Load the data
Download rawcashflows.csv and save the file in the data directory.
Enter the following code in the console to open the project
> load.project()
ProjectTemplate will print out a number of messages as the project opens. As part of this process, all data in the data directory is automatically loaded into memory. You can check the data has been loaded by running
> head(rawcashflows)
# A tibble: 6 x 7
X line1 line2 line3 line4 line5 line6
<int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 5419. 15614. 16292. 12964. 19732. 4725.
2 2 5579. 15929. 16591. 13897. 20291. 3520.
3 3 5450. 15523. 16307. 14247. 20555. 3349.
4 4 4916. 15189. 16794. 14597. 20652. 3221.
5 5 3708. 14143. 15792. 13759. 20371. 2728.
6 6 3516. 13726. 15792. 13702. 20802. 2606.
Data processing
Data pre-processing scripts are saved in the munge folder. By default, ProjectTemplate will run these scripts when a project is loaded.
The data represents monthly cashflows for six business lines, covering a six year period starting from January 2019. For the purposes of this analysis, we will aggregate the cashflows for each business line and store them as a time series object.
Replace the contents of 01-A.R in the munge folder with the following code and run it by clicking Source at the top right of the script window.
# Extract cashflows for analysis
cashflows_to_use = rawcashflows[-1]
# Sum the rows of the cashflows_to_use table and store them in cashflows
cashflows = apply(cashflows_to_use, 1, sum)
# Convert cashflows to a time series object (monthly frequency starting 01/2019)
cashflows = ts(cashflows, start=c(2019,1), frequency=12)
Review the processed cashflows by running
> cashflows
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2019 74745.73 75806.47 75431.92 75369.54 70499.94 70145.14 68768.38 69180.25 67806.93 67137.06 65975.45 67501.55
2020 69021.06 71125.27 71904.86 73737.79 75219.03 77089.06 78891.84 81858.21 84104.65 85313.66 86211.66 85504.44
2021 88967.93 90945.45 93367.84 96273.92 97944.35 100950.73 103267.23 104752.50 104971.76 106221.24 108087.61 110795.66
2022 112267.34 113324.90 115041.22 116655.58 118671.40 118646.21 117415.46 115567.68 115938.50 114935.11 114686.74 115052.86
2023 116854.92 118285.90 120318.21 120647.65 124066.59 127266.34 131204.09 134259.75 135086.55 137808.61 141751.25 143700.78
2024 146171.69 149603.43 150657.80 154338.23 157246.42 160183.02 162482.05 163495.04 164494.54 166231.03 166576.49 169832.96
For larger projects with substantial amounts of pre-processing, you may wish to divide your pre-processing across several scripts.
When you have multiple pre-processing scripts in your munge directory, ProjectTemplate will execute these in a predictable order, based on file name. For example the following files would be executed in this order:
01-A.r
01-B.r
02-A.r
02-AA.r
02-B.r