Plotly R Open Source Graphing Library

https://plotly.com/r
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Plotly's R graphing library makes interactive, publication-quality graphs. Examples of how to make...
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### Initialize Pie Trace

Source: https://plotly.com/r/reference

Shows how to initialize a pie trace using plot_ly or add_trace functions.

```r
plot_ly(df, type="pie"[, ...])  
 add_trace(p, type="pie"[, ...])
```

--------------------------------

### Set start position and distance between ticks

Source: https://plotly.com/r/axes

Use `tick0` and `dtick` to define the starting point and interval for axis ticks. This example sets ticks at intervals of 0.5, starting at 0.25 on the y-axis.

```r
library(plotly)
data(iris)

fig <- iris %>%
  group_by(Species) %>%
  do(p=plot_ly(., x = ~Sepal.Width, y = ~Sepal.Length, type = "scatter", opacity = 0.5,
               mode = "markers",
               marker = list(
                 color = "blue"
               ))) %>%
  subplot(nrows = 1, shareX = TRUE, shareY = TRUE)
fig <- fig %>%
  layout(
    annotations = list(
      list(
        x = 0.16,
        y = 1,
        font = list(size = 10),
        text = "species=setosa",
        xref = "paper",
        yref = "paper",
        xanchor = "center",
        yanchor = "bottom",
        showarrow = FALSE
      ),
      list(
        x = 0.5,
        y = 1,
        font = list(size = 10),
        text = "species=versicolor",
        xref = "paper",
        yref = "paper",
        xanchor = "center",
        yanchor = "bottom",
        showarrow = FALSE
      ),
      list(
        x = 0.85,
        y = 1,
        font = list(size = 10),
        text = "species=virginica",
        xref = "paper",
        yref = "paper",
        xanchor = "center",
        yanchor = "bottom",
        showarrow = FALSE
      )))
  %>%layout(plot_bgcolor='#e5ecf6',
                   xaxis = list(
                     zerolinecolor = '#ffff',
                     zerolinewidth = 2,
                     gridcolor = 'ffff'),
                   xaxis2 = list(
                     zerolinecolor = '#ffff',
                     zerolinewidth = 2,
                     gridcolor = 'ffff'),
                   xaxis3 = list(
                     zerolinecolor = '#ffff',
                     zerolinewidth = 2,
                     gridcolor = 'ffff'),
                   yaxis = list(
                                  zerolinecolor = '#ffff',
                                  zerolinewidth = 2,
                                  gridcolor = 'ffff',
                                  tick0=0.25, dtick=0.5)
                   ,showlegend = F)

fig
```

--------------------------------

### 3D Streamtube Plot with Custom Starts and Segments in R

Source: https://plotly.com/r/streamtube-plot

Generate a 3D streamtube plot with specified starting positions and maximum displayed segments. This example uses wind data and customizes the 'starts' parameter to define multiple origins for the streamtubes, and 'maxdisplayed' to control segment density.

```r
library(plotly)

df = read.csv('https://raw.githubusercontent.com/plotly/datasets/master/streamtube-wind.csv')

fig <- df %>%
  plot_ly(
    type = 'streamtube',
    x = ~x,
    y = ~y,
    z = ~z,
    u = ~u,
    v = ~v,
    w = ~w,
    starts = list(
      x = rep(80, 16),
      y = rep(c(20,30,40,50), 4),
      z = c(rep(0,4),rep(5,4),rep(10,4),rep(15,4))
    ),
    sizeref = 0.3,
    showscale = F,
    maxdisplayed = 3000
  ) 
fig <- fig %>%
  layout(
    scene = list(
      aspectratio = list(
        x = 2,
        y = 1,
        z = 0.3
      )
    ),
    margin = list(
      t = 20, b = 20, l = 20, r = 20
    )
  )

fig
```

--------------------------------

### Initialize Violin Trace

Source: https://plotly.com/r/reference

Shows the standard initialization methods for a violin trace using plot_ly or add_trace.

```r
plot_ly(df, type="violin"[, ...])  
 add_trace(p, type="violin"[, ...])
```

--------------------------------

### Create a Heatmap with WebGL Annotations

Source: https://plotly.com/r/webgl-text-and-annotations

This example generates a heatmap and overlays text labels using the scattergl trace type for high-performance rendering. It includes a slider to navigate through the data range.

```R
library(plotly)
library(random)

n=250
t=12
x = c(rep(0:(n-1), times=t))
y = c(rep(0:(t-1), each=n))
z = randomNumbers(n=n*t, min=1, max=10, col=n)
text = c(replicate(t*n, sample(c("A","T","G","C"), 1)))

steps = list()
for (e in c(0:(n-30))){
  step <-  list(
    list(
      args = list('xaxis.range', c(-0.5 + e, 30.5 + e)),
      method = "relayout",
      label = e,
      value = e
    )
  )
  steps[e] <- step
}

fig <- plot_ly() 
fig <- fig %>%
  add_trace(
    type='heatmap',
    z = z
  ) 
fig <- fig %>%
  add_trace(
    mode = "text",
    text = text,
    type = "scattergl",
    textfont = list(
      size = 20
    ),
    x = x,
    y = y
  ) 
fig <- fig %>%
  layout(
    xaxis = list(
      range = c(-0.5, 30.5),
      showline = F,
      zeroline = F,
      showgrid = F
    ),
    yaxis = list(
      showline = F,
      zeroline = F,
      showgrid = F
    ),
    sliders=list(
      list(
        active = 0,
        steps = steps
      )
    )
  )

fig
```

--------------------------------

### Exporting a Plotly graph as an SVG using orca()

Source: https://plotly.com/r/static-image-export

This example demonstrates how to export a surface plot to an SVG file. It requires the processx package to be installed.

```R
library(plotly)

if (!require("processx")) install.packages("processx")
fig <- plot_ly(z = ~volcano) %>% add_surface()
orca(fig, "surface-plot.svg")
```

--------------------------------

### Distplot with Normal Curve

Source: https://plotly.com/r/distplot

This example shows how to plot a distplot with overlaid normal distribution curves for different groups. It utilizes `ggplot2` for the base plot and `plotly` for interactive visualization. Ensure the `ggplot2` and `plotly` libraries are installed.

```r
library(ggplot2)
library(plotly)
set.seed(1)
x1 <- rnorm(200, mean = 0, sd = 1)
x2 <- rnorm(200, mean = 0, sd = 1) + 2

group_labels = c('Group 1', 'Group 2')

df1 <- data.frame(x1, group_labels[1])
colnames(df1) <- c('x', 'Group')
df2 <- data.frame(x2, group_labels[2])
colnames(df2) <- c('x', 'Group')

df <- rbind(df1,df2)
colnames(df) <- c('x', 'Group')

gg <- ggplot(data = df , aes(x=x)) + 
  geom_histogram(aes(y = ..density.., fill=Group),bins = 30, alpha = 0.7)+
  geom_density(aes(color=Group))+ 
  geom_rug(aes(color=Group))+ 
  labs(x = '',
       y = '',
       title = 'Distplot with Normal Distribution')

ggplotly(gg)%>% 
  layout(plot_bgcolor='#e5ecf6',
             xaxis = list(
               title='Time',
               zerolinecolor = '#ffff',
               zerolinewidth = 2,
               gridcolor = 'ffff'),
             yaxis = list(
               title='Value A',
               zerolinecolor = '#ffff',
               zerolinewidth = 2,
               gridcolor = 'ffff'))
```

--------------------------------

### Initialize Parcats Trace

Source: https://plotly.com/r/reference

Shows how to initialize a parallel categories diagram using plot_ly or add_trace.

```r
plot_ly(df, type="parcats"[, ...])  
 add_trace(p, type="parcats"[, ...])
```

--------------------------------

### Install Plotly R Package from GitHub

Source: https://plotly.com/r/getting-started

Install the latest development version of the plotly R package from GitHub using the devtools package. Ensure you have devtools installed.

```r
devtools::install_github("ropensci/plotly")
```
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