- Overview of Data Visualization
- When to Use Bar Chart, Column Chart, and Area Chart
- What is Line Chart and When to Use It
- What are Pie Chart and Donut Chart and When to Use Them
- How to Read Scatter Chart and Bubble Chart
- What is a Box Plot and How to Read It
- Understanding Japanese Candlestick Charts and OHLC Charts
- Understanding Treemap, Heatmap and Other Map Charts
- Visualization in Data Science
- Graphic Systems in R
- Accessing Built-in Datasets in R
- How to Create a Scatter Plot in R
- Create a Scatter Plot in R with Multiple Groups
- Creating a Bar Chart in R
- Creating a Line Chart in R
- Plotting Multiple Datasets on One Chart in R
- Adding Details and Features to R Plots
- Introduction to ggplot2
- Grammar of Graphics in ggplot
- Data Import and Basic Manipulation in R - German Credit Dataset
- Create ggplot Graph with German Credit Data in R
- Splitting Plots with Facets in ggplots
- ggplot2 - Chart Aesthetics and Position Adjustments in R
- Creating a Line Chart in ggplot 2 in R
- Add a Statistical Layer on Line Chart in ggplot2
- stat_summary for Statistical Summary in ggplot2 R
- Facets for ggplot2 Charts in R (Faceting Layer)
- Coordinates in ggplot2 in R
- Changing Themes (Look and Feel) in ggplot2 in R

# Splitting Plots with Facets in ggplots

Faceting refers to splitting the data into multiple subsets and then displaying plots for the specific subsets in a panel. Such plots are also called small-multiple plots. The facet approach partitions a plot into a matrix of panels. Each panel shows a different subset of the data.

In our scatter plot we grouped the data into bad and good loans using colors. We can use the faceting concept to split the entire graph into two separate panels one for each, good and bad loans. To do so we will use the `facet_grid(. ~ ___)`

layer. `facet_grid`

forms a matrix of panels defined by row and column faceting variables. `facet_grid(x ~ y)`

will display x*y plots even if some plots are empty.

```
g <- ggplot(df,aes(x=Duration.of.Credit..in.months.,y=Credit.amount))
g+geom_point()+geom_smooth(se=FALSE)+
facet_grid(. ~ Loan.Quality)
```

We split our data only by columns. That's why we specify the rows by a simple. The columns are split by Loan.Quality. Since this variable has only two values, the graph will be split into two columns.

Faceting can be specially useful when we want to bring another variable into our plot. For example in this case, we can now add the Job variable to see how the type of job of the borrower impacts the credit.

```
g <- ggplot(df,aes(x=Duration.of.Credit..in.months.,y=Credit.amount, color=Job))
g+geom_point()+geom_smooth(se=FALSE,method='lm')+
facet_grid(. ~ Loan.Quality)
```

`ggplot2`

is beautiful and to slice the data in a different way is extremely easy. In the following graph, we exchange the two parameters `Loan.Quality`

and `Job`

.

```
g <- ggplot(df,aes(x=Duration.of.Credit..in.months.,y=Credit.amount, color=Loan.Quality))
g+geom_point()+geom_smooth(se=FALSE,method='lm')+
facet_grid(. ~ Job)
```

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