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Gathering Tweets, geocoding users, and plotting them
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doInstall <- TRUE | |
toInstall <- c("twitteR", "dismo", "maps", "ggplot2") | |
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")} | |
lapply(toInstall, library, character.only = TRUE) | |
searchTerm <- "#rstats" | |
searchResults <- searchTwitter(searchTerm, n = 1000) # Gather Tweets | |
tweetFrame <- twListToDF(searchResults) # Convert to a nice dF | |
userInfo <- lookupUsers(tweetFrame$screenName) # Batch lookup of user info | |
userFrame <- twListToDF(userInfo) # Convert to a nice dF | |
locatedUsers <- !is.na(userFrame$location) # Keep only users with location info | |
locations <- geocode(userFrame$location[locatedUsers]) # Use amazing API to guess | |
# approximate lat/lon from textual location data. | |
with(locations, plot(lon, lat)) | |
worldMap <- map_data("world") # Easiest way to grab a world map shapefile | |
zp1 <- ggplot(worldMap) | |
zp1 <- zp1 + geom_path(aes(x = long, y = lat, group = group), # Draw map | |
colour = gray(2/3), lwd = 1/3) | |
zp1 <- zp1 + geom_point(data = locations, # Add points indicating users | |
aes(x = lon, y = lat), | |
colour = "RED", alpha = 1/2, size = 1) | |
zp1 <- zp1 + coord_equal() # Better projections are left for a future post | |
zp1 <- zp1 + theme_minimal() # Drop background annotations | |
print(zp1) |
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