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Introduction
Open Data Blend Datasets
Datasets
Dataset Versions
Dataset UI
Dataset API
Frictionless Data Compatibility
Modelling Conventions
Loading Data Files in Excel
Loading Data Files in Power BI Desktop
Loading Data Files in Tableau Desktop
Loading Data Files in Python
Loading Data Files in R
Loading Data Files in Other Tools
Open Data Blend Analytics
Analytics
Analytics Queries
Analytics Users
Connecting from Excel
Connecting from Power BI Desktop
Connecting from Tableau Desktop
Connecting from Other Tools
Open Data Blend Insights
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Report Drill Throughs
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Subscription Management
Subscription Portal
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Loading Data Files in R

Supported Formats

R can load the following data files formats:
  • Compressed CSV (.csv.gz)
  • Parquet (.parquet)
​

Download the Data Files

Download and save the data files to a suitable location. In the examples that follow, the data has been saved to C:\data.
Although you could load data files directly from the data file URLs, this is not recommended because you may quickly hit usage limits or incur additional costs. We always recommend saving the files locally or to cloud storage first using the Open Data Blend Dataset UI, Open Data Blend Dataset API, or Open Data Blend for Python.
​

Loading Compressed (Gzip) CSV Data Files

You can use the below steps as a guide on how you can load compressed (Gzip) data files in R.
Reading the entire compressed (Gzip) CSV data file directly into a data frame.
df_date <- read.csv("C:\\data\\date\\date.csv.gz")

​

Loading Parquet Data Files

You can use the below steps as a guide on how you can load Parquet data files in R.
Install the arrow package.
install.packages("arrow")
​
Import the arrow library.
library(arrow)
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Read the Parquet data file into a data frame.
df_date <- read_parquet("C:\\data\\date\\date.parquet")
df_anonymised_mot_test_result_info <- read_parquet("C:\\data\\anonymised_mot_test_result_info\\anonymised_mot_test_result_info.parquet")
​
Read a subset of the columns from the Parquet data file into a data frame.
df_mot_results_2017 <- read_parquet("C:\\data\\anonymised_mot_test_result\\anonymised_mot_test_result_2017.parquet", col_select = c("drv_anonymised_mot_test_date_key", "drv_anonymised_mot_test_result_info_key"))
When working with larger data files, it is a good practice to only read the required columns because it will reduce the read times, memory footprint, and processing times.
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Using R for Data Analysis

Guidance on how to analyse data in R is beyond the scope of this documentation.
You may find the following helpful:
Open Data Blend Datasets - Previous
Loading Data Files in Python
Next - Open Data Blend Datasets
Loading Data Files in Other Tools
Last modified 3mo ago
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Outline
Supported Formats
Download the Data Files
Loading Compressed (Gzip) CSV Data Files
Loading Parquet Data Files
Using R for Data Analysis