textshape is small suite of text reshaping and restructuring functions. Many of these functions are descended from tools in the qdapTools package. This brings reshaping tools under one roof with specific functionality of the package limited to text reshaping.
Other R packages provide some of the same functionality. textshape differs from these packages in that it is designed to help the user take unstructured data (or implicitly structured), extract it into a structured format, and then restructure into common text analysis formats for use in the next stage of the text analysis pipeline. The implicit structure of seemingly unstructured data is often detectable/expressible by the researcher. textshape provides tools (e.g., split_match) to enable the researcher to convert this tacit knowledge into a form that can be used to reformat data into more structured formats. This package is meant to be used jointly with the textclean package, which provides cleaning and text normalization functionality. Additionally, the textreadr package is designed to import various common text data sources into R for reshaping and cleaning.
Most of the functions split, expand, grab, or tidy a vector, list, data.frame, or DocumentTermMatrix. The combine, duration, mtabulate, & flatten functions are notable exceptions. The table below describes the functions and their use:
| Function | Used On | Description |
|---|---|---|
combine
|
vector, list, data.frame
|
Combine and collapse elements |
tidy_list
|
list of vectors or data.frames
|
Row bind a list and repeat list names as id column |
tidy_vector
|
vector
|
Column bind a named atomic vector’s names and values
|
tidy_table
|
table
|
Column bind a table’s names and values
|
tidy_matrix
|
matrix
|
Stack values, repeat column row names accordingly |
tidy_dtm/tidy_tdm
|
DocumentTermMatrix
|
Tidy format DocumentTermMatrix/TermDocumentMatrix
|
tidy_colo_dtm/tidy_colo_tdm
|
DocumentTermMatrix
|
Tidy format of collocating words from a DocumentTermMatrix/TermDocumentMatrix
|
duration
|
vector, data.frame
|
Get duration (start-end times) for turns of talk in n words |
from_to
|
vector, data.frame
|
Prepare speaker data for a flow network |
mtabulate
|
vector, list, data.frame
|
Dataframe/list version of tabulate to produce count matrix
|
flatten
|
list
|
Flatten nested, named list to single tier |
unnest_text
|
data.frame
|
Unnest a nested text column |
split_index
|
vector, list, data.frame
|
Split at specified indices |
split_match
|
vector
|
Split vector at specified character/regex match |
split_portion
|
vector*
|
Split data into portioned chunks |
split_run
|
vector, data.frame
|
Split runs (e.g., “aaabbbbcdddd”) |
split_sentence
|
vector, data.frame
|
Split sentences |
split_speaker
|
data.frame
|
Split combined speakers (e.g., “Josh, Jake, Jim”) |
split_token
|
vector, data.frame
|
Split words and punctuation |
split_transcript
|
vector
|
Split speaker and dialogue (e.g., “greg: Who me”) |
split_word
|
vector, data.frame
|
Split words |
grab_index
|
vector, data.frame, list
|
Grab from an index up to a second index |
grab_match
|
vector, data.frame, list
|
Grab from a regex match up to a second regex match |
column_to_rownames
|
data.frame
|
Add a column as rownames |
cluster_matrix
|
matrix
|
Reorder column/rows of a matrix via hierarchical clustering |
*Note: Text vector accompanied by aggregating grouping.var argument, which can be in the form of a vector, list, or data.frame
To download the development version of textshape:
Download the zip ball or tar ball, decompress and run R CMD INSTALL on it, or use the pacman package to install the development version:
if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh("trinker/textshape")You are welcome to:
Contributions are welcome from anyone subject to the following rules:
The main shaping functions can be broken into the categories of (a) binding, (b) combining, (c) tabulating, (d) spanning, (e) splitting, (f) grabbing & (e) tidying. The majority of functions in textshape fall into the category of splitting and expanding (the semantic opposite of combining). These sections will provide example uses of the functions from textshape within the three categories.
if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse, magrittr, ggstance, viridis, gridExtra, textreadr, quanteda)
pacman::p_load_current_gh('trinker/gofastr', 'trinker/textshape')The tidy_xxx functions convert untidy structures into tidy format. Tidy formatted text data structures are particularly useful for interfacing with ggplot2, which expects this form.
The tidy_list function is used in the style of do.call(rbind, list(x1, x2)) as a convenient way to bind together multiple named data.frames or vectorss into a single data.frame with the list names acting as an id column. The data.frame bind is particularly useful for binding transcripts from different observations. Additionally, tidy_vector and tidy_table are provided for cbinding a table’s or named atomic vector’s values and names as separate columns in a data.frame. Lastly, tidy_dtm/tidy_tdm provide convenient ways to tidy a DocumentTermMatrix or TermDocumentMatrix.
x <- list(p=1:500, r=letters)
tidy_list(x)
## id content
## 1: p 1
## 2: p 2
## 3: p 3
## 4: p 4
## 5: p 5
## ---
## 522: r v
## 523: r w
## 524: r x
## 525: r y
## 526: r zx <- list(p=mtcars, r=mtcars, z=mtcars, d=mtcars)
tidy_list(x)
## id mpg cyl disp hp drat wt qsec vs am gear carb
## 1: p 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2: p 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3: p 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4: p 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5: p 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## ---
## 124: d 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 125: d 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 126: d 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 127: d 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## 128: d 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2x <- setNames(
sample(LETTERS[1:6], 1000, TRUE),
sample(state.name[1:5], 1000, TRUE)
)
tidy_vector(x)
## id content
## 1: Arkansas D
## 2: Alaska B
## 3: Arizona E
## 4: Arizona C
## 5: California A
## ---
## 996: Arizona F
## 997: Alaska E
## 998: Alabama F
## 999: Alaska C
## 1000: Arizona Ex <- table(sample(LETTERS[1:6], 1000, TRUE))
tidy_table(x)
## id content
## 1: A 156
## 2: B 174
## 3: C 179
## 4: D 149
## 5: E 170
## 6: F 172mat <- matrix(1:16, nrow = 4,
dimnames = list(LETTERS[1:4], LETTERS[23:26])
)
mat
## W X Y Z
## A 1 5 9 13
## B 2 6 10 14
## C 3 7 11 15
## D 4 8 12 16
tidy_matrix(mat)
## row col value
## 1: A W 1
## 2: B W 2
## 3: C W 3
## 4: D W 4
## 5: A X 5
## 6: B X 6
## 7: C X 7
## 8: D X 8
## 9: A Y 9
## 10: B Y 10
## 11: C Y 11
## 12: D Y 12
## 13: A Z 13
## 14: B Z 14
## 15: C Z 15
## 16: D Z 16With clustering (column and row reordering) via the cluster_matrix function.
## plot heatmap w/o clustering
wo <- mtcars %>%
cor() %>%
tidy_matrix('car', 'var') %>%
ggplot(aes(var, car, fill = value)) +
geom_tile() +
scale_fill_viridis(name = expression(r[xy])) +
theme(
axis.text.y = element_text(size = 8) ,
axis.text.x = element_text(size = 8, hjust = 1, vjust = 1, angle = 45),
legend.position = 'bottom',
legend.key.height = grid::unit(.1, 'cm'),
legend.key.width = grid::unit(.5, 'cm')
) +
labs(subtitle = "With Out Clustering")
## plot heatmap w clustering
w <- mtcars %>%
cor() %>%
cluster_matrix() %>%
tidy_matrix('car', 'var') %>%
mutate(
var = factor(var, levels = unique(var)),
car = factor(car, levels = unique(car))
) %>%
group_by(var) %>%
ggplot(aes(var, car, fill = value)) +
geom_tile() +
scale_fill_viridis(name = expression(r[xy])) +
theme(
axis.text.y = element_text(size = 8) ,
axis.text.x = element_text(size = 8, hjust = 1, vjust = 1, angle = 45),
legend.position = 'bottom',
legend.key.height = grid::unit(.1, 'cm'),
legend.key.width = grid::unit(.5, 'cm')
) +
labs(subtitle = "With Clustering")
grid.arrange(wo, w, ncol = 2)
The tidy_dtm and tidy_tdm functions convert a DocumentTermMatrix or TermDocumentMatrix into a tidied data set.
my_dtm <- with(presidential_debates_2012, q_dtm(dialogue, paste(time, tot, sep = "_")))
tidy_dtm(my_dtm) %>%
tidyr::extract(doc, c("time", "turn", "sentence"), "(\\d)_(\\d+)\\.(\\d+)") %>%
mutate(
time = as.numeric(time),
turn = as.numeric(turn),
sentence = as.numeric(sentence)
) %>%
tbl_df() %T>%
print() %>%
group_by(time, term) %>%
summarize(n = sum(n)) %>%
group_by(time) %>%
arrange(desc(n)) %>%
slice(1:10) %>%
mutate(term = factor(paste(term, time, sep = "__"), levels = rev(paste(term, time, sep = "__")))) %>%
ggplot(aes(x = n, y = term)) +
geom_barh(stat='identity') +
facet_wrap(~time, ncol=2, scales = 'free_y') +
scale_y_discrete(labels = function(x) gsub("__.+$", "", x))
## # A tibble: 42,057 x 7
## time turn sentence term n i j
## <dbl> <dbl> <dbl> <chr> <dbl> <int> <int>
## 1 1 1 1 we'll 1 1 1
## 2 1 1 1 talk 1 1 2
## 3 1 1 1 about 2 1 3
## 4 1 1 1 specifically 1 1 4
## 5 1 1 1 health 1 1 5
## 6 1 1 1 care 1 1 6
## 7 1 1 1 in 1 1 7
## 8 1 1 1 a 1 1 8
## 9 1 1 1 moment 1 1 9
## 10 1 1 1 . 1 1 10
## # ... with 42,047 more rows
## `summarise()` regrouping output by 'time' (override with `.groups` argument)
The tidy_colo_dtm and tidy_colo_tdm functions convert a DocumentTermMatrix or TermDocumentMatrix into a collocation matrix and then a tidied data set.
my_dtm <- with(presidential_debates_2012, q_dtm(dialogue, paste(time, tot, sep = "_")))
## Warning: NA is replaced by empty string
sw <- unique(c(
lexicon::sw_jockers,
lexicon::sw_loughran_mcdonald_long,
lexicon::sw_fry_1000
))
tidy_colo_dtm(my_dtm) %>%
tbl_df() %>%
filter(!term_1 %in% c('i', sw) & !term_2 %in% sw) %>%
filter(term_1 != term_2) %>%
unique_pairs() %>%
filter(n > 15) %>%
complete(term_1, term_2, fill = list(n = 0)) %>%
ggplot(aes(x = term_1, y = term_2, fill = n)) +
geom_tile() +
scale_fill_gradient(low= 'white', high = 'red') +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
The combine function acts like paste(x, collapse=" ") on vectors and lists of vectors. On dataframes multiple text cells are pasted together within grouping variables.
x <- c("Computer", "is", "fun", ".", "Not", "too", "fun", ".")
combine(x)
## [1] "Computer is fun. Not too fun."(dat <- split_sentence(DATA))
## person sex adult state code element_id
## 1: sam m 0 Computer is fun. K1 1
## 2: sam m 0 Not too fun. K1 1
## 3: greg m 0 No it's not, it's dumb. K2 2
## 4: teacher m 1 What should we do? K3 3
## 5: sam m 0 You liar, it stinks! K4 4
## 6: greg m 0 I am telling the truth! K5 5
## 7: sally f 0 How can we be certain? K6 6
## 8: greg m 0 There is no way. K7 7
## 9: sam m 0 I distrust you. K8 8
## 10: sally f 0 What are you talking about? K9 9
## 11: researcher f 1 Shall we move on? K10 10
## 12: researcher f 1 Good then. K10 10
## 13: greg m 0 I'm hungry. K11 11
## 14: greg m 0 Let's eat. K11 11
## 15: greg m 0 You already? K11 11
## sentence_id
## 1: 1
## 2: 2
## 3: 1
## 4: 1
## 5: 1
## 6: 1
## 7: 1
## 8: 1
## 9: 1
## 10: 1
## 11: 1
## 12: 2
## 13: 1
## 14: 2
## 15: 3
combine(dat[, 1:5, with=FALSE])
## person sex adult state code
## 1: sam m 0 Computer is fun. Not too fun. K1
## 2: greg m 0 No it's not, it's dumb. K2
## 3: teacher m 1 What should we do? K3
## 4: sam m 0 You liar, it stinks! K4
## 5: greg m 0 I am telling the truth! K5
## 6: sally f 0 How can we be certain? K6
## 7: greg m 0 There is no way. K7
## 8: sam m 0 I distrust you. K8
## 9: sally f 0 What are you talking about? K9
## 10: researcher f 1 Shall we move on? Good then. K10
## 11: greg m 0 I'm hungry. Let's eat. You already? K11mtabulate allows the user to transform data types into a dataframe of counts.
(x <- list(w=letters[1:10], x=letters[1:5], z=letters))
## $w
## [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j"
##
## $x
## [1] "a" "b" "c" "d" "e"
##
## $z
## [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
## [20] "t" "u" "v" "w" "x" "y" "z"
mtabulate(x)
## a b c d e f g h i j k l m n o p q r s t u v w x y z
## w 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## x 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## z 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## Dummy coding
mtabulate(mtcars$cyl[1:10])
## 4 6 8
## 1 0 1 0
## 2 0 1 0
## 3 1 0 0
## 4 0 1 0
## 5 0 0 1
## 6 0 1 0
## 7 0 0 1
## 8 1 0 0
## 9 1 0 0
## 10 0 1 0(dat <- data.frame(matrix(sample(c("A", "B"), 30, TRUE), ncol=3)))
## X1 X2 X3
## 1 A A B
## 2 B A A
## 3 B A B
## 4 B A A
## 5 A A B
## 6 A A B
## 7 B B B
## 8 A B B
## 9 A B A
## 10 A B B
mtabulate(dat)
## A B
## X1 6 4
## X2 6 4
## X3 3 7
t(mtabulate(dat))
## X1 X2 X3
## A 6 6 3
## B 4 4 7flatten allows the user to flatten a named, nested list of atomic vectors to a single level using the concatenated list/atomic vector names as the names of the single tiered list. This is particularly useful for flattening dictionaries as seen below. First we see the quanteda dictionary.
mydict <- textreadr::download("https://provalisresearch.com/Download/LaverGarry.zip") %>%
unzip(exdir = tempdir()) %>%
`[`(1) %>%
quanteda::dictionary(file = .)
## LaverGarry.zip read into C:\Users\TYLERR~1\AppData\Local\Temp\RtmpyAtc0H
mydict
## Dictionary object with 9 primary key entries and 2 nested levels.
## - [CULTURE]:
## - people, war_in_iraq, civil_war
## - [CULTURE-HIGH]:
## - art, artistic, dance, galler*, museum*, music*, opera*, theatre*
## - [CULTURE-POPULAR]:
## - media
## - [SPORT]:
## - angler*
## - [ECONOMY]:
## - [+STATE+]:
## - accommodation, age, ambulance, assist, benefit, care, carer*, child*, class, classes, clinics, collective*, contribution*, cooperative*, co-operative*, deprivation, disabilities, disadvantaged, educat*, elderly [ ... and 30 more ]
## - [=STATE=]:
## - accountant, accounting, accounts, advert*, airline*, airport*, audit*, bank*, bargaining, breadwinner*, budget*, buy*, cartel*, cash*, charge*, commerce*, compensat*, consum*, cost*, credit* [ ... and 51 more ]
## - [-STATE-]:
## - assets, autonomy, barrier*, bid, bidders, bidding, burden*, charit*, choice*, compet*, confidence, confiscatory, constrain*, contracting*, contractor*, controlled, controlling, controls, corporate, corporation* [ ... and 42 more ]
## - [ENVIRONMENT]:
## - [CON ENVIRONMENT]:
## - produc*
## - [PRO ENVIRONMENT]:
## - car, catalytic, chemical*, chimney*, clean*, congestion, cyclist*, deplet*, ecolog*, emission*, energy-saving, environment*, fur, green, habitat*, hedgerow*, husbanded, litter*, opencast, open-cast* [ ... and 8 more ]
## - [GROUPS]:
## - [ETHNIC]:
## - asian*, buddhist*, ethnic*, race, raci*
## - [WOMEN]:
## - girls, woman, women
## - [INSTITUTIONS]:
## - [CONSERVATIVE]:
## - authority, continu*, disrupt*, inspect*, jurisdiction*, legitimate, manag*, moratorium, rul*, strike*, whitehall
## - [NEUTRAL]:
## - administr*, advis*, agenc*, amalgamat*, appoint*, assembly, chair*, commission*, committee*, constituen*, council*, department*, directorate*, executive*, headquarters, legislat*, mechanism*, minister*, office, offices [ ... and 18 more ]
## - [RADICAL]:
## - abolition, accountable, answerable, consult*, corrupt*, democratic*, elect*, implement*, modern*, monitor*, rebuild*, reexamine*, reform*, re-organi*, repeal*, replace*, representat*, scandal*, scrap, scrap* [ ... and 3 more ]
## - [LAW_AND_ORDER]:
## - [LAW-CONSERVATIVE]:
## - assaults, bail, burglar*, constab*, convict*, court, courts, custod*, dealing, delinquen*, deter, deter*, disorder, drug*, fine, fines, firmness, force*, fraud*, guard* [ ... and 32 more ]
## - [LAW-LIBERAL]:
## - harassment, non-custodial
## [ reached max_nkey ... 3 more keys ]And now we coerce the dictionary to a list and flatten it. This could have been combined with the chain above but I wanted the user to see the original dictionary structure as well.
mydict %>%
as.list() %>%
flatten()
## $CULTURE___
## [1] "people" "war_in_iraq" "civil_war"
##
## $`CULTURE_CULTURE-HIGH`
## [1] "art" "artistic" "dance" "galler*" "museum*" "music*" "opera*"
## [8] "theatre*"
##
## $`CULTURE_CULTURE-POPULAR`
## [1] "media"
##
## $CULTURE_SPORT
## [1] "angler*"
##
## $`ECONOMY_-STATE-`
## [1] "assets" "autonomy" "barrier*" "bid"
## [5] "bidders" "bidding" "burden*" "charit*"
## [9] "choice*" "compet*" "confidence" "confiscatory"
## [13] "constrain*" "contracting*" "contractor*" "controlled"
## [17] "controlling" "controls" "corporate" "corporation*"
## [21] "deregulating" "dismantl*" "entrepreneur*" "expensive"
## [25] "flexib*" "franchise*" "fundhold*" "fund-holding"
## [29] "homestead*" "initiative" "intrusive" "investor*"
## [33] "liberali*" "market*" "monetary" "money"
## [37] "own*" "private" "privately" "privatisations"
## [41] "privatised" "privatising" "produce*" "profitable"
## [45] "regulat*" "retail*" "risk" "risks"
## [49] "savings" "sell*" "shares" "simplif*"
## [53] "spend*" "sponsorship" "taxable" "taxes"
## [57] "tax-free" "thrift*" "trading" "value"
## [61] "volunt*" "voucher*"
##
## $`ECONOMY_+STATE+`
## [1] "accommodation" "age" "ambulance" "assist"
## [5] "benefit" "care" "carer*" "child*"
## [9] "class" "classes" "clinics" "collective*"
## [13] "contribution*" "cooperative*" "co-operative*" "deprivation"
## [17] "disabilities" "disadvantaged" "educat*" "elderly"
## [21] "equal*" "establish" "fair*" "guarantee*"
## [25] "hardship" "health*" "homeless*" "hospital*"
## [29] "hunger" "inequal*" "invest" "investing"
## [33] "investment" "means-test*" "nurse*" "patients"
## [37] "pension" "poor" "poorer" "poorest"
## [41] "poverty" "rehouse*" "re-house*" "school"
## [45] "teach*" "transport" "underfund*" "unemploy*"
## [49] "vulnerable" "widow*"
##
## $`ECONOMY_=STATE=`
## [1] "accountant" "accounting" "accounts" "advert*" "airline*"
## [6] "airport*" "audit*" "bank*" "bargaining" "breadwinner*"
## [11] "budget*" "buy*" "cartel*" "cash*" "charge*"
## [16] "commerce*" "compensat*" "consum*" "cost*" "credit*"
## [21] "customer*" "debt*" "deficit*" "dwelling*" "earn*"
## [26] "econ*" "electricity" "estate*" "export*" "fee"
## [31] "fees" "financ*" "hous*" "import" "imports"
## [36] "industr*" "jobs" "lease*" "loan*" "manufactur*"
## [41] "mortgage*" "negotiat*" "opportunity" "partnership*" "passenger*"
## [46] "pay*" "performance" "port*" "productivity" "profession*"
## [51] "purchas*" "railway*" "rebate*" "recession*" "research*"
## [56] "revenue*" "salar*" "sell*" "settlement" "software"
## [61] "supplier*" "supply" "telecom*" "telephon*" "tenan*"
## [66] "touris*" "trade" "train*" "wage*" "welfare"
## [71] "work*"
##
## $`ENVIRONMENT_CON ENVIRONMENT`
## [1] "produc*"
##
## $`ENVIRONMENT_PRO ENVIRONMENT`
## [1] "car" "catalytic" "chemical*" "chimney*"
## [5] "clean*" "congestion" "cyclist*" "deplet*"
## [9] "ecolog*" "emission*" "energy-saving" "environment*"
## [13] "fur" "green" "habitat*" "hedgerow*"
## [17] "husbanded" "litter*" "opencast" "open-cast*"
## [21] "ozone" "planet" "population" "recycl*"
## [25] "re-cycl*" "re-use" "toxic" "warming"
##
## $GROUPS_ETHNIC
## [1] "asian*" "buddhist*" "ethnic*" "race" "raci*"
##
## $GROUPS_WOMEN
## [1] "girls" "woman" "women"
##
## $INSTITUTIONS_CONSERVATIVE
## [1] "authority" "continu*" "disrupt*" "inspect*"
## [5] "jurisdiction*" "legitimate" "manag*" "moratorium"
## [9] "rul*" "strike*" "whitehall"
##
## $INSTITUTIONS_NEUTRAL
## [1] "administr*" "advis*" "agenc*" "amalgamat*"
## [5] "appoint*" "assembly" "chair*" "commission*"
## [9] "committee*" "constituen*" "council*" "department*"
## [13] "directorate*" "executive*" "headquarters" "legislat*"
## [17] "mechanism*" "minister*" "office" "offices"
## [21] "official" "operat*" "opposition" "organisation*"
## [25] "parliament*" "presiden*" "procedur*" "process*"
## [29] "queen" "regist*" "scheme*" "secretariat*"
## [33] "sovereign*" "subcommittee*" "tribunal*" "vote*"
## [37] "voting" "westminster"
##
## $INSTITUTIONS_RADICAL
## [1] "abolition" "accountable" "answerable" "consult*" "corrupt*"
## [6] "democratic*" "elect*" "implement*" "modern*" "monitor*"
## [11] "rebuild*" "reexamine*" "reform*" "re-organi*" "repeal*"
## [16] "replace*" "representat*" "scandal*" "scrap" "scrap*"
## [21] "scrutin*" "transform*" "voice*"
##
## $`LAW_AND_ORDER_LAW-CONSERVATIVE`
## [1] "assaults" "bail" "burglar*" "constab*" "convict*"
## [6] "court" "courts" "custod*" "dealing" "delinquen*"
## [11] "deter" "deter*" "disorder" "drug*" "fine"
## [16] "fines" "firmness" "force*" "fraud*" "guard*"
## [21] "hooligan*" "illegal*" "intimidat*" "joy-ride*" "lawless*"
## [26] "magistrat*" "offence*" "officer*" "penal*" "police"
## [31] "policemen" "policing" "prison*" "probation" "prosecution"
## [36] "punish*" "re-offend" "ruc" "seiz*" "sentence*"
## [41] "shop-lifting" "squatting" "terror*" "theft*" "thug*"
## [46] "tough*" "trafficker*" "uniformed" "unlawful" "vandal*"
## [51] "victim*" "vigilan*"
##
## $`LAW_AND_ORDER_LAW-LIBERAL`
## [1] "harassment" "non-custodial"
##
## $RURAL
## [1] "agricultur*" "badgers" "bird*" "countryside" "farm*"
## [6] "feed" "fish*" "forest*" "hens" "horse*"
## [11] "landscape*" "lane*" "livestock" "meadows" "village*"
## [16] "wildlife"
##
## $URBAN
## [1] "town*"
##
## $VALUES_CONSERVATIVE
## [1] "defend" "defended" "defending" "discipline"
## [5] "glories" "glorious" "grammar" "heritage"
## [9] "histor*" "honour*" "immigra*" "inherit*"
## [13] "integrity" "jubilee*" "leader*" "maintain"
## [17] "majesty" "marriage" "obscen*" "past"
## [21] "pornograph*" "preserv*" "pride" "principl*"
## [25] "probity" "professionalism" "proud" "punctual*"
## [29] "recapture*" "reliab*" "threat*" "tradition*"
##
## $VALUES_LIBERAL
## [1] "cruel*" "discriminat*" "human*" "injustice*" "innocent"
## [6] "inter_racial" "minorit*" "repressi*" "rights" "sex*"Often it is useful to know the duration (start-end) of turns of talk. The duration function calculates start-end durations as n words.
(x <- c(
"Mr. Brown comes! He says hello. i give him coffee.",
"I'll go at 5 p. m. eastern time. Or somewhere in between!",
"go there"
))
## [1] "Mr. Brown comes! He says hello. i give him coffee."
## [2] "I'll go at 5 p. m. eastern time. Or somewhere in between!"
## [3] "go there"
duration(x)
## all word.count start end
## 1: all 10 1 10
## 2: all 12 11 22
## 3: all 2 23 24
## text.var
## 1: Mr. Brown comes! He says hello. i give him coffee.
## 2: I'll go at 5 p. m. eastern time. Or somewhere in between!
## 3: go there
# With grouping variables
groups <- list(group1 = c("A", "B", "A"), group2 = c("red", "red", "green"))
duration(x, groups)
## group1 group2 word.count start end
## 1: A red 10 1 10
## 2: B red 12 11 22
## 3: A green 2 23 24
## text.var
## 1: Mr. Brown comes! He says hello. i give him coffee.
## 2: I'll go at 5 p. m. eastern time. Or somewhere in between!
## 3: go thereduration(DATA)
## person sex adult code word.count start end
## 1: sam m 0 K1 6 1 6
## 2: greg m 0 K2 5 7 11
## 3: teacher m 1 K3 4 12 15
## 4: sam m 0 K4 4 16 19
## 5: greg m 0 K5 5 20 24
## 6: sally f 0 K6 5 25 29
## 7: greg m 0 K7 4 30 33
## 8: sam m 0 K8 3 34 36
## 9: sally f 0 K9 5 37 41
## 10: researcher f 1 K10 6 42 47
## 11: greg m 0 K11 6 48 53
## state
## 1: Computer is fun. Not too fun.
## 2: No it's not, it's dumb.
## 3: What should we do?
## 4: You liar, it stinks!
## 5: I am telling the truth!
## 6: How can we be certain?
## 7: There is no way.
## 8: I distrust you.
## 9: What are you talking about?
## 10: Shall we move on? Good then.
## 11: I'm hungry. Let's eat. You already?library(ggplot2)
ggplot(duration(DATA), aes(x = start, xend = end, y = person, yend = person, color = sex)) +
geom_segment(size=4) +
xlab("Duration (Words)") +
ylab("Person")
The following section provides examples of available splitting functions.
split_index allows the user to supply the integer indices of where to split a data type.
split_index(
LETTERS,
indices = c(4, 10, 16),
names = c("dog", "cat", "chicken", "rabbit")
)
## $dog
## [1] "A" "B" "C"
##
## $cat
## [1] "D" "E" "F" "G" "H" "I"
##
## $chicken
## [1] "J" "K" "L" "M" "N" "O"
##
## $rabbit
## [1] "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y" "Z"Here I calculate the indices of every time the vs variable in the mtcars data set changes and then split the dataframe on those indices. The change_index function is handy for extracting the indices of changes in runs within an atomic vector.
(vs_change <- change_index(mtcars[["vs"]]))
## [1] 3 5 6 7 8 12 18 22 26 27 28 29 32
split_index(mtcars, indices = vs_change)
## [[1]]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21 6 160 110 3.9 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4
##
## [[2]]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
##
## [[3]]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2
##
## [[4]]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1
##
## [[5]]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4
##
## [[6]]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Merc 240D 24.4 4 146.7 62 3.69 3.19 20.0 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4
##
## [[7]]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
##
## [[8]]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
##
## [[9]]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Dodge Challenger 15.5 8 318 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2
##
## [[10]]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1
##
## [[11]]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2
##
## [[12]]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2
##
## [[13]]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
## Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
## Maserati Bora 15.0 8 301 335 3.54 3.57 14.6 0 1 5 8
##
## [[14]]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2split_match splits on elements that match exactly or via a regular expression match.
set.seed(15)
(x <- sample(c("", LETTERS[1:10]), 25, TRUE, prob=c(.2, rep(.08, 10))))
## [1] "C" "" "A" "C" "D" "A" "I" "B" "H" "I" "" "C" "E" "H" "J" "J" "E" "A" ""
## [20] "I" "I" "I" "G" "" "F"
split_match(x)
## $`1`
## [1] "C"
##
## $`2`
## [1] "A" "C" "D" "A" "I" "B" "H" "I"
##
## $`3`
## [1] "C" "E" "H" "J" "J" "E" "A"
##
## $`4`
## [1] "I" "I" "I" "G"
##
## $`5`
## [1] "F"
split_match(x, split = "C")
## $`1`
## [1] "" "A"
##
## $`2`
## [1] "D" "A" "I" "B" "H" "I" ""
##
## $`3`
## [1] "E" "H" "J" "J" "E" "A" "" "I" "I" "I" "G" "" "F"
split_match(x, split = c("", "C"))
## $`1`
## [1] "A"
##
## $`2`
## [1] "D" "A" "I" "B" "H" "I"
##
## $`3`
## [1] "E" "H" "J" "J" "E" "A"
##
## $`4`
## [1] "I" "I" "I" "G"
##
## $`5`
## [1] "F"
## Don't include
split_match(x, include = 0)
## $`1`
## [1] "C"
##
## $`2`
## [1] "A" "C" "D" "A" "I" "B" "H" "I"
##
## $`3`
## [1] "C" "E" "H" "J" "J" "E" "A"
##
## $`4`
## [1] "I" "I" "I" "G"
##
## $`5`
## [1] "F"
## Include at beginning
split_match(x, include = 1)
## $`1`
## [1] "C"
##
## $`2`
## [1] "" "A" "C" "D" "A" "I" "B" "H" "I"
##
## $`3`
## [1] "" "C" "E" "H" "J" "J" "E" "A"
##
## $`4`
## [1] "" "I" "I" "I" "G"
##
## $`5`
## [1] "" "F"
## Include at end
split_match(x, include = 2)
## [[1]]
## [1] "C" ""
##
## [[2]]
## [1] "A" "C" "D" "A" "I" "B" "H" "I" ""
##
## [[3]]
## [1] "C" "E" "H" "J" "J" "E" "A" ""
##
## [[4]]
## [1] "I" "I" "I" "G" ""
##
## [[5]]
## [1] "F"Here I use the regex "^I" to break on any vectors containing the capital letter I as the first character.
split_match(DATA[["state"]], split = "^I", regex=TRUE, include = 1)
## $`1`
## [1] "Computer is fun. Not too fun." "No it's not, it's dumb."
## [3] "What should we do?" "You liar, it stinks!"
##
## $`2`
## [1] "I am telling the truth!" "How can we be certain?"
## [3] "There is no way."
##
## $`3`
## [1] "I distrust you." "What are you talking about?"
## [3] "Shall we move on? Good then."
##
## $`4`
## [1] "I'm hungry. Let's eat. You already?"At times it is useful to split texts into portioned chunks, operate on the chunks and aggregate the results. split_portion allows the user to do this sort of text shaping. We can split into n chunks per grouping variable (via n.chunks) or into chunks of n length (via n.words).
with(DATA, split_portion(state, n.chunks = 10))
## all index text.var
## 1: all 1 Computer is fun. Not too
## 2: all 2 fun. No it's not, it's
## 3: all 3 dumb. What should we do?
## 4: all 4 You liar, it stinks! I
## 5: all 5 am telling the truth! How
## 6: all 6 can we be certain? There
## 7: all 7 is no way. I distrust
## 8: all 8 you. What are you talking
## 9: all 9 about? Shall we move on?
## 10: all 10 Good then. I'm hungry. Let's
## 11: all 11 eat. You already?
with(DATA, split_portion(state, n.words = 10))
## all index text.var
## 1: all 1 Computer is fun. Not too fun. No it's not, it's
## 2: all 2 dumb. What should we do? You liar, it stinks! I
## 3: all 3 am telling the truth! How can we be certain? There
## 4: all 4 is no way. I distrust you. What are you talking
## 5: all 5 about? Shall we move on? Good then. I'm hungry. Let's
## 6: all 6 eat. You already?with(DATA, split_portion(state, list(sex, adult), n.words = 10))
## sex adult index text.var
## 1: f 0 1 How can we be certain? What are you talking about?
## 2: f 1 1 Shall we move on? Good then.
## 3: m 0 1 Computer is fun. Not too fun. No it's not, it's
## 4: m 0 2 dumb. You liar, it stinks! I am telling the truth!
## 5: m 0 3 There is no way. I distrust you. I'm hungry. Let's
## 6: m 0 4 eat. You already?
## 7: m 1 1 What should we do?split_run allows the user to split up runs of identical characters.
x1 <- c(
"122333444455555666666",
NA,
"abbcccddddeeeeeffffff",
"sddfg",
"11112222333"
)
x <- c(rep(x1, 2), ">>???,,,,....::::;[[")
split_run(x)
## [[1]]
## [1] "1" "22" "333" "4444" "55555" "666666"
##
## [[2]]
## [1] NA
##
## [[3]]
## [1] "a" "bb" "ccc" "dddd" "eeeee" "ffffff"
##
## [[4]]
## [1] "s" "dd" "f" "g"
##
## [[5]]
## [1] "1111" "2222" "333"
##
## [[6]]
## [1] "1" "22" "333" "4444" "55555" "666666"
##
## [[7]]
## [1] NA
##
## [[8]]
## [1] "a" "bb" "ccc" "dddd" "eeeee" "ffffff"
##
## [[9]]
## [1] "s" "dd" "f" "g"
##
## [[10]]
## [1] "1111" "2222" "333"
##
## [[11]]
## [1] ">>" "???" ",,,," "...." "::::" ";" "[["DATA[["run.col"]] <- x
split_run(DATA)
## person sex adult state code run.col element_id sentence_id
## 1: sam m 0 C K1 122333444455555666666 1 1
## 2: sam m 0 o K1 122333444455555666666 1 2
## 3: sam m 0 m K1 122333444455555666666 1 3
## 4: sam m 0 p K1 122333444455555666666 1 4
## 5: sam m 0 u K1 122333444455555666666 1 5
## ---
## 206: greg m 0 e K11 >>???,,,,....::::;[[ 11 26
## 207: greg m 0 a K11 >>???,,,,....::::;[[ 11 27
## 208: greg m 0 d K11 >>???,,,,....::::;[[ 11 28
## 209: greg m 0 y K11 >>???,,,,....::::;[[ 11 29
## 210: greg m 0 ? K11 >>???,,,,....::::;[[ 11 30
## Reset the DATA dataset
DATA <- textshape::DATAsplit_sentece provides a mapping + regex approach to splitting sentences. It is less accurate than the Stanford parser but more accurate than a simple regular expression approach alone.
(x <- paste0(
"Mr. Brown comes! He says hello. i give him coffee. i will ",
"go at 5 p. m. eastern time. Or somewhere in between!go there"
))
## [1] "Mr. Brown comes! He says hello. i give him coffee. i will go at 5 p. m. eastern time. Or somewhere in between!go there"
split_sentence(x)
## [[1]]
## [1] "Mr. Brown comes!" "He says hello."
## [3] "i give him coffee." "i will go at 5 p.m. eastern time."
## [5] "Or somewhere in between!" "go there"split_sentence(DATA)
## person sex adult state code element_id
## 1: sam m 0 Computer is fun. K1 1
## 2: sam m 0 Not too fun. K1 1
## 3: greg m 0 No it's not, it's dumb. K2 2
## 4: teacher m 1 What should we do? K3 3
## 5: sam m 0 You liar, it stinks! K4 4
## 6: greg m 0 I am telling the truth! K5 5
## 7: sally f 0 How can we be certain? K6 6
## 8: greg m 0 There is no way. K7 7
## 9: sam m 0 I distrust you. K8 8
## 10: sally f 0 What are you talking about? K9 9
## 11: researcher f 1 Shall we move on? K10 10
## 12: researcher f 1 Good then. K10 10
## 13: greg m 0 I'm hungry. K11 11
## 14: greg m 0 Let's eat. K11 11
## 15: greg m 0 You already? K11 11
## sentence_id
## 1: 1
## 2: 2
## 3: 1
## 4: 1
## 5: 1
## 6: 1
## 7: 1
## 8: 1
## 9: 1
## 10: 1
## 11: 1
## 12: 2
## 13: 1
## 14: 2
## 15: 3Often speakers may talk in unison. This is often displayed in a single cell as a comma separated string of speakers. Some analysis may require this information to be parsed out and replicated as one turn per speaker. The split_speaker function accomplishes this.
DATA$person <- as.character(DATA$person)
DATA$person[c(1, 4, 6)] <- c("greg, sally, & sam",
"greg, sally", "sam and sally")
DATA
## person sex adult state code
## 1 greg, sally, & sam m 0 Computer is fun. Not too fun. K1
## 2 greg m 0 No it's not, it's dumb. K2
## 3 teacher m 1 What should we do? K3
## 4 greg, sally m 0 You liar, it stinks! K4
## 5 greg m 0 I am telling the truth! K5
## 6 sam and sally f 0 How can we be certain? K6
## 7 greg m 0 There is no way. K7
## 8 sam m 0 I distrust you. K8
## 9 sally f 0 What are you talking about? K9
## 10 researcher f 1 Shall we move on? Good then. K10
## 11 greg m 0 I'm hungry. Let's eat. You already? K11
split_speaker(DATA)
## person sex adult state code element_id
## 1: greg m 0 Computer is fun. Not too fun. K1 1
## 2: sally m 0 Computer is fun. Not too fun. K1 1
## 3: sam m 0 Computer is fun. Not too fun. K1 1
## 4: greg m 0 No it's not, it's dumb. K2 2
## 5: teacher m 1 What should we do? K3 3
## 6: greg m 0 You liar, it stinks! K4 4
## 7: sally m 0 You liar, it stinks! K4 4
## 8: greg m 0 I am telling the truth! K5 5
## 9: sam f 0 How can we be certain? K6 6
## 10: sally f 0 How can we be certain? K6 6
## 11: greg m 0 There is no way. K7 7
## 12: sam m 0 I distrust you. K8 8
## 13: sally f 0 What are you talking about? K9 9
## 14: researcher f 1 Shall we move on? Good then. K10 10
## 15: greg m 0 I'm hungry. Let's eat. You already? K11 11
## split_id
## 1: 1
## 2: 2
## 3: 3
## 4: 1
## 5: 1
## 6: 1
## 7: 2
## 8: 1
## 9: 1
## 10: 2
## 11: 1
## 12: 1
## 13: 1
## 14: 1
## 15: 1
## Reset the DATA dataset
DATA <- textshape::DATAThe split_token function split data into words and punctuation.
(x <- c(
"Mr. Brown comes! He says hello. i give him coffee.",
"I'll go at 5 p. m. eastern time. Or somewhere in between!",
"go there"
))
## [1] "Mr. Brown comes! He says hello. i give him coffee."
## [2] "I'll go at 5 p. m. eastern time. Or somewhere in between!"
## [3] "go there"
split_token(x)
## [[1]]
## [1] "mr" "." "brown" "comes" "!" "he" "says" "hello"
## [9] "." "i" "give" "him" "coffee" "."
##
## [[2]]
## [1] "i'll" "go" "at" "5" "p" "."
## [7] "m" "." "eastern" "time" "." "or"
## [13] "somewhere" "in" "between" "!"
##
## [[3]]
## [1] "go" "there" split_token(DATA)
## person sex adult state code element_id token_id
## 1: sam m 0 computer K1 1 1
## 2: sam m 0 is K1 1 2
## 3: sam m 0 fun K1 1 3
## 4: sam m 0 . K1 1 4
## 5: sam m 0 not K1 1 5
## 6: sam m 0 too K1 1 6
## 7: sam m 0 fun K1 1 7
## 8: sam m 0 . K1 1 8
## 9: greg m 0 no K2 2 1
## 10: greg m 0 it's K2 2 2
## 11: greg m 0 not K2 2 3
## 12: greg m 0 , K2 2 4
## 13: greg m 0 it's K2 2 5
## 14: greg m 0 dumb K2 2 6
## 15: greg m 0 . K2 2 7
## 16: teacher m 1 what K3 3 1
## 17: teacher m 1 should K3 3 2
## 18: teacher m 1 we K3 3 3
## 19: teacher m 1 do K3 3 4
## 20: teacher m 1 ? K3 3 5
## 21: sam m 0 you K4 4 1
## 22: sam m 0 liar K4 4 2
## 23: sam m 0 , K4 4 3
## 24: sam m 0 it K4 4 4
## 25: sam m 0 stinks K4 4 5
## 26: sam m 0 ! K4 4 6
## 27: greg m 0 i K5 5 1
## 28: greg m 0 am K5 5 2
## 29: greg m 0 telling K5 5 3
## 30: greg m 0 the K5 5 4
## 31: greg m 0 truth K5 5 5
## 32: greg m 0 ! K5 5 6
## 33: sally f 0 how K6 6 1
## 34: sally f 0 can K6 6 2
## 35: sally f 0 we K6 6 3
## 36: sally f 0 be K6 6 4
## 37: sally f 0 certain K6 6 5
## 38: sally f 0 ? K6 6 6
## 39: greg m 0 there K7 7 1
## 40: greg m 0 is K7 7 2
## 41: greg m 0 no K7 7 3
## 42: greg m 0 way K7 7 4
## 43: greg m 0 . K7 7 5
## 44: sam m 0 i K8 8 1
## 45: sam m 0 distrust K8 8 2
## 46: sam m 0 you K8 8 3
## 47: sam m 0 . K8 8 4
## 48: sally f 0 what K9 9 1
## 49: sally f 0 are K9 9 2
## 50: sally f 0 you K9 9 3
## 51: sally f 0 talking K9 9 4
## 52: sally f 0 about K9 9 5
## 53: sally f 0 ? K9 9 6
## 54: researcher f 1 shall K10 10 1
## 55: researcher f 1 we K10 10 2
## 56: researcher f 1 move K10 10 3
## 57: researcher f 1 on K10 10 4
## 58: researcher f 1 ? K10 10 5
## 59: researcher f 1 good K10 10 6
## 60: researcher f 1 then K10 10 7
## 61: researcher f 1 . K10 10 8
## 62: greg m 0 i'm K11 11 1
## 63: greg m 0 hungry K11 11 2
## 64: greg m 0 . K11 11 3
## 65: greg m 0 let's K11 11 4
## 66: greg m 0 eat K11 11 5
## 67: greg m 0 . K11 11 6
## 68: greg m 0 you K11 11 7
## 69: greg m 0 already K11 11 8
## 70: greg m 0 ? K11 11 9
## person sex adult state code element_id token_idThe split_transcript function splits vectors with speaker prefixes (e.g., c("greg: Who me", "sarah: yes you!")) into a two column data.frame.
(x <- c(
"greg: Who me",
"sarah: yes you!",
"greg: well why didn't you say so?",
"sarah: I did but you weren't listening.",
"greg: oh :-/ I see...",
"dan: Ok let's meet at 4:30 pm for drinks"
))
## [1] "greg: Who me"
## [2] "sarah: yes you!"
## [3] "greg: well why didn't you say so?"
## [4] "sarah: I did but you weren't listening."
## [5] "greg: oh :-/ I see..."
## [6] "dan: Ok let's meet at 4:30 pm for drinks"
split_transcript(x)
## person dialogue
## 1: greg Who me
## 2: sarah yes you!
## 3: greg well why didn't you say so?
## 4: sarah I did but you weren't listening.
## 5: greg oh :-/ I see...
## 6: dan Ok let's meet at 4:30 pm for drinksThe split_word function splits data into words.
(x <- c(
"Mr. Brown comes! He says hello. i give him coffee.",
"I'll go at 5 p. m. eastern time. Or somewhere in between!",
"go there"
))
## [1] "Mr. Brown comes! He says hello. i give him coffee."
## [2] "I'll go at 5 p. m. eastern time. Or somewhere in between!"
## [3] "go there"
split_word(x)
## [[1]]
## [1] "mr" "brown" "comes" "he" "says" "hello" "i" "give"
## [9] "him" "coffee"
##
## [[2]]
## [1] "i'll" "go" "at" "5" "p" "m"
## [7] "eastern" "time" "or" "somewhere" "in" "between"
##
## [[3]]
## [1] "go" "there" split_word(DATA)
## person sex adult state code element_id word_id
## 1: sam m 0 computer K1 1 1
## 2: sam m 0 is K1 1 2
## 3: sam m 0 fun K1 1 3
## 4: sam m 0 not K1 1 4
## 5: sam m 0 too K1 1 5
## 6: sam m 0 fun K1 1 6
## 7: greg m 0 no K2 2 1
## 8: greg m 0 it's K2 2 2
## 9: greg m 0 not K2 2 3
## 10: greg m 0 it's K2 2 4
## 11: greg m 0 dumb K2 2 5
## 12: teacher m 1 what K3 3 1
## 13: teacher m 1 should K3 3 2
## 14: teacher m 1 we K3 3 3
## 15: teacher m 1 do K3 3 4
## 16: sam m 0 you K4 4 1
## 17: sam m 0 liar K4 4 2
## 18: sam m 0 it K4 4 3
## 19: sam m 0 stinks K4 4 4
## 20: greg m 0 i K5 5 1
## 21: greg m 0 am K5 5 2
## 22: greg m 0 telling K5 5 3
## 23: greg m 0 the K5 5 4
## 24: greg m 0 truth K5 5 5
## 25: sally f 0 how K6 6 1
## 26: sally f 0 can K6 6 2
## 27: sally f 0 we K6 6 3
## 28: sally f 0 be K6 6 4
## 29: sally f 0 certain K6 6 5
## 30: greg m 0 there K7 7 1
## 31: greg m 0 is K7 7 2
## 32: greg m 0 no K7 7 3
## 33: greg m 0 way K7 7 4
## 34: sam m 0 i K8 8 1
## 35: sam m 0 distrust K8 8 2
## 36: sam m 0 you K8 8 3
## 37: sally f 0 what K9 9 1
## 38: sally f 0 are K9 9 2
## 39: sally f 0 you K9 9 3
## 40: sally f 0 talking K9 9 4
## 41: sally f 0 about K9 9 5
## 42: researcher f 1 shall K10 10 1
## 43: researcher f 1 we K10 10 2
## 44: researcher f 1 move K10 10 3
## 45: researcher f 1 on K10 10 4
## 46: researcher f 1 good K10 10 5
## 47: researcher f 1 then K10 10 6
## 48: greg m 0 i'm K11 11 1
## 49: greg m 0 hungry K11 11 2
## 50: greg m 0 let's K11 11 3
## 51: greg m 0 eat K11 11 4
## 52: greg m 0 you K11 11 5
## 53: greg m 0 already K11 11 6
## person sex adult state code element_id word_idThe following section provides examples of available grabbing (from a starting point up to an ending point) functions.
grab_index allows the user to supply the integer indices of where to grab (from - up to) a data type.
grab_index(DATA$state, from = 2, to = 4)
## [1] "No it's not, it's dumb." "What should we do?"
## [3] "You liar, it stinks!"
grab_index(DATA$state, from = 9)
## [1] "What are you talking about?"
## [2] "Shall we move on? Good then."
## [3] "I'm hungry. Let's eat. You already?"
grab_index(DATA$state, to = 3)
## [1] "Computer is fun. Not too fun." "No it's not, it's dumb."
## [3] "What should we do?"grab_index(DATA, from = 2, to = 4)
## person sex adult state code
## 2 greg m 0 No it's not, it's dumb. K2
## 3 teacher m 1 What should we do? K3
## 4 sam m 0 You liar, it stinks! K4grab_index(as.list(DATA$state), from = 2, to = 4)
## [[1]]
## [1] "No it's not, it's dumb."
##
## [[2]]
## [1] "What should we do?"
##
## [[3]]
## [1] "You liar, it stinks!"grab_match grabs (from - up to) elements that match a regular expression.
grab_match(DATA$state, from = 'dumb', to = 'liar')
## [1] "No it's not, it's dumb." "What should we do?"
## [3] "You liar, it stinks!"
grab_match(DATA$state, from = '^What are')
## [1] "What are you talking about?"
## [2] "Shall we move on? Good then."
## [3] "I'm hungry. Let's eat. You already?"
grab_match(DATA$state, to = 'we do[?]')
## [1] "Computer is fun. Not too fun." "No it's not, it's dumb."
## [3] "What should we do?"
grab_match(DATA$state, from = 'no', to = 'the', ignore.case = TRUE,
from.n = 'last', to.n = 'first')
## [1] "There is no way." "How can we be certain?"
## [3] "I am telling the truth!"grab_match(DATA, from = 'dumb', to = 'liar')
## person sex adult state code
## 2 greg m 0 No it's not, it's dumb. K2
## 3 teacher m 1 What should we do? K3
## 4 sam m 0 You liar, it stinks! K4grab_match(as.list(DATA$state), from = 'dumb', to = 'liar')
## [[1]]
## [1] "No it's not, it's dumb."
##
## [[2]]
## [1] "What should we do?"
##
## [[3]]
## [1] "You liar, it stinks!"Eduardo Flores blogged about What the candidates say, analyzing republican debates using R where he demonstrated some scraping and analysis techniques. Here I highlight a combination usage of textshape tools to scrape and structure the text from 4 of the 2015 Republican debates within a magrittr pipeline. The result is a single data.table containing the dialogue from all 4 debates. The code highlights the conciseness and readability of textshape by restructuring Flores scraping with textshape replacements.
if (!require("pacman")) install.packages("pacman")
## Loading required package: pacman
pacman::p_load(rvest, magrittr, xml2)
debates <- c(
wisconsin = "110908",
boulder = "110906",
california = "110756",
ohio = "110489"
)
lapply(debates, function(x){
xml2::read_html(paste0("http://www.presidency.ucsb.edu/ws/index.php?pid=", x)) %>%
rvest::html_nodes("p") %>%
rvest::html_text() %>%
textshape::split_index(., grep("^[A-Z]+:", .)) %>%
#textshape::split_match("^[A-Z]+:", TRUE, TRUE) %>% #equal to line above
textshape::combine() %>%
textshape::split_transcript() %>%
textshape::split_sentence()
}) %>%
textshape::tidy_list("location")
## location person
## 1: wisconsin About Search
## 2: wisconsin PARTICIPANTS
## 3: wisconsin MODERATORS
## 4: wisconsin CAVUTO
## 5: wisconsin CAVUTO
## ---
## 7527: ohio KELLY
## 7528: ohio KELLY
## 7529: ohio NOTE
## 7530: ohio NOTE
## 7531: ohio NOTE
## dialogue
## 1: About Search
## 2: Former Governor Jeb Bush (FL);Ben Carson;Senator Ted Cruz (TX);Carly Fiorina;Governor John Kasich (OH);Senator Rand Paul (KY);Senator Marco Rubio (FL); andDonald Trump;
## 3: Gerard Baker (The Wall Street Journal);Maria Bartiromo (Fox Business Network); andNeil Cavuto (Fox Business Network)
## 4: It is 9:00 p.m. on the East Coast, 8:00 p.m. here inside the Milwaukee theater.
## 5: Welcome to the Republican presidential debate here on the Fox Business Network.
## ---
## 7527: Thank you all very much, and that will do it for the first Republican primary debate night of the 2016 presidential race.
## 7528: Our thanks to the candidates, who will now be joined by their families on stage.
## 7529: A candidate must rank in the top ten candidates in Fox News polls in order to appear in this main debate.
## 7530: The remaining candidates were invited to appear in the "undercard" debate.
## 7531: Presidential Candidate Debates, Republican Candidates Debate in Cleveland, Ohio Online by Gerhard Peters and John T. Woolley, The American Presidency Project https://www.presidency.ucsb.edu/node/310229 The American Presidency ProjectJohn Woolley and Gerhard PetersContact Twitter Facebook Copyright © The American Presidency ProjectTerms of Service | Privacy | Accessibility
## element_id sentence_id
## 1: 1 1
## 2: 2 1
## 3: 3 1
## 4: 4 1
## 5: 4 2
## ---
## 7527: 305 4
## 7528: 305 5
## 7529: 306 1
## 7530: 306 2
## 7531: 306 3