Lab 7: Dates & Times; The Apply Family

BI 255 · Bethune-Cookman University · Fall 2026

Author

Dr. Rosie Stanbrook-Buyer

Published

September 2, 2026

Dates, Times & the Apply Family — with MLB Data

Baseball has more data than almost any other sport — games played every day for six months, timestamps on every pitch, player performance tracked by date. Today we learn how to work with dates and times in R, and how to apply functions efficiently across rows, columns, or groups using the apply family.

Learning Objectives

By the end of this lab you will be able to:

Create and format dates using as.Date() and format()
Calculate time differences between dates
Extract components from dates (month, year, day of week)
Use apply() on matrices
Use lapply() and sapply() on lists and vectors
Use tapply() to calculate group summaries

Part 1: Working with Dates

In R, dates are a special data type. They look like text but behave like numbers — you can subtract them, compare them, and extract parts.

Creating Dates with `as.Date()`

# The 2026 MLB season Opening Day
opening_day <- as.Date("2026-03-26")
opening_day

[1] "2026-03-26"

class(opening_day)

[1] "Date"

The Standard Date Format in R

R expects dates in YYYY-MM-DD format (year-month-day) by default. This is called ISO 8601 format.

"2026-09-01" — works perfectly
"Sept 1, 2026" — R won’t understand this without extra instructions
"09/01/2026" — you need to specify the format

If your date is in a different format, use the format argument:

as.Date("09/01/2026", format = "%m/%d/%Y")

# Key MLB 2026 season dates
spring_training <- as.Date("2026-02-14")
opening_day     <- as.Date("2026-03-26")
all_star_game   <- as.Date("2026-07-14")
playoff_start   <- as.Date("2026-10-01")
world_series    <- as.Date("2026-10-21")

Calculating Time Differences

# How many days from Opening Day to the All-Star Game?
all_star_game - opening_day

Time difference of 110 days

# How many days is the regular season?
as.numeric(playoff_start - opening_day)

[1] 189

# Days between Spring Training and World Series
as.numeric(world_series - spring_training)

[1] 249

as.numeric() on Date Differences

When you subtract two dates, R returns a “difftime” object. Wrapping it in as.numeric() gives you a plain number (in days) that you can use in calculations.

Today’s Date

# Sys.Date() gives today's date automatically
today <- Sys.Date()
today

[1] "2026-06-23"

# How many days until the World Series?
as.numeric(world_series - today)

[1] 120

Part 2: Formatting and Extracting Date Components

The format() function lets you display dates in any way you like, and extract specific components:

# Display opening day in a friendly format
format(opening_day, "%B %d, %Y")      # "March 26, 2026"

[1] "March 26, 2026"

format(opening_day, "%m/%d/%Y")       # "03/26/2026"

[1] "03/26/2026"

format(opening_day, "%A")             # Day of the week

[1] "Thursday"

Date Format Codes

Code	Meaning	Example
`%Y`	4-digit year	2026
`%y`	2-digit year	26
`%m`	Month (number)	03
`%B`	Month (full name)	March
`%b`	Month (abbreviated)	Mar
`%d`	Day of month	26
`%A`	Day of week (full)	Thursday
`%a`	Day of week (abbreviated)	Thu

# Extract just the month number
as.numeric(format(opening_day, "%m"))    # 3 = March

[1] 3

# What month is the World Series?
format(world_series, "%B %Y")

[1] "October 2026"

Part 3: Working with Sequences of Dates

# Generate all game dates for the first week of the 2026 MLB season
first_week <- seq(from = opening_day,
                  to   = opening_day + 6,
                  by   = "day")
first_week

[1] "2026-03-26" "2026-03-27" "2026-03-28" "2026-03-29" "2026-03-30"
[6] "2026-03-31" "2026-04-01"

# Show day names for the first week
format(first_week, "%A %b %d")

[1] "Thursday Mar 26"  "Friday Mar 27"    "Saturday Mar 28"  "Sunday Mar 29"   
[5] "Monday Mar 30"    "Tuesday Mar 31"   "Wednesday Apr 01"

# Generate monthly milestones through the season
monthly <- seq(from = opening_day,
               to   = playoff_start,
               by   = "month")
format(monthly, "%B %d, %Y")

[1] "March 26, 2026"     "April 26, 2026"     "May 26, 2026"      
[4] "June 26, 2026"      "July 26, 2026"      "August 26, 2026"   
[7] "September 26, 2026"

Part 4: The Apply Family

The apply family of functions lets you apply the same function to many things at once — without writing a repetitive loop. This is very important in biology when you have many samples, genes, or games to process.

`apply()` — For Matrices (Rows or Columns)

# Season stats matrix for 5 Dodgers players
# Columns: Batting Average, Home Runs, RBIs, Stolen Bases
dodgers_stats <- matrix(
  c(0.312, 32, 95, 8,
    0.285, 28, 87, 22,
    0.301, 19, 74, 35,
    0.267, 41, 108, 3,
    0.320, 12, 58, 44),
  nrow  = 5,
  byrow = TRUE
)
rownames(dodgers_stats) <- c("Betts", "Freeman", "Hernandez", "Muncy", "Outman")
colnames(dodgers_stats) <- c("AVG", "HR", "RBI", "SB")

dodgers_stats

            AVG HR RBI SB
Betts     0.312 32  95  8
Freeman   0.285 28  87 22
Hernandez 0.301 19  74 35
Muncy     0.267 41 108  3
Outman    0.320 12  58 44

# apply() syntax: apply(matrix, MARGIN, function)
# MARGIN = 1 means apply to each ROW
# MARGIN = 2 means apply to each COLUMN

# Average value for each stat (column averages)
apply(dodgers_stats, 2, mean)

   AVG     HR    RBI     SB 
 0.297 26.400 84.400 22.400

# Total for each player across all stats (not meaningful here, but shows the concept)
apply(dodgers_stats, 1, sum)

    Betts   Freeman Hernandez     Muncy    Outman 
  135.312   137.285   128.301   152.267   114.320

# Which player has the highest value in each stat?
apply(dodgers_stats, 2, which.max)

AVG  HR RBI  SB 
  5   4   4   5

Remember: MARGIN = 1 (Rows) or 2 (Columns)?

A handy way to remember: - 1 = rows → think of the 1st dimension - 2 = columns → think of the 2nd dimension

Or: “1 goes across, 2 goes down”

`sapply()` — Apply to a Vector or List, Get Simple Output

sapply() applies a function to each element of a vector or list and tries to return a simple result (a vector or matrix):

# Player names vector
players <- c("Mookie Betts", "Freddie Freeman", "Miguel Rojas",
              "Max Muncy", "James Outman")

# Apply nchar() to get the length of each player's name
sapply(players, nchar)

   Mookie Betts Freddie Freeman    Miguel Rojas       Max Muncy    James Outman 
             12              15              12               9              12

# Calculate the square root of each HR total
hr_totals <- c(32, 28, 19, 41, 12)
sapply(hr_totals, sqrt)

[1] 5.656854 5.291503 4.358899 6.403124 3.464102

# Custom function: categorize HR totals
hr_category <- function(hr) {
  if (hr >= 35) return("Power Hitter")
  if (hr >= 20) return("Average Power")
  return("Contact Hitter")
}

sapply(hr_totals, hr_category)

[1] "Average Power"  "Average Power"  "Contact Hitter" "Power Hitter"  
[5] "Contact Hitter"

Writing Your Own Functions

You just saw R let you define your own function with function() { }. We’ll go deeper into custom functions in future labs, but notice how natural the syntax is:

my_function <- function(input) {
  # do something with input
  return(result)
}

`lapply()` — Apply to a List, Always Return a List

lapply() is like sapply() but always returns a list (useful when results have different lengths):

# A list of game scores for 3 Dodgers games
game_scores <- list(
  game_1 = c(7, 2, 4, 0, 3, 1, 2, 0, 0),   # Runs per inning
  game_2 = c(0, 0, 3, 5, 0, 2, 0, 1, 2),
  game_3 = c(1, 1, 0, 0, 4, 0, 3, 2, 1)
)

# Calculate total runs in each game
lapply(game_scores, sum)

$game_1
[1] 19

$game_2
[1] 13

$game_3
[1] 12

# Use sapply to get a simpler vector output
sapply(game_scores, sum)

game_1 game_2 game_3 
    19     13     12

# Which inning did they score the most runs in each game?
sapply(game_scores, which.max)

game_1 game_2 game_3 
     1      4      5

`tapply()` — Apply by Group

tapply() is extremely useful in biology and sports: it applies a function to a variable split by a grouping variable.

# MLB player data: batting average by position
mlb_players <- data.frame(
  player   = c("Betts", "Freeman", "Turner", "Muncy",
                "Smith", "Outman", "Hernandez", "Barnes", "Buehler"),
  position = c("OF", "1B", "SS", "3B",
                "C", "OF", "2B", "C", "SP"),
  avg      = c(0.312, 0.285, 0.271, 0.267,
                0.250, 0.320, 0.301, 0.239, 0.098),
  hr       = c(32, 28, 15, 41, 18, 12, 19, 11, 2)
)

# Average batting average by position
tapply(mlb_players$avg, mlb_players$position, mean)

    1B     2B     3B      C     OF     SP     SS 
0.2850 0.3010 0.2670 0.2445 0.3160 0.0980 0.2710

# Total home runs by position
tapply(mlb_players$hr, mlb_players$position, sum)

1B 2B 3B  C OF SP SS 
28 19 41 29 44  2 15

tapply() in Biology

tapply() is exactly what you use to compare groups in biology — e.g., average gene expression in treatment vs. control, or species richness in disturbed vs. undisturbed habitats. This pattern is at the heart of almost all biological data analysis.

Part 5: Putting It All Together

Let’s analyze a simulated MLB pitch dataset combining dates and apply functions:

# Simulated pitching data for 3 Dodgers starting pitchers over 6 starts each
set.seed(123)
pitching <- data.frame(
  pitcher     = rep(c("Buehler", "Kershaw", "May"), each = 6),
  start_date  = as.Date(c("2026-04-05","2026-04-11","2026-04-17",
                           "2026-04-23","2026-04-29","2026-05-05",
                           "2026-04-06","2026-04-12","2026-04-18",
                           "2026-04-24","2026-04-30","2026-05-06",
                           "2026-04-07","2026-04-13","2026-04-19",
                           "2026-04-25","2026-05-01","2026-05-07")),
  innings     = c(6.0, 5.2, 7.0, 6.1, 5.0, 6.2,
                  7.0, 7.1, 6.0, 8.0, 5.2, 7.0,
                  5.0, 6.0, 5.1, 6.2, 4.1, 5.0),
  earned_runs = c(3, 4, 1, 2, 5, 3, 2, 1, 3, 0, 4, 2, 4, 3, 5, 2, 6, 4)
)

# Calculate ERA per start: ERA = (earned_runs / innings) * 9
pitching$era_per_start <- round((pitching$earned_runs / pitching$innings) * 9, 2)

# Average ERA by pitcher using tapply
cat("Average ERA by Pitcher:\n")

Average ERA by Pitcher:

tapply(pitching$era_per_start, pitching$pitcher, mean)

 Buehler  Kershaw      May 
4.835000 2.971667 7.298333

# Which month do most starts occur in?
pitching$month <- format(pitching$start_date, "%B")
table(pitching$month)


April   May 
   14     4

# Visualize ERA by pitcher
boxplot(era_per_start ~ pitcher, data = pitching,
        col  = c("#005A9C", "#EF3E42", "#A5ACAF"),  # Dodger colors
        ylab = "ERA per Start",
        xlab = "Pitcher",
        main = "Starting Pitcher ERA Distribution\nLos Angeles Dodgers, Spring 2026")
abline(h = 4.50, col = "red", lty = 2)
text(0.5, 4.7, "4.50 ERA threshold", col = "red", cex = 0.8, adj = 0)

3-Minute Knowledge Check

Close your notes. Answer these on your own — you have 3 minutes. We’ll go through the answers together.

Knowledge Check Questions

1. Which of these correctly creates a date in R?

a) `as.Date("July 4, 2026")`  &nbsp;&nbsp; b) `as.Date("2026-07-04")`  &nbsp;&nbsp; c) `date("04/07/2026")`  &nbsp;&nbsp; d) `"2026-07-04"`

2. You subtract two date objects: as.Date("2026-10-31") - as.Date("2026-09-01"). What type of result does R return, and how would you convert it to a plain number?

3. In apply(my_matrix, 2, mean), what does the 2 refer to?

a) Apply to every 2nd row  &nbsp;&nbsp; b) Apply to columns  &nbsp;&nbsp; c) Apply to rows  &nbsp;&nbsp; d) Apply the function twice

4. What is the main difference between sapply() and lapply()?

5. Which function from the apply family would you use to calculate the average score separately for each team in a data frame?

a) `apply()`  &nbsp;&nbsp; b) `lapply()`  &nbsp;&nbsp; c) `sapply()`  &nbsp;&nbsp; d) `tapply()`

Answers (Only reveal if you have answered the above questions)

1. b) as.Date("2026-07-04") — R’s default date format is YYYY-MM-DD (ISO 8601). Other formats require you to specify format = "%m/%d/%Y" etc.

2. R returns a difftime object. Wrap it in as.numeric() to get a plain number of days: as.numeric(as.Date("2026-10-31") - as.Date("2026-09-01")) → 60.

3. b) Apply to columns — In apply(), MARGIN = 1 applies to rows, MARGIN = 2 applies to columns. Here, mean is calculated for each column of the matrix.

4. sapply() tries to simplify the output to a vector or matrix. lapply() always returns a list. Use sapply() when you want a clean vector; use lapply() when results might have different lengths.

5. d) tapply() — This function applies a function to a variable split by a grouping variable, making it ideal for group-wise summaries like average score per team.

Lab 7 Checklist

Before you leave, make sure you can:

Create a date with as.Date() in YYYY-MM-DD format
Calculate the number of days between two dates
Use format() to display dates in different formats
Extract date components (month, day, weekday)
Use apply() with MARGIN = 1 (rows) and 2 (columns)
Use sapply() to apply a function to a vector
Use lapply() when the output is a list
Use tapply() to get group summaries

Bonus Challenge

Create a data frame of any sports team’s game schedule with at least 10 dates. Use format() to find the day of the week for each game. Use tapply() to calculate the average score on weekday games vs. weekend games (hint: Saturday and Sunday are weekends).

Before Next Class

Monday is Labor Day — No Class ️
Wednesday Sep 9 (Lab 8): Hypothesis Testing & the Null Hypothesis
Read Intro2r Ch. 4
Remember: Quiz 2 is Friday Sep 4 — covers Labs 3–7

Quiz 2 — This Friday!

Topics covered: sequences, vectors, missing values, data frames, matrices, subsetting, dates, and apply functions.

Best way to prepare: open a new Quarto document and try to reproduce the key examples from Labs 3–7 from memory.

--- title: "Lab 7: Dates & Times; The Apply Family" subtitle: "BI 255 · Bethune-Cookman University · Fall 2026" author: "Dr. Rosie Stanbrook-Buyer" date: "September 2, 2026" format: html: theme: cosmo toc: true toc-location: left toc-title: "In This Lab" toc-depth: 3 code-fold: false code-tools: true highlight-style: github smooth-scroll: true embed-resources: true callout-appearance: default execute: warning: false message: false echo: true --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` ------------------------------------------------------------------------ ## Dates, Times & the Apply Family — with MLB Data Baseball has more data than almost any other sport — games played every day for six months, timestamps on every pitch, player performance tracked by date. Today we learn how to work with **dates and times** in R, and how to apply functions efficiently across rows, columns, or groups using the **apply family**. ::: callout-note ## Learning Objectives By the end of this lab you will be able to: 1. Create and format dates using `as.Date()` and `format()` 2. Calculate time differences between dates 3. Extract components from dates (month, year, day of week) 4. Use `apply()` on matrices 5. Use `lapply()` and `sapply()` on lists and vectors 6. Use `tapply()` to calculate group summaries ::: ------------------------------------------------------------------------ ## Part 1: Working with Dates In R, dates are a special data type. They look like text but behave like numbers — you can subtract them, compare them, and extract parts. ### Creating Dates with `as.Date()` ```{r} # The 2026 MLB season Opening Day opening_day <- as.Date("2026-03-26") opening_day class(opening_day) ``` ::: callout-important ## The Standard Date Format in R R expects dates in **YYYY-MM-DD** format (year-month-day) by default. This is called ISO 8601 format. - `"2026-09-01"` — works perfectly - `"Sept 1, 2026"` — R won't understand this without extra instructions - `"09/01/2026"` — you need to specify the format If your date is in a different format, use the `format` argument: ```r as.Date("09/01/2026", format = "%m/%d/%Y") ``` ::: ```{r} # Key MLB 2026 season dates spring_training <- as.Date("2026-02-14") opening_day <- as.Date("2026-03-26") all_star_game <- as.Date("2026-07-14") playoff_start <- as.Date("2026-10-01") world_series <- as.Date("2026-10-21") ``` ### Calculating Time Differences ```{r} # How many days from Opening Day to the All-Star Game? all_star_game - opening_day ``` ```{r} # How many days is the regular season? as.numeric(playoff_start - opening_day) ``` ```{r} # Days between Spring Training and World Series as.numeric(world_series - spring_training) ``` ::: callout-tip ## `as.numeric()` on Date Differences When you subtract two dates, R returns a "difftime" object. Wrapping it in `as.numeric()` gives you a plain number (in days) that you can use in calculations. ::: ### Today's Date ```{r} # Sys.Date() gives today's date automatically today <- Sys.Date() today # How many days until the World Series? as.numeric(world_series - today) ``` ------------------------------------------------------------------------ ## Part 2: Formatting and Extracting Date Components The `format()` function lets you display dates in any way you like, and extract specific components: ```{r} # Display opening day in a friendly format format(opening_day, "%B %d, %Y") # "March 26, 2026" ``` ```{r} format(opening_day, "%m/%d/%Y") # "03/26/2026" ``` ```{r} format(opening_day, "%A") # Day of the week ``` ::: callout-note ## Date Format Codes | Code | Meaning | Example | |------|---------|---------| | `%Y` | 4-digit year | 2026 | | `%y` | 2-digit year | 26 | | `%m` | Month (number) | 03 | | `%B` | Month (full name) | March | | `%b` | Month (abbreviated) | Mar | | `%d` | Day of month | 26 | | `%A` | Day of week (full) | Thursday | | `%a` | Day of week (abbreviated) | Thu | ::: ```{r} # Extract just the month number as.numeric(format(opening_day, "%m")) # 3 = March ``` ```{r} # What month is the World Series? format(world_series, "%B %Y") ``` ------------------------------------------------------------------------ ## Part 3: Working with Sequences of Dates ```{r} # Generate all game dates for the first week of the 2026 MLB season first_week <- seq(from = opening_day, to = opening_day + 6, by = "day") first_week ``` ```{r} # Show day names for the first week format(first_week, "%A %b %d") ``` ```{r} # Generate monthly milestones through the season monthly <- seq(from = opening_day, to = playoff_start, by = "month") format(monthly, "%B %d, %Y") ``` ------------------------------------------------------------------------ ## Part 4: The Apply Family The apply family of functions lets you apply the same function to many things at once — without writing a repetitive loop. This is very important in biology when you have many samples, genes, or games to process. ### `apply()` — For Matrices (Rows or Columns) ```{r} # Season stats matrix for 5 Dodgers players # Columns: Batting Average, Home Runs, RBIs, Stolen Bases dodgers_stats <- matrix( c(0.312, 32, 95, 8, 0.285, 28, 87, 22, 0.301, 19, 74, 35, 0.267, 41, 108, 3, 0.320, 12, 58, 44), nrow = 5, byrow = TRUE ) rownames(dodgers_stats) <- c("Betts", "Freeman", "Hernandez", "Muncy", "Outman") colnames(dodgers_stats) <- c("AVG", "HR", "RBI", "SB") dodgers_stats ``` ```{r} # apply() syntax: apply(matrix, MARGIN, function) # MARGIN = 1 means apply to each ROW # MARGIN = 2 means apply to each COLUMN # Average value for each stat (column averages) apply(dodgers_stats, 2, mean) ``` ```{r} # Total for each player across all stats (not meaningful here, but shows the concept) apply(dodgers_stats, 1, sum) ``` ```{r} # Which player has the highest value in each stat? apply(dodgers_stats, 2, which.max) ``` ::: callout-tip ## Remember: MARGIN = 1 (Rows) or 2 (Columns)? A handy way to remember: - **1** = rows → think of the **1st dimension** - **2** = columns → think of the **2nd dimension** Or: "1 goes across, 2 goes down" ::: ------------------------------------------------------------------------ ### `sapply()` — Apply to a Vector or List, Get Simple Output `sapply()` applies a function to each element of a vector or list and tries to return a simple result (a vector or matrix): ```{r} # Player names vector players <- c("Mookie Betts", "Freddie Freeman", "Miguel Rojas", "Max Muncy", "James Outman") # Apply nchar() to get the length of each player's name sapply(players, nchar) ``` ```{r} # Calculate the square root of each HR total hr_totals <- c(32, 28, 19, 41, 12) sapply(hr_totals, sqrt) ``` ```{r} # Custom function: categorize HR totals hr_category <- function(hr) { if (hr >= 35) return("Power Hitter") if (hr >= 20) return("Average Power") return("Contact Hitter") } sapply(hr_totals, hr_category) ``` ::: callout-note ## Writing Your Own Functions You just saw R let you define your own function with `function() { }`. We'll go deeper into custom functions in future labs, but notice how natural the syntax is: ```r my_function <- function(input) { # do something with input return(result) } ``` ::: ------------------------------------------------------------------------ ### `lapply()` — Apply to a List, Always Return a List `lapply()` is like `sapply()` but always returns a **list** (useful when results have different lengths): ```{r} # A list of game scores for 3 Dodgers games game_scores <- list( game_1 = c(7, 2, 4, 0, 3, 1, 2, 0, 0), # Runs per inning game_2 = c(0, 0, 3, 5, 0, 2, 0, 1, 2), game_3 = c(1, 1, 0, 0, 4, 0, 3, 2, 1) ) # Calculate total runs in each game lapply(game_scores, sum) ``` ```{r} # Use sapply to get a simpler vector output sapply(game_scores, sum) ``` ```{r} # Which inning did they score the most runs in each game? sapply(game_scores, which.max) ``` ------------------------------------------------------------------------ ### `tapply()` — Apply by Group `tapply()` is extremely useful in biology and sports: it applies a function to a variable **split by a grouping variable**. ```{r} # MLB player data: batting average by position mlb_players <- data.frame( player = c("Betts", "Freeman", "Turner", "Muncy", "Smith", "Outman", "Hernandez", "Barnes", "Buehler"), position = c("OF", "1B", "SS", "3B", "C", "OF", "2B", "C", "SP"), avg = c(0.312, 0.285, 0.271, 0.267, 0.250, 0.320, 0.301, 0.239, 0.098), hr = c(32, 28, 15, 41, 18, 12, 19, 11, 2) ) # Average batting average by position tapply(mlb_players$avg, mlb_players$position, mean) ``` ```{r} # Total home runs by position tapply(mlb_players$hr, mlb_players$position, sum) ``` ::: callout-tip ## `tapply()` in Biology `tapply()` is exactly what you use to compare groups in biology — e.g., average gene expression in treatment vs. control, or species richness in disturbed vs. undisturbed habitats. This pattern is at the heart of almost all biological data analysis. ::: ------------------------------------------------------------------------ ## Part 5: Putting It All Together Let's analyze a simulated MLB pitch dataset combining dates and apply functions: ```{r} # Simulated pitching data for 3 Dodgers starting pitchers over 6 starts each set.seed(123) pitching <- data.frame( pitcher = rep(c("Buehler", "Kershaw", "May"), each = 6), start_date = as.Date(c("2026-04-05","2026-04-11","2026-04-17", "2026-04-23","2026-04-29","2026-05-05", "2026-04-06","2026-04-12","2026-04-18", "2026-04-24","2026-04-30","2026-05-06", "2026-04-07","2026-04-13","2026-04-19", "2026-04-25","2026-05-01","2026-05-07")), innings = c(6.0, 5.2, 7.0, 6.1, 5.0, 6.2, 7.0, 7.1, 6.0, 8.0, 5.2, 7.0, 5.0, 6.0, 5.1, 6.2, 4.1, 5.0), earned_runs = c(3, 4, 1, 2, 5, 3, 2, 1, 3, 0, 4, 2, 4, 3, 5, 2, 6, 4) ) # Calculate ERA per start: ERA = (earned_runs / innings) * 9 pitching$era_per_start <- round((pitching$earned_runs / pitching$innings) * 9, 2) # Average ERA by pitcher using tapply cat("Average ERA by Pitcher:\n") tapply(pitching$era_per_start, pitching$pitcher, mean) ``` ```{r} # Which month do most starts occur in? pitching$month <- format(pitching$start_date, "%B") table(pitching$month) ``` ```{r} # Visualize ERA by pitcher boxplot(era_per_start ~ pitcher, data = pitching, col = c("#005A9C", "#EF3E42", "#A5ACAF"), # Dodger colors ylab = "ERA per Start", xlab = "Pitcher", main = "Starting Pitcher ERA Distribution\nLos Angeles Dodgers, Spring 2026") abline(h = 4.50, col = "red", lty = 2) text(0.5, 4.7, "4.50 ERA threshold", col = "red", cex = 0.8, adj = 0) ``` ------------------------------------------------------------------------ ## 3-Minute Knowledge Check *Close your notes. Answer these on your own — you have 3 minutes. We'll go through the answers together.* ::: callout-caution ## Knowledge Check Questions **1.** Which of these correctly creates a date in R? a) `as.Date("July 4, 2026")`    b) `as.Date("2026-07-04")`    c) `date("04/07/2026")`    d) `"2026-07-04"` **2.** You subtract two date objects: `as.Date("2026-10-31") - as.Date("2026-09-01")`. What type of result does R return, and how would you convert it to a plain number? **3.** In `apply(my_matrix, 2, mean)`, what does the `2` refer to? a) Apply to every 2nd row    b) Apply to columns    c) Apply to rows    d) Apply the function twice **4.** What is the main difference between `sapply()` and `lapply()`? **5.** Which function from the apply family would you use to calculate the average score **separately for each team** in a data frame? a) `apply()`    b) `lapply()`    c) `sapply()`    d) `tapply()` ::: ::: {.callout-caution collapse="true"} ## Answers (Only reveal if you have answered the above questions) **1.** b) `as.Date("2026-07-04")` — R's default date format is **YYYY-MM-DD** (ISO 8601). Other formats require you to specify `format = "%m/%d/%Y"` etc. **2.** R returns a **difftime** object. Wrap it in `as.numeric()` to get a plain number of days: `as.numeric(as.Date("2026-10-31") - as.Date("2026-09-01"))` → `60`. **3.** b) Apply to columns — In `apply()`, MARGIN = 1 applies to **rows**, MARGIN = 2 applies to **columns**. Here, `mean` is calculated for each column of the matrix. **4.** `sapply()` tries to simplify the output to a **vector or matrix**. `lapply()` always returns a **list**. Use `sapply()` when you want a clean vector; use `lapply()` when results might have different lengths. **5.** d) `tapply()` — This function applies a function to a variable **split by a grouping variable**, making it ideal for group-wise summaries like average score per team. ::: ------------------------------------------------------------------------ ## Lab 7 Checklist Before you leave, make sure you can: - [ ] Create a date with `as.Date()` in YYYY-MM-DD format - [ ] Calculate the number of days between two dates - [ ] Use `format()` to display dates in different formats - [ ] Extract date components (month, day, weekday) - [ ] Use `apply()` with MARGIN = 1 (rows) and 2 (columns) - [ ] Use `sapply()` to apply a function to a vector - [ ] Use `lapply()` when the output is a list - [ ] Use `tapply()` to get group summaries ::: callout-tip ## Bonus Challenge Create a data frame of any sports team's game schedule with at least 10 dates. Use `format()` to find the day of the week for each game. Use `tapply()` to calculate the average score on weekday games vs. weekend games (hint: Saturday and Sunday are weekends). ::: ------------------------------------------------------------------------ ## Before Next Class - Monday is **Labor Day — No Class** ️ - **Wednesday Sep 9** (Lab 8): Hypothesis Testing & the Null Hypothesis - Read **Intro2r Ch. 4** - Remember: **Quiz 2 is Friday Sep 4** — covers Labs 3–7 ::: callout-important ## Quiz 2 — This Friday! Topics covered: sequences, vectors, missing values, data frames, matrices, subsetting, dates, and apply functions. Best way to prepare: open a new Quarto document and try to reproduce the key examples from Labs 3–7 from memory. :::