Trev Makes Charts

Worst of the Worst: The Ten Worst Fantasy Football Outings of All Time

2023-08-25T00:00:00+00:00

Load in the required libraries

library(reactable)
library(reactablefmtr)
library(tidyverse)
library(htmltools)
library(htmlwidgets)

Load in and begin to wrangle the data

ffp_wurst <- read.csv('/Users/trev/Desktop/Data Projects/R Files/all_time_ffp.csv')
ffp_wurst <- ffp_wurst %>%
  mutate(headshot_url = coalesce(headshot_url, 'https://static.www.nfl.com/image/private/f_auto,q_auto/league/mdrlzgankwwjldxllgcx')) %>%
  mutate(ffp = round(ffp, digits = 2)) %>%
  mutate(rank = dense_rank(ffp)) %>%
  filter(pos != 'P', pos != 'K') %>%
  slice_min(ffp, n = 10, with_ties = FALSE) %>%
  select(rank, headshot_url, player, team, pos, season, week, ffp:int)

Edit team names and clean variable names

ffp_wurst$team <- gsub('BUF', 'Buffalo Bills',
                  gsub('ARI', 'Arizona Cardinals',
                  gsub('NYJ', 'New York Jets', 
                  gsub('DEN', 'Denver Broncos',
                  gsub('NYG', 'New York Giants', 
                  gsub('TEN', 'Tennesee Titans',
                  gsub('HOU', 'Houston Texans',
                  ffp_wurst$team)))))))

ffp_wurst <- ffp_wurst %>%
  mutate(pos_team = paste(pos, team, sep = ' | ')) %>%
  select(rank, headshot_url, player, pos_team, season:int)

Assign color variables for table styling

back_blk = '#1A1A19'
text_white = '#F5F5EB'
button_red = '#FC6A55'
border = '#A8A89D'

Create reactable table

wurst_table <- ffp_wurst %>% reactable(data = .,
                                       highlight = TRUE,
                                       striped = FALSE,
                                       borderless = FALSE,
                                       fullWidth = FALSE,
                                       pagination = FALSE,
                                       theme = reactableTheme(
                                         color = back_blk,
                                         backgroundColor = text_white,
                                         borderColor = border,
                                         borderWidth = '2px',
                                         cellPadding = '8px 12px'),
                                       defaultColDef = colDef(align = 'center', vAlign = 'center', style = list(fontSize =18)),
                                       style = list(fontFamily = 'urbanist'),
                                       columnGroups = list(
                                         colGroup(name = 'PASSING', columns = c('pass_yds', 'pass_tds', 'int')),
                                         colGroup(name = 'RUSHING', columns = c('rush_yds', 'rush_tds', 'fumbles'))),
                                       columns = list(
                                         rank = colDef(name = '',
                                                       minWidth = 65,
                                                       style = list(fontFamily = 'Fjalla One', fontSize = 28)),
                                         headshot_url = colDef(name = '',
                                                        minWidth = 110,
                                                        align = 'center',
                                                        cell = embed_img(height = 75, width = 105)),
                                         player = colDef(name = 'PLAYER',
                                                         style = list(fontFamily = 'Fjalla One', fontSize = 24),
                                                         align = 'center', 
                                                         minWidth = 200,
                                                         cell = merge_column(., 'pos_team', 'below', size = 20, merged_size = 14)),
                                         pos_team = colDef(show = FALSE),
                                         season = colDef(name = 'GAME',
                                                         minWidth = 105,
                                                         cell = merge_column(., "week", "right")),
                                         week = colDef(show = FALSE),
                                         ffp = colDef(name = 'POINTS',
                                                      cell = pill_buttons(., colors = button_red, bold_text = TRUE),
                                                      style = list(borderLeft = '2px solid #A8A89D', borderRight = '2px solid #A8A89D')),
                                        
                                         pass_yds = colDef(name = 'YDS',
                                                           width = 72),
                                         pass_tds = colDef(name = 'TDS',
                                                           width = 72),
                                         int = colDef(name = 'INT',
                                                      width = 72,
                                                      style = list(borderRight = '2px solid #A8A89D')),
                                         rush_yds = colDef(name = 'YDS',
                                                           width = 72),
                                         rush_tds = colDef(name = 'TDS',
                                                           width = 72),
                                         fumbles = colDef(name = 'FUM',
                                                          width = 72),
                                         rec = colDef(show = FALSE),
                                         rec_yds = colDef(show = FALSE),
                                         rec_tds = colDef(show = FALSE),
                                         twopoints = colDef(show = FALSE)))

#wurst_table

Add title and subtile to the table

wurst_table_final <- wurst_table %>%
  htmlwidgets::prependContent(
    tagList(
      tags$div('Worst of the Worst', style = 'font-size:48px;
                                              font-weight:bold;
                                              font-family:Fjalla One;
                                              color:#1A1A19;
                                              margin-bottom:0;
                                              display:inline-block;
                                              vertical-align:middle;'),
      tags$h3('The ten worst fantasy football performances since 2012 (ESPN standard PPR scoring)',style="font-family:urbanist;
                                                                                                          font-size:18px;
                                                                                                          margin-bottom:0;
                                                                                                          margin-top:0;
                                                                                                          font-weight:400;
                                                                                                          color:#A8A89D;
                                                                                                          padding-left:0px;")))

#wurst_table_final

saveWidget(wurst_table_final, '/Users/trev/Desktop/Data Projects/R Files/wurst_table_final.html', background = text_white)

Finding the 100 best Fantasy Football Performances of the last 10 years

2023-08-20T00:00:00+00:00

Loading in the necessary Libraries

library(reactable)
library(reactablefmtr)
library(htmltools)
library(tidyverse)
library(htmlwidgets)
library(gt)

Setting color variables for use in reactable table

wsj_cream = '#F7F2E6'
highlight_cream = '#FEFCF4'
text_black = '#292727'
glizzy_grey = '#C4C4C4'
almost_white = '#F1F1F1'

Wrangle and clean data

ffp_react <- read.csv('/Users/trev/Desktop/Data Projects/R Files/all_time_ffp.csv')
ffp_react <- ffp_react %>%
  slice_max(ffp, n = 100, with_ties = FALSE) %>%
  mutate(rank = row_number()) %>%
  mutate(headshot_url = coalesce(headshot_url, 'https://static.www.nfl.com/image/private/f_auto,q_auto/league/mdrlzgankwwjldxllgcx')) %>%
  select(rank, headshot_url, 'Player' = player, 'Pos' = pos, team, 'Season' = season, 'Week' = week, 'Points' = ffp, pass_yds, pass_tds, rush_yds, rush_tds, rec, rec_yds, rec_tds, twopoints, fumbles, int) 

ffp_react$Points <- round(ffp_react$Points, 1)
head(ffp_react, 5) %>% gt()

rank	headshot_url	Player	Pos	team	Season	Week	Points	rush_yds	rush_tds	rec	rec_yds	rec_tds
1	https://static.www.nfl.com/image/private/f_auto,q_auto/league/mdrlzgankwwjldxllgcx	Jamaal Charles	RB	KC	2013	Week 15	59.5	20	1	8	195	4
2	https://static.www.nfl.com/image/private/f_auto,q_auto/league/lsszbdnkusxc7mduw5be	Tyreek Hill	WR	KC	2020	Week 12	57.9	0	0	13	269	3
3	https://static.www.nfl.com/image/private/f_auto,q_auto/league/uulstglrpmhqsvqyr5v3	Alvin Kamara	RB	NO	2020	Week 16	56.2	155	6	3	17	0
4	https://static.www.nfl.com/image/private/f_auto,q_auto/league/hwrzdfsng9bh4tba7uoe	Ja'Marr Chase	WR	CIN	2021	Week 17	55.6	0	0	11	266	3
5	https://static.www.nfl.com/image/private/f_auto,q_auto/league/lhazttk4tjpi7ggf7du4	Doug Martin	RB	TB	2012	Week 9	55.2	251	4	4	21	0

Create interactive table

ffp_table <- ffp_react %>% reactable(
          ., theme = reactableTheme(
            color = text_black,
            backgroundColor = almost_white,
            borderColor = '#B8B8B8',
            borderWidth = '2px',
            cellPadding = '8px 12px'),
          highlight = TRUE,
          striped = TRUE,
          bordered = FALSE,
          fullWidth = TRUE,
          style = list(fontFamily = 'Roboto Mono'),
          defaultColDef = colDef(align = 'center', vAlign = 'center', style = list(fontSize =14)),
          defaultPageSize = 8,
          columnGroups = list(
            colGroup(name = 'PASSING',
                     columns = c('pass_yds', 'pass_tds', 'int')),
            colGroup(name = 'RUSHING',
                     columns = c('rush_yds', 'rush_tds', 'fumbles')),
            colGroup(name = 'RECIEVING',
                     columns = c('rec', 'rec_yds', 'rec_tds'))),
          columns = list(
            rank = colDef(name = '#',
                          minWidth = 65,
                          style = list(fontFamily = 'staatliches', fontSize = 28, borderRight = '2px solid ##B8B8B8')
                          ),
            headshot_url = colDef(name = '',
                                  minWidth = 110,
                                  align = 'center',
                                  cell = embed_img(height = 75, width = 105)),
            Player = colDef(name = 'NAME',
                            style = list(fontFamily = 'staatliches', fontSize = 24),
                            align = 'center', 
                            minWidth = 200,
                            cell = merge_column(., 'team', 'below', size = 20, merged_size = 14)),
            Pos = colDef(name = 'POS',
                         minWidth = 75,
                         style = list(fontSize = 19, fontWeight = 700)),
            team = colDef(show = FALSE),
            Season = colDef(name = 'GAME',
                            minWidth = 75,
                            cell = merge_column(., "Week", "below")),
            Week = colDef(show = FALSE),
            Points = colDef(name = 'POINTS',
                            minWidth = 100,
                            style = list(fontSize = 19, borderLeft = '2px solid #B8B8B8', borderRight = '2px solid ##B8B8B8'),
                            cell = pill_buttons(., colors = '#292727', bold_text = TRUE)),
            pass_yds = colDef(name = 'YDS',
                              width = 72,
                              style = highlight_max(.,font_color = 'green')),
            pass_tds = colDef(name = 'TDS',
                              width = 72,
                              style = highlight_max(.,font_color = 'green')),
            int = colDef(name = 'INT',
                         width = 72,
                         style = list(borderRight = '2px solid #B8B8B8')),
            rush_yds = colDef(name = 'YDS',
                              width = 72,
                              style = highlight_max(.,font_color = 'green')),
            rush_tds = colDef(name = 'TDS',
                              width = 72,
                              style = highlight_max(.,font_color = 'green')),
            fumbles = colDef(name = 'FUM',
                             width = 72,
                             style = list(borderRight = '2px solid #B8B8B8')),
            rec = colDef(name = 'REC',
                         width = 72,
                         style = highlight_max(.,font_color = 'green')),
            rec_yds = colDef(name = 'YDS',
                             width = 72,
                             style = highlight_max(.,font_color = 'green')),
            rec_tds = colDef(name = 'TDS',
                             width = 72,
                             style = highlight_max(., font_color = 'green')),
            twopoints = colDef(name = '2PT',
                               width = 72,
                               style = list(borderLeft = '2px solid #B8B8B8'))))

ffp_table_final <- ffp_table %>%
  htmlwidgets::prependContent(
    tagList(
      tags$div('Top 100 Fantasy Football Performances since 2012', style = 'font-size:42px;font-weight:bold;font-family:Staatliches;color:#292727;margin-bottom:0;display:inline-block;vertical-align:middle;'),
      tags$h3('Based on ESPN standard PPR scoring', style="font-family:Roboto Mono;font-size:18px;margin-bottom:0;margin-top:0;font-weight:400;color:#292727;padding-left:0px;")))

ffp_table_final

saveWidget(ffp_table_final, 'ffp_table_final.html', background = almost_white)

The United States of Buc’ee: Finding and mapping the most efficient route to visit all 46 Buc’ees locations

2023-08-04T00:00:00+00:00

Loading in the necessary Libraries

library(plotly)
library(tidyverse)
library(knitr)
library(kableExtra)
library(geosphere)
library(doParallel)
library(ggmap)
library(leaflet)
library(leaflet.extras)
library(htmlwidgets)
library(htmltools)
library(TSP)

Reading in csv file created through webscraping with python

bucees <- read.csv('bucees.csv')
bucees <- bucees %>%
  select(city, state, full_addy, lat, lon)

bucees

Create distance matrix between locations to run TSP model

bucees_coords <- bucees %>% select(lon, lat)

dist_matrix <- as.matrix(
  distm(bucees_coords, fun = distHaversine))/1000

rownames(dist_matrix) <- bucees$city
colnames(dist_matrix) <- bucees$city

dist_matrix %>%
  kbl(digits = 2)

Run TSP Model to find most efficient path and output to a dataframe

tsp_prob <- TSP(dist_matrix)

tsp_prob <- insert_dummy(tsp_prob, label = 'dummy')

tour <- solve_TSP(
  tsp_prob,
  method = 'two_opt',
  control = list(rep =16)
)

path <- names(cut_tour(tour, 'dummy'))

route <- as.data.frame(path)
route$order <- 1:nrow(route)
route <- route %>%
  select(order, city = path)

bucees_plot <- left_join(route, bucees, by = 'city')
bucees_plot

Create interactive map of each Bucee’s location using the leaflet package

middle_lon = mean(bucees_plot$lon)
middle_lat = mean(bucees_plot$lat)

map <- leaflet(bucees_plot) %>% 
  addTiles() %>% 
  setView( lng = middle_lon, lat = middle_lat, zoom = 5.25 ) %>%
  addCircleMarkers(popup = ~city) %>%
  addPolylines(lat = ~lat, lng = ~lon, color = 'green')

Load in API key for ggmaps package

ggmap::register_google(key = "KEY_GOES_HERE", write = FALSE)

Create model to loop through location data and find most efficient driving routes between Buc’ees using Google Maps API

route <- data.frame(matrix(ncol = 3, nrow = 0))
x <- c("lat", "lon", "route")
colnames(route) <- x


i = 1
while (i < 47) {
  singlepath <-trek(bucees_plot$full_addy[i], bucees_plot$full_addy[(i + 1)])
  i <- (i + 1)
  route <- rbind(route, singlepath)
}

Read in the saved route info and plot the path

road_path <- read.csv('bucees_route.csv')

map <- leaflet(road_path,
               options = leafletOptions(attributionControl = FALSE,
                                        minZoom = 5.25
                                        )) %>% 
  addTiles('https://tile.jawg.io/9675b164-059d-4351-aa07-ed736a72cf8d/{z}/{x}/{y}{r}.png?access-token=KEY_GOES_HERE') %>% 
  setView( lng = -89.8485, lat = 33.7966, zoom = 5.25 ) %>%
  addPolylines(lat = ~lat, 
               lng = ~lon, 
               color = '#D31145',
               opacity = 0.9)

map <- map %>%
  setMaxBounds(-110, 25, -75, 45)

Convert Buc’ees logo to a plotabble icon and add markers for each location

bucee_icon <- makeIcon(
  iconUrl = "https://upload.wikimedia.org/wikipedia/en/thumb/2/2c/Buc-ee%27s_beaver.svg/352px-Buc-ee%27s_beaver.svg.png?20210624041100",
  iconWidth = 25, iconHeight = 25)

map_with_bucee <- map %>%
  addMarkers(
    data = bucees_plot,
    label = ~city,
    icon = bucee_icon) %>%
  addResetMapButton()

Overlay title card that was made in Adobe Illustrator

bucee_with_title <- map_with_bucee %>%
  htmlwidgets::onRender("
      function(el, x) {
        console.log(this);
        var myMap = this;
        var imageUrl ='https://raw.githubusercontent.com/trevriekenberg/data/main/Bucees%20Sign-02-02.png';
        var imageBounds = [[40.00, -101.00], [36.00, -91]];

        L.imageOverlay(imageUrl, imageBounds, { opacity: 0.95}).addTo(myMap);
      }
      ")

bucee_with_title

saveWidget(bucee_with_title, 'bucees_map.html')

Cataloging every Buc’ees Location

2023-07-30T00:00:00+00:00

Loading in the required packages

from bs4 import BeautifulSoup as bs
import requests
import re
import pandas as pd
from geopy.geocoders import ArcGIS
import os

Using the requests and beautifulsoup packages to load in the url and convert the webpage to ‘soup’

url = 'https://buc-ees.com/locations/'
page = requests.get(url)
soup = bs(page.text, 'html')

Searching for address and location info by using specific html tags from the webpage

locations = soup.find_all('div', class_='bucees-location-address')
cities = soup.find_all('h4')

Looping through specified div and class tags on the webpage to extract and clean location data

cities_list = [city.text.strip() for city in cities]
#print(cities_list)

address = [location.text for location in locations]
address_clean = []
for x in address:
    street_address = re.sub('(Ethanol-Free:\d+\s\w+\s\d+\sOctane|Ethanol-Free:\d+\sOctane)', '', x)
    address_clean.append(street_address)

Further cleaning the data and stripping whitespace from the entries to isolate city and state values for each location

address_clean

city_state_df = pd.DataFrame(cities_list)
city_state_df.reset_index(inplace=True)
city_state_df.rename( columns={0:'raw', 'index':'#'}, inplace=True )
city_state_df['state'] = city_state_df['raw'].str.extract(r'(\w+$)')
city_state_df['city'] = city_state_df['raw'].str.extract(r'([A-Za-z]+,|[A-Za-z]+\s\w+,)')
city_state_df['city'] = city_state_df['city'].str.replace(',', '')
#city_state_df 
city_state = city_state_df[['#', 'city', 'state']]
city_state.head().style    

	#	city	state
0	0	Athens	AL
1	1	Auburn	AL
2	2	Leeds	AL
3	3	Loxley	AL
4	4	Daytona Beach	FL

Extracting zip codes and street numbers for each location using the regex package

table = pd.DataFrame(columns = ['raw', 'city', 'state', 'zip', 'street_address'])
df = pd.DataFrame(address_clean)
#df['num'] = len(address_clean) - row
df.reset_index(inplace=True)
df.rename( columns={0:'raw','index':'#'}, inplace=True)
df['zip_code'] = df['raw'].str.extract(r'(\d+$)')
df['state'] = df['raw'].str.extract(r'(,\s\D+)')
df['state'] = df['state'].str.extract(r'(\w+\s\w+|\w+)')
df['street_num'] = df['raw'].str.extract(r'(\d+)')
df['city'] = df['raw'].str.extract(r'(\w+,|\w+\s\w+,)')

zip_df = df[['#', 'zip_code', 'street_num']]
zip_df.head()

	#	zip_code	street_num
0	0	35613	2328
1	1	36832	2500
2	2	35094	6900
3	3	36567	20403
4	4	32117	2330

Combining street numbers with street names to form full addresses for each location

address_list = []

for i in locations:
    local = re.search(r'(>(.*?)<)', str(i))
    address_list.append(local[0])
    
add_list_clean = []

for row in address_list:
    
    street = re.sub('>|<', '', str(row))
    add_list_clean.append(street)
    
add_list_clean

addy = pd.DataFrame(add_list_clean)
addy.reset_index(inplace=True)
addy.rename( columns={0:'street_address', 'index':'#'}, inplace=True )
addy.head()

	#	street_address
0	0	2328 Lindsay Lane South
1	1	2500 Buc-ee’s Blvd
2	2	6900 Buc-ee’s Blvd.
3	3	20403 County Rd. 68
4	4	2330 Gateway North Drive

Merging all 3 dataframes together to create a full dataset

half_bucee = addy.merge(zip_df, how='right')
full_bucee = city_state.merge(half_bucee, how='right')
full_bucee['street_name'] = full_bucee['street_address'].replace(r'(^\d+\s)','', regex=True)
full_bucee['full_addy'] = full_bucee['street_address']+', '+full_bucee['city']+', '+full_bucee['state']+' '+full_bucee['zip_code']
full_bucee[['#', 'full_addy', 'street_num', 'street_name', 'city', 'state', 'zip_code']]
full_bucee['store_code'] = full_bucee['#'].apply(str) + '_' + full_bucee['city']
full_bucee.head()

	#	city	state	street_address	zip_code	street_num	street_name	full_addy	store_code
0	0	Athens	AL	2328 Lindsay Lane South	35613	2328	Lindsay Lane South	2328 Lindsay Lane South, Athens, AL 35613	0_Athens
1	1	Auburn	AL	2500 Buc-ee’s Blvd	36832	2500	Buc-ee’s Blvd	2500 Buc-ee’s Blvd, Auburn, AL 36832	1_Auburn
2	2	Leeds	AL	6900 Buc-ee’s Blvd.	35094	6900	Buc-ee’s Blvd.	6900 Buc-ee’s Blvd., Leeds, AL 35094	2_Leeds
3	3	Loxley	AL	20403 County Rd. 68	36567	20403	County Rd. 68	20403 County Rd. 68, Loxley, AL 36567	3_Loxley
4	4	Daytona Beach	FL	2330 Gateway North Drive	32117	2330	Gateway North Drive	2330 Gateway North Drive, Daytona Beach, FL 32117	4_Daytona Beach

Assigning the ‘nom’ variable to the ArcGIS function to create latitude/longitude data for each Buc’ees location

nom=ArcGIS()

Creating a dummy column in the dataset to hold GIS data

full_bucee['coord'] = full_bucee['full_addy'].apply(nom.geocode)

Splitting the ‘coord’ column into seperate latitude and longitude data

full_bucee['lat'] = full_bucee['coord'].apply(lambda x: x.latitude if x != None else None)
full_bucee['lon'] = full_bucee['coord'].apply(lambda x: x.longitude if x != None else None)
clean_beaver = full_bucee[['city', 'state', 'store_code','full_addy', 'lat', 'lon']]
clean_beaver.head()

	city	state	store_code	full_addy	lat	lon
0	Athens	AL	0_Athens	2328 Lindsay Lane South, Athens, AL 35613	34.730718	-86.930749
1	Auburn	AL	1_Auburn	2500 Buc-ee’s Blvd, Auburn, AL 36832	32.610800	-85.494635
2	Leeds	AL	2_Leeds	6900 Buc-ee’s Blvd., Leeds, AL 35094	33.543646	-86.587598
3	Loxley	AL	3_Loxley	20403 County Rd. 68, Loxley, AL 36567	30.634522	-87.676438
4	Daytona Beach	FL	4_Daytona Beach	2330 Gateway North Drive, Daytona Beach, FL 32117	29.224078	-81.099183

Exporting dataframe to csv and saving to local drive

os.makedirs('/User/rieke/R FILES', exist_ok=True)
clean_beaver.to_csv('/Users/rieke/R FILES/bucees.csv')

A look at Premier League Wages

2023-07-07T00:00:00+00:00

1. Loading in the Necessary Packages

library(worldfootballR)
library(tidyverse)
library(reactable)
library(ggthemes)
library(htmltools)
library(scales)
library(ggchicklet)
library(sysfonts)
library(showtext)

2. Using the WorldFootballR package to read in squad wages for each team and save the data as a csv file

prem <- fb_league_urls(country = 'ENG', gender = 'M', season_end_year = 2023, tier = "1st")
prem_teams <- fb_teams_urls(prem, time_pause = 3)

wages <- data.frame()

for (i in prem_teams) {
  wage <- fb_squad_wages(team_urls = i)
  wages <- rbind(wages,wage)
}

wages %>%
  arrange(desc(Nation))


#write.csv(wages, 'wages.csv')
wages_csv <- read.csv('wages.csv')

3. Cleaning the Data and calculating averages for each team

wages$AnnualWageUSD <- as.integer(wages$AnnualWageUSD)
wages_clean <- wages_csv %>%
  select(Team, 'Name' = Player, 'Nationality' = Nation, 'Position' = Pos, Age, 'Salary' = AnnualWageUSD) %>%
  na.omit() %>%
  mutate(is_english = grepl('ENG', Nationality)) %>%
  arrange(desc(Salary))

wages_clean
wagebyteam <- wages_clean %>%
  group_by(Team) %>%
  summarise(avg = mean(Salary))

wagebyteam <- wagebyteam %>%
  mutate(avg_salary = (avg/1000000)) %>%
  arrange(desc(Team))

wagebyteam$avg_salary <- round(wagebyteam$avg_salary, 3)

#EDITING TEAM NAMES
wagebyteam$Team <- gsub("Wolverhampton Wanderers", "Wolverhampton", wagebyteam$Team)
wagebyteam$Team <- gsub("Tottenham Hotspur", "Tottenham", wagebyteam$Team)
wagebyteam$Team <- gsub("Brighton and Hove Albion", "Brighton", wagebyteam$Team)

wagebyteam$avg_salary <- as.numeric(wagebyteam$avg_salary)

4. Creating list of team color hex codes and loading in custom fonts from Google Fonts

teammain <- c('Arsenal' = '#EF0107',
              'Aston Villa' = '#670E36',
              'Bournemouth' = '#DA291C',
              'Brentford' = '#D20000',
              'Brighton' = '#0057B8',
              'Chelsea' = '#034694',
              'Crystal Palace' = '#1B458F',
              'Everton' = '#003399',
              'Fulham' = '#000000',
              'Leeds United' = '#FFCD00',
              'Leicester City' = '#003090',
              'Liverpool' = '#00B2A9',
              'Manchester City' = '#6CABDD',
              'Manchester United' = '#DA291C',
              'Newcastle United' = '#241F20',
              'Nottingham Forest' = '#E53233',
              'Southampton' = '#D71920',
              'Tottenham' = '#132257',
              'West Ham United' = '#7A263A',
              'Wolverhampton' = '#FDB913')

font.add.google(name = 'Source Code Pro', family = 'source-code-pro')
font.add.google(name = 'Staatliches', family = 'staatliches')
showtext.auto()

5. Plotting the data and customizing the chart using the ggplot2 package

wagebyteam.epl.plot <- ggplot(data = wagebyteam, aes( y = avg_salary, x = Team, fill = Team)) +
  geom_bar(stat = 'identity', width = .6) +
  geom_chicklet(radius = grid::unit(1.5, "mm")) +
  scale_y_continuous(limits=c(0, 11), breaks=c(0, 2, 4, 6, 8, 10)) +
  scale_fill_manual(values = teammain) +
  coord_flip() +
  theme_wsj() +
  ylab("Average Player Salary (in Millions)") +
  labs(title = "Average Annual Wages") +
  labs(subtitle = "2022-23 Premier League Season") +
  theme(
    plot.title = element_text(size = 22,
                              family = 'staatliches', 
                              color  = '#292727'),
    plot.subtitle = element_text(size=9),
    legend.position = "none",
    axis.title.x  = element_text(
      size = 9, lineheight = 1,
      family = "source-code-pro",
      face = 'plain',
      colour = "#292727"),
    axis.text.y = element_text(
      size = 9, lineheight = 1, 
      family = 'source-code-pro', 
      face = 'plain',
      color = '#292727')
    )

6. Creating an interactive and searchable database of player wages using the reactable package

wage_search <- wages %>%
  mutate('Percentile' = ntile(WeeklyWageGBP, 100)) %>%
  select(Team, 'Name' =  Player, 'Nationality' = Nation, 'Position' = Pos, WeeklyWageGBP, Percentile) %>%
  na.omit()

wage_search$Team <- gsub("Wolverhampton Wanderers", "Wolverhampton", wage_search$Team)
wage_search$Team <- gsub("Tottenham Hotspur", "Tottenham", wage_search$Team)
wage_search$Team <- gsub("Brighton and Hove Albion", "Brighton", wage_search$Team)

wage_search <- wage_search %>%
  mutate(Nationality = replace(Nationality, Name == 'Alejandro Garnacho', 'ARG')) %>%
  mutate(Nationality = replace(Nationality, Name == 'Samuel Edozie', 'ENG')) %>%
  mutate(Nationality = replace(Nationality, Name == 'Roméo Lavia', 'BEL' )) %>%
  mutate(Nationality = replace(Nationality, Name == 'Juan Larios', 'ESP'))
  
wsj_cream = '#F7F2E6'
highlight_cream = '#FEFCF4'
text_black = '#292727'
glizzy_grey = '#C4C4C4'
green_gradient <- function(x) rgb(colorRamp(c('#F7F2E6','#4DFF6B'))(x), maxColorValue = 255)

wage_search_db <- reactable(wage_search,
          columns = list(
            Team = colDef(width = 200),
            Name = colDef(width = 250),
            Nationality = colDef(name = 'NAT',
                                 align = 'center', 
                                 width = 75),
            Position = colDef(name = 'POS',
                              align = 'center',
                              width = 75,),
            WeeklyWageGBP = colDef(name = "Weekly Wage (£)", 
                                   align = 'center',
                                   width = 200,
                                   format = colFormat(separators = TRUE)),
            Percentile = colDef(name = '%', 
                                align = 'center', 
                                width = 75, 
                                style = function(value) {
                                  normalized <- (value - min(wage_search$Percentile)) /   (max(wage_search$Percentile) - min(wage_search$Percentile))
                                  color <- green_gradient(normalized)
                                  list(background = color)
                                })),
          language = reactableLang(searchPlaceholder = 'Filter Data',
                                   noData = 'No Results'),
          theme = reactableTheme(
            backgroundColor = wsj_cream,
            highlightColor = highlight_cream,
            borderColor = glizzy_grey,
            cellPadding = '8px 12px',
            style = list(fontFamily = 'Roboto Mono',
                         fontSize = '15px',
                         color = text_black),
            searchInputStyle = list(width = '100%',
                                    backgroundColor = wsj_cream,
                                    color = text_black,
                                    borderColor = glizzy_grey)),
                                    
          defaultSorted = 'Name',
          fullWidth = FALSE,
          searchable = TRUE, 
          highlight = TRUE,
          bordered = TRUE,
          wrap = FALSE,
          paginationType = "simple",
          minRows = 10)

Trev Makes Charts

Worst of the Worst: The Ten Worst Fantasy Football Outings of All Time

Load in the required libraries

Load in and begin to wrangle the data

Edit team names and clean variable names

Assign color variables for table styling

Create reactable table

Add title and subtile to the table

Save the table as an HTML widget

Finding the 100 best Fantasy Football Performances of the last 10 years

Loading in the necessary Libraries

Setting color variables for use in reactable table

Wrangle and clean data

Create interactive table

Save table as an HTML widget

The United States of Buc’ee: Finding and mapping the most efficient route to visit all 46 Buc’ees locations

Loading in the necessary Libraries

Reading in csv file created through webscraping with python

Create distance matrix between locations to run TSP model

Run TSP Model to find most efficient path and output to a dataframe

Create interactive map of each Bucee’s location using the leaflet package

Load in API key for ggmaps package

Create model to loop through location data and find most efficient driving routes between Buc’ees using Google Maps API

Read in the saved route info and plot the path

Convert Buc’ees logo to a plotabble icon and add markers for each location

Overlay title card that was made in Adobe Illustrator

Save leaflet map as an html widget

Cataloging every Buc’ees Location

Loading in the required packages

Using the requests and beautifulsoup packages to load in the url and convert the webpage to ‘soup’

Searching for address and location info by using specific html tags from the webpage

Looping through specified div and class tags on the webpage to extract and clean location data

Further cleaning the data and stripping whitespace from the entries to isolate city and state values for each location

Extracting zip codes and street numbers for each location using the regex package

Combining street numbers with street names to form full addresses for each location

Merging all 3 dataframes together to create a full dataset

Assigning the ‘nom’ variable to the ArcGIS function to create latitude/longitude data for each Buc’ees location

Creating a dummy column in the dataset to hold GIS data

Splitting the ‘coord’ column into seperate latitude and longitude data

Exporting dataframe to csv and saving to local drive

A look at Premier League Wages

1. Loading in the Necessary Packages

2. Using the WorldFootballR package to read in squad wages for each team and save the data as a csv file

3. Cleaning the Data and calculating averages for each team

4. Creating list of team color hex codes and loading in custom fonts from Google Fonts

5. Plotting the data and customizing the chart using the ggplot2 package

6. Creating an interactive and searchable database of player wages using the reactable package