Introduction Statistical Data Simulation

Using Statistical Distributions to Generate Data that Mimics the Real World Scenario

0.0.0.1 Data Simulation

# load packages
library(here)
library(tidyverse)

Simulation of data used by the Government in MTI to place university students into 5 bands for award of Government scholarship

Disclaimer!

The data is simulated, and therefore substantially differs with the actual scenario! The data is for learning purposes only, and the statistical estimates reported MUST NOT be taken as true reflection of the real-word situation.

# table display setup
#| label: tbl-simulated_data .striped .hover .primary .bordered
#| tbl-cap: "Simulated data"
#| tbl-cap-location: bottom 

# for reproducibility
set.seed(1) 

# generate data
simulated_data <- data.frame(Bands = rbinom(n = 1000, 
                                        size = 5, 
                                        prob = 0.5),
                         Gross_Family_Income = rnbinom(n = 1000, 
                                                         size = 5, 
                                                         mu = 20000),
                         Geographical_Location = as.factor(ceiling(runif(n = 1000, 
                                                                           min = 1, 
                                                                           max = 47))),
                         Poverty_Probability_Index = abs(rnorm(n = 1000, 
                                                                 mean = 0.3, 
                                                                 sd = 0.2)),
                         Orphans = as.factor(rbinom(n = 1000,
                                                    size = 1,
                                                    prob = 0.4)),
                         Disability = as.factor(rbinom(n = 1000,
                                                       size = 1,
                                                       prob = 0.022)),
                         Number_of_Dependents = rpois(n = 1000, 
                                                        lambda = 4),
                         Program_Costs_KES = abs(rnorm(n = 1000,
                                                     mean = 500000,
                                                     sd = 50000)),
                         Gender = as.factor(rbinom(n = 1000,
                                                   size = 3,
                                                   prob = 0.017)))

# view data (printed on your browser)
knitr::kable(head(x = simulated_data, n = 5))

Bands	Gross_Family_Income	Geographical_Location	Poverty_Probability_Index	Orphans	Disability	Number_of_Dependents	Program_Costs_KES	Gender
2	18621	36	0.1751991	1	0	4	510735.4	0
2	6235	39	0.2081443	1	0	5	466547.2	0
3	33812	33	0.5994036	0	0	3	458893.0	0
4	30154	22	0.3511708	0	0	3	554462.2	0
2	27145	14	0.1675043	0	0	5	464203.2	1

# write data to disc
write.csv(x = simulated_data, row.names = FALSE, file = here::here("./Data/simulated_data.csv"))
saveRDS(object = simulated_data, file = here::here("./Data/simulated_data.rds"))