setup

## set up search terms
searchString.x <- "#HillaryWon"    # search term
n.x <- 3000                     # number of tweets
radius <- "30mi"               # radius around selected geo-location
duration.days <- 1             # how many days
since.date <- (Sys.Date() - duration.days) %>% as.character # calculated starting date

#HillaryWon geo-preference.

[1] "Using direct authentication"

Get city geo data from maps::cities

Use the twitteR::searchTwitter command.

n.cities <- 40

Data collection for the top 40 cities (by population) in the U.S. This includes cities from New York to Nashville.

Tweet-Map for #HillaryWon?

map.plot +
    geom_point(aes(x = lon, y = lat, fill = tweet.flux, size = n.tweets), data=analyzed_df, pch=21, color = "#33333399") +
    ggtitle(paste0(searchString.x, " tweets in ", duration.days," days since ", since.date, " r = ", radius)) +
    scale_fill_gradient(low = "#BBBBFF", high = "#EE3300", space = "Lab", na.value = "grey50", guide = "colourbar")

#HillaryWon AMB tweet-flux

Here are the top few cities by tweet flux (in “twipermipeds”).

name tweet.flux n.tweets population
Washington 39.20 239 6097684
Los Angeles 19.42 259 13340068
Atlanta 18.74 107 5710795
Baltimore 15.73 44 2797407
New York 9.17 185 20182305
Houston 6.61 44 6656947
San Diego 5.76 19 3299521
Denver 5.69 16 2814330
Chicago 4.92 47 9551031
Sacramento 4.84 11 2274194
Las Vegas 4.73 10 2114801
Charlotte 4.53 11 2426363
Columbus 4.45 9 2021632
Austin 4.00 8 2000860
San Francisco 3.87 18 4656132

#HillaryWon AMB tweet count

Here are the top few cities sorted by raw tweets, again with major metro areas leading. Note that some other cities, like Chicago, have a large number of tweets but a lower flux because of their higher population.

name tweet.flux n.tweets population
Los Angeles 19.42 259 13340068
Washington 39.20 239 6097684
New York 9.17 185 20182305
Atlanta 18.74 107 5710795
Chicago 4.92 47 9551031
Houston 6.61 44 6656947
Baltimore 15.73 44 2797407
Philadelphia 3.79 23 6069875
San Diego 5.76 19 3299521
San Francisco 3.87 18 4656132
Denver 5.69 16 2814330
Dallas 1.97 14 7102796
Minneapolis 3.69 13 3524583
Boston 2.30 11 4774321
Riverside 2.45 11 4489159