You can use View(data) to inspect the imported dataset and names(data) to see the column names. # Import the data from the Excel file and call it data In R, you can use the readxl library to import Excel data. ![]() It is a famous dataset that has been utilized severally, including by authors such as Sarkis and Talluri (2004), Tyteca (1998), and Fare et al. was from the Energy Information Administration of the United States Department of Energy (EIA). The original source of this environmental data for 48 electric utility plants in the U.S. To get started, you can access the dataset from my Github repository, and download the data, file named "e-data.xlsx." Let's dive into the world of advanced DEA! Step 1: Accessing the Dataset I'll guide you through the process of obtaining the dataset from my GitHub repository, implementing DEA in R using my codes, and comparing the results with those presented in the paper. In this blog, we will explore advanced applications of DEA using the renowned dataset from Sarkis and Talluri (2004). Data Envelopment Analysis (DEA) is a versatile mathematical technique (specifically, linear programming) used for relative performance evaluation and benchmarking in various fields.
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