geom_count

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

@@ -0,0 +1,177 @@
+---
+title: geom_count | Examples | Plotly
+name: geom_count
+permalink: ggplot2/geom_count/
+description: How to make a 2-dimensional frequency graph in ggplot2 using geom_count Examples of coloured and facetted graphs.
+layout: base
+thumbnail: thumbnail/geom_count.jpg
+language: ggplot2
+page_type: example_index
+has_thumbnail: true
+display_as: statistical
+order: 2
+output:
+  html_document:
+    keep_md: true
+---
+
+```{r, echo = FALSE, message=FALSE}
+knitr::opts_chunk$set(message = FALSE, warning=FALSE)
+Sys.setenv("plotly_username"="RPlotBot")
+Sys.setenv("plotly_api_key"="q0lz6r5efr")
+```
+
+### New to Plotly?
+
+Plotly's R library is free and open source!<br>
+[Get started](https://plot.ly/r/getting-started/) by downloading the client and [reading the primer](https://plot.ly/r/getting-started/).<br>
+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>
+We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/r_cheat_sheet.pdf) (new!) to help you get started!
+
+### Version Check
+
+Version 4 of Plotly's R package is now [available](https://plot.ly/r/getting-started/#installation)!<br>
+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.
+
+```{r}
+library(plotly)
+packageVersion('plotly')
+```
+
+### Basic geom\_count Plot
+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.
+
+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.
+
+```{r, results='hide'}
+library(plotly)
+flightdata <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/flightdata.csv", stringsAsFactors = FALSE)
+
+p <- ggplot(flightdata, aes(y=airline, x=dest, colour = dest, group=airport)) +
+  geom_count(alpha=0.5) +
+  labs(title = "Flights from New York to major domestic destinations",
+       x = "Origin and destination",
+       y = "Airline",
+       size = "")
+ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_count/basic-plot")
+chart_link
+```
+
+```{r echo=FALSE}
+chart_link
+```
+
+### Adding a Third Variable
+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.
+
+```{r, results='hide'}
+library(plotly)
+flightdata <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/flightdata.csv", stringsAsFactors = FALSE)
+
+p <- ggplot(flightdata, aes(y=airline, x=origin, colour=origin, group=airport)) +
+  geom_count(alpha=0.5) +
+  facet_grid(. ~ dest) +
+  labs(title = "Flights from New York to major domestic destinations",
+       x = "Origin and destination",
+       y = "Airline",
+       size = "")
+ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_count/three-variables")
+chart_link
+```
+
+```{r echo=FALSE}
+chart_link
+```
+
+### Customized appearance
+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.
+
+```{r, results='hide'}
+library(plotly)
+library(LaCroixColoR)
+flightdata <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/flightdata.csv", stringsAsFactors = FALSE)
+
+p <- ggplot(flightdata, aes(y=airline, x=origin, colour=origin, group=airport)) +
+  geom_count(alpha=0.5) +
+  facet_grid(. ~ dest) +
+  scale_colour_manual(values = lacroix_palette("PassionFruit", n=3)) +
+  theme(axis.text.x = element_blank(),
+        axis.ticks.x = element_blank()) +
+  labs(title = "Flights from New York to major domestic destinations",
+       x = "Origin and destination",
+       y = "Airline",
+       size = "")
+ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_count/customize-theme")
+chart_link
+```
+
+```{r echo=FALSE}
+chart_link
+```
+
+### geom\_count vs geom\_point
+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.
+
+```{r, results='hide'}
+library(plotly)
+library(dplyr)
+beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
+
+df <- beers %>%
+  mutate(abv = round(abv*100),
+         ibu = round(ibu/10)*10) %>%
+  filter(!is.na(style2))
+
+p <- ggplot(df, aes(x=abv, y=ibu, colour=style2)) +
+  geom_count(alpha=0.5) +
+  theme(legend.position = "none") +
+  facet_wrap(~style2)
+ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_count/compare-count")
+chart_link
+```
+
+```{r echo=FALSE}
+chart_link
+```
+
+```{r, results='hide'}
+library(plotly)
+library(dplyr)
+beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
+
+df <- filter(beers, !is.na(style2))
+
+p <- ggplot(df, aes(x=abv, y=ibu, colour=style2)) +
+  geom_point(alpha=0.2, aes(text = label)) +
+  theme(legend.position = "none") +
+  facet_wrap(~style2) +
+  labs(y = "bitterness (IBU)",
+       x = "alcohol volume (ABV)",
+       title = "Craft beers from American breweries")
+ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_count/compare-point")
+chart_link
+```
+
+```{r echo=FALSE}
+chart_link
+```

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

@@ -0,0 +1,173 @@
+---
+title: geom_count | Examples | Plotly
+name: geom_count
+permalink: ggplot2/geom_count/
+description: How to make a 2-dimensional frequency graph in ggplot2 using geom_count Examples of coloured and facetted graphs.
+layout: base
+thumbnail: thumbnail/geom_count.jpg
+language: ggplot2
+page_type: example_index
+has_thumbnail: true
+display_as: statistical
+order: 2
+output:
+  html_document:
+    keep_md: true
+---
+
+
+
+### New to Plotly?
+
+Plotly's R library is free and open source!<br>
+[Get started](https://plot.ly/r/getting-started/) by downloading the client and [reading the primer](https://plot.ly/r/getting-started/).<br>
+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>
+We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/r_cheat_sheet.pdf) (new!) to help you get started!
+
+### Version Check
+
+Version 4 of Plotly's R package is now [available](https://plot.ly/r/getting-started/#installation)!<br>
+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.
+
+
+```r
+library(plotly)
+packageVersion('plotly')
+```
+
+```
+## [1] '4.8.0.9000'
+```
+
+### Basic geom\_count Plot
+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.
+
+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.
+
+
+```r
+library(plotly)
+flightdata <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/flightdata.csv", stringsAsFactors = FALSE)
+
+p <- ggplot(flightdata, aes(y=airline, x=dest, colour = dest, group=airport)) +
+  geom_count(alpha=0.5) +
+  labs(title = "Flights from New York to major domestic destinations",
+       x = "Origin and destination",
+       y = "Airline",
+       size = "")
+ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_count/basic-plot")
+chart_link
+```
+
+<iframe src="https://plot.ly/~RPlotBot/5819.embed" width="800" height="600" id="igraph" scrolling="no" seamless="seamless" frameBorder="0"> </iframe>
+
+### Adding a Third Variable
+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.
+
+
+```r
+library(plotly)
+flightdata <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/flightdata.csv", stringsAsFactors = FALSE)
+
+p <- ggplot(flightdata, aes(y=airline, x=origin, colour=origin, group=airport)) +
+  geom_count(alpha=0.5) +
+  facet_grid(. ~ dest) +
+  labs(title = "Flights from New York to major domestic destinations",
+       x = "Origin and destination",
+       y = "Airline",
+       size = "")
+ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_count/three-variables")
+chart_link
+```
+
+<iframe src="https://plot.ly/~RPlotBot/5821.embed" width="800" height="600" id="igraph" scrolling="no" seamless="seamless" frameBorder="0"> </iframe>
+
+### Customized appearance
+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.
+
+
+```r
+library(plotly)
+library(LaCroixColoR)
+flightdata <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/flightdata.csv", stringsAsFactors = FALSE)
+
+p <- ggplot(flightdata, aes(y=airline, x=origin, colour=origin, group=airport)) +
+  geom_count(alpha=0.5) +
+  facet_grid(. ~ dest) +
+  scale_colour_manual(values = lacroix_palette("PassionFruit", n=3)) +
+  theme(axis.text.x = element_blank(),
+        axis.ticks.x = element_blank()) +
+  labs(title = "Flights from New York to major domestic destinations",
+       x = "Origin and destination",
+       y = "Airline",
+       size = "")
+ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_count/customize-theme")
+chart_link
+```
+
+<iframe src="https://plot.ly/~RPlotBot/5823.embed" width="800" height="600" id="igraph" scrolling="no" seamless="seamless" frameBorder="0"> </iframe>
+
+### geom\_count vs geom\_point
+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.
+
+
+```r
+library(plotly)
+library(dplyr)
+beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
+
+df <- beers %>%
+  mutate(abv = round(abv*100),
+         ibu = round(ibu/10)*10) %>%
+  filter(!is.na(style2))
+
+p <- ggplot(df, aes(x=abv, y=ibu, colour=style2)) +
+  geom_count(alpha=0.5) +
+  theme(legend.position = "none") +
+  facet_wrap(~style2)
+ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_count/compare-count")
+chart_link
+```
+
+<iframe src="https://plot.ly/~RPlotBot/5825.embed" width="800" height="600" id="igraph" scrolling="no" seamless="seamless" frameBorder="0"> </iframe>
+
+
+```r
+library(plotly)
+library(dplyr)
+beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
+
+df <- filter(beers, !is.na(style2))
+
+p <- ggplot(df, aes(x=abv, y=ibu, colour=style2)) +
+  geom_point(alpha=0.2, aes(text = label)) +
+  theme(legend.position = "none") +
+  facet_wrap(~style2) +
+  labs(y = "bitterness (IBU)",
+       x = "alcohol volume (ABV)",
+       title = "Craft beers from American breweries")
+ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_count/compare-point")
+chart_link
+```
+
+<iframe src="https://plot.ly/~RPlotBot/5827.embed" width="800" height="600" id="igraph" scrolling="no" seamless="seamless" frameBorder="0"> </iframe>