4 Ways to make Data Frames in R!
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This article is part of a RTips Weekly, a weekly video tutorial that shows you stepbystep how to do common R coding tasks.
Data frames (like Excel tables) are the main way for storing, organizing, and analyzing data in R. Here are 4 ways using the tidyverse: tibble
, as_tibble()
, read_excel()
, and enframe()
/deframe()
.
Here are the links to get set up. 👇
Making Data Frames in R
Data frames are the most important data structure in R. They are just like Excel Tables. They keep your data organized.
We’re going to shed light on 4 SUPER POWERFUL ways to create data frames (from beginner to intermediate):
tibble()
– For making simple data frames from scratchread_excel()
– For importing data from Excel worksheets as data frames.as_tibble()
– For converting lists and matrix objects to data framesenframe()
– A SUPERPOWER. Convert ANYTHING to a data frame 🤯
As you go along, you can use my Ultimate R Cheatsheet for getting R importing & data wrangling down. It consolidates the most important R packages I use every day into one cheatsheet.
Method 1: Using tibble()
Make simple data frames from scratch.
The tidyverse
uses a structure called a “tibble”, which is simply a Data Frame (like an excel table) but with more informative printing than the default data frame.
We use the tibble()
function to create a “tibble” from scratch. Here’s a simple tibble I created and compared to a basic R dataframe. The tibble printing is much more informative.
Method 2: Using read_excel()
Use read_excel() to read excel worksheets.
Data importing is how we get data into R. There are a ton of ways to import data (check out my Ultimate R Cheatsheet for getting R importing down).
If we are working in Excel, we can import the data as a tibble using the readxl
package’s read_excel()
function.
Method 3: Using as_tibble()
For converting from other data structures
The next function, as_tibble()
, helps convert from list or matrix data structures to tibbles. Here we have a pretty complex (nested) list.
Using as_tibble()
, we just made it an organized data frame that’s ready for analysis!
Method 4: Using enframe()
For converting ANYTHING to a data frame.
The last function, enframe()
, is a MORE POWERFUL / FLEXIBLE version of as_tibble()
.
Why do we need enframe()?
When as_tibble()
fails, enframe()
is your Plan B.
You’re becoming a data ninja one Rtip at a time
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