README.Rmd

---
output: github_document
---

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

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

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The goal of `fantasypros` is to provide easy and reproducable access to data
provided on [fantasypros](https://www.fantasypros.com). The intital focus is on
NFL and fantasy football data, but other sports are planned to be added

## Installation

You can install the released version of fantasypros from [CRAN](https://CRAN.R-project.org) with:

``` r
# not on CRAN
install.packages("fantasypros")
```

And the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("jpiburn/fantasypros")
```
## Example

This is a basic example which shows you how to solve a common problem:

## Visualizing Expert Consensus Ranking
```{r rb-erc-chart, fig.height=8, message=FALSE, warning=FALSE}
library(fantasypros)
library(ggplot2)
library(dplyr)

fp_draft_rankings("RB") %>%
  filter(rank <= 40) %>%
  ggplot(
    aes(x = avg, y = adp, colour = factor(tier), label = player)
  ) +
  geom_abline(
    slop = 1, 
    intercept = 0,
    linetype = 2,
    color = "grey"
  ) +
  geom_errorbarh(
    aes(xmin = avg - std_dev, xmax = avg + std_dev), 
    height = 0, 
    alpha = 0.6, 
    size = 0.9,  
    show.legend = FALSE
  ) +
  geom_point(size = 1.5) +
  scale_x_reverse(
    breaks = c(1, seq(10,70, 10))
  ) +
  scale_y_reverse(
    breaks = c(1, seq(10,60, 10))
  ) +
  ggsci::scale_color_npg() +
  hrbrthemes::theme_ipsum_rc(
    base_size = 10, 
    axis_title_size = 9,
    plot_title_size = 14
  ) +
  labs(
    title   = "RB Expert Consensus Rank vs Average Draft Position",
    colour  = "Tier",
    x       = "Expert Consensus Rank",
    y       = "Average Draft Position",
    caption = "Data: fantasypros.com"
  ) + 
  geom_text(
    aes(x = avg + std_dev), 
    size = 2, 
    nudge_x = -3.5, 
    show.legend = FALSE,
    fontface = "bold"
  ) +
  geom_text(
    aes(x = 10, y = 35), label = "Under\nDrafted", color = "light grey", 
    size = 8, family = "Roboto Condensed", fontface = "italic"
  ) +
  geom_text(
    aes(x = 45, y = 10), label = "Over\nDrafted", color = "light grey", 
    size = 8, family = "Roboto Condensed", fontface = "italic"
  ) +
  theme(
    legend.position = "bottom",
  ) +
  guides(
    colour = guide_legend(nrow = 1)
  )
```


## Team Target Distributions
```{r target-dist, message=FALSE, warning=FALSE, fig.height=7}
library(fantasypros)
library(tidyverse)
library(ggplot2)

fp_team_targets(season = 2018) %>%
  select(
    team, 
    ends_with("percent")
  ) %>%
  mutate(
    team = forcats::fct_reorder(team, rb_percent)
  ) %>%
  gather("position", "percent", -team) %>%
  mutate(
    pos = factor(
      position, 
      levels = c("te_percent", "wr_percent", "rb_percent"), 
      labels = c("TE", "WR", "RB")
      )
  ) %>%
  ggplot() +
  geom_col(
    aes(team, percent, fill = pos), alpha = 0.9
  ) +
  scale_x_discrete(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  ggsci::scale_fill_jama() +
  hrbrthemes::theme_ipsum_rc() + 
  coord_flip() +
  labs(
    title   = "Team Target Distribution",
    fill  = NULL,
    y       = "Target %",
    x       = NULL,
    caption = "Data: fantasypros.com"
  ) +
  theme(
    legend.position = "bottom"
  )
```

## Season Stats
```{r season-stats, message=FALSE}
library(fantasypros)

fp_stats("QB", season = 2018, start_week = 3, end_week = 8)

```


## Weekly Snap Counts
```{r snap-counts, message=FALSE}
library(fantasypros)

fp_snap_counts(season = 2018)

fp_snap_counts(pos = "defense", season = 2018, percentage = TRUE)

```


## Detailed Snap Analysis
```{r snap-analysis, message=FALSE}
library(fantasypros)

# all offensive positions for weeks 5-9 of the 2018 season
fp_snap_analysis(season = 2018, start_week = 5, end_week = 9)
```

## Weekly Targets
```{r targets, message=FALSE}
library(fantasypros)

# total targets for TE's in the 2014 season
fp_targets(pos = "TE", season = 2014)
```