Line
set.seed(1234)
dat<-data.frame(cond=factor(rep(c("A","B"),each=200)),rating=c(rnorm(200),rnorm(200,mean=.8)))
p<- ggplot(dat,aes(x=rating))+geom_histogram(binwidth = .5, colour="black",fill="white")+geom_vline(aes(xintercept=mean(rating,na.rm=TRUE)),color="red",linetype="dashed",size=1)+theme_minimal()
fig<-ggplotly(p)
figStacked Bar Chart
DF <- read.table(text="Rank F1 F2 F3
1 500 250 50
2 400 100 30
3 300 155 100
4 200 90 10", header=TRUE)
library(reshape2)
DF1 <- melt(DF, id.var="Rank")
p <- ggplot(DF1, aes(x = Rank, y = value, fill = variable)) +
geom_bar(stat = "identity")
fig <- ggplotly(p)
figgeom_contour produces a similar output to geom_density_2d, except it uses a third variable for the values rather than frequency. The volcano dataset comes pre-loaded on R
library(plotly)
library(reshape2)
df <- melt(volcano)
p <- ggplot(df, aes(Var1, Var2, z= value)) +
geom_contour() +
scale_fill_distiller(palette = "Spectral", direction = -1)
fig <- ggplotly(p)
figlibrary(plotly)
library(reshape2)
df <- melt(volcano)
p <- ggplot(df, aes(Var1, Var2, z= value)) +
stat_contour(geom="polygon",aes(fill=stat(level))) +
scale_fill_distiller(palette = "Spectral", direction = -1)
ggplotly(p)Add a smooth density estimate calculated by stat_density with ggplot2 and R
set.seed(1234)
dfGamma = data.frame(nu75 = rgamma(100, 0.75),
nu1 = rgamma(100, 1),
nu2 = rgamma(100, 2))
dfGamma = stack(dfGamma)
p <- ggplot(dfGamma, aes(x = values)) +
stat_density(aes(group = ind, color = ind),position="identity",geom="line")
fig <- ggplotly(p)
figlibrary(plotly)
set.seed(123)
df <- data.frame(x <- rchisq(1000, 10, 10),
y <- rnorm(1000))
p <- ggplot(df, aes(x, y)) +
geom_point(alpha = 0.5) +
geom_density_2d() +
theme(panel.background = element_rect(fill = '#ffffff')) +
ggtitle("2D density plot with scatterplot overlay")
fig <- ggplotly(p)
figp <- ggplot(diamonds, aes(x = price)) +
geom_density(aes(fill = "epanechnikov"), kernel = "epanechnikov") +
facet_grid(~cut) +
ggtitle("Kernel density estimate with Facets")
fig <- ggplotly(p)
figlibrary(plotly)
district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv",
stringsAsFactors = FALSE)
district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))
p <- ggplot(district_density,aes(x=cluster, y=dem_margin, colour=region)) +
geom_jitter(aes(text=paste("district: ", cd_code)), width=0.25, alpha=0.5, ) +
geom_hline(yintercept=0) +
theme(axis.text.x = element_text(angle = -30, hjust = 0.1)) +
labs(title = "Democratic performance in the 2018 House elections, by region and density",
x = "Density Index from CityLab",
y = "Democratic Margin of Victory/Defeat")## Warning: Ignoring unknown aesthetics: textfig <- ggplotly(p)
figlibrary(plotly)
district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv",
stringsAsFactors = FALSE)
district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))
p <- ggplot(district_density,aes(x=cluster, y=dem_margin, colour=region)) +
geom_jitter(aes(text=paste("district: ", cd_code)), width=0.25, alpha=0.5, ) +
geom_hline(yintercept=0) +
theme(axis.text.x = element_text(angle = -30, hjust = 0.1)) +
labs(title = "Democratic performance in the 2018 House elections, by region and density",
x = "Density Index from CityLab",
y = "Democratic Margin of Victory/Defeat")## Warning: Ignoring unknown aesthetics: textfig <- ggplotly(p)
figtest_data <-
data.frame(
var0 = 100 + c(0, cumsum(runif(49, -20, 20))),
var1 = 150 + c(0, cumsum(runif(49, -10, 10))),
date = seq(as.Date("2002-01-01"), by="1 month", length.out=100)
)
test_data_long <- melt(test_data, id="date") # convert to long format
p <- ggplot(data=test_data_long,
aes(x=date, y=value, colour=variable)) +
geom_line()
fig <- ggplotly(p)
figlibrary(plotly)
set.seed(955)
# Make some noisily increasing data
dat <- data.frame(cond = rep(c("A", "B"), each=10),
xvar = 1:20 + rnorm(20,sd=3),
yvar = 1:20 + rnorm(20,sd=3))
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
geom_point(shape=1) # Use hollow circles
fig <- ggplotly(p)
figlibrary(plotly)
set.seed(955)
# Make some noisily increasing data
dat <- data.frame(cond = rep(c("A", "B"), each=10),
xvar = 1:20 + rnorm(20,sd=3),
yvar = 1:20 + rnorm(20,sd=3))
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
geom_point(shape=1) + # Use hollow circles
geom_smooth(method=lm) # Add linear regression line
fig <- ggplotly(p)## `geom_smooth()` using formula 'y ~ x'figlibrary(reshape2)
library(plotly)
df <- melt(volcano)
p <- ggplot(df, aes(Var1, Var2)) +
geom_raster(aes(fill=value)) +
labs(x="West to East",
y="North to South",
title = "Elevation map of Maunga Whau")
ggplotly(p)## Warning: 'heatmap' objects don't have these attributes: 'showlegend'
## Valid attributes include:
## 'type', 'visible', 'opacity', 'name', 'uid', 'ids', 'customdata', 'meta', 'hoverinfo', 'hoverlabel', 'stream', 'transforms', 'uirevision', 'z', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'transpose', 'xtype', 'ytype', 'zsmooth', 'connectgaps', 'xgap', 'ygap', 'zhoverformat', 'hovertemplate', 'zauto', 'zmin', 'zmax', 'zmid', 'colorscale', 'autocolorscale', 'reversescale', 'showscale', 'colorbar', 'coloraxis', 'xcalendar', 'ycalendar', 'xaxis', 'yaxis', 'idssrc', 'customdatasrc', 'metasrc', 'hoverinfosrc', 'zsrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'hovertemplatesrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'library(reshape2)
library(plotly)
df <- melt(volcano)
p <- ggplot(df, aes(Var1, Var2)) +
geom_raster(aes(fill=value)) +
scale_fill_distiller(palette = "Spectral", direction = -1) +
labs(x="West to East",
y="North to South",
title = "Elevation map of Maunga Whau",
fill = "Elevation") +
theme(text = element_text(family = 'Fira Sans'),
plot.title = element_text(hjust = 0.5))
ggplotly(p)## Warning: 'heatmap' objects don't have these attributes: 'showlegend'
## Valid attributes include:
## 'type', 'visible', 'opacity', 'name', 'uid', 'ids', 'customdata', 'meta', 'hoverinfo', 'hoverlabel', 'stream', 'transforms', 'uirevision', 'z', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'transpose', 'xtype', 'ytype', 'zsmooth', 'connectgaps', 'xgap', 'ygap', 'zhoverformat', 'hovertemplate', 'zauto', 'zmin', 'zmax', 'zmid', 'colorscale', 'autocolorscale', 'reversescale', 'showscale', 'colorbar', 'coloraxis', 'xcalendar', 'ycalendar', 'xaxis', 'yaxis', 'idssrc', 'customdatasrc', 'metasrc', 'hoverinfosrc', 'zsrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'hovertemplatesrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'library(plotly)
ids <- factor(c("1.1", "2.1", "1.2", "2.2", "1.3", "2.3"))
values <- data.frame(
id = ids,
value = c(3, 3.1, 3.1, 3.2, 3.15, 3.5)
)
positions <- data.frame(
id = rep(ids, each = 4),
x = c(2, 1, 1.1, 2.2, 1, 0, 0.3, 1.1, 2.2, 1.1, 1.2, 2.5, 1.1, 0.3,
0.5, 1.2, 2.5, 1.2, 1.3, 2.7, 1.2, 0.5, 0.6, 1.3),
y = c(-0.5, 0, 1, 0.5, 0, 0.5, 1.5, 1, 0.5, 1, 2.1, 1.7, 1, 1.5,
2.2, 2.1, 1.7, 2.1, 3.2, 2.8, 2.1, 2.2, 3.3, 3.2)
)
datapoly <- merge(values, positions, by=c("id"))
p <- ggplot(datapoly, aes(x=x, y=y)) + geom_polygon(aes(fill=value, group=id))
fig <- ggplotly(p)
figlibrary(plotly)
x=seq(-2,2,length=200)
dat <- data.frame(
norm = dnorm(x,mean=0,sd=0.2),
logistic = dlogis(x,location=0,scale=0.2), x = x
)
p <- ggplot(data=dat, aes(x=x)) +
geom_polygon(aes(y=norm), fill="red", alpha=0.6) +
geom_polygon(aes(y=logistic), fill="blue", alpha=0.6) +
xlab("z") + ylab("") +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
fig <- ggplotly(p)
figlibrary(plotly)
spinrates <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/spinrates.csv",
stringsAsFactors = FALSE)
p <- ggplot(spinrates, aes(x=velocity, y=spinrate)) +
geom_tile(aes(fill = swing_miss)) +
scale_fill_distiller(palette = "YlGnBu") +
labs(title = "Likelihood of swinging and missing on a fastball",
y = "spin rate (rpm)")
ggplotly(p)## Warning: 'heatmap' objects don't have these attributes: 'showlegend'
## Valid attributes include:
## 'type', 'visible', 'opacity', 'name', 'uid', 'ids', 'customdata', 'meta', 'hoverinfo', 'hoverlabel', 'stream', 'transforms', 'uirevision', 'z', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'transpose', 'xtype', 'ytype', 'zsmooth', 'connectgaps', 'xgap', 'ygap', 'zhoverformat', 'hovertemplate', 'zauto', 'zmin', 'zmax', 'zmid', 'colorscale', 'autocolorscale', 'reversescale', 'showscale', 'colorbar', 'coloraxis', 'xcalendar', 'ycalendar', 'xaxis', 'yaxis', 'idssrc', 'customdatasrc', 'metasrc', 'hoverinfosrc', 'zsrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'hovertemplatesrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'library(plotly)
spinrates <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/spinrates.csv",
stringsAsFactors = FALSE)
p <- ggplot(spinrates, aes(x=velocity, y=spinrate)) +
geom_tile(aes(fill = swing_miss)) +
scale_fill_distiller(palette = "YlGnBu", direction = 1) +
theme_light() +
labs(title = "Likelihood of swinging and missing on a fastball",
y = "spin rate (rpm)")
ggplotly(p)## Warning: 'heatmap' objects don't have these attributes: 'showlegend'
## Valid attributes include:
## 'type', 'visible', 'opacity', 'name', 'uid', 'ids', 'customdata', 'meta', 'hoverinfo', 'hoverlabel', 'stream', 'transforms', 'uirevision', 'z', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'transpose', 'xtype', 'ytype', 'zsmooth', 'connectgaps', 'xgap', 'ygap', 'zhoverformat', 'hovertemplate', 'zauto', 'zmin', 'zmax', 'zmid', 'colorscale', 'autocolorscale', 'reversescale', 'showscale', 'colorbar', 'coloraxis', 'xcalendar', 'ycalendar', 'xaxis', 'yaxis', 'idssrc', 'customdatasrc', 'metasrc', 'hoverinfosrc', 'zsrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'hovertemplatesrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'df <- data.frame(name = c("Nixon", "Ford", "Carter", "Reagan", "Bush", "Clinton", "Bush", "Obama"),
start = as.Date(c("1969-01-20", "1974-08-09", "1977-01-20", "1981-01-20",
"1989-01-20", "1993-01-20", "2001-01-20", "2009-01-20")),
end = as.Date(c("1974-08-09", "1977-01-20", "1981-01-20", "1989-01-20",
"1993-01-20", "2001-01-20", "2009-01-20", "2017-01-20")),
party = c("R", "R", "D", "R", "R", "D", "R", "D"),
stringsAsFactors = FALSE) %>%
mutate(median_x = start + floor((end-start)/2))
p <- ggplot(economics, aes(x=date,y=unemploy)) +
geom_rect(data=df, aes(NULL,NULL,xmin=start,xmax=end,fill=party),
ymin=0,ymax=16000, colour="white", size=0.5, alpha=0.2) +
scale_fill_manual(values=c("R" = "red", "D" = "blue")) +
geom_line() +
geom_text(data=df,aes(x=median_x,y=3000,label=name), size=3) +
labs(title = "Unemmployment numbers since 1967",
y = "No. unemployed (x 1000)")
fig <- ggplotly(p)
figlibrary(plotly)
df <- MASS::birthwt
df <- with(df, {
race <- factor(race, labels = c("white", "black", "other"))
ptd <- factor(ptl > 0)
ftv <- factor(ftv)
levels(ftv)[-(1:2)] <- "2+"
data.frame(low = factor(low), age, lwt, race, smoke = (smoke > 0),
ptd, ht = (ht > 0), ui = (ui > 0), ftv, bwt)
})
p <- ggplot(df, aes(lwt, bwt, colour = smoke)) +
geom_point(size = 1) +
geom_rug()
fig <- ggplotly(p)
figlibrary(plotly)
english_french <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/english_french.csv",stringsAsFactors = FALSE)
p<- ggplot(english_french,aes(x=engperc,y=frenperc))+geom_bin2d()+labs(title= "Distribution of Canadian areas by English and French fluency", x="% fluent in English",y="% fluent in French",fill="# of census \ nsubdivisions")+scale_fill_gradient(low="lightblue1",high="darkblue",na.value = "black")
fig<- ggplotly(p)
fig## Warning: 'heatmap' objects don't have these attributes: 'showlegend'
## Valid attributes include:
## 'type', 'visible', 'opacity', 'name', 'uid', 'ids', 'customdata', 'meta', 'hoverinfo', 'hoverlabel', 'stream', 'transforms', 'uirevision', 'z', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'transpose', 'xtype', 'ytype', 'zsmooth', 'connectgaps', 'xgap', 'ygap', 'zhoverformat', 'hovertemplate', 'zauto', 'zmin', 'zmax', 'zmid', 'colorscale', 'autocolorscale', 'reversescale', 'showscale', 'colorbar', 'coloraxis', 'xcalendar', 'ycalendar', 'xaxis', 'yaxis', 'idssrc', 'customdatasrc', 'metasrc', 'hoverinfosrc', 'zsrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'hovertemplatesrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'p <- ggplot(english_french, aes(x=engperc,y=frenperc, weight=total)) +
geom_bin2d(bins = 20) +
facet_wrap(~factor(region, levels = c("Atlantic","Québec","Ontario","Prairies","British Columbia"))) +
scale_fill_gradient(low="lightblue1",high="darkblue",trans="log10") +
labs(title = "Distribution of Canadian towns by English and French fluency",
x = "% fluent in English",
y = "% fluent in French",
fill = "population")
fig <- ggplotly(p)
fig## Warning: 'heatmap' objects don't have these attributes: 'showlegend'
## Valid attributes include:
## 'type', 'visible', 'opacity', 'name', 'uid', 'ids', 'customdata', 'meta', 'hoverinfo', 'hoverlabel', 'stream', 'transforms', 'uirevision', 'z', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'transpose', 'xtype', 'ytype', 'zsmooth', 'connectgaps', 'xgap', 'ygap', 'zhoverformat', 'hovertemplate', 'zauto', 'zmin', 'zmax', 'zmid', 'colorscale', 'autocolorscale', 'reversescale', 'showscale', 'colorbar', 'coloraxis', 'xcalendar', 'ycalendar', 'xaxis', 'yaxis', 'idssrc', 'customdatasrc', 'metasrc', 'hoverinfosrc', 'zsrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'hovertemplatesrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'heatmap' objects don't have these attributes: 'showlegend'
## Valid attributes include:
## 'type', 'visible', 'opacity', 'name', 'uid', 'ids', 'customdata', 'meta', 'hoverinfo', 'hoverlabel', 'stream', 'transforms', 'uirevision', 'z', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'transpose', 'xtype', 'ytype', 'zsmooth', 'connectgaps', 'xgap', 'ygap', 'zhoverformat', 'hovertemplate', 'zauto', 'zmin', 'zmax', 'zmid', 'colorscale', 'autocolorscale', 'reversescale', 'showscale', 'colorbar', 'coloraxis', 'xcalendar', 'ycalendar', 'xaxis', 'yaxis', 'idssrc', 'customdatasrc', 'metasrc', 'hoverinfosrc', 'zsrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'hovertemplatesrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'heatmap' objects don't have these attributes: 'showlegend'
## Valid attributes include:
## 'type', 'visible', 'opacity', 'name', 'uid', 'ids', 'customdata', 'meta', 'hoverinfo', 'hoverlabel', 'stream', 'transforms', 'uirevision', 'z', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'transpose', 'xtype', 'ytype', 'zsmooth', 'connectgaps', 'xgap', 'ygap', 'zhoverformat', 'hovertemplate', 'zauto', 'zmin', 'zmax', 'zmid', 'colorscale', 'autocolorscale', 'reversescale', 'showscale', 'colorbar', 'coloraxis', 'xcalendar', 'ycalendar', 'xaxis', 'yaxis', 'idssrc', 'customdatasrc', 'metasrc', 'hoverinfosrc', 'zsrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'hovertemplatesrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'heatmap' objects don't have these attributes: 'showlegend'
## Valid attributes include:
## 'type', 'visible', 'opacity', 'name', 'uid', 'ids', 'customdata', 'meta', 'hoverinfo', 'hoverlabel', 'stream', 'transforms', 'uirevision', 'z', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'transpose', 'xtype', 'ytype', 'zsmooth', 'connectgaps', 'xgap', 'ygap', 'zhoverformat', 'hovertemplate', 'zauto', 'zmin', 'zmax', 'zmid', 'colorscale', 'autocolorscale', 'reversescale', 'showscale', 'colorbar', 'coloraxis', 'xcalendar', 'ycalendar', 'xaxis', 'yaxis', 'idssrc', 'customdatasrc', 'metasrc', 'hoverinfosrc', 'zsrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'hovertemplatesrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'heatmap' objects don't have these attributes: 'showlegend'
## Valid attributes include:
## 'type', 'visible', 'opacity', 'name', 'uid', 'ids', 'customdata', 'meta', 'hoverinfo', 'hoverlabel', 'stream', 'transforms', 'uirevision', 'z', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'transpose', 'xtype', 'ytype', 'zsmooth', 'connectgaps', 'xgap', 'ygap', 'zhoverformat', 'hovertemplate', 'zauto', 'zmin', 'zmax', 'zmid', 'colorscale', 'autocolorscale', 'reversescale', 'showscale', 'colorbar', 'coloraxis', 'xcalendar', 'ycalendar', 'xaxis', 'yaxis', 'idssrc', 'customdatasrc', 'metasrc', 'hoverinfosrc', 'zsrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'hovertemplatesrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'library(plotly)
flightdata <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/flightdata.csv", stringsAsFactors = FALSE)
p <- ggplot(flightdata, aes(y=airline, x=dest, colour = dest, group=airport)) +
geom_count(alpha=0.5) +
labs(title = "Flights from New York to major domestic destinations",
x = "Origin and destination",
y = "Airline",
size = "")
ggplotly(p)set.seed(1234)
dat <- data.frame(cond = factor(rep(c("A","B"), each=200)), rating = c(rnorm(200),rnorm(200, mean=.8)))
p <- ggplot(dat, aes(x=cond, y=rating)) + geom_boxplot()
fig <- ggplotly(p)library(plotly)
set.seed(1234)
dat <- data.frame(cond = factor(rep(c("A","B"), each=200)), rating = c(rnorm(200),rnorm(200, mean=.8)))
p <- ggplot(dat, aes(x=cond, y=rating, fill=cond)) + geom_boxplot() +
guides(fill=FALSE) + coord_flip()
fig <- ggplotly(p)
figlibrary(plotly)
english_french <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/english_french.csv",stringsAsFactors = FALSE)
p <- ggplot(english_french, aes(x=engperc,y=frenperc)) +
geom_hex() +
labs(title = "Distribution of Canadian areas by English and French fluency",
x = "% fluent in English",
y = "% fluent in French",
fill = "# of census \nsubdivisions")
fig <- ggplotly(p)
figlibrary(plotly)
english_french <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/english_french.csv",stringsAsFactors = FALSE)
p <- ggplot(english_french, aes(x=engperc,y=frenperc)) +
geom_hex() +
scale_fill_gradient(low="lightblue1",high="darkblue",trans="log10") +
labs(title = "Distribution of Canadian towns by English and French fluency",
x = "% fluent in English",
y = "% fluent in French",
fill = "# of census \nsubdivisions")
fig <- ggplotly(p)
figlibrary(plotly)
english_french <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/english_french.csv",stringsAsFactors = FALSE)
p <- ggplot(english_french, aes(x=engperc, y=frenperc, weight=total)) +
geom_hex() +
scale_fill_gradient(low="lightblue1",high="darkblue",trans="log10") +
labs(title = "Distribution of the Canadian population by English and French fluency",
x = "% fluent in English",
y = "% fluent in French",
fill = "population")
fig <- ggplotly(p)
figlibrary(plotly)
dat <- data.frame(xx = c(runif(100,20,50),runif(100,40,80),runif(100,0,30)),yy = rep(letters[1:3],each = 100))
p <- ggplot(dat,aes(x=xx)) +
geom_histogram(data=subset(dat,yy == 'a'),fill = "red", alpha = 0.2) +
geom_histogram(data=subset(dat,yy == 'b'),fill = "blue", alpha = 0.2) +
geom_histogram(data=subset(dat,yy == 'c'),fill = "green", alpha = 0.2)
fig <- ggplotly(p)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.figlibrary(plotly)
df1 <- data.frame(cond = factor( rep(c("A","B"), each=200) ),
rating = c(rnorm(200),rnorm(200, mean=.8)))
df2 <- data.frame(x=c(.5,1),cond=factor(c("A","B")))
p <- ggplot(data=df1, aes(x=rating, fill=cond)) +
geom_vline(xintercept=c(.5,1)) +
geom_histogram(binwidth=.5, position="dodge")
fig <- ggplotly(p)
figlibrary(plotly)
df <- data.frame(x = rnorm(1000))
p <- ggplot(df, aes(x=x)) +
geom_histogram(aes(y = ..density..), binwidth=density(df$x)$bw) +
geom_density(fill="red", alpha = 0.2)
fig <- ggplotly(p)
figWhile common linear regrssion is a method of estimating the conditional mean of variable y based on the values of variables x, quantile regression is a method that can give the conditional median (50th percentile) as well as any other quantile
This dataset gives the effect of the mother’s weight on her baby’s birth weight, further divided according to whether the smokers or not
df <- MASS::birthwt
df <- with(df, { #Make sure variables properly show up as categories
race <- factor(race, labels = c("white", "black", "other"))
ptd <- factor(ptl > 0)
ftv <- factor(ftv)
levels(ftv)[-(1:2)] <- "2+"
data.frame(low = factor(low), age, lwt, race, smoke = (smoke > 0),
ptd, ht = (ht > 0), ui = (ui > 0), ftv, bwt)
})
p <- ggplot(df, aes(lwt, bwt, colour = smoke)) +
geom_point(size = 1) +
geom_quantile(quantiles = 0.5)
fig <- ggplotly(p)## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ xfiglibrary(plotly)
library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## select## The following object is masked from 'package:plotly':
##
## selectlibrary(dplyr)
df <- MASS::birthwt
df <- with(df, {
race <- factor(race, labels = c("white", "black", "other"))
ptd <- factor(ptl > 0)
ftv <- factor(ftv)
levels(ftv)[-(1:2)] <- "2+"
data.frame(low = factor(low), age, lwt, race, smoke = (smoke > 0),
ptd, ht = (ht > 0), ui = (ui > 0), ftv, bwt)
})
p <- ggplot(df, aes(lwt, bwt, colour=smoke)) +
geom_point(size = 1) +
geom_quantile(quantiles = c(0.1, 0.5, 0.9), size = 2, aes(alpha = ..quantile..)) +
scale_alpha(range = c(0.3, 0.7))
fig <- ggplotly(p)## Smoothing formula not specified. Using: y ~ x## Smoothing formula not specified. Using: y ~ xfiglibrary(plotly)
set.seed(1)
y <- sin(seq(1, 2*pi, length.out = 100))
x <- 1:100
plotdata <- data.frame(x=x, y=y, lower = (y+runif(100, -1, -0.5)), upper = (y+runif(100, 0.5, 1)))
p <- ggplot(plotdata) + geom_line(aes(y=y, x=x, colour = "sin"))+
geom_ribbon(aes(ymin=lower, ymax=upper, x=x, fill = "band"), alpha = 0.3)+
scale_colour_manual("",values="blue")+
scale_fill_manual("",values="grey12")
fig <- ggplotly()
figlibrary(plotly)
the.data <- read.table( header=TRUE, sep=",",
text="source,year,value
S1,1976,56.98
S1,1977,55.26
S1,1978,68.83
S1,1979,59.70
S1,1980,57.58
S1,1981,61.54
S1,1982,48.65
S1,1983,53.45
S1,1984,45.95
S1,1985,51.95
S1,1986,51.85
S1,1987,54.55
S1,1988,51.61
S1,1989,52.24
S1,1990,49.28
S1,1991,57.33
S1,1992,51.28
S1,1993,55.07
S1,1994,50.88
S2,1993,54.90
S2,1994,51.20
S2,1995,52.10
S2,1996,51.40
S3,2002,57.95
S3,2003,47.95
S3,2004,48.15
S3,2005,37.80
S3,2006,56.96
S3,2007,48.91
S3,2008,44.00
S3,2009,45.35
S3,2010,49.40
S3,2011,51.19")
cutoff <- data.frame( x = c(-Inf, Inf), y = 50, cutoff = factor(50) )
p <- ggplot(the.data, aes( year, value ) ) +
geom_point(aes( colour = source )) +
geom_smooth(aes( group = 1 )) +
geom_hline(yintercept = 50)
fig <- ggplotly(p)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'figlibrary(plotly)
beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
p <- ggplot(beers, aes(x=abv, y=ibu)) +
geom_density2d() +
labs(y = "bitterness (IBU)",
x = "alcohol volume (ABV)",
title = "Craft beers from American breweries")
ggplotly(p)## Warning: Removed 1005 rows containing non-finite values (stat_density2d).library(plotly)
beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
p <- ggplot(beers, aes(x=abv, y=ibu)) +
stat_density2d(aes(fill = stat(level)), geom="polygon") +
scale_fill_viridis_c(option = "plasma") +
theme(legend.position = "magma") +
labs(y = "bitterness (IBU)",
x = "alcohol volume (ABV)",
title = "Craft beers from American breweries")
ggplotly(p)## Warning: Removed 1005 rows containing non-finite values (stat_density2d).library(plotly)
district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv", stringsAsFactors = FALSE)
district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))
p <- ggplot(district_density,aes(x=cluster, y=dem_margin, fill=cluster)) +
geom_violin(colour=NA) +
geom_hline(yintercept=0, alpha=0.5) +
labs(title = "Democratic performance in the 2018 House elections, by region and density",
x = "Density Index\nfrom CityLab",
y = "Margin of Victory/Defeat") +
coord_flip()
ggplotly(p)library(plotly)
library(gapminder)
p <- ggplot(gapminder, aes(gdpPercap, lifeExp, color = continent)) +
geom_point(aes(size = pop, frame = year, ids = country)) +
scale_x_log10()## Warning: Ignoring unknown aesthetics: frame, idsfig <- ggplotly(p)
figlibrary(plotly)
fig <- fig %>%
animation_opts(
1000, easing = "elastic", redraw = FALSE
)
fig