Data Wrangling and Data Tiding - Replicate Measurements - Computer Science Assignment Help

Assignment Task :

Objective:
The purpose of this tutorial project is to tidy and wrangle data with methods from the “tidyr” and “dplyr” libraries so that the data can be used by clients, in an easy-to-use form. This project will focus on the following tasks:

•  Removing missing data, out-of-bounds values, and unexpected values
•  Transformation to calculate new variables
•  Concatenate information regarding replicate measurements
• Sub-sampling and stratification to make balanced distributions
•  Re-scaling and other transformations
•  Saving data sets in easy to read formats

Tasks
The client requires the data to be cleaned, tidied, transformed, appropriately sub-sampled, then saved in a form that they can use to input into a third-party NIRS training/calibration software (a CSV file). Also, the client wants all the data to be saved in a single CSV file for future reference. So, in total, there will be five outputted files, a file for each of the four laboratory measurements, which will be used to train (calibrate) the NIRS instrument, and a single file containing all the cleaned and tidied lab data, to be archived.

Data
Sugar factories measure sugar cane juice at the start of the factory process to determine factory settings and to determine the economic value of the supplied sugar cane. To enable real-time factory optimisation, a real-time measurement technology called near-infrared spectroscopy (NIRS) is used. NIRS analyses the light spectrum that the sugar cane absorbs – the absorbance spectrum is correlated to the chemical composition of the sugar cane. However, the NIRS instruments need to be calibrated to measure specific components of the sugar cane. To do this, traditional laboratory measurements are collected and used to train (calibrate) the NIRS instruments.
The first step in training the NIRS instruments is to prepare laboratory measurements. Because laboratories use multiple assays (different measurement types), measurement information is typically stored in different files or databases. This information needs to be collated, cleaned and appropriately sub-sampled to make training datasets for NIRS instruments.
The laboratory measures are:

•  Pol: the amount of optical rotation of light due to different sugars in sugar cane (there are three main types of sugar in sugar cane – sucrose, fructose and dextrose)
•  Brix: the total amount of dissolved solids in the sugar cane juice (sugar is a dissolved solid!)
•  Fibre: multiple weight measurements are taken to estimate the percentage of fibre (or cellulose) in sugar cane. Fibre is important as it is a key factor in optimising factory settings, like how much energy to use to squeeze out the juice from the sugar cane.
•  Ash: multiple weight measurements are taken to estimate the percentage ash content. Ash is the amount of dirt that enters with the sugar cane (usually picked up by the sugar cane harvester). It is important to know the ash content as flocculants are added to the squeezed juice to remove it during the refining process
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