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I have the following sample file:
"id";"PCA0";"PCA1";"PCA2"
1;6.142741644872954;1.2075898020608253;1.8946959360032403
2;-0.5329026419681557;-8.586870627925729;4.510113575138726
When I try to read it with:
d <- read.table("file.csv", sep=";", header=T)
id is a integer column, PCA0 a numeric an all subsequent columns are factors
class(d$iid)
[1] "integer"
class(d$PCA0)
[1] "numeric"
class(d$PCA1)
[1] "factor"
class(d$PCA2)
[1] "factor"
Why aren't the other columns numeric as well?
I know how to convert the columns, but I want my script to work without manually casting the types. Why doesn't R recognize the numeric columns?
755
A table can be read from left to right or from top to bottom. If you read a table across the row, you read the information from left to right. In the Cats and Dogs Table, the number of black animals is 2 + 2 = 4. You'll see that those are the numbers in the row directly to the right of the word 'Black.
table() function in R Language is used to read data from a text file. It returns the data in the form of a table.
Remember that the read. csv() as well as the read. csv2() function are almost identical to the read. table() function, with the sole difference that they have the header and fill arguments set as TRUE by default.
You can use the read. table function to read in a file that contains tabular data into R. By default, the read. table function assumes there is no header row in the file and that the values are separated by white space.
as @MrFlick says: too many digits.
you can force what you want by specifying colClasses argument:
read.table("test.csv",
sep=";",
header=TRUE,
colClasses=c("integer","numeric","numeric","numeric"))
require(data.table)
d <- fread("test.csv")
Then modify to maximum precision stored:
d[,PCA0 := sprintf("%.15E",PCA0)]
d[,PCA1 := sprintf("%.15E",PCA1)]
d[,PCA2 := sprintf("%.15E",PCA2)]
gives:
> d
id PCA0 PCA1 PCA2
1: 1 6.142741644872954E+00 1.207589802060825E+00 1.8946959360032403
2: 2 -5.329026419681557E-01 -8.586870627925729E+00 4.510113575138726
note: fread should be smater + faster.
169
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