`r params$set_title`"

In wpa: Tools for Analysing and Visualising Viva Insights Data

library(wpa)

## Get user data
data <- params$data
stopwords <- params$stopwords
keep <- params$keep
seed <- params$seed

Introduction

Column {data-width=40%}

Introduction

In this report, you will find analysis on meeting subject lines. This will allow you to refine meeting exclusion rules and identify topics that drive collaboration patterns.

Column {data-width=60%}

Word cloud

data %>% tm_wordcloud(stopwords = stopwords, keep = keep)

Word Frequency

Column {data-width=50%}

Word Frequency - Plot

data %>% tm_freq(token = "words", stopwords = stopwords, keep = keep)

Column {data-width=50%}

Word Frequency - Table

data %>%
  tm_freq(
    token = "words",
    stopwords = stopwords,
    keep = keep,
    return = "table"
    ) %>%
  create_dt()

Phrase Frequency

Column {data-width=50%}

Phrase Frequency - Plot

data %>% tm_freq(token = "ngrams", stopwords = stopwords, keep = keep)

Column {data-width=50%}

Phrase Frequency - Table

data %>%
  tm_freq(
    token = "ngrams",
    stopwords = stopwords,
    keep = keep,
    return = "table"
    ) %>%
  create_dt()

Word co-occurrence

Column {data-width=50%}

Word co-occurrence - Plot

data %>% tm_cooc(stopwords = stopwords, seed = seed, return = "plot")

Column {data-width=50%}

Word co-occurrence - Table

data %>%
  tm_cooc(stopwords = stopwords, seed = seed, return = "table") %>%
  create_dt()

Top terms

Column {data-width=50%}

Top terms - Days

data %>% subject_scan(mode = "days", stopwords = stopwords)

Column {data-width=50%}

Top terms - Hours

data %>% subject_scan(mode = "hours", stopwords = stopwords)

wpa documentation built on Aug. 21, 2023, 5:11 p.m.