Here are some plots

today we’re making interactive plots in plotly. we’ll make examples using the NYC airbnb dataset.

library(tidyverse)
library(p8105.datasets)

library(plotly)

Do some initial data cleaning / subsetting.

data("nyc_airbnb")

nyc_airbnb = 
  nyc_airbnb |> 
  mutate(rating = review_scores_location / 2) |> 
  select(
    rating, neighbourhood_group, neighbourhood, 
    room_type, lat, long, price) |> 
  drop_na(rating) |> 
  filter(
    neighbourhood_group == "Manhattan", 
    room_type == "Entire home/apt", 
    price %in% 100:500
  )

Use plotly to make some quick plots.

First a scatterplot.

nyc_airbnb |> 
  mutate(
    text_label = 
      str_c("Price: ", price, "\nNeighborhood: ", neighbourhood)) |> 
  plot_ly(
    x = ~lat, y = ~long, color = ~price, text = ~text_label,
    type = "scatter", mode = "markers", alpha = 0.5
  )

Next up – box plot.

nyc_airbnb |> 
  mutate(neighbourhood = fct_reorder(neighbourhood, price)) |> 
  plot_ly(
    x = ~neighbourhood, y = ~price, color = ~neighbourhood,
    type = "box", colors = "viridis")

Let’s do a bar chart with number of rentals.

nyc_airbnb |> 
  count(neighbourhood) |> 
  mutate(neighbourhood = fct_reorder(neighbourhood, n)) |> 
  plot_ly(
    x = ~neighbourhood, y = ~n, 
    type = "bar")

plot_ly(
  z = volcano, type = "heatmap"
)

Here’s a chloropleth

plot_ly(
  type = "choropleth",
  locations = c( "AZ", "CA", "VT" ) , 
  locationmode = "USA-states" , 
  colorscale = "Viridis" ,
  z = c( 10, 20, 40 )) %>%
  layout ( geo = list ( scope = "usa" ))

Plotly

Here are some plots