HW 1

# Step 1: Load required libraries
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.0     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.1     ✔ tibble    3.1.8
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors

library(lubridate)
library(tidyquant)

## Loading required package: PerformanceAnalytics
## Loading required package: xts
## Loading required package: zoo
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## Attaching package: 'zoo'
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## ################################### WARNING ###################################
## # We noticed you have dplyr installed. The dplyr lag() function breaks how    #
## # base R's lag() function is supposed to work, which breaks lag(my_xts).      #
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## # Calls to lag(my_xts) that you enter or source() into this session won't     #
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## # All package code is unaffected because it is protected by the R namespace   #
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## # Set `options(xts.warn_dplyr_breaks_lag = FALSE)` to suppress this warning.  #
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## # You can use stats::lag() to make sure you're not using dplyr::lag(), or you #
## # can add conflictRules('dplyr', exclude = 'lag') to your .Rprofile to stop   #
## # dplyr from breaking base R's lag() function.                                #
## ################################### WARNING ###################################
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## Attaching package: 'xts'
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## Attaching package: 'PerformanceAnalytics'
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## Loading required package: quantmod
## Loading required package: TTR
## Registered S3 method overwritten by 'quantmod':
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##   as.zoo.data.frame zoo

library(readxl)
library(writexl)

# Step 2: Import the data files using read_excel()
bikes_data <- read_excel("bikes.xlsx")

# Step 3: Show "model" and "price" columns with "price" in descending order
bikes_sorted <- bikes_data %>%
  select(model, price) %>%
  arrange(desc(price))

# Display the result
bikes_sorted

## # 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
## # … with 87 more rows

# Step 4: Show "model" and "price" columns with "price" greater than mean price
mean_price <- mean(bikes_data$price)

bikes_filtered <- bikes_data %>%
  select(model, price) %>%
  filter(price > mean_price)

# Display the result
bikes_filtered

## # 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
## # … with 25 more rows