hw1 Khuslen. B

# Load required libraries
library(tidyverse)

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library(lubridate)
library(tidyquant)

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library(readxl)
library(writexl)

# Import bikes data from Excel
bikes <- read_excel("bikes.xlsx")

# Select "model" and "price" columns and arrange in descending order
bikes_desc_price <- bikes %>%
  select(model, price) %>%
  arrange(desc(price))

bikes_desc_price

## # A tibble: 97 × 2
##    model                          price
##    <chr>                          <dbl>
##  1 Supersix Evo Black Inc.        12790
##  2 Scalpel-Si Black Inc.          12790
##  3 Habit Hi-Mod Black Inc.        12250
##  4 F-Si Black Inc.                11190
##  5 Supersix Evo Hi-Mod Team       10660
##  6 Synapse Hi-Mod Disc Black Inc.  9590
##  7 Scalpel-Si Race                 9060
##  8 F-Si Hi-Mod Team                9060
##  9 Trigger Carbon 1                8200
## 10 Supersix Evo Hi-Mod Dura Ace 1  7990
## # ℹ 87 more rows

# Calculate the mean price
mean_price <- mean(bikes$price, na.rm = TRUE)

# Filter for rows where price is greater than the mean and select relevant columns
bikes_above_mean_price <- bikes %>%
  filter(price > mean_price) %>%
  select(model, price)

bikes_above_mean_price

## # A tibble: 35 × 2
##    model                          price
##    <chr>                          <dbl>
##  1 Supersix Evo Black Inc.        12790
##  2 Supersix Evo Hi-Mod Team       10660
##  3 Supersix Evo Hi-Mod Dura Ace 1  7990
##  4 Supersix Evo Hi-Mod Dura Ace 2  5330
##  5 Supersix Evo Hi-Mod Utegra      4260
##  6 CAAD12 Black Inc                5860
##  7 CAAD12 Disc Dura Ace            4260
##  8 Synapse Hi-Mod Disc Black Inc.  9590
##  9 Synapse Hi-Mod Disc Red         7460
## 10 Synapse Hi-Mod Dura Ace         5860
## # ℹ 25 more rows