Can I teach you R in 10 minutes?

...no   😞

But we can...

• talk about what's easier to do in R
• go over some common functions you might use for this project
• discuss a roadmap for learning more

Disclaimer
The speaker herein pledges as follows: no declarative statements will be uttered about how R is objectively "the best" and no moral judgments will be made on which software, IDE, programming language, packages, dark mode, etc. the viewer chooses to use and/or not use.

tl;dr
I don't care what you use for your project! Use Excel! Use Python! Use SAS! Use an abacus!

But these are my 10 minutes, so we're going to talk about R.

Step 0: How does one use R, exactly?

1️⃣   the language itself: https://cloud.r-project.org/

2️⃣   the IDE (integrated development environment): https://rstudio.com/products/rstudio/download/

Step 1: Data!

library(tidyverse)
library(janitor)
scouting <- read_csv("https://tinyurl.com/BDCscouting") %>% 
  clean_names()
nwhl <- read_csv("https://tinyurl.com/BDCnwhl") %>% 
  clean_names()
women <- read_csv("https://tinyurl.com/BDCwomens") %>% 
  clean_names()

Step 1: Data!

library(tidyverse)
library(janitor) 
scouting <- read_csv("https://tinyurl.com/BDCscouting") %>%
  clean_names()
nwhl <- read_csv("https://tinyurl.com/BDCnwhl") %>%
  clean_names()
women <- read_csv("https://tinyurl.com/BDCwomens") %>%
  clean_names()

Step 1: Data!

library(tidyverse) 
library(janitor) 
scouting <- read_csv("https://tinyurl.com/BDCscouting") %>%
  clean_names() 
nwhl <- read_csv("https://tinyurl.com/BDCnwhl") %>%
  clean_names() 
women <- read_csv("https://tinyurl.com/BDCwomens") %>%
  clean_names()

Step 1: Data!

library(tidyverse) 
library(janitor)
scouting <- read_csv("https://tinyurl.com/BDCscouting") %>%
  clean_names()
nwhl <- read_csv("https://tinyurl.com/BDCnwhl") %>% 
  clean_names()
women <- read_csv("https://tinyurl.com/BDCwomens") %>% 
  clean_names()

Step 2: Explore

View(scouting)
scouting %>%
  glimpse()
scouting %>% 
  count(event)
scouting %>% 
  count(event, detail_1)

Step 2: Explore

View(scouting)
scouting %>%
  glimpse() 
scouting %>%
  count(event)
scouting %>% 
  count(event, detail_1)

Step 2: Explore

View(scouting)
scouting %>%
  glimpse() 
scouting %>%
  count(event) 
scouting %>%
  count(event, detail_1)

Step 3: A Question

From the scouting data, among players who've taken at least 50 faceoffs, who has the best faceoff percentage?

(Is this the most rigorous Big Data Cup question? No, but I only have 10 minutes!)

Step 3: A Question

From the scouting data, among players who've taken at least 50 faceoffs, who has the best faceoff percentage?

(Is this the most rigorous Big Data Cup question? No, but I only have 10 minutes!)

Step 3: A Question

Step 3: A Question

faceoffs <- scouting %>% 
  filter(event == "Faceoff Win") %>% 
  select(player, player_2) %>% 
  rename(winner = player,
         loser = player_2) %>% 
  pivot_longer(winner:loser, 
               names_to = "status", 
               values_to = "player") %>% 
  mutate(win = ifelse(status == "winner", 1, 0)) %>% 
  group_by(player) %>% 
  summarize(faceoffs = n(),
            faceoff_wins = sum(win)) %>% 
  mutate(faceoff_perc = faceoff_wins / faceoffs) %>% 
  filter(faceoffs >= 50) %>% 
  arrange(desc(faceoff_perc))

Step 3: A Question

faceoffs <- scouting %>%
  filter(event == "Faceoff Win") %>% 
  select(player, player_2) %>% 
  rename(winner = player,
         loser = player_2) %>% 
  pivot_longer(winner:loser, 
               names_to = "status", 
               values_to = "player") %>% 
  mutate(win = ifelse(status == "winner", 1, 0)) %>% 
  group_by(player) %>% 
  summarize(faceoffs = n(),
            faceoff_wins = sum(win)) %>% 
  mutate(faceoff_perc = faceoff_wins / faceoffs) %>% 
  filter(faceoffs >= 50) %>% 
  arrange(desc(faceoff_perc))

Step 3: A Question

faceoffs <- scouting %>% 
  filter(event == "Faceoff Win") %>%
  select(player, player_2) %>%
  rename(winner = player,
         loser = player_2) %>% 
  pivot_longer(winner:loser, 
               names_to = "status", 
               values_to = "player") %>% 
  mutate(win = ifelse(status == "winner", 1, 0)) %>% 
  group_by(player) %>% 
  summarize(faceoffs = n(),
            faceoff_wins = sum(win)) %>% 
  mutate(faceoff_perc = faceoff_wins / faceoffs) %>% 
  filter(faceoffs >= 50) %>% 
  arrange(desc(faceoff_perc))

Step 3: A Question

faceoffs <- scouting %>% 
  filter(event == "Faceoff Win") %>% 
  select(player, player_2) %>% 
  rename(winner = player,
         loser = player_2) %>%
  pivot_longer(winner:loser, 
               names_to = "status", 
               values_to = "player") %>% 
  mutate(win = ifelse(status == "winner", 1, 0)) %>% 
  group_by(player) %>% 
  summarize(faceoffs = n(),
            faceoff_wins = sum(win)) %>% 
  mutate(faceoff_perc = faceoff_wins / faceoffs) %>% 
  filter(faceoffs >= 50) %>% 
  arrange(desc(faceoff_perc))

Step 3: A Question

faceoffs <- scouting %>% 
  filter(event == "Faceoff Win") %>% 
  select(player, player_2) %>% 
  rename(winner = player,
         loser = player_2) %>% 
  pivot_longer(winner:loser,
               names_to = "status",
               values_to = "player") %>%
  mutate(win = ifelse(status == "winner", 1, 0)) %>% 
  group_by(player) %>% 
  summarize(faceoffs = n(),
            faceoff_wins = sum(win)) %>% 
  mutate(faceoff_perc = faceoff_wins / faceoffs) %>% 
  filter(faceoffs >= 50) %>% 
  arrange(desc(faceoff_perc))

Step 3: A Question

Step 3: A Question

faceoffs <- scouting %>% 
  filter(event == "Faceoff Win") %>% 
  select(player, player_2) %>%
  rename(winner = player,
         loser = player_2) %>% 
  pivot_longer(winner:loser, 
               names_to = "status", 
               values_to = "player") %>% 
  mutate(win = ifelse(status == "winner", 1, 0)) %>%
  group_by(player) %>% 
  summarize(faceoffs = n(),
            faceoff_wins = sum(win)) %>% 
  mutate(faceoff_perc = faceoff_wins / faceoffs) %>% 
  filter(faceoffs >= 50) %>% 
  arrange(desc(faceoff_perc))

Step 3: A Question

faceoffs <- scouting %>% 
  filter(event == "Faceoff Win") %>% 
  select(player, player_2) %>% 
  rename(winner = player,
         loser = player_2) %>% 
  pivot_longer(winner:loser, 
               names_to = "status", 
               values_to = "player") %>% 
  mutate(win = ifelse(status == "winner", 1, 0)) %>% 
  group_by(player) %>%
  summarize(faceoffs = n(),
            faceoff_wins = sum(win)) %>%
  mutate(faceoff_perc = faceoff_wins / faceoffs) %>% 
  filter(faceoffs >= 50) %>% 
  arrange(desc(faceoff_perc))

Step 3: A Question

faceoffs <- scouting %>% 
  filter(event == "Faceoff Win") %>% 
  select(player, player_2) %>% 
  rename(winner = player,
         loser = player_2) %>% 
  pivot_longer(winner:loser, 
               names_to = "status", 
               values_to = "player") %>% 
  mutate(win = ifelse(status == "winner", 1, 0)) %>% 
  group_by(player) %>% 
  summarize(faceoffs = n(),
            faceoff_wins = sum(win)) %>% 
  mutate(faceoff_perc = faceoff_wins / faceoffs) %>%
  filter(faceoffs >= 50) %>%
  arrange(desc(faceoff_perc))

Step 3: A Question

faceoffs <- scouting %>% 
  filter(event == "Faceoff Win") %>% 
  select(player, player_2) %>% 
  rename(winner = player,
         loser = player_2) %>% 
  pivot_longer(winner:loser, 
               names_to = "status", 
               values_to = "player") %>% 
  mutate(win = ifelse(status == "winner", 1, 0)) %>% 
  group_by(player) %>% 
  summarize(faceoffs = n(),
            faceoff_wins = sum(win)) %>% 
  mutate(faceoff_perc = faceoff_wins / faceoffs) %>% 
  filter(faceoffs >= 50) %>% 
  arrange(desc(faceoff_perc))

Step 3: A Question

Step 4: A graph!

Step 4: A graph!

faceoffs_team %>% 
  ggplot(aes(x = reorder(player, faceoff_perc), 
             y = faceoff_perc, fill = team)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_fill_manual(values = c("#F2A900", "#FF6720", "#862633", 
                               "#00205B", "#010101", "#C8C9C7")) +
  labs(title = "Faceoff Percentages",
       subtitle = "Among players with 50+ faceoffs, from Big Data 
       Cup scouting data set") +
  ylab("Faceoff Win Percentage") +
  geom_text(aes(label = scales::percent(faceoff_perc, 
                                        accuracy = 0.1)), 
            family = "Seravek", hjust = -0.15) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1), 
                     limits = c(0, 0.72)) +
  hanic_theme() +
  theme(legend.title = element_blank(),
        legend.position = "bottom",
        panel.grid.major.y = element_blank(),
        axis.title.y = element_blank())

Step 4: A graph!

faceoffs_team %>% 
  ggplot(aes(x = reorder(player, faceoff_perc),
             y = faceoff_perc, fill = team)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_fill_manual(values = c("#F2A900", "#FF6720", "#862633", 
                               "#00205B", "#010101", "#C8C9C7")) +
  labs(title = "Faceoff Percentages",
       subtitle = "Among players with 50+ faceoffs, from Big Data 
       Cup scouting data set") +
  ylab("Faceoff Win Percentage") +
  geom_text(aes(label = scales::percent(faceoff_perc, 
                                        accuracy = 0.1)), 
            family = "Seravek", hjust = -0.15) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1), 
                     limits = c(0, 0.72)) +
  hanic_theme() +
  theme(legend.title = element_blank(),
        legend.position = "bottom",
        panel.grid.major.y = element_blank(),
        axis.title.y = element_blank())

Step 4: A graph!

faceoffs_team %>% 
  ggplot(aes(x = reorder(player, faceoff_perc), 
             y = faceoff_perc, fill = team)) + 
  geom_bar(stat = "identity") + 
  coord_flip() + 
  scale_fill_manual(values = c("#F2A900", "#FF6720", "#862633",
                               "#00205B", "#010101", "#C8C9C7")) +
  labs(title = "Faceoff Percentages",
       subtitle = "Among players with 50+ faceoffs, from Big Data
       Cup scouting data set") +
  ylab("Faceoff Win Percentage") +
  geom_text(aes(label = scales::percent(faceoff_perc,
                                        accuracy = 0.1)),
            family = "Seravek", hjust = -0.15) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1),
                     limits = c(0, 0.72)) +
  hanic_theme() +
  theme(legend.title = element_blank(),
        legend.position = "bottom",
        panel.grid.major.y = element_blank(),
        axis.title.y = element_blank())

Learn More

If you want to stick with the hockey theme:

• my workshop from CMSAC last year
• interactive tutorials in RStudio

install.packages("devtools")
devtools::install_github("meghall06/betweenthepipes")
# look in the Tutorial pane in the upper-right of RStudio
# or...run:
betweenthepipes::intro()
betweenthepipes::data_manip()

Learn More

If you want to stick with the hockey theme:

• my workshop from CMSAC last year
• interactive tutorials in RStudio

Learn More

If you want to stick with the hockey theme:

• my workshop from CMSAC last year
• interactive tutorials in RStudio

install.packages("devtools")
devtools::install_github("meghall06/betweenthepipes")
# look in the Tutorial pane in the upper-right of RStudio
# or...run:
betweenthepipes::intro()
betweenthepipes::data_manip()

Elsewhere:

• R for Data Science   ⭐
• R for Excel Users
• STAT 545