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update with slashes and dataframe assignment
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‎_posts/ggplot2/2019-07-30-geom_text.Rmd

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@@ -42,10 +42,10 @@ packageVersion('plotly')
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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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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_text(aes(size=population/3.5, label=abbreviation, colour=region), alpha=1) +
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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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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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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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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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```
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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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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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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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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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```
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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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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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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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m <- lm(euro_turnout ~ nat_turnout, data = recent_turnout)
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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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stat_smooth(geom="line", method="lm", alpha=0.3, se=FALSE) +
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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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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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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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m <- lm(euro_turnout ~ nat_turnout, data = recent_turnout)
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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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stat_smooth(geom="line", method="lm", alpha=0.3, se=FALSE) +

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

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@@ -44,10 +44,10 @@ Sources: [International IDEA](https://www.idea.int/data-tools/continent-view/Eur
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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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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_text(aes(size=population/3.5, label=abbreviation, colour=region), alpha=1) +
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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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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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```r
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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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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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```r
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library(plotly)
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library(LaCroixColoR)
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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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m <- lm(euro_turnout ~ nat_turnout, data = recent_turnout)
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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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stat_smooth(geom="line", method="lm", alpha=0.3, se=FALSE) +
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```r
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library(plotly)
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library(LaCroixColoR)
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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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m <- lm(euro_turnout ~ nat_turnout, data = recent_turnout)
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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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stat_smooth(geom="line", method="lm", alpha=0.3, se=FALSE) +

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