Data Analysis in RStudio

Setup

1. Install R. Digital Ocean has a nice guide on this (and many other things Linux-related, they are one of my preferred documentation sources)
2. Install RStudio. The RStudio Downloads Page has a Debian package you can download. Install with sudo dpkg -i <pkgname>.deb
3. Add data.table with Tools->Install Packages…, enter data.table
4. Install texlive full with sudo apt install texlive-full to enable PDF output

Datasets

Load profiles come from http://wzy.ece.iastate.edu/Testsystem.html

Analysis

Load Data

feeder_a <- read.csv("feeder_a_data.csv")
names(feeder_a)

 [1] "Time"          "Year"          "Month"         "Day.of.Week"   "Hour"          "Elapsed.Days"  "Elapsed.Hours"
 [8] "Total.Power"   "Bus.1001"      "Bus.1002"      "Bus.1003"      "Bus.1004"      "Bus.1005"      "Bus.1006"     
[15] "Bus.1007"      "Bus.1008"      "Bus.1009"      "Bus.1010"      "Bus.1011"      "Bus.1012"      "Bus.1013"     
[22] "Bus.1014"      "Bus.1015"      "Bus.1016"      "Bus.1017"     

There’s a lot of columns here from the different smart meters. Let’s just take the total power

fa <- feeder_a[,c("Month","Day.of.Week","Hour","Elapsed.Days","Total.Power")]

Take a quick look at the loaded data. You can also use the “Environment” tab, but this won’t show up in the notebook

head(fa)

Visualize Data

hist(fa$Total.Power)

How to do arithmetic with data frame columns

fa$b12 <- feeder_a$Bus.1001 + feeder_a$Bus.1002

mean(fa$Total.Power)

[1] 105.8696

There’s a convenient command to print summary stastics for each column

summary(fa)

     Month         Day.of.Week         Hour        Elapsed.Days     Total.Power          b12   
 Min.   : 1.000   Min.   :1.000   Min.   : 0.00   Min.   :  0.00   Min.   : 42.68   Min.   :0  
 1st Qu.: 4.000   1st Qu.:2.000   1st Qu.: 5.75   1st Qu.: 91.24   1st Qu.: 68.07   1st Qu.:0  
 Median : 7.000   Median :4.000   Median :11.50   Median :182.48   Median : 94.11   Median :0  
 Mean   : 6.526   Mean   :3.992   Mean   :11.50   Mean   :182.48   Mean   :105.87   Mean   :0  
 3rd Qu.:10.000   3rd Qu.:6.000   3rd Qu.:17.25   3rd Qu.:273.72   3rd Qu.:141.18   3rd Qu.:0  
 Max.   :12.000   Max.   :7.000   Max.   :23.00   Max.   :364.96   Max.   :284.09   Max.   :0  
                                                                                    NA's   :1  

Plot the power over time. Yikes! There’s a lot of data here? Just how long is it?

plot(fa$Total.Power,type="l")

A whole year! That’s too much!

max(fa$Elapsed.Days)

[1] 364.96

We can plot just the first week by taking a subset of rows

plot(fa[fa$Elapsed.Days<=7,"Total.Power"],type="l")

names(fa)

[1] "Month"        "Day.of.Week"  "Hour"         "Elapsed.Days" "Total.Power"  "b12"         

How to Replace Excel Pivot Tables with R Data Tables

require(data.table)

Loading required package: data.table
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
data.table 1.14.0 using 1 threads (see ?getDTthreads).  Latest news: r-datatable.com
**********
This installation of data.table has not detected OpenMP support. It should still work but in single-threaded mode.
This is a Mac. Please read https://mac.r-project.org/openmp/. Please engage with Apple and ask them for support. Check r-datatable.com for updates, and our Mac instructions here: https://github.com/Rdatatable/data.table/wiki/Installation. After several years of many reports of installation problems on Mac, it's time to gingerly point out that there have been no similar problems on Windows or Linux.
**********

ta <- as.data.table(fa)

Let’s look at how much power is used per day of week (1 = Sunday). People are a little lazy on Mondays

ta[,mean(Total.Power),by=Day.of.Week]

Now let’s look at power vs month. It looks like air conditioning loads cause a peak in July

ta[,mean(Total.Power),by=Month]

Finally, let’s look at power vs time of day. The peak is at noon, though in some regions/seasons it is common two have two peaks in mornings/evenings

ta[,mean(Total.Power),by=Hour]