Here, you will find some examples of graphs.
library(ggplot2)
library(ggthemes)
iris <- iris
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) +
geom_point(size =2) + theme_solarized() +
labs(title ="My title",
subtitle = "My subtitle",
caption = "An explanation about the graph",
x = "My title for the x-axis (suggestion: Sepal Length)",
y = "Sepal Width")
The same thing with a different pair of variables, but now I am including the linear relationship between the variables.
ggplot(iris, aes(x=Petal.Length, y=Petal.Width)) +
geom_point(size =2) + theme_solarized() +
geom_point(aes(color = Species))+
geom_smooth(method='lm')
I could actually include a linear relationship by group. In all cases, we observe a positive relationship between the variables, but the relationship seens stonger in versicolor.
ggplot(iris, aes(x=Petal.Length, y=Petal.Width, color = Species)) +
geom_point(size =2) + theme_solarized() +
geom_point(aes(color = Species))+
geom_smooth(method='lm')
I could also create boxplots using the iris dataset. Notice there is one observation with very low Length in the virginica group.
ggplot(iris, aes(Species, Sepal.Length, fill=Species)) +
geom_boxplot()+ theme_solarized() +
scale_y_continuous(breaks= seq(0,30, by=.5)) 
Here is a nice-looking density plot.
ggplot(iris, aes(x=Petal.Length, colour=Species, fill=Species)) +
geom_density(alpha=.3) + theme_solarized()