I would like to clarify my understanding here on both converting a file into CSV and also reading it. Let's use a dataset from R for instance, titled longley.
To set up a data frame, I can just use the write.table command as follows, right?
d1<-longley
write.table(d1, file="", sep="1,16", row.names=TRUE, col.names=TRUE)
Has this already become a data frame or am I missing something here?
Now let's say if I want to read this CSV file. Then would my code be something like:
read.table(<dframe1>, header=FALSE, sep="", quote="\"")
It seems like before that I have to use a function called setwd(). I'm not really sure what it does or how it helps. Can someone help me here?
longley and, therefore, d1 are already data frames (type class(d1) in the console). A data frame is a fundamental data structure in R. Writing a data frame to a file saves the data in the data frame. In this case, you're trying to save the data in the data frame in CSV format, which you would do like this:
write.csv(d1, "myFileName.csv")
write.csv is a wrapper for write.table that takes care of the settings needed for saving in CSV format. You could also do:
write.table(d1, "myFileName.csv", sep=",")
sep="," tells R to write the file with values separated by a comma.
Then, to read the file into an R session you can do this:
df = read.csv("myFileName.csv", header=TRUE, stringsAsFactors=FALSE)
This creates a new object called df, which is the data frame created from the data in myFileName.csv. Once again, read.csv is a wrapper for read.table that takes care of the settings for reading a CSV file.
setwd is how you change the working directory--that is, the default directory where R writes to and reads from. But you can also keep the current working directory unchanged and just give write.csv or read.csv (or any other function that writes or reads R objects) the full path to wherever you want to read from or write to. For example:
write.csv(d1, "/path/for/saving/file/myFileName.csv")
Related
This is more of a curiosity.
Sometimes I modify csv files from Excel rather than R (suppose I manage to find a missing piece of info and I type it in the csv file), of course maintaining commas and quotes as they were.
Every time I do this, R becomes unable to read the csv file, i.e. it imports a single column as it appears on Excel, rather than separating the values (no options like sep= or quote= change this).
Does anyone know why this happens?
Thanks a lot
An example
This was readable:
state,"city","county"
AK,"Anchorage",""
AK,"Haines",""
AK,"Juneau","Juneau"
After adding the missing info under "county", R fails to import it as a data frame, reading it instead as a single vector.
state,"city","county"
AK,"Anchorage","Anchorage"
AK,"Haines","Haines"
AK,"Juneau","Juneau"
Edit:
I'm just running the basic read.csv
df <- read.csv("C:/directory/df.csv")
I define a DataFrame named data and want to write it into .csv file. I used writetable("result_data.csv", data) but it doesn't work.
This is the dataframe
error details
To write a data frame to a disk you should use the CSV.jl package like this (also make sure that you have write right to the directory you want to save the file on Juliabox):
using CSV
CSV.write("result_data.csv", data)
If this fails then please report back in the comment I will investigate it further.
I have an SPSS file from which I am creating a data table using R script. But the problem is it's taking a bit of time to load the data table. I want to convert the SPSS(.sav) file into CSV file first and then read the CSV file to create a data table. So far I have tried multiple codes but that didn't work out properly.
Here's the code which I got from this.
I think foreign package in r can be used to solve this problem.
library(foreign)
write.table(read.spss("inFile.sav"), file="outFile.csv", quote = TRUE, sep = ",")
Writing a fresh .Rda file to save a data.frame is easy:
df <- data.frame(a=c(1,2,3,4), b=c(5,6,7,8))
save(df,file="data.Rda")
But is it possible to write more data afterwards, there is no append=TRUE option using save.
Similarly, writing new lines to a text file is easy using:
write.table(df, file = 'data.txt', append=T)
However for large data.frames, the resulting file is much larger.
If you use Microsoft R, you might want to check RevoScaler package, rxImport function in particular. It allows you to store compressed data.frame in file, it also allows you to append new lines to existing file without loading it into environment.
Hope this helps. Link on function documentation below.
https://learn.microsoft.com/en-us/machine-learning-server/r-reference/revoscaler/rximport
I tried opening the file in excel and it is being displayed in proper format. Now how do i read it in R? I tried using read.csv function. It takes all the columns together without any separator.
You cannot directly load it to dataframe, first you have to load it as xml and then you can process it further.
Try following
require(XML)
data <- xmlParse('sample.dxl')
xml_data <- xmlToList(data)
You this list further to make your dataframe.