left_join( )

library(tidyverse)

left_join(left_table, right_table, by = "shared_column_name")

anti_join( )

anti_join(left_table, right_table, by = "shared_column_name")

Joining monsters

• monsters
• left_join
• anti_join
• R code

# Create monsters df
monsters <- data.frame(id = 1:5,
                       color = c("pink", "green",  "pink", "green", "green"),
                       age = c(8,2,7,3,1),
                       hat = c(T,T,F,T,F) )


# Create monster nicknames df
nicknames <- data.frame(id = c(1,2,3,5,7),
                        name = c("Kid", "Cheeky", "Spritz", "Littles", "Lucky"))


#  LEFT_JOIN ----
named_monsters <- left_join(monsters, nicknames, by = "id")

named_monsters


#  ANTI_JOIN ----
no_name_monsters <- anti_join(monsters, nicknames, by = "id")

# filter() alternative: 
## monster id "NOT IN" nicknames id column
no_name_monsters <- filter(monsters, !id %in% nicknames$id)

Dates with lubridate

It’s about time! Lubridate makes working with dates much easier.

We can find how much time has elapsed, add or subtract days, and find season and day of the week averages. The package is included in the tidyverse bundle of packages, so it’s already installed!

View the date cheatsheet. This is a great reference when you need to manipulate dates or timezones in your data.

Step 1: Become a date

Step 2: Sort your components

Step 3: Show your style

library(tidyverse)

report_style <- stamp_date("Reported on December 15th, 2023")

new_date <- ymd("2024-05-15")

report_style(new_date)

## [1] "Reported on May 15th, 2024"

Monsters EVERYWHERE!

It seemed like a good idea to bring those helpful monsters along, and they’re so so cute. People hate to say it, but they are starting to become a real nuisance.

To get a grasp on the population explosion of monsters our research vessel is launching a full-scale monster survey. And they need our help.

library(tidyverse)

start_date <- ymd("2024-05-01")

end_date   <- ymd("2024-10-31")

# Sequence from start to end, counting by 1 day interval
survey_dates <- seq(from = start_date, 
                    to = end_date, 
                    by = "day")

survey <- data.frame(count_date = survey_dates)

# or

survey <- tibble(count_date = survey_dates)

head(survey)

## # A tibble: 6 × 1
##   count_date
##   <date>    
## 1 2024-05-01
## 2 2024-05-02
## 3 2024-05-03
## 4 2024-05-04
## 5 2024-05-05
## 6 2024-05-06

1. Scheduling weekdays

Scientists on our boat are busy people. They only have one day per week when they can volunteer.

Here is the weekday when volunteers are available for each island:

• Dream: Saturdays
• Biscoe: Tuesdays

When is the first Saturday in May of 2024?

For that, we can use the wday() (weekday) function.

Try changing some of the TRUE/FALSE values to see what the arguments are doing.

new_date <- ymd('2024-05-01')

# wday tells you the day of week (Sun, Mon, etc..) for a specific date
wday(new_date, label = TRUE, abbr = FALSE)

## [1] Wednesday
## 7 Levels: Sunday < Monday < Tuesday < Wednesday < Thursday < ... < Saturday

So the 1st of May will be a Wednesday. That means….May the 4th will be on Saturday. Perfect! That’s my favorite day.

Mutate to the rescue

We really don’t want to check every date one by one do we?

Let’s add a new week_day column to our survey table that checks ALL the dates ALL at once. To add a new column we call on mutate().

Complete the code below to add a week_day column to our survey table.

survey <- mutate(survey, 
                 week_day = ________ )

survey <- mutate(survey, 
                 week_day = wday(count_date, label = TRUE, abbr = FALSE))

filter() week days

With filter we can pick out the days of the week that we want.

Split the schedule in two by filtering the survey to only the week day needed at each island:

• Saturday for Dream
• Tuesday for Biscoe

dream_dates <- filter(survey,  ______ ==  ________ )

biscoe_dates <- filter(survey, ______ ==  ________ )

dream_dates <- filter(survey, week_day ==  "Saturday")

biscoe_dates <- filter(survey, week_day ==  "Tuesday")

How many survey dates will occur at each island?

Hint: It’s less than 50.

2. Custom date formats

Oh no! Each survey island has been assigned a very-very particular Assistant to the Regional Manager. And they are demanding a very specific date format be used for their work schedules.

Before you send off the survey dates, you’ll need to adjust the dates to match the requested formats below.

Preferred date formats

• Dream: 08-18-2024
• Biscoe: Aug 18, 2024

For the first case we will do things the more manual way and use format with specific date expressions to layout each piece of the date

Use format(count_date, ...) and the date expressions below to format the schedule for each region accordingly.

For example: format(count_date, "%b, %Y") prints the date as May, 2024.

• %b stands for 3-letter month abbreviation
• %Y% stands for the full 4 digit year

# Set date format to match 08-18-2024
dream_dates <- mutate(dream_dates, pretty_date = format(count_date, _______  ))

# Set date format to 08-18-2024
dream_dates <- mutate(dream_dates, pretty_date = format(count_date, "%m-%d-%Y"))

# Dream island schedule
head(dream_dates, 3)

# Create date stamp with format of Aug 18, 2024
biscoe_stamp <- stamp_date("Aug 18, 2024")

# Test the stamp
test_date <- ymd("1985-10-26")

biscoe_stamp(test_date)

## [1] "Oct 26, 1985"

# Set date format to match Aug 18, 2024
biscoe_dates <- mutate(biscoe_dates, pretty_date = ___________ )

# Set date format to match Aug 18, 2024
biscoe_dates <- mutate(biscoe_dates, pretty_date = biscoe_stamp(count_date))

# Biscoe island schedule
head(biscoe_dates, 3)

3. Results

monsters <- read_csv("https://tidy-mn.github.io/R-camp-penguins/data/2024_monster_survey.csv") 

mystery_island_date <- "2024-06-25"

monsters <- filter(monsters, !is.na(island))  

4. The best time for monsters

What is the best month to see monsters?

First, add a month column to the data with the function month() and the column count_date.

monsters <- mutate(monsters, month = month(count_date))

Next, use ggplot() and geom_col() to plot the monster sightings by month.

ggplot(monsters, aes(x = month, y = ______ , fill = island)) +
    geom_col()

ggplot(monsters, aes(x = month, y = monster_count, fill = island)) +
    geom_col()

Why might June be the lowest month?

Hint: When was that data we dropped?

5. Time series: All the data

Plot all the data with geom_point(). Put count_date on the x-axis, and the monster_count on the y-axis. Set the color to match the island column.

ggplot(monsters, aes(x = _______, y = ______ , color = _____ )) +
    geom_point(size = 5)

ggplot(monsters, aes(x = count_date, y = monster_count, color = island)) +
    geom_point(size = 5) 

Oof! That’s a busy plot. Try adding + facet_wrap("island") to the end. What happens? Then try adding + geom_line().

ggplot(monsters, aes(x = count_date, y = monster_count, color = island)) +
  geom_point(size = 5) +
  facet_wrap("island") +
  geom_line()

Great work

The researchers were deeply thankful and rewarded you with a shiny penguin magnet. They’ll be in touch for Monster Survey 2025.