I am new to R and need suggestions.
I have a dataframe with 1 text field in it. I need to fix the misspelled words in that text field. To help with that, I have a second file (dictionary) with 2 columns - the misspelled words and the correct words to replace them.
How would you recommend doing it? I wrote a simple "for loop" but the performance is an issue.
The file has ~120K rows and the dictionary has ~5k rows and the program's been running for hours. The text can have a max of 2000 characters.
Here is the code:
output<-source_file$MEMO_MANUAL_TXT
for (i in 1:nrow(fix_file)) { #dictionary file
target<-paste0(" ", fix_file$change_to_target[i], " ")
replace<-paste0(" ", fix_file$target[i], " ")
output<-gsub(target, replace, output, fixed = TRUE)
I would try agrep. I'm not sure how well it scales though.
Eg.
> agrep("laysy", c("1 lazy", "1", "1 LAZY"), max = 2, value = TRUE)
[1] "1 lazy"
Also check out pmatch and charmatch although I feel they won't be as useful to you.
here an example , to show #joran comment using a data.table left join. It is very fast (instantaneously here).
library(data.table)
n1 <- 120e3
n2 <- 1e3
set.seed(1)
## create vocab
tt <- outer(letters,letters,paste0)
vocab <- as.vector(outer(tt,tt,paste0))
## create the dictionary
dict <- data.table(miss=sample(vocab,n2,rep=F),
good=sample(letters,n2,rep=T),key='miss')
## the text table
orig <- data.table(miss=sample(vocab,n1,rep=TRUE),key='miss')
orig[dict]
orig[dict]
miss good
1: aakq v
2: adac t
3: adxj r
4: aeye t
5: afji g
---
1027: zvia d
1028: zygp p
1029: zyjm x
1030: zzak t
1031: zzvs q
Related
I have to do a topic modeling based on pieces of texts containing emojis with R. Using the replace_emoji() and replace_emoticon functions let me analyze them, but there is a problem with the results.
A red heart emoji is translated as "red heart ufef". These words are then treated separately during the analysis and compromise the results.
Terms like "heart" can have a very different meaning as can be seen with "red heart ufef" and "broken heart"
The function replace_emoji_identifier() doesn't help either, as the identifiers make an analysis hard.
Dummy data set reproducible with by using dput() (including the step force to lowercase:
Emoji_struct <- c(
list(content = "🔥🔥 wow", "😮 look at that", "😤this makes me angry😤", "😍❤\ufe0f, i love it!"),
list(content = "😍😍", "😊 thanks for helping", "😢 oh no, why? 😢", "careful, challenging ❌❌❌")
)
Current coding (data_orig is a list of several files):
library(textclean)
#The rest should be standard r packages for pre-processing
#pre-processing:
data <- gsub("'", "", data)
data <- replace_contraction(data)
data <- replace_emoji(data) # replace emoji with words
data <- replace_emoticon(data) # replace emoticon with words
data <- replace_hash(data, replacement = "")
data <- replace_word_elongation(data)
data <- gsub("[[:punct:]]", " ", data) #replace punctuation with space
data <- gsub("[[:cntrl:]]", " ", data)
data <- gsub("[[:digit:]]", "", data) #remove digits
data <- gsub("^[[:space:]]+", "", data) #remove whitespace at beginning of documents
data <- gsub("[[:space:]]+$", "", data) #remove whitespace at end of documents
data <- stripWhitespace(data)
Desired output:
[1] list(content = c("fire fire wow",
"facewithopenmouth look at that",
"facewithsteamfromnose this makes me angry facewithsteamfromnose",
"smilingfacewithhearteyes redheart \ufe0f, i love it!"),
content = c("smilingfacewithhearteyes smilingfacewithhearteyes",
"smilingfacewithsmilingeyes thanks for helping",
"cryingface oh no, why? cryingface",
"careful, challenging crossmark crossmark crossmark"))
Any ideas? Lower cases would work, too.
Best regards. Stay safe. Stay healthy.
Answer
Replace the default conversion table in replace_emoji with a version where the spaces/punctuation is removed:
hash2 <- lexicon::hash_emojis
hash2$y <- gsub("[[:space:]]|[[:punct:]]", "", hash2$y)
replace_emoji(Emoji_struct[,1], emoji_dt = hash2)
Example
Single character string:
replace_emoji("wow!😮 that is cool!", emoji_dt = hash2)
#[1] "wow! facewithopenmouth that is cool!"
Character vector:
replace_emoji(c("1: 😊", "2: 😍"), emoji_dt = hash2)
#[1] "1: smilingfacewithsmilingeyes "
#[2] "2: smilingfacewithhearteyes "
List:
list("list_element_1: 🔥", "list_element_2: ❌") %>%
lapply(replace_emoji, emoji_dt = hash2)
#[[1]]
#[1] "list_element_1: fire "
#
#[[2]]
#[1] "list_element_2: crossmark "
Rationale
To convert emojis to text, replace_emoji uses lexicon::hash_emojis as a conversion table (a hash table):
head(lexicon::hash_emojis)
# x y
#1: <e2><86><95> up-down arrow
#2: <e2><86><99> down-left arrow
#3: <e2><86><a9> right arrow curving left
#4: <e2><86><aa> left arrow curving right
#5: <e2><8c><9a> watch
#6: <e2><8c><9b> hourglass done
This is an object of class data.table. We can simply modify the y column of this hash table so that we remove all the spaces and punctuation. Note that this also allows you to add new ASCII byte representations and an accompanying string.
I'm trying to read a table using fread.
The txt file has text which look like:
"No","Comment","Type"
"0","he said:"wonderful|"","A"
"1","Pr/ "d/s". "a", n) ","B"
R codes I'm using is: dataset0 <- fread("data/test.txt", stringsAsFactors = F) with the development version of data.table R package.
Expect to see a dataset with three columns; however:
Error in fread(input = "data/stackoverflow.txt", stringsAsFactors = FALSE) :
Line 3 starting <<"1","Pr/ ">> has more than the expected 3 fields.
Separator 3 occurs at position 26 which is character 6 of the last field: << n) ","B">>.
Consider setting 'comment.char=' if there is a trailing comment to be ignored.
How to solve it?
The development version of data.table handles files like this where the embedded quotes have not been escaped. See point 10 on the wiki page.
I just tested it on your input and it works.
$ more unescaped.txt
"No","Comment","Type"
"0","he said:"wonderful."","A"
"1","The problem is: reading table, and also "a problem, yes." keep going on.","A"
> DT = fread("unescaped.txt")
> DT
No Comment Type
1: 0 he said:"wonderful." A
2: 1 The problem is: reading table, and also "a problem, yes." keep going on. A
> ncol(DT)
[1] 3
Use readLines to read line by line, then replace delimiter and read.table:
# read with no sep
x <- readLines("test.txt")
# introduce new sep - "|"
x <- gsub("\",\"", "\"|\"", x)
# read with new sep
read.table(text = x, sep = "|", header = TRUE)
# No Comment Type
# 1 0 he said:"wonderful." A
# 2 1 The problem is: reading table, and also "a problem, yes." keep going on. A
I have problem with code in R.
I have a data-set(questions) with 4 columns and over 600k observation, of which one column is named 'V3'.
This column has questions like 'what is the day?'.
I have second data-set(voc) with 2 columns, of which one column name 'word' and other column name 'synonyms'. If In my first data-set (questions )exists word from second data-set(voc) from column 'synonyms' then I want to replace it word from 'word' column.
questions = cbind(V3=c("What is the day today?","Tom has brown eyes"))
questions <- data.frame(questions)
V3
1 what is the day today?
2 Tom has brown eyes
voc = cbind(word=c("weather", "a","blue"),synonyms=c("day", "the", "brown"))
voc <- data.frame(voc)
word synonyms
1 weather day
2 a the
3 blue brown
Desired output
V3 V5
1 what is the day today? what is a weather today?
2 Tom has brown eyes Tom has blue eyes
I wrote simple code but it doesn't work.
for (k in 1:nrow(question))
{
for (i in 1:nrow(voc))
{
question$V5<- gsub(do.call(rbind,strsplit(question$V3[k]," "))[which (do.call(rbind,strsplit(question$V3[k]," "))== voc[i,2])], voc[i,1], question$V3)
}
}
Maybe someone will try to help me? :)
I wrote second code, but it doesn't work too..
for( i in 1:nrow(questions))
{
for( j in 1:nrow(voc))
{
if (grepl(voc[j,k],do.call(rbind,strsplit(questions[i,]," "))) == TRUE)
{
new=matrix(gsub(do.call(rbind,strsplit(questions[i,]," "))[which(do.call(rbind,strsplit(questions[i,]," "))== voc[j,2])], voc[j,1], questions[i,]))
questions[i,]=new
}
}
questions = cbind(questions,c(new))
}
First, it is important that you use the stringsAsFactors = FALSE option, either at the program level, or during your data import. This is because R defaults to making strings into factors unless you otherwise specify. Factors are useful in modeling, but you want to do analysis of the text itself, and so you should be sure that your text is not coerced to factors.
The way I approached this was to write a function that would "explode" each string into a vector, and then uses match to replace the words. The vector gets reassembled into a string again.
I'm not sure how performant this will be given your 600K records. You might look into some of the R packages that handle strings, like stringr or stringi, since they will probably have functions that do some of this. match tends to be okay on speed, but %in% can be a real beast depending on the length of the string and other factors.
# Start with options to make sure strings are represented correctly
# The rest is your original code (mildly tidied to my own standard)
options(stringsAsFactors = FALSE)
questions <- cbind(V3 = c("What is the day today?","Tom has brown eyes"))
questions <- data.frame(questions)
voc <- cbind(word = c("weather","a","blue"),
synonyms = c("day","the","brown"))
voc <- data.frame(voc)
# This function takes:
# - an input string
# - a vector of words to replace
# - a vector of the words to use as replacements
# It returns a list of the original input and the changed version
uFunc_FindAndReplace <- function(input_string,words_to_repl,repl_words) {
# Start by breaking the input string into a vector
# Note that we use [[1]] to get first list element of strsplit output
# Obviously this relies on breaking sentences by spacing
orig_words <- strsplit(x = input_string,split = " ")[[1]]
# If we find at least one of the words to replace in the original words, proceed
if(sum(orig_words %in% words_to_repl) > 0) {
# The right side selects the elements of orig_words that match words to be replaced
# The left side uses match to find the numeric index of those replacements within the words_to_repl vector
# This numeric vector is used to select the values from repl_words
# These then replace the values in orig_words
orig_words[orig_words %in% words_to_repl] <- repl_words[match(x = orig_words,table = words_to_repl,nomatch = 0)]
# We rebuild the sentence again, and return a list with original and new version
new_sent <- paste(orig_words,collapse = " ")
return(list(original = input_string,new = new_sent))
} else {
# Otherwise we return the original version since no changes are needed
return(list(original = input_string,new = input_string))
}
}
# Using do.call and rbind.data.frame, we can collapse the output of a lapply()
do.call(what = rbind.data.frame,
args = lapply(X = questions$V3,
FUN = uFunc_FindAndReplace,
words_to_repl = voc$synonyms,
repl_words = voc$word))
>
original new
1 What is the day today? What is a weather today?
2 Tom has brown eyes Tom has blue eyes
Working in R, I'm trying to find an efficient way to search through a file of texts and remove or replace all instances of proper names (e.g., Thomas). I assume there is something available to do this but have been unable to locate.
So, in this example the words "Susan" and "Bob" would be removed. This is a simplified example, when in reality would want this to apply to hundreds of documents and therefore a fairly large list of names.
texts <- as.data.frame (rbind (
'This text stuff if quite interesting',
'Where are all the names said Susan',
'Bob wondered what happened to all the proper nouns'
))
names(texts) [1] <- "text"
Here's one approach based upon a data set of firstnames:
install.packages("gender")
library(gender)
install_genderdata_package()
sets <- data(package = "genderdata")$results[,"Item"]
data(list = sets, package = "genderdata")
stopwords <- unique(kantrowitz$name)
texts <- as.data.frame (rbind (
'This text stuff if quite interesting',
'Where are all the names said Susan',
'Bob wondered what happened to all the proper nouns'
))
removeWords <- function(txt, words, n = 30000L) {
l <- cumsum(nchar(words)+c(0, rep(1, length(words)-1)))
groups <- cut(l, breaks = seq(1,ceiling(tail(l, 1)/n)*n+1, by = n))
regexes <- sapply(split(words, groups), function(words) sprintf("(*UCP)\\b(%s)\\b", paste(sort(words, decreasing = TRUE), collapse = "|")))
for (regex in regexes) txt <- gsub(regex, "", txt, perl = TRUE, ignore.case = TRUE)
return(txt)
}
removeWords(texts[,1], stopwords)
# [1] "This text stuff if quite interesting"
# [2] "Where are all the names said "
# [3] " wondered what happened to all the proper nouns"
It may need some tuning for your specific data set.
Another approach could be based upon part-of-speech tagging.
What is the fastest way to parse a text file such as the example below into a two column data.frame which then then be transformed into a wide format?
FN Thomson Reuters Web of Science™
VR 1.0
PT J
AU Panseri, Sara
Chiesa, Luca Maria
Brizzolari, Andrea
Santaniello, Enzo
Passero, Elena
Biondi, Pier Antonio
TI Improved determination of malonaldehyde by high-performance liquid
chromatography with UV detection as 2,3-diaminonaphthalene derivative
SO JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL
AND LIFE SCIENCES
VL 976
BP 91
EP 95
DI 10.1016/j.jchromb.2014.11.017
PD JAN 22 2015
PY 2015
Using readLines is problematic because the multi-line fields don't have the keys. Reading as fixed width table also doesn't work. Suggestions? If not for the multiline issue, this would be easily accomplished with a function that operates on each row/record like so:
x <- "FN Thomson Reuters Web of Science"
re <- "^([^\\s]+)\\s*(.*)$"
key <- sub(re, "\\1", x, perl=TRUE)
value <- sub(re, "\\2", x, perl=TRUE)
data.frame(key, value)
key value
1 FN Thomson Reuters Web of Science
Notes: The fields will always be uppercase and two characters. The entire title and list of authors can be concatenated into a single cell.
This should work:
library(zoo)
x <- read.fwf(file="tempSO.txt",widths=c(2,500),as.is=TRUE)
x$V1[x$V1==" "] <- NA
x$V1 <- na.locf(x$V1)
res <- aggregate(V2 ~ V1, data = x, FUN = paste, collapse = "")
Here's another idea, that might be useful if you want to stay in base R:
parseEntry <- function(entry) {
## Split at beginning of each line that starts with a non-space character
ll <- strsplit(entry, "\\n(?=\\S)", perl=TRUE)[[1]]
## Clean up empty characters at beginning of continuation lines
ll <- gsub("\\n(\\s){3}", "", ll)
## Split each field into its two components
read.fwf(textConnection(ll), c(2, max(nchar(ll))))
}
## Read in and collapse entry into one long character string.
## (If file contained more than one entry, you could preprocess it accordingly.)
ee <- paste(readLines("egFile.txt"), collapse="\n")
## Parse the entry
parseEntry(ee)
Read lines of the file into a character vector using readLines and append a colon to each key. The result is then in DCF format so we can read it using read.dcf - this is the function used to read R package DESCRIPTION files. The result of read.dcf is wide, a matrix with one column per key. Finally we create long, a long data.frame with one row per key:
L <- readLines("myfile.dat")
L <- sub("^(\\S\\S)", "\\1:", L)
wide <- read.dcf(textConnection(L))
long <- data.frame(key = colnames(wide), value = wide[1,], stringsAsFactors = FALSE)