Working with dates and times is crucial for time series analysis, scheduling, and understanding temporal patterns in data. The lubridate package makes date manipulation intuitive and powerful, providing functions that closely match how we naturally think about dates and times.
# Different date formats commonly found in datadate_strings <-c("2024-01-15", # ISO format"01/15/2024", # US format"15/01/2024", # European format"January 15, 2024", # Written format"15-Jan-2024", # Mixed format"2024-01-15 14:30:00"# With time)# Use appropriate parsing functions based on orderiso_dates <-ymd("2024-01-15") # Year-Month-Dayus_dates <-mdy("01/15/2024") # Month-Day-Yeareuro_dates <-dmy("15/01/2024") # Day-Month-Yearwritten_dates <-mdy("January 15, 2024") # Month-Day-Year (text month)cat("Parsed dates (all represent the same date):\n")
Parsed dates (all represent the same date):
print(iso_dates)
[1] "2024-01-15"
print(us_dates)
[1] "2024-01-15"
print(euro_dates)
[1] "2024-01-15"
print(written_dates)
[1] "2024-01-15"
# Verify they're all the sameidentical(iso_dates, us_dates, euro_dates, written_dates)
# Business days (excluding weekends)business_days <-seq(from = start_date, to = start_date +days(20), by ="day")business_days[!wday(business_days) %in%c(1, 7)] # Remove weekends
# Periods: Human-friendly units (months, years can vary)period_1_month <-period(1, "month")period_30_days <-period(30, "days")# Duration: Exact time spans (always in seconds)duration_1_month <-duration(30, "days") # Assumes 30 daysduration_exactly <-ddays(30) # Exactly 30 days# Compare period vs durationleap_year_date <-ymd("2024-01-31") # 2024 is a leap yearcat("Starting date:", as.character(leap_year_date), "\n")
cat("Plus 30 days (duration):", as.character(leap_year_date +days(30)), "\n")
Plus 30 days (duration): 2024-03-01
# Intervals: Specific time spans between two datesstart_date <-ymd("2024-01-01")end_date <-ymd("2024-12-31")year_interval <-interval(start_date, end_date)cat("Interval:", as.character(year_interval), "\n")
Interval: 2024-01-01 UTC--2024-12-31 UTC
cat("Duration in days:", as.numeric(year_interval, "days"), "\n")
Duration in days: 365
cat("Duration in months:", as.numeric(year_interval, "months"), "\n")
Duration in months: 11.99179
# Check if dates fall within intervaltest_dates <-ymd(c("2023-12-31", "2024-06-15", "2025-01-01"))test_dates %within% year_interval
[1] FALSE TRUE FALSE
Date Rounding and Truncation
# Sample datetime datasample_datetime <-ymd_hms("2024-03-15 14:37:23")# Round to different unitsfloor_date(sample_datetime, "month") # Beginning of month
[1] "2024-03-01 UTC"
floor_date(sample_datetime, "week") # Beginning of week (Sunday)
[1] "2024-03-10 UTC"
floor_date(sample_datetime, "day") # Beginning of day
[1] "2024-03-15 UTC"
floor_date(sample_datetime, "hour") # Beginning of hour
[1] "2024-03-15 14:00:00 UTC"
ceiling_date(sample_datetime, "month") # End of month
[1] "2024-04-01 UTC"
ceiling_date(sample_datetime, "week") # End of week
# Create datetime in different time zonesutc_time <-ymd_hms("2024-01-15 12:00:00", tz ="UTC")ny_time <-ymd_hms("2024-01-15 12:00:00", tz ="America/New_York")london_time <-ymd_hms("2024-01-15 12:00:00", tz ="Europe/London")tokyo_time <-ymd_hms("2024-01-15 12:00:00", tz ="Asia/Tokyo")cat("Same wall clock time in different zones:\n")
Same wall clock time in different zones:
cat("UTC:", as.character(utc_time), "\n")
UTC: 2024-01-15 12:00:00
cat("New York:", as.character(ny_time), "\n")
New York: 2024-01-15 12:00:00
cat("London:", as.character(london_time), "\n")
London: 2024-01-15 12:00:00
cat("Tokyo:", as.character(tokyo_time), "\n")
Tokyo: 2024-01-15 12:00:00
# Convert between time zonescat("\nSame moment in different zones:\n")
Same moment in different zones:
cat("UTC:", as.character(utc_time), "\n")
UTC: 2024-01-15 12:00:00
cat("In New York:", as.character(with_tz(utc_time, "America/New_York")), "\n")
cat("Difference:", as.numeric(difftime(ny_forced, utc_forced, units ="hours")), "hours\n")
Difference: 5 hours
Handling Daylight Saving Time
# Daylight saving time transitions in 2024# Spring forward: March 10, 2024 (2:00 AM becomes 3:00 AM)# Fall back: November 3, 2024 (2:00 AM becomes 1:00 AM)# Create times around DST transitionspring_transition <-ymd_hms("2024-03-10 01:30:00", tz ="America/New_York")spring_after <- spring_transition +hours(1)fall_transition <-ymd_hms("2024-11-03 01:30:00", tz ="America/New_York")fall_after <- fall_transition +hours(1)cat("Spring DST transition (spring forward):\n")
# Function to check if date is a business dayis_business_day <-function(date) {# Exclude weekends weekday <-wday(date)!weekday %in%c(1, 7) # Not Sunday or Saturday}# Function to add business daysadd_business_days <-function(start_date, days_to_add) { current_date <- start_date days_added <-0while (days_added < days_to_add) { current_date <- current_date +days(1)if (is_business_day(current_date)) { days_added <- days_added +1 } }return(current_date)}# Calculate business days between datescount_business_days <-function(start_date, end_date) {if (start_date > end_date) return(0) date_seq <-seq(from = start_date +days(1), to = end_date, by ="day")sum(is_business_day(date_seq))}# Test business day functionsstart_date <-ymd("2024-01-15") # Mondaycat("Start date:", format(start_date, "%A, %B %d, %Y"), "\n")
Start date: Monday, January 15, 2024
# Add 5 business daysend_date <-add_business_days(start_date, 5)cat("5 business days later:", format(end_date, "%A, %B %d, %Y"), "\n")
5 business days later: Monday, January 22, 2024
# Count business days in January 2024jan_start <-ymd("2024-01-01")jan_end <-ymd("2024-01-31")business_days_jan <-count_business_days(jan_start, jan_end)cat("Business days in January 2024:", business_days_jan, "\n")
Business days in January 2024: 22
# Create business day sequencebusiness_day_seq <-seq(from =ymd("2024-01-01"), to =ymd("2024-01-31"), by ="day")business_days_only <- business_day_seq[is_business_day(business_day_seq)]cat("First 10 business days of 2024:\n")
# Best Practice 1: Always specify format when possible# Goodexplicit_dates <-ymd("2024-01-15")# Less reliable (could be misinterpreted)ambiguous_dates <-as.Date("01/02/2024", format ="%m/%d/%Y") # Is this Jan 2 or Feb 1?# Best Practice 2: Handle parsing failures gracefullymessy_input <-c("2024-01-15", "invalid", "2024/01/16", NA)safe_parse <-function(date_strings) { results <-ymd(date_strings, quiet =TRUE)# Report parsing issues failed <-is.na(results) &!is.na(date_strings)if (any(failed)) {cat("Failed to parse:", sum(failed), "dates\n")cat("Failed values:", paste(date_strings[failed], collapse =", "), "\n") }return(results)}parsed_safely <-safe_parse(messy_input)
Found 2 dates outside valid range
Invalid dates: 1850-01-01, 2050-01-01
Common Mistakes
# Mistake 1: Confusing time zones# Problem: Creating datetime without specifying timezoneambiguous_time <-ymd_hms("2024-01-15 12:00:00") # What timezone?cat("Ambiguous timezone:", tz(ambiguous_time), "\n")
Ambiguous timezone: UTC
# Better: Always specify timezone when it mattersexplicit_time <-ymd_hms("2024-01-15 12:00:00", tz ="America/New_York")cat("Explicit timezone:", tz(explicit_time), "\n")
Explicit timezone: America/New_York
# Mistake 2: Not handling leap years# Problem: Assuming February always has 28 daysleap_year_check <-function(year) { feb_29 <-ymd(paste(year, "02", "29", sep ="-"))!is.na(feb_29)}cat("Is 2024 a leap year?", leap_year_check(2024), "\n")
Is 2024 a leap year? TRUE
cat("Is 2023 a leap year?", leap_year_check(2023), "\n")
Is 2023 a leap year? FALSE
# Mistake 3: Ignoring DST in calculations# Problem: Assuming all days have 24 hoursdst_spring <-ymd("2024-03-10", tz ="America/New_York") # Spring forwardhours_in_day <-as.numeric(difftime(dst_spring +days(1), dst_spring, units ="hours"))cat("Hours in DST transition day:", hours_in_day, "\n")
Hours in DST transition day: 23
# Mistake 4: Not validating date arithmetic# Problem: Invalid dates from arithmeticjan_31 <-ymd("2024-01-31")invalid_result <- jan_31 +months(1) # What's January 31 + 1 month?cat("Jan 31 + 1 month:", as.character(invalid_result), "\n")
Jan 31 + 1 month: NA
# Better: Use %m+% for month arithmetic that handles thisvalid_result <- jan_31 %m+%months(1)cat("Jan 31 %m+% 1 month:", as.character(valid_result), "\n")
Jan 31 %m+% 1 month: 2024-02-29
Exercises
Exercise 1: Sales Data Analysis
Given daily sales data with various date formats: 1. Parse dates from mixed formats 2. Extract seasonal and trend components 3. Calculate rolling averages and growth rates 4. Identify business day vs weekend patterns
Exercise 2: Employee Time Tracking
Create a time tracking system that: 1. Handles different time zones for global employees 2. Calculates overtime based on business rules 3. Accounts for holidays and vacation days 4. Generates payroll reports by pay period
Exercise 3: Project Timeline Management
Build a project management system that: 1. Tracks milestones and deadlines 2. Calculates critical path and delays 3. Handles business day scheduling 4. Predicts completion dates based on current progress
Exercise 4: Event Scheduling
Design an event scheduling application that: 1. Handles recurring events (daily, weekly, monthly) 2. Manages time zone conversions for global events 3. Avoids scheduling conflicts 4. Sends reminders based on time until event
Summary
The lubridate package makes date and time manipulation intuitive and powerful:
Handle parsing failures gracefully with error checking
Validate date ranges to catch data entry errors
Use appropriate period types (period vs duration)
Account for leap years and DST in calculations
Common Applications:
Time series analysis: Extracting seasonal patterns
Business analytics: Calculating rolling metrics
Scheduling: Managing appointments and deadlines
International applications: Handling multiple time zones
Remember:
Dates in R are stored as numbers (days since 1970-01-01)
Time zones can be tricky - always be explicit
Business day calculations need custom logic
DST transitions affect duration calculations
Use %m+% for safer month arithmetic
Date and time manipulation is essential for many data analysis tasks. With lubridate, you can handle even complex temporal data scenarios with confidence!
This completes our exploration of data types in the tidyverse. You now have the tools to work effectively with strings, factors, and dates in your data analysis projects!
Source Code
---title: "Working with Dates and Times using lubridate"author: "IND215"date: todayformat: html: toc: true toc-depth: 3 code-fold: false code-tools: true---## Introduction to Date and Time DataWorking with dates and times is crucial for time series analysis, scheduling, and understanding temporal patterns in data. The `lubridate` package makes date manipulation intuitive and powerful, providing functions that closely match how we naturally think about dates and times.```{r}#| label: setup#| message: falselibrary(tidyverse)library(lubridate)# Key lubridate functions we'll explorecat("Key lubridate functions:\n")cat("- Parsing: ymd(), mdy(), dmy(), ymd_hms()\n")cat("- Extracting: year(), month(), day(), hour(), minute()\n")cat("- Arithmetic: +, -, years(), months(), days(), hours()\n")cat("- Rounding: floor_date(), ceiling_date(), round_date()\n")cat("- Intervals: interval(), duration(), period()\n")cat("- Time zones: with_tz(), force_tz()\n")```## Parsing Dates from Text### Basic Date Parsing```{r}#| label: date-parsing-basic# Different date formats commonly found in datadate_strings <-c("2024-01-15", # ISO format"01/15/2024", # US format"15/01/2024", # European format"January 15, 2024", # Written format"15-Jan-2024", # Mixed format"2024-01-15 14:30:00"# With time)# Use appropriate parsing functions based on orderiso_dates <-ymd("2024-01-15") # Year-Month-Dayus_dates <-mdy("01/15/2024") # Month-Day-Yeareuro_dates <-dmy("15/01/2024") # Day-Month-Yearwritten_dates <-mdy("January 15, 2024") # Month-Day-Year (text month)cat("Parsed dates (all represent the same date):\n")print(iso_dates)print(us_dates)print(euro_dates)print(written_dates)# Verify they're all the sameidentical(iso_dates, us_dates, euro_dates, written_dates)```### Parsing with Times```{r}#| label: datetime-parsing# Different datetime formatsdatetime_strings <-c("2024-01-15 14:30:00","01/15/2024 2:30 PM","15-01-2024 14:30","2024-01-15T14:30:00Z")# Parse datetimesymd_hms("2024-01-15 14:30:00")mdy_hms("01/15/2024 2:30:00 PM")dmy_hm("15-01-2024 14:30")# Handle different separatorsymd_hms("2024/01/15 14:30:00")ymd_hms("2024.01.15 14.30.00")# Parse times onlyhms("14:30:00")hm("14:30")ms("30:45") # Minutes:Seconds```### Handling Messy Date Data```{r}#| label: messy-dates# Real-world messy date datamessy_dates <-c("2024-01-15","01/16/2024","17-Jan-2024","2024-1-18", # Single digit month"01/19/24", # Two-digit year"20th January 2024", # Ordinal day"invalid date",NA,"2024/01/21")# Parse with error handlingparse_date_safely <-function(date_string) {# Try different parsing functions parsed <-ymd(date_string, quiet =TRUE)if (is.na(parsed)) parsed <-mdy(date_string, quiet =TRUE)if (is.na(parsed)) parsed <-dmy(date_string, quiet =TRUE)return(parsed)}# Apply to messy dataparsed_dates <-map(messy_dates, parse_date_safely)parsed_dates <-as.Date(unlist(parsed_dates), origin ="1970-01-01")cat("Original messy dates:\n")print(messy_dates)cat("\nParsed dates:\n")print(parsed_dates)# Count parsing failurescat("\nParsing success rate:", mean(!is.na(parsed_dates)), "\n")```## Extracting Date Components### Basic Date Components```{r}#| label: date-components# Sample datessample_dates <-ymd(c("2024-01-15", "2024-06-30", "2024-12-25"))sample_datetimes <-ymd_hms(c("2024-01-15 09:30:00", "2024-06-30 15:45:30", "2024-12-25 20:15:45"))# Extract date componentsyear(sample_dates)month(sample_dates)month(sample_dates, label =TRUE) # Month namesmonth(sample_dates, label =TRUE, abbr =FALSE) # Full month namesday(sample_dates)mday(sample_dates) # Day of month (same as day())yday(sample_dates) # Day of yearwday(sample_dates) # Day of week (numeric)wday(sample_dates, label =TRUE) # Day of week nameswday(sample_dates, label =TRUE, abbr =FALSE) # Full day names# Week informationweek(sample_dates) # Week of yearisoweek(sample_dates) # ISO week (Monday as week start)```### Time Components```{r}#| label: time-components# Extract time componentshour(sample_datetimes)minute(sample_datetimes)second(sample_datetimes)# AM/PMam(sample_datetimes)pm(sample_datetimes)# Extract multiple components at oncedatetime_components <-tibble(datetime = sample_datetimes,year =year(datetime),month =month(datetime, label =TRUE),day =day(datetime),weekday =wday(datetime, label =TRUE),hour =hour(datetime),minute =minute(datetime),am_pm =ifelse(am(datetime), "AM", "PM"))print(datetime_components)```### Creating Date Summaries```{r}#| label: date-summaries# Create sample transaction dataset.seed(123)transactions <-tibble(transaction_id =1:100,date =sample(seq(ymd("2024-01-01"), ymd("2024-12-31"), by ="day"), 100),amount =round(runif(100, 10, 500), 2))# Add date components for analysistransactions <- transactions %>%mutate(year =year(date),month =month(date, label =TRUE),quarter =quarter(date),weekday =wday(date, label =TRUE),is_weekend =wday(date) %in%c(1, 7), # Sunday = 1, Saturday = 7week_of_year =week(date) )# Analyze patternscat("Sales by day of week:\n")transactions %>%group_by(weekday) %>%summarise(total_sales =sum(amount),avg_sale =round(mean(amount), 2),transaction_count =n(),.groups ="drop" ) %>%print()cat("\nWeekend vs Weekday sales:\n")transactions %>%group_by(is_weekend) %>%summarise(total_sales =sum(amount),avg_sale =round(mean(amount), 2),.groups ="drop" ) %>%mutate(day_type =ifelse(is_weekend, "Weekend", "Weekday")) %>%select(day_type, everything(), -is_weekend) %>%print()```## Date Arithmetic and Manipulation### Basic Date Arithmetic```{r}#| label: date-arithmetic# Start with a datestart_date <-ymd("2024-01-15")# Add and subtract time periodsstart_date +days(30) # 30 days laterstart_date +months(3) # 3 months laterstart_date +years(1) # 1 year later# Combine periodsstart_date +years(1) +months(6) +days(15)# Subtract periodsstart_date -days(10)start_date -months(2)# Date sequencesseq(from = start_date, to = start_date +months(6), by ="month")seq(from = start_date, length.out =10, by ="2 weeks")# Business days (excluding weekends)business_days <-seq(from = start_date, to = start_date +days(20), by ="day")business_days[!wday(business_days) %in%c(1, 7)] # Remove weekends```### Period vs Duration vs Interval```{r}#| label: periods-durations-intervals# Periods: Human-friendly units (months, years can vary)period_1_month <-period(1, "month")period_30_days <-period(30, "days")# Duration: Exact time spans (always in seconds)duration_1_month <-duration(30, "days") # Assumes 30 daysduration_exactly <-ddays(30) # Exactly 30 days# Compare period vs durationleap_year_date <-ymd("2024-01-31") # 2024 is a leap yearcat("Starting date:", as.character(leap_year_date), "\n")cat("Plus 1 month (period):", as.character(leap_year_date +months(1)), "\n")cat("Plus 30 days (duration):", as.character(leap_year_date +days(30)), "\n")# Intervals: Specific time spans between two datesstart_date <-ymd("2024-01-01")end_date <-ymd("2024-12-31")year_interval <-interval(start_date, end_date)cat("Interval:", as.character(year_interval), "\n")cat("Duration in days:", as.numeric(year_interval, "days"), "\n")cat("Duration in months:", as.numeric(year_interval, "months"), "\n")# Check if dates fall within intervaltest_dates <-ymd(c("2023-12-31", "2024-06-15", "2025-01-01"))test_dates %within% year_interval```### Date Rounding and Truncation```{r}#| label: date-rounding# Sample datetime datasample_datetime <-ymd_hms("2024-03-15 14:37:23")# Round to different unitsfloor_date(sample_datetime, "month") # Beginning of monthfloor_date(sample_datetime, "week") # Beginning of week (Sunday)floor_date(sample_datetime, "day") # Beginning of dayfloor_date(sample_datetime, "hour") # Beginning of hourceiling_date(sample_datetime, "month") # End of monthceiling_date(sample_datetime, "week") # End of weekceiling_date(sample_datetime, "day") # End of dayround_date(sample_datetime, "hour") # Nearest hourround_date(sample_datetime, "15 minutes") # Nearest 15 minutes# Useful for grouping datasales_data <-tibble(timestamp =ymd_hms("2024-01-15 09:00:00") +minutes(seq(0, 480, 30)), # Every 30 min for 8 hourssales =round(runif(17, 100, 500), 2))# Group by hourhourly_sales <- sales_data %>%mutate(hour =floor_date(timestamp, "hour")) %>%group_by(hour) %>%summarise(total_sales =sum(sales), .groups ="drop")print(hourly_sales)```## Time Zones and International Dates### Working with Time Zones```{r}#| label: time-zones# Create datetime in different time zonesutc_time <-ymd_hms("2024-01-15 12:00:00", tz ="UTC")ny_time <-ymd_hms("2024-01-15 12:00:00", tz ="America/New_York")london_time <-ymd_hms("2024-01-15 12:00:00", tz ="Europe/London")tokyo_time <-ymd_hms("2024-01-15 12:00:00", tz ="Asia/Tokyo")cat("Same wall clock time in different zones:\n")cat("UTC:", as.character(utc_time), "\n")cat("New York:", as.character(ny_time), "\n")cat("London:", as.character(london_time), "\n")cat("Tokyo:", as.character(tokyo_time), "\n")# Convert between time zonescat("\nSame moment in different zones:\n")cat("UTC:", as.character(utc_time), "\n")cat("In New York:", as.character(with_tz(utc_time, "America/New_York")), "\n")cat("In London:", as.character(with_tz(utc_time, "Europe/London")), "\n")cat("In Tokyo:", as.character(with_tz(utc_time, "Asia/Tokyo")), "\n")# Force timezone (changes the timezone label without converting)cat("\nForcing timezone (wall clock time stays same):\n")utc_forced <-force_tz(ymd_hms("2024-01-15 12:00:00"), "UTC")ny_forced <-force_tz(ymd_hms("2024-01-15 12:00:00"), "America/New_York")cat("UTC forced:", as.character(utc_forced), "\n")cat("NY forced:", as.character(ny_forced), "\n")cat("Difference:", as.numeric(difftime(ny_forced, utc_forced, units ="hours")), "hours\n")```### Handling Daylight Saving Time```{r}#| label: daylight-saving# Daylight saving time transitions in 2024# Spring forward: March 10, 2024 (2:00 AM becomes 3:00 AM)# Fall back: November 3, 2024 (2:00 AM becomes 1:00 AM)# Create times around DST transitionspring_transition <-ymd_hms("2024-03-10 01:30:00", tz ="America/New_York")spring_after <- spring_transition +hours(1)fall_transition <-ymd_hms("2024-11-03 01:30:00", tz ="America/New_York")fall_after <- fall_transition +hours(1)cat("Spring DST transition (spring forward):\n")cat("Before:", as.character(spring_transition), "\n")cat("After +1 hour:", as.character(spring_after), "\n")cat("\nFall DST transition (fall back):\n")cat("Before:", as.character(fall_transition), "\n")cat("After +1 hour:", as.character(fall_after), "\n")# Working with DST-aware periodscat("\nDST-aware periods:\n")dst_start <-ymd_hms("2024-03-09 12:00:00", tz ="America/New_York")dst_plus_24h <- dst_start +hours(24) # Exactly 24 hoursdst_plus_1d <- dst_start +days(1) # 1 calendar day (23 hours due to DST)cat("Start:", as.character(dst_start), "\n")cat("Plus 24 hours:", as.character(dst_plus_24h), "\n")cat("Plus 1 day:", as.character(dst_plus_1d), "\n")```## Real-World Date Applications### Example 1: Business Analytics```{r}#| label: business-analytics# Create realistic business dataset.seed(123)business_data <-tibble(date =sample(seq(ymd("2023-01-01"), ymd("2024-12-31"), by ="day"), 500),revenue =round(rnorm(500, 1000, 300), 2),customers =round(rnorm(500, 50, 15)),product_category =sample(c("Electronics", "Clothing", "Home", "Sports"), 500, replace =TRUE)) %>%arrange(date)# Add comprehensive date featuresbusiness_data <- business_data %>%mutate(year =year(date),month =month(date, label =TRUE),quarter =paste0("Q", quarter(date)),weekday =wday(date, label =TRUE),is_weekend =wday(date) %in%c(1, 7),week_of_year =week(date),month_year =floor_date(date, "month"),# Business calendar featuresis_holiday_season =month(date) %in%c(11, 12), # Nov-Decis_summer =month(date) %in%c(6, 7, 8), # Jun-Augdays_since_start =as.numeric(difftime(date, min(date), units ="days")),# Seasonal indicatorsseason =case_when(month(date) %in%c(12, 1, 2) ~"Winter",month(date) %in%c(3, 4, 5) ~"Spring",month(date) %in%c(6, 7, 8) ~"Summer",month(date) %in%c(9, 10, 11) ~"Fall" ) )# Analyze seasonal patternsseasonal_analysis <- business_data %>%group_by(season, year) %>%summarise(avg_daily_revenue =round(mean(revenue), 2),avg_daily_customers =round(mean(customers), 1),total_days =n(),.groups ="drop" )cat("Seasonal business patterns:\n")print(seasonal_analysis)# Monthly trendsmonthly_trends <- business_data %>%group_by(month_year) %>%summarise(total_revenue =sum(revenue),total_customers =sum(customers),avg_order_value =round(total_revenue / total_customers, 2),.groups ="drop" ) %>%mutate(revenue_growth =round((total_revenue /lag(total_revenue) -1) *100, 1),customer_growth =round((total_customers /lag(total_customers) -1) *100, 1) )cat("\nRecent monthly trends:\n")monthly_trends %>%tail(6) %>%print()```### Example 2: Employee Scheduling```{r}#| label: employee-scheduling# Create employee shift datashift_schedule <-tibble(employee_id =rep(1:10, each =30),shift_date =rep(seq(ymd("2024-01-01"), by ="day", length.out =30), 10),shift_start =sample(c("06:00", "14:00", "22:00"), 300, replace =TRUE),hours_worked =sample(c(8, 10, 12), 300, replace =TRUE)) %>%mutate(shift_start_time =ymd_hm(paste(shift_date, shift_start)),shift_end_time = shift_start_time +hours(hours_worked),# Shift classificationshift_type =case_when(hour(shift_start_time) <10~"Morning",hour(shift_start_time) <18~"Afternoon",TRUE~"Night" ),# Date featuresweekday =wday(shift_date, label =TRUE),is_weekend =wday(shift_date) %in%c(1, 7),week_of_year =week(shift_date) )# Analyze shift patternsshift_analysis <- shift_schedule %>%group_by(shift_type, weekday) %>%summarise(total_shifts =n(),total_hours =sum(hours_worked),avg_hours_per_shift =round(mean(hours_worked), 1),.groups ="drop" )cat("Shift distribution by type and day:\n")print(shift_analysis)# Identify potential overtime issuesovertime_analysis <- shift_schedule %>%group_by(employee_id, week =floor_date(shift_date, "week")) %>%summarise(weekly_hours =sum(hours_worked),shifts_worked =n(),weekend_shifts =sum(is_weekend),.groups ="drop" ) %>%mutate(potential_overtime = weekly_hours >40,excessive_hours = weekly_hours >50 )cat("\nEmployees with potential overtime (>40 hours/week):\n")overtime_analysis %>%filter(potential_overtime) %>%arrange(desc(weekly_hours)) %>%head(10) %>%print()```### Example 3: Event Planning and Deadlines```{r}#| label: event-planning# Create project timelineproject_events <-tibble(event_name =c("Project Kickoff", "Requirements Complete", "Design Review","Development Start", "Alpha Release", "Beta Release","User Testing", "Final Review", "Launch"),planned_date =ymd(c("2024-01-15", "2024-02-01", "2024-02-15","2024-03-01", "2024-04-15", "2024-05-15","2024-06-01", "2024-06-15", "2024-07-01")),actual_date =ymd(c("2024-01-15", "2024-02-03", "2024-02-18","2024-03-05", "2024-04-20", "2024-05-22","2024-06-05", NA, NA)) # Future events not yet complete)# Calculate delays and time to completionproject_analysis <- project_events %>%mutate(# Days between planned and actualdelay_days =as.numeric(difftime(actual_date, planned_date, units ="days")),# Time since project startdays_from_start_planned =as.numeric(difftime(planned_date, first(planned_date), units ="days")),days_from_start_actual =as.numeric(difftime(actual_date, first(planned_date), units ="days")),# Statusstatus =case_when(is.na(actual_date) ~"Pending", delay_days >0~"Delayed", delay_days ==0~"On Time", delay_days <0~"Early" ),# Days until planned completion (for pending items)days_until_planned =ifelse(is.na(actual_date),as.numeric(difftime(planned_date, Sys.Date(), units ="days")),NA) )cat("Project timeline analysis:\n")project_analysis %>%select(event_name, planned_date, actual_date, delay_days, status, days_until_planned) %>%print()# Calculate project health metricsproject_health <- project_analysis %>%filter(!is.na(actual_date)) %>%summarise(completed_events =n(),avg_delay =round(mean(delay_days, na.rm =TRUE), 1),total_delay =sum(pmax(delay_days, 0), na.rm =TRUE),on_time_rate =round(mean(delay_days <=0, na.rm =TRUE) *100, 1) )cat("\nProject health metrics:\n")print(project_health)# Predict remaining timelineremaining_events <- project_events %>%filter(is.na(actual_date))if (nrow(remaining_events) >0) { avg_delay <- project_health$avg_delay predicted_completion <- remaining_events %>%mutate(predicted_date = planned_date +days(ceiling(avg_delay)),days_until_predicted =as.numeric(difftime(predicted_date, Sys.Date(), units ="days")) )cat("\nPredicted completion dates (based on average delay):\n") predicted_completion %>%select(event_name, planned_date, predicted_date, days_until_predicted) %>%print()}```## Advanced Date Techniques### Rolling Date Windows```{r}#| label: rolling-windows# Create daily sales datadaily_sales <-tibble(date =seq(ymd("2024-01-01"), ymd("2024-12-31"), by ="day"),sales =round(rnorm(366, 1000, 200) +sin(seq_along(date) *2* pi /365) *100, 2) # Seasonal pattern)# Calculate rolling averagesdaily_sales <- daily_sales %>%mutate(# Rolling 7-day averagesales_7d_avg = zoo::rollmean(sales, 7, fill =NA, align ="right"),# Rolling 30-day averagesales_30d_avg = zoo::rollmean(sales, 30, fill =NA, align ="right"),# Year-over-year comparison (simulated)sales_yoy = sales *runif(n(), 0.9, 1.2), # Simulate YoY growthyoy_change =round((sales / sales_yoy -1) *100, 1),# Month-to-date and quarter-to-datemonth_start =floor_date(date, "month"),quarter_start =floor_date(date, "quarter") )# Show recent rolling averagescat("Recent sales with rolling averages:\n")daily_sales %>%filter(date >=ymd("2024-12-20")) %>%select(date, sales, sales_7d_avg, sales_30d_avg) %>%print()```### Business Day Calculations```{r}#| label: business-days# Function to check if date is a business dayis_business_day <-function(date) {# Exclude weekends weekday <-wday(date)!weekday %in%c(1, 7) # Not Sunday or Saturday}# Function to add business daysadd_business_days <-function(start_date, days_to_add) { current_date <- start_date days_added <-0while (days_added < days_to_add) { current_date <- current_date +days(1)if (is_business_day(current_date)) { days_added <- days_added +1 } }return(current_date)}# Calculate business days between datescount_business_days <-function(start_date, end_date) {if (start_date > end_date) return(0) date_seq <-seq(from = start_date +days(1), to = end_date, by ="day")sum(is_business_day(date_seq))}# Test business day functionsstart_date <-ymd("2024-01-15") # Mondaycat("Start date:", format(start_date, "%A, %B %d, %Y"), "\n")# Add 5 business daysend_date <-add_business_days(start_date, 5)cat("5 business days later:", format(end_date, "%A, %B %d, %Y"), "\n")# Count business days in January 2024jan_start <-ymd("2024-01-01")jan_end <-ymd("2024-01-31")business_days_jan <-count_business_days(jan_start, jan_end)cat("Business days in January 2024:", business_days_jan, "\n")# Create business day sequencebusiness_day_seq <-seq(from =ymd("2024-01-01"), to =ymd("2024-01-31"), by ="day")business_days_only <- business_day_seq[is_business_day(business_day_seq)]cat("First 10 business days of 2024:\n")print(head(business_days_only, 10))```## Best Practices and Common Pitfalls### Date Parsing Best Practices```{r}#| label: date-best-practices# Best Practice 1: Always specify format when possible# Goodexplicit_dates <-ymd("2024-01-15")# Less reliable (could be misinterpreted)ambiguous_dates <-as.Date("01/02/2024", format ="%m/%d/%Y") # Is this Jan 2 or Feb 1?# Best Practice 2: Handle parsing failures gracefullymessy_input <-c("2024-01-15", "invalid", "2024/01/16", NA)safe_parse <-function(date_strings) { results <-ymd(date_strings, quiet =TRUE)# Report parsing issues failed <-is.na(results) &!is.na(date_strings)if (any(failed)) {cat("Failed to parse:", sum(failed), "dates\n")cat("Failed values:", paste(date_strings[failed], collapse =", "), "\n") }return(results)}parsed_safely <-safe_parse(messy_input)# Best Practice 3: Validate date rangesvalidate_date_range <-function(dates, min_date =ymd("1900-01-01"), max_date =Sys.Date()) { valid <-!is.na(dates) & dates >= min_date & dates <= max_dateif (!all(valid, na.rm =TRUE)) { invalid_dates <- dates[!valid &!is.na(dates)]cat("Found", length(invalid_dates), "dates outside valid range\n")cat("Invalid dates:", paste(as.character(invalid_dates), collapse =", "), "\n") }return(valid)}# Test validationtest_dates <-ymd(c("1850-01-01", "2024-01-15", "2050-01-01"))validation_results <-validate_date_range(test_dates)```### Common Mistakes```{r}#| label: date-mistakes# Mistake 1: Confusing time zones# Problem: Creating datetime without specifying timezoneambiguous_time <-ymd_hms("2024-01-15 12:00:00") # What timezone?cat("Ambiguous timezone:", tz(ambiguous_time), "\n")# Better: Always specify timezone when it mattersexplicit_time <-ymd_hms("2024-01-15 12:00:00", tz ="America/New_York")cat("Explicit timezone:", tz(explicit_time), "\n")# Mistake 2: Not handling leap years# Problem: Assuming February always has 28 daysleap_year_check <-function(year) { feb_29 <-ymd(paste(year, "02", "29", sep ="-"))!is.na(feb_29)}cat("Is 2024 a leap year?", leap_year_check(2024), "\n")cat("Is 2023 a leap year?", leap_year_check(2023), "\n")# Mistake 3: Ignoring DST in calculations# Problem: Assuming all days have 24 hoursdst_spring <-ymd("2024-03-10", tz ="America/New_York") # Spring forwardhours_in_day <-as.numeric(difftime(dst_spring +days(1), dst_spring, units ="hours"))cat("Hours in DST transition day:", hours_in_day, "\n")# Mistake 4: Not validating date arithmetic# Problem: Invalid dates from arithmeticjan_31 <-ymd("2024-01-31")invalid_result <- jan_31 +months(1) # What's January 31 + 1 month?cat("Jan 31 + 1 month:", as.character(invalid_result), "\n")# Better: Use %m+% for month arithmetic that handles thisvalid_result <- jan_31 %m+%months(1)cat("Jan 31 %m+% 1 month:", as.character(valid_result), "\n")```## Exercises### Exercise 1: Sales Data AnalysisGiven daily sales data with various date formats:1. Parse dates from mixed formats2. Extract seasonal and trend components3. Calculate rolling averages and growth rates4. Identify business day vs weekend patterns### Exercise 2: Employee Time TrackingCreate a time tracking system that:1. Handles different time zones for global employees2. Calculates overtime based on business rules3. Accounts for holidays and vacation days4. Generates payroll reports by pay period### Exercise 3: Project Timeline ManagementBuild a project management system that:1. Tracks milestones and deadlines2. Calculates critical path and delays3. Handles business day scheduling4. Predicts completion dates based on current progress### Exercise 4: Event SchedulingDesign an event scheduling application that:1. Handles recurring events (daily, weekly, monthly)2. Manages time zone conversions for global events3. Avoids scheduling conflicts4. Sends reminders based on time until event## SummaryThe `lubridate` package makes date and time manipulation intuitive and powerful:### Key Functions:- **Parsing**: `ymd()`, `mdy()`, `dmy()`, `ymd_hms()`- **Extracting**: `year()`, `month()`, `day()`, `hour()`, `minute()`- **Arithmetic**: `+`, `-`, `years()`, `months()`, `days()`- **Rounding**: `floor_date()`, `ceiling_date()`, `round_date()`- **Time zones**: `with_tz()`, `force_tz()`, `tz()`### Best Practices:- **Specify time zones explicitly** when they matter- **Handle parsing failures gracefully** with error checking- **Validate date ranges** to catch data entry errors- **Use appropriate period types** (period vs duration)- **Account for leap years and DST** in calculations### Common Applications:- **Time series analysis**: Extracting seasonal patterns- **Business analytics**: Calculating rolling metrics- **Scheduling**: Managing appointments and deadlines- **International applications**: Handling multiple time zones### Remember:- Dates in R are stored as numbers (days since 1970-01-01)- Time zones can be tricky - always be explicit- Business day calculations need custom logic- DST transitions affect duration calculations- Use `%m+%` for safer month arithmeticDate and time manipulation is essential for many data analysis tasks. With lubridate, you can handle even complex temporal data scenarios with confidence!This completes our exploration of data types in the tidyverse. You now have the tools to work effectively with strings, factors, and dates in your data analysis projects!