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‎_posts/ggplot2/2019-07-30-geom_text.Rmd

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---
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title: geom_text | Examples | Plotly
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name: geom_text
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permalink: ggplot2/geom_text/
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description: How to add text .
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layout: base
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thumbnail: thumbnail/geom_text.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: basic
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order: 11
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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 Text Graph
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Sources: [International IDEA](https://www.idea.int/data-tools/continent-view/Europe/40?st=par#rep) for national turnout and [European Parliament](https://election-results.eu/turnout/) for European turnout, while regional classifications are based on [EuroVoc](https://publications.europa.eu/en/web/eu-vocabularies/th-concept-scheme/-/resource/eurovoc/100277?target=Browse).
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```{r, results='hide'}
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library(plotly)
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recent_turnout <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/european_turnout.csv",stringsAsFactors = FALSE)
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recent_turnout$region <- factor(recent_turnout$region, levels=c("British","Northern","Western","Mediterranean","Central/Eastern"))
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p <- recent_turnout %>%
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ggplot(aes(x=nat_turnout,y=euro_turnout)) +
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geom_text(aes(size=population/3.5, label=abbreviation, colour=region), alpha=1) +
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labs(title = "Recent turnout in European Union countries",
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x = "Latest legislative or presidential election (whichever had higher turnout)",
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y = "May 2019 European Parliament election")
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p <- 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_text/basic-chart")
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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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### Overlaid Points
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Colour-coding the text itself might present readability issues. Another possible use of geom_text is to keep the text grey, but overlay it on a coloured point graph.
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Adding the *text* option within aes() allows us to control the text that appears when hovering over a point.
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```{r, results='hide'}
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library(plotly)
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p <- recent_turnout %>%
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ggplot(aes(x=nat_turnout,y=euro_turnout)) +
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geom_point(aes(size=population, colour=region, text=paste("country:", country)), alpha=0.4) +
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geom_text(aes(size=population/3.5, label=abbreviation), colour="gray20", alpha=1) +
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labs(title = "Recent turnout in European Union countries",
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x = "Latest legislative or presidential election (whichever had higher turnout)",
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y = "May 2019 European Parliament election")
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p <- 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_text/overlaid-points")
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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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### Customed Colour and Size Scale
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Let's use the LaCroixColoR package to spruce up the colour scheme. In addition, by using scale_size_continuous, we can make sure that none of the text is too small.
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```{r, results='hide'}
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library(plotly)
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library(LaCroixColoR)
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p <- recent_turnout %>%
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ggplot(aes(x=nat_turnout,y=euro_turnout)) +
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geom_point(aes(size=population, colour=region, text=paste("country:", country)), alpha=0.4) +
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geom_text(aes(size=population/3.5, label=abbreviation), colour="gray20", alpha=1) +
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scale_colour_manual(values=lacroix_palette(n=6, name="PeachPear")) +
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scale_size_continuous(range = c(3, 8)) +
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labs(title = "Recent turnout in European Union countries",
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x = "Latest legislative or presidential election (whichever had higher turnout)",
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y = "May 2019 European Parliament election")
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p <- 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_text/customized-scales")
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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 regression
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Adding a regression line as well as a label. geom_smooth does not allow for adjusting the transparency of the line (using alpha), which is why stat_smooth is used here. annotate is used to include a single text label (geom_text would create one label for every data point, all overlapped with each other).
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```{r, results='hide'}
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library(plotly)
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library(LaCroixColoR)
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m <- lm(euro_turnout ~ nat_turnout, data = recent_turnout)
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p <- recent_turnout %>%
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ggplot(aes(x=nat_turnout,y=euro_turnout)) +
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stat_smooth(geom="line", method="lm", alpha=0.3, se=FALSE) +
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geom_point(aes(size=population, colour=region, text=paste("country:", country)), alpha=0.4) +
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geom_text(aes(size=population/3.5, label=abbreviation), colour="gray20", alpha=1) +
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scale_colour_manual(values=lacroix_palette(n=6, name="PeachPear")) +
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scale_size_continuous(range = c(3, 8)) +
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labs(title = "Recent turnout in European Union countries",
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x = "Latest legislative or presidential election (whichever had higher turnout)",
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y = "May 2019 European Parliament election") +
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annotate(geom="text", x=60, y=80, label = paste("European turnout = \n",
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round(unname(coef(m)[2]),2),
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"x national turnout",
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round(unname(coef(m)[1]),1)))
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p <- 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_text/add-regression")
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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 Formatting
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Changed the font of the geom_text and of the graph (these must be done separately!), corrected the size label, centre-aligned the title.
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```{r, results='hide'}
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library(plotly)
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library(LaCroixColoR)
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m <- lm(euro_turnout ~ nat_turnout, data = recent_turnout)
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p <- recent_turnout %>%
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ggplot(aes(x=nat_turnout,y=euro_turnout)) +
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stat_smooth(geom="line", method="lm", alpha=0.3, se=FALSE) +
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geom_point(aes(size=population, colour=region, text=paste("country:", country)), alpha=0.4) +
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geom_text(aes(size=population/3.5, label=abbreviation), colour="gray20", alpha=1, family="Fira Sans") +
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scale_colour_manual(values=lacroix_palette(n=6, name="PeachPear")) +
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scale_size_continuous(range = c(3, 8)) +
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labs(title = "Recent turnout in European Union countries",
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x = "Latest legislative or presidential election (whichever had higher turnout)",
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y = "May 2019 European Parliament election",
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size = "") +
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annotate(geom="text", x=60, y=80, label = paste("European turnout = \n",
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round(unname(coef(m)[2]),2),
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"x national turnout",
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round(unname(coef(m)[1]),1))) +
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theme(plot.title = element_text(hjust = 0.5)) +
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guides(size=guide_legend(""), fill = FALSE) +
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theme(text = element_text(family = 'Fira Sans'))
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p <- 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_text/add-formatting")
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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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‎_posts/ggplot2/2019-07-30-geom_text.md

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---
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title: geom_text | Examples | Plotly
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name: geom_text
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permalink: ggplot2/geom_text/
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description: How to add text .
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layout: base
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thumbnail: thumbnail/geom_text.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: basic
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order: 11
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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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### 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!
26+
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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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```
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## [1] '4.9.0.9000'
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```
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### Basic Text Graph
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Sources: [International IDEA](https://www.idea.int/data-tools/continent-view/Europe/40?st=par#rep) for national turnout and [European Parliament](https://election-results.eu/turnout/) for European turnout, while regional classifications are based on [EuroVoc](https://publications.europa.eu/en/web/eu-vocabularies/th-concept-scheme/-/resource/eurovoc/100277?target=Browse).
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```r
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library(plotly)
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recent_turnout <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/european_turnout.csv",stringsAsFactors = FALSE)
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recent_turnout$region <- factor(recent_turnout$region, levels=c("British","Northern","Western","Mediterranean","Central/Eastern"))
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p <- recent_turnout %>%
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ggplot(aes(x=nat_turnout,y=euro_turnout)) +
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geom_text(aes(size=population/3.5, label=abbreviation, colour=region), alpha=1) +
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labs(title = "Recent turnout in European Union countries",
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x = "Latest legislative or presidential election (whichever had higher turnout)",
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y = "May 2019 European Parliament election")
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p <- 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_text/basic-chart")
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chart_link
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```
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<iframe src="https://plot.ly/~RPlotBot/5778.embed" width="800" height="600" id="igraph" scrolling="no" seamless="seamless" frameBorder="0"> </iframe>
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### Overlaid Points
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Colour-coding the text itself might present readability issues. Another possible use of geom_text is to keep the text grey, but overlay it on a coloured point graph.
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Adding the *text* option within aes() allows us to control the text that appears when hovering over a point.
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```r
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library(plotly)
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p <- recent_turnout %>%
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ggplot(aes(x=nat_turnout,y=euro_turnout)) +
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geom_point(aes(size=population, colour=region, text=paste("country:", country)), alpha=0.4) +
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geom_text(aes(size=population/3.5, label=abbreviation), colour="gray20", alpha=1) +
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labs(title = "Recent turnout in European Union countries",
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x = "Latest legislative or presidential election (whichever had higher turnout)",
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y = "May 2019 European Parliament election")
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p <- 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_text/overlaid-points")
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chart_link
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```
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<iframe src="https://plot.ly/~RPlotBot/5780.embed" width="800" height="600" id="igraph" scrolling="no" seamless="seamless" frameBorder="0"> </iframe>
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### Customed Colour and Size Scale
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Let's use the LaCroixColoR package to spruce up the colour scheme. In addition, by using scale_size_continuous, we can make sure that none of the text is too small.
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```r
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library(plotly)
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library(LaCroixColoR)
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p <- recent_turnout %>%
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ggplot(aes(x=nat_turnout,y=euro_turnout)) +
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geom_point(aes(size=population, colour=region, text=paste("country:", country)), alpha=0.4) +
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geom_text(aes(size=population/3.5, label=abbreviation), colour="gray20", alpha=1) +
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scale_colour_manual(values=lacroix_palette(n=6, name="PeachPear")) +
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scale_size_continuous(range = c(3, 8)) +
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labs(title = "Recent turnout in European Union countries",
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x = "Latest legislative or presidential election (whichever had higher turnout)",
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y = "May 2019 European Parliament election")
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p <- 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_text/customized-scales")
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chart_link
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```
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<iframe src="https://plot.ly/~RPlotBot/5782.embed" width="800" height="600" id="igraph" scrolling="no" seamless="seamless" frameBorder="0"> </iframe>
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### Adding a regression
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Adding a regression line as well as a label. geom_smooth does not allow for adjusting the transparency of the line (using alpha), which is why stat_smooth is used here. annotate is used to include a single text label (geom_text would create one label for every data point, all overlapped with each other).
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```r
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library(plotly)
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library(LaCroixColoR)
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m <- lm(euro_turnout ~ nat_turnout, data = recent_turnout)
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p <- recent_turnout %>%
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ggplot(aes(x=nat_turnout,y=euro_turnout)) +
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stat_smooth(geom="line", method="lm", alpha=0.3, se=FALSE) +
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geom_point(aes(size=population, colour=region, text=paste("country:", country)), alpha=0.4) +
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geom_text(aes(size=population/3.5, label=abbreviation), colour="gray20", alpha=1) +
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scale_colour_manual(values=lacroix_palette(n=6, name="PeachPear")) +
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scale_size_continuous(range = c(3, 8)) +
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labs(title = "Recent turnout in European Union countries",
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x = "Latest legislative or presidential election (whichever had higher turnout)",
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y = "May 2019 European Parliament election") +
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annotate(geom="text", x=60, y=80, label = paste("European turnout = \n",
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round(unname(coef(m)[2]),2),
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"x national turnout",
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round(unname(coef(m)[1]),1)))
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p <- 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_text/add-regression")
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chart_link
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```
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<iframe src="https://plot.ly/~RPlotBot/5774.embed" width="800" height="600" id="igraph" scrolling="no" seamless="seamless" frameBorder="0"> </iframe>
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### Customized Formatting
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Changed the font of the geom_text and of the graph (these must be done separately!), corrected the size label, centre-aligned the title.
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```r
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library(plotly)
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library(LaCroixColoR)
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m <- lm(euro_turnout ~ nat_turnout, data = recent_turnout)
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p <- recent_turnout %>%
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ggplot(aes(x=nat_turnout,y=euro_turnout)) +
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stat_smooth(geom="line", method="lm", alpha=0.3, se=FALSE) +
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geom_point(aes(size=population, colour=region, text=paste("country:", country)), alpha=0.4) +
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geom_text(aes(size=population/3.5, label=abbreviation), colour="gray20", alpha=1, family="Fira Sans") +
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scale_colour_manual(values=lacroix_palette(n=6, name="PeachPear")) +
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scale_size_continuous(range = c(3, 8)) +
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labs(title = "Recent turnout in European Union countries",
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x = "Latest legislative or presidential election (whichever had higher turnout)",
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y = "May 2019 European Parliament election",
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size = "") +
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annotate(geom="text", x=60, y=80, label = paste("European turnout = \n",
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round(unname(coef(m)[2]),2),
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"x national turnout",
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round(unname(coef(m)[1]),1))) +
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theme(plot.title = element_text(hjust = 0.5)) +
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guides(size=guide_legend(""), fill = FALSE) +
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theme(text = element_text(family = 'Fira Sans'))
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p <- 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_text/add-formatting")
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chart_link
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```
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<iframe src="https://plot.ly/~RPlotBot/5776.embed" width="800" height="600" id="igraph" scrolling="no" seamless="seamless" frameBorder="0"> </iframe>
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