# Radar Chart in R with ggradar

A radar chart, additionally called a spider plot is used to visualize the values over more than one quantitative variable.

A radar chart is an informative graphical tool in which multiple variables and compared on a two-dimensional space.

In this tutorial we are going to describes how to create a radar chart in R, using ggradar R packages.

Is it possible to compare multiple variables in one place instead of a table?”

Web, polar, star, radar, or spider charts, these diagrams help us to convert complex piece of information in a simpler manner.

apply family in r apply(), lapply(), sapply(), mapply() and tapply() »

In this article you will be familiar how to create spider graphs in R.

Basically, a spider chart can be used in any situation when you need to compare multivariable information in a 2D plane.

## Elements

A spider plot is easy to interpret and it contains following elements.

**Center point: **Core of a spider chart from which different axes are drawn.

**Axis:** Each axis indicates a variable

**Grids: **When axes are connected in a spider chart, it divides the complete graph into distinct grids that assist us to constitute facts in a higher way.

**Values: **Once the graph is drawn, we constitute numerous values on every axis and plot the chart for each access through allocating extraordinary colors.

Principal component analysis (PCA) in R »

### Getting Data

data<- data.frame(

row.names = c("A", "B", "C"),

Thickness = c(7.9, 3.9, 9.4),

Apperance = c(10, 7, 5),

Spredability = c(3.7, 6, 2.5),

Likeability = c(8.7, 6, 4)

)

data

In this data frame contains 3 observations with 4 variables.

summarize in r, Data Summarization In R »

Thickness Apperance Spredability Likeability A 7.9 10 3.7 8.7 B 3.9 7 6.0 6.0 C 9.4 5 2.5 4.0

Let’s Load basic packages in R,

library(tidyverse) #devtools::install_github("ricardo-bion/ggradar") library("ggradar") df<-data %>% rownames_to_column("group") df

Output:-

Cluster Meaning-Cluster or area sampling in a nutshell »

group Thickness Apperance Spredability Likeability 1 A 7.9 10 3.7 8.7 2 B 3.9 7 6.0 6.0 3 C 9.4 5 2.5 4.0

ggradar( df, values.radar = c("0", "5", "10"), grid.min = 0, grid.mid = 5, grid.max = 10, # Polygons group.line.width = 1, group.point.size = 3, group.colours = c("#00AFBB", "#E7B800", "#FC4E07"), background.circle.colour = "white", gridline.mid.colour = "grey", legend.position = "bottom" )

KNN Algorithm Machine Learning » Classification & Regression »