The approach for this assignment is collecting the net data from project 1 and adding to the my R interface and then over viewing the data. The original data I.D. have kept the chess player by their performance so I will avoid adjusting the data sets order at first. I found a general calculation of expected value which gave a value of QA/ (QA+QB) where Q is the 10 ^ (Player ELO/400). I will calculate the average opponents that the player had compared to their real pre-performance score. Then by calculating that data we can made a column for the different getting the best best and worst performers will be a matter of using the tail and head functions.
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 4.0.0 ✔ tibble 3.2.1
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.0.4
── 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
New names:
Rows: 64 Columns: 5
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(2): Name, State dbl (3): ...1, ID, Total Points
ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
Specify the column types or set `show_col_types = FALSE` to quiet this message.
• `` -> `...1`
# A tibble: 64 × 5
...1 ID Name State `Total Points`
<dbl> <dbl> <chr> <chr> <dbl>
1 1 1 GARY HUA ON 6
2 2 2 DAKSHESH DARURI MI 6
3 3 3 ADITYA BAJAJ MI 6
4 4 4 PATRICK H SCHILLING MI 5.5
5 5 5 HANSHI ZUO MI 5.5
6 6 6 HANSEN SONG OH 5
7 7 7 GARY DEE SWATHELL MI 5
8 8 8 EZEKIEL HOUGHTON MI 5
9 9 9 STEFANO LEE ON 5
10 10 10 ANVIT RAO MI 5
# ℹ 54 more rows