I know that there are many threads called this but either the advice within hasn't worked or I haven't understood it.
I have read what was an SPSS file into R.
I cleaned some variables and added new ones.
By this point the file size is 1,000 MB.
I wanted to write it into a CSV to look at it more easily but it just stops responding - file too big I guess.
So instead I want to create a subset of only the variables I need. I tried a couple of things
(besb <- bes[, c(1, 7, 8)])
data1 <- bes[,1:8]
I also tried referring to variables by name:
nf <- c(bes$approveGov, bes$politmoney)
All these attempts return errors with number of dimensions.
Therefore could somebody please explain to me how to create a reduced subset of variables preferably using variable names?
An easy way to subset variables from a data.frame is with the dplyr package. You can select variables with their bare names. For example:
library(dplyr)
nf <- select(bes, approveGov, politmoney)
It's fast for large data frames too.
Related
I'm brand new to programming and an picking up Rstudio as a stats tool.
I have a dataset which includes multiple questionnaires divided by weeks, and I'm trying to organize the data into meaningful chunks.
Right now this is what my code looks like:
w1a=table(qwest1,talm1)
w2a=table(qwest2,talm2)
w3a=table(quest3,talm3)
Where quest and talm are the names of the variable and the number denotes the week.
Is there a way to compress all those lines into one line of code so that I could make w1a,w2a,w3a... each their own object with the corresponding questionnaire added in?
Thank you for your help, I'm very new to coding and I don't know the etiquette or all the vocabulary.
This might do what you wanted (but not what you asked for):
tbl_list <- mapply(table, list(qwest1, qwest2, quest3),
list(talm1, talm2, talm3) )
names(tbl_list) <- c('w1a', 'w2a','w3a')
You are committing a fairly typical new-R-user error in creating multiple similarly named and structured objects but not putting them in a list. This is my effort at pushing you in that direction. Could also have been done via:
qwest_lst <- list(qwest1, qwest2, quest3)
talm_lst <- list(talm1, talm2, talm3)
tbl_lst <- mapply(table, qwest_lst, talm_lst)
names(tbl_list) <- paste0('w', 1:3, 'a')
There are other ways to programmatically access objects with character vectors using get or wget.
I am trying to transfer data from one data frame to other. I want to copy all 8 columns from a huge data frame to a smaller one and name the columns n1, n2, etc..
first I am trying to find the column number from which I need to copy by using this
x=as.numeric(which(colnames(old_df)=='N1_data'))
Then I am pasting it in new data frame this way
new_df[paste('N',1:8,'new',sep='')]=old_df[x:x+7]
However, when I run this, all the new 8 columns have exactly same data. However, instead if I directly use the value of x, then I get what I want like
new_df[paste('N',1:8,'new',sep='')]=old_df[10:17]
So my questions are
Why I am not able to use the variable x. I added as.numeric just to make sure it is a number not a list. However, that does not seem to help.
Is there any better or more efficient way to achieve this?
If I'm understanding your question correctly, you may be overthinking the problem.
library(dplyr);
new_df <- select(old_df, N1_data, N2_data, N3_data, N4_data,
N5_data, N6_data, N7_data, N8_data);
colnames(new_df) <- sub("N(\\d)_data", "n\\\\1", colnames(new_df));
I'm a beginner in R and i'm working on a automation,i have a list of variables in a separate file based on which the values needs to be aggregated in the master dataset.The Master datastructure is attached Master Dataset
and the referal dataset contains the vars to be aggregated Referal dataset
Of the 6 variables i need to aggregate the Variables D,E,F by Sum(C)(as per the referal dataset).
The below code does my requirement manually,
X<-aggregate(C,by=list(D,E,F),FUN=sum)
But i need a code which does the same funtionality automatically.I tried making loops but the problem i face is that both datasets dont have same data.frame size. Can someone help me on this ?
So, it seems like you want to do a few things:
1) read in the master/referent datasets
2) subset the master according to the values in the referent
3) compute column sums on the master?
also, is there a specific reason you want to use aggregate()? there are probably lots of ways to do this. In any case, here is what i would do:
# assuming master is a dataframe or matrix, referent is a vector
# just simulating them here because not clear how you are reading them in
master = matrix(rnorm(36),6)
colnames(master) = c('A','B','C','D','E','F')
referent = c('D','E','F')
colSums(master[,referent])
so is that doing what you want to do? I like colSums because it's a handy built-in. I am not an R superstar though so it is possible that other ways are better for some reason.
I have to manually collect some rows so based on the R Cookbook, it recommended me to pre-allocate some memory for a large data frame. Say my code is
dataSize <- 500000;
shoesRead <- read.csv(file="someShoeCsv.csv", head=TRUE, sep=",");
shoes <- data.frame(size=integer(dataSize), price=double(dataSize),
cost=double(dataSize), retail=double(dataSize));
So now, I have some data about shoes which I imported via csv, and then I perform some calculation and want to insert into the data frame shoes. Let's say the someShoeCsv.csv has a column called ukSize and so
usSize <- ukSize * 1.05 #for example
My question is how do I do so? Running the code, noting now I have a usSize variable which was transformed from the ukSize column, read from the csv file:
shoes <- rbind(shoes,
data.frame("size"=usSize, "price"=price,
"cost"=cost, "retail"=retail));
adds to the already large data frame.
I have experimented with doing the list and then rbind but understand that it is tedious and so I am thinking of using this method but still to no avail.
I'm not quite sure what you're trying to do, but if you're trying to replace some of the pre-allocated rows with new data, you could do so like this:
Nreplace = length(usSize)
shoes$size[1:Nreplace] <- usSize
shoes$price[1:Nreplace] <- shoesRead$price
And so on, for the rest of the columns.
Here's some unsolicited advice. Looking at the code you've included, you reference ukSize and price etc without referencing the data frame, which makes it appear like you've done attach(shoesRead). Definitely never use attach(). If you want the price vector, for example, just do shoesRead$price. It's just a little bit more typing for the sake of much more readable code.
I have a dataset in SPSS that has 100K+ rows and over 100 columns. I want to filter both the rows and columns at the same time into a new SPSS dataset.
I can accomplish this very easily using the subset command in R. For example:
new_data = subset(old_data, select = ColumnA >10, select = c(ColumnA, ColumnC, ColumnZZ))
Even easier would be:
new data = old_data[old_data$ColumnA >10, c(1, 4, 89)]
where I am passing the column indices instead.
What is the equivalent in SPSS?
I love R, but the read/write and data management speed of SPSS is significantly better.
I am not sure what exactly you are referring to when you write that "the read/write and data management speed of SPSS being significantly better" than R. Your question itself demonstrates how flexible R is at data management! And, a dataset of 100k rows and 100 columns is by no means a large one.
But, to answer your question, perhaps you are looking for something like this. I'm providing a "programmatic" solution, rather than the GUI one, because you're asking the question on Stack Overflow, where the focus is more on the programming side of things. I'm using a sample data file that can be found here: http://www.ats.ucla.edu/stat/spss/examples/chp/p004.sav
Save that file to your SPSS working directory, open up your SPSS syntax editor, and type the following:
GET FILE='p004.sav'.
SELECT IF (lactatio <= 3).
SAVE OUTFILE= 'mynewdatafile.sav'
/KEEP currentm previous lactatio.
GET FILE='mynewdatafile.sav'.
More likely, though, you'll have to go through something like this:
FILE HANDLE directoryPath /NAME='C:\path\to\working\directory\' .
FILE HANDLE myFile /NAME='directoryPath/p004.sav' .
GET FILE='myFile'.
SELECT IF (lactatio <= 3).
SAVE OUTFILE= 'directoryPath/mynewdatafile.sav'
/KEEP currentm previous lactatio.
FILE HANDLE myFile /NAME='directoryPath/mynewdatafile.sav'.
GET FILE='myFile'.
You should now have a new file created that has just three columns, and where no value in the "lactatio" column is greater than 3.
So, the basic steps are:
Load the data you want to work with.
Subset for all columns from all the cases you're interested in.
Save a new file with only the variables you're interested in.
Load that new file before you proceed.
With R, the basic steps are:
Load the data you want to work with.
Create an object with your subset of rows and columns (which you know how to do).
Hmm.... I don't know about you, but I know which method I prefer ;)
If you're using the right tools with R, you can also directly read in the specific subset you are interested in without first loading the whole dataset if speed really is an issue.
In spss you can't combine the two actions in one command, but it's easy enough to do it in two:
dataset copy old_data. /* delete this if you don't need to keep both old and new data.
select if ColumnA>10.
add files /file=* /keep=ColumnA ColumnC ColumnZZ.