Reading Tabular Data

This repository contains the R implementation of reading and analyzing tabular/rectangular data from this database.

Overview: Read solar and climate-related data from a variety of locations in California. The data is related to solar performance for buildings and simulation models for understanding this solar performance.

For this project, we will focus on 5 zip files:

• USA_CA_Point.Reyes.Lighthouse.724959_TMYx.2007-2021
• USA_CA_Fairfield-San.Francisco.Bay.Reserve.998011_TMYx.2007-2021
• USA_CA_Napa.County.AP.724955_TMYx.2007-2021
• USA_CA_Marin.County.AP-Gnoss.Field.720406_TMYx.2007-2021
• USA_CA_UC-Davis-University.AP.720576_TMYx.2007-2021

Task

For the 5 zip files, read data from

• .wea file
• .pvsyst file
• .stat file

For the .stat file, read the tables for

• Monthly Statistics for Dry Bulb temperatures
• Monthly Statistics for Dew Point temperatures
• Average Hourly Statistics for Dry Bulb temperatures
• Average Hourly Statistics for Dew Point temperatures
• Average Hourly Relative Humidity
• Monthly Wind Direction (Interval 11.25 deg from displayed deg)
• Average Hourly Statistics for Direct Normal Solar Radiation
• Monthly Statistics for Wind Speed
• Average Hourly Statistics for Wind Speed

For the monthly data sets:

• Verify that the max hour and min hour are correct
• Convert the data so that the rows corresponding to measured variables and dates (e.g. maximum, minimum, daily avg, ...) are columns and the columns corresponding to months are rows
• Convert the Day:Hour values to a time (POSIXct).
• Convert the measurements for other variables to numbers

For the hourly data sets:

• Convert each to a data.frame with 3 columns:
1. Convert the month-hour pairs to rows with the single variable as a column
2. One column for the month
3. One column for the hour

Finally, combine the average hourly tables into a single data frame with a column for each variable. For each variable, plot the values against the hour for each month.