Source code for http://rpubs.com/plotly/user2014-demographics

UseR2014demog

---
title: "Conference Demographics"
output: html_document
---
The R User Conference 2014 Demographics
========================================================
(View source here: https://gist.github.com/mkcor/d54cc5eb4d3b826d093b)

Some informal data and graphs created for David McArthur by Tom Filloon and Ari
Lamstein and Marianne Corvellec. 

Made with R, RStudio, knitr, ggplot2, Hmisc and plotly. Thanks R Community for
making this so easy!

```{r, cache=TRUE}
# create some random data that matches with our experiences during the conference
sex <- sample(c("M", "F"), 700, prob=c(0.75, .25), T)
age <- rnorm(700, 35, 5)
continent <- sample(c("North America", "Europe", "Asia", "South America"), 700,
                   prob=c(.5, .3, .15, .05), T)
year_first_contact <- age
year_first_contact <- year_first_contact + 1949
year_first_contact <- year_first_contact + sample(-3:3, 700, T) # to add some jitter
affiliation <- sample(c("A", "G", "I", "S"), 700, prob=c(0.3, 0.03, 0.49, 0.18),
                      replace=T)
df <- data.frame(sex=sex, age=age, continent=continent,
                 year_first_contact=year_first_contact, affiliation=affiliation)

# force first row to be David McArthur
df[1, "sex"] <- "M"
df[1, "age"] <- 70.0
df[1, "continent"] <- "North America"
df[1, "year_first_contact"] <- 1958
df[1, "affiliation"] <- "A"
```

# Now, let's view the data
```{r, echo=FALSE, message=FALSE}
library(ggplot2)
library(Hmisc)
library(devtools)
install_github("ropensci/plotly")
library(plotly)
```

```{r, eval=FALSE}
library(ggplot2)
library(Hmisc)
library(devtools)
install_github("ropensci/plotly")
library(plotly)
```

```{r}
continent <- ggplot(df, aes(x=continent)) + geom_bar() + ggtitle("Continent of Origin")
py <- plotly()
```
You can view an interactive version of this plot at https://plot.ly/~mkcor/414
and get R code for it at https://plot.ly/~mkcor/414.r
```{r, plotly=TRUE, width=600, height=600}
py$ggplotly(continent, session="knitr")
```

```{r}
affiliation <- ggplot(df, aes(x=affiliation)) + geom_bar() +
  ggtitle("Affiliation (Academic, Government, Industry, Student)")
```

Again, view an interactive version of this plot at https://plot.ly/~mkcor/395
```{r, plotly=TRUE, width=600, height=600}
py$ggplotly(affiliation, session="knitr")
```

```{r}
sex <- ggplot(df, aes(x=sex)) + geom_bar() + ggtitle("Sex of Attendees")
```

You can use the link in the iframe above and append file extensions to it, like
.r, .json, .png, etc.

```{r, plotly=TRUE, width=600, height=600}
py$ggplotly(sex, session="knitr")
```

```{r}
bpplot(df$age, main="Age of Attendees")

bpplot(df$year_first_contact, main="First Computer Contact")
```