added geom_text

_posts/ggplot2/2016-11-29-geom_tile.Rmd

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 page_type: example_index
 has_thumbnail: true
 display_as: basic
-order: 8
+order: 12
 output:
   html_document:
     keep_md: true

_posts/ggplot2/2016-11-29-geom_tile.md

@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: basic
-order: 8
+order: 12
 output:
   html_document:
     keep_md: true

_posts/ggplot2/2017-04-21-geom_spoke.Rmd

@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: basic
-order: 11
+order: 10
 output:
   html_document:
     keep_md: true

_posts/ggplot2/2017-04-21-geom_spoke.md

@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: basic
-order: 11
+order: 10
 output:
   html_document:
     keep_md: true

_posts/ggplot2/2018-06-22-geom_sf.Rmd

@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: basic
-order: 12
+order: 9
 output:
   html_document:
     keep_md: true

_posts/ggplot2/2018-06-22-geom_sf.md

@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: basic
-order: 12
+order: 9
 output:
   html_document:
     keep_md: true

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

@@ -0,0 +1,201 @@
+---
+title: geom_text | Examples | Plotly
+name: geom_text
+permalink: ggplot2/geom_text/
+description: How to make a text graph using ggplotly.
+layout: base
+thumbnail: thumbnail/geom_text.jpg
+language: ggplot2
+page_type: example_index
+has_thumbnail: true
+display_as: basic
+order: 11
+output:
+  html_document:
+    keep_md: true
+---
+
+```{r, echo = FALSE, message=FALSE}
+knitr::opts_chunk$set(message = FALSE, warning=FALSE)
+Sys.setenv("plotly_username"="RPlotBot")
+Sys.setenv("plotly_api_key"="q0lz6r5efr")
+```
+
+### New to Plotly?
+
+Plotly's R library is free and open source!<br>
+[Get started](https://plot.ly/r/getting-started/) by downloading the client and [reading the primer](https://plot.ly/r/getting-started/).<br>
+You can set up Plotly to work in [online](https://plot.ly/r/getting-started/#hosting-graphs-in-your-online-plotly-account) or [offline](https://plot.ly/r/offline/) mode.<br>
+We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/r_cheat_sheet.pdf) (new!) to help you get started!
+
+### Version Check
+
+Version 4 of Plotly's R package is now [available](https://plot.ly/r/getting-started/#installation)!<br>
+Check out [this post](http://moderndata.plot.ly/upgrading-to-plotly-4-0-and-above/) for more information on breaking changes and new features available in this version.
+
+```{r}
+library(plotly)
+packageVersion('plotly')
+```
+
+### Basic Text Graph
+Sources: [International IDEA](https://www.idea.int/data-tools/continent-view/Europe/40?st=par#rep) for national turnout and [European Parliament](https://election-results.eu/turnout/) for European turnout, while regional classifications are based on [EuroVoc](https://publications.europa.eu/en/web/eu-vocabularies/th-concept-scheme/-/resource/eurovoc/100277?target=Browse).
+
+```{r, results='hide'}
+recent_turnout <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/european_turnout.csv",stringsAsFactors = FALSE)
+recent_turnout$region <- factor(recent_turnout$region, levels=c("British","Northern","Western","Mediterranean","Central/Eastern"))
+
+library(plotly)
+p <- recent_turnout %>%
+  ggplot(aes(x=nat_turnout,y=euro_turnout)) + 
+  geom_text(aes(size=population/3.5, label=abbreviation, colour=region), alpha=1) +
+  labs(title = "Recent turnout in European Union countries",
+       x = "Latest legislative or presidential election (whichever had higher turnout)",
+       y = "May 2019 European Parliament election")
+p <- ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_text/basic-chart")
+chart_link
+```
+
+```{r echo=FALSE}
+chart_link
+```
+
+### Overlaid Points
+Colour-coding the text itself might present readability issues. Another possible use of geom\_text is to keep the text grey, but overlay it on a coloured point graph.
+
+Adding the *text* option within aes() allows us to control the text that appears when hovering over a point.
+
+```{r, results='hide'}
+recent_turnout <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/european_turnout.csv",stringsAsFactors = FALSE)
+recent_turnout$region <- factor(recent_turnout$region, levels=c("British","Northern","Western","Mediterranean","Central/Eastern"))
+
+library(plotly)
+p <- recent_turnout %>%
+  ggplot(aes(x=nat_turnout,y=euro_turnout)) + 
+  geom_point(aes(size=population, colour=region, text=paste("country:", country)), alpha=0.4) +
+  geom_text(aes(size=population/3.5, label=abbreviation), colour="gray20", alpha=1) +
+  labs(title = "Recent turnout in European Union countries",
+       x = "Latest legislative or presidential election (whichever had higher turnout)",
+       y = "May 2019 European Parliament election")
+p <- ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_text/overlaid-points")
+chart_link
+```
+
+```{r echo=FALSE}
+chart_link
+```
+
+### Customed Colour and Size Scale
+Let's use the LaCroixColoR package to spruce up the colour scheme. In addition, by using scale\_size\_continuous, we can make sure that none of the text is too small.
+
+```{r, results='hide'}
+recent_turnout <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/european_turnout.csv",stringsAsFactors = FALSE)
+recent_turnout$region <- factor(recent_turnout$region, levels=c("British","Northern","Western","Mediterranean","Central/Eastern"))
+
+library(plotly)
+library(LaCroixColoR)
+p <- recent_turnout %>%
+  ggplot(aes(x=nat_turnout,y=euro_turnout)) + 
+  geom_point(aes(size=population, colour=region, text=paste("country:", country)), alpha=0.4) +
+  geom_text(aes(size=population/3.5, label=abbreviation), colour="gray20", alpha=1) +
+  scale_colour_manual(values=lacroix_palette(n=6, name="PeachPear")) +
+  scale_size_continuous(range = c(3, 8)) +
+  labs(title = "Recent turnout in European Union countries",
+       x = "Latest legislative or presidential election (whichever had higher turnout)",
+       y = "May 2019 European Parliament election")
+p <- ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_text/customized-scales")
+chart_link
+```
+
+```{r echo=FALSE}
+chart_link
+```
+
+### Adding a regression
+Adding a regression line as well as a label. geom\_smooth does not allow for adjusting the transparency of the line (using alpha), which is why stat\_smooth is used here. annotate is used to include a single text label (geom\_text would create one label for every data point, all overlapped with each other).
+
+```{r, results='hide'}
+recent_turnout <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/european_turnout.csv",stringsAsFactors = FALSE)
+recent_turnout$region <- factor(recent_turnout$region, levels=c("British","Northern","Western","Mediterranean","Central/Eastern"))
+m <- lm(euro_turnout ~ nat_turnout, data = recent_turnout)
+
+library(plotly)
+library(LaCroixColoR)
+p <- recent_turnout %>%
+  ggplot(aes(x=nat_turnout,y=euro_turnout)) + 
+  stat_smooth(geom="line", method="lm", alpha=0.3, se=FALSE) + 
+  geom_point(aes(size=population, colour=region, text=paste("country:", country)), alpha=0.4) +
+  geom_text(aes(size=population/3.5, label=abbreviation), colour="gray20", alpha=1) +
+  scale_colour_manual(values=lacroix_palette(n=6, name="PeachPear")) +
+  scale_size_continuous(range = c(3, 8)) +
+  labs(title = "Recent turnout in European Union countries",
+       x = "Latest legislative or presidential election (whichever had higher turnout)",
+       y = "May 2019 European Parliament election") +
+  annotate(geom="text", x=60, y=80, label = paste("European turnout = \n",
+                                                  round(unname(coef(m)[2]),2),
+                                                  "x national turnout",
+                                                  round(unname(coef(m)[1]),1)))
+p <- ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_text/add-regression")
+chart_link
+```
+
+```{r echo=FALSE}
+chart_link
+```
+
+### Customized Formatting
+Changed the font of the geom\_text and of the graph (these must be done separately!), corrected the size label, centre-aligned the title.
+
+```{r, results='hide'}
+recent_turnout <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/european_turnout.csv",stringsAsFactors = FALSE)
+recent_turnout$region <- factor(recent_turnout$region, levels=c("British","Northern","Western","Mediterranean","Central/Eastern"))
+m <- lm(euro_turnout ~ nat_turnout, data = recent_turnout)
+
+library(plotly)
+library(LaCroixColoR)
+p <- recent_turnout %>%
+  ggplot(aes(x=nat_turnout,y=euro_turnout)) + 
+  stat_smooth(geom="line", method="lm", alpha=0.3, se=FALSE) + 
+  geom_point(aes(size=population, colour=region, text=paste("country:", country)), alpha=0.4) +
+  geom_text(aes(size=population/3.5, label=abbreviation), colour="gray20", alpha=1, family="Fira Sans") +
+  scale_colour_manual(values=lacroix_palette(n=6, name="PeachPear")) +
+  scale_size_continuous(range = c(3, 8)) +
+  labs(title = "Recent turnout in European Union countries",
+       x = "Latest legislative or presidential election (whichever had higher turnout)",
+       y = "May 2019 European Parliament election",
+       size = "") +
+  annotate(geom="text", x=60, y=80, label = paste("European turnout = \n",
+                                                  round(unname(coef(m)[2]),2),
+                                                  "x national turnout",
+                                                  round(unname(coef(m)[1]),1))) +
+  theme(plot.title = element_text(hjust = 0.5)) +
+  guides(size=guide_legend(""), fill = FALSE) +
+  theme(text = element_text(family = 'Fira Sans'))
+p <- 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_text/add-formatting")
+chart_link
+```
+
+```{r echo=FALSE}
+chart_link
+```
+