library (readxl)library (tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.2
✔ ggplot2 4.0.0 ✔ tibble 3.3.0
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.1.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
<- read_excel ("bikes.xlsx" )head (bikesData)
# A tibble: 6 × 4
bike.id model description price
<dbl> <chr> <chr> <dbl>
1 1 Supersix Evo Black Inc. Road - Elite Road - Carbon 12790
2 2 Supersix Evo Hi-Mod Team Road - Elite Road - Carbon 10660
3 3 Supersix Evo Hi-Mod Dura Ace 1 Road - Elite Road - Carbon 7990
4 4 Supersix Evo Hi-Mod Dura Ace 2 Road - Elite Road - Carbon 5330
5 5 Supersix Evo Hi-Mod Utegra Road - Elite Road - Carbon 4260
6 6 Supersix Evo Red Road - Elite Road - Carbon 3940
arrange (select (bikesData, model, price), 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
filter (select (bikesData, model, price), price > mean (bikesData$ 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