NV Energy Bill 13 Month Analysis

EnergyBill.R

library(dplyr)

# Read in the data from a comma seperated value file.
data <- read.csv("BillHistory.csv", stringsAsFactors=TRUE,skip=6)

# transform it into a data.table
data <- tbl_df(data)

# print the data table
data

# Source: local data frame [13 x 5]
#
#     Bill.Date Balance.Forward Electric.Usage..kWh. Current..Charges
# 1   5/13/2015           $0.00                  643           $97.28
# 2   4/14/2015           $0.00                  820          $120.29
# 3   3/13/2015           $0.00                  812          $119.22
# 4   2/11/2015           $0.00                  741          $109.96
# 5   1/13/2015           $0.00                  935          $133.65
# 6  12/11/2014           $0.00                  833          $119.73
# 7  11/08/2014           $0.00                  769          $111.32
# 8  10/11/2014           $0.00                 1316          $182.73
# 9   9/13/2014           $0.00                 1913          $260.78
# 10  8/14/2014           $0.00                 2383          $322.27
# 11  7/15/2014           $0.00                 2060          $281.56
# 12  6/14/2014           $0.00                 1660          $229.73
# 13  5/14/2014           $0.00                  779          $113.37
# Variables not shown: Total.Amount (fctr)

# Keep only three columns from the table, dropping the rest
data <- select(data,Bill.Date, Electric.Usage..kWh.,Current..Charges)

# Rename the column names to something a little less strange (removing
# the double dots .. )
colnames(data) <- c("Bill.Date","Electric.Usage.kWh","Current.Charges")

# Change the Bill.Date into a date data type
data$Bill.Date <- as.Date(data$Bill.Date,"%m/%d/%Y")

# Remove the dollar-signs from the Current..Charges field and
# then turn them in to a numerical data type since we 
# need to do math on them
data$Current.Charges <- gsub("\\$","",data$Current.Charges)
data$Current.Charges <- as.numeric(data$Current.Charges)

# print the data table again (notice is has only three columns)
data
 
# Source: local data frame [13 x 3]
#
#    Bill.Date Electric.Usage.kWh Current.Charges
# 1  2015-05-13                643           97.28
# 2  2015-04-14                820          120.29
# 3  2015-03-13                812          119.22
# 4  2015-02-11                741          109.96
# 5  2015-01-13                935          133.65
# 6  2014-12-11                833          119.73
# 7  2014-11-08                769          111.32
# 8  2014-10-11               1316          182.73
# 9  2014-09-13               1913          260.78
# 10 2014-08-14               2383          322.27
# 11 2014-07-15               2060          281.56
# 12 2014-06-14               1660          229.73
# 13 2014-05-14                779          113.37

# Show some summary statistics from about the data
summary(data)

#      Bill.Date Electric.Usage.kWh Current.Charges
# 1/13/2015 :1   Min.   : 643       Min.   : 97.28
# 10/11/2014:1   1st Qu.: 779       1st Qu.:113.37
# 11/08/2014:1   Median : 833       Median :120.29
# 12/11/2014:1   Mean   :1205       Mean   :169.38
# 2/11/2015 :1   3rd Qu.:1660       3rd Qu.:229.73
# 3/13/2015 :1   Max.   :2383       Max.   :322.27
# (Other)   :7

# Plot the amount of energy used by date
plot(data$Bill.Date, data$Electric.Usage.kWh)