I have a dataframe I want to share using dput.
I want all of it and not just some of it.
When I use dput(), the result is so big that it does not all fit in the Rstudio Console.
I tried assigning the dput to a variable and saving that variable as a txt file but it did not work, the txt file was made of unreadable characters.
Does anyone know a way of copying the whole length of a dput eventhough it's huge so that I can then share it on here with a google doc link or whatever ?
You are looking for dump().
try:
dump("iris", "iris.txt")
Related
I'm trying to get R to read data from a txt file, but the file is not properly made, so it's giving me lots of errors.
Ideally, I'd like to be able to extract a dataframe to be able to work with from this, but I trully don't know how to.
The files are all in this link.
An example with any of them would work.
Thanks a lot!
I am using R to analyze some text data. After doing some aggregation, I had a new dataframe I wanted to write to a csv file, so I can use it in other analyses. The dataframe looks correct in r- it only has 2 columns with text data- but once I write the csv and open it, the text is scattered across different columns. Here is the code I was using:
write.csv(new_df, "4.19 Group 1_agg by user try 2.csv")
I tried adding in an extra bit of code to specify that it should be using UTF-8, since I've heard this could be an encoding error, so the code then looked like this:
write.csv(new_df, "4.19 Group 1_agg by user try 2.csv", fileEncoding = "UTF-8")
I also tried reading in the file differently (using fread instead of read.csv)
Still, the csv file looks wrong/messy in many places. Here is what it should look like:
This is what it looks like currently:
Again, I think the error must be in writing the csv file, because everything looks good in R when I check it using names and head. Any help is appreciated, thank you!
hi literally day one new coder
On the excel sheet, my data looks organized, but when I upload my file to R, it's not able to read the excel properly and the column headers are in the rows and the data seems randomized.
So far I have tried:
library(readxl)
dataset <-read_excel("pathname")
View(dataset)
Also tried:
dataset <-read_excel("pathname", sheet=1, colNames=TRUE)
Also tried to use the package openxlsx
but nothing is giving me the correct, organized data set.
I tried formatting my Excel to a CSV file, and the CSV file looks exactly like the data that shows up on R (both are messed up).
How should I approach this problem?
I deal with importing .xlsx into R frequently. It can be challenging due to the flexibility of the excel platform. I generally use readxl::read_xlsx() to fetch data from .xlsx files. My suggestions:
First, specify exactly the data you want to import with the range argument.
A cell range to read from, as described in cell-specification. Includes typical Excel
ranges like "B3:D87", possibly including the sheet name like "Budget!B2:G14"
Second, if there are there merged cells or other formatting challenges in column headers, I resort to setting col_names = FALSE. And supplying clean names after import with names(df) <- c("first_col", "second_col")
Third, if there are merged cells elsewhere in the spreadsheet I generally I resort to "fixing" them in excel (not ideal but easier for my use case), however, others may have suggestions on a programmatic fix.
It may be helpful to provide a screenshot of your spreadsheet.
I got a question about reading a file into data frame using R.
I don't understand "getwd" and "setwd", do we must do these before reading the files?
and also i need to print some of the columns in the data frame, and only need to print 1 to 30,how to do this?
Kinds regards
getwd tells you what your current working directory is. setwd is used to change your working directory to a specified path. See the relevant documentation here or by typing ? getwd or ? setwd in your R console.
Using these allows you to shorten what you type into, e.g., read.csv by just specifying a filename without specifying its full path, like:
setwd('C:/Users/Me/Documents')
read.csv('myfile.csv')
instead of:
read.csv('C:/Users/Me/Documents/myfile.csv')
I am looking for an easy way to get objects into MS Excel.
(I am using the preinstalled "Puromycin"-dataset for the examples)
I would like to place the contents of these objects to a single excel file:
Puromycin
summary(Puromycin$rate)
summary(Purymycin$conc)
table(Puromycin$state)
lm( conc ~ rate , data=Puromycin)
By "contents" i mean what is shown in the console when i press enter. I dont know what to call it.
I tried to do this:
sink("datafilewhichexcelhopefullyunderstands.csv")
Puromycin
summary(Puromycin$rate)
summary(Purymycin$conc)
table(Puromycin$state)
lm( conc ~ rate , data=Puromycin)
sink()
This gives med a file with the CSV-extension, however when i open the file in notepad,
there is comma-separation. That means that i cant get Excel to open it properly. By properly
i mean that each number is in its own cell.
Others have suggested this for a similar problem
https://stackoverflow.com/a/13007555/1831980
But as a novice i feel that the solution is too complex, and I am hoping for a simpler method.
What I am doing now is this:
write.table(Puromycin, file="clipboard" , sep=";" , row.names=FALSE )
write.table(summary(Purymycin$conc), file="clipboard" , sep=";" , row.names=FALSE )
... etc...
But this requires i lot of copy-ing and pasting, which I hope to eliminate.
Any help would appreciated.
write.table and its friends are intended to write out columns of data separated by whatever separator is specified. Your clipboard contains several data types because you are using summary which always gives a unique output.
For writing the data values out, you can use write.csv on a data frame and then open with Excel. For example, Puromycin is already a data frame (which you can see with str(Puromycin)) so you can just write it out directly:
write.csv(file = "some file.csv", x = Puromycin)
Which will go into the current working directory (which can be determined with getwd()).
To write out/save the results of the regression model is a bit more of a challenge. You could definitely use sink as you did, but specify an extension of .txt on your file so a text editor can open it. There are fancier methods (sweave, knitr) which you might want to look into in the long run, as they can write really nice reports automatically.
In the meantime, get to know str(any R object) as it will be your friend. You can see all the objects in your workspace with ls().
This will only be helpful if you are prepared to use Excel's Data/Text to Columns functions:
capture.output( sapply( c(Puromycin,
summary(Puromycin$rate),
summary(Puromycin$conc),
table(Puromycin$state),
lm( conc ~ rate , data=Puromycin) ), FUN=print), file="datafilewhichexcelhopefullyunderstands.csv", append=TRUE)
The problem being that Excel will not read the whitespace as a cell separator unless you specifically tell it to. You can (and I have often done so) use the fixed filed input features offered by the Text-to-Columns dialog interface.
Your simplest option may be to use the RExcel tool, it transfers information between R and Excel. However it is not free software.
The XLConnect package is another option, it can be used to write information directly to an Excel file.
The tricky part is the lm call. lm does not return a simple vector, matrix, or data frame (all of which are easy to convert to csv or send directly) and there is not a clear way to convert the various parts of a list to cells in a spreadsheet. What would be better is to use extractor functions to pull the important parts from the return of lm or the summary of the lm object and send those to Excel using the other tools.
If you can tell us more about why you want the numbers in Excel and what you plan to do with them after, then we may be able to offer better help (you may be able to completely skip excel).
If the main goal is to share output with others then you should really look at the knitr package (or other related packages). This will not create Excel files, but can be used (along with the pandoc program and possibly other tools) to create a report file in a format easy to share with others not familiar with R. You could put everything into a .pdf file or a .docx file (the latter read by MS Word and would have tables wich can be edited using Word). There is not a simple way to get edits back into R, but with the track changes you can easily see what changes have been made and hand edit your R script/template accordingly.