library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.1 ✔ purrr 1.0.1
## ✔ tibble 3.1.8 ✔ dplyr 1.1.0
## ✔ tidyr 1.3.0 ✔ stringr 1.5.0
## ✔ readr 2.1.4 ✔ forcats 1.0.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()stats <- read_csv("../00_data/myData.csv")## New names:
## Rows: 39 Columns: 8
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (2): Country, ...8 dbl (5): Kill/Death Ratio, Player Rating, Headshot
## Percentage, Kills Per Rou... lgl (1): ...7
## 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.
## • `` -> `...7`
## • `` -> `...8`stats <- stats %>%
janitor::clean_names()filter(stats, player_impact == 1.05)## # A tibble: 7 × 8
## country kill_death_ratio player_rating headsh…¹ kills…² playe…³ x7 x8
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <chr>
## 1 Belgium 1.03 1.01 46.9 0.68 1.05 NA <NA>
## 2 Bulgaria 1.03 1.01 45.6 0.69 1.05 NA <NA>
## 3 Finland 1.02 1 43.2 0.68 1.05 NA <NA>
## 4 Kazakhstan 1.02 1 48.2 0.68 1.05 NA <NA>
## 5 Norway 1 0.99 49.2 0.68 1.05 NA <NA>
## 6 Russia 1.04 1.01 45.3 0.68 1.05 NA <NA>
## 7 Slovakia 1.07 1.04 47.8 0.7 1.05 NA <NA>
## # … with abbreviated variable names ¹headshot_percentage, ²kills_per_round,
## # ³player_impactfilter(stats, player_impact == 1.05 & player_rating == 1)## # A tibble: 2 × 8
## country kill_death_ratio player_rating headsh…¹ kills…² playe…³ x7 x8
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <chr>
## 1 Finland 1.02 1 43.2 0.68 1.05 NA <NA>
## 2 Kazakhstan 1.02 1 48.2 0.68 1.05 NA <NA>
## # … with abbreviated variable names ¹headshot_percentage, ²kills_per_round,
## # ³player_impactarrange(stats, player_impact)## # A tibble: 39 × 8
## country kill_death_ratio player_rating headshot…¹ kills…² playe…³ x7 x8
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <chr>
## 1 Belarus 0.99 0.98 48.2 0.67 1.01 NA <NA>
## 2 Germany 1 0.99 45.9 0.67 1.02 NA <NA>
## 3 Ukraine 1.02 1 46.2 0.68 1.02 NA <NA>
## 4 France 1.03 1.01 47.3 0.68 1.03 NA <NA>
## 5 Serbia 1.03 1 43.8 0.68 1.03 NA <NA>
## 6 Canada 1.01 1 47.1 0.68 1.04 NA <NA>
## 7 Denmark 1.03 1 45.8 0.68 1.04 NA <NA>
## 8 Latvia 1.07 1.03 50.4 0.7 1.04 NA <NA>
## 9 Poland 1.01 1 44.2 0.68 1.04 NA <NA>
## 10 Spain 1.02 1 43.7 0.68 1.04 NA <NA>
## # … with 29 more rows, and abbreviated variable names ¹headshot_percentage,
## # ²kills_per_round, ³player_impactarrange(stats, desc(player_impact))## # A tibble: 39 × 8
## country kill_death_ratio player_…¹ heads…² kills…³ playe…⁴ x7 x8
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <chr>
## 1 Singapore 1.08 1.05 45.0 0.73 1.16 NA <NA>
## 2 New Zealand 1.14 1.08 37.4 0.73 1.15 NA <NA>
## 3 Indonesia 1.12 1.07 38.3 0.74 1.12 NA <NA>
## 4 Turkey 1.08 1.05 46.1 0.71 1.11 NA <NA>
## 5 Netherlands 1.07 1.04 39.8 0.71 1.1 NA <NA>
## 6 China 1.05 1.03 48.0 0.71 1.09 NA <NA>
## 7 Korea 1.07 1.04 44.3 0.71 1.09 NA <NA>
## 8 Mongolia 1.06 1.03 50.4 0.71 1.09 NA <NA>
## 9 Australia 1.05 1.02 44.7 0.7 1.08 NA <NA>
## 10 Czech Republic 1.06 1.03 46.2 0.7 1.08 NA <NA>
## # … with 29 more rows, and abbreviated variable names ¹player_rating,
## # ²headshot_percentage, ³kills_per_round, ⁴player_impactselect(stats, country, player_rating)## # A tibble: 39 × 2
## country player_rating
## <chr> <dbl>
## 1 Argentina 1.01
## 2 Australia 1.02
## 3 Belarus 0.98
## 4 Belgium 1.01
## 5 Brazil 1.03
## 6 Bulgaria 1.01
## 7 Canada 1
## 8 China 1.03
## 9 Czech Republic 1.03
## 10 Denmark 1
## # … with 29 more rowsmutate(stats,
gain = player_rating - player_impact)## # A tibble: 39 × 9
## country kill_dea…¹ playe…² heads…³ kills…⁴ playe…⁵ x7 x8 gain
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <chr> <dbl>
## 1 Argentina 1.03 1.01 46.2 0.69 1.07 NA Filt… -0.0600
## 2 Australia 1.05 1.02 44.7 0.7 1.08 NA <NA> -0.0600
## 3 Belarus 0.99 0.98 48.2 0.67 1.01 NA <NA> -0.0300
## 4 Belgium 1.03 1.01 46.9 0.68 1.05 NA <NA> -0.0400
## 5 Brazil 1.07 1.03 44.0 0.7 1.07 NA <NA> -0.0400
## 6 Bulgaria 1.03 1.01 45.6 0.69 1.05 NA <NA> -0.0400
## 7 Canada 1.01 1 47.1 0.68 1.04 NA <NA> -0.0400
## 8 China 1.05 1.03 48.0 0.71 1.09 NA <NA> -0.0600
## 9 Czech Republic 1.06 1.03 46.2 0.7 1.08 NA <NA> -0.0500
## 10 Denmark 1.03 1 45.8 0.68 1.04 NA <NA> -0.0400
## # … with 29 more rows, and abbreviated variable names ¹kill_death_ratio,
## # ²player_rating, ³headshot_percentage, ⁴kills_per_round, ⁵player_impactstats## # A tibble: 39 × 8
## country kill_death_ratio player_…¹ heads…² kills…³ playe…⁴ x7 x8
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <chr>
## 1 Argentina 1.03 1.01 46.2 0.69 1.07 NA Filt…
## 2 Australia 1.05 1.02 44.7 0.7 1.08 NA <NA>
## 3 Belarus 0.99 0.98 48.2 0.67 1.01 NA <NA>
## 4 Belgium 1.03 1.01 46.9 0.68 1.05 NA <NA>
## 5 Brazil 1.07 1.03 44.0 0.7 1.07 NA <NA>
## 6 Bulgaria 1.03 1.01 45.6 0.69 1.05 NA <NA>
## 7 Canada 1.01 1 47.1 0.68 1.04 NA <NA>
## 8 China 1.05 1.03 48.0 0.71 1.09 NA <NA>
## 9 Czech Republic 1.06 1.03 46.2 0.7 1.08 NA <NA>
## 10 Denmark 1.03 1 45.8 0.68 1.04 NA <NA>
## # … with 29 more rows, and abbreviated variable names ¹player_rating,
## # ²headshot_percentage, ³kills_per_round, ⁴player_impactAverage Player Rating
summarise(stats, rating = mean(player_rating, na.rm = TRUE))## # A tibble: 1 × 1
## rating
## <dbl>
## 1 1.02