Importing data into к from excel

###Chapter 3 Importing Excel Data ###List the Sheets of an Excel File # Load the readxl package library(readxl) # Print out the names of both spreadsheets excel_sheets(«urbanpop.xlsx«) ###Import an Excel Sheet # The readxl package is already loaded # Read the sheets, one by one pop_1 <- read_excel(«urbanpop.xlsx«, sheet = 1) pop_2 <- read_excel(«urbanpop.xlsx«, sheet = 2) pop_3 <- read_excel(«urbanpop.xlsx«, sheet = 3) # Put pop_1, pop_2 and pop_3 in a list: pop_list pop_list <- list(pop_1, pop_2, pop_3) # Display the structure of pop_list str(pop_list) ###Reading a Workbook # The readxl package is already loaded # Read all Excel sheets with lapply(): pop_list pop_list <- lapply(excel_sheets(«urbanpop.xlsx«), read_excel, path = «urbanpop.xlsx«) # Display the structure of pop_list str(pop_list) ###The col_names Argument # The readxl package is already loaded # Import the the first Excel sheet of urbanpop_nonames.xlsx (R gives names): pop_a pop_a <- read_excel(«urbanpop_nonames.xlsx«, col_names = FALSE) # Import the the first Excel sheet of urbanpop_nonames.xlsx (specify col_names): pop_b cols <- c(«country«, paste0(«year_«, 1960:1966)) pop_b <- read_excel(«urbanpop_nonames.xlsx«, col_names = cols) # Print the summary of pop_a summary(pop_a) # Print the summary of pop_b summary(pop_b) ###The skip Argument # The readxl package is already loaded # Import the second sheet of urbanpop.xlsx, skipping the first 21 rows: urbanpop_sel urbanpop_sel <- read_excel(«urbanpop.xlsx«, sheet = 2, col_names = FALSE, skip = 21) # Print out the first observation from urbanpop_sel urbanpop_sel[1,] ###Import a Local File # Load the gdata package library(gdata) # Import the second sheet of urbanpop.xls: urban_pop urban_pop <- read.xls(«urbanpop.xls«, sheet = «1967-1974«) # Print the first 11 observations using head() head(urban_pop, n = 11) ###read.xls() Wraps Around read.table() # The gdata package is alreaded loaded # Column names for urban_pop columns <- c(«country«, paste0(«year_«, 1967:1974)) # Finish the read.xls call urban_pop <- read.xls(«urbanpop.xls«, sheet = 2, skip = 50, header = FALSE, stringsAsFactors = FALSE, col.names = columns) # Print first 10 observation of urban_pop head(urban_pop, n = 10) ###Work that Excel Data! # Import all sheets from urbanpop.xls path <- «urbanpop.xls« urban_sheet1 <- read.xls(path, sheet = 1, stringsAsFactors = FALSE) urban_sheet2 <- read.xls(path, sheet = 2, stringsAsFactors = FALSE) urban_sheet3 <- read.xls(path, sheet = 3, stringsAsFactors = FALSE) # Extend the cbind() call to include urban_sheet3: urban_all urban <- cbind(urban_sheet1, urban_sheet2[—1], urban_sheet3[—1]) # Remove all rows with NAs from urban: urban_clean urban_clean <- na.omit(urban) # Print out a summary of urban_clean summary(urban_clean)

Importing Data from Excel

Excel is a spreadsheet application, which is widely used by many institutions to store data. This tutorial will give a brief of reading, writing and manipulating the data in Excel files using R. We will learn about various R packages and extensions to read and import Excel files. At the end of this section, we have written about some common problems encountered while loading Excel files and spreadsheet data.

Before we import the data from Excel spreadsheet into R, there are some standard practices to tone your data, to avoid any unnecessary error.

• The first column of the spreadsheet is used to identify the sample dataset, therefore it should be a unique key id. Similarly the first row is reserved for header, describing the scheme of the data.
• Concatenating words in the cells should be done using ‘.’. For example, ‘Sample.data’.
• The names and header of the data scheme should usually avoid symbols.
• All missing data points in the Excel spreadsheet should be indicated with ‘NA’.

Before you import the Excel data in R, you would need to set the console in R to working directory.

>getwd()//To get the working directory at the moment
>setwd(“”)

 Before we look into the packages available to extract data from Excel spreadsheet, we will show you simple R commands that can do the job. Utlis package is one of the core packages which contains bunch of basic utility functions and the following commands are part of this package.

• read.table()

dataset <-read.table(“”,
                     header =TRUE)

The first argument of read.table() function is the name of the text file within the double quotes and if the data file has a header for data schema in the top row, the second argument will be true. This function will work for files, which are saved in .txt format.

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Reading data from an excel file is incredibly easy and it can be done using several packages. You can export the Excel file to a Comma delimited file and import it using the method shown in the tutorial Importing Data from Flat Files in R. Another method to Import Data in R from Excel is using xlsx package, which I used to access Excel files. The first row should contain variable names.

//read in the excel sheet from workbook sample_excel.xls
//variable name in the first row
library(xlsx)
sampledata<- read.xlsx(“sample_excel.xls”,
                       sheetName=”sample_sheet1”)

It is necessary that while using read.xlsx function, we mention the sheet index or the sheet name. If the required dataset is bigger, then read.xlsx2() function is used.

Sample.data <- read.xlsx2(“sample_excel.xls”,
                          sheetName=”sample_sheeet1”,
                          startRow = 100,
                          colIndex = 100)

Additionally in the function above, user can mention the end row or the data import can be limited to certain row and column index. xlsx package does a lot more than importing data from Excel files, it can also manipulate the data and write data frames into the spreadsheets. The data frames can be written to Excel workbook using the function write.xlsx().

>write.xlsx(Sample.data,
            “Sample_Sheet.xls”,
            sheetName=”sample_sheet1”)

---

Apart from the xlsx package, we have gdata package, which has functions that can read from data in the Excel format. gdata provides a cross platform solution for importing data from Excel files into R. The read.xls function in particular can read data from an Excel spreadsheet and gives data frame as output. Take for example a sample Excel spreadsheet, named ‘Sample_Sheet.xls’ and to use this method, you would require Perl runtime in your system.

>library(gdata)//Load gdata package
>sample_data = read.xls(“Sample_Sheet.xls”)//Read data from the sheet

This function converts the Sample_Sheet.xls file into a temporary .csv or .tab limited file using Perl. While executing read.xls function, R will search for a path to the excel file and looks out for Perl on its way. If it doesn’t find perl.exe, then R will return an error. To avoid this error, another argument for the function can be given to search for the Perl executable file.

>sample.data <- read.xlsx(“Sample_Sheet.xlsx,
                          sheetIndex = 1,
                          perl = “C:/Perl/bin/perl.exe””)

gdata has several other functions to convert the Excel file into various other formats. Such as:

• xls2sep()
• xls2csv()
• xls2tab()
• xls2tsv()

The input arguments for these functions are same as that for read.xlsx() function.

Another package that can do the job of importing data from Excel is the XLConnect package using the loadWorkbook function. This function can be used to read the entire workbook, followed by readWorksheet function to load the worksheets into R. Java is required to be pre-installed for this package to work. This package also provides function to create Excel workbooks, and export data to them.

>library(XLConnect)
>Sample_Workbook = loadWorkbook(“Sample_Sheet.xls”)
>Sample_Data = readWorksheet (Sample_Workbook,
                              sheet=”Sheet1”)

Other arguments can also be added after the Index argument such as startCol or StartRow or endCol or endRow to indicate and limit the cells that are required to be imported from the Excel workbook. Another argument ‘region’ can also be used in this function to highlight the range of starting and ending rows and columns.

 

• Preleminary tasks
• Copying data from Excel and import into R
  • On Windows system
  • On Mac OSX system
• Importing Excel files into R using readxl package
  • Installing and loading readxl package
  • Using readxl package
• Importing Excel files using xlsx package
  • Installing and loading xlsx package
  • Using xlsx package
  • Read more
• Summary
• Related articles
• Infos

Previously, we described the essentials of R programming and some best practices for preparing your data. We also provided quick start guides for reading and writing txt and csv files using R base functions as well as using a most modern R package named readr, which is faster (X10) than R base functions.

In this article, you’ll learn how to read data from Excel xls or xlsx file formats into R. This can be done either by:

• copying data from Excel
• using readxl package
• or using xlsx package

Preleminary tasks

1. Launch RStudio as described here: Running RStudio and setting up your working directory

2. Prepare your data as described here: Best practices for preparing your data

Copying data from Excel and import into R

On Windows system

1. Open the Excel file containing your data: select and copy the data (ctrl + c)

2. Type the R code below to import the copied data from the clipboard into R and store the data in a data frame (my_data):

my_data <- read.table(file = "clipboard", 
                      sep = "t", header=TRUE)

On Mac OSX system

1. Select and copy the data (Cmd + c)

2. Use the function pipe(pbpaste) to import the data you’ve copied (with Cmd + c):

my_data <- read.table(pipe("pbpaste"), sep="t", header = TRUE)

Importing Excel files into R using readxl package

The readxl package, developed by Hadley Wickham, can be used to easily import Excel files (xls|xlsx) into R without any external dependencies.

Installing and loading readxl package

• Install

install.packages("readxl")

• Load

library("readxl")

Using readxl package

The readxl package comes with the function read_excel() to read xls and xlsx files

1. Read both xls and xlsx files

# Loading
library("readxl")
# xls files
my_data <- read_excel("my_file.xls")
# xlsx files
my_data <- read_excel("my_file.xlsx")

The above R code, assumes that the file “my_file.xls” and “my_file.xlsx” is in your current working directory. To know your current working directory, type the function getwd() in R console.

• It’s also possible to choose a file interactively using the function file.choose(), which I recommend if you’re a beginner in R programming:

my_data <- read_excel(file.choose())

If you use the R code above in RStudio, you will be asked to choose a file.

1. Specify sheet with a number or name

# Specify sheet by its name
my_data <- read_excel("my_file.xlsx", sheet = "data")
  
# Specify sheet by its index
my_data <- read_excel("my_file.xlsx", sheet = 2)

1. Case of missing values: NA (not available). If NAs are represented by something (example: “—”) other than blank cells, set the na argument:

my_data <- read_excel("my_file.xlsx", na = "---")

Importing Excel files using xlsx package

The xlsx package, a java-based solution, is one of the powerful R packages to read, write and format Excel files.

Installing and loading xlsx package

• Install

install.packages("xlsx")

• Load

library("xlsx")

Using xlsx package

There are two main functions in xlsx package for reading both xls and xlsx Excel files: read.xlsx() and read.xlsx2() [faster on big files compared to read.xlsx function].

The simplified formats are:

read.xlsx(file, sheetIndex, header=TRUE)
read.xlsx2(file, sheetIndex, header=TRUE)

• file: file path
• sheetIndex: the index of the sheet to be read
• header: a logical value. If TRUE, the first row is used as column names.

Example of usage:

library("xlsx")
my_data <- read.xlsx(file.choose(), 1)  # read first sheet

Summary

• Read Excel files using readxl package: read_excel(file.choose(), sheet = 1)

• Read Excel files using xlsx package: read.xlsx(file.choose(), sheetIndex = 1)

Related articles

• Previous chapters
  • R programming basics
  • Best practices in preparing data files for importing into R
  • Reading data from txt|csv files: R base functions
  • Fast Reading of Data From txt|csv Files into R: readr package
• Next chapters
  • Exporting data from R

Infos

This analysis has been performed using R (ver. 3.2.3).

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Import from an Excel Table

When you load the sales data into Power Query and apply some transformations, Power Query creates a new worksheet, but the original sales data worksheet stays the same. This makes it easy to experiment with the data without changing the data source.

1. Notice that there are product categories and sales data worksheets. Open the Sales Data worksheet.

2. Position the cursor on the Excel table, Select Data > Get & Transform Data > From Table/Range.

   Excel opens the Power Query Editor with your data displayed in a preview pane.

3. To display all query tables in the workbook from the Queries pane, select the arrow to the left of the preview pane.

   The Product Sales table is listed last, then the World Cup Results table, and then the tables used earlier in the tutorial workbook. As you add new tables, they are automatically listed here.

   1. The Query Settings pane shows the transformation steps you have taken. You can preview, change, and delete any steps. Power Query has added a few steps for you. The following table summarizes important features:

      Feature	Description
      Source	Defines the source for your data.
      Changed Type	Power Query interprets the data types. You can adjust these later.
      Home Tab	Use to preview your data and make transformations.
      Close & Load	When finished, select to return the data to the worksheet.
      Transform Tab	Provides advanced transformation options.
      Add Column	Calculates data from existing columns, such as the day of the week from a date or custom calculations.
      View Tab	Provides additional options, such as opening the Advanced Query Editor.

4. To return the transformed data to Excel, select Home > Close & Load.

Note   Whether or not you transformed the data, a new worksheet is created. It’s a good idea to rename the new worksheet to clearly distinguish it from the original worksheet. Additional transformations are always added to the new worksheet.