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readxl

Overview

The readxl package makes it easy to get data out of Excel and into R. Compared to many of the existing packages (e.g. gdata, xlsx, xlsReadWrite) readxl has no external dependencies, so it’s easy to install and use on all operating systems. It is designed to work with tabular data.

readxl supports both the legacy .xls format and the modern xml-based .xlsx format. The libxls C library is used to support .xls, which abstracts away many of the complexities of the underlying binary format. To parse .xlsx, we use the RapidXML C++ library.

Installation

The easiest way to install the latest released version from CRAN is to install the whole tidyverse.

install.packages("tidyverse")

NOTE: you will still need to load readxl explicitly, because it is not a core tidyverse package loaded via library(tidyverse).

Alternatively, install just readxl from CRAN:

install.packages("readxl")

Or install the development version from GitHub:

#install.packages("pak")
pak::pak("tidyverse/readxl")

Cheatsheet

You can see how to read data with readxl in the data import cheatsheet, which also covers similar functionality in the related packages readr and googlesheets4.

Usage

library(readxl)

readxl includes several example files, which we use throughout the documentation. Use the helper readxl_example() with no arguments to list them or call it with an example filename to get the path.

readxl_example()
#>  [1] "clippy.xls"    "clippy.xlsx"   "datasets.xls"  "datasets.xlsx"
#>  [5] "deaths.xls"    "deaths.xlsx"   "geometry.xls"  "geometry.xlsx"
#>  [9] "type-me.xls"   "type-me.xlsx"
readxl_example("clippy.xls")
#> [1] "/private/tmp/RtmpM1GkLC/temp_libpatha8e46f7f62bf/readxl/extdata/clippy.xls"

read_excel() reads both xls and xlsx files and detects the format from the extension.

xlsx_example <- readxl_example("datasets.xlsx")
read_excel(xlsx_example)
#> # A tibble: 150 × 5
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>          <dbl>       <dbl>        <dbl>       <dbl> <chr>  
#> 1          5.1         3.5          1.4         0.2 setosa 
#> 2          4.9         3            1.4         0.2 setosa 
#> 3          4.7         3.2          1.3         0.2 setosa 
#> # ℹ 147 more rows

xls_example <- readxl_example("datasets.xls")
read_excel(xls_example)
#> # A tibble: 150 × 5
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>          <dbl>       <dbl>        <dbl>       <dbl> <chr>  
#> 1          5.1         3.5          1.4         0.2 setosa 
#> 2          4.9         3            1.4         0.2 setosa 
#> 3          4.7         3.2          1.3         0.2 setosa 
#> # ℹ 147 more rows

List the sheet names with excel_sheets().

excel_sheets(xlsx_example)
#> [1] "iris"     "mtcars"   "chickwts" "quakes"

Specify a worksheet by name or number.

read_excel(xlsx_example, sheet = "chickwts")
#> # A tibble: 71 × 2
#>   weight feed     
#>    <dbl> <chr>    
#> 1    179 horsebean
#> 2    160 horsebean
#> 3    136 horsebean
#> # ℹ 68 more rows
read_excel(xls_example, sheet = 4)
#> # A tibble: 1,000 × 5
#>     lat  long depth   mag stations
#>   <dbl> <dbl> <dbl> <dbl>    <dbl>
#> 1 -20.4  182.   562   4.8       41
#> 2 -20.6  181.   650   4.2       15
#> 3 -26    184.    42   5.4       43
#> # ℹ 997 more rows

There are various ways to control which cells are read. You can even specify the sheet here, if providing an Excel-style cell range.

read_excel(xlsx_example, n_max = 3)
#> # A tibble: 3 × 5
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>          <dbl>       <dbl>        <dbl>       <dbl> <chr>  
#> 1          5.1         3.5          1.4         0.2 setosa 
#> 2          4.9         3            1.4         0.2 setosa 
#> 3          4.7         3.2          1.3         0.2 setosa
read_excel(xlsx_example, range = "C1:E4")
#> # A tibble: 3 × 3
#>   Petal.Length Petal.Width Species
#>          <dbl>       <dbl> <chr>  
#> 1          1.4         0.2 setosa 
#> 2          1.4         0.2 setosa 
#> 3          1.3         0.2 setosa
read_excel(xlsx_example, range = cell_rows(1:4))
#> # A tibble: 3 × 5
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>          <dbl>       <dbl>        <dbl>       <dbl> <chr>  
#> 1          5.1         3.5          1.4         0.2 setosa 
#> 2          4.9         3            1.4         0.2 setosa 
#> 3          4.7         3.2          1.3         0.2 setosa
read_excel(xlsx_example, range = cell_cols("B:D"))
#> # A tibble: 150 × 3
#>   Sepal.Width Petal.Length Petal.Width
#>         <dbl>        <dbl>       <dbl>
#> 1         3.5          1.4         0.2
#> 2         3            1.4         0.2
#> 3         3.2          1.3         0.2
#> # ℹ 147 more rows
read_excel(xlsx_example, range = "mtcars!B1:D5")
#> # A tibble: 4 × 3
#>     cyl  disp    hp
#>   <dbl> <dbl> <dbl>
#> 1     6   160   110
#> 2     6   160   110
#> 3     4   108    93
#> # ℹ 1 more row

If NAs are represented by something other than blank cells, set the na argument.

read_excel(xlsx_example, na = "setosa")
#> # A tibble: 150 × 5
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>          <dbl>       <dbl>        <dbl>       <dbl> <chr>  
#> 1          5.1         3.5          1.4         0.2 <NA>   
#> 2          4.9         3            1.4         0.2 <NA>   
#> 3          4.7         3.2          1.3         0.2 <NA>   
#> # ℹ 147 more rows

If you are new to the tidyverse conventions for data import, you may want to consult the data import chapter in R for Data Science. readxl will become increasingly consistent with other packages, such as readr.

Articles

Broad topics are explained in these articles:

• Cell and Column Types
• Sheet Geometry: how to specify which cells to read
• readxl Workflows: Iterating over multiple tabs or worksheets, stashing a csv snapshot

We also have some focused articles that address specific aggravations presented by the world’s spreadsheets:

• Column Names
• Multiple Header Rows

Features

• No external dependency on, e.g., Java or Perl.

• Re-encodes non-ASCII characters to UTF-8.

• Loads datetimes into POSIXct columns. Both Windows (1900) and Mac (1904) date specifications are processed correctly.

• Discovers the minimal data rectangle and returns that, by default. User can exert more control with range, skip, and n_max.

• Column names and types are determined from the data in the sheet, by default. User can also supply via col_names and col_types and control name repair via .name_repair.

• Returns a tibble, i.e. a data frame with an additional tbl_df class. Among other things, this provide nicer printing.

Other relevant packages

Here are some other packages with functionality that is complementary to readxl and that also avoid a Java dependency.

Writing Excel files: The example files datasets.xlsx and datasets.xls were created with the help of openxlsx (and Excel). openxlsx provides “a high level interface to writing, styling and editing worksheets”.

l <- list(iris = iris, mtcars = mtcars, chickwts = chickwts, quakes = quakes)
openxlsx::write.xlsx(l, file = "inst/extdata/datasets.xlsx")

writexl is a new option in this space, first released on CRAN in August 2017. It’s a portable and lightweight way to export a data frame to xlsx, based on libxlsxwriter. It is much more minimalistic than openxlsx, but on simple examples, appears to be about twice as fast and to write smaller files.

Non-tabular data and formatting: tidyxl is focused on importing awkward and non-tabular data from Excel. It also “exposes cell content, position and formatting in a tidy structure for further manipulation”.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.