plotly examples 2

Some more plotly examples

Today we are going to discuss a number of additional examples of using plotly.

To get started, plotly uses htmlwidgets. Take a look at the htmlwidgets website and give a few examples a try.

plotly uses htmlwidgets. So we should become familiar with them.

Example 1:

Using leaflet to make maps.

library(leaflet)
m <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=174.768, lat=-36.852, popup="The birthplace of R")
m  # Print the map

Go to google and ask for the location of CSU East Bay. Search for “CSU East Bay lat and long”

Answer: 37.6563° N, 122.0567° W

First try. What goes google give us?

m <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=37.6563 , lat=122.0567, popup="CSU East Bay?")
m  # Print the map

That is totally wrong.

m <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=122.0567, lat=37.6563, popup="CSU East Bay?")
m  # Print the map

Still wrong, but at least we are on the planet now.

m <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=-122.0567, lat=37.6563, popup="CSU East Bay")
m  # Print the map

Here is a link to the google developer page about lat and long. Key thing is lat comes before long.

m <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lat=37.6563, lng=-122.0567, popup="CSU East Bay")
m  # Print the map

Here is the link to Google Maps API.

Finally here is OpenStreetMap. This is where the maps come from for leaflet. Can you use googlemaps?

Example 2:

plotly is given as a example.

library(ggplot2)
library(plotly)
p <- ggplot(data = diamonds, aes(x = cut, fill = clarity)) +
            geom_bar(position = "dodge")
ggplotly(p)

d <- diamonds[sample(nrow(diamonds), 500), ]
plot_ly(d, x = d$carat, y = d$price, 
        text = paste("Clarity: ", d$clarity),
        mode = "markers", color = d$carat, size = d$carat)

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

Example 3.

The python ploting library rbokeh.

library(rbokeh)
figure() %>%
  ly_points(Sepal.Length, Sepal.Width, data = iris,
    color = Species, glyph = Species,
    hover = list(Sepal.Length, Sepal.Width))

Example 4:

visNetwork.

library(networkD3)
data(MisLinks, MisNodes)
forceNetwork(Links = MisLinks, Nodes = MisNodes, Source = "source",
             Target = "target", Value = "value", NodeID = "name",
             Group = "group", opacity = 0.4)

Example 5:

Making tables DT.

library(DT)
datatable(iris, options = list(pageLength = 5))

Example 6:

One more thing, flexdashboards which used shiny. Here is a one example geoms dash.

Back to plotly.

Example.

A case study of housing sales in Texas. See this page in the book.

This is a very nice case study. You should read the chapter and try all of the plots.

library(plotly)
txhousing

p <- ggplot(txhousing, aes(date, median)) +
  geom_line(aes(group = city), alpha = 0.2)
p

subplot(
  p, ggplotly(p, tooltip = "city"), 
  ggplot(txhousing, aes(date, median)) + geom_bin2d(),
  ggplot(txhousing, aes(date, median)) + geom_hex(),
  nrows = 2, shareX = TRUE, shareY = TRUE,
  titleY = FALSE, titleX = FALSE
)

Removed 616 rows containing non-finite values (stat_bin2d).Removed 616 rows containing non-finite values (stat_binhex).

library(dplyr)
tx <- group_by(txhousing, city)
# initiate a plotly object with date on x and median on y
p <- plot_ly(tx, x = ~date, y = ~median)
# plotly_data() returns data associated with a plotly object
plotly_data(p)

p

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
Ignoring 616 observationsNo trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
Ignoring 616 observations

# add a line highlighting houston
add_lines(
  # plots one line per city since p knows city is a grouping variable
  add_lines(p, alpha = 0.2, name = "Texan Cities", hoverinfo = "none"),
  name = "Houston", data = filter(txhousing, city == "Houston")
)

data-plot-pipeline

allCities <- txhousing %>%
  group_by(city) %>%
  plot_ly(x = ~date, y = ~median) %>%
  add_lines(alpha = 0.2, name = "Texan Cities", hoverinfo = "none")
allCities %>%
  filter(city == "Houston") %>%
  add_lines(name = "Houston")

allCities %>%
  add_fun(function(plot) {
    plot %>% filter(city == "Houston") %>% add_lines(name = "Houston")
  }) %>%
  add_fun(function(plot) {
    plot %>% filter(city == "San Antonio") %>% 
      add_lines(name = "San Antonio")
  })

Using a function.

# reusable function for highlighting a particular city
layer_city <- function(plot, name) {
  plot %>% filter(city == name) %>% add_lines(name = name)
}
# reusable function for plotting overall median & IQR
layer_iqr <- function(plot) {
  plot %>%
    group_by(date) %>% 
    summarise(
      q1 = quantile(median, 0.25, na.rm = TRUE),
      m = median(median, na.rm = TRUE),
      q3 = quantile(median, 0.75, na.rm = TRUE)
    ) %>%
    add_lines(y = ~m, name = "median", color = I("black")) %>%
    add_ribbons(ymin = ~q1, ymax = ~q3, name = "IQR", color = I("black"))
}
allCities %>%
  add_fun(layer_iqr) %>%
  add_fun(layer_city, "Houston") %>%
  add_fun(layer_city, "San Antonio")

library(forecast)
layer_forecast <- function(plot) {
  d <- plotly_data(plot)
  series <- with(d, 
    ts(median, frequency = 12, start = c(2000, 1), end = c(2015, 7))
  )
  fore <- forecast(ets(series), h = 48, level = c(80, 95))
  plot %>%
    add_ribbons(x = time(fore$mean), ymin = fore$lower[, 2],
                ymax = fore$upper[, 2], color = I("gray95"), 
                name = "95% confidence", inherit = FALSE) %>%
    add_ribbons(x = time(fore$mean), ymin = fore$lower[, 1],
                ymax = fore$upper[, 1], color = I("gray80"), 
                name = "80% confidence", inherit = FALSE) %>%
    add_lines(x = time(fore$mean), y = fore$mean, color = I("blue"), 
              name = "prediction")
}
txhousing %>%
  group_by(city) %>%
  plot_ly(x = ~date, y = ~median) %>%
  add_lines(alpha = 0.2, name = "Texan Cities", hoverinfo = "none") %>%
  add_fun(layer_iqr) %>%
  add_fun(layer_forecast)

Click-and-drag

p <- ggplot(fortify(gold), aes(x, y)) + geom_line()
gg <- ggplotly(p)
layout(gg, dragmode = "pan")

rangeslider(gg)

mtcars

p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
   geom_point() + geom_smooth()
p %>%
  ggplotly(layerData = 2, originalData = FALSE) %>%
  plotly_data()

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

p %>%
  ggplotly(layerData = 2, originalData = F) %>%
  add_fun(function(p) {
    p %>% slice(which.max(se)) %>%
      add_segments(x = ~x, xend = ~x, y = ~ymin, yend = ~ymax) %>%
      add_annotations("Maximum uncertainty", ax = 60)
  }) %>%
  add_fun(function(p) {
    p %>% slice(which.min(se)) %>%
      add_segments(x = ~x, xend = ~x, y = ~ymin, yend = ~ymax) %>%
      add_annotations("Minimum uncertainty")
  })

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

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