README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```
# statskier2

This package is a collection of functions I use to explore and analyze cross-
country skiing results data that I have collected over the years. The data itself
is not included in the package, so the this package isn't actually functional
on its own.

## Examples

Some basic plots of results by skier. Standardized percent behind the median skier
is a custom metric that takes the percent back for each racer relative to the
median skier and then performs a simple adjustment to account for differences
between race formats (individual start races and mass start races result in very
different distributions of percent backs due to tactical differences in the race
formats).

```{r ath-plots,warning=FALSE}
library(statskier2)
p <- ath_plot_dst(ath_names = c("WENG Heidi","SOMMERFELDT Rene","BJOERGEN Marit"))
print(p$plot)

p <- ath_plot_spr(ath_name = c("NEWELL Andrew","RANDALL Kikkan"))
print(p$plot)

p <- spr_plot(ath_names = c('NEWELL Andrew','HAMILTON Simeon'))
print(p$plot)
```

Another way to asses race performance for a given athlete in a single race is 
to compare how they did against those specific opponents relative to their 
historical results against just those opponents. The first is a crude attempt
using violin plots, where the shaded regions represent past (and future) 
performances and the black outline represents the race in question.

The second example fits a simple mixed-effects model using race length & technique
as fixed effects and random intercept for each opponent to estimate expected
results against each opponent.

```{r ath-hth}
p <- hth_race(ath_name = 'DIGGINS Jessica',race_id = 9232,num_opp = 30,min_encounters = 3,measure = 'pb')
print(p$plot)

p <- hth_lmer(ath_names = 'DIGGINS Jessica',race_id = 9232,num_opp = 30,min_encounters = 3,measure = 'pb')
print(p$plot)
```

Simple results trends by nation:

```{r nation-trend}
p <- nation_trend(nations = c('USA','CAN','RUS','SWE'),
                  race_gender = 'Men',
                  race_type = 'Distance')
print(p$plot)
```

Individual race snapshots, to give a quick sense of who raced well or poorly:

```{r race-snapshot}
p <- race_snapshot_dst(race_id = 7902,title = "Men's 15k Classic - 2014-11-30")
print(p$plot)
```

World Junior & U23 results over time by nation:

```{r wjc-u23}
p <- wjc_u23_plot(nations = c('USA','CAN'),races = 'WJC')
print(p$plots$USA)
print(p$plots$CAN)
```