- 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

# stat_summary for Statistical Summary in ggplot2 R

`stat_summary`

is a unique statistical function and allows a lot of flexibility in terms of specifying the summary. Using this, you can add a variety of summary on your plots. For example, in a bar chart, you can plot the bars based on a summary statistic such as mean or median. Similarly, `stat_summary()`

can be used to add mean/median points to a dot plot.

`stat_summary()`

takes a few different arguments.

`fun.y`

: A function to produce y aesthetics`fun.ymax`

: A function to produce ymax aesthetics`fun.ymin`

: A function to produce ymin aesthetics`fun.data`

: A function to produce a named vector of aesthetics

We can pass a function to each of these arguments, and `ggplot2`

will use the value returned by that function for the corresponding aesthetic. If you pass a function to fun.data, you can compute many summary statistics and return them as a vector, where each element in the vector is named for the aesthetic it should be used for.

Let's understand this with two examples:

### Bar Chart with Median Values

We will use the `stock_prices.tidy`

dataframe we created earlier to plot a bar chart with the stock symbols on the x-axis and the median stock price for each stock on y-axis. We can achieve this using the `stat_summary()`

function as follows:

```
ggplot(stock_prices.tidy,aes(x=Symbol,y=Prices,fill=Symbol))+
stat_summary(fun.y = median, geom = "bar")
```

### Quartile Points

Following is another example where we plot quartile points for each stock. We first create a new function to calculate the quartile and then supply that function as argument to `fun.data`

in `stat_summary()`

.

```
median.quartile <- function(x){
out <- quantile(x, probs = c(0.25,0.5,0.75))
names(out) <- c("ymin","y","ymax")
return(out)
}
ggplot(stock_prices.tidy, aes(x=Symbol,y=Prices,col=Symbol)) +
stat_summary(fun.data = median.quartile, geom = "pointrange")
```

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