loops.R

##################################
##                              ##
##  Imperative Programming      ##
##      for and while loops     ##
##                              ##
##################################

rm(list = ls())

# Case 1) purest form of a for loop
for (i in 1 : 5){
  print(i)
}

# Case 2) 
for (i in seq(50, 58)){
  print(i)
}

# Case 3) 
for (i in c(10,9,-10,8)){
  print(i)
}

# Play around with lists
for (i in list(2, 'a', TRUE, sqrt(2))){
  print(i)
}

# Create a loop which gives the cumulative sum:
v <- c(10, 6, 5, 32, 45, 23)
cs_v <- v
for (i in 2 : length(v)){
  cs_v[ i ] <- cs_v[ i - 1 ] + cs_v[ i ]
}
v
cs_v
cumsum(v)

# Also good to know
seq_along(v)

cs_v2 <- 0
for (i in seq_along(v)){
  cs_v2 <- cs_v2 + v[ i ]
}
cs_v2

# Task check if all the elements in cs_v is the same as
# the cumsum(v) function and if it is true
#  print out Good job! otherwise: there is a mistake!

if (all(cs_v == cumsum(v))){
  print('Good job!')
} else {
  print('There is a mistake!')
}

## Measure CPU time
if (!require(tictoc)){
  install.packages('tictoc')
  library(tictoc)
}


iter_num <- 10000

# Sloppy way to do loops:
tic('Sloppy way')
q <- c()
for (i in 1 : iter_num){
  q <- c(q, i)
}
toc()

# Proper way
tic('Good way')
r <- double(length = iter_num)
for (i in 1 : iter_num){
  r[ i ] <- i
}
toc()

##
# While loop
x <- 0
while (x < 10) {
  x <- x + 1
  print(x)
}
x

# Instead use a for loop with break
max_iter <- 10000
x <- 0
flag <- FALSE
for (i in 1 : max_iter){
  if (x < 10){
    x <- x + 1 
  } else{
    flag <- TRUE
    break
  }
}
x
if (flag) {
  print('Successful iteration!')
}else{
  print('Did not satisfied stopping criterion!')
}

####
# Exercise sp500
library(tidyverse)
# Load data
sp500 <- read_csv('https://osf.io/h64z2/download', na = c('', '#N/A'))
# Filter out missing and create a year variable
sp500 <- sp500 %>% filter(!is.na(VALUE)) %>% 
  mutate(year = format(DATE, '%Y')) 

# Get unique years and create tibble
years <- unique(sp500$year)
return_yearly <- tibble(years = years, return = NA)

# Initialize
aux <- sp500$VALUE[ sp500$year == years[ 1 ] ]
lyp <- aux[ length(aux) ]
rm(aux)
# start from 2007
for (i in 2 : length(years)){
  # get the values for specific year
  value_year_i <- sp500$VALUE[ sp500$year == years[ i ] ]
  # last day's price
  ldp <- value_year_i[ length(value_year_i) ]
  # calculate the return
  return_yearly$return[ i ] <- (ldp - lyp) / lyp * 100
  # save this years last value as last year value
  lyp <- ldp
}

# Check results
return_yearly