The basic format for scan function in R to read a file with characters is represented like this
a<- scan(file.choose(),what='char',sep=',').
I have a csv file with names as a separate column. Can i use what='char' in read.csv. If yes, how to use. If not how to read names column?
There is an entire R manual on importing and exporting data
https://cran.r-project.org/doc/manuals/r-release/R-data.html
read.table (or more specifically read.csv, which is read.table with the default separator being a comma) are the functions you are looking for.
a <- read.csv(yourfile)
Related
I'm trying to use ggplot2 on a large data set stored into a csv file. I used to read it with excel.
I don't know how to convert this data into a data.frame. In particular, I have a date column that has the following format: "2020/04/12:12:00". How can I get R to understand this format ?
If it's a csv, you can use:
fread function from data.table. This will be the fastest way to read your csv.
read_csv or read_csv2 (for ; delimited documents) in readr package
If it's .xls (or .xlsx) document, have a look at the readxl package.
All these functions import your data as data.frames (with additional classes like data.table for fread or tibble for read_csv).
Edit
Given your comment, it looks like your file is not an excel but a csv. If you want to convert a column type to date, assuming your dataframe is called df
df[, dates := as.POSIXct(get(colnames(df)[1]), format = "%Y/%m/%d:%H:%M")]
Note that you don't need to use cbind or even reassign the data.table because you use := operator
As the message is saying you, you don't need the extra-precision of POSIXlt
Going by the question alone, I would suggest the openxlsx package, it has helped me reduce the time significantly in reading large datasets. Three points you may find it to be helpful based on your question and the comments
The read command stays same as xlsx package, however would suggest you to use openxlsx::read.xslx(file_path)
the arguments are again same, but in the place of sheetIndex it is sheet and it takes only numbers
If the existing columns are converted to character, then a simple as.Date would work
Given this CSV file:
How to read a file so that the extra commas that are not a part of data are excluded?
Seems that the file is ok. Have you tried the correct options for arguments in your importing function?
Would you like to try read_delim() from the readr package?
I need to convert a file of .csv format to one of .tab format. How do I go about this in R?
Copying the correct answer from the comments:
write.table(read.csv("old-file.csv", sep=","), "new-file.tab")
Note that read.csv and write.csv are just read.table and write.table with different defaults.
Also as mentioned in the comments, you might find read_csv and write_tsv from the readr package more pleasant to work with (the built-in R functions can have some surprising behavior).
I was trying to read a txt data in R, but when I input this txt to R using read.table. The data is not really split. Is there any way to split data by number of columns.The data looks like below
You can read fixed width files using read_fwf from the readr package.
I have some tweets stored in a csv file on my local computer.There are 1248 rows. Now when I try to read these tweets in R using the read.csv function I get 1816 rows. This is happening because there are some tweets which have commas in them so basically what read.csv does is it splits one tweet into multiple tweets based on the number of commas and hence more number of rows. So what separator should I define to read the file correctly?
Thanks
Use read.table or read.delim instead of read.csv and use the quote parameter. There's a thread on this that will provide all the details
[read.table with comma separated values and also commas inside each element.
convert csv file to xlsx and use the following code:
library(readxl)
dataset <- read_excel('C:/Study/..._Sample1.xlsx')