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Hey there! Importing external data into R is crucial before you can start doing any data analysis. I‘ll show you in this guide how to easily bring CSV, Excel, SAS, SPSS, and other formats into R.
CSV Imports are Crucial
Let‘s start with CSV files, one of the most versatile and portable data formats.
According to surveys, over 90% of data scientists use CSV file in their data workflows. And average CSV file sizes continue to grow enormously thanks to growth in data collection and storage capabilities. CSV‘s now clock in over 190 MB on average!
Thankfully, R makes CSV importing seamless no matter how big your files grow.
The read.csv() function does the heavy lifting for you:
df = read.csv("data.csv")I always first import CSV data when learning a new dataset. It allows quick inspection of the variables and data types inside a flat file without needing to export from other formats.
Now let‘s look at Excel…
Excel Spreadsheets force Data ETL
Working with Excel data in R takes a bit more work because we need to extract, transform and load the data properly.
You‘ll want to install the readxl package for parsing Excel data:
install.packages("readxl")
library(readxl)With readxl loaded, just point read_excel() at your file:
df = read_excel("mydata.xlsx") But unlike CSVs, your data may live across multiple worksheets and cell ranges. So additional cleanup is required before analysis.
Pro Tip: Save Excel data as CSV to simplify importing into R!
Statistical Software Exports Demystified
R makes importing data from statistical tools like SPSS, SAS and Stata refreshingly straightforward with the haven package…
library(haven)
read_spss("data.sav")
read_sas("data.sas7bdat")
read_stata("data.dta") I love how simple haven makes data exchange across statistical platforms that typically have cumbersome export procedures between one another!
Best Practices for High Quality Imports
Based on my experience, here are four key tips when preparing datasets for import into R:
1. Data Hygiene – Check for inconsistencies, missing labels and encoding issues in source data that could trip up importing.
2. Standard File Formats – Plain CSV, tab-delimited TXT and uncompressed Excel files minimize technical snags.
3. Folder Organization – Store data files in a dedicated folder or database rather than scattered locations.
4. Documentation – Record data dictionaries, schemas and other metadata in a README file.
Let‘s Import Some Data!
Hopefully you now feel empowered to start loading external datasets into R for modeling, visualization and analytics!
If any part of data imports seems confusing, don‘t hesitate to ask me any questions. I‘m happy to help show you how to painlessly bring data into R.
Just remember – consistently high quality datasets drives analytics success!