mini proj

data(cars)

median(cars[, 1])

## [1] 15

library(jsonlite)

url <- "https://min-api.cryptocompare.com/data/v2/histoday?fsym=BTC&tsym=USD&limit=100"

btc_data <- fromJSON(url)

btc_prices <- btc_data$Data$Data

max_close_price <- max(btc_prices$close)

max_close_price

## [1] 124723

Student Habits vs Academic Performance

df <- read.csv("student_lifestyle_dataset.csv")

# 1. What is the average number of study hours per day among students?
mean(df$Study_Hours_Per_Day, na.rm = TRUE)

## [1] 7.4758

median(df$Study_Hours_Per_Day, na.rm = TRUE)

## [1] 7.4

# 2. Do students who sleep more have higher GPAs on average?
# Create two groups: <8 hours sleep vs >=8 hours
df$sleep_group <- ifelse(df$Sleep_Hours_Per_Day >= 8, "8+ hours", "<8 hours")

# Compare group averages
tapply(df$GPA, df$sleep_group, mean, na.rm = TRUE)

## <8 hours 8+ hours 
## 3.116544 3.115138

# 3. What is the relationship (correlation) between study hours and GPA?
cor(df$Study_Hours_Per_Day, df$GPA, use = "complete.obs")

## [1] 0.734468

# 4. Which daily activity takes the most time on average for students?
means <- c(
  study = mean(df$Study_Hours_Per_Day, na.rm = TRUE),
  sleep = mean(df$Sleep_Hours_Per_Day, na.rm = TRUE),
  social = mean(df$Social_Hours_Per_Day, na.rm = TRUE),
  physical = mean(df$Physical_Activity_Hours_Per_Day, na.rm = TRUE),
  extracurricular = mean(df$Extracurricular_Hours_Per_Day, na.rm = TRUE)
)
means

##           study           sleep          social        physical extracurricular 
##         7.47580         7.50125         2.70455         4.32830         1.99010

# 5. Study Hours vs GPA
plot(df$Study_Hours_Per_Day, df$GPA,
     xlab = "Study Hours Per Day", ylab = "GPA",
     main = "Study Hours vs GPA")