# 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, '
', "Beef", beef, "Dairy", dairy, "
", "Fruits", total.fruits, "Veggies", total.veggies, "
", "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
(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? ( doesn't seem to work) layout(title = '2014 Global GDP
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
Source: HDX', 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$id <- seq_len(nrow(flights)) # map projection geo <- list( scope = 'north america', projection = list(type = 'azimuthal equal area'), showland = TRUE, landcolor = toRGB("gray95"), countrycolor = toRGB("gray80") ) plot_ly(air, lon = long, lat = lat, text = airport, type = 'scattergeo', locationmode = 'USA-states', marker = list(size = 2, color = 'red'), inherit = FALSE) %>% add_trace(lon = list(start_lon, end_lon), lat = list(start_lat, end_lat), group = id, opacity = cnt/max(cnt), data = flights, mode = 'lines', line = list(width = 1, color = 'red'), type = 'scattergeo', locationmode = 'USA-states') %>% layout(title = 'Feb. 2011 American Airline flight paths
(Hover for airport names)', geo = geo, showlegend = FALSE) ########################################################################## plot_ly(lat = c(40.7127, 51.5072), lon = c(-74.0059, 0.1275), type = 'scattergeo', mode = 'lines', line = list(width = 2, color = 'blue')) %>% layout( title = 'London to NYC Great Circle', showlegend = FALSE, geo = list( resolution = 50, showland = TRUE, showlakes = TRUE, landcolor = toRGB("grey80"), countrycolor = toRGB("grey80"), lakecolor = toRGB("white"), projection = list(type = "equirectangular"), coastlinewidth = 2, lataxis = list( range = c(20, 60), showgrid = TRUE, tickmode = "linear", dtick = 10 ), lonaxis = list( range = c(-100, 20), showgrid = TRUE, tickmode = "linear", dtick = 20 ) ) ) ########################################################################## df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/globe_contours.csv') df$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$line)) p <- add_trace(p, lat = lat, lon = lon, data = subset(d, line == i)) geo <- list( showland = TRUE, showlakes = TRUE, showcountries = TRUE, showocean = TRUE, countrywidth = 0.5, landcolor = toRGB("grey90"), lakecolor = toRGB("white"), oceancolor = toRGB("white"), projection = list( type = 'orthographic', rotation = list( lon = -100, lat = 40, roll = 0 ) ), lonaxis = list( showgrid = TRUE, gridcolor = toRGB("gray40"), gridwidth = 0.5 ), lataxis = list( showgrid = TRUE, gridcolor = toRGB("gray40"), gridwidth = 0.5 ) ) layout(p, showlegend = FALSE, geo = geo, title = 'Contour lines over globe
(Click and drag to rotate)') # ---------------------------------------------------------------------------- # https://plot.ly/python/scatter-plots-on-maps/ # ---------------------------------------------------------------------------- df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv') df$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
(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
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
(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") ) )