You will learn how to use the highcharter R package for creating **interactive plots** from the results of advanced statistical methods, such as:

- Network analysis
- Survival models
- Principal component analysis
- Correlation matrix
- Distance matrix

Contents:

## Prerequisites

```
# Load required R packages
library(tidyverse)
library(highcharter)
# Set highcharter options
options(highcharter.theme = hc_theme_smpl(tooltip = list(valueDecimals = 2)))
```

## Network analysis

```
library(igraph)
N <- 40
net <- sample_gnp(N, p = 2 / N)
wc <- cluster_walktrap(net)
V(net)$label <- seq(N)
V(net)$name <- paste("I'm #", seq(N))
V(net)$page_rank <- round(page.rank(net)$vector, 2)
V(net)$betweenness <- round(betweenness(net), 2)
V(net)$degree <- degree(net)
V(net)$size <- V(net)$degree
V(net)$comm <- membership(wc)
V(net)$color <- colorize(membership(wc))
hc <- hchart(net, layout = layout_with_fr)
```

`hc`

## Survival models

```
library(survival)
data(lung)
lung <- mutate(lung, sex = ifelse(sex == 1, "Male", "Female"))
fit <- survfit(Surv(time, status) ~ sex, data = lung)
hc <- hchart(fit, ranges = TRUE)
```

`hc`

## Principal component analysis

`hc <- hchart(princomp(USArrests, cor = TRUE))`

`hc`

## Distance matrix

```
mtcars2 <- mtcars[1:20, ]
x <- dist(mtcars2)
hc <- hchart(x)
```

`hc`

## Correlation matrix

`hc <- hchart(cor(mtcars))`

`hc`

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