To run the State of the Union demo app, run the following in the R console:

library(corporaexplorer)
run_sotu_app()

To run an alternative State of the Union app, organised by decade rather than by president, run the following in the R console:

library(corporaexplorer)
run_sotu_decade_app()

These two apps are created with the code below. The State of the Union texts and metadata are accessed through the sotu package.

Step 1: Preprocessing before “corporaexplorer”

Loading packages

library(sotu)
library(stringr)
library(corporaexplorer)

Creating data frame

## Merge data from 'sotu' package into one df
df <- sotu::sotu_meta
df$Text <- sotu::sotu_text %>%
    stringr::str_trim()

## Avoid clutter in corpus plot
# A. Distinguish between non-consecutive terms
df$president[97:100] <- "Grover Cleveland 1"
df$president[105:108] <- "Grover Cleveland 2"

# B. Get correct order of rows in data frame
    # in the cases where incumbent holds a final sotu before leaving office,
    # resulting in two sotus in one year
presidents <- unique(df$president)
df$president <- factor(df$president, levels = presidents)
df <- df[order(df$president),]
df$president <- as.character(df$president)

## Add decade variable for variation of app
df$decade <- stringr::str_sub(df$year, 1, 3) %>%
    paste0("0s")

# And add variable for informative document tab title in that app variation
df$for_tab_title <- paste(df$president, df$year)

Step 2: Creating app with “corporaexplorer”

App 1: State of the Union addresses grouped by president

Create ‘corporaexplorerobject’

corpus <- prepare_data(
    df,                                # the data frame created above
    date_based_corpus = FALSE,         # dates are not the organising principle in the corpus
    grouping_variable = "president",   # group the sotu addresses by president

# The remaining arguments are not strictly necessary, but we use them to fine-tune
      # how the corpus will be presented in the app

    within_group_identifier = "year",  # The tab header in document view will then be e.g. 
                                         # "Theodore Roosevelt – 1901"
    columns_doc_info =                 # metadata to be included in a "Document info" tab,
        colnames(df)[1:5],               # in this case the first five columns in the data frame
    tile_length_range = c(2, 10),      # fine-tuning the length of the tiles representing
                                         # the length of the addrsses
    use_matrix = FALSE                 # we don't create a document term matrix, as the corpus
                                         # is very small and searches will be fast anyway
)

Run app

explore(corpus)

App 2: State of the Union addresses grouped by decade

By just changing two arguments (or even one), we create an app with a quite different organisation of the texts.

corpus <- prepare_data(
    df,
    date_based_corpus = FALSE,
    grouping_variable = "decade",                # change grouping variable
    within_group_identifier = "for_tab_title",   # adjust tab header in document view
    columns_doc_info =
        colnames(df)[1:5],
    tile_length_range = c(2, 10),
    use_matrix = FALSE
)
explore(corpus)

Examples of app adjustments

See the documentation for explore() for all runtime options.

Example 1: Tile length By default, the length of the tiles representing documents in the app have varying lengths, depending on document length. For all tiles to of the same length:

explore(corpus,
                    plot_options = list(
                        tile_length = "uniform"
                    ))

Example 2: Plot colours To change the use of colours in the corpus map, use e.g.:

explore(corpus,
                    plot_options = list(
                        colours = "green"
                    ))

Examples of app usage

Some explore() calls with pre-filled sidebar input:

explore(corpus,
        search_input = list(search_terms = c("socialis",
                                             "communis",
                                             "soviet")))
explore(corpus,
        search_input = list(filter_terms = "speech--sotu_type"))
explore(corpus,
        search_input = list(
            search_terms = c("democracy",
                             "freedom",
                             "democratic--party"),
            filter_terms = "speech--sotu_type"
        ))