Create line plot with categorical data
Ubiqum Code Academy
1 Goal
The goal of this tutorial is to create a line plot having categorical data in the x axis.
2 Loading libraries
library(dplyr)
library(ggplot2)
library(reshape2)3 Data import
# In this tutorial we are going to use the dataset iris
data(iris)
str(iris)## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...4 Data preparation
# We are going to take the average of every variable per species
my_group <- group_by(iris, Species)
iris_mean <- summarise_all(my_group, mean)
iris_mean## # A tibble: 3 x 5
## Species Sepal.Length Sepal.Width Petal.Length Petal.Width
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 setosa 5.01 3.43 1.46 0.246
## 2 versicolor 5.94 2.77 4.26 1.33
## 3 virginica 6.59 2.97 5.55 2.03# Now we melt the table
iris_melt <- melt(iris_mean, id = "Species")
iris_melt## Species variable value
## 1 setosa Sepal.Length 5.006
## 2 versicolor Sepal.Length 5.936
## 3 virginica Sepal.Length 6.588
## 4 setosa Sepal.Width 3.428
## 5 versicolor Sepal.Width 2.770
## 6 virginica Sepal.Width 2.974
## 7 setosa Petal.Length 1.462
## 8 versicolor Petal.Length 4.260
## 9 virginica Petal.Length 5.552
## 10 setosa Petal.Width 0.246
## 11 versicolor Petal.Width 1.326
## 12 virginica Petal.Width 2.0265 Making a line plot for every characteristic
ggplot(data = iris_melt) +
geom_line(aes(x = Species, y = value, colour = variable, group = variable))
6 Conclusion
In this tutorial we have learnt how to make line plots using categorical data. It is important to prepare the dataset properly and to use the group function for drawing the proper line.