CODEWITHSUNDEEP
rcsvdataframelarge-data
MaskedMonkey
MaskedMonkey

Reputation: 117

Load large csv's into dataframe while maintaining column structure

I am trying to load a folder of large files (35million Rows Total) into R and have it as a data frame.

I have managed to load the data in, although it does take 10/15 mins to do so using the code below, however, the problem is that all the columns from the csv's are becoming 1 column. Here is my code:

# Load files

temp = list.files(path ="D:/", pattern="*.csv", full.names = TRUE)
myfiles = lapply(temp, read.delim)

# Make Dataframe

df_list = lapply(seq(length(myfiles)),function(i){
  df = as.data.frame(myfiles[i], stringsAsFactors = FALSE)
})

head(do.call(bind_rows,df_list))

df = as.data.frame(data.table::rbindlist(df_list, use.names=TRUE, fill=TRUE))

The column of a csv could look like:

|A|B|C|D1|E|

However is output in my dataframe like:

|A.B.C.D1..E|

Any help with solving this maintaining columns issue would be appriciated.

Upvotes: 0

Views: 52

Answers (1)

clemens
clemens

Reputation: 6813

You can use fread() to read the csv faster and rbindlist() to combine the data in the lists. Both come from the data.table package.

library(data.table)


# Load files
temp = list.files(path ="D:/", pattern="*.csv", full.names = TRUE)

Use fread() instead of read.delim():

myfiles = lapply(temp, fread)

Since no reproducible data is provided:

df_list <- lapply(1:5, function(x) {
  set.seed(x)

  rows <- sample(1:32, 2)
  mtcars[rows, ]
})

Combine the data in the lists:

df <- rbindlist(df_list)

This is the result:

     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
 1: 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
 2: 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
 3: 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
 4: 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
 5: 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
 6: 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
 7: 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
 8: 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
 9: 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
10: 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2

Upvotes: 1

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