```
data(diamonds, package = 'ggplot2')
diamonds <- data.frame(diamonds)
head(diamonds)
```

```
## carat cut color clarity depth table price x y z
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
```

`nrow(diamonds)`

`## [1] 53940`

In this example we sample the `diamonds`

data set and pick a subset of 100 individuals using the cLHS method. To reduce the length of the optimisation step to 1000 iterations to save computing time. This is controlled through the `iter`

option. The progress bar is disabled because it doesn’t renders well in the vignette. By default, the index of the selected individuals in the original object are returned.

```
library(clhs)
res <- clhs(diamonds, size = 100, progress = FALSE, iter = 1000)
str(res)
```

`## int [1:100] 49345 34614 27998 35433 38025 50691 30214 53320 48832 24098 ...`

(work in progress)

(work in progress)

```
diamonds$cost <- runif(nrow(diamonds))
res_cost <- clhs(diamonds, size = 100, progress = FALSE, iter = 1000, cost = 'cost')
```

If you want to report on the cLHS results, e.g. plot the evolution of the objective function, or compare the distribution of attributes in the initial object and in the sampled subset, you need to switch the `simple`

option to `FALSE`

. Instead f simply returning a numeric vector giving the index of the sampled individuals in the original object, a specific, more complex will be returned. This object can be handled by a specific `plot`

method:

```
res <- clhs(diamonds, size = 100, simple = FALSE, progress = FALSE, iter = 1000)
plot(res)
```

The default plotting method plots the evolution of the objective function with the number of iterations. However, you can get more details using the `modes`

option, which controls which indicators are plotted. Three `modes`

can be simultaneously plotted:

`obj`

: evolution of the objective function (default)`cost`

: evolution of the cost function (if present)`dens`

OR`box`

OR`hist`

: comparison of the distributions of each attribute in the original object and in the proposed sample, respectively using probability density functions, boxplots or histograms. Note that categorical attributes are always reported using dotplots.

These modes should be given as a vector of characters.

```
res_cost <- clhs(diamonds, size = 100, progress = FALSE, iter = 1000, cost = 'cost', simple = FALSE)
plot(res_cost, c('obj', 'cost'))
```

`plot(res_cost, c('obj', 'cost', 'box'))`