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```{r}
#packages

library(rvest)
library(tidyverse)
library(magrittr)
library(scales)
library(knitr)
library(lubridate)
```

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lattice

examples inspired by this book

ggplot2

examples inspired by this book

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Nowhere near as spectacular as the Upshot/New York Times 3d yield curve by Amanda Cox and Gregor Aisch, but not bad at all for a couple of lines of R code with the plotly htmlwidget.

library(plotly)
library(dplyr)
library(tidyr)
library(purrr)
library(quantmod)
library(magrittr)

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R Cheat Sheet : Applying functions

• R Cheat Sheet : Applying functions
  • apply(x,index,function)
  • lapply(x,function)
  • sapply(x,function)
  • tapply(x,y,function)
  • mapply(function,x,y,...)
  • References

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Simple data manipulations in R

Many years ago, I was introduced to R by Cam Webb . At the time, his website contained a list of common data manipulations (original here). This list dated from Cam's early experience with R, and contained the R-help mailing list responses to a series of data manipulations. For a long time, I kept this file as a handy reference. I printed it out. I recommended it to friends.

Now I have been using R for years, and the state of the art has advanced considerably. Particulary, Hadley Wickham's reshape2 and dplyr packages have transformed the way most useRs manipulate their data. I decided that it would be interesting to revisit my favourite resource and try my hand at solving these problems with tools from these two packages.

library(reshape2)
library(dplyr)

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• Eurostat, World Bank and others: https://ikashnitsky.github.io/2017/data-acquisition-two/
• Star Wars data: in dplyr, http://dplyr.tidyverse.org/reference/starwars.html
• Baby names data: babynames package, https://cran.r-project.org/web/packages/babynames/index.html
• Movies data: https://cran.r-project.org/web/packages/ggplot2movies/index.html ggplot2movies package
• Game of Thrones screen time: https://github.com/Preetish/GoT_screen_time
• Open Bike Data: https://github.com/ropensci/bikedata
• Tons of data through 538: https://cran.r-project.org/web/packages/fivethirtyeight/vignettes/fivethirtyeight.html
• Public health data England: fingertipsR, https://cran.r-project.org/web/packages/fingertipsR/
• Financial data via Quandl: https://www.quandl.com/tools/r
• Cyclones: https://github.com/ropensci/rrricanesdat

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1.) If I have a data.frame df <- data.frame(a = c(1, 2, 3), b = c(4, 5, 6), c(7, 8, 9))...

1a.) How do I select the c(4, 5, 6)?

1b.) How do I select the 1?

1c.) How do I select the 5?

1d.) What is df[, 3]?

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---
title: "Beginner R Workshop"
output: html_document
date: October 27, 2016
author: Keegan Korthauer and Joseph N. Paulson
---

Today's workshop will be a gentle introduction to the
[R programming language](https://cran.r-project.org/).  We have provided RStudio Server instances that you can access on the web, but we'll also briefly overview how you can install R and RStudio on your own computer.  We'll start with some basics about programming and then get some hands-on experience with analyzing a real-world messy dataset that we'll collect from everyone live.  We hope that you'll get a feel for what R can do as well as learn where you can learn more to use it on your own (great resources are listed at the end).

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---
title: "Untitled"
author: "Ben Marwick"
date: "Wednesday, September 24, 2014"
output: html_document
---

## Introduction

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>. You should read through this entire document very carefully before making any changes or pressing any buttons. 

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---
title: "Hands-on with dplyr"
author: "Dmitry Grapov"
output:
  html_document:
    keep_md: yes
---

## Introduction