#Step 1 : Load Required Libraries
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.4.3## Warning: package 'ggplot2' was built under R version 4.4.2## Warning: package 'readr' was built under R version 4.4.3## Warning: package 'forcats' was built under R version 4.4.3## Warning: package 'lubridate' was built under R version 4.4.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.1 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(lubridate)
library(tidyquant)## Warning: package 'tidyquant' was built under R version 4.4.3## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo## Warning: package 'xts' was built under R version 4.4.3## Warning: package 'zoo' was built under R version 4.4.3## Warning: package 'quantmod' was built under R version 4.4.3## Warning: package 'TTR' was built under R version 4.4.3## Warning: package 'PerformanceAnalytics' was built under R version 4.4.3## ── Attaching core tidyquant packages ─────────────────────── tidyquant 1.0.11 ──
## ✔ PerformanceAnalytics 2.0.8 ✔ TTR 0.24.4
## ✔ quantmod 0.4.28 ✔ xts 0.14.1
## ── Conflicts ────────────────────────────────────────── tidyquant_conflicts() ──
## ✖ zoo::as.Date() masks base::as.Date()
## ✖ zoo::as.Date.numeric() masks base::as.Date.numeric()
## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(readxl)## Warning: package 'readxl' was built under R version 4.4.3library(writexl)## Warning: package 'writexl' was built under R version 4.4.3#Step 2 : Import Data
bikes <- read_excel("bikes.xlsx")#Step 3 : take a glance on the data
glimpse(bikes)## Rows: 97
## Columns: 4
## $ bike.id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,…
## $ model <chr> "Supersix Evo Black Inc.", "Supersix Evo Hi-Mod Team", "Su…
## $ description <chr> "Road - Elite Road - Carbon", "Road - Elite Road - Carbon"…
## $ price <dbl> 12790, 10660, 7990, 5330, 4260, 3940, 3200, 2660, 2240, 18…#Step 4 : Show Model & Price in Descending Order
bikes_desc <- bikes %>%
select(model, price) %>%
arrange(desc(price))
bikes_desc## # 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#Step 5 : Show Model & Price with Price Greater than Mean Price
# Calculate the mean
mean_price <- mean(bikes$price, na.rm = TRUE)
#Show Value of Mean Price
mean_price## [1] 3953.763# Filter the data with price greater than mean price
bikes_above_mean <- bikes %>%
select(model, price) %>%
filter(price > mean_price)
bikes_above_mean## # 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