R uses a system of libraries to add additional statistical functionality. ProjectTemplate can automatically load libraries when a project is opened.
In addition to using libraries, it is often helpful to write helper functions to streamline analysis. This exercise demonstrates how to do this with ProjectTemplate.
Loading libraries
We will use the forecast library in our analysis.
To add this library to the project, go to config/global.dcf, set load_libraries to TRUE (so that the libraries are loaded when a project is opened) and add it to the list of libraries.
Following this, global.dcf should look like this
version: 0.8.2
data_loading: TRUE
data_loading_header: TRUE
data_ignore:
cache_loading: TRUE
recursive_loading: FALSE
munging: TRUE
logging: FALSE
logging_level: INFO
load_libraries: TRUE
libraries: reshape2, plyr, tidyverse, stringr, lubridate, forecast
as_factors: TRUE
data_tables: FALSE
attach_internal_libraries: FALSE
cache_loaded_data: TRUE
sticky_variables: NONE
If you are working with a large dataset, it can be helpful to switch off data_loading and munging (data pre-processing) once these steps have been completed. If caching is set to true, the data will be loaded from the cache when the project is opened which will be quicker.
Helper functions
We will also add a helper function that calculates the present value of a vector of cashflows.
Helper functions are saved in lib/helpers.R. These functions are loaded when a project is opened.
Replace the contents of helpers.R in the lib folder with the code below and load the function by clicking Source.
# discount(cashflows, disc, freq)
# Helper function to calculate the present value of a vector of cashflows.
# cashflows = input vector of cashflows
# disc = discount rate
# freq = payment frequency p.a. of cashflows e.g. 1 is annual, 12 is monthly
discount = function(cashflows, disc, freq) {
t = 1:length(cashflows) - 0.5
D = (1 + disc)^-(t/freq)
pv = as.numeric(cashflows %*% D)
}