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‎_posts/ggplot2/2019-08-08-geom_count.Rmd

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---
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title: geom_count | Examples | Plotly
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name: geom_count
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permalink: ggplot2/geom_count/
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description: How to make a 2-dimensional frequency graph in ggplot2 using geom_count Examples of coloured and facetted graphs.
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layout: base
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thumbnail: thumbnail/geom_count.jpg
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language: ggplot2
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page_type: example_index
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has_thumbnail: true
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display_as: statistical
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order: 2
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output:
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html_document:
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keep_md: true
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---
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```{r, echo = FALSE, message=FALSE}
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knitr::opts_chunk$set(message = FALSE, warning=FALSE)
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Sys.setenv("plotly_username"="RPlotBot")
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Sys.setenv("plotly_api_key"="q0lz6r5efr")
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```
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### New to Plotly?
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Plotly's R library is free and open source!<br>
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[Get started](https://plot.ly/r/getting-started/) by downloading the client and [reading the primer](https://plot.ly/r/getting-started/).<br>
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You can set up Plotly to work in [online](https://plot.ly/r/getting-started/#hosting-graphs-in-your-online-plotly-account) or [offline](https://plot.ly/r/offline/) mode.<br>
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We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/r_cheat_sheet.pdf) (new!) to help you get started!
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### Version Check
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Version 4 of Plotly's R package is now [available](https://plot.ly/r/getting-started/#installation)!<br>
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Check out [this post](http://moderndata.plot.ly/upgrading-to-plotly-4-0-and-above/) for more information on breaking changes and new features available in this version.
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```{r}
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library(plotly)
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packageVersion('plotly')
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```
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### Basic geom\_count Plot
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geom\_count is a way to plot two variables that are not continuous. Here's a modified version of the nycflights13 dataset that comes with R; it shows 2013 domestic flights leaving New York's three airports. This graph maps two categorical variables: which of America's major airports it was headed to, and which major carrier was operating it.
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It's good to show the full airport names for destinations, rather than just the airport codes. You can use aes(group = ), which doesn't modify the graph in any way but adds information to the labels.
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```{r, results='hide'}
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library(plotly)
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flightdata <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/flightdata.csv", stringsAsFactors = FALSE)
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p <- ggplot(flightdata, aes(y=airline, x=dest, colour = dest, group=airport)) +
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geom_count(alpha=0.5) +
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labs(title = "Flights from New York to major domestic destinations",
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x = "Origin and destination",
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y = "Airline",
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size = "")
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ggplotly(p)
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# Create a shareable link to your chart
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# Set up API credentials: https://plot.ly/r/getting-started
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chart_link = api_create(p, filename="geom_count/basic-plot")
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chart_link
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```
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```{r echo=FALSE}
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chart_link
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```
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### Adding a Third Variable
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By using facets, we can add a third variable: which of New York's three airports it departed from. We can also colour-code by this variable.
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```{r, results='hide'}
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library(plotly)
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flightdata <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/flightdata.csv", stringsAsFactors = FALSE)
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p <- ggplot(flightdata, aes(y=airline, x=origin, colour=origin, group=airport)) +
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geom_count(alpha=0.5) +
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facet_grid(. ~ dest) +
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labs(title = "Flights from New York to major domestic destinations",
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x = "Origin and destination",
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y = "Airline",
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size = "")
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ggplotly(p)
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# Create a shareable link to your chart
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# Set up API credentials: https://plot.ly/r/getting-started
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chart_link = api_create(p, filename="geom_count/three-variables")
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chart_link
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```
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```{r echo=FALSE}
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chart_link
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```
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### Customized appearance
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The airport labels at the bottom aren't very visible and aren't very important, since there's a colour key to the side; we can get rid of the text and ticks using theme() options. Let's also use the LaCroixColoR package to give this geom\_count chart a new colour scheme.
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```{r, results='hide'}
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library(plotly)
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library(LaCroixColoR)
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flightdata <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/flightdata.csv", stringsAsFactors = FALSE)
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p <- ggplot(flightdata, aes(y=airline, x=origin, colour=origin, group=airport)) +
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geom_count(alpha=0.5) +
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facet_grid(. ~ dest) +
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scale_colour_manual(values = lacroix_palette("PassionFruit", n=3)) +
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theme(axis.text.x = element_blank(),
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axis.ticks.x = element_blank()) +
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labs(title = "Flights from New York to major domestic destinations",
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x = "Origin and destination",
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y = "Airline",
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size = "")
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ggplotly(p)
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# Create a shareable link to your chart
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# Set up API credentials: https://plot.ly/r/getting-started
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chart_link = api_create(p, filename="geom_count/customize-theme")
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chart_link
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```
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```{r echo=FALSE}
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chart_link
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```
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### geom\_count vs geom\_point
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Here's a comparison of geom\_count and geom\_point on the same dataset (rounded for geom\_count). Geom\_point has the advantage of allowing multiple colours on the same graph, as well as a label for each point. But even with a low alpha, there are too many overlapping points to understand what the actual distribution looks like, only a general impression.
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```{r, results='hide'}
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library(plotly)
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library(dplyr)
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beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
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df <- beers %>%
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mutate(abv = round(abv*100),
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ibu = round(ibu/10)*10) %>%
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filter(!is.na(style2))
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p <- ggplot(df, aes(x=abv, y=ibu, colour=style2)) +
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geom_count(alpha=0.5) +
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theme(legend.position = "none") +
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facet_wrap(~style2)
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ggplotly(p)
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# Create a shareable link to your chart
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# Set up API credentials: https://plot.ly/r/getting-started
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chart_link = api_create(p, filename="geom_count/compare-count")
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chart_link
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```
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```{r echo=FALSE}
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chart_link
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```
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```{r, results='hide'}
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library(plotly)
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library(dplyr)
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beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
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df <- filter(beers, !is.na(style2))
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p <- ggplot(df, aes(x=abv, y=ibu, colour=style2)) +
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geom_point(alpha=0.2, aes(text = label)) +
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theme(legend.position = "none") +
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facet_wrap(~style2) +
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labs(y = "bitterness (IBU)",
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x = "alcohol volume (ABV)",
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title = "Craft beers from American breweries")
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ggplotly(p)
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# Create a shareable link to your chart
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# Set up API credentials: https://plot.ly/r/getting-started
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chart_link = api_create(p, filename="geom_count/compare-point")
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chart_link
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```
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```{r echo=FALSE}
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chart_link
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```

‎_posts/ggplot2/2019-08-08-geom_count.md

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---
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title: geom_count | Examples | Plotly
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name: geom_count
4+
permalink: ggplot2/geom_count/
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description: How to make a 2-dimensional frequency graph in ggplot2 using geom_count Examples of coloured and facetted graphs.
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layout: base
7+
thumbnail: thumbnail/geom_count.jpg
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language: ggplot2
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page_type: example_index
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has_thumbnail: true
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display_as: statistical
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order: 2
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output:
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html_document:
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keep_md: true
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---
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18+
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### New to Plotly?
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Plotly's R library is free and open source!<br>
23+
[Get started](https://plot.ly/r/getting-started/) by downloading the client and [reading the primer](https://plot.ly/r/getting-started/).<br>
24+
You can set up Plotly to work in [online](https://plot.ly/r/getting-started/#hosting-graphs-in-your-online-plotly-account) or [offline](https://plot.ly/r/offline/) mode.<br>
25+
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/r_cheat_sheet.pdf) (new!) to help you get started!
26+
27+
### Version Check
28+
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Version 4 of Plotly's R package is now [available](https://plot.ly/r/getting-started/#installation)!<br>
30+
Check out [this post](http://moderndata.plot.ly/upgrading-to-plotly-4-0-and-above/) for more information on breaking changes and new features available in this version.
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```r
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library(plotly)
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packageVersion('plotly')
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```
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```
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## [1] '4.8.0.9000'
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```
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### Basic geom\_count Plot
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geom\_count is a way to plot two variables that are not continuous. Here's a modified version of the nycflights13 dataset that comes with R; it shows 2013 domestic flights leaving New York's three airports. This graph maps two categorical variables: which of America's major airports it was headed to, and which major carrier was operating it.
44+
45+
It's good to show the ful airport names for destinations, rather than just the airport codes. You can use aes(group = ), which doesn't modify the graph in any way but adds information to the labels.
46+
47+
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```r
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library(plotly)
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flightdata <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/flightdata.csv", stringsAsFactors = FALSE)
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p <- ggplot(flightdata, aes(y=airline, x=dest, colour = dest, group=airport)) +
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geom_count(alpha=0.5) +
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labs(title = "Flights from New York to major domestic destinations",
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x = "Origin and destination",
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y = "Airline",
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size = "")
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ggplotly(p)
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# Create a shareable link to your chart
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# Set up API credentials: https://plot.ly/r/getting-started
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chart_link = api_create(p, filename="geom_count/basic-plot")
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chart_link
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```
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<iframe src="https://plot.ly/~RPlotBot/5819.embed" width="800" height="600" id="igraph" scrolling="no" seamless="seamless" frameBorder="0"> </iframe>
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### Adding a Third Variable
69+
By using facets, we can add a third variable: which of New York's three airports it departed from. We can also colour-code by this variable.
70+
71+
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```r
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library(plotly)
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flightdata <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/flightdata.csv", stringsAsFactors = FALSE)
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p <- ggplot(flightdata, aes(y=airline, x=origin, colour=origin, group=airport)) +
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geom_count(alpha=0.5) +
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facet_grid(. ~ dest) +
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labs(title = "Flights from New York to major domestic destinations",
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x = "Origin and destination",
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y = "Airline",
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size = "")
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ggplotly(p)
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# Create a shareable link to your chart
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# Set up API credentials: https://plot.ly/r/getting-started
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chart_link = api_create(p, filename="geom_count/three-variables")
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chart_link
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```
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<iframe src="https://plot.ly/~RPlotBot/5821.embed" width="800" height="600" id="igraph" scrolling="no" seamless="seamless" frameBorder="0"> </iframe>
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### Customized appearance
94+
The airport labels at the bottom aren't very visible and aren't very important, since there's a colour key to the side; we can get rid of the text and ticks using theme() options. Let's also use the LaCroixColoR package to give this geom\_count chart a new colour scheme.
95+
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```r
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library(plotly)
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library(LaCroixColoR)
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flightdata <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/flightdata.csv", stringsAsFactors = FALSE)
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p <- ggplot(flightdata, aes(y=airline, x=origin, colour=origin, group=airport)) +
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geom_count(alpha=0.5) +
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facet_grid(. ~ dest) +
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scale_colour_manual(values = lacroix_palette("PassionFruit", n=3)) +
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theme(axis.text.x = element_blank(),
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axis.ticks.x = element_blank()) +
108+
labs(title = "Flights from New York to major domestic destinations",
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x = "Origin and destination",
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y = "Airline",
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size = "")
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ggplotly(p)
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# Create a shareable link to your chart
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# Set up API credentials: https://plot.ly/r/getting-started
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chart_link = api_create(p, filename="geom_count/customize-theme")
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chart_link
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```
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<iframe src="https://plot.ly/~RPlotBot/5823.embed" width="800" height="600" id="igraph" scrolling="no" seamless="seamless" frameBorder="0"> </iframe>
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### geom\_count vs geom\_point
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Here's a comparison of geom\_count and geom\_point on the same dataset (rounded for geom\_count). Geom\_point has the advantage of allowing multiple colours on the same graph, as well as a label for each point. But even with a low alpha, there are too many overlapping points to understand what the actual distribution looks like, only a general impression.
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```r
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library(plotly)
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library(dplyr)
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beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
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df <- beers %>%
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mutate(abv = round(abv*100),
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ibu = round(ibu/10)*10) %>%
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filter(!is.na(style2))
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p <- ggplot(df, aes(x=abv, y=ibu, colour=style2)) +
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geom_count(alpha=0.5) +
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theme(legend.position = "none") +
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facet_wrap(~style2)
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ggplotly(p)
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# Create a shareable link to your chart
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# Set up API credentials: https://plot.ly/r/getting-started
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chart_link = api_create(p, filename="geom_count/compare-count")
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chart_link
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```
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<iframe src="https://plot.ly/~RPlotBot/5825.embed" width="800" height="600" id="igraph" scrolling="no" seamless="seamless" frameBorder="0"> </iframe>
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```r
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library(plotly)
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library(dplyr)
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beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
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df <- filter(beers, !is.na(style2))
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p <- ggplot(df, aes(x=abv, y=ibu, colour=style2)) +
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geom_point(alpha=0.2, aes(text = label)) +
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theme(legend.position = "none") +
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facet_wrap(~style2) +
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labs(y = "bitterness (IBU)",
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x = "alcohol volume (ABV)",
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title = "Craft beers from American breweries")
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ggplotly(p)
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# Create a shareable link to your chart
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# Set up API credentials: https://plot.ly/r/getting-started
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chart_link = api_create(p, filename="geom_count/compare-point")
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chart_link
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```
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<iframe src="https://plot.ly/~RPlotBot/5827.embed" width="800" height="600" id="igraph" scrolling="no" seamless="seamless" frameBorder="0"> </iframe>

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