# welcome to week 10, our last week together!

# intro to writing functions

my_sum <- function(a, b){
  the_sum <- a + b
  return(the_sum)
}

the_sum_2 <- 10 + 4 # this object will appear in the global environment

my_sum(a = 5, b = 10) # the object my_sum is self-contained within this function

a <- 20
b <- 2

my_sum() # my_sum can't find a and b from the global environment, you need to give them as arguments

my_sum(10, 35)

my_sum2 <- function(a = 2, b = 5){
  the_sum <- a + b
  return(the_sum)
}

my_sum2()
my_sum2(12)
my_sum2(b = 25)
my_sum2(4, 1)

# functions to convert temperatures

F_to_K <- function(temp){
  K <- ((temp - 32) * (5/9)) + 273.15
  return(K)
}

# source()-ing functions

source("code/functions.R")

F_to_K(32)
F_to_K(212)

library(tidyverse)
d <- read_csv("data/gapminder.csv")

d

# making a function that returns a line plot of pop over time for a single country

d %>% 
  filter(country == "Afghanistan") %>% 
  ggplot(aes(x = year, y = pop)) +
  geom_line()

plot_pop_growth <- function(data, pcountry){
  plot <- data %>% 
    filter(country == pcountry) %>% 
    ggplot(aes(x = year, y = pop)) +
    geom_line()
  
  return(plot)
}

plot_pop_growth(data = d, pcountry = "Bulgaria")

# intro to iteration


# lots of base functions are already vectorized

x <- 1:10
log(x)

# for loops

for(i in 1:10){
  print(i)
}

# multiple lines of code in one loop

for(i in 1:10){
  print(i)
  print(i^2)
}

# using i as an index to subset
for(i in 1:10){
  print(letters[i])
  print(mtcars$mpg[i])
}

for(cat in 1:10){
  print(letters[cat])
  print(mtcars$mpg[cat])
}

# making an empty results vector and filling it in
results <- rep(NA, nrow(mtcars))

for(i in seq_along(results)){
  results[i] <- mtcars$wt[i] * 1000
}

results

gapminder <- read_csv("data/gapminder.csv")

countries <- unique(gapminder$country)[1:5]

for (i in seq_along(countries)) {
  p <- plot_pop_growth(data = gapminder, pcountry = countries[i]) + ggtitle(countries[i])
  print(p)
  ggsave(filename = paste0("images/gg_example_", countries[i], ".jpg"), plot = p)
}


# conditional statements
# sometimes you want to run some code, but only if a certain condition is met

for(i in 1:10){
  if(i < 5){
    print(paste(i, "is less than 5"))
  } else {
    print(paste(i, "is greater than or equal to 5"))
  }
}

# case_when is a nested conditional

mtcars %>% 
  mutate(car_size = case_when(
    wt > 3.5 | cyl == 8 ~ "big",
    wt > 2.5 ~ "medium",
    TRUE ~ "small"
  ))

if(x > 10){
  
} else if(x < 10){
  
} else if(x = 10){
  
}


rep(5, 8)
rep(NA, 12)

rep(NA, nrow(mtcars))

1:nrow(mtcars)
seq_along(results)

results <- NULL
1:results
seq_along(results)