• Reading And Writing Files In C#
• Reading And Writing Files In Java

What’s Excel’s Connection To R? As most of you know, Excel is a spreadsheet application developed by Microsoft.

For reading and writing data in R, there are many functions available. R can read and write many file format such as txt, csv, xls, sav, dat, etc.

It is an easily accessible tool for organizing, analyzing and storing data in tables and has a widespread use in many different application fields all over the world. It doesn't need to surprise that R has implemented some ways to read, write and manipulate Excel files (and spreadsheets in general). This tutorial on reading and importing Excel files into R will give an overview of some of the options that exist to import Excel files and spreadsheets of different extensions to R. Both basic commands in R and dedicated packages are covered. At the same time, some of the most common problems that you can face when loading Excel files and spreadsheets into R will be addressed. Want to dive deeper? Check out, which has a chapter on importing Excel data.

Steps. Loading your Spreadsheets And Files Into R After saving your data set in Excel and some adjusting your workspace, you can finally start with the real importing of your file into R! This can happen in two ways: either through basic R commands or through packages. Go through these two options and discover which option is easiest and fastest for you. Basic R Commands The following commands are all part of R’s Utils package, which is one of the core and built-in packages that contains a collection of utility functions.

You will see that these basic functions focus on getting Excel spreadsheets into R, rather than the Excel files themselves. If you are more interested in the latter, scroll just a bit to discover the packages that are specifically designed for this purpose. Read.table As described in Step Two, Excel offers many options for saving your data sets and one of them is the tab-delimited text file or.txt file. If your data is saved as such, you can use one of the easiest and most general options to import your file to R: the read.table function.

Df.txt', header = TRUE) You fill in the first argument of the read.table function with the name of your text file in between ' and its extension, while you specify in the second argument header if your excel file has names in the first line or top row. The TRUE value for the header argument is the default. Remember that by executing setwd R knows in which folder you’re working. This means that you can also just write the file’s name as an argument of the read.table function without specifying the file’s location, just like this: df.txt', header = TRUE) Note that the field separator character for this function is set to ' or white space because it is meant to work for tab-delimited.txt files, which separate fields based on tabs. Indeed, white spaces here indicate not only one or more spaces, but also tabs, newlines or carriage returns. But what if your file uses another symbol to separate the fields of your data set, like in the following data set? 1/6/12:01:03/0.50/WORST 2/16/07:42:51/0.32/ BEST 3/19/12:01:29/0.50/'EMPTY' 4/13/03:22:50/0.14/INTERMEDIATE 5/8/09:30:03/0.40/WORST You can easily indicate this by adding the sep argument to the read.table function.

XLConnect XLConnect is a “comprehensive and cross-platform R package for manipulating Microsoft Excel files from within R”. You can make use of functions to create Excel workbooks, with multiple sheets if desired, and import data to them. Read in existing Excel files into R through: df ', sheet=1, startRow = 4, endCol = 2) The sheet argument specifies which sheet you exactly want to import into R. You can also add more specifications, such as startRow or startCol to indicate from which row or column the data set should be imported, or endRow or endCol to indicate the point up until where you want the data to be read in.

Alternatively, the argument region allows you to specify a range, like A5:B5 to indicate starting and ending rows and columns. Alternatively, you can also load in a whole workbook with the loadWorkbook function, to then read in worksheets that you desire to appear as data frames in R through readWorksheet: # Load in Workbook wb ') # Load in Worksheet df. Xlsx Package This is a second package that you can use to load in Excel files in R. The function to read in the files is just the same as the basic read.table or its variants: df ', sheetIndex = 1) Note that it is necessary to add a sheet name or a sheet index to this function.

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In the example above, the first sheet of the Excel file was assigned. If you have a bigger data set, you might get better performance when using the read.xlsx2 function: df ', sheetIndex = 1, startRow=2, colIndex = 2) Fun fact: according to the package information, the function achieves a performance of an order of magnitude faster on sheets with 100,000 cells or more. This is because this function does more work in Java. Note that the command above is the exact same that you can use in the readWorkSheetFromFile from the XLConnect package and that it specifies that you start reading the data set from the second row onwards. Additionally, you might want to specify the endRow, or you can limit yourself to colIndex and rowIndex to indicate the rows and columns you want to extract.

Reading And Writing Files In C#

Just like XLConnect, the xlsx package can do a lot more than just reading data: it can also be used to write data frames to Excel workbooks and to manipulate the data further into those files. If you would also like to write a data frame to an Excel workbook, you can just use write.xlsx and write.xlsx2. Note the analogy with read.xlsx and read.xlsx2!

For example: write.xlsx(df, 'df.xlsx', sheetName='Data Frame') The function requires you first to specify what data frame you want to export. In the second argument, you specify the name of the file that you are outputting. Note that this file will appear in the folder that you designated as your working directory. If, however, you want to write the data frame to a file that already exists, you can execute the following command: write.xlsx(df, ', sheetName='Data Frame' append=TRUE) Note that, in addition to changing the name of the output file, you also add the argument append to indicate that the data frame sheet should be added to the given file. For more details on this package and its functions, go to.

Gdata Package This package provides another cross-platform solution to load in Excel files into R. It contains various tools for data manipulation, among which the read.xls function, which is used as follows: df ', perl='.xls', sheet = 1, na.strings = 'EMPTY', perl=') The output of this function, df, will contain the temporary.csv file of the first sheet of the.xls or.xlsx file with stringS “EMPTY” defined as NA values.

You can subsequently read in this temporary file with any of the previous functions that is fit to read in files with the.csv extension, like read.csv: df ') In other words, the default is to read the first sheet(tab) in the specified workbook. If your workbook is a little more complicated than this, you can crack it open and list the sheet names with the following excelsheets function: excelsheets(') From there, you can then choose which sheet to read with the sheet argument: either referencing the sheet’s name or its index (number). References to sheet names are direct and therefore do require quotes: readexcel(', sheet='Sheet 3') Sheet indexing starts at 1, so alternatively, you could load in the third tab in with the following code: readexcel(', sheet=3) In the readexcel function, if the colnames argument is left to its default value of True, you will import the first line of the worksheet as the header names. In line with tibble and tidyverse standards, the readxl column header names are formed exactly as they were written in Excel.

Reading And Writing Files In Java

This results in behaviour that is much more in line with the expectations of Excel and tidy data users. If you want to convert column names to classic Base R valid identifiers, base R’s make.names is able to quickly perform the necessary conversions. Leading numbers and symbols will be prefixed or replaced with X’s and spaces will be replaced with.’s. Alternatively, if you wish to skip using header specified column-names and instead “number columns sequentially from X1 to Xn”, then set this argument to false: i.e. Colnames = FALSE Leaving the coltypes argument in its default state will cause types to be automatically registered when readexcel samples the first 10 rows and assigns each column to the most applicable class.

As with read.table’s colClasses argument that you’ve seen earlier, you can also manually classify column types on entry. As before, you will construct a complete vector specifying types for each column; however, this time be sure to use the following classification options of “blank”, “numeric”, “date”, or “text”.

For example, if you want to set a three column excel sheet to contain the data as dates in the first column, characters in the second, and numeric values in the third, you would need the following lines of code: readexcel(', coltypes = c('date', 'numeric', 'text')) While this is easy enough for tall datasets, with wider dataframes you want to transform only a few column types after the import using as.character or as.numeric type mutations. If you wish to avoid all issues from the beginning, and bring all your excel data into R in the most encompassing way possible, you can simply specify each column to be cast as characters. For a ten-column sheet this would look like the following: readexcel(', coltypes = rep('text', 10)) For the final of the most useful additional arguments available in readexcel, if you wish to skip rows before setting column names, there is the skip argument. This works exceptionally well for dealing with those intricately crafted database reports you enjoy so much. Let’s say, for example, those daily reports you receive with a lovely logo, five rows of report generation details, and the column headers in the sixth row.

Getting this imported quickly and tidily into R requires only the following code: readexcel(', skip = 5) For more details on this package and its functions, please see. Final Checkup After executing the command to read in the file in which your data set is stored, you might want to check one last time to see if you imported the file correctly. Remember to type in the following command to check the attributes’ data types of your data set: str(') Alternatively, you can also type in: head(') By executing this command, you will get to see the first rows of your data frame. This will allow you to check if the data set’s fields were correctly separated, if you didn’t forget to specify or indicate the header, etc. Note that you can add an argument n to head to specify the number of data frame rows you want to return, like in: head(df, 5) to return the first five lines of the data frame df. There And Back Again Importing your files is only one small but essential step in your endeavours with R. From this point, you are ready to start analyzing, manipulating or visualizing the imported data.

Do you want to continue already and get started with the data of your newly imported Excel file? Check out our tuturials for beginners on and. This tutorial was written in collaboration with, Data Quality Analyst with a passion for resolving data quality issues at scale in large, documentation sparse environments.