# When to Apply approxfun vs. approx in R (2 Examples)

This tutorial shows how to use the approx and approxfun interpolation functions in R.

## Example Data

```vec_a <- 1:3 # Create example vectors vec_a # [1] 1 2 3```
```vec_b <- c(1, 2, 10) vec_b # [1] 1 2 10```

## Example 1: Linearly Interpolate Given Data Points Using approx() Function

```approx_out <- approx(vec_a, # Using approx() function vec_b) approx_out # \$x # [1] 1.000000 1.040816 1.081633 1.122449 1.163265 1.204082 1.244898 1.285714 # [9] 1.326531 1.367347 1.408163 1.448980 1.489796 1.530612 1.571429 1.612245 # [17] 1.653061 1.693878 1.734694 1.775510 1.816327 1.857143 1.897959 1.938776 # [25] 1.979592 2.020408 2.061224 2.102041 2.142857 2.183673 2.224490 2.265306 # [33] 2.306122 2.346939 2.387755 2.428571 2.469388 2.510204 2.551020 2.591837 # [41] 2.632653 2.673469 2.714286 2.755102 2.795918 2.836735 2.877551 2.918367 # [49] 2.959184 3.000000 # # \$y # [1] 1.000000 1.040816 1.081633 1.122449 1.163265 1.204082 1.244898 # [8] 1.285714 1.326531 1.367347 1.408163 1.448980 1.489796 1.530612 # [15] 1.571429 1.612245 1.653061 1.693878 1.734694 1.775510 1.816327 # [22] 1.857143 1.897959 1.938776 1.979592 2.163265 2.489796 2.816327 # [29] 3.142857 3.469388 3.795918 4.122449 4.448980 4.775510 5.102041 # [36] 5.428571 5.755102 6.081633 6.408163 6.734694 7.061224 7.387755 # [43] 7.714286 8.040816 8.367347 8.693878 9.020408 9.346939 9.673469 # [50] 10.000000```
```plot(approx_out\$x, # Create plot of approx output approx_out\$y)```

## Example 2: Create Own Function for Interpolation Using approxfun()

```manual_approxfun <- approxfun(vec_a, # Using approxfun() function vec_b)```
```plot(vec_a, vec_b) # Create plot of approxfun curve(manual_approxfun, add = TRUE)```