Hadley Wickham
GitHub
@@ -10,9 +10,8 @@ status("complete")
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## Introduction
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So far, you have learned about importing data from plain text files, e.g. `.csv` and `.tsv` files.
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Sometimes you need to analyze data that lives in a spreadsheet.
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This chapter will introduce you to tools for working with data in Excel spreadsheets and Google Sheets.
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In @sec-data-import you learned about importing data from plain text files like `.csv` and `.tsv`.
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Now it's time to learn how to get data out of a spreadsheet, either an Excel spreadsheet or a Google Sheet.
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This will build on much of what you've learned in @sec-data-import, but we will also discuss additional considerations and complexities when working with data from spreadsheets.
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If you or your collaborators are using spreadsheets for organizing data, we strongly recommend reading the paper "Data Organization in Spreadsheets" by Karl Broman and Kara Woo: <https://doi.org/10.1080/00031305.2017.1375989>.
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@@ -24,18 +23,16 @@ The best practices presented in this paper will save you much headache when you
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In this section, you'll learn how to load data from Excel spreadsheets in R with the **readxl** package.
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This package is non-core tidyverse, so you need to load it explicitly, but it is installed automatically when you install the tidyverse package.
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Later, we'll also use the writexl package, which allows us to create Excel spreadsheets.
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```{r}
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#| message: false
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library(readxl)
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library(tidyverse)
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```
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**openxlsx**, **xlsx**, and **XLConnect** can also be used for reading data from and writing data to Excel spreadsheets.
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We will discuss openxlsx in @sec-writing-to-excel.
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The latter two packages require Java installed on your machine and the rJava package.
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Due to potential challenges with installation, we recommend using alternative packages we're introducing in this chapter.
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library(writexl)
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```
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### Getting started
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@@ -201,6 +198,7 @@ knitr::include_graphics("screenshots/import-spreadsheets-penguins-islands.png")
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```
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You can read a single worksheet from a spreadsheet with the `sheet` argument in `read_excel()`.
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The default, which we've been relying on up until now, is the first sheet.
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```{r}
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read_excel("data/penguins.xlsx", sheet = "Torgersen Island")
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@@ -280,45 +278,19 @@ deaths
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```
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The top three rows and the bottom four rows are not part of the data frame.
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We could skip the top three rows with `skip`.
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Note that we set `skip = 4` since the fourth row contains column names, not the data.
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```{r}
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read_excel(deaths_path, skip = 4)
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```
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We could also set `n_max` to omit the extraneous rows at the bottom.
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```{r}
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read_excel(deaths_path, skip = 4, n_max = 10)
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```
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Another approach is using cell ranges.
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It's possible to eliminate these extraneous rows using the `skip` and `n_max` arguments, but we recommend using cell ranges.
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In Excel, the top left cell is `A1`.
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As you move across columns to the right, the cell label moves down the alphabet, i.e.
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`B1`, `C1`, etc.
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And as you move down a column, the number in the cell label increases, i.e.
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`A2`, `A3`, etc.
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The data we want to read in starts in cell `A5` and ends in cell `F15`.
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In spreadsheet notation, this is `A5:F15`.
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Here the data we want to read in starts in cell `A5` and ends in cell `F15`.
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In spreadsheet notation, this is `A5:F15`, which we supply to the `range` argument:
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- Supply this information to the `range` argument:
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```{r}
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#| results: "hide"
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read_excel(deaths_path, range = "A5:F15")
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```
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- Specify rows:
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```{r}
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#| results: "hide"
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read_excel(deaths_path, range = cell_rows(c(5, 15)))
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```
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```{r}
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read_excel(deaths_path, range = "A5:F15")
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```
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### Data types
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@@ -326,17 +298,17 @@ In CSV files, all values are strings.
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This is not particularly true to the data, but it is simple: everything is a string.
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The underlying data in Excel spreadsheets is more complex.
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A cell can be one of five things:
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A cell can be one of four things:
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- A boolean, like TRUE, FALSE, or NA
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- A boolean, like `TRUE`, `FALSE`, or `NA`.
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- A number, like "10" or "10.5"
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- A number, like "10" or "10.5".
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- A datetime, which can also include time like "11/1/21" or "11/1/21 3:00 PM"
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- A datetime, which can also include time like "11/1/21" or "11/1/21 3:00 PM".
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- A text string, like "ten"
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- A text string, like "ten".
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When working with spreadsheet data, it's important to keep in mind that how the underlying data is stored can be very different than what you see in the cell.
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When working with spreadsheet data, it's important to keep in mind that the underlying data can be very different than what you see in the cell.
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For example, Excel has no notion of an integer.
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All numbers are stored as floating points, but you can choose to display the data with a customizable number of decimal points.
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Similarly, dates are actually stored as numbers, specifically the number of seconds since January 1, 1970.
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@@ -353,8 +325,8 @@ In these cases you can set the type for this column to `"list"`, which will load
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### Data not in cell values
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**tidyxl** is useful for importing non-tabular data from Excel files into R.
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For example, tidyxl doesn't coerce a pivot table into a data frame.
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Sometimes data is stored in more exotic ways, like the color of the cell background, or whether or not the text is bold.
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In such cases, you might find the [tidyxl package](https://nacnudus.github.io/tidyxl/) useful.
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See <https://nacnudus.github.io/spreadsheet-munging-strategies/> for more on strategies for working with non-tabular data from Excel.
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### Writing to Excel {#sec-writing-to-excel}
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@@ -371,12 +343,11 @@ bake_sale <- tibble(
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bake_sale
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```
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You can write data back to disk as an Excel file using the `write_xlsx()` from the **writexl** package.
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You can write data back to disk as an Excel file using the `write_xlsx()` from the [writexl package](https://docs.ropensci.org/writexl/):
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```{r}
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#| eval: false
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library(writexl)
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write_xlsx(bake_sale, path = "data/bake-sale.xlsx")
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```
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@@ -406,7 +377,7 @@ read_excel("data/bake-sale.xlsx")
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### Formatted output
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The writexl package is a light-weight solution for writing a simple Excel spreadsheet, but if you're interested in additional features like writing to sheets within a spreadsheet and styling, you will want to use the **openxlsx** package.
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The writexl package is a light-weight solution for writing a simple Excel spreadsheet, but if you're interested in additional features like writing to sheets within a spreadsheet and styling, you will want to use the [openxlsx package](https://ycphs.github.io/openxlsx).
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We won't go into the details of using this package here, but we recommend reading <https://ycphs.github.io/openxlsx/articles/Formatting.html> for an extensive discussion on further formatting functionality for data written from R to Excel with openxlsx.
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Note that this package is not part of the tidyverse so the functions and workflows may feel unfamiliar.
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@@ -578,8 +549,7 @@ This is the same dataset as in @fig-students-excel, except it's stored in a Goog
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knitr::include_graphics("screenshots/import-googlesheets-students.png")
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```
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The first argument to `read_sheet()` is the URL of the file to read.
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You can also access this file via <https://pos.it/r4ds-students>, however note that at the time of writing this book you can't read a sheet directly from a short link.
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The first argument to `read_sheet()` is the URL of the file to read, and it returns a tibble:
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```{r}
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#| include: false
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@@ -590,11 +560,6 @@ gs4_deauth()
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```{r}
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students_url <- "https://docs.google.com/spreadsheets/d/1V1nPp1tzOuutXFLb3G9Eyxi3qxeEhnOXUzL5_BcCQ0w"
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students <- read_sheet(students_url)
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```
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`read_sheet()` will read the file in as a tibble.
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```{r}
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students
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```
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@@ -606,12 +571,8 @@ students <- read_sheet(
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col_names = c("student_id", "full_name", "favourite_food", "meal_plan", "age"),
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skip = 1,
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na = c("", "N/A"),
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col_types = c("dcccc")
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) |>
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mutate(
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age = if_else(age == "five", "5", age),
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age = parse_number(age)
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)
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col_types = "dcccc"
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)
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students
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```
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@@ -620,7 +581,7 @@ Note that we defined column types a bit differently here, using short codes.
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For example, "dcccc" stands for "double, character, character, character, character".
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It's also possible to read individual sheets from Google Sheets as well.
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Let's read the penguins Google Sheet at <https://pos.it/r4ds-penguins>, and specifically the "Torgersen Island" sheet in it.
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Let's read the "Torgersen Island" sheet from the [penguins Google Sheet](https://pos.it/r4ds-penguins):
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```{r}
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penguins_url <- "https://docs.google.com/spreadsheets/d/1aFu8lnD_g0yjF5O-K6SFgSEWiHPpgvFCF0NY9D6LXnY"
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@@ -644,7 +605,8 @@ deaths
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### Write sheets
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You can write from R to Google Sheets with `write_sheet()`:
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You can write from R to Google Sheets with `write_sheet()`.
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The first argument is the data frame to write, and the second argument is the name (or other identifier) of the Google Sheet to write to:
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```{r}
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#| eval: false
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Reference in New Issue
Block a user