This is an simple example of an R Markdown created GitHub Pages page.

Plots

Here are some examples of plots you can create and include in your page.

A Map

library(WDI)
library(googleVis)
co2 <- WDI(indicator = 'EN.ATM.CO2E.PC', start = 2010, end = 2010)
co2 <- co2[, c('iso2c','EN.ATM.CO2E.PC')]

# Clean
names(co2) <- c('iso2c', 'CO2 Emissions per Capita')
co2[, 2] <- round(log(co2[, 2]), digits = 2)

# Plot
co2_map <- gvisGeoChart(co2, locationvar = 'iso2c',
                      colorvar = 'CO2 Emissions per Capita',
                      options = list(
                          colors = "['#fff7bc', '#d95f0e']"
                          ))

print(co2_map, tag = 'chart')

A ggplot2 graph with ggplotly

library(devtools)
library(ggplot2)
library(plotly)

source_url("http://bit.ly/OTWEGS")

# Create data with no missing values of infant mortality
InfantNoMiss <- subset(MortalityGDP,
                           !is.na(InfantMortality))

# Create High/Low Income Variable
InfantNoMiss$DumMort[InfantNoMiss$InfantMortality
                     >= 15] <- "high"
InfantNoMiss$DumMort[InfantNoMiss$InfantMortality
                     < 15] <- "low"

# Create ggplot2 object
mort_plot <- ggplot(data = MortalityGDP, aes(x = InfantMortality,
        y = GDPperCapita)) + geom_point() + 
        stat_smooth(se = FALSE) +
        ylab('GDP per Capita') + xlab('Infant Mortality') + 
        theme_bw(base_size = 9)

# Plot interactive
ggplotly(mort_plot)

Plotly native graph

plot_ly(MortalityGDP, x = InfantMortality, y = GDPperCapita,
        mode = 'markers')

Simulated probabilities with simGLM and plotly

library(simGLM) # if not installed use devtools::github_install('christophergandrud/simGLM')

# Download data
URL <- 'http://www.ats.ucla.edu/stat/data/binary.csv'
Admission <- read.csv(URL)
Admission$rank <- as.factor(Admission$rank)

# Estimate model
m2 <- glm(admit ~ gre + gpa + rank, data = Admission, family = 'binomial')

# Create fitted values
fitted_admit <- expand.grid(gre = seq(220, 800, by = 10), gpa = c(1, 4), 
                            rank4 = 1)

# Simulate and plot
sim_gpa <- sim_glm(obj = m2, newdata = fitted_admit, model = 'logit', x_coef = 'gre', 
                   group_coef = 'gpa') + theme_bw(base_size = 9)

# Plot with plotly
ggplotly(sim_gpa)

A time series graph with dynsim

library(dygraphs)
lungDeaths <- cbind(mdeaths, fdeaths)
dygraph(lungDeaths) %>% dyRangeSelector()

A network plot

library(networkD3)
data(MisLinks); data(MisNodes)
forceNetwork(Links = MisLinks, Nodes = MisNodes,
            Source = "source", Target = "target",
            Value = "value", NodeID = "name",
            Group = "group", opacity = 0.8)