Nmf jmm

.Rhistory

@@ -0,0 +1,149 @@
+# Query Twitter API to obtain data ----------------------------------------
+## Query API with these parameters and save as JSON files
+tweets <- get_all_tweets(query = "disruptJMM",
+start_tweets = "2006-04-01T00:00:00Z",
+end_tweets = "2021-10-01T00:00:00Z",
+bearer_token = get_bearer(),
+n = Inf,
+data_path = "data/",
+bind_tweets = FALSE)
+# Bind JSONs into data.frame; Multiple options depending on need ----------
+## Tidy: "Opinionated” format the devs believe to contain all essential columns for social media research
+twitterData_tidy  <- bind_tweets(data_path = "data/", user = TRUE, output_format = "tidy")
+#### Note you will need to load the twitterData_tidy to run the code.
+library(readxl)
+twitterData_tidy <- read_excel("twitterData_tidy.xlsx")
+View(twitterData_tidy)
+View(twitterData_tidy)
+View(twitterData_tidy)
+# - Library's to load
+library(academictwitteR)
+library(tidytags)
+library(tidyverse)
+library(lubridate)
+library(stringr)
+library(visNetwork)
+library(igraph)
+############## - Data Sorted by Month #####################
+timeSeriesMonth <- twitterData_tidy
+## Convert dates in new data frame from characters to format year-month-date
+timeSeriesMonth$created_at <- as.POSIXct(timeSeriesMonth$created_at)
+## Create a month variable then group by months to summarize tweet numbers
+timeSeriesMonth <- timeSeriesMonth %>%
+select(created_at) %>%
+mutate(month = floor_date(created_at, "month")) %>%
+group_by(month) %>%
+summarize(tweets = n())
+## Plot #disruptJMM Tweets Over Time (2020-2021)
+plot_timeSeriesMonth <- ggplot(timeSeriesMonth, aes(x = month, y = tweets)) +
+geom_point() +
+geom_line() +
+theme_minimal() +
+scale_x_datetime(date_breaks = "2 months",
+date_labels = "%b, \n%Y") +
+theme(axis.text.x = element_text(angle = 0, vjust = 1, hjust = 0.5),
+plot.title = element_text(face = "bold")) +
+labs (x = "Date", y = "Tweet Count",
+title = "#disruptJMM Tweets Over Time (2020-2021)",
+subtitle = "JMM 2020: Jan. 15th - 18th \nJMM 2021: Jan. 06th - 09th",
+caption = "\nSource: Twitter's API via academictwitteR")
+## View plot
+plot_timeSeriesMonth
+############## - By Day of 2020 During JMM #####################
+## Create new data frame from twitterData_tidy
+timeSeriesDay2020 <- twitterData_tidy %>%
+filter(str_detect(created_at, "2020-01"))
+## Convert dates in new data frame from characters to format year-month-date
+timeSeriesDay2020$created_at <- as.POSIXct(timeSeriesDay2020$created_at)
+## Create a month variable then group by days to summarize tweet numbers
+timeSeriesDay2020 <- timeSeriesDay2020 %>%
+select(created_at) %>%
+mutate(day = floor_date(created_at, "day")) %>%
+group_by(day) %>%
+summarize(tweets = n())
+## Plot #disruptJMM Tweets Over Time During JMM Conference (2020)
+plot_timeSeriesDay2020 <- ggplot(timeSeriesDay2020, aes(x = day, y = tweets)) +
+geom_point() +
+geom_line() +
+theme_minimal() +
+scale_x_datetime(date_breaks = "1 day",
+date_labels = "%a, \n%d") +
+theme(axis.text.x = element_text(angle = 0,
+vjust = 1,
+hjust = 0.5),
+plot.title = element_text(face = "bold")) +
+labs (x = "Date", y = "Tweet Count",
+title = "#disruptJMM Tweets During JMM (2020)",
+subtitle = "JMM 2020: Jan. 15th - 18th",
+caption = "\nSource: Twitter's API via academictwitteR")
+## View plot
+plot_timeSeriesDay2020
+############## - By Day of 2020 During JMM #####################
+## Create new data frame from twitterData_tidy
+timeSeriesDay2021 <- twitterData_tidy %>%
+filter(str_detect(created_at, "2021-01"))
+## Convert dates in new data frame from characters to format year-month-date
+timeSeriesDay2021$created_at <- as.POSIXct(timeSeriesDay2021$created_at)
+## Create a month variable then group by days to summarize tweet numbers
+timeSeriesDay2021 <- timeSeriesDay2021 %>%
+select(created_at) %>%
+mutate(day = floor_date(created_at, "day")) %>%
+group_by(day) %>%
+summarize(tweets = n())
+## Plot #disruptJMM Tweets Over Time During JMM Conference (2021)
+plot_timeSeriesDay2021 <- ggplot(timeSeriesDay2021, aes(x = day, y = tweets)) +
+theme_minimal() +
+geom_point() +
+geom_line() +
+scale_x_datetime(date_breaks = "1 day",
+date_labels = "%a, \n%d") +
+theme(axis.text.x = element_text(angle = 0,
+vjust = 1,
+hjust = 0.5),
+plot.title = element_text(face = "bold")) +
+labs (x = "Date", y = "Tweet Count",
+title = "#disruptJMM Tweets During JMM (2021)",
+subtitle = "JMM 2021: Jan. 6th - 9th",
+caption = "\nSource: Twitter's API via academictwitteR")
+## View plot
+plot_timeSeriesDay2021
+## Plot the top #disruptJMM Tweeters in the dataset
+plot_topTweeters <- twitterData_tidy %>%
+select(user_username) %>%
+group_by(user_username) %>%
+summarize(n=n()) %>%
+arrange(desc(n)) %>%
+top_n(8, n) %>%
+ggplot(aes(x = reorder(user_username, - n), y = n)) +
+theme_classic() +
+scale_fill_grey(start = 0.25, end = 2.75) +
+geom_bar(stat = "identity") +
+theme(axis.text.x = element_text(angle = 335,
+vjust = 0.5,
+hjust = 0.5),
+plot.title = element_text(face = "bold")) +
+labs (x = "Username", y = "Tweet Count",
+title = "Top #disruptJMM Tweeters",
+subtitle = "2020 - 2021",
+caption = "\nSource: Twitter's API via academictwitteR")
+## View plot
+plot_topTweeters
+####################### Save Dave Visualizations #######################
+## Create a folder for data visualizations
+dir.create("visualizations")
+## Save plot as a png file
+ggsave(filename = "plot_timeSeriesMonth.png",
+plot = plot_timeSeriesMonth,
+path = "visualizations/")
+## Save plot as a png file
+ggsave(filename = "plot_timeSeriesDay2020.png",
+plot = plot_timeSeriesDay2020,
+path = "visualizations/")
+## Save plot as a png file
+ggsave(filename = "plot_timeSeriesDay2021.png",
+plot = plot_timeSeriesDay2021,
+path = "visualizations/")
+## Save plot as a png file
+ggsave(filename = "plot_topTweeters.png",
+plot = plot_topTweeters,
+path = "visualizations/")

.devcontainer/devcontainer.json

@@ -0,0 +1,25 @@
+// For format details, see https://aka.ms/devcontainer.json. For config options, see the
+// README at: https://github.com/devcontainers/templates/tree/main/src/python
+{
+	"name": "Python 3",
+	// Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+	"image": "mcr.microsoft.com/devcontainers/python:2-3.14-bookworm",
+
+	// Features to add to the dev container. More info: https://containers.dev/features.
+	// "features": {},
+
+	// Use 'forwardPorts' to make a list of ports inside the container available locally.
+	// "forwardPorts": [],
+
+	// Use 'postCreateCommand' to run commands after the container is created.
+	"postCreateCommand": "pip3 install --user -r requirements.txt",
+	"features": {
+		"ghcr.io/devcontainers/features/python:1": {}
+	},
+
+	// Configure tool-specific properties.
+	// "customizations": {},
+
+	// Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
+	// "remoteUser": "root"
+}

.github/dependabot.yml

@@ -0,0 +1,12 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for more information:
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+# https://containers.dev/guide/dependabot
+
+version: 2
+updates:
+ - package-ecosystem: "devcontainers"
+   directory: "/"
+   schedule:
+     interval: weekly

.gitignore

@@ -0,0 +1,2 @@
+.DS_Store
+.Rproj.user