Radar Chart in R, 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)
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,
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 » 