7  Visualisasi Kualitatif

8 Qualitative

Visualisasi data kualitatif membantu menyampaikan informasi yang lebih deskriptif.

8.1 Word Clouds and Specific Words

Menampilkan distribusi kata dalam teks berdasarkan frekuensi penggunaannya.

Code 
df <- tibble(
  iso_639_3 = c("zho", "wuu", "vie", "urd", "tur", "tha",
                "tgl", "tel", "tam", "swa", "spa", "rus",
                "pus", "por", "pnb", "pan", "msa", "mar",
                "kor", "kan", "jpn", "jav", "ita", "ind",
                "hin", "hau", "guj", "fra", "fas", "eng",
                "deu", "ben", "arz", "ara"),
  word = c("謝謝", "謝謝", "Cảm ơn ông", "بہت شکریہ", "Teşekkür ederim", "ขอบคุณ",
           "Salamat", "ధన్యవాదాలు", "நன்றி", "Asante", "Gracias", "Спасибо!",
           "شكرًا", "Obrigado", "شکریہ", "ਮੁਰਕੁਪਾ", "Terima kasih", "आभार आहे",
           "감사합니다", "ಧನ್ಯವಾದಗಳು", "ありがとう", "Matur nuwun sanget", "Grazie", "Terima kasih",
           "धन्यवाद", "Na gode", "આભાર", "Merci", "متشكرم", "Thank you",
           "Danke", "ধন্যবাদ", "شكراً", "شكراً"),
  name = c("Chinese", "Wu Chinese", "Vietnamese", "Urdu", "Turkish", "Thai",
           "Tagalog", "Telugu", "Tamil", "Swahili", "Spanish", "Russian",
           "Pushto", "Portuguese", "Western Panjabi", "Panjabi", "Malay", "Marathi",
           "Korean", "Kannada", "Japanese", "Javanese", "Italian", "Indonesian",
           "Hindi", "Hausa", "Gujarati", "French", "Persian", "English",
           "German", "Bengali", "Egyptian Arabic", "Arabic"),
  native_speakers = c(1200, 80, 75, 67, 78.5, 28,
                      28, 81, 69, 8, 480, 154,
                      55, 220, 120, 120, 77, 83,
                      77.2, 69, 125, 82, 90, 43,
                      322, 43.7, 55, 76.8, 60, 400,
                      95, 260, 65, 245),
  speakers = c(1200, 80, 75, 67, 88, 72,
               73, 81, 77, 98, 555, 239,
               55, 243, 120, 120, 277, 83,
               77.2, 69, 125, 82, 114, 199,
               442, 63.2, 55, 350.8, 110, 800,
               107.5, 280, 65, 515)
)
Code 
set.seed(1)
ggplot(df, aes(label = word)) +
  geom_text_wordcloud() +
  theme_minimal()

Code 
set.seed(1)
ggplot(df, aes(label = word, size = speakers)) +
  geom_text_wordcloud() +
  theme_minimal()

Code 
set.seed(1)
ggwordcloud(words = df$word, freq = df$speakers)

Code 
set.seed(1)
ggplot(df, aes(label = word, size = speakers)) +
  geom_text_wordcloud() +
  scale_size_area(max_size = 20) +
  theme_minimal()

Code 
set.seed(1)
ggplot(df, aes(label = word, size = speakers)) +
  geom_text_wordcloud(rm_outside = TRUE) +
  scale_size_area(max_size = 60) +
  theme_minimal()
Warning in wordcloud_boxes(data_points = points_valid_first, boxes = boxes, :
Some words could not fit on page. They have been removed.

Code 
set.seed(1)

# Data
df <- thankyou_words_small
df$angle <- sample(c(0, 45, 60, 90, 120, 180), nrow(df), replace = TRUE)

ggplot(df, aes(label = word, size = speakers, angle = angle)) +
  geom_text_wordcloud() +
  scale_size_area(max_size = 20) +
  theme_minimal()

Code 
set.seed(1)
ggplot(df, aes(label = word, size = speakers)) +
  geom_text_wordcloud(shape = "diamond") +
  scale_size_area(max_size = 20) +
  theme_minimal()

Code 
set.seed(1)
ggplot(df, aes(label = word, size = speakers)) +
  geom_text_wordcloud(shape = "star") +
  scale_size_area(max_size = 20) +
  theme_minimal()

8.2 Word Trees

Menampilkan struktur hierarki dari kata atau frasa dalam teks.