read.csv

I'm writing an R package where the R code talks to a Java application. The Java application outputs a CSV formatted string and I want the R code to be able to directly read the string and convert it into a data.frame. Dirk Eddelbuettel Editing a 7-year old answer: By now, this is much simpler thanks to the text= argument which has been added to read.csv() and alike: R> data <- read.csv(text="flim,flam + 1.2,2.2 + 77.1,3.14") R> data flim flam 1 1.2 2.20 2 77.1 3.14 R> Yes, look at the help for textConnection() -- the very powerful notion in R is that essentially all readers (as e.g. read.table

I'm trying to read a column oriented csv file into R as a data frame. the first line of the file is like so: sDATE, sTIME,iGPS_ALT, ... and then each additional line is a measurement: 4/10/2011,2:15,78, ... when I try to read this into R, via d = read.csv('filename') I get a duplicate row.names error since R thinks that the first column of the data is the row names, and since all of the measurements were taken on the same day, the values in the first column do not change. If I put in row.names = NULL into the read.csv call, I get an extraneous column d$row.names which corresponds to the sDATE

This question already has an answer here: How can I read the header but also skip lines - read.table()? 5 answers I wish to skip the 1st and 3rd rows of my csv file when importing the file into a data frame in R. In the original file my headers are on line 2. Using the skip argument in read.csv I can skip the 1st line and set the header argument to TRUE by I still have the 3rd line from the original file in my data frame. Can anyone suggest how to skip multiple specific rows in R, below is what I was able to cobble together? Can I pass a vector to the skip argument specifying the exact rows to

In R , what is the difference between read.csv() and read.csv2() The official documentation says, In various European locales, as the comma character serves as the decimal point, the function read.csv2 should be used instead What does this mean. I don't see any difference at the superficial level. Can anybody give out a concrete example to clarify it further. They are (almost) the same functions - read.table . The only difference is default parameters. Look at source code: > read.csv function (file, header = TRUE, sep = ",", quote = "\"", dec = ".", fill = TRUE, comment.char = "", ...) read

I am working on a project that imports all csv files from a given folder and merges them into one file. I was able to import the rows and columns I wanted from each of the files from the folder but now need help merging them all into one file. I do not know how many files I will eventually end up with (probably around 120) so I do not want to merge them 1 by 1. Here is what I have so far: # Import All files rowsToUse <- c(9:104,657:752) colsToUse <- c(15,27,28,29,30,33,35) filenames <- list.files("save", pattern="*.csv", full.names=TRUE) for (i in seq_along(filenames)) { assign(paste("df", i,

I have a problem when I import a csv file with R: example lines to import: 2010-07-27;91 2010-07-26;93 2010-07-23;88 I use the statement: data <- read.csv2(file="...", sep=";", dec=".", header=FALSE) when I try to aggregate this data with other ones originated by statistical analysis using cbind , the date is showed as an integer number because it was imported as factor. If I try to show it as a string using as.character , the numerical data are transformed into characters too so they are unusable for statistical procedures. Use colClasses argument: data <- read.csv2(file="...", sep=";", dec="

I am importing a csv that has a single column which contains very long integers (for example: 2121020101132507598) a<-read.csv('temp.csv',as.is=T) When I import these integers as strings they come through correctly, but when imported as integers the last few digits are changed. I have no idea what is going on... 1 "4031320121153001444" 4031320121153001472 2 "4113020071082679601" 4113020071082679808 3 "4073020091116779570" 4073020091116779520 4 "2081720101128577687" 2081720101128577792 5 "4041720081087539887" 4041720081087539712 6 "4011120071074301496" 4011120071074301440 7 "4021520051054304372