entering_saving.R

install.packages("Hmisc",dependencies=TRUE)
update.packages()
q()

setwd()
getwd()
ls()           # lists objects
rm(object)     # deletes an object

library(RColorBrewer)  # load ColorBrewer palettes
library(vegan)         # load vegan package
head(mydata)
tail(mydata)

##########
# Entering data

c = scan() # prompts for input from the use at the cmd
           # works for columns from excel

# values are separated by whitespace
# can be cut and pasted from excel

read.table(file, header = FALSE, sep = "", quote = "\"'",
           dec = ".", row.names, col.names,
           as.is = !stringsAsFactors,
           na.strings = "NA", colClasses = NA, nrows = -1,
           skip = 0, check.names = TRUE, fill = !blank.lines.skip,
           strip.white = FALSE, blank.lines.skip = TRUE,
           comment.char = "#",
           allowEscapes = FALSE, flush = FALSE,
           stringsAsFactors = default.stringsAsFactors(),
           fileEncoding = "", encoding = "unknown", text)

# tab-delimited
read.delim(file, header = TRUE, sep = "\t", quote="\"", dec=".",
           fill = TRUE, comment.char="", ...)

read.delim2(file, header = TRUE, sep = "\t", quote="\"", dec=",",
            fill = TRUE, comment.char="", ...)


# comma-delimited
read.csv(file, header = TRUE, sep = ",", quote="\"", dec=".",
         fill = TRUE, comment.char="", ...)

read.csv2(file, header = TRUE, sep = ";", quote="\"", dec=",",
          fill = TRUE, comment.char="", ...)


data1 = read.table('c:/aaron/R/file1.txt')  
data1 = read.table(file.choose())  # opens a file browser
data1 = read.csv(file = 'c:/aaron/R/file1.csv', head = TRUE, sep = ";")  # reads cvs with ';' delimiter. first row are column names

# Opening Excel
# Datasets are often saved as Excel spreadsheets. Here we utilize the xlsx package and Java to download an Excel dataset.

# these two steps only needed to read excel files from the internet
f <- tempfile("hsb2", fileext=".xls")

download.file("http://www.ats.ucla.edu/stat/data/hsb2.xls", f, mode="wb")

dat.xls <- read.xlsx(f, sheetIndex=1)
# If you have trouble getting Java and the xlsx package installed and working, just click "save as" in Excel and export the data to a comma separated values file (.csv).

#################
# Saving data

write.table(x, file = "", append = FALSE, quote = TRUE, sep = " ",
            eol = "\n", na = "NA", dec = ".", row.names = TRUE,
            col.names = TRUE, qmethod = c("escape", "double"),
            fileEncoding = "")
write.csv(...)
write.csv2(...)

x <- data.frame(a = I("a \" quote"), b = pi)
write.table(x, file = "foo.txt", sep = "\t", col.names = TRUE)

tempfile(pattern = "file", tmpdir = tempdir(), fileext = "")
tempdir()

# write.xlsx comes from the xlsx package.

write.xlsx(dat.csv, file = "path/to/save/filename.xlsx", sheetName="hsb2")

# save to binary R format (can save multiple datasets and R objects)
# save(dat.csv, dat.dta, dat.spss, dat.txt, file = "path/to/save/filename.RData")

files <- list.files(pattern=".txt$") 
for(i in files) { 
        x <- read.table(i, header=TRUE, comment.char = "A", sep="\t")
        assign(i, x)
        print(i)
        write.table(x, paste(i, c(".out"), sep=""), quote=FALSE, sep="\t", col.names = NA) 
}

######################
# Binary R obejects

save.image()  # saves the workspace
save(object, file='filename.RData')
load('filename.RData')