Time Series Data in R - r

Question

I have a basic understanding of R that mostly entails the ability to run regressions and summary statistics, so if there appear any gaps in my knowledge I would appreciate being pointed in the correct direction.
I have time series data in CSV that is formatted as follows:
Facility ID, Utility Type, Account No, Unit Name, Date 1, Date 2, Date 3, Date 4
There will be multiple rows for a specific account number referencing a unique utility type and facility (i.e., one row entry for Unit Name = L, one row entry for Unit Name = USD). The account number values for a particular unit at every date are entered in each "date" column. I would like to be able to write a script that enables me to re-export the data where each Date column doesn't contain entries for multiple units. I would also like to then designate to R that the Date columns represent monthly time series data points, and from there do various time series analysis.
I appreciate your help in telling me how to clean up this data.
As requested, sample data:
Facility ID, Facility Name, State, Utility Type, Supplier, Account No., Unit Name, 7/1/14, 8/1/14
4015, Palm Court Apts, CA, Chilled Water, PG&E, 87993, USD, 42333, 41775
4015, Palm Court Apts, CA, Chilled Water, PG&E, 87993, ton-hr, 244278, 238035
4044, 18 Sawtelle, CA, Natural Gas, Chevron, 17965, USD, 4860, 5890
4044, 18 Sawtelle, CA, Natural Gas, Chevron, 17965, M^3, 7639, 8895
Example output:
Facility ID, Facility Name, State, Utility Type, Supplier, Account No., Quantity Consumed, Unit of Measure, Utility Bill, Currency, Date
4015, Palm Court Apts, CA, Chilled Water, PG&E, 87993, 244278, ton-hr, 42333, USD, 7/1/14
4015, Palm Court Apts, CA, Chilled Water, PG&E, 87993, 238035, ton-hr, 41775, USD, 8/1/14
4044, 18 Sawtelle, CA, Natural Gas, Chevron, 17965, 7639, M^3, 4860, USD, 7/1/14
4044, 18 Sawtelle, CA, Natural Gas, Chevron, 17965, 8895, M^3, 5890, USD, 8/1/14

Answer 1

library(reshape2)
d = read.csv("data.csv")
d.molten = melt(d,
id.vars=c("Facility.ID", "Facility.Name", "State", "Utility.Type", "Supplier", "Account.No.", "Unit.Name"),
variable.name = "Date"
)
The melt function breaks up a "wide" format (with an undefined numbers of columns) to a "long" format, where each row is an observation. This is actually the preferred format for most things you'd do in R, at least when using packages from the "Hadleyverse". Especially for time series.
But we're not done yet. Now you have the following structure:
Facility.ID Facility.Name … Date value
4015 Palm Court Apts X7.1.14 42333
We have to fix the dates that are currently just "strings". They had an "X" prepended since column names cannot start with a number, and cannot contain spaces.
d.molten$Date=as.Date(d.molten$Date, "X%m.%d.%y")
Now your dates will look correct, and you have one row for each observation:
Facility.ID Facility.Name … Date value
4015 Palm Court Apts 2014-07-01 42333
And now we can easily plot time series:
library(ggplot2)
ggplot(d.molten,
aes(x = Date, y = value, color = Facility.Name)) +
geom_point()