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https://github.com/mkearney/cspan_data

A repo for tracking the number of followers of Congress, the Cabinet, and Governors
https://github.com/mkearney/cspan_data

congress dataset governors mkearney-dataset r rtweet the-cabinet twitter-api twitter-data

Last synced: 3 months ago
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A repo for tracking the number of followers of Congress, the Cabinet, and Governors

Awesome Lists containing this project

README 

        
---

output: github_document

---



```{r setup, include=FALSE}

knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, comment = "#>")

```



## cspan_data



Tracking users-level data of (a) [Members of Congress](https://twitter.com/cspan/lists/members-of-congress), 

(b) [The Cabinet](https://twitter.com/cspan/lists/the-cabinet/), and (c) [Governors](https://twitter.com/cspan/lists/governors) 

using CSPAN Twitter lists and the [rtweet package](http://rtweet.info).



## \#dataviz



### Members of Congress






 



### The Cabinet






 



### Governors






 



## Data collection script



Data collected using [rtweet](http://rtweet.info)



```{r, eval=FALSE}

## load rtweet and tidyverse

library(rtweet)



## 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)

````



## Data visualization script



Plots created using [ggplot2](http://ggplot2.org/) and [ggrepel](https://github.com/slowkow/ggrepel)



```{r, eval=FALSE}

## load tidyverse

suppressPackageStartupMessages(library(tidyverse))



## read all files

data_files <- list.files("data", full.names = TRUE)

cspan_data <- map(data_files, readRDS)



## merge into single data set

cspan_data <- bind_rows(cspan_data)



## 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

library(ggrepel)



## 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

cspan_data %>%

  filter(followers_count > 3e5) %>%

  congress_data() %>%

  mutate(followers_count = log10(followers_count)) %>%

  arrange(timestamp) %>%

  mutate(x = timestamp_range(timestamp)) %>%

  group_by(screen_name) %>%

  mutate(mean = mean(followers_count)) %>%

  ungroup() %>%

  ggplot(aes(x = timestamp, y = followers_count, colour = screen_name, label = screen_name)) +

  theme_mwk(base_family = "Roboto Condensed") +

  theme(legend.position = "none") +

  geom_line() +

#  geom_point() +

  geom_label_repel(aes(x = x, y = mean), family = "Roboto Condensed") +

  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 = "Number of followers (logged)",

    caption = "\nSource: Data collected via Twitter's REST API using rtweet (http://rtweet.info") +

  ggsave("plots/members-of-congress.png", width = 7, height = 13, units = "in")



## cabinet members

cspan_data %>%

  cabinet_data() %>%

  mutate(followers_count = log10(followers_count)) %>%

  arrange(timestamp) %>%

  mutate(x = timestamp_range(timestamp)) %>%

  group_by(screen_name) %>%

  mutate(mean = mean(followers_count)) %>%

  ungroup() %>%

  ggplot(aes(x = timestamp, y = followers_count, colour = screen_name, label = screen_name)) +

  theme_mwk(base_family = "Roboto Condensed") +

  theme(legend.position = "none") +

  geom_line() +

#  geom_point() +

  geom_label_repel(aes(x = x, y = mean), family = "Roboto Condensed") +

  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 = "Number of followers (logged)",

    caption = "\nSource: Data collected via Twitter's REST API using rtweet (http://rtweet.info") +

  ggsave("plots/the-cabinet.png", width = 7, height = 13, units = "in")



## governors

cspan_data %>%

  governors_data() %>%

  mutate(followers_count = log10(followers_count)) %>%

  arrange(timestamp) %>%

  mutate(x = timestamp_range(timestamp)) %>%

  group_by(screen_name) %>%

  mutate(mean = mean(followers_count)) %>%

  ungroup() %>%

  ggplot(aes(x = timestamp, y = followers_count, colour = screen_name, label = screen_name)) +

  theme_mwk(base_family = "Roboto Condensed") +

  theme(legend.position = "none") +

  geom_line() +

#  geom_point() +

  geom_label_repel(aes(x = x, y = mean), family = "Roboto Condensed") +

  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 = "Number of followers (logged)",

    caption = "\nSource: Data collected via Twitter's REST API using rtweet (http://rtweet.info)") +

  ggsave("plots/governors.png", width = 7, height = 9, units = "in")

```