class_5.Rmd

---
title: "in-class coding"
author: "Julia Gallucci"
date: "16/12/2023"
output: html_document
---

From last class
We can also iterate over two lists simultaneously and apply a function to each corresponding pair of elements.
```{r}
library(purrr)
x <- list(1, 1, 1)
y <- list(10, 20, 30)
map2(x, y, ~ .x + .y)

#Note, anonymous function is a function defined without a specific name. Instead, it is defined inline within the code where it is needed. In the function above, we used the ~ (tilde) symbol followed by the expression .x + .y . The .x + .y represents the input parameter of the function

#the anonymous function ( ~ .x + .y ) takes two arguments, .x and .y , which represent the corresponding elements from x and y in each iteration. The function adds .x and .y to calculate the sum.
```
Simulation
```{r}
#uniform distribution
set.seed(123)
runif(n = 5)

hist(runif(10000))

set.seed(123)
runif(n = 10, min = 1, max = 20)


#normal distribution
set.seed(123)
rnorm(n = 10, mean = 5, sd = 1)
hist(rnorm(10000))

#sample
set.seed(123)
sample(c("a","b","c"),
       size = 100,
       replace = TRUE,
       prob = c(0.1, 0.2, 0.7))

#random sample
set.seed(123)
simulated_data <-
  tibble(X = runif(10, min = 0, max = 20),
        Y = 3*X + rnorm(10, mean = 0, sd = 1))

simulated_data

simulated_data %>%
  ggplot(aes(x = X, y = Y)) +
  geom_point()
```


1. Write a greeting function that says “good morning”, “good afternoon”, or “good evening”, depending on the time of day.
```{r}
library(lubridate)
greeting <- function(){
  current <- now()
  current_hour <- hour(current)
  greeting_output <- 
  case_when(
    current_hour > 4 && current_hour < 12 ~ "good morning",
    current_hour < 16 ~ "good afternoon",
    TRUE ~ "good evening"
  )
  return(greeting_output)
}

greeting()
```

2-Simulate a dataset using a normal distribution with mean 100 and standard deviation 15 as variable X, and a quadratic transformation of X as variable Y. Graph your data.
```{r}
simulated_data <-
  tibble(X = rnorm(n = 1000, mean = 100, sd = 15),
         Y = X ^ 2)

simulated_data

simulated_data %>%
  ggplot(aes(x = X, y =Y)) +
  geom_point()
```

*Learners worked on exercise slide deck and presented their findings