`comperes`

offers a pipe (`%>%`

) friendly set of tools for storing and managing competition results. Understanding of **competition** is quite general: it is a set of **games** (abstract event) in which **players** (abstract entity) gain some abstract **scores** (typically numeric). The most natural example is sport results, however not the only one. For example, product rating can be considered as a competition between products as "players". Here a "game" is a customer that reviews a set of products by rating them with numerical "score" (stars, points, etc.).

This package leverages dplyr's grammar of data manipulation. Only basic knowledge is enough to use `comperes`

.

`comperes`

provides the following functionality:

**Store and convert**competition results:- In
*long format*as a tibble with one row per game-player pair. Functions:`as_longcr()`

,`is_longcr()`

. - In
*wide format*as a`tibble`

with one row per game with fixed amount of players. Functions:`as_widecr()`

,`is_widecr()`

.

- In
**Summarise**:- Compute
*item summaries*with functions using`dplyr`

's grammar. Functions:`summarise_item()`

,`summarise_game()`

,`summarise_player()`

. - Compute and
*join*item summaries to data for easy transformation. Functions:`join_item_summary()`

,`join_game_summary()`

,`join_player_summary()`

. - Use
*common item summary functions*with rlang's unquoting mechanism. Example:`. %>% summarise_player(!!! summary_funs["mean_score"])`

.

- Compute
**Compute Head-to-Head values**(a summary statistic of direct confrontation between two players) with functions also using`dplyr`

's grammar:- Store output in
*long format*as a`tibble`

with one row per pair of players. Function:`h2h_long()`

. - Store output in
*matrix format*as a matrix with rows and columns describing players and entries - Head-to-Head values. Function:`h2h_mat()`

. - Use
*common Head-to-Head functions*with rlang's unquoting mechanism. Example:`. %>% h2h_mat(!!! h2h_funs["num_wins"])`

.

- Store output in

You can install `comperes`

from CRAN with:

```
install.packages("comperes")
```

To install the most recent development version from GitHub use:

```
# install.packages("devtools")
devtools::install_github("echasnovski/comperes")
```

We will be using `ncaa2005`

, data from `comperes`

package. It is an example competition results (hereafter - results) of an isolated group of Atlantic Coast Conference teams provided in book "Who's #1" by Langville and Meyer. It looks like this:

```
library(comperes)
ncaa2005
#> # A longcr object:
#> # A tibble: 20 x 3
#> game player score
#> <int> <chr> <int>
#> 1 1 Duke 7
#> 2 1 Miami 52
#> 3 2 Duke 21
#> 4 2 UNC 24
#> 5 3 Duke 7
#> 6 3 UVA 38
#> # ... with 14 more rows
```

This is an object of class `longcr`

which describes results in long form (*each row represents the score of particular player in particular game*). Because in this competition a game always consists from two players, more natural way to look at `ncaa2005`

is in wide format:

```
as_widecr(ncaa2005)
#> # A widecr object:
#> # A tibble: 10 x 5
#> game player1 score1 player2 score2
#> <int> <chr> <int> <chr> <int>
#> 1 1 Duke 7 Miami 52
#> 2 2 Duke 21 UNC 24
#> 3 3 Duke 7 UVA 38
#> 4 4 Duke 0 VT 45
#> 5 5 Miami 34 UNC 16
#> 6 6 Miami 25 UVA 17
#> # ... with 4 more rows
```

This converted `ncaa2005`

into an object of `widecr`

class which describes results in wide format (*each row represents scores of all players in particular game*). All `comperes`

functions expect either a data frame with results structured in long format or one of supported classes: `longcr`

, `widecr`

.

With `compere`

the following summaries are possible:

```
ncaa2005 %>%
summarise_player(min_score = min(score), mean_score = mean(score))
#> # A tibble: 5 x 3
#> player min_score mean_score
#> <chr> <dbl> <dbl>
#> 1 Duke 0. 8.75
#> 2 Miami 25. 34.5
#> 3 UNC 3. 12.5
#> 4 UVA 5. 18.5
#> 5 VT 7. 33.5
# Using list of common summary functions
library(rlang)
ncaa2005 %>%
summarise_game(!!! summary_funs[c("sum_score", "num_players")])
#> # A tibble: 10 x 3
#> game sum_score num_players
#> <int> <int> <int>
#> 1 1 59 2
#> 2 2 45 2
#> 3 3 45 2
#> 4 4 45 2
#> 5 5 50 2
#> 6 6 42 2
#> # ... with 4 more rows
```

Supplied list of common summary functions has 8 entries, which are quoted expressions to be used in `dplyr`

grammar:

```
summary_funs
#> $min_score
#> min(score)
#>
#> $max_score
#> max(score)
#>
#> $mean_score
#> mean(score)
#>
#> $median_score
#> median(score)
#>
#> $sd_score
#> sd(score)
#>
#> $sum_score
#> sum(score)
#>
#> $num_games
#> length(unique(game))
#>
#> $num_players
#> length(unique(player))
ncaa2005 %>% summarise_player(!!! summary_funs)
#> # A tibble: 5 x 9
#> player min_score max_score mean_score median_score sd_score sum_score
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 Duke 0. 21. 8.75 7.00 8.81 35
#> 2 Miami 25. 52. 34.5 30.5 12.3 138
#> 3 UNC 3. 24. 12.5 11.5 9.40 50
#> 4 UVA 5. 38. 18.5 15.5 14.0 74
#> 5 VT 7. 52. 33.5 37.5 19.9 134
#> # ... with 2 more variables: num_games <int>, num_players <int>
```

To modify scores based on the rest of results one can use `join_*_summary()`

functions:

```
suppressPackageStartupMessages(library(dplyr))
ncaa2005_mod <- ncaa2005 %>%
join_player_summary(player_mean_score = mean(score)) %>%
join_game_summary(game_mean_score = mean(score)) %>%
mutate(score = player_mean_score - game_mean_score)
ncaa2005_mod
#> # A longcr object:
#> # A tibble: 20 x 5
#> game player score player_mean_score game_mean_score
#> <int> <chr> <dbl> <dbl> <dbl>
#> 1 1 Duke -20.8 8.75 29.5
#> 2 1 Miami 5.00 34.5 29.5
#> 3 2 Duke -13.8 8.75 22.5
#> 4 2 UNC -10.0 12.5 22.5
#> 5 3 Duke -13.8 8.75 22.5
#> 6 3 UVA -4.00 18.5 22.5
#> # ... with 14 more rows
ncaa2005_mod %>% summarise_player(mean_score = mean(score))
#> # A tibble: 5 x 2
#> player mean_score
#> <chr> <dbl>
#> 1 Duke -15.5
#> 2 Miami 11.4
#> 3 UNC -5.00
#> 4 UVA -2.12
#> 5 VT 11.2
```

This code modifies `score`

to be average player score minus average game score. Negative values indicate poor game performance.

Computation of Head-to-Head performance is done with `h2h_long()`

(output is a tibble; allows multiple Head-to-Head values per pair of players) or `h2h_mat()`

(output is a matrix; only one value per pair of players).

Head-to-Head functions should be supplied in `dplyr`

grammar but for players' matchups: direct confrontation between **ordered** pairs of players (including playing with themselves) stored in wide format:

```
ncaa2005 %>% get_matchups()
#> # A widecr object:
#> # A tibble: 40 x 5
#> game player1 score1 player2 score2
#> <int> <chr> <int> <chr> <int>
#> 1 1 Duke 7 Duke 7
#> 2 1 Duke 7 Miami 52
#> 3 1 Miami 52 Duke 7
#> 4 1 Miami 52 Miami 52
#> 5 2 Duke 21 Duke 21
#> 6 2 Duke 21 UNC 24
#> # ... with 34 more rows
```

Typical Head-to-Head computation is done like this:

```
ncaa2005 %>%
h2h_long(
mean_score_diff = mean(score1 - score2),
num_wins = sum(score1 > score2)
)
#> # A long format of Head-to-Head values:
#> # A tibble: 25 x 4
#> player1 player2 mean_score_diff num_wins
#> <chr> <chr> <dbl> <int>
#> 1 Duke Duke 0. 0
#> 2 Duke Miami -45. 0
#> 3 Duke UNC -3. 0
#> 4 Duke UVA -31. 0
#> 5 Duke VT -45. 0
#> 6 Miami Duke 45. 1
#> # ... with 19 more rows
ncaa2005 %>% h2h_mat(mean(score1 - score2))
#> # A matrix format of Head-to-Head values:
#> Duke Miami UNC UVA VT
#> Duke 0 -45 -3 -31 -45
#> Miami 45 0 18 8 20
#> UNC 3 -18 0 2 -27
#> UVA 31 -8 -2 0 -38
#> VT 45 -20 27 38 0
```

Supplied list of common Head-to-Head functions has 9 entries, which are also quoted expressions:

```
h2h_funs
#> $mean_score_diff
#> mean(score1 - score2)
#>
#> $mean_score_diff_pos
#> max(mean(score1 - score2), 0)
#>
#> $mean_score
#> mean(score1)
#>
#> $sum_score_diff
#> sum(score1 - score2)
#>
#> $sum_score_diff_pos
#> max(sum(score1 - score2), 0)
#>
#> $sum_score
#> sum(score1)
#>
#> $num_wins
#> num_wins(score1, score2, half_for_draw = FALSE)
#>
#> $num_wins2
#> num_wins(score1, score2, half_for_draw = TRUE)
#>
#> $num
#> n()
ncaa2005 %>% h2h_long(!!! h2h_funs)
#> # A long format of Head-to-Head values:
#> # A tibble: 25 x 11
#> player1 player2 mean_score_diff mean_score_diff_pos mean_score
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Duke Duke 0. 0. 8.75
#> 2 Duke Miami -45. 0. 7.00
#> 3 Duke UNC -3. 0. 21.0
#> 4 Duke UVA -31. 0. 7.00
#> 5 Duke VT -45. 0. 0.
#> 6 Miami Duke 45. 45. 52.0
#> # ... with 19 more rows, and 6 more variables: sum_score_diff <int>,
#> # sum_score_diff_pos <dbl>, sum_score <int>, num_wins <dbl>,
#> # num_wins2 <dbl>, num <int>
```

To compute Head-to-Head for only subset of players or include values for players that are not in the results, use factor `player`

column:

```
ncaa2005 %>%
mutate(player = factor(player, levels = c("Duke", "Miami", "Extra"))) %>%
h2h_mat(!!! h2h_funs["num_wins"], fill = 0)
#> # A matrix format of Head-to-Head values:
#> Duke Miami Extra
#> Duke 0 0 0
#> Miami 1 0 0
#> Extra 0 0 0
```

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