Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling 'rhs(lhs)'.

Child Barometer 2016 response data

Description

This data contains background variables and the responses to q3 "Missä asioissa olet hyvä? (Avokysymys)", q7 "Kertoisitko, mitä sinun mielestäsi kiusaaminen on? (Avokysymys)", and q11 "Mikä tekee sinut iloiseksi? (Avokysymys)" in the FSD3134 Lapsibarometri 2016 dataset.

Usage

child

Format

## 'child' A dataframe with 414 rows and 8 columns:

fsd_id

FSD case id

q3

'Which things are you good at?' response text

q7

'What do you think bullying is?' response text

q11

'What makes you happy?' response text

paino

Weight

gender

Gender)

major_region

Major region)

daycare_before_school

Daycare before pre-school

Source

<https://urn.fi/urn:nbn:fi:fsd:T-FSD3134>

Young People's Views on Development Cooperation 2012 response data

Description

This data contains background variables and the responses to q11_1 'Jatka lausetta: Kehitysmaa on maa, jossa... (Avokysymys)', q11_2 'Jatka lausetta: Kehitysyhteistyö on toimintaa, jossa... (Avokysymys)', q11_3' Jatka lausetta: Maailman kolme suurinta ongelmaa ovat... (Avokysymys)' in the FSD2821 Nuorten ajatuksia kehitysyhteistyöstä 2012 dataset.

Usage

dev_coop

Format

## 'dev_coop' A dataframe with 925 rows and 9 columns:

fsd_id

FSD case id

q11_1

response text for q11_1

q11_2

response text for q11_2

q11_3

response text for q11_3

paino

Weight

gender

Gender

year_of_birth

Year of Birth

region

Region of Residence

education_level

Education level

Source

<https://urn.fi/urn:nbn:fi:fsd:T-FSD2821>

English Sample Survey Data: Patient Joe

Description

This data contains English text responses to ""Joe’s doctor told him that he would need to return in two weeks to find out whether or not his condition had improved. But when Joe asked the receptionist for an appointment, he was told that it would be over a month before the next available appointment. What should Joe do?" as well as categorisation of these responses by two coders as either destructive, passive, somewhat proactive, or proactive.

Usage

english_sample_survey

Format

## 'english_sample_survey' A dataframe with 585 rows and 5 columns:

id

ID

label

Label: destructive, passive, somewhat proactive, or proactive

label_coder1

Label from coder 1

label_coder2

Label from coder 2

text

Text of response

Source

<https://doi.org/10.7802/2474>

Child Barometer 2016 Bullying response data in CoNLL-U format with NLTK stopwords removed and background variables

Description

This data contains the responses to q7 "Kertoisitko, mitä sinun mielestäsi kiusaaminen on? (Avokysymys)" in the FSD3134 Lapsibarometri 2016 dataset in CoNLL-U format with NLTK stopwords and punctuation removed plus weights and background variables.

Usage

fst_child

Format

## 'fst_child' A dataframe with 1580 rows and 18 columns:

doc_id

the identifier of the document

paragraph_id

the identifier of the paragraph

sentence_id

the identifier of the sentence

sentence

the text of the sentence for which this token is part of

token_id

Word index, integer starting at 1 for each new sentence; may be a range for multi-word tokens; may be a decimal number for empty nodes.

token

Word form or punctuation symbol.

lemma

Lemma or stem of word form.

upos

Universal part-of-speech tag.

xpos

Language-specific part-of-speech tag; underscore if not available.

feats

List of morphological features from the universal feature inventory or from a defined language-specific extension; underscore if not available.

head_token_id

Head of the current word, which is either a value of token_id or zero (0).

dep_rel

Universal dependency relation to the HEAD (root iff HEAD = 0) or a defined language-specific subtype of one.

deps

Enhanced dependency graph in the form of a list of head-deprel pairs.

misc

Any other annotation.

weight

Weight

gender

Gender

major_region

Major region

daycare_before_school

Daycare before pre-school

Source

<https://urn.fi/urn:nbn:fi:fsd:T-FSD3134>

Child Barometer 2016 Bullying response data in CoNLL-U format with NLTK stopwords removed

Description

This data contains the responses to q7 "Kertoisitko, mitä sinun mielestäsi kiusaaminen on? (Avokysymys)" in the FSD3134 Lapsibarometri 2016 dataset in CoNLL-U format with NLTK stopwords and punctuation removed.

Usage

fst_child_2

Format

## 'fst_child_2' A dataframe with 1580 rows and 14 columns:

doc_id

the identifier of the document

paragraph_id

the identifier of the paragraph

sentence_id

the identifier of the sentence

sentence

the text of the sentence for which this token is part of

token_id

Word index, integer starting at 1 for each new sentence; may be a range for multi-word tokens; may be a decimal number for empty nodes.

token

Word form or punctuation symbol.

lemma

Lemma or stem of word form.

upos

Universal part-of-speech tag.

xpos

Language-specific part-of-speech tag; underscore if not available.

feats

List of morphological features from the universal feature inventory or from a defined language-specific extension; underscore if not available.

head_token_id

Head of the current word, which is either a value of token_id or zero (0).

dep_rel

Universal dependency relation to the HEAD (root iff HEAD = 0) or a defined language-specific subtype of one.

deps

Enhanced dependency graph in the form of a list of head-deprel pairs.

misc

Any other annotation.

Source

<https://urn.fi/urn:nbn:fi:fsd:T-FSD3134>

Concept Network- Plot comparison Concept Network

Description

Creates a Concept Network plot from a list of edges and nodes (and their respective weights) which indicates unique words in this plot in comparison to another Network.

Usage

fst_cn_compare_plot(
  edges,
  nodes,
  concepts,
  unique_lemmas,
  name = NULL,
  concept_colour = "#cd1719",
  unique_colour = "#4DAF4A",
  min_edge = NULL,
  max_edge = NULL,
  min_node = NULL,
  max_node = NULL,
  title_size = 20
)

Arguments

Value

Plot of concept network with concept and unique words (nodes) highlighted.

Examples

pos_filter <- c("NOUN", "VERB", "ADJ", "ADV")
e1 <- fst_cn_edges(fst_child, "lyödä", pos_filter = pos_filter)
e2 <- fst_cn_edges(fst_child, "lyöminen", pos_filter = pos_filter)
n1 <- fst_cn_nodes(fst_child, e1)
n2 <- fst_cn_nodes(fst_child, e2)
u <- fst_cn_get_unique_separate(n1, n2)

fst_cn_compare_plot(e1, n1, "lyödä", unique_lemma = u)
fst_cn_compare_plot(e2, n2, "lyöminen", u, unique_colour = "purple")

Concept Network - Get TextRank edges

Description

This function takes a string of terms (separated by commas) or a single term and, using 'fst_cn_search()' find words connected to these searched terms. Then, a dataframe is returned of 'edges' between two words which are connected together in an frequently-occurring n-gram containing a concept term.

Usage

fst_cn_edges(
  data,
  concepts,
  threshold = NULL,
  norm = "number_words",
  pos_filter = NULL
)

Arguments

Value

Dataframe of co-occurrences between two connected words.

Examples

con <- "kiusata, lyöminen"
fst_cn_edges(fst_child, con, pos_filter = c("NOUN", "VERB", "ADJ", "ADV"))
fst_cn_edges(fst_child, con, pos_filter = 'VERB, NOUN')
fst_cn_edges(fst_child, "lyöminen", threshold = 2, norm = "number_resp")

Concept Network- Get unique nodes from a list of top n-grams tables

Description

Takes at least two tables of nodes and pagerank (output of 'fst_cn_nodes()') and finds nodes unique to one table.

Usage

fst_cn_get_unique(list)

Arguments

Value

Dataframe of words and whether word is unique or not.

Examples

pos_filter <- 'NOUN, VERB, ADJ, ADV'
e1 <- fst_cn_edges(fst_child, "lyödä", pos_filter = pos_filter)
e2 <- fst_cn_edges(fst_child, "lyöminen", pos_filter = pos_filter)
n1 <- fst_cn_nodes(fst_child, e1)
n2 <- fst_cn_nodes(fst_child, e2)
list_of_nodes <- list()
list_of_nodes <- append(list_of_nodes, list(n1))
list_of_nodes <- append(list_of_nodes, list(n2))
fst_cn_get_unique(list_of_nodes)

Concept Network- Get unique nodes from separate top n-grams tables

Description

Takes at least two tables of nodes and pagerank (output of 'fst_cn_nodes()') and finds nodes unique to one table.

Usage

fst_cn_get_unique_separate(table1, table2, ...)

Arguments

Value

Dataframe of words and whether word is unique or not.

Examples

pos_filter <- c("NOUN", "VERB", "ADJ", "ADV")
e1 <- fst_cn_edges(fst_child, "lyödä", pos_filter = pos_filter)
e2 <- fst_cn_edges(fst_child, "lyöminen", pos_filter = pos_filter)
n1 <- fst_cn_nodes(fst_child, e1)
n2 <- fst_cn_nodes(fst_child, e2)
fst_cn_get_unique_separate(n1, n2)

Concept Network - Get TextRank nodes

Description

This function takes a string of terms (separated by commas) or a single term and, using 'textrank_keywords()' from 'textrank' package, filters data based on 'pos_filter' ranks words which are the filtered for those connected to search terms.

Usage

fst_cn_nodes(data, edges, pos_filter = NULL)

Arguments

Value

A dataframe containing relevant lemmas and their associated pagerank.

Examples

con <- "kiusata, lyöminen"
cb <- fst_child
edges <- fst_cn_edges(cb, con, pos_filter = c("NOUN", "VERB", "ADJ", "ADV"))
edges2 <- fst_cn_edges(cb, con, pos_filter = 'NOUN, VERB, ADJ, ADV')
fst_cn_nodes(cb, edges, c("NOUN", "VERB", "ADJ", "ADV"))
fst_cn_nodes(cb, edges, 'NOUN, VERB, ADJ, ADV')

Plot Concept Network

Description

Creates a Concept Network plot from a list of edges and nodes (and their respective weights).

Usage

fst_cn_plot(edges, nodes, concepts, title = NULL)

Arguments

Value

Plot of Concept Network.

Examples

con <- "kiusata, lyöminen"
cb <- fst_child
edges <- fst_cn_edges(cb, con, pos_filter = c("NOUN", "VERB", "ADJ", "ADV"))
nodes <- fst_cn_nodes(cb, edges, c("NOUN", "VERB", "ADJ", "ADV"))
fst_cn_plot(edges = edges, nodes = nodes, concepts = con)

Concept Network - Search TextRank for concepts

Description

This function takes a string of terms (separated by commas) or a single term and, using 'textrank_keywords()' from 'textrank' package, filters data based on 'pos_filter' and finds words connected to search terms.

Usage

fst_cn_search(data, concepts, pos_filter = NULL)

Arguments

Value

Dataframe of n-grams containing searched terms.

Examples

con <- "kiusata, lyöminen, lyödä, potkia"
pf <- c("NOUN", "VERB", "ADJ", "ADV")
pf2 <- "NOUN, VERB, ADJ, ADV"
fst_cn_search(fst_child, concepts = con, pos_filter = pf)
fst_cn_search(fst_child, concepts = con, pos_filter = pf2)
fst_cn_search(fst_child, concepts = con)

Make comparison cloud

Description

Creates a comparison wordcloud showing words that occur differently between each group. Data is split based on different values in the 'field' column of formatted data. Results will be shown within the plots pane.

Usage

fst_comparison_cloud(
  data,
  field,
  pos_filter = NULL,
  max = 100,
  norm = NULL,
  use_svydesign_weights = FALSE,
  use_svydesign_field = FALSE,
  id = "",
  svydesign = NULL,
  use_column_weights = FALSE,
  exclude_nulls = FALSE,
  rename_nulls = "null_data"
)

Arguments

Value

A comparison cloud from wordcloud package.

Examples

fst_comparison_cloud(fst_child, 'gender', max = 50)
s <- survey::svydesign(id=~1, weights= ~paino, data = child)
i <- 'fsd_id'
c2 <- fst_child_2
fst_comparison_cloud(c2, 'gender', NULL, 100, NULL, TRUE, TRUE, i, s)
T <- TRUE
fst_comparison_cloud(fst_dev_coop, 'education_level', use_column_weights = T)
pf <- c("NOUN", "VERB", "ADJ", "ADV")
pf2 <- "NOUN, VERB, ADJ, ADV"
fst_comparison_cloud(fst_dev_coop, 'gender', pos_filter = pf)
fst_comparison_cloud(fst_dev_coop, 'gender', pos_filter = pf2)
fst_comparison_cloud(fst_dev_coop, 'gender', norm = 'number_resp')

Concept Network - Make Concept Network plot

Description

This function takes a string of terms (separated by commas) or a single term and, using 'textrank_keywords()' from 'textrank' package, filters data based on 'pos_filter' and finds words connected to search terms. Then it plots a Concept Network based on the calculated weights of these terms and the frequency of co-occurrences.

Usage

fst_concept_network(
  data,
  concepts,
  threshold = NULL,
  norm = "number_words",
  pos_filter = NULL,
  title = NULL
)

Arguments

Value

Plot of Concept Network.

Examples

data <- fst_child
con <- "kiusata, lyöminen"
pf <- c("NOUN", "VERB", "ADJ", "ADV")
title <- "Bullying Concept Network"
fst_concept_network(data, concepts = con, pos_filter = pf, title = title)

Concept Network- Compare and plot Concept Network

Description

This function takes a string of terms (separated by commas) or a single term and, using 'textrank_keywords()' from 'textrank' package, filters data based on 'pos_filter' and finds words connected to search terms for each group. Then it plots a Concept Network for each group based on the calculated weights of these terms and the frequency of co-occurrences, indicating any words that are unique to each group's Network plot.

Usage

fst_concept_network_compare(
  data,
  concepts,
  field,
  norm = NULL,
  threshold = NULL,
  pos_filter = NULL,
  use_svydesign_field = FALSE,
  id = "",
  svydesign = NULL,
  exclude_nulls = FALSE,
  rename_nulls = "null_data",
  title_size = 20,
  subtitle_size = 15
)

Arguments

Value

Multiple concept network plots with concept and unique words highlighted.

Examples

con1 <- "lyödä, lyöminen"
fst_concept_network_compare(fst_child, concepts = con1, field = 'gender')
s <- survey::svydesign(id=~1, weights= ~paino, data = child)
c2 <- fst_child_2
i <- 'fsd_id'
fst_concept_network_compare(c2, con1, 'gender', NULL, NULL, NULL, TRUE, i, s)
con2 <- "köyhyys, nälänhätä, sota"
fst_concept_network_compare(fst_dev_coop, con2, 'gender')

Young People's Views on Development Cooperation 2012 q11_3 response data in CoNLL-U format with NTLK stopwords removed and background variables.

Description

This data contains the responses to Development Cooperation q11_3 dataset in CoNLL-U format with NLTK stopwords and punctuation removed plus weights and background variables.

Usage

fst_dev_coop

Format

## 'fst_dev_coop' A dataframe with 4192 rows and 19 columns:

doc_id

the identifier of the document

paragraph_id

the identifier of the paragraph

sentence_id

the identifier of the sentence

sentence

the text of the sentence for which this token is part of

token_id

Word index, integer starting at 1 for each new sentence; may be a range for multi-word tokens; may be a decimal number for empty nodes.

token

Word form or punctuation symbol.

lemma

Lemma or stem of word form.

upos

Universal part-of-speech tag.

xpos

Language-specific part-of-speech tag; underscore if not available.

feats

List of morphological features from the universal feature inventory or from a defined language-specific extension; underscore if not available.

head_token_id

Head of the current word, which is either a value of token_id or zero (0).

dep_rel

Universal dependency relation to the HEAD (root iff HEAD = 0) or a defined language-specific subtype of one.

deps

Enhanced dependency graph in the form of a list of head-deprel pairs.

misc

Any other annotation.

weight

Weight

gender

Gender

year_of_birth

Year of Birth

region

Region of Residence

Source

<https://urn.fi/urn:nbn:fi:fsd:T-FSD2821>

Young People's Views on Development Cooperation 2012 q11_3 response data in CoNLL-U format with NTLK stopwords removed

Description

This data contains the responses to Development Cooperation q11_3 dataset in CoNLL-U format with NLTK stopwords and punctuation removed.

Usage

fst_dev_coop_2

Format

## 'fst_dev_coop_2' A dataframe with 4192 rows and 14 columns:

doc_id

the identifier of the document

paragraph_id

the identifier of the paragraph

sentence_id

the identifier of the sentence

sentence

the text of the sentence for which this token is part of

token_id

Word index, integer starting at 1 for each new sentence; may be a range for multi-word tokens; may be a decimal number for empty nodes.

token

Word form or punctuation symbol.

lemma

Lemma or stem of word form.

upos

Universal part-of-speech tag.

xpos

Language-specific part-of-speech tag; underscore if not available.

feats

List of morphological features from the universal feature inventory or from a defined language-specific extension; underscore if not available.

head_token_id

Head of the current word, which is either a value of token_id or zero (0).

dep_rel

Universal dependency relation to the HEAD (root iff HEAD = 0) or a defined language-specific subtype of one.

deps

Enhanced dependency graph in the form of a list of head-deprel pairs.

misc

Any other annotation.

Source

<https://urn.fi/urn:nbn:fi:fsd:T-FSD2821>

Get available stopwords lists

Description

Returns a tibble containing all available stopword lists for the language, their contents, and the size of the lists.

Usage

fst_find_stopwords(language = "fi")

Arguments

Value

A tibble containing the stopwords lists.

Examples

fst_find_stopwords()
fst_find_stopwords(language = 'et')

Annotate open-ended survey responses in into CoNLL-U format

Description

Creates a dataframe in CoNLL-U format from a dataframe containing text from using the [udpipe] package and a language model plus any additional columns that are included such as 'weights' or columns added through 'add_cols'.

Usage

fst_format(data, question, id, model = "ftb", weights = NULL, add_cols = NULL)

Arguments

Value

Dataframe of annotated text in CoNLL-U format plus any additional columns.

Examples

## Not run: 
i <- "fsd_id"
fst_format(data = child, question = "q7", id = i)
fst_format(data = child, question = "q7", id = i, model = "tdt")
fst_format(data = child, question = "q7", id = i, weights="paino")
cols <- c("gender", "major_region", "daycare_before_school")
fst_format(child, question = "q7", id = i, add_cols = cols)
fst_format(child, question = "q7", id = i, add_cols = "gender, major_region")
fst_format(child, question = 'q7', id = i, model = 'swedish-talbanken')
unlink("finnish-ftb-ud-2.5-191206.udpipe")
unlink("finnish-tdt-ud-2.5-191206.udpipe")
unlink("swedish-talkbanken-ud-2.5-191206.udpipe")

## End(Not run)

Annotate open-ended survey responses within a 'svydesign' object into CoNLL-U format

Description

Creates a dataframe in CoNLL-U format from a 'svydesign' object including text using the [udpipe] package and a language model plus weights if these are included in the 'svydesign' object and any columns added through 'add_cols'.

Usage

fst_format_svydesign(
  svydesign,
  question,
  id,
  model = "ftb",
  use_weights = TRUE,
  add_cols = NULL
)

Arguments

Value

Dataframe of annotated text in CoNLL-U format plus any additional columns.

Examples

## Not run: 
i <- "fsd_id"
svy_child <- survey::svydesign(id=~1, weights= ~paino, data = child)
fst_format_svydesign(svy_child, question = 'q7', id = 'fsd_id')
fst_format_svydesign(svy_child, question = 'q7', id = i, use_weights = FALSE)
cols <- c('gender', 'major_region')
fst_format_svydesign(svy_child, 'q7', 'fsd_id', add_cols = cols)

svy_dev <- survey::svydesign(id = ~1, weights = ~paino, data = dev_coop)
fst_format_svydesign(svy_dev, 'q11_1', 'fsd_id', add_cols = 'gender, region')

fst_format_svydesign(svy_dev, 'q11_2', 'fsd_id', 'finnish-ftb')
unlink("finnish-ftb-ud-2.5-191206.udpipe")
unlink("finnish-tdt-ud-2.5-191206.udpipe")

## End(Not run)

Find and Plot Top Words

Description

Creates a plot of the most frequently-occurring words (unigrams) within the data. Optionally, weights can be provided either through a 'weight' column in the formatted data, or from a 'svydesign' object with the raw (preformatted) data.

Usage

fst_freq(
  data,
  number = 10,
  norm = NULL,
  pos_filter = NULL,
  strict = TRUE,
  name = NULL,
  use_svydesign_weights = FALSE,
  id = "",
  svydesign = NULL,
  use_column_weights = FALSE
)

Arguments

Value

Plot of top words.

Examples

fst_freq(fst_child, number = 12, norm = 'number_resp',  name = "All")
fst_freq(fst_child, use_column_weights = TRUE)
s <- survey::svydesign(id=~1, weights= ~paino, data = child)
i <- 'fsd_id'
fst_freq(fst_child_2, use_svydesign_weights = TRUE, svydesign = s, id = i)

Compare and plot top words

Description

Find top and unique top words for different groups of participants. Data is split based on different values in the 'field' column of formatted data. Results will be shown within the plots pane.

Usage

fst_freq_compare(
  data,
  field,
  number = 10,
  norm = NULL,
  pos_filter = NULL,
  strict = TRUE,
  use_svydesign_weights = FALSE,
  use_svydesign_field = FALSE,
  id = "",
  svydesign = NULL,
  use_column_weights = FALSE,
  exclude_nulls = FALSE,
  rename_nulls = "null_data",
  unique_colour = "indianred",
  title_size = 20,
  subtitle_size = 15
)

Arguments

Value

Plots of most frequent words in the plots pane with unique words highlighted.

Examples

fst_freq_compare(fst_child, 'gender', number = 10, norm = "number_resp")
fst_freq_compare(fst_child, 'gender', number = 10, norm = NULL)
s <- survey::svydesign(id=~1, weights= ~paino, data = child)
c2 <- fst_child_2
c <- fst_child
g <- 'gender'
fst_freq_compare(c2, g, 10, NULL, NULL, TRUE, TRUE, TRUE, 'fsd_id', s)
fst_freq_compare(c, g, use_column_weights = TRUE, strict = FALSE)

Make Top Words plot

Description

Plots most common words.

Usage

fst_freq_plot(table, number = NULL, name = NULL)

Arguments

Value

Plot of top words.

Examples

pf <- c("NOUN", "VERB", "ADJ", "ADV")
top_words <- fst_freq_table(fst_child, number = 15, pos_filter = pf)
fst_freq_plot(top_words, number = 15, name = "Bullying")

Make Top Words Table

Description

Creates a table of the most frequently-occurring words (unigrams) within the data. Optionally, weights can be provided either through a 'weight' column in the formatted data, or from a 'svydesign' object with the raw (preformatted) data.

Usage

fst_freq_table(
  data,
  number = 10,
  norm = NULL,
  pos_filter = NULL,
  strict = TRUE,
  use_svydesign_weights = FALSE,
  id = "",
  svydesign = NULL,
  use_column_weights = FALSE
)

Arguments

Value

A table of the most frequently occurring words in the data.

Examples

pf <- c("NOUN", "VERB", "ADJ", "ADV")
pf2 <- "NOUN, VERB, ADJ, ADV"
fst_freq_table(fst_child, number = 15, strict = FALSE, pos_filter = pf)
fst_freq_table(fst_child, number = 15, strict = FALSE, pos_filter = pf2)
fst_freq_table(fst_child, norm = 'number_words')
fst_freq_table(fst_child, use_column_weights = TRUE)
c2 <- fst_child_2
s <- survey::svydesign(id=~1, weights= ~paino, data = child)
i <- 'fsd_id'
fst_freq_table(c2, use_svydesign_weights = TRUE, svydesign = s, id = i)

Get unique n-grams from a list of top n-grams tables

Description

Takes a list containing at least two tables of n-grams and frequencies (either output of 'fst_freq_table()' or 'fst_ngrams_table()') and finds n-grams unique to one table.

Usage

fst_get_unique_ngrams(list_of_top_ngrams)

Arguments

Value

Dataframe of words and whether word is unique or not.

Examples

top_child <- fst_freq_table(fst_child)
top_dev <- fst_freq_table(fst_dev_coop)
list_of_top_words <- list()
list_of_top_words <- append(list_of_top_words, list(top_child))
list_of_top_words <- append(list_of_top_words, list(top_dev))
fst_get_unique_ngrams(list_of_top_words)

Get unique n-grams from separate top n-grams tables

Description

Takes at least two separate tables of n-grams and frequencies (either output of 'fst_freq_table()' or 'fst_ngrams_table()') and finds n-grams unique to one table.

Usage

fst_get_unique_ngrams_separate(table1, table2, ...)

Arguments

Value

Dataframe of words and whether word is unique or not.

Examples

top_child <- fst_freq_table(fst_child)
top_dev <- fst_freq_table(fst_dev_coop)
fst_get_unique_ngrams_separate(top_child, top_dev)

Merge N-grams table with unique words

Description

Merges list of unique words from 'fst_get_unique_ngrams()' with output of 'fst_freq_table()' or 'fst_ngrams_table()' so that unique words can be displayed on comparison plots.

Usage

fst_join_unique(table, unique_table)

Arguments

Value

A table of top n-grams, frequency, and whether the n-gram is "unique".

Examples

top_child <- fst_freq_table(fst_child)
top_dev <- fst_freq_table(fst_dev_coop)
unique_words <- fst_get_unique_ngrams_separate(top_child, top_dev)
fst_join_unique(top_child, unique_words)
fst_join_unique(top_dev, unique_words)

Compare response lengths

Description

Compare length of text responses for different groups of participants. Data is split based on different values in the 'field' column of formatted data. Results will be shown within the plots pane.

Usage

fst_length_compare(
  data,
  field,
  incl_sentences = TRUE,
  exclude_nulls = FALSE,
  rename_nulls = "null_data"
)

Arguments

Value

Dataframe summarising response lengths.

Examples

fst_length_compare(fst_child, 'gender')
fst_length_compare(fst_dev_coop, 'education_level', incl_sentences = FALSE)

Make Length Summary Table

Description

Creates a table summarising distribution of the length of responses.

Usage

fst_length_summary(data, desc = "All responses", incl_sentences = TRUE)

Arguments

Value

Table summarising distribution of lengths of responses.

Examples

fst_length_summary(fst_child, incl_sentences = FALSE)
fst_length_summary(fst_dev_coop, desc = "Q11_3")

Find and Plot Top N-grams

Description

Creates a plot of the most frequently-occurring n-grams within the data. Optionally, weights can be provided either through a 'weight' column in the formatted data, or from a 'svydesign' object with the raw (preformatted) data.

Usage

fst_ngrams(
  data,
  number = 10,
  ngrams = 1,
  norm = NULL,
  pos_filter = NULL,
  strict = TRUE,
  name = NULL,
  use_svydesign_weights = FALSE,
  id = "",
  svydesign = NULL,
  use_column_weights = FALSE
)

Arguments

Value

Plot of top n-grams

Examples

fst_ngrams(fst_child, 12, ngrams = 2, strict = FALSE, name = "All")
c <- fst_child_2
s <- survey::svydesign(id=~1, weights= ~paino, data = child)
i <- 'fsd_id'
T <- TRUE
fst_ngrams(c, ngrams = 3, use_svydesign_weights = T, svydesign = s, id = i)

Compare and plot top n-grams

Description

Find top and unique top n-grams for different groups of participants. Data is split based on different values in the 'field' column of formatted data. Results will be shown within the plots pane.

Usage

fst_ngrams_compare(
  data,
  field,
  number = 10,
  ngrams = 1,
  norm = NULL,
  pos_filter = NULL,
  strict = TRUE,
  use_svydesign_weights = FALSE,
  use_svydesign_field = FALSE,
  id = "",
  svydesign = NULL,
  use_column_weights = FALSE,
  exclude_nulls = FALSE,
  rename_nulls = "null_data",
  unique_colour = "indianred",
  title_size = 20,
  subtitle_size = 15
)

Arguments

Value

Plots of top n-grams in the plots pane with unique n-grams highlighted.

Examples

c <- fst_child
g <- 'gender'
fst_ngrams_compare(c, g, ngrams = 4, number = 10, norm = "number_resp")
fst_ngrams_compare(c, g, ngrams = 2, number = 10, norm = NULL)
s <- survey::svydesign(id=~1, weights= ~paino, data = child)
c2 <- fst_child_2
fst_ngrams_compare(c2, g, 10, 3, NULL, NULL, TRUE, TRUE, TRUE, 'fsd_id', s)
fst_ngrams_compare(c, g, 10, 2, use_column_weights = TRUE, strict = TRUE)

Plot comparison n-grams

Description

Plots frequency n-grams with unique n-grams highlighted.

Usage

fst_ngrams_compare_plot(
  table,
  number = 10,
  ngrams = 1,
  unique_colour = "indianred",
  name = NULL,
  override_title = NULL,
  title_size = 20
)

Arguments

Value

Plot of top n-grams with unique terms highlighted.

Examples

top_child <- fst_freq_table(fst_child)
top_dev <- fst_freq_table(fst_dev_coop)
unique_words <- fst_get_unique_ngrams_separate(top_child, top_dev)
top_child_u <- fst_join_unique(top_child, unique_words)
top_dev_u <- fst_join_unique(top_dev, unique_words)
fst_ngrams_compare_plot(top_child_u, ngrams = 1, name = "Child")
fst_ngrams_compare_plot(top_dev_u, ngrams = 1, name = "Dev", title_size = 10)

Make N-grams plot

Description

Plots frequency n-grams.

Usage

fst_ngrams_plot(table, number = NULL, ngrams = 1, name = NULL)

Arguments

Value

Plot of top n-grams.

Examples

top_bigrams <- fst_ngrams_table(fst_child, ngrams = 2, number = 15)
fst_ngrams_plot(top_bigrams, ngrams = 2, number = 15, name = "Children")

Make Top N-grams Table

Description

Creates a table of the most frequently-occurring n-grams within the data. Optionally, weights can be provided either through a 'weight' column in the formatted data, or from a 'svydesign' object with the raw (preformatted) data.

Usage

fst_ngrams_table(
  data,
  number = 10,
  ngrams = 1,
  norm = NULL,
  pos_filter = NULL,
  strict = TRUE,
  use_svydesign_weights = FALSE,
  id = "",
  svydesign = NULL,
  use_column_weights = FALSE
)

Arguments

Value

A table of the most frequently occurring n-grams in the data.

Examples

pf <- c("NOUN", "VERB", "ADJ", "ADV")
pf2 <- "NOUN, VERB, ADJ, ADV"
fst_ngrams_table(fst_child, norm = NULL)
fst_ngrams_table(fst_child, ngrams = 2, norm = "number_resp")
fst_ngrams_table(fst_child, ngrams = 2, pos_filter = pf)
fst_ngrams_table(fst_child, ngrams = 2, pos_filter = pf2)
c2 <- fst_child_2
s <- survey::svydesign(id=~1, weights= ~paino, data = child)
i <- 'fsd_id'
fst_ngrams_table(c2, use_svydesign_weights = TRUE, svydesign = s, id = i)
fst_ngrams_table(fst_child, use_column_weights = TRUE, ngrams = 3)

Make Top N-grams Table 2

Description

Creates a table of the most frequently-occurring n-grams within the data. Optionally, weights can be provided either through a 'weight' column in the formatted data, or from a 'svydesign' object with the raw (preformatted) data. Equivalent to ‘fst_get_top_ngrams' but doesn’t print message about ties.

Usage

fst_ngrams_table2(
  data,
  number = 10,
  ngrams = 1,
  norm = NULL,
  pos_filter = NULL,
  strict = TRUE,
  use_svydesign_weights = FALSE,
  id = "",
  svydesign = NULL,
  use_column_weights = FALSE
)

Arguments

Value

A table of the most frequently occurring n-grams in the data.

Examples

fst_ngrams_table2(fst_child, norm = NULL)
fst_ngrams_table2(fst_child, ngrams = 2, norm = "number_resp")
c <- fst_child_2
s <- survey::svydesign(id=~1, weights= ~paino, data = child)
i <- 'fsd_id'
T <- TRUE
fst_ngrams_table2(c, 10, 2, use_svydesign_weights = T, svydesign = s, id = i)

Make POS Summary Table

Description

Creates a summary table for the input CoNLL-U data which counts the number of words of each part-of-speech tag within the data.

Usage

fst_pos(data)

Arguments

Value

A dataframe with a count and proportion of each UPOS tag in the data and the full name of the tag.

Examples

fst_pos(fst_child)
fst_pos(fst_dev_coop)

Compare parts-of-speech

Description

Count each POS type for different groups of participants. Data is split based on different values in the 'field' column of formatted data. Results will be shown within the plots pane.

Usage

fst_pos_compare(data, field, exclude_nulls = FALSE, rename_nulls = "null_data")

Arguments

Value

Table of POS tag counts for the groups.

Examples

fst_pos_compare(fst_child, 'gender')
fst_pos_compare(fst_dev_coop, 'region')

Read In and format survey text responses

Description

Creates a dataframe in CoNLL-U format from a dataframe containing text from using the [udpipe] package and a language model plus any additional columns that are included such as 'weights' or columns added through 'add_cols'. Stopwords and punctuation are optionally removed if the the 'stopword_list' argument is not "none".

Usage

fst_prepare(
  data,
  question,
  id,
  model = "ftb",
  stopword_list = "nltk",
  language = "fi",
  weights = NULL,
  add_cols = NULL,
  manual = FALSE,
  manual_list = ""
)

Arguments

Details

'fst_prepare_conllu()' produces a dataframe containing survey text responses in CoNLL-U format with stopwords optionally removed.

Value

A dataframe of text in CoNLL-U format.

Examples

## Not run: 
i <- "fsd_id"
cb <- child
dev <- dev_coop
fst_prepare(data = cb, question = "q7", id = 'fsd_id', weights = 'paino')
fst_prepare(data = dev, question = "q11_2", id = i, add_cols = c('gender'))
fst_prepare(data = dev, question = "q11_3", id = i, add_cols = 'gender')
fst_prepare(data = child, question = "q7", id = i, model = 'swedish-lines')
unlink("finnish-ftb-ud-2.5-191206.udpipe")
unlink("finnish-tdt-ud-2.5-191206.udpipe")
unlink("swedish-lines-ud-2.5-191206.udpipe")

## End(Not run)

Read In and format survey text responses from 'svydesign' object

Description

Creates a dataframe in CoNLL-U format from a 'svydesign' object including text using the [udpipe] package and a language model plus weights if these are included in the 'svydesign' object and any columns added through 'add_cols'.Stopwords and punctuation are optionally removed if the the 'stopword_list' argument is not "none".

Usage

fst_prepare_svydesign(
  svydesign,
  question,
  id,
  model = "ftb",
  stopword_list = "nltk",
  language = "fi",
  use_weights = TRUE,
  add_cols = NULL,
  manual = FALSE,
  manual_list = ""
)

Arguments

Details

'fst_prepare_svydesign()' produces a dataframe containing survey text responses in CoNLL-U format with stopwords optionally removed.

Value

A dataframe of text in CoNLL-U format.

Examples

## Not run: 
i <- "fsd_id"
svy_child <- survey::svydesign(id=~1, weights= ~paino, data = child)
fst_prepare_svydesign(svy_child, question = "q7", id = i, use_weights = TRUE)

svy_d <- survey::svydesign(id = ~1, weights = ~paino, data =dev_coop)
fst_prepare_svydesign(svy_d, question = "q11_2", id = i, add_cols = 'gender')

fst_prepare_svydesign(svy_d, 'q11_2', i, 'finnish-ftb', 'nltk', 'fi')
unlink("finnish-ftb-ud-2.5-191206.udpipe")
unlink("finnish-tdt-ud-2.5-191206.udpipe")

## End(Not run)

Find treebanks available for use

Description

Find treebanks available for use

Usage

fst_print_available_models(search = NULL)

Arguments

Value

List of available treebanks, filtered

Examples

fst_print_available_models()
fst_print_available_models(search = "swedish")

Remove stopwords and punctuation from CoNLL-U dataframe

Description

Removes stopwords and punctuation from a dataframe containing survey text data which is already in CoNLL-U format.

Usage

fst_rm_stop_punct(
  data,
  stopword_list = "nltk",
  language = "fi",
  manual = FALSE,
  manual_list = ""
)

Arguments

Value

A dataframe of text in CoNLL-U format without stopwords and punctuation.

Examples

## Not run: 
c <- fst_format(child, question = 'q7', id = 'fsd_id')
fst_rm_stop_punct(c)
fst_rm_stop_punct(c, stopword_list = "snowball")
fst_rm_stop_punct(c, "stopwords-iso")

mlist <- c('en', 'et', 'ei', 'emme', 'ette', 'eivät', 'minä', 'minum')
mlist2 <- "en, et, ei, emme, ette, eivät, minä, minum"
fst_rm_stop_punct(c, manual = TRUE, manual_list = mlist)
fst_rm_stop_punct(c, stopword_list = "manual", manual_list = mlist)
unlink("finnish-ftb-ud-2.5-191206.udpipe")

## End(Not run)

Make Summary Table

Description

Creates a summary table for the input CoNLL-U data which provides the response count and proportion, total number of words, the number of unique words, and the number of unique lemmas.

Usage

fst_summarise(data, desc = "All responses")

Arguments

Value

A dataframe with summary information for the data including response rate and word counts.

Examples

fst_summarise(fst_child)
fst_summarise(fst_dev_coop, "Q11_3")

Make comparison summary

Description

Compare text responses for different groups of participants. Data is split based on different values in the 'field' column of formatted data. Results will be shown within the plots pane.

Usage

fst_summarise_compare(
  data,
  field,
  exclude_nulls = FALSE,
  rename_nulls = "null_data"
)

Arguments

Value

Summary table of responses between groups.

Examples

fst_summarise_compare(fst_child, 'gender')
fst_summarise_compare(fst_dev_coop, 'gender')

Make Simple Summary Table

Description

Creates a summary table for the input CoNLL-U data which provides the total number of words, the number of unique words, and the number of unique lemmas.

Usage

fst_summarise_short(data)

Arguments

Value

A dataframe with summary information on word counts for the data.

Examples

fst_summarise_short(fst_child)
fst_summarise_short(fst_dev_coop)

Add 'svydesign' weights to CoNLL-U data

Description

This function takes data in CoNLL-U format and a 'svydesign' (from 'survey' package) object with weights in it and merges the weights, and any additional columns into the formatted data.

Usage

fst_use_svydesign(data, svydesign, id, add_cols = NULL, add_weights = TRUE)

Arguments

Value

A dataframe of text in CoNLL-U format plus a ''weight'' column and optional other columns

Examples

svy_child <- survey::svydesign(id=~1, weights= ~paino, data = child)
fst_use_svydesign(data = fst_child_2, svydesign = svy_child, id = 'fsd_id')

svy_dev <- survey::svydesign(id = ~1, weights = ~paino, data = dev_coop)
fst_use_svydesign(data = fst_dev_coop_2, svydesign = svy_dev, id = 'fsd_id')

Make Wordcloud

Description

Creates a wordcloud from CoNLL-U data of frequently-occurring words. Optionally, weights can be provided either through a 'weight' column in the formatted data, or from a 'svydesign' object with the raw (preformatted) data.

Usage

fst_wordcloud(
  data,
  pos_filter = NULL,
  max = 100,
  use_svydesign_weights = FALSE,
  id = "",
  svydesign = NULL,
  use_column_weights = FALSE
)

Arguments

Value

A wordcloud from the data.

Examples

fst_wordcloud(fst_child)
fst_wordcloud(fst_child, pos_filter = c("NOUN", "VERB", "ADJ", "ADV"))
fst_wordcloud(fst_child, pos_filter = 'NOUN, VERB, ADJ')
fst_wordcloud(fst_child, use_column_weights = TRUE)
i <- 'fsd_id'
c <- fst_child_2
s <- survey::svydesign(id=~1, weights= ~paino, data = child)
fst_wordcloud(c, use_svydesign_weights = TRUE, id = i, svydesign = s)

Run Shiny App Demo

Description

Run Shiny App Demo

Usage

runDemo()

Value

launches the RShiny demo

Examples

## Not run: 
  runDemo()

## End(Not run)