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.
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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
I have the following problem with exporting data from excel, and importing them into R.
My data in excel contains "," so excel interprets them as numbers.
But when I have this data in R I have "." so R interprets them as text instead of numbers.
For example,
In excel I have 12,765 and in R I have 12.765
Do you have any idea how can I fix this ?
I use the following code to import the data file into R:
library(openxlsx)
read.xlsx("pk.xlsx")
I suggest using the nice package readxl from the tidyverse (https://readxl.tidyverse.org/) :
library(readxl)
read_excel("pk.xlsx")
Your should find everything you need with the tidyverse to import different format of files (readr for all csv, tsv and so on... ).
I have to read an excel file in R. The excelfile has a column with values such as 50%,20%... and another column with dates in the format "12-December-2017" but R converts both the column datas.
I am using readxl package and i specified in col_types parameter all the columns to be read as text but when i check the dataframe all the column types are characters but the percentage data and date changes to decimals and numbers respectively.
excelfile2<-read_excel(filePath,col_types=rep("text",8))
I want to read the excel file as is.Any help will be appreciated.
This is because what you visualize inside the Excel is not what actually is stored.
For example, if in excel you visualize "12-December-2017", what is stored in reality is the number of days since 1-1-1899.
My suggestion is to open the Excel file with the TextReader so you have a grasp what really you are reading in R.
Then, you can either define everything as text in excel or you can apply some transformations in R in order to convert the days since 1-1-1899 into a POSIXct format.
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)
I would like to clarify my understanding here on both converting a file into CSV and also reading it. Let's use a dataset from R for instance, titled longley.
To set up a data frame, I can just use the write.table command as follows, right?
d1<-longley
write.table(d1, file="", sep="1,16", row.names=TRUE, col.names=TRUE)
Has this already become a data frame or am I missing something here?
Now let's say if I want to read this CSV file. Then would my code be something like:
read.table(<dframe1>, header=FALSE, sep="", quote="\"")
It seems like before that I have to use a function called setwd(). I'm not really sure what it does or how it helps. Can someone help me here?
longley and, therefore, d1 are already data frames (type class(d1) in the console). A data frame is a fundamental data structure in R. Writing a data frame to a file saves the data in the data frame. In this case, you're trying to save the data in the data frame in CSV format, which you would do like this:
write.csv(d1, "myFileName.csv")
write.csv is a wrapper for write.table that takes care of the settings needed for saving in CSV format. You could also do:
write.table(d1, "myFileName.csv", sep=",")
sep="," tells R to write the file with values separated by a comma.
Then, to read the file into an R session you can do this:
df = read.csv("myFileName.csv", header=TRUE, stringsAsFactors=FALSE)
This creates a new object called df, which is the data frame created from the data in myFileName.csv. Once again, read.csv is a wrapper for read.table that takes care of the settings for reading a CSV file.
setwd is how you change the working directory--that is, the default directory where R writes to and reads from. But you can also keep the current working directory unchanged and just give write.csv or read.csv (or any other function that writes or reads R objects) the full path to wherever you want to read from or write to. For example:
write.csv(d1, "/path/for/saving/file/myFileName.csv")