Understanding Plotly

MATH/COSC 3570 Spring 2023

R

Tables in R

fig <- plot_ly(
  type = 'table',
  columnwidth = c(100, 100),
  columnorder = c(0, 1),
  header = list(
    values = c("Name","Grade"),
    align = c("center", "center"),
    line = list(width = 1, color = 'black'),
    fill = list(color = c("lightblue", "lightblue")),
    font = list(family = "Arial", size = 14, color = "white")
  ),
  cells = list(
    values = rbind(
      c("Steve", "Carol", "Bob", "Tina", "Clay"),
      c("97", "34", "73", "52", "88")
    ),
    align = c("center", "center"),
    line = list(color = "black", width = 1),
    font = list(family = "Arial", size = 12, color = c("black"))
  ))

fig

Pie Charts in R

labels = c('Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen')
values = c(4500, 2500, 1053, 500)
fig <- plot_ly(type='pie', labels=labels, values=values, 
               textinfo='label+percent',
               insidetextorientation='radial')
fig

##ScatterPlots in R

fig <- plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length)
fig

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plotly.com/r/reference/#scatter

No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

Bubblegraphs in R

##Bubble
plot_ly() |>
  add_markers(data = mtcars,
              x = ~mpg,
              y = ~hp,
              hoverinfo = 'text',
              text = ~paste('Car Model:', rownames(mtcars)),
              marker = list(size = ~2*cyl, opacity = 0.5, color = 'purple')) |>
  layout(title = 'Horsepower Vs. MPG',
         xaxis = list(title = 'Miles Per Gallon'),
         yaxis = list(title = 'Horsepower'))

Sunburst Graphs in R

##Sunburst
library(plotly)

labels <- c("Andrew", "Alex", "Sophia", "Seth", "Max", "Lisa", "Mark", "Louise",
            "Kevin", "Martha", "Jane", "Abby")
parents <- c("", "Andrew", "Andrew", 'Andrew', "Andrew", "Andrew", "Alex", "Alex",
             "Sophia", "Seth", "Max", "Seth")
values <- c(20,34,25,20,8, 13, 17, 7, 25, 10, 20, 8)

fig <- plot_ly(
  labels = labels,
  parents = parents,
  values = values,
  type = "sunburst"
)

fig

3D Surface Plot in R

library(plotly)
# volcano is a numeric matrix that ships with R
fig <- plot_ly(z = ~volcano)
fig <- fig %>% add_surface()
fig

Map Graph in R

##Map
g <- list(showland = TRUE,
          showcountries = TRUE,
          countrycolor = "orange",
          landcolor = "#e5ecf6",
          projection = list(type = "natural earth"))
fig <- plot_ly(type = 'scattergeo', mode = 'markers')
fig <- fig |> layout(geo = g)
fig

Python

Tables in Python

import pandas as pd
import plotly.graph_objects as go

data = pd.read_csv('./data/mtcars.csv')
data = data.head(7)
data = data.filter(items = ['mpg', 'cyl', 'disp', 'hp'])


fig1 = go.Figure(data=[go.Table(
  header = dict(values = list(data.columns),
    fill_color = 'paleturquoise',
    align = 'left'),
  cells = dict(values=[data.mpg, data.cyl, data.disp, data.hp],
  fill_color = 'lavender',
  align = 'left'))
  ])
fig1

Pie Charts in Python

import plotly.graph_objects as go

labels = ['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen']
values = [4500, 2500, 1053, 500]

fig = go.Figure(data=[go.Pie(labels=labels, values=values)])
fig.show()

##ScatterPlots in Python

import plotly.express as px
df = px.data.iris() 
fig = px.scatter(df, x="sepal_width", y="sepal_length")
fig.show()

Bubblegraphs in Python

import plotly.graph_objects as go
import pandas as pd


cars = pd.read_csv('./data/mtcars.csv')
size = 0.5*cars['cyl']

bubble2 = go.Figure(data=[go.Scatter(
    x=cars['mpg'],
    y=cars['hp'],
    mode='markers',
    marker=dict(
        size=size,
        sizemode='area',
        sizeref=2.*max(size)/(40.**2),
        sizemin=4,
        opacity = 0.5
    )
)])
bubble2

Sunburst Graphs in Python

import plotly.express as px

data = dict(
  character=["Andrew", "Alex", "Sophia", "Seth", "Max", "Lisa", "Mark", "Louise",
             "Kevin", "Martha", "Jane", "Abby"],
  parent=["", "Andrew", "Andrew", 'Andrew', "Andrew", "Andrew", "Alex", "Alex",
          "Sophia", "Seth", "Max", "Seth"],
  value=[20,34,25,20,8, 13, 17, 7, 25, 10, 20, 8])

fig = px.sunburst(
  data,
  names='character',
  parents='parent',
  values='value',
)
fig

3d Surface Plot in Python

import plotly.graph_objects as go
import pandas as pd

# Read data from a csv
z_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv')

fig = go.Figure(data=[go.Surface(z=z_data.values)])

fig.update_layout(title='Mt Bruno Elevation', autosize=False,
                  width=500, height=500,
                  margin=dict(l=65, r=50, b=65, t=90))

Map Graph in Python

import pandas as pd
import plotly.express as px
import plotly.graph_objects as go

world = go.Figure(go.Scattergeo())
world.update_geos(
  projection_type = "orthographic",
  visible = True, resolution = 110, scope = "world",
  showcountries = True, countrycolor = "Black",
  coastlinecolor = "Black", showcoastlines = True
)

world.update_layout(height = 300, margin = {"r":0, "t":0, "l":0,"b":0})