fread/fwrite introduces garbage values

user_10518066 0
Viewed 92

Data file data.dat:

5625010350032.36719 5627008621379.12591 5628763999478.55791 5630383772880.98831 5632384688238.96095 5633992371569.87936 5635830220975.76879 5637713568911.67183 5639436594135.51215 5641160625591.58400 5643072053703.23919 5644920788572.33232 5646668772882.99855 5648398453919.33759 5650178043246.84799 5651842484825.03887 5653671759113.42399 5655374735235.55599 5657184594518.72287 5658951103084.33839 5660687853998.58127 5662491073242.24399

The following code

x1 <- data.matrix(data.table::fread("data.dat")) # Read it
plot(x1[1,])                                     
data.table::fwrite(x=x1, file="xout.dat", sep=" ") # Write it 
x2 <- data.matrix(data.table::fread("xout.dat"))   # Read it again
lines(x2[1,], col='red')

reveals that the element x2[1,13] takes the value 2.7898250541260385e-311 when it should in fact be equal to x1[1,13]. What is causing the garbage values to be introduced?

The data.dat file is written from a C++ file in the following way

    std::ofstream file("data.dat", std::ios::out);
    file << std::setprecision(std::numeric_limits<long double>::digits10) << std::showpoint;
    for (size_t i = 0; i < v.size(); ++i)
        file << v[i] << " ";
    file << std::endl;

where the vector v contains the values written to data.dat. I am using data.table version 1.14.2 and R 4.1.3.

data.table precision r c++
1 Answers

Appearantly it does some rounding somewhere in the process and fread stores that 13th value as integer64 "integer64" (default) reads columns detected as containing integers larger than 2^31 as type bit64::integer64.

enter image description here

What you can do is force it to be interpretted as numeric, by adding colClasses = c("numeric") to your fread.

x2 <- data.matrix(data.table::fread("xout.dat", colClasses = c("numeric")))

This does not prevent the floating point issues but does not make the 13th value be changed completely.

If we now do x1-x2 we see for all values we have the same sort of differences.

x1-x2

#              V1         V2         V3         V4         V5          V6          V7        V8        V9       V10         V11       V12
# [1,] -0.0029297 -0.0039062 -0.0019531 -0.0019531 0.00097656 -0.00097656 -0.00097656 0.0019531 0.0019531 0.0039062 -0.00097656 0.0019531
#              V13        V14        V15         V16       V17        V18       V19         V20        V21       V22
# [1,] -0.00097656 -0.0019531 -0.0019531 -0.00097656 0.0039062 -0.0039062 0.0029297 -0.00097656 0.00097656 0.0039062
user_104157490
Related