I am attempting to add a new stemmer that works using a table look up method. if h is the hash the contains the stemming operation, it is encoded as follows: keys as words before stemming and values as words post-stemming.
I would like to ideally add a custom hash that allows me to do the following
myCorpus = tm_map(myCorpus, replaceWords, h)
the replaceWords function is applied to each document in myCorpus and uses the hash to stem the contents of the document
Here is the sample code from my replaceWords function
$hash_replace <- function(x,h) {
if (length(h[[x]])>0) {
return(h[[x]])
} else {
return(x)
}
}
replaceWords <- function(x,h) {
y = tolower(unlist(strsplit(x," ")))
y=y[which(as.logical(nchar(y)))]
z = unlist(lapply(y,hash_replace,h))
return(paste(unlist(z),collapse=' '))
}
Although this works, the transformed corpus is no longer contains content of type "TextDocument" or "PlainTextDocument" but of type "character"
I tried using
return(as.PlainTextDocument(paste(unlist(z),collapse=' ')))
but that that gives me an error while trying to run.
In the previous versions of the R's tm package, I did see a replaceWords function that allowed for synonym and WORDNET based subtitution. But I no longer see it in the current version of tm package (especially when I call the function getTransformations())
Does anybody out there have ideas on how I can make this happen?
Any help is greatly appreciated.
Cheers,
Shivani
Thanks,
Shivani Rao
You just need to use the PlainTextDocument function instead of as.PlainTextDocument. R will automatically return the last statement in your function, so it works if you just make the last line
PlainTextDocument(paste(unlist(z),collapse=' '))
Related
I'm writing out some functions for Inventory management. I've recently wanted to add a "photo url column" to my spreadsheet by using an API I've used successfully while initially building my inventory. My Spreadsheet header looks like the following:
SKU | NAME | OTHER STUFF
I have a getProductInfo function that returns a list of product info from an API I'm calling.
getProductInfo<- function(barcode) {
#Input UPC
#Output List of product info
CallAPI(barcode)
Process API return, remove garbage
return(info)
}
I made a new function that takes my inventory csv as input, and attempts to add a new column with product photo url.
get_photo_url_from_product_info_output <- function(in_list){
#Input GetProductInfo Output. Returns Photo URL, or nothing if
#it doesn't exist
if(in_list$DisplayStockPhotos == TRUE){
return(in_list$StockPhotoURL)
} else {
return("")
}
}
add_Photo_URL <- function(in_csv){
#Input CSV data frame, appends photourl column
#Requires SKU (UPC) assumes no photourl column
out_csv <- mutate(in_csv, photo =
get_photo_url_from_product_info_output(
getProductInfo(SKU)
)
)
}
return (out_csv)
}
#Call it
new <- add_Photo_URL(old)
My thinking was that R would simply input the SKU of the from the row, and put it through the double function call "as is", and the vectorized DPLYR function mutate would just vectorize it. Unfortunately I was running into all sorts of problems I couldn't understand. Eventually I figured out that API call was crashing because the SKU field was all messed up as it was being passed in. I put in a breakpoint and found out that it wasn't just passing in the SKU, but instead an entire list (I think?) of SKUs. Every Row all at once. Something like this:
#Variable 'barcode' inside getProductInfo function contains:
[1] 7.869368e+11 1.438175e+10 1.256983e+10 2.454357e+10 3.139814e+10 1.256983e+10 1.313260e+10 4.339643e+10 2.454328e+10
[10] 1.313243e+10 6.839046e+11 2.454367e+10 2.454363e+10 2.454367e+10 2.454348e+10 8.418870e+11 2.519211e+10 2.454375e+10
[19] 2.454381e+10 2.454381e+10 2.454383e+10 2.454384e+10 7.869368e+11 2.454370e+10 2.454390e+10 1.913290e+11 2.454397e+10
[28] 2.454399e+10 2.519202e+10 2.519205e+10 7.742121e+11 8.839291e+11 8.539116e+10 2.519211e+10 2.519211e+10 2.519211e+10
Obviously my initial getProductInfo function can't handle that, so it'll crash.
How should I modify my code, whether it be in the input or API call to avoid this vectorized operation issue?
Well, it's not totally elegant but it works.
I figured out I need to use lapply, which is usually not my strong suit. Initally I tried to nest them like so:
lapply(SKU, get_photo_url_from_product_info_output(getProductInfo())
But that didn't work. So I just came up with bright idea of making another function
get_photo_url_from_sku <- function(barcode){
return(get_photo_url_from_product_info_output(getProductInfo(barcode)))
}
Call that in the lapply:
out_csv<- mutate(in_csv, photocolumn = lapply(SKU, get_photo_url_from_sku))
And it works great. My speed is only limited by my API calls.
i tried updating data in dataframe but its unable to get updating
//Initialize data and dataframe here
user_data=read.csv("train_5.csv")
baskets.df=data.frame(Sequence=character(),
Challenge=character(),
countno=integer(),
stringsAsFactors=FALSE)
/Updating data in dataframe here
for(i in 1:length((user_data)))
{
for(j in i:length(user_data))
{
if(user_data$challenge_sequence[i]==user_data$challenge_sequence[j]&&user_data$challenge[i]==user_data$challenge[j])
{
writedata(user_data$challenge_sequence[i],user_data$challenge[i])
}
}
}
writedata=function( seqnn,challng)
{
#print(seqnn)
#print(challng)
newRow <- data.frame(Sequence=seqnn,Challenge=challng,countno=1)
baskets.df=rbind(baskets.df,newRow)
}
//view data here
View(baskets.df)
I've modified your code to what I believe will work. You haven't provided sample data, so I can't verify that it works the way you want. I'm basing my attempt here on a couple of common novice mistakes that I'll do my best to explain.
Your writedata function was written to be a little loose with it's scope. When you create a new function, what happens in the function technically happens in its own environment. That is, it tries to look for things defined within the function, and then any new objects it creates are created only within that environment. R also has this neat (and sometimes tricky) feature where, if it can't find an object in an environment, it will try to look up to the parent environment.
The impact this has on your writedata function is that when R looks for baskets.df in the function and can't find it, R then turns to the Global Environment, finds baskets.df there, and then uses it in rbind. However, the result of rbind gets saved to a baskets.df in the function environment, and does not update the object of the same name in the global environment.
To address this, I added an argument to writedata that is simply named data. We can then use this argument to pass a data frame to the function's environment and do everything locally. By not making any assignment at the end, we implicitly tell the function to return it's result.
Then, in your loop, instead of simply calling writedata, we assign it's result back to baskets.df to replace the previous result.
for(i in 1:length((user_data)))
{
for(j in i:length(user_data))
{
if(user_data$challenge_sequence[i] == user_data$challenge_sequence[j] &&
user_data$challenge[i] == user_data$challenge[j])
{
baskets.df <- writedata(baskets.df,
user_data$challenge_sequence[i],
user_data$challenge[i])
}
}
}
writedata=function(data, seqnn,challng)
{
#print(seqnn)
#print(challng)
newRow <- data.frame(Sequence = seqnn,
Challenge = challng,
countno = 1)
rbind(data, newRow)
}
I'm not sure what you're programming background is, but your loops will be very slow in R because it's an interpreted language. To get around this, many functions are vectorized (which simply means that you give them more than one data point, and they do the looping inside compiled code where the loops are fast).
With that in mind, here's what I believe will be a much faster implementation of your code
user_data=read.csv("train_5.csv")
# challenge_indices will be a matrix with TRUE at every place "challenge" and "challenge_sequence" is the same
challenge_indices <- outer(user_data$challenge_sequence, user_data$challenge_sequence, "==") &
outer(user_data$challenge, user_data$challenge, "==")
# since you don't want duplicates, get rid of them
challenge_indices[upper.tri(challenge_indices, diag = TRUE)] <- FALSE
# now let's get the indices of interest
index_list <- which(challenge_indices,arr.ind = TRUE)
# now we make the resulting data set all at once
# this is much faster, because it does not require copying the data frame many times - which would be required if you created a new row every time.
baskets.df <- with(user_data, data.frame(
Sequence = challenge_sequence[index_list[,"row"]],
challenge = challenge[index_list[,"row"]]
)
When I start up and R script and I like to check their package versions. I tend to run something like
library(dplyr); packageVersion("dplyr")
This works fine, but I'd like to shorten this to a single function that would load a library and then return its version.
I want the libary function to accept either a string of the library name, or just the library name typed in by itself.
I tried this function:
libver <- function(pac){
if(!is.character(pac)){
pac <- deparse(substitute(pac))
}
library(pac, character.only=TRUE)
packageVersion(pac)
}
But this works for string inputs but not non string inputs
libver(MASS)
Error in libver(MASS): object 'MASS' not found
I can hard code it to take objects rather than strings as follows,
libver <- function(pac){
library( deparse(substitute(pac), character.only=TRUE)
packageVersion(deparse(substitute(pac))
}
but I'd like to keep the flexability to do either one if I can.
!is.character(pac) returns an error when pac is the bare package name, without quotation marks. Instead, you can do pac = as.character(substitute(pac)) which will return a character string, regardless of whether the argument was originally a character string.
libver <- function(pac) {
pac = as.character(substitute(pac))
library(pac, character.only=TRUE)
packageVersion(pac)
}
libver <- function(pac){
pac <- gsub("\"","",deparse(substitute(pac)))
library(pac,character.only = T)
packageVersion(pac)
}
libver(dplyr)
[1] ‘0.7.2’
libver("dplyr")
[1] ‘0.7.2’
Been going around for hours with this. My 1st question online on R. Trying to creat a function that contains a loop. The function takes a vector that the user submits like in pollutantmean(4:6) and then it loads a bunch of csv files (in the directory mentioned) and binds them. What is strange (to me) is that if I assign the variable id and then run the loop without using a function, it works! When I put it inside a function so that the user can supply the id vector then it does nothing. Can someone help ? thank you!!!
pollutantmean<-function(id=1:332)
{
#read files
allfiles<-data.frame()
id<-str_pad(id,3,pad = "0")
direct<-"/Users/ped/Documents/LearningR/"
for (i in id) {
path<-paste(direct,"/",i,".csv",sep="")
file<-read.csv(path)
allfiles<-rbind(allfiles,file)
}
}
Your function is missing a return value. (#Roland)
pollutantmean<-function(id=1:332) {
#read files
allfiles<-data.frame()
id<-str_pad(id,3,pad = "0")
direct<-"/Users/ped/Documents/LearningR/"
for (i in id) {
path<-paste(direct,"/",i,".csv",sep="")
file<-read.csv(path)
allfiles<-rbind(allfiles,file)
}
return(allfiles)
}
Edit:
Your mistake was that you did not specify in your function what you want to get out from the function. In R, you create objects inside of function (you could imagine it as different environment) and then specify which object you want it to return.
With my comment about accepting my answer, I meant this: (...To mark an answer as accepted, click on the check mark beside the answer to toggle it from greyed out to filled in...).
Consider even an lapply and do.call which would not need return being last line of function:
pollutantmean <- function(id=1:332) {
id <- str_pad(id,3,pad = "0")
direct_files <- paste0("/Users/ped/Documents/LearningR/", id, ".csv")
# READ FILES INTO LIST AND ROW BIND
allfiles <- do.call(rbind, lapply(direct_files, read.csv))
}
ok, I got it. I was expecting the files that are built to be actually created and show up in the environment of R. But for some reason they don't. But R still does all the calculations. Thanks lot for the replies!!!!
pollutantmean<-function(directory,pollutant,id)
{
#read files
allfiles<-data.frame()
id2<-str_pad(id,3,pad = "0")
direct<-paste("/Users/pedroalbuquerque/Documents/Learning R/",directory,sep="")
for (i in id2) {
path<-paste(direct,"/",i,".csv",sep="")
file<-read.csv(path)
allfiles<-rbind(allfiles,file)
}
#averaging polutants
mean(allfiles[,pollutant],na.rm = TRUE)
}
pollutantmean("specdata","nitrate",23:35)
I'm trying to use dump() to save the settings of my analysis so I can examine them in a text editor or reload them at a later date.
In my code I'm using the command
dump(ls(), settingsOutput, append=TRUE)
The file defined by `settingsOutput' gets created, but the larger objects and locally defined functions are truncated. Here's an excerpt from such a file. Note these files are generally on the order of a few kb.
createFilePrefix <-
function (runDesc, runID, restartNumber)
{
...
createRunDesc <-
function (genomeName, nGenes, nMix, mixDef, phiFlag)
{
...
datasetID <-
"02"
descriptionPartsList <-
c("genomeNameTest", "nGenesTest", "numMixTest", "mixDefTest",
"phiFlagTest", "runDescTest", "runIDTest", "restartNumberTest"
...
diffTime <-
structure(0.531, units = "hours", class = "difftime")
dissectObjectFileName <-
function (objectFileName)
{
...
divergence <-
0
Just for reference, here's one of the functions defined above
createFilePrefix <- function(runDesc, runID, restartNumber){
paste(runDesc, "_run-", runID, "_restartNumber-", restartNumber, sep="")
}
Right now I'm going back and removing the problematic lines and then loading the files, but I'd prefer to actually have code that works as intended.
Can anyone explain to me why I'm getting this behavior and what to do to fix it?