fread is not reading the columns names properly - r
I am trying to use a csv generated from the Apple mobility reports, which can be found here.
Now everything works relatively fine, and I am able to get the .csv as intended, which looks something like this text:
csvtxt <- "geo_type,region,2020-01-14,2020-01-15,2020-01-16
country/region,Albania,50.1,100.2,75.3"
But when I fread it, the first line, which is unsurprisingly a column name line, is not recognized as so, even with the option check.names = FALSE that I found somewhere here but cannot find again.
library(data.table)
fread(csvtxt, check.names = FALSE)
# V1 V2 V3 V4 V5
#1: geo_type region 2020-01-14 2020-01-15 2020-01-16
#2: country/region Albania 50.1 100.2 75.3
Is there a way to get this data to import so that the column name line is recognized properly?
We need to force the header by setting it to TRUE.
library(data.table) # R version 4.0.2, data.table_1.13.2
fread(csvtxt, header = TRUE)
# geo_type region 2020-01-14 2020-01-15 2020-01-16
# 1: country/region Albania 50.1 100.2 75.3
From the manuals:
header
Does the first data line contain column names? Defaults according to whether every non-empty field on the first data line is
type character. If so, or TRUE is supplied, any empty column names are
given a default name.
Confusion might be from read.csv where header is TRUE by default:
read.csv(text = csvtxt)
# geo_type region X2020.01.14 X2020.01.15 X2020.01.16
# 1 country/region Albania 50.1 100.2 75.3
Related
`fread` with headers with special characters (latin1) and unusual nested quotes
I have a latin1 encoded csv-file with nested quotes:
Ort;Stra▒e;Bezeichnung
Vienna;Testgasse 1;"Ministerium ""Pestalozzi"""
Graz;Teststra▒e 3;HS
Salzburg;Beispielstra▒e 9;"NMS ""Die Schlauen"""
Vienna;Wolfgang-Stra▒e 7;"Wirtshaus ""Wien III"""
Using fread from data.table 1.9.6 gives a wrong special character (ß) in the header while all ß below are correct - the quoted quotes stay "".
dat <- fread("latin1quotedat.csv", encoding = "Latin-1")
dat # wrong header, wrong quotes
Ort Stra\xdfe Bezeichnung
1: Vienna Testgasse 1 Ministerium ""Pestalozzi""
2: Graz Teststraße 3 HS
3: Salzburg Beispielstraße 9 NMS ""Die Schlauen""
4: Vienna Wolfgang-Straße 7 Wirtshaus ""Wien III""
Using read.csv2 from base R everything is as expected:
dat1 <- read.csv2("latin1quotedat.csv", encoding = "latin1")
dat1 # ok
Ort Straße Bezeichnung
1 Vienna Testgasse 1 Ministerium "Pestalozzi"
2 Graz Teststraße 3 HS
3 Salzburg Beispielstraße 9 NMS "Die Schlauen"
4 Vienna Wolfgang-Straße 7 Wirtshaus "Wien III"
Maybe there is an option for the quotes (although I didn't find one).
The misinterpreted special character in the header looks like a bug.
The code and an example csv can be found here: https://github.com/nachti/datatable_test.
Clone the repository and run latin1quotedat.R.
Gerhard
Now fixed with commit f91bba1 in current devel, v1.9.7. From NEWS:
fread() did not respect encoding on header column. Now fixed, #1680. Thanks #nachti.
With this, I get:
names(fread("~/Downloads/latin1quotedat.csv", encoding = "Latin-1"))
# [1] "Ort" "Straße" "Bezeichnung"
Is there anyway to read .dat file from movielens to R studio
I am trying to use Import Dataset in R Studio to read ratings.dat from movielens. Basically it has this format: 1::1::5::978824268 1::1022::5::978300055 1::1028::5::978301777 1::1029::5::978302205 1::1035::5::978301753 So I need to replace :: by : or ' or white spaces, etc. I use notepad++, it helps to load the file quite fast (compare to note) and can view very big file easily. However, when I do replacement, it shows some strange characters: "LF" as I do some research here, it said that it is \n (line feed or line break). But I do not know why when it load the file, it do not show these, only when I do replacement then they appear. And when I load into R Studio, it still detect as "LF", not line break and cause error in data reading. What is the solution for that ? Thank you ! PS: I know there is python code for converting this but I don't want to use it, is there any other ways ?
Try this:
url <- "http://files.grouplens.org/datasets/movielens/ml-10m.zip"
## this part is agonizingly slow
tf <- tempfile()
download.file(url,tf, mode="wb") # download archived movielens data
files <- unzip(tf, exdir=tempdir()) # unzips and returns a vector of file names
ratings <- readLines(files[grepl("ratings.dat$",files)]) # read rating.dat file
ratings <- gsub("::", "\t", ratings)
# this part is much faster
library(data.table)
ratings <- fread(paste(ratings, collapse="\n"), sep="\t")
# Read 10000054 rows and 4 (of 4) columns from 0.219 GB file in 00:00:07
head(ratings)
# V1 V2 V3 V4
# 1: 1 122 5 838985046
# 2: 1 185 5 838983525
# 3: 1 231 5 838983392
# 4: 1 292 5 838983421
# 5: 1 316 5 838983392
# 6: 1 329 5 838983392
Alternatively (use the d/l code from jlhoward but he also updated his code to not use built-in functions and switch to data.table while i wrote this, but mine's still faster/more efficient :-)
library(data.table)
# i try not to use variable names that stomp on function names in base
URL <- "http://files.grouplens.org/datasets/movielens/ml-10m.zip"
# this will be "ml-10m.zip"
fil <- basename(URL)
# this will download to getwd() since you prbly want easy access to
# the files after the machinations. the nice thing about this is
# that it won't re-download the file and waste bandwidth
if (!file.exists(fil)) download.file(URL, fil)
# this will create the "ml-10M100K" dir in getwd(). if using
# R 3.2+ you can do a dir.exists() test to avoid re-doing the unzip
# (which is useful for large archives or archives compressed with a
# more CPU-intensive algorithm)
unzip(fil)
# fast read and slicing of the input
# fread will only spit on a single delimiter so the initial fread
# will create a few blank columns. the [] expression filters those
# out. the "with=FALSE" is part of the data.table inanity
mov <- fread("ml-10M100K/ratings.dat", sep=":")[, c(1,3,5,7), with=FALSE]
# saner column names, set efficiently via data.table::setnames
setnames(mov, c("user_id", "movie_id", "tag", "timestamp"))
mov
## user_id movie_id tag timestamp
## 1: 1 122 5 838985046
## 2: 1 185 5 838983525
## 3: 1 231 5 838983392
## 4: 1 292 5 838983421
## 5: 1 316 5 838983392
## ---
## 10000050: 71567 2107 1 912580553
## 10000051: 71567 2126 2 912649143
## 10000052: 71567 2294 5 912577968
## 10000053: 71567 2338 2 912578016
## 10000054: 71567 2384 2 912578173
It's quite a bit faster than built-in functions.
Small improvement to #hrbrmstr's answer:
mov <- fread("ml-10M100K/ratings.dat", sep=":", select=c(1,3,5,7))
R correct use of read.csv
I must be misunderstanding how read.csv works in R. I have read the help file, but still do not understand how a csv file containing:
40900,-,-,-,241.75,0
40905,244,245.79,241.25,244,22114
40906,244,246.79,243.6,245.5,18024
40907,246,248.5,246,247,60859
read into R using: euk<-data.matrix(read.csv("path\to\csv.csv"))
produces this as a result (using tail):
Date Open High Low Close Volume
[2713,] 15329 490 404 369 240.75 62763
[2714,] 15330 495 409 378 242.50 127534
[2715,] 15331 1 1 1 241.75 0
[2716,] 15336 504 425 385 244.00 22114
[2717,] 15337 504 432 396 245.50 18024
[2718,] 15338 512 442 405 247.00 60859
It must be something obvious that I do not understand. Please be kind in your responses, I am trying to learn.
Thanks!
The issue is not with read.csv, but with data.matrix. read.csv imports any column with characters in it as a factor. The '-' in the first row for your dataset are character, so the column is converted to a factor. Now, you pass the result of the read.csv into data.matrix, and as the help states, it replaces the levels of the factor with it's internal codes.
Basically, you need to insure that the columns of your data are numeric before you pass the data.frame into data.matrix.
This should work in your case (assuming the only characters are '-'):
euk <- data.matrix(read.csv("path/to/csv.csv", na.strings = "-", colClasses = 'numeric'))
I'm no R expert, but you may consider using scan() instead, eg:
> data = scan("foo.csv", what = list(x = numeric(), y = numeric()), sep = ",")
Where foo.csv has two columns, x and y, and is comma delimited. I hope that helps.
I took a cut/paste of your data, put it in a file and I get this using 'R'
> c<-data.matrix(read.csv("c:/DOCUME~1/Philip/LOCALS~1/Temp/x.csv",header=F))
> c
V1 V2 V3 V4 V5 V6
[1,] 40900 1 1 1 241.75 0
[2,] 40905 2 2 2 244.00 22114
[3,] 40906 2 3 3 245.50 18024
[4,] 40907 3 4 4 247.00 60859
>
There must be more in your data file, for one thing, data for the header line. And the output you show seems to start with row 2713. I would check:
The format of the header line, or get rid of it and add it manually later.
That each row has exactly 6 values.
The the filename uses forward slashes and has no embedded spaces
(use the 8.3 representation as shown in my filename).
Also, if you generated your csv file from MS Excel, the internal representation for a date is a number.
read.table and files with excess commas
I am trying to import a CSV file into R using the read.table command. I keep getting the error message "more columns than column names", even though I have set the strip.white to TRUE. The program that makes the csv files adds a large number of comma characters to the end of each line, which I think is the source of the extra columns.
read.table("filename.csv", sep=",", fill=T, header=TRUE, strip.white = T,
as.is=T,row.names = NULL, quote = "")
How can I get R to strip away the extraneous columns of commas from the header line and from the rest of the CSV file as it reads it into the R console?
Also, numerous cells in the csv file do not contain any data. Is it possible to get R to fill in these empty cells with "NA"?
The first two lines of the csv file:
Document_Name,Sequence_Name,Track_Name,Type,Name,Sequence,Minimum,Min_(with_gaps),Maximum,Max_(with_gaps),Length,Length_(with_gaps),#_Intervals,Direction,Average_Quality,Coverage,modified_by,Polymorphism_Type,Strand-Bias,Strand-Bias_>50%_P-value,Strand-Bias_>65%_P-value,Variant_Frequency,Variant_Nucleotide(s),Variant_P-Value_(approximate),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
Chr2_FT,Chr2,Chr2.bed,CDS,10000_ARHGAP15,GAAAGAATCATTAACAGTTAGAAGTTGATG-AAGTTTCAATAACAAGTGGGCACTGAGAGAAAG,55916421,56019336,55916483,56019399,63,64,1,forward,,,User,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
You can use a combination of colClasses with "NULL" entries to "blank-out" the commas (also still needing , fill=TRUE:
read.table(text="1,2,3,4,5,6,7,8,,,,,,,,,,,,,,,,,,
9,9,9,9,9,9,9,9,,,,,,,,,,,,,,,,,", sep=",", fill=TRUE, colClasses=c(rep("numeric", 8), rep("NULL", 30)) )
#------------------
V1 V2 V3 V4 V5 V6 V7 V8
1 1 2 3 4 5 6 7 8
2 9 9 9 9 9 9 9 9
Warning message:
In read.table(text = "1,2,3,4,5,6,7,8,,,,,,,,,,,,,,,,,,\n9,9,9,9,9,9,9,9,,,,,,,,,,,,,,,,,", :
cols = 26 != length(data) = 38
I needed to add back in the missing linefeed at the end of the first line. (Yet another reason why you should edit questions rather than putting data examples in the comments.) There was an octothorpe in the header which required the comment.char be set to "":
read.table(text="Document_Name,Sequence_Name,Track_Name,Type,Name,Sequence,Minimum,Min_(with_gaps),Maximum,Max_(with_gaps),Length,Length_(with_gaps),#_Intervals,Direction,Average_Quality,Coverage,modified_by,Polymorphism_Type,Strand-Bias,Strand-Bias_>50%_P-value,Strand-Bias_>65%_P-value,Variant_Frequency,Variant_Nucleotide(s),Variant_P-Value_(approximate),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,\nChr2_FT,Chr2,Chr2.bed,CDS,10000_ARHGAP15,GAAAGAATCATTAACAGTTAGAAGTTGATG-AAGTTTCAATAACAAGTGGGCACTGAGAGAAAG,55916421,56019336,55916483,56019399,63,64,1,forward,,,User,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,", header=TRUE, colClasses=c(rep("character", 24), rep("NULL", 41)), comment.char="", sep=",")
Document_Name Sequence_Name Track_Name Type Name
1 Chr2_FT Chr2 Chr2.bed CDS 10000_ARHGAP15
Sequence Minimum Min_.with_gaps... Maximum
1 GAAAGAATCATTAACAGTTAGAAGTTGATG-AAGTTTCAATAACAAGTGGGCACTGAGAGAAAG 55916421 56019336 55916483
Max_.with_gaps. Length Length_.with_gaps. X._Intervals Direction Average.._Quality Coverage modified_by
1 56019399 63 64 1 forward User
Polymorphism_Type Strand.Bias Strand.Bias_.50._P.va..lue Strand.Bias_.65._P.value Variant_Frequency
1
Variant_Nucleotide.s. Variant_P.Va..lue_.approximate.
1
If you know what your colClasses will be, then you can get missing values to be NA in the numeric columns automatically. You could also use the na.strings setting to accomplish this. You could also do some editing on the header to take out the illegal characters in the column names. (I didn't think I needed to be the one to do that though.)
read.table(text="Document_Name,Sequence_Name,Track_Name,Type,Name,Sequence,Minimum,Min_(with_gaps),Maximum,Max_(with_gaps),Length,Length_(with_gaps),#_Intervals,Direction,Average_Quality,Coverage,modified_by,Polymorphism_Type,Strand-Bias,Strand-Bias_>50%_P-value,Strand-Bias_>65%_P-value,Variant_Frequency,Variant_Nucleotide(s),Variant_P-Value_(approximate),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
Chr2_FT,Chr2,Chr2.bed,CDS,10000_ARHGAP15,GAAAGAATCATTAACAGTTAGAAGTTGATG-AAGTTTCAATAACAAGTGGGCACTGAGAGAAAG,55916421,56019336,55916483,56019399,63,64,1,forward,,,User,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,", header=TRUE, colClasses=c(rep("character", 24), rep("NULL", 41)), comment.char="", sep=",", na.strings="")
#------------------------------------------------------
Document_Name Sequence_Name Track_Name Type Name
1 Chr2_FT Chr2 Chr2.bed CDS 10000_ARHGAP15
Sequence Minimum Min_.with_gaps... Maximum
1 GAAAGAATCATTAACAGTTAGAAGTTGATG-AAGTTTCAATAACAAGTGGGCACTGAGAGAAAG 55916421 56019336 55916483
Max_.with_gaps. Length Length_.with_gaps. X._Intervals Direction Average.._Quality Coverage modified_by
1 56019399 63 64 1 forward <NA> <NA> User
Polymorphism_Type Strand.Bias Strand.Bias_.50._P.va..lue Strand.Bias_.65._P.value Variant_Frequency
1 <NA> <NA> <NA> <NA> <NA>
Variant_Nucleotide.s. Variant_P.Va..lue_.approximate.
1 <NA> <NA>
I have been fiddling with the first two lines of your file, and the problem appears to be the # in one of your column names. read.table treats # as a comment character by default, so it reads in your header, ignores everything after # and returns 13 columns. You will be able to read in your file with read.table using the argument comment.char="". Incidentally, this is yet another reason why those who ask questions should include examples of the files/datasets they are working with.
Use the string of characters from a cell in a dataframe to create a vector
>titletool<-read.csv("TotalCSVData.csv",header=FALSE,sep=",")
> class(titletool)
[1] "data.frame"
>titletool[1,1]
[1] Experiment name : CONTROL DB AD_1
>t<-titletool[1,1]
>t
[1] Experiment name : CONTROL DB AD_1
>class(t)
[1] "character"
now i want to create an object (vector) with the name "Experiment name : CONTROL DB AD_1" , or even better if possible CONTROL DB AD_1
Thank you
Use assign:
varname <- "Experiment name : CONTROL DB AD_1"
assign(varname, 3.14158)
get("Experiment name : CONTROL DB AD_1")
[1] 3.14158
And you can use a regular expression and sub or gsub to remove some text from a string:
cleanVarname <- sub("Experiment name : ", "", varname)
assign(cleanVarname, 42)
get("CONTROL DB AD_1")
[1] 42
But let me warn you this is an unusual thing to do.
Here be dragons.
If I understand correctly, you have a bunch of CSV files, each with multiple experiments in them, named in the pattern "Experiment ...". You now want to read each of these "experiments" into R in an efficient way.
Here's a not-so-pretty (but not-so-ugly either) function that might get you started in the right direction.
What the function basically does is read in the CSV, identify the line numbers where each new experiment starts, grabs the names of the experiments, then does a loop to fill in a list with the separate data frames. It doesn't really bother making "R-friendly" names though, and I've decided to leave the output in a list, because as Andrie pointed out, "R has great tools for working with lists."
read.funkyfile = function(funkyfile, expression, ...) {
temp = readLines(funkyfile)
temp.loc = grep(expression, temp)
temp.loc = c(temp.loc, length(temp)+1)
temp.nam = gsub("[[:punct:]]", "",
grep(expression, temp, value=TRUE))
temp.out = vector("list")
for (i in 1:length(temp.nam)) {
temp.out[[i]] = read.csv(textConnection(
temp[seq(from = temp.loc[i]+1,
to = temp.loc[i+1]-1)]),
...)
names(temp.out)[i] = temp.nam[i]
}
temp.out
}
Here is an example CSV file. Copy and paste it into a text editor and save it as "funkyfile1.csv" in the current working directory. (Or, read it in from Dropbox: http://dl.dropbox.com/u/2556524/testing/funkyfile1.csv)
"Experiment Name: Here Be",,
1,2,3
4,5,6
7,8,9
"Experiment Name: The Dragons",,
10,11,12
13,14,15
16,17,18
Here is a second CSV. Again, copy-paste and save it as "funkyfile2.csv" in your current working directory. (Or, read it in from Dropbox: http://dl.dropbox.com/u/2556524/testing/funkyfile2.csv)
"Promises: I vow to",,
"H1","H2","H3"
19,20,21
22,23,24
25,26,27
"Promises: Slay the dragon",,
"H1","H2","H3"
28,29,30
31,32,33
34,35,36
Notice that funkyfile1 has no column names, while funkyfile2 does. That's what the ... argument in the function is for: to specify header=TRUE or header=FALSE. Also the "expression" identifying each new set of data is "Promises" in funkyfile2.
Now, use the function:
read.funkyfile("funkyfile1.csv", "Experiment", header=FALSE)
# read.funkyfile("http://dl.dropbox.com/u/2556524/testing/funkyfile1.csv",
# "Experiment", header=FALSE) # Uncomment to load remotely
# $`Experiment Name Here Be`
# V1 V2 V3
# 1 1 2 3
# 2 4 5 6
# 3 7 8 9
#
# $`Experiment Name The Dragons`
# V1 V2 V3
# 1 10 11 12
# 2 13 14 15
# 3 16 17 18
read.funkyfile("funkyfile2.csv", "Promises", header=TRUE)
# read.funkyfile("http://dl.dropbox.com/u/2556524/testing/funkyfile2.csv",
# "Experiment", header=TRUE) # Uncomment to load remotely
# $`Promises I vow to`
# H1 H2 H3
# 1 19 20 21
# 2 22 23 24
# 3 25 26 27
#
# $`Promises Slay the dragon`
# H1 H2 H3
# 1 28 29 30
# 2 31 32 33
# 3 34 35 36
Go get those dragons.
Update
If your data are all in the same format, you can use the lapply solution mentioned by Andrie along with this function. Just make a list of the CSVs that you want to load, as below. Note that the files all need to use the same "expression" and other arguments the way the function is currently written....
temp = list("http://dl.dropbox.com/u/2556524/testing/funkyfile1.csv",
"http://dl.dropbox.com/u/2556524/testing/funkyfile3.csv")
lapply(temp, read.funkyfile, "Experiment", header=FALSE)