# Draw a trend line using ggplot-Quick Guide

Draw a trend line using ggplot, Technical analysts utilize the trendline as one of their most significant tools. A trendline aids technical analysts in determining the data set direction.

Let’s start by making a data frame.

```data <- data.frame(x=c(1, 2, 5, 3, 5, 5, 9,10,12),
y=c(18, 10, 10, 20, 22, 13, 15,16,17))
data```
```   x  y
1  1 18
2  2 10
3  5 10
4  3 20
5  5 22
6  5 13
7  9 15
8 10 16
9 12 17```

## Approach1: Add Linear Trend Line

In ggplot2, the following code demonstrates how to add a linear trend line to a scatterplot.

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```library(ggplot2)
ggplot(data, aes(x=x, y=y)) +
geom_point() +
geom_smooth(method=lm)``` ## Approach 2: Add Linear Trend Line & Confidence Region

The level option can be used to determine the level of confidence to use for the shaded confidence region in the plot.

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The default level of confidence is 0.95. Our shaded confidence region on the plot became much bigger when we specified a confidence level of 0.99.

```ggplot(data, aes(x=x, y=y)) +
geom_point() +
geom_smooth(method=lm, level=0.99)``` ## Approach 3: Add Linear Trend Line & No-Confidence Region

We can use the se=FALSE argument to hide the shaded confidence region around the trend line

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```ggplot(data, aes(x=x, y=y)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, col='blue', size=2)``` ## Approach 4: Curved loess Trend Line

If we don’t specify a technique for geom smooth(), we’ll get a curved loess line by default

```ggplot(data, aes(x=x, y=y)) +
geom_point() +
geom_smooth()``` Note: Traders can utilize trendlines to identify probable locations of support and resistance, which can assist assess the possibility of the trend continuing.

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