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# install the new/experimental plotly R package
# devtools::install_github("ropensci/plotly@carson-dsl")
# ----------------------------------------------------------------------
# https://plot.ly/r/3d-line-plots/
# ----------------------------------------------------------------------
library(plotly)
# initiate a 100 x 3 matrix filled with zeros
m <- matrix(numeric(300), ncol = 3)
# simulate a 3D random-walk
for (i in 2:100) m[i, ] <- m[i-1, ] + rnorm(3)
# collect everything in a data-frame
df <- setNames(
data.frame(m, seq(1, 100)),
c("x", "y", "z", "time")
)
# create the plotly
plot_ly(df, x = x, y = y, z = z, color = time, type = "scatter3d")
# ----------------------------------------------------------------------
# https://plot.ly/r/3d-scatter-plots/
# ----------------------------------------------------------------------
library(plotly)
# variance-covariance matrix for a multivariate normal distribution
s <- matrix(c(1, .5, .5,
.5, 1, .5,
.5, .5, 1), ncol = 3)
# use the mvtnorm package to sample 200 observations
obs <- mvtnorm::rmvnorm(200, sigma = s)
# collect everything in a data-frame
df <- setNames(data.frame(obs), c("x", "y", "z"))
plot_ly(df, x = x, y = y, z = z, type = "scatter3d", mode = "markers")
# ----------------------------------------------------------------------
# https://plot.ly/r/3d-surface-plots/
# ----------------------------------------------------------------------
library(plotly)
# Note that volcano is a numeric matrix that ships with R
plot_ly(z = volcano, type = "surface")
# 2D kernel density estimation
kd <- with(geyser, MASS::kde2d(duration, waiting, n = 50))
with(kd, plot_ly(x = x, y = y, z = z, type = "surface"))
# ----------------------------------------------------------------------
# https://plot.ly/r/filled-area-plots/
# ----------------------------------------------------------------------
library(plotly)
p <- plot_ly(x = c(1, 2, 3, 4), y = c(0, 2, 3, 5), fill = "tozeroy")
add_trace(p, x = c(1, 2, 3, 4), y = c(3, 5, 1, 7), fill = "tonexty")
# ----------------------------------------------------------------------
# https://plot.ly/r/bar-charts/
# ----------------------------------------------------------------------
library(plotly)
p <- plot_ly(
x = c("giraffes", "orangutans", "monkeys"),
y = c(20, 14, 23),
name = "SF Zoo",
type = "bar"
)
p
p2 <- add_trace(p,
x = c("giraffes", "orangutans", "monkeys"),
y = c(12, 18, 29),
name = "LA Zoo"
)
p2
layout(p2, barmode = "stack")
## customizing colors
library(dplyr)
ggplot2::diamonds %>% count(cut) %>%
plot_ly(x = cut, y = n, type = "bar", marker = list(color = toRGB("black")))
# mapping a color variable
ggplot2::diamonds %>% count(cut, clarity) %>%
plot_ly(x = cut, y = n, type = "bar", color = clarity)
# ----------------------------------------------------------------------
# https://plot.ly/r/box-plots/
# ----------------------------------------------------------------------
library(plotly)
#' basic boxplot
plot_ly(y = rnorm(50), type = "box") %>%
add_trace(y = rnorm(50, 1))
#' adding jittered points
plot_ly(y = rnorm(50), type = "box", boxpoints = "all", jitter = 0.3,
pointpos = -1.8)
#' several box plots
data(diamonds, package = "ggplot2")
plot_ly(diamonds, y = price, color = cut, type = "box")
#' grouped box plots
plot_ly(diamonds, x = cut, y = price, color = clarity, type = "box") %>%
layout(boxmode = "group")
# ----------------------------------------------------------------------
# https://plot.ly/r/bubble-charts/
# ----------------------------------------------------------------------
# why do we need a separate page from this?? -> https://plot.ly/r/line-and-scatter/
d <- diamonds[sample(nrow(diamonds), 1000), ]
plot_ly(d, x = carat, y = price, text = paste("Clarity: ", clarity),
mode = "markers", marker = list(size = depth))
# TODO: automatic scaling for marker size/opacity
# ----------------------------------------------------------------------
# https://plot.ly/r/contour-plots/
# ----------------------------------------------------------------------
#' Basic contour
library(plotly)
plot_ly(z = volcano, type = "contour")
#' Advanced
x <- rnorm(200)
y <- rnorm(200)
p1 <- plot_ly(x = x, type = "histogram")
p2 <- plot_ly(x = x, y = y, type = "histogram2dcontour")
p3 <- plot_ly(y = y, type = "histogram")
a1 <- list(domain = c(0, .85))
a2 <- list(domain = c(.85, 1))
subplot(
layout(p1, xaxis = a1, yaxis = a2),
layout(p2, xaxis = a1, yaxis = a1),
layout(p3, xaxis = a2, yaxis = a1)
)
#TODO: fix this -> https://plot.ly/~botty/2038
# ----------------------------------------------------------------------
# https://plot.ly/r/error-bars/
# ----------------------------------------------------------------------
library(dplyr)
library(plotly)
p <- ggplot2::mpg %>% group_by(class) %>%
summarise(mn = mean(hwy), sd = 1.96 * sd(hwy)) %>%
arrange(desc(mn)) %>%
plot_ly(x = class, y = mn, error_y = list(value = sd),
mode = "markers", name = "Highway") %>%
layout(yaxis = list(title = "Miles Per Gallon"))
p
df2 <- mpg %>% group_by(class) %>%
summarise(mn = mean(cty), sd = 1.96 * sd(cty))
add_trace(p, y = mn, error_y = list(value = sd),
name = "City", data = df2)
# ----------------------------------------------------------------------
# https://plot.ly/r/heatmaps/
# ----------------------------------------------------------------------
library(plotly)
plot_ly(z = volcano, type = "heatmap")
#' categorical x/y axis
m <- matrix(rnorm(9), nrow = 3, ncol = 3)
plot_ly(z = m, x = c("a", "b", "c"), y = c("d", "e", "f"), type = "heatmap")
#' Sequential Colorscales (Hot)
plot_ly(z = volcano, colorscale = "Hot", type = "heatmap")
#' Sequential Colorscales (Greys)
plot_ly(z = volcano, colorscale = "Greys", type = "heatmap")
#' Sequential Colorscales (Greens)
plot_ly(z = volcano, colorscale = "Greens", type = "heatmap")
#' Custom colorscale via scales package
vals <- unique(scales::rescale(c(volcano)))
o <- order(vals, decreasing = FALSE)
cols <- scales::col_numeric("Blues", domain = NULL)(vals)
colz <- setNames(data.frame(vals[o], cols[o]), NULL)
plot_ly(z = volcano, colorscale = colz, type = "heatmap")
# ----------------------------------------------------------------------
# https://plot.ly/r/2D-Histogram/
# ----------------------------------------------------------------------
library(plotly)
s <- matrix(c(1, -.75, -.75, 1), ncol = 2)
obs <- mvtnorm::rmvnorm(500, sigma = s)
plot_ly(x = obs[,1], y = obs[,2], type = "histogram2d")
# ----------------------------------------------------------------------
# https://plot.ly/r/histograms/
# ----------------------------------------------------------------------
#' Basic histogram
plot_ly(x = rnorm(50), type = "histogram")
#' Vertical histogram
plot_ly(y = rnorm(50), type = "histogram")
#' Overlayed histograms
plot_ly(x = rnorm(500), opacity = 0.6, type = "histogram") %>%
add_trace(x = rnorm(500))
# ----------------------------------------------------------------------
# https://plot.ly/r/line-and-scatter/
# ----------------------------------------------------------------------
#' Simple scatterplot
plot_ly(data = iris, x = Sepal.Length, y = Petal.Length, mode = "markers")
#' Scatterplot with qualitative colorscale
plot_ly(data = iris, x = Sepal.Length, y = Petal.Length, color = Species, mode = "markers")
#' Scatterplot with sequential colorscale
plot_ly(data = iris, x = Sepal.Length, y = Petal.Length, color = Petal.Width, mode = "markers")
#' Scatterplot with custom colorscale (TODO: how to add legend entries?)
pal <- RColorBrewer::brewer.pal(3, "Set1")
names(pal) <- levels(iris$Species)
cols <- as.character(pal[iris$Species])
plot_ly(data = iris, x = Sepal.Length, y = Petal.Length, marker = list(color = cols),
mode = "markers")
#' Basic time-series (line) plot with loess smooth
plot_ly(economics, x = date, y = uempmed, name = "unemployment")
add_trace(y = fitted(loess(uempmed ~ as.numeric(date))))
#' Density plot
dens <- with(diamonds, tapply(price, INDEX = cut, density))
df <- data.frame(
x = unlist(lapply(dens, "[[", "x")),
y = unlist(lapply(dens, "[[", "y")),
cut = rep(names(dens), each = length(dens[[1]]$x))
)
plot_ly(df, x = x, y = y, color = cut)
#' Different line interpolation options
x <- 1:5
y <- c(1, 3, 2, 3, 1)
plot_ly(x = x, y = y, name = "linear", line = list(shape = "linear")) %>%
add_trace(y = y + 5, name = "spline", line = list(shape = "spline")) %>%
add_trace(y = y + 10, name = "vhv", line = list(shape = "vhv")) %>%
add_trace(y = y + 15, name = "hvh", line = list(shape = "hvh")) %>%
add_trace(y = y + 20, name = "vh", line = list(shape = "vh")) %>%
add_trace(y = y + 25, name = "hv", line = list(shape = "hv"))
# ----------------------------------------------------------------------
# https://plot.ly/r/log-plot/
# ----------------------------------------------------------------------
d <- diamonds[sample(nrow(diamonds), 1000), ]
#' Without log scales
(p <- plot_ly(d, x = carat, y = price, mode = "markers"))
#' With log scales
layout(p, xaxis = list(type = "log", autorange = T),
yaxis = list(type = "log", autorange = T))
# ----------------------------------------------------------------------
# https://plot.ly/r/graphing-multiple-chart-types/
# ----------------------------------------------------------------------
# necessary?
# ----------------------------------------------------------------------
# https://plot.ly/r/polar-chart/
# ----------------------------------------------------------------------
p <- plot_ly(plotly::mic, r = r, t = t, color = nms, mode = "lines")
layout(p, title = "Mic Patterns", orientation = -90)
p <- plot_ly(plotly::hobbs, r = r, t = t, color = nms, opacity = 0.7, mode = "markers")
layout(p, title = "Hobbs-Pearson Trials", plot_bgcolor = toRGB("grey90"))
p <- plot_ly(plotly::wind, r = r, t = t, color = nms, type = "area")
layout(p, radialaxis = list(ticksuffix = "%"), orientation = 270)
# ----------------------------------------------------------------------
# https://plot.ly/r/time-series/
# ----------------------------------------------------------------------
#' POSIXlt date/time class
now_lt <- as.POSIXlt(Sys.time(), tz = "GMT")
tm <- seq(0, 600, by = 10)
x <- now_lt - tm
y <- rnorm(length(x))
plot_ly(x = x, y = y, text = paste(tm, "seconds from now in GMT"))
#' POSIXct date/time class
now_ct <- as.POSIXct(Sys.time())
tm <- seq(0, 600, by = 10)
x <- now_ct - tm
y <- rnorm(length(x))
plot_ly(x = x, y = y, text = paste(tm, "seconds from now in", Sys.timezone()))
#' Dates
today <- Sys.Date()
tm <- seq(0, 600, by = 10)
x <- today - tm
y <- rnorm(length(x))
plot_ly(x = x, y = y, text = paste(tm, "days from today"))
# ----------------------------------------------------------------------------
# https://plot.ly/python/choropleth-maps/
# ----------------------------------------------------------------------------
df <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv")
df$hover <- with(df, paste(state, '<br>', "Beef", beef, "Dairy", dairy, "<br>",
"Fruits", total.fruits, "Veggies", total.veggies,
"<br>", "Wheat", wheat, "Corn", corn))
# give state boundaries a white border
l <- list(
color = toRGB("white"),
width = 2
)
# specify some map projection/options
g <- list(
scope = 'usa',
projection = list(type = 'albers usa'),
showlakes = TRUE,
lakecolor = toRGB('white')
)
plot_ly(df, z = total.exports, text = hover, locations = code, type = 'choropleth',
locationmode = 'USA-states', color = total.exports, colors = 'Purples',
marker = list(line = l)), colorbar = list(title = "Millions USD")) %>%
layout(title = '2011 US Agriculture Exports by State<br>(Hover for breakdown)', geo = g)
##########################################################################
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')
# light grey boundaries
l <- list(
color = toRGB("grey"),
width = 0.5
)
# specify map projection/options
g <- list(
showframe = FALSE,
showcoastlines = FALSE,
projection = list(type = 'Mercator')
)
plot_ly(df, z = GDP..BILLIONS., text = COUNTRY, locations = CODE, type = 'choropleth',
color = GDP..BILLIONS., colors = 'Blues', marker = list(line = l),
colorbar = list(tickprefix = '$', title = 'GDP Billions US$')) %>%
# TODO: how to add the hyperlink? (<a href=""> doesn't seem to work)
layout(title = '2014 Global GDP<br>Source: CIA World Factbook', geo = g)
##########################################################################
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_ebola.csv')
# restrict from June to September
df <- subset(df, Month %in% 6:9)
# ordered factor variable with month abbreviations
df$abbrev <- ordered(month.abb[df$Month], levels = month.abb[6:9])
# September totals
df9 <- subset(df, Month == 9)
# common plot options
g <- list(
scope = 'africa',
showframe = F,
showland = T,
landcolor = toRGB("grey90")
)
# styling for "zoomed in" map
g1 <- c(
g,
resolution = 50,
showcoastlines = T,
countrycolor = toRGB("white"),
coastlinecolor = toRGB("white"),
projection = list(type = 'Mercator'),
list(lonaxis = list(range = c(-15, -5))),
list(lataxis = list(range = c(0, 12))),
list(domain = list(x = c(0, 1), y = c(0, 1)))
)
g2 <- c(
g,
showcountries = F,
bgcolor = toRGB("white", alpha = 0),
list(domain = list(x = c(0, .6), y = c(0, .6)))
)
plot_ly(df, type = 'scattergeo', mode = 'markers', locations = Country,
locationmode = 'country names', text = paste(Value, "cases"),
color = as.ordered(abbrev), marker = list(size = Value/50), inherit = F) %>%
add_trace(type = 'scattergeo', mode = 'text', geo = 'geo2', showlegend = F,
# plotly should support "unboxed" constants
lon = list(21.0936), lat = list(7.1881), text = list('Africa')) %>%
add_trace(type = 'choropleth', locations = Country, locationmode = 'country names',
z = Month, colors = "black", showscale = F, geo = 'geo2', data = df9) %>%
layout(title = 'Ebola cases reported by month in West Africa 2014<br> Source: <a href="https://data.hdx.rwlabs.org/dataset/rowca-ebola-cases">HDX</a>',
geo = g1, geo2 = g2)
# ----------------------------------------------------------------------------
# https://plot.ly/python/lines-on-maps/
# ----------------------------------------------------------------------------
# airport locations
air <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
# flights between airports
flights <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_aa_flight_paths.csv')
flights$id <- seq_len(nrow(flights))
# map projection
geo <- list(
scope = 'north america',
projection = list(type = 'azimuthal equal area'),
showland = TRUE,
landcolor = toRGB("gray95"),
countrycolor = toRGB("gray80")
)
plot_ly(air, lon = long, lat = lat, text = airport, type = 'scattergeo',
locationmode = 'USA-states', marker = list(size = 2, color = 'red'),
inherit = FALSE) %>%
add_trace(lon = list(start_lon, end_lon), lat = list(start_lat, end_lat),
group = id, opacity = cnt/max(cnt), data = flights,
mode = 'lines', line = list(width = 1, color = 'red'),
type = 'scattergeo', locationmode = 'USA-states') %>%
layout(title = 'Feb. 2011 American Airline flight paths<br>(Hover for airport names)',
geo = geo, showlegend = FALSE)
##########################################################################
plot_ly(lat = c(40.7127, 51.5072), lon = c(-74.0059, 0.1275), type = 'scattergeo',
mode = 'lines', line = list(width = 2, color = 'blue')) %>%
layout(
title = 'London to NYC Great Circle',
showlegend = FALSE,
geo = list(
resolution = 50,
showland = TRUE,
showlakes = TRUE,
landcolor = toRGB("grey80"),
countrycolor = toRGB("grey80"),
lakecolor = toRGB("white"),
projection = list(type = "equirectangular"),
coastlinewidth = 2,
lataxis = list(
range = c(20, 60),
showgrid = TRUE,
tickmode = "linear",
dtick = 10
),
lonaxis = list(
range = c(-100, 20),
showgrid = TRUE,
tickmode = "linear",
dtick = 20
)
)
)
##########################################################################
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/globe_contours.csv')
df$id <- seq_len(nrow(df))
library(tidyr)
d <- df %>%
gather(key, value, -id) %>%
separate(key, c("l", "line"), "\\.") %>%
spread(l, value)
p <- plot_ly(type = 'scattergeo', mode = 'lines',
line = list(width = 2, color = 'violet'))
for (i in unique(d$line))
p <- add_trace(p, lat = lat, lon = lon, data = subset(d, line == i))
geo <- list(
showland = TRUE,
showlakes = TRUE,
showcountries = TRUE,
showocean = TRUE,
countrywidth = 0.5,
landcolor = toRGB("grey90"),
lakecolor = toRGB("white"),
oceancolor = toRGB("white"),
projection = list(
type = 'orthographic',
rotation = list(
lon = -100,
lat = 40,
roll = 0
)
),
lonaxis = list(
showgrid = TRUE,
gridcolor = toRGB("gray40"),
gridwidth = 0.5
),
lataxis = list(
showgrid = TRUE,
gridcolor = toRGB("gray40"),
gridwidth = 0.5
)
)
layout(p, showlegend = FALSE, geo = geo,
title = 'Contour lines over globe<br>(Click and drag to rotate)')
# ----------------------------------------------------------------------------
# https://plot.ly/python/scatter-plots-on-maps/
# ----------------------------------------------------------------------------
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
df$hover <- with(df, paste(airport, city, state, "Arrivals: ", cnt))
# TODO: rework utils so that marker specs aren't written over
plot_ly(df, lat = lat, lon = long, text = hover, color = cnt,
type = 'scattergeo', locationmode = 'USA-states', mode = 'markers',
marker = list(size = 8, opacity = 0.8, symbol = 'square')) %>%
layout(
title = 'Most trafficked US airports<br>(Hover for airport names)',
geo = list(
scope = 'usa',
projection = list(type = 'albers usa'),
showland = TRUE,
landcolor = toRGB("gray95"),
subunitcolor = toRGB("gray85"),
countrycolor = toRGB("gray85"),
countrywidth = 0.5,
subunitwidth = 0.5
)
)
##########################################################################
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2015_06_30_precipitation.csv')
df$hover <- paste(df$Globvalue, "inches")
# change default color scale title
m <- list(colorbar = list(title = "Total Inches"))
plot_ly(df, lat = Lat, lon = Lon, text = hover, color = Globvalue,
type = 'scattergeo', marker = m) %>%
layout(title = 'US Precipitation 06-30-2015<br>Source: NOAA',
geo = list(
scope = 'north america',
showland = TRUE,
landcolor = toRGB("grey83"),
subunitcolor = toRGB("white"),
countrycolor = toRGB("white"),
showlakes = TRUE,
lakecolor = toRGB("white"),
showsubunits = TRUE,
showcountries = TRUE,
resolution = 50,
projection = list(
type = 'conic conformal',
rotation = list(
lon = -100
)
),
lonaxis = list(
showgrid = TRUE,
gridwidth = 0.5,
range= c(-140, -55),
dtick = 5
),
lataxis = list(
showgrid = TRUE,
gridwidth = 0.5,
range= c(20, 60),
dtick = 5
)
)
)
# ----------------------------------------------------------------------------
# https://plot.ly/python/bubble-maps/
# ----------------------------------------------------------------------------
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_us_cities.csv')
df$hover <- paste(df$name, "Population", df$pop/1e6, " million")
df$q <- with(df, cut(pop, quantile(pop)))
levels(df$q) <- paste(c("1st", "2nd", "3rd", "4th", "5th"), "Quantile")
df$q <- as.ordered(df$q)
plot_ly(df, lon = lon, lat = lat, text = hover,
marker = list(size = sqrt(pop/10000) + 1),
color = q, type = 'scattergeo', locationmode = 'USA-states') %>%
layout(
title = '2014 US city populations<br>(Click legend to toggle traces)',
geo = list(
scope = 'usa',
projection = list(type = 'albers usa'),
showland = TRUE,
landcolor = toRGB("gray85"),
subunitwidth = 1,
countrywidth = 1,
subunitcolor = toRGB("white"),
countrycolor = toRGB("white")
)
)
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