Rev

0.1 28 Sept 2016 supporess printing of all the code
0.2 30 Sept 2016 use metro area populations from wikipedia
0.5 1 Oct 2016 add ChoroplethRmaps to reduce map clutter and add date span

Set-up

[1] "Using direct authentication"

Get city geo data from maps::cities

Select number of cities

    n.cities <- 40

The top Cities are:

rank	population	metro	metro_match	name	country.etc	pop	lat	long	city_name
1	20182305	New York	new york	New York NY	NY	8124427	40.67	-73.94	New York
2	13340068	Los Angeles	los angeles	Los Angeles CA	CA	3911500	34.11	-118.41	Los Angeles
3	9551031	Chicago	chicago	Chicago IL	IL	2830144	41.84	-87.68	Chicago
4	7102796	Dallas	dallas	Dallas TX	TX	1216543	32.79	-96.77	Dallas
5	6656947	Houston	houston	Houston TX	TX	2043005	29.77	-95.39	Houston
6	6097684	Washington	washington	WASHINGTON DC	DC	548359	38.91	-77.02	WASHINGTON
7	6069875	Philadelphia	philadelphia	Philadelphia PA	PA	1439814	40.01	-75.13	Philadelphia
8	6012331	Miami	miami	Miami FL	FL	386740	25.78	-80.21	Miami
9	5710795	Atlanta	atlanta	Atlanta GA	GA	424096	33.76	-84.42	Atlanta
10	4774321	Boston	boston	Boston MA	MA	567759	42.34	-71.02	Boston

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

Keeping first 40 metro areas comprises a total population of 174.3 million people.

search

## set up search terms
searchString.x <- "#AI"    # search term
n.x <- 3000                     # number of tweets
radius <- "30mi"               # radius around selected geo-location
days.ago <-10
duration.days <- 7             # how many days
since.date <- (Sys.Date() - days.ago) %>% as.character # calculated starting date
until.date <- (Sys.Date() - days.ago + duration.days) %>% as.character # calculated ending date

#AI geo-preference.

Use the twitteR::searchTwitter command.

[1] "Rate limited .... blocking for a minute and retrying up to 119 times ..."
[1] "Rate limited .... blocking for a minute and retrying up to 118 times ..."
[1] "Rate limited .... blocking for a minute and retrying up to 117 times ..."
[1] "Rate limited .... blocking for a minute and retrying up to 116 times ..."
[1] "Rate limited .... blocking for a minute and retrying up to 115 times ..."
[1] "Rate limited .... blocking for a minute and retrying up to 114 times ..."
[1] "Rate limited .... blocking for a minute and retrying up to 113 times ..."
[1] "Rate limited .... blocking for a minute and retrying up to 112 times ..."
[1] "Rate limited .... blocking for a minute and retrying up to 111 times ..."

Tweet-Map for #AI?

    #map.plot +  ## use this to underlay with google map
    ggplot() +   ## use this to underlay with simple border outlines
    geom_polygon(data = state.map %>% filter(region != "alaska" & region != "hawaii"), aes(x=long, y=lat, group = group), fill = "#DAC143", color = "#36180D") +
    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 from ", since.date, " until ", until.date)) +
    scale_fill_gradient(low = "#92A0CD", high = "#B32F2A", space = "Lab", na.value = "grey50", guide = "colourbar") +
        theme_bw()

#AI AMB tweet-flux

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

name	tweet.flux	n.tweets	population
San Jose CA	105.80	1464	1976836
San Francisco CA	67.50	2200	4656132
Seattle WA	47.22	1234	3733580
Boston MA	39.74	1328	4774321
Orlando FL	28.19	471	2387138
Austin TX	21.42	300	2000860
Nashville TN	18.97	243	1830345
Denver CO	18.93	373	2814330
Los Angeles CA	17.20	1606	13340068
New York NY	16.70	2360	20182305
San Diego CA	14.59	337	3299521
WASHINGTON DC	12.25	523	6097684
Phoenix AZ	10.59	339	4574531
Chicago IL	7.64	511	9551031
Portland OR	7.47	125	2389228

#AI 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
New York NY	16.70	2360	20182305
San Francisco CA	67.50	2200	4656132
Los Angeles CA	17.20	1606	13340068
San Jose CA	105.80	1464	1976836
Boston MA	39.74	1328	4774321
Seattle WA	47.22	1234	3733580
WASHINGTON DC	12.25	523	6097684
Chicago IL	7.64	511	9551031
Orlando FL	28.19	471	2387138
Denver CO	18.93	373	2814330
Phoenix AZ	10.59	339	4574531
San Diego CA	14.59	337	3299521
Austin TX	21.42	300	2000860
Atlanta GA	6.28	251	5710795
Nashville TN	18.97	243	1830345

Metro Areas with #AI tweet flux

Winston Saunders

Oct 1, 2016