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make.R
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make.R
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##----------------------------------------------------------------------------##
## DATA COLLECTION ##
##----------------------------------------------------------------------------##
## load rtweet, dplyr, and tidyverse
library(rtweet)
library(dataviz)
suppressPackageStartupMessages(library(tidyverse))
## also need installed
#pkgs <- c("ggrepel", "purrr", "tfse", "hms")
## define function for getting CSPAN Twitter lists data
get_cspan_list <- function(slug) {
## get users data of list members
x <- lists_members(slug = slug, owner_user = "CSPAN")
## document slug
x$cspan_list <- slug
## timestamp observations
x$timestamp <- Sys.time()
## return data
x
}
## cspan lists
cspan_lists <- c("members-of-congress", "the-cabinet", "governors")
## members of congress
cspan_data <- purrr::map(cspan_lists, get_cspan_list)
## merge into single data frame
cspan_data <- dplyr::bind_rows(cspan_data)
## file name to save as (timestamped)
save_as <- sprintf("data/twitter_cspan_lists%s.rds",
stringr::str_replace_all(Sys.time(), " |:", "-"))
## save file
saveRDS(cspan_data, save_as)
##----------------------------------------------------------------------------##
## #DATAVIZ ##
##----------------------------------------------------------------------------##
## load tidyverse
#suppressPackageStartupMessages(library(tidyverse))
## read timestamped followers count file
data <- readRDS("data/timestamped-followers-data.rds")
## merge with running data set
cspan_data <- bind_rows(data,
select(cspan_data, user_id, screen_name,
followers_count, timestamp, cspan_list))
## save minimal form for public data
saveRDS(cspan_data, "data/timestamped-followers-data.rds")
## fast unique
funique <- function(x) {
## identify date-time, date, and character columns
psx <- vapply(x, inherits, "POSIXct",
FUN.VALUE = logical(1), USE.NAMES = FALSE)
## convert those columns to factors
x[psx] <- lapply(x[psx], as.integer)
## return only unique rows
x <- unique(x)
## convert columns back to date-time, date, and character
x[psx] <- lapply(x[psx], as.POSIXct, origin = "1970-01-01")
## return data
x
}
## round by hours
round2hour <- function(x) {
n <- 60 * 60
as.POSIXct(hms::hms(as.numeric(x) %/% n * n))
}
## save trimmed down CSV version for public data
#cspan_data %>%
# select(screen_name, followers_count, timestamp) %>%
# mutate(timestamp = round2hour(timestamp),
# timestamp = as.integer(timestamp)) %>%
# readr::write_csv("data/timestamped-followers-data.csv")
## shortcuts for subsetting into data sets
congress_data <- function(cspan_data) filter(
cspan_data, cspan_list == "members-of-congress")
cabinet_data <- function(cspan_data) filter(
cspan_data, cspan_list == "the-cabinet")
governors_data <- function(cspan_data) filter(
cspan_data, cspan_list == "governors")
## plot most popular congress accounts
## hacky function for labels
timestamp_range <- function(timestamp) {
n <- length(unique(timestamp))
x <- seq(min(timestamp), max(timestamp),
length.out = (length(timestamp) / n))
nas <- rep(as.POSIXct(NA_character_), length(x))
c(x, rep(nas, n - 1L))
}
## member of congress
p <- cspan_data %>%
filter(followers_count > 6e5) %>%
congress_data() %>%
mutate(followers_count = sqrt(followers_count)) %>%
group_by(screen_name) %>%
mutate(mean = mean(followers_count)) %>%
ungroup() %>%
arrange(rev(timestamp)) %>%
mutate(lab = ifelse(duplicated(screen_name), NA, screen_name)) %>%
ggplot(aes(x = timestamp, y = followers_count,
colour = screen_name, label = lab)) +
theme_mwk(base_family = "Roboto Condensed") +
theme(legend.position = "none",
axis.title = element_text(hjust = .95, face = "italic")) +
geom_line(size = .6) +
ggrepel::geom_label_repel(family = "Roboto Condensed", size = 2.5,
label.size = .05, label.padding = .05, fill = "#ffffffbb") +
labs(title = "Tracking follower counts for members of Congress on Twitter",
subtitle = "Tracking the number of Twitter followers of members of the Congress over time",
x = NULL, y = "\u221a\u02c9Followers",
caption = "\nSource: Data collected via Twitter's REST API using rtweet (http://rtweet.info)")
pp <- p +
ggsave("plots/members-of-congress.png",
width = 9, height = 9, units = "in", device = "png")
## cabinet members
p <- cspan_data %>%
filter(followers_count > 6e5) %>%
cabinet_data() %>%
mutate(followers_count = sqrt(followers_count)) %>%
group_by(screen_name) %>%
mutate(mean = mean(followers_count)) %>%
ungroup() %>%
arrange(rev(timestamp)) %>%
mutate(lab = ifelse(duplicated(screen_name), NA, screen_name)) %>%
ggplot(aes(x = timestamp, y = followers_count,
colour = screen_name, label = lab)) +
theme_mwk(base_family = "Roboto Condensed") +
theme(legend.position = "none",
axis.title = element_text(hjust = .95, face = "italic")) +
geom_line(size = .6) +
ggrepel::geom_label_repel(family = "Roboto Condensed", size = 2.5,
label.size = .05, label.padding = .05, fill = "#ffffffbb") +
labs(title = "Tracking follower counts for Cabinet members on Twitter",
subtitle = "Tracking the number of Twitter followers of members of the Cabinet over time",
x = NULL, y = "\u221a\u02c9Followers",
caption = "\nSource: Data collected via Twitter's REST API using rtweet (http://rtweet.info)")
pp <- p +
ggsave("plots/the-cabinet.png",
width = 9, height = 9, units = "in", device = "png")
## governors
p <- cspan_data %>%
filter(followers_count > 100000) %>%
governors_data() %>%
mutate(followers_count = sqrt(followers_count)) %>%
group_by(screen_name) %>%
mutate(mean = mean(followers_count)) %>%
ungroup() %>%
arrange(rev(timestamp)) %>%
mutate(lab = ifelse(duplicated(screen_name), NA, screen_name)) %>%
ggplot(aes(x = timestamp, y = followers_count,
colour = screen_name, label = lab)) +
theme_mwk(base_family = "Roboto Condensed") +
theme(legend.position = "none",
axis.title = element_text(hjust = .95, face = "italic")) +
geom_line(size = .6) +
ggrepel::geom_label_repel(family = "Roboto Condensed", size = 2.5,
label.size = .05, label.padding = .05, fill = "#ffffffbb") +
labs(title = "Tracking follower counts for U.S. Governors on Twitter",
subtitle = "Tracking the number of Twitter followers of Governors over time",
x = NULL, y = "\u221a\u02c9Followers",
caption = "\nSource: Data collected via Twitter's REST API using rtweet (http://rtweet.info)")
pp <- p +
ggsave("plots/governors.png", width = 9,
height = 9, units = "in", device = "png")
## add, commit, and push to git
git2r::add(path = ".")
git2r::commit(message = "auto update")
system("git push")