## ----eval=FALSE---------------------------------------------------------------
#  library(vosonSML)

## ----eval=FALSE---------------------------------------------------------------
#  twitterAuth <- Authenticate("twitter", bearerToken = "xxxxxxxxxxxx")

## ----eval=FALSE---------------------------------------------------------------
#  twitterAuth <- Authenticate(
#    "twitter",
#    appName = "My App",
#    apiKey = "xxxxxxxx",
#    apiSecret = "xxxxxxxx",
#    accessToken = "xxxxxxxx",
#    accessTokenSecret = "xxxxxxxx"
#  )

## ----eval=FALSE---------------------------------------------------------------
#  twitterAuth <- Authenticate(
#    "twitter",
#    appName = "An App",
#    apiKey = "xxxxxxxxxxxx",
#    apiSecret = "xxxxxxxxxxxx"
#  )

## ----eval=FALSE---------------------------------------------------------------
#  saveRDS(twitterAuth, file = "twitter_auth")

## ----eval=FALSE---------------------------------------------------------------
#  twitterAuth <- readRDS("twitter_auth")

## ----eval=FALSE---------------------------------------------------------------
#  twitterData <- twitterAuth |>
#    Collect(
#      searchTerm = "#auspol",
#      numTweets = 1000,
#      includeRetweets = FALSE,
#      writeToFile = TRUE,
#      verbose = TRUE
#    )

## ----eval=FALSE---------------------------------------------------------------
#  > twitterData$tweets
#  # A tibble: 999 x 90
#     user_id status_id created_at          screen_name text  source
#     <chr>   <chr>     <dttm>              <chr>       <chr> <chr>
#   1 xxxxxx… xxxxxxxx… 2020-01-09 12:02:13 xxxx        "htt… Twitt…
#   2 xxxxxx… xxxxxxxx… 2020-01-09 12:01:32 xxxxxxxxx   "Fir… Twitt…
#   3 xxxxxx… xxxxxxxx… 2020-01-09 12:00:44 xxxxxxxxxxx "Ser… Twitt…
#   [snip]
#   … with 989 more rows, and 84 more variables: display_text_width <dbl>,
#  ...

## ----eval=FALSE---------------------------------------------------------------
#  twitterData <- readRDS("2020-09-26_095354-TwitterData.rds")

## ----eval=FALSE---------------------------------------------------------------
#  actorNetwork <- twitterData |>
#    Create("actor", writeToFile = TRUE, verbose = TRUE)
#  
#  actorGraph <- actorNetwork |> Graph(writeToFile = TRUE, verbose = TRUE)

## ----eval=FALSE---------------------------------------------------------------
#  > actorNetwork
#  $edges
#  # A tibble: 1,725 x 5
#     from             to              edge_type timestamp         status_id
#     <fct>            <fct>           <fct>     <fct>             <fct>
#   1 xxxxxxxx         xxxxxxxx        quote     2020-01-09 12:00… xxxxxxxxxxxx…
#   2 xxxxxxxx         xxxxxxxxx       quote     2020-01-09 09:37… xxxxxxxxxxxx…
#  [snip]
#  # … with 1,715 more rows
#  
#  $nodes
#  # A tibble: 1,158 x 2
#     user_id             screen_name
#     <fct>               <fct>
#   1 xxxxxxxx            xxxx
#   2 xxxxxxxx            xxxxxxxxx
#  [snip]
#  # … with 1,148 more rows
#  
#  attr(,"class")
#  [1] "list"    "network" "actor"   "twitter"

## ----eval=FALSE---------------------------------------------------------------
#  > actorGraph
#  IGRAPH bc177a6 DN-- 1158 1725 --
#  + attr: type (g/c), name (v/c), screen_name (v/c), label (v/c),
#  | edge_type (e/c), timestamp (e/c), status_id (e/c)
#  + edges from bc177a6 (vertex names):
#   [1] xxxxxxxx           ->xxxxxxxx
#   [2] xxxxxxxx           ->xxxxxxxxx
#  [snip]
#  + ... omitted several edges

## ----eval=FALSE---------------------------------------------------------------
#  library(igraph)
#  
#  # remove edges that are not reply edges
#  g2 <- delete.edges(actorGraph, which(E(actorGraph)$edge_type != "reply"))
#  
#  # get the giant component
#  cc <- clusters(g2)
#  g2 <- induced_subgraph(g2, which(cc$membership == which.max(cc$csize)))
#  
#  # open and write plot to a png file
#  png("twitter_actor_reply_gc.png", width = 600, height = 600)
#  plot(g2, vertex.label = "", vertex.size = 4, edge.arrow.size = 0.5)
#  dev.off()

## ----eval=FALSE---------------------------------------------------------------
#  actorGraphWithText <- twitterData |>
#    Create("actor") |> AddText(twitterData) |> Graph()

## ----eval=FALSE---------------------------------------------------------------
#  # get the index of nodes or users who tweeted the word "bushfire"
#  ind <- tail_of(
#    actorGraphWithText,
#    grep("bushfire", tolower(E(actorGraphWithText)$vosonTxt_tweet))
#  )
#  
#  # set node attribute
#  V(actorGraphWithText)$tweetedBushfires <- "no"
#  V(actorGraphWithText)$tweetedBushfires[ind] <- "yes"

## ----eval=FALSE---------------------------------------------------------------
#  # remove edges that are not reply edges
#  g3 <- delete.edges(
#    actorGraphWithText, which(E(actorGraphWithText)$edge_type != "reply")
#  )
#  
#  # get the giant component
#  cc <- clusters(g3)
#  g3 <- induced_subgraph(g3, which(cc$membership == which.max(cc$csize)))
#  
#  # set node colour based on tweeted bushfires attribute value
#  V(g3)$color <- ifelse(V(g3)$tweetedBushfires == "yes", "red", "grey")
#  
#  # open and write plot to a png file
#  png("twitter_actor_reply_gc_bushfires.png", width = 600, height = 600)
#  plot(g3, vertex.label = "", vertex.size = 4, edge.arrow.size = 0.5)
#  dev.off()

## ----eval=FALSE---------------------------------------------------------------
#  # save the graph as a graphml file
#  write.graph(g3, "twitter_reply_gc_bushfires.graphml", format = "graphml")

## ----eval=FALSE---------------------------------------------------------------
#  # create an actor network with user metadata
#  actorGraphWithUserAttr <- actorNetwork |>
#    AddUserData(twitterData, lookupUsers = TRUE, twitterAuth = twitterAuth) |>
#    Graph(writeToFile = TRUE)

## ----eval=FALSE---------------------------------------------------------------
#  # create an activity network with tweet text
#  activityNetwork <- twitterData |> Create("activity") |> AddText(twitterData)
#  activityGraph <- activityNetwork |> Graph(writeToFile = TRUE)

## ----eval=FALSE---------------------------------------------------------------
#  > activityNetwork
#  $nodes
#  # A tibble: 1,408 x 5
#     status_id    user_id    screen_name created_at   vosonTxt_tweet
#     <chr>        <chr>      <chr>       <chr>        <chr>
#   1 xxxxxxxxxxx… xxxxxxxx   xxxx        2020-01-09 … "xxxxxxxxxxxxxxxxxxxxx…
#   2 xxxxxxxxxxx… xxxxxxxx   xxxxxxxxx   2020-01-09 … "xxxxxxxxxxxxxxxxxxxxx…
#   [snip]
#   # … with 1,398 more rows
#  
#  $edges
#  # A tibble: 662 x 3
#     from                to                  edge_type
#     <chr>               <chr>               <chr>
#   1 xxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxx quote
#   2 xxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxx quote
#  [snip]
#  # … with 652 more rows
#  
#  attr(,"class")
#  [1] "list"       "network"    "activity"   "twitter"    "voson_text"

## ----eval=FALSE---------------------------------------------------------------
#  > activityGraph
#  IGRAPH e60c486 DN-- 1408 662 --
#  + attr: type (g/c), name (v/c), user_id (v/c), screen_name (v/c),
#  | created_at (v/c), vosonTxt_tweet (v/c), label (v/c), edge_type (e/c)
#  + edges from e60c486 (vertex names):
#   [1] xxxx->xxxx
#   [2] xxxx->xxxx
#  [snip]
#  + ... omitted several edges

## ----eval=FALSE---------------------------------------------------------------
#  # create a subgraph containing nodes of components that have more than 5 nodes
#  cc <- clusters(activityGraph)
#  g4 <- induced_subgraph(
#    activityGraph,
#    which(cc$membership %in% which(cc$csize > 5))
#  )
#  
#  # set node colour based on if tweet contains the word "bushfire"
#  ind <- grep("bushfire", tolower(V(g4)$vosonTxt_tweet))
#  V(g4)$color <- "grey"
#  V(g4)$color[ind] <- "red"
#  
#  # open and write plot to a png file
#  png("twitter_activity.png", width = 600, height = 600)
#  plot(g4, vertex.label = "", vertex.size = 4, edge.arrow.size = 0.5)
#  dev.off()

## ----eval=FALSE---------------------------------------------------------------
#  # create a 2-mode network with the hashtag "#auspol" removed
#  twomodeNetwork <- twitterData |>
#    Create("twomode", removeTermsOrHashtags = c("#auspol"))
#  twomodeGraph <- twomodeNetwork |> Graph()

## ----eval=FALSE---------------------------------------------------------------
#  > twomodeNetwork
#  $nodes
#  # A tibble: 1,146 x 2
#     entity_id                    display_name
#     <chr>                        <chr>
#   1 xxxxxxxx                     xxxx
#   2 xxxxxxxx                     xxxxxxxxx
#   3 #auspol2020                  #auspol2020
#   4 #australianbushfiredisaster  #australianbushfiredisaster
#  [snip]
#  # … with 1,136 more rows
#  
#  $edges
#  # A tibble: 1,675 x 5
#     from      to                      edge_type timestamp        status_id
#     <fct>     <fct>                   <fct>     <fct>            <fct>
#   1 xxxxxxxx  #auspol2020             hashtag   2020-01-09 12:0… xxxxxxxxxxxx…
#   2 xxxxxxxx  #australianbushfiredis… hashtag   2020-01-09 12:0… xxxxxxxxxxxx…
#  [snip]
#  # … with 1,665 more rows
#  
#  attr(,"class")
#  [1] "list"    "network" "twomode" "twitter"

## ----eval=FALSE---------------------------------------------------------------
#  > twomodeGraph
#  IGRAPH 68bd240 DN-- 1146 1675 --
#  + attr: type (g/c), name (v/c), display_name (v/c), label (v/c),
#  | edge_type (e/c), timestamp (e/c), status_id (e/c)
#  + edges from 68bd240 (vertex names):
#   [1] xxxx -> #auspol2020
#   [2] xxxx -> #australianbushfiredisaster
#  [snip]
#  + ... omitted several edges

## ----eval=FALSE---------------------------------------------------------------
#  # get index of nodes that are in the top 5 by highest in-degree
#  # this is the top 5 used hashtags, as all users have 0 in-degree
#  # in this network
#  ind <- order(degree(twomodeGraph, mode = "in"), decreasing = TRUE)[1:5]
#  
#  # get index of nodes with an edge directed to the top 5 hashtags
#  # this is users who have tweeted with these hashtags
#  ind2 <- unlist(
#    lapply(ind, function(x) neighbors(twomodeGraph, x, mode = "in"))
#  )
#  
#  # create a subgraph containing only the top 5 used hashtags and related users
#  g5 <- induced_subgraph(twomodeGraph, c(ind, as.numeric(ind2)))
#  
#  # set node colour and label based on in-degree
#  # only hashtag nodes are set to blue and with label attribute set
#  V(g5)$color <- "grey"
#  V(g5)$color[which(degree(g5, mode = "in") > 0)] <- "blue"
#  V(g5)$label2 <- ifelse(degree(g5, mode = "in") > 0, V(g5)$label, "")
#  
#  # open and write plot to a png file
#  png("twitter_twomode.png", width = 600, height = 600)
#  plot(g5, vertex.label = V(g5)$label2, vertex.size = 4, edge.arrow.size = 0.5,
#       vertex.label.cex = 1.8, vertex.label.color = "red")
#  dev.off()

## ----eval=FALSE---------------------------------------------------------------
#  # additional required packages for tokenization
#  install.packages("stopwords")
#  
#  # create a semantic network with some common terms removed
#  # include only the top 5% occurring terms in the network
#  semanticNetwork <- twitterData |> Create(
#     "semantic",
#     removeTermsOrHashtags = c("#auspol", "auspol", "australia"),
#     termFreq = 5
#  )
#  
#  # create an undirected graph
#  semanticGraph <- semanticNetwork |> Graph(directed = FALSE)

## ----eval=FALSE---------------------------------------------------------------
#  > semanticNetwork
#  $nodes
#  # A tibble: 799 x 1
#     value
#     <fct>
#   1 just
#   2 one
#   3 fire
#   4 going
#   5 still
#   6 hard
#   7 trying
#   8 since
#   9 try
#  10 sick
#  # … with 789 more rows
#  
#  $edges
#  # A tibble: 10,990 x 3
#     from        to                           weight
#     <fct>       <fct>                         <int>
#   1 #auspol2020 #australianbushfiredisaster       2
#   2 #auspol2020 government                        2
#   3 #auspol2020 fire                              4
#   4 #auspol2020 australian                        2
#   5 #auspol2020 bushfire                          2
#   6 #auspol2020 fires                             4
#   7 #auspol2020 #australiafires                   1
#   8 #auspol2020 #australianbushfiresdisaster      1
#   9 #auspol2020 #australia                        4
#  10 #auspol2020 bushfires                         2
#  # … with 10,980 more rows
#  
#  attr(,"class")
#  [1] "list"     "network"  "semantic" "twitter"

## ----eval=FALSE---------------------------------------------------------------
#  > semanticGraph
#  IGRAPH cb8c381 UNW- 799 10990 --
#  + attr: type (g/c), name (v/c), label (v/c), weight (e/n)
#  + edges from cb8c381 (vertex names):
#   [1] #australianbushfiredisaster --#auspol2020
#   [2] government                  --#auspol2020
#   [3] fire                        --#auspol2020
#   [4] australian                  --#auspol2020
#   [5] bushfire                    --#auspol2020
#   [6] fires                       --#auspol2020
#   [7] #australiafires             --#auspol2020
#   [8] #australianbushfiresdisaster--#auspol2020
#  + ... omitted several edges

## ----eval=FALSE---------------------------------------------------------------
#  # get index of the nodes whose term contains "bushfire"
#  ind <- grep("bushfire", tolower(V(semanticGraph)$name))
#  
#  # create a subgraph containing only bushfire terms
#  g6 <- induced_subgraph(semanticGraph, ind)
#  
#  # open and write plot to a png file
#  # plotted with large-graph-layout algorithm and edge weights
#  png("twitter_semantic.png", width = 600, height = 600)
#  plot(g6, layout = layout_with_lgl(g6), vertex.shape = "none", vertex.size = 4,
#       edge.width = 1 + log(E(g2)$weight))
#  dev.off()

## ----eval=FALSE---------------------------------------------------------------
#  # create auth object with api key
#  youtubeAuth <- Authenticate("youtube", apiKey = "xxxxxxxx")

## ----eval=FALSE---------------------------------------------------------------
#  videoIDs <- c(
#    "xxxxxx",
#    "https://www.youtube.com/watch?v=xxxxxxxx",
#    "https://youtu.be/xxxxxxxx")
#  )

## ----eval=FALSE---------------------------------------------------------------
#  videoID <- "https://www.youtube.com/watch?v=pJ_NyEYRkLQ"
#  youtubeData <- youtubeAuth |>
#    Collect(videoID, maxComments = 500, writeToFile = TRUE)

## ----eval=FALSE---------------------------------------------------------------
#  > str(youtubeData)
#  Classes ‘dataource’, ‘youtube’ and 'data.frame':	603 obs. of  12 variables:
#   $ Comment              : chr  "xxxxx"
#   $ AuthorDisplayName    : chr  "xx" "xx" "xx" "xx"
#   $ AuthorProfileImageUrl: chr  "https://xx" "https://xx" "https://xx"
#   $ AuthorChannelUrl     : chr  "http://xx" "http://xx" "http://xx" "http://xx"
#   $ AuthorChannelID      : chr  "xx" "xx" "xx" "xx"
#   $ ReplyCount           : chr  "0" "0" "0" "0"
#   $ LikeCount            : chr  "0" "0" "0" "0"
#   $ PublishedAt          : chr  "2020-01-10T02:23:43" "2020-01-09T20:56:23"
#                                 "2020-01-09T20:44:00" "2020-01-09T19:31:32"
#   $ UpdatedAt            : chr  "2020-01-10T02:23:43" "2020-01-09T20:56:23"
#                                 "2020-01-09T20:44:00" "2020-01-09T19:31:32"
#   $ CommentID            : chr  "xx" "xx" "xx" "xx"
#   $ ParentID             : chr  NA NA NA NA
#   $ VideoID              : chr  "pJ_NyLQ" "pJ_NyLQ" "pJ_NyLQ" "pJ_NyLQ"

## ----eval=FALSE---------------------------------------------------------------
#  # read dataframe from file
#  youtubeData <- readRDS("2020-09-26_095354-YoutubeData.rds")

## ----eval=FALSE---------------------------------------------------------------
#  actorNetwork <- youtubeData |> Create("actor") |> AddText(youtubeData)
#  actorGraph <- actorNetwork |> Graph(writeToFile = TRUE)

## ----eval=FALSE---------------------------------------------------------------
#  > actorNetwork
#  $nodes
#  # A tibble: 522 x 3
#     id                       screen_name                         node_type
#     <chr>                    <chr>                               <chr>
#   1 xxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxx                      actor
#   2 xxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxx                       actor
#  [snip]
#  # … with 512 more rows
#  
#  $edges
#  # A tibble: 604 x 6
#     from      to       video_id comment_id   edge_type vosonTxt_comment
#     <chr>     <chr>    <chr>    <chr>        <chr>     <chr>
#   1 xxxxxxxx… VIDEOID… pJ_NyEY… xxxxxxxxxxx… comment   "xxxxx"
#   2 xxxxxxxx… VIDEOID… pJ_NyEY… xxxxxxxxxxx… comment   "xxxxx"
#  [snip]
#  # … with 594 more rows
#  
#  attr(,"class")
#  [1] "list"       "network"    "actor"      "youtube"    "voson_text"

## ----eval=FALSE---------------------------------------------------------------
#  actorGraph <- youtubeData |> Create("actor") |> AddText(youtubeData) |> Graph()

## ----eval=FALSE---------------------------------------------------------------
#  > actorGraph
#  IGRAPH 79e5456 DN-- 522 604 --
#  + attr: type (g/c), name (v/c), screen_name (v/c), node_type (v/c),
#  | label (v/c), video_id (e/c), comment_id (e/c), edge_type (e/c),
#  | vosonTxt_comment (e/c)
#  + edges from 79e5456 (vertex names):
#  [1] xxxx->VIDEOID:pJ_NyEYRkLQ
#  [2] xxxx->VIDEOID:pJ_NyEYRkLQ
#  [snip]
#  + ... omitted several edges

## ----eval=FALSE---------------------------------------------------------------
#  > table(E(actorGraph)$edge_type)
#     comment reply-comment     self-loop
#         500           103             1

## ----eval=FALSE---------------------------------------------------------------
#  # change color of nodes with type video to red and others grey
#  V(actorGraph)$color <- ifelse(
#    V(actorGraph)$node_type == "video", "red", "grey"
#  )
#  
#  # open and write plot to a png file
#  png("youtube_actor.png", width = 600, height = 600)
#  plot(actorGraph, vertex.label = "", vertex.size = 4, edge.arrow.size = 0.5)
#  dev.off()

## ----eval=FALSE---------------------------------------------------------------
#  # removed edges that are not of type reply-comment
#  g2 <- delete.edges(
#    actorGraph, which(E(actorGraph)$edge_type != "reply-comment")
#  )
#  
#  # check number of isolates
#  > length(which(degree(g2) == 0))
#  [1] 417
#  
#  # remove isolates
#  g2 <- delete.vertices(g2, which(degree(g2) == 0))
#  
#  # get node indexes for the tails of edges that have comments containing
#  # words of interest change the indexed node colors to red and others grey
#  V(g2)$color <- "grey"
#  ind <- tail_of(
#    actorGraph,
#    grep("arson|backburn|climate change", tolower(E(g2)$vosonTxt_comment))
#  )
#  V(g2)$color[ind] <- "red"
#  
#  # open and write plot to a png file
#  png("youtube_actor_reply.png", width = 600, height = 600)
#  plot(g2, vertex.label = "", vertex.size = 4, edge.arrow.size = 0.5)
#  dev.off()

## ----eval=FALSE---------------------------------------------------------------
#  actorNetwork_withVideoInfo <- actorNetwork |> AddVideoData(youtubeAuth)

## ----eval=FALSE---------------------------------------------------------------
#  > actorNetwork_withVideoInfo
#  $nodes
#  # A tibble: 522 x 3
#     id                       screen_name                         node_type
#     <chr>                    <chr>                               <chr>
#   1 xxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxx                       actor
#   2 xxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxx                       actor
#  [snip]
#  # … with 512 more rows
#  
#  $edges
#  # A tibble: 604 x 9
#     from  to    video_id comment_id edge_type vosonTxt_comment video_title
#     <chr> <chr> <chr>    <chr>      <chr>     <chr>            <chr>
#   1 xxxx… xxxx… pJ_NyEY… xxxxxxxxx… comment   xxxxxxxxxxxx … Australia …
#   2 xxxx… xxxx… pJ_NyEY… xxxxxxxxx… comment   "xxxx"         Australia …
#  [snip]
#  # … with 594 more rows, and 2 more variables: video_description <chr>,
#  #   video_published_at <chr>
#  
#  $videos
#  # A tibble: 1 x 6
#   VideoID  VideoTitle  VideoDescription VideoPublishedAt ChannelID ChannelTitle
#   <chr>    <chr>       <chr>            <chr>            <chr>     <chr>
#  1 pJ_NyEY… Australia … "As Australia ba… 2020-01-05T12:3… UCknLrEd… DW News
#  
#  attr(,"class")
#  [1] "list"             "network"          "actor"            "youtube"
#  [5] "voson_text"       "voson_video_data"

## ----eval=FALSE---------------------------------------------------------------
#  activityNetwork <- youtubeData |> Create("activity") |> AddText(youtubeData)
#  activityGraph <- activityNetwork |> Graph()

## ----eval=FALSE---------------------------------------------------------------
#  > activityNetwork
#  $edges
#  # A tibble: 603 x 3
#     from                       to                  edge_type
#     <chr>                      <chr>               <chr>
#   1 xxxxxxxxxxxxxxxxxxxxxxxxxx VIDEOID:pJ_NyEYRkLQ comment
#   2 xxxxxxxxxxxxxxxxxxxxxxxxxx VIDEOID:pJ_NyEYRkLQ comment
#  [snip]
#  # … with 593 more rows
#  
#  $nodes
#  # A tibble: 604 x 8
#     id    video_id published_at updated_at author_id screen_name node_type
#     <chr> <chr>    <chr>        <chr>      <chr>     <chr>       <chr>
#   1 xxxx… pJ_NyEY… 2020-01-10T… 2020-01-1… xxxxxxxx… xxxxxxxxxx… comment
#   2 xxxx… pJ_NyEY… 2020-01-09T… 2020-01-0… xxxxxxxx… xxxxxxxxxx… comment
#  [snip]
#  # … with 594 more rows, and 1 more variable: vosonTxt_comment <chr>
#  
#  attr(,"class")
#  [1] "list"       "network"    "activity"   "youtube"    "voson_text"

## ----eval=FALSE---------------------------------------------------------------
#  IGRAPH 02664d1 DN-- 604 603 --
#  + attr: type (g/c), name (v/c), video_id (v/c), published_at (v/c),
#  | updated_at (v/c), author_id (v/c), screen_name (v/c), node_type
#  | (v/c), vosonTxt_comment (v/c), label (v/c), edge_type (e/c)
#  + edges from 02664d1 (vertex names):
#  [1] xxxx->VIDEOID:pJ_NyEYRkLQ
#  [2] xxxx->VIDEOID:pJ_NyEYRkLQ
#  [3] xxxx->VIDEOID:pJ_NyEYRkLQ
#  [4] xxxx->VIDEOID:pJ_NyEYRkLQ
#  [5] xxxx->VIDEOID:pJ_NyEYRkLQ
#  [6] xxxx->VIDEOID:pJ_NyEYRkLQ
#  + ... omitted several edges

## ----eval=FALSE---------------------------------------------------------------
#  # set all video node colors to red and others to grey
#  V(activityGraph)$color <- "grey"
#  V(activityGraph)$color[which(V(activityGraph)$node_type == "video")] <- "red"
#  
#  # get node indexes of comments that contain terms of interest
#  # set their node colors to blue
#  ind <- grep(
#    "arson|backburn|climate change", tolower(V(activityGraph)$vosonTxt_comment)
#  )
#  V(activityGraph)$color[ind] <- "blue"
#  
#  # open and write plot to a png file
#  png("youtube_activity.png", width = 600, height = 600)
#  plot(activityGraph, vertex.label = "", vertex.size = 4, edge.arrow.size = 0.5)
#  dev.off()

## ----eval=FALSE---------------------------------------------------------------
#  myThreadUrls <- c(
#    "https://www.reddit.com/r/xxxxxx/comments/xxxxxx/x_xxxx_xxxxxxxxx/",
#    "https://www.reddit.com/r/xxxxxx/comments/xxxxxx/x_xxxx_xxxxxxxxx/"
#  )

## ----eval=FALSE---------------------------------------------------------------
#  myThreadUrls <- "https://www.reddit.com/r/worldnews/comments/elcb9b/australias_leaders_deny_link_between_climate/"
#  redditData <- Authenticate("reddit") |>
#                Collect(threadUrls = myThreadUrls, writeToFile = TRUE)

## ----eval=FALSE---------------------------------------------------------------
#  > str(redditData)
#  Classes ‘tbl_df’, ‘tbl’, ‘datasource’, ‘reddit’ and 'data.frame':	
#    767 obs. of  22 variables:
#   $ id              : int  1 2 3 4 5 6 7 8 9 10 ...
#   $ structure       : chr  "1" "4_1_1_1_1_1_1_1_1_1" "4_1_1_4_2_1_1_1_1_1" ...
#   $ post_date       : chr  "2020-01-07 14:34:58" "2020-01-07 14:34:58" ...
#   $ post_date_unix  : num  1.58e+09 1.58e+09 1.58e+09 1.58e+09 1.58e+09 ...
#   $ comm_id         : chr  "xxxx" "xxxx" "xxxx" "xxxx" ...
#   $ comm_date       : chr  "2020-01-07 19:11:10" "2020-01-07 21:04:05" ...
#   $ comm_date_unix  : num  1.58e+09 1.58e+09 1.58e+09 1.58e+09 1.58e+09 ...
#   $ num_comments    : int  4435 4435 4435 4435 4435 4435 4435 4435 4435 4435 ...
#   $ subreddit       : chr  "worldnews" "worldnews" "worldnews" "worldnews" ...
#   $ upvote_prop     : num  0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 ...
#   $ post_score      : int  45714 45714 45714 45712 45714 45710 45720 45712 ..
#   $ author          : chr  "xxxx" "xxxx" "xxxx" "xxxx" ...
#   $ user            : chr  "xxxx" "xxxx" "xxxx" "xxxx" ...
#   $ comment_score   : int  1904 136 17 13 9 9 125 4 6 12 ...
#   $ controversiality: int  0 0 0 0 0 0 0 0 0 0 ...
#   $ comment         : chr  "xxxx...
#   $ title           : chr  "Australia’s leaders deny link between climate change and the country’s devastating bushfires" "Australia’s leaders deny link between climate change and the country’s devastating bushfires" "Australia’s leaders deny link between climate change and the country’s devastating bushfires" "Australia’s leaders deny link between climate change and the country’s devastating bushfires" ...
#   $ post_text       : chr  "" "" "" "" ...
#   $ link            : chr  "https://www.theglobeandmail.com/world/article-australias-leaders-unmoved-on-climate-action-after-devastating-2/" "https://www.theglobeandmail.com/world/article-australias-leaders-unmoved-on-climate-action-after-devastating-2/" "https://www.theglobeandmail.com/world/article-australias-leaders-unmoved-on-climate-action-after-devastating-2/" "https://www.theglobeandmail.com/world/article-australias-leaders-unmoved-on-climate-action-after-devastating-2/" ...
#   $ domain          : chr  "theglobeandmail.com" "theglobeandmail.com" "theglobeandmail.com" "theglobeandmail.com" ...
#   $ url             : chr  "https://www.reddit.com/r/worldnews/comments/elcb9b/australias_leaders_deny_link_between_climate/" "https://www.reddit.com/r/worldnews/comments/elcb9b/australias_leaders_deny_link_between_climate/" "https://www.reddit.com/r/worldnews/comments/elcb9b/australias_leaders_deny_link_between_climate/" "https://www.reddit.com/r/worldnews/comments/elcb9b/australias_leaders_deny_link_between_climate/" ...
#   $ thread_id       : chr  "elcb9b" "elcb9b" "elcb9b" "elcb9b" ...

## ----eval=FALSE---------------------------------------------------------------
#  redditData <- readRDS("2020-09-26_095354-RedditData.rds")

## ----eval=FALSE---------------------------------------------------------------
#  actorNetwork <- redditData |> Create("actor") |> AddText(redditData)
#  actorGraph <- actorNetwork |> Graph(writeToFile = TRUE)

## ----eval=FALSE---------------------------------------------------------------
#  > actorNetwork
#  $nodes
#  # A tibble: 439 x 2
#        id user
#     <int> <chr>
#   1     1 xxxxxxxxxx
#   2     2 xxxxxxxxxxxxxx
#  [snip]
#  # … with 429 more rows
#  
#  $edges
#  # A tibble: 768 x 8
#      from    to subreddit thread_id comment_id comm_id vosonTxt_comment   title
#     <int> <int> <chr>     <chr>        <dbl> <chr>   <chr>                <chr>
#   1     1   439 worldnews elcb9b           1 xxxxxxx "xxxxxxxxxxxxxxxxxxx NA
#   2     2    73 worldnews elcb9b           2 xxxxxxx "xxxxxxxxxxxxxxxxxxx NA
#  [snip]
#  … with 758 more rows
#  
#  attr(,"class")
#  [1] "list"       "network"    "actor"      "reddit"     "voson_text"

## ----eval=FALSE---------------------------------------------------------------
#  > actorGraph
#  IGRAPH 5a5d5b9 DN-- 439 768 --
#  + attr: type (g/c), name (v/c), user (v/c), label (v/c), subreddit
#  | (e/c), thread_id (e/c), comment_id (e/n), comm_id (e/c),
#  | vosonTxt_comment (e/c), title (e/c)
#  + edges from 5a5d5b9 (vertex names):
#   [1] 1 ->439 2 ->73  3 ->113 4 ->120 5 ->120 6 ->17  7 ->194 8 ->20  9 ->20
#  [10] 10->165 11->165 12->1   13->2   14->3   15->4   16->5   17->6   18->7
#  [19] 19->8   20->9   21->10  22->11  23->12  2 ->13  24->3   7 ->18  25->23
#  [28] 26->2   3 ->24  27->18  28->1   29->2   18->27  1 ->28  30->2   31->7
#  [37] 25->1   32->2   33->31  34->1   2 ->32  35->7   25->34  36->2   7 ->35
#  [46] 37->1   38->2   39->7   40->1   41->2   42->7   43->1   2 ->41  44->7
#  + ... omitted several edges

## ----eval=FALSE---------------------------------------------------------------
#  # set node color of original post to red based on presence of title edge
#  # attribute set other node colors to grey
#  V(actorGraph)$color <- "grey"
#  V(actorGraph)$color[tail_of(
#    actorGraph, which(!is.na(E(actorGraph)$title))
#  )] <- "red"
#  
#  # get node indexes for the tails of edges that have comments containing
#  # words of interest set their node colors to blue
#  ind <- tail_of(
#    actorGraph,
#    grep("arson|starting fires",
#         tolower(E(actorGraph)$vosonTxt_comment))
#  )
#  V(actorGraph)$color[ind] <- "blue"
#  
#  # open and write plot to a png file
#  png("reddit_actor.png", width = 600, height = 600)
#  plot(actorGraph, vertex.label = "", vertex.size = 4, edge.arrow.size = 0.5)
#  dev.off()

## ----eval=FALSE---------------------------------------------------------------
#  activityNetwork <- redditData |> Create("activity") |> AddText(redditData)
#  activityGraph <- activityNetwork |> Graph(writeToFile = TRUE)

## ----eval=FALSE---------------------------------------------------------------
#  > activityNetwork
#  $nodes
#  # A tibble: 768 x 10
#     id    thread_id comm_id datetime     ts subreddit user  node_type
#     <chr> <chr>     <chr>   <chr>     <dbl> <chr>     <chr> <chr>
#   1 elcb… elcb9b    xxxxxxx 2020-01… 1.58e9 worldnews xxxx… comment
#   2 elcb… elcb9b    xxxxxxx 2020-01… 1.58e9 worldnews xxxx… comment
#  [snip]
#  # … with 758 more rows, and 2 more variables: vosonTxt_comment <chr>,
#  #   title <chr>
#  
#  $edges
#  # A tibble: 767 x 3
#     from                       to                       edge_type
#     <chr>                      <chr>                    <chr>
#   1 elcb9b.1                   elcb9b.0                 comment
#   2 elcb9b.4_1_1_1_1_1_1_1_1_1 elcb9b.4_1_1_1_1_1_1_1_1 comment
#  [snip]
#  # … with 757 more rows
#  
#  attr(,"class")
#  [1] "list"       "network"    "activity"   "reddit"     "voson_text"
#  

## ----eval=FALSE---------------------------------------------------------------
#  > activityGraph
#  IGRAPH 09e30ea DN-- 768 767 --
#  + attr: type (g/c), name (v/c), thread_id (v/c), comm_id (v/c),
#  | datetime (v/c), ts (v/n), subreddit (v/c), user (v/c), node_type
#  | (v/c), vosonTxt_comment (v/c), title (v/c), label (v/c), edge_type
#  | (e/c)
#  + edges from 09e30ea (vertex names):
#  [1] elcb9b.1                  ->elcb9b.0
#  [2] elcb9b.4_1_1_1_1_1_1_1_1_1->elcb9b.4_1_1_1_1_1_1_1_1
#  [3] elcb9b.4_1_1_4_2_1_1_1_1_1->elcb9b.4_1_1_4_2_1_1_1_1
#  [4] elcb9b.4_1_1_4_3_1_1_1_3_1->elcb9b.4_1_1_4_3_1_1_1_3
#  [5] elcb9b.4_1_1_4_3_1_1_1_3_2->elcb9b.4_1_1_4_3_1_1_1_3
#  + ... omitted several edges

## ----eval=FALSE---------------------------------------------------------------
#  # set original post node colors to red based on a node type of thread
#  # set other node colors to grey
#  V(activityGraph)$color <- "grey"
#  V(activityGraph)$color[which(V(activityGraph)$node_type == "thread")] <- "red"
#  
#  # get node indexes for nodes that have comment attributes containing words of interest
#  # set their node colors to blue
#  ind <- grep("arson|starting fires", tolower(V(activityGraph)$vosonTxt_comment))
#  V(activityGraph)$color[ind] <- "blue"
#  
#  # open and write plot to a png file
#  png("reddit_activity.png", width = 600, height = 600)
#  plot(activityGraph, vertex.label = "", vertex.size = 4, edge.arrow.size = 0.5)
#  dev.off()

## ----eval=FALSE---------------------------------------------------------------
#  # collect twitter data for the #auspol hashtag
#  auspolTwitterData <- twitterAuth |>
#    Collect(searchTerm = "#auspol", searchType = "recent", numTweets = 100)
#  
#  # collect twitter data for the #bushfire hashtag
#  bushfireTwitterData <- twitterAuth |>
#    Collect(searchTerm = "#bushfire", searchType = "popular", numTweets = 50)
#  
#  # combine the collected data for the different hashtags
#  twitterData <- Merge(auspolTwitterData, bushfireTwitterData, writeToFile = TRUE)

## ----eval=FALSE---------------------------------------------------------------
#  twitterData <- MergeFiles(
#    "2019TwitterBushfireData", pattern = "*TwitterData.rds"
#  )

## ----eval=FALSE---------------------------------------------------------------
#  # create an igraph of twitter actor network
#  actorGraph <- twitterData |> Create("actor") |> Graph(writeToFile = TRUE)

## ----eval=FALSE---------------------------------------------------------------
#  V(g3)$vosonCA_tweetedBushfires <- V(g3)$tweetedBushfires
#  write.graph(g3, "g3.graphml", format = "graphml")

