 --- a/tidy.Rmd +++ b/tidy.Rmd @@ -24,12 +24,11 @@ In *Section 4.1*, you will learn how the features of R determine the best way to ```{r message=FALSE} library(tidyr) library(dplyr) -library(DSR) ``` ## Tidy data -You can organize tabular data in many ways. For example, the data sets below show the same data organized in four different ways. Each data set shows the same values of four variables *country*, *year*, *population*, and *cases*, but each data set organizes the values into a different layout . You can access the data sets in the `DSR` package. +You can organize tabular data in many ways. For example, the data sets below show the same data organized in four different ways. Each data set shows the same values of four variables *country*, *year*, *population*, and *cases*, but each data set organizes the values into a different layout . You can access the data sets in tidyr. ```{r} # Data set one @@ -374,7 +373,7 @@ You can further customize `separate()` with the `remove`, `convert`, and `extra` **TODO: UNITE DESCRIPTION** -We can use `unite()` to rejoin the *century* and *year* columns that we created in the last example. That data is saved in the `DSR` package as `table6`. +We can use `unite()` to rejoin the *century* and *year* columns that we created in the last example. That data is saved as `tidyr::table6`. ```{r} table6 @@ -392,7 +391,7 @@ You can also use integers or the syntax of the `dplyr::select()` function to spe ## Case Study -The `who` data set in the `DSR` package contains cases of tuberculosis (TB) reported between 1995 and 2013 sorted by country, age, and gender. The data comes in the *2014 World Health Organization Global Tuberculosis Report*, available for download at [www.who.int/tb/country/data/download/en/](http://www.who.int/tb/country/data/download/en/). The data provides a wealth of epidemiological information, but it would be difficult to work with the data as it is. +The `who` data set in tidyr contains cases of tuberculosis (TB) reported between 1995 and 2013 sorted by country, age, and gender. The data comes in the *2014 World Health Organization Global Tuberculosis Report*, available for download at [www.who.int/tb/country/data/download/en/](http://www.who.int/tb/country/data/download/en/). The data provides a wealth of epidemiological information, but it would be difficult to work with the data as it is. ```{r} who