Filter

Please create a data frame of Orca whales only from the bo data frame.

We’ll name the new data frame orcas.

# Add to the line below 

orcas <- bo %>%
  
unique(orcas$Species)

orcas <- bo %>% filter(Species=="Killer Whale; Orca")
  
unique(orcas$Species)

Select

Drop the column/variable Species since there’s only one variable in it now.

# Add to the line below

orcas2 <- orcas
  
colnames(orcas2)

orcas2 <- select(orcas, -Species)

colnames(orcas2)

Arrange

Sort the orcas data frame based on the Facility column in alphabetical order.

# Add to the line below

orcas3 <- orcas2
  

head(orcas3)

orcas3 <- orcas2 %>% arrange(Facility)
  

head(orcas3)

Mutate

Create a new column called status_num to fill with a 1 if Status is “DEAD” or 0 if alive. Save the transformed data frame back into orcas

# Transform the code below

orcas4 <- orcas3
  

head(orcas4)

orcas4 <- mutate(orcas3, status_num=case_when(Status=="DEAD" ~ 1, TRUE ~ 0))
  

head(orcas4)

Summarize

Count up the number of total Orcas by counting the unique NOAA.IDs and how many have died by adding up the status_num. You want to summarize with two columns: total (by counting distinct NOAA.IDS and adding up number of those that have died)

# Add to the line below

alive_dead <- orcas4
  
alive_dead

alive_dead <- orcas4 %>%
  summarize(total=n_distinct(NOAA.ID), died=sum(status_num))

Percent

What’s the percent of dead Orcas?

Create a new column/variable called percent_dead.

You do not need to round the results.

# Add to the line below

perc_dead <- alive_dead

perc_dead

perc_dead <- alive_dead %>%
  mutate(percent_dead=died/total*100)

perc_dead

Now figure how the percent of Orcas still alive.

Create a new column/variable called percent_alive.

You do not need to round the results.

# Add to the line below

percent_dead_alive <- perc_dead

percent_dead_alive

percent_dead_alive <- perc_dead %>%
  mutate(percent_alive=100-percent_dead)

percent_dead_alive

Group By

Consider the bo data frame again.

Which Facility had the most Orcas and Belugas combined?

Go ahead and call the new column mammal_count.

List them from highest count to lowest.

# Add to the line below

facilities_count <- bo

facilities_count

facilities_count <- bo %>%
  group_by(Facility) %>%
  summarize(mammal_count=n()) %>%
  arrange(-mammal_count)

facilities_count

Turn a tall data frame wide

Summarize the data and count the total number of Orcas and Belugas (separately) per facility from the Species variable. Use the mammal_count as a column name again.

Then turn the tall data into a wide data frame that has these columns: Facility, Belugas, Orcas.

# Add to the line below

facilities_count <- bo %>%
   group_by(Facility, ________) %>%
   summarize(_______=___) %>%
   ______(_______, _____)

facilities_count

facilities_count <- bo %>%
  group_by(Facility, Species) %>%
  summarize(mammal_count=n()) %>%
  spread(Species, mammal_count)

facilities_count

Turn a wide data frame into a tall tidy data frame

Here’s a wide data frame with Owners and percent of Belugas compared to Orca whales owned.

Make this dataframe called owner_percent tidy for me (So the mammal types are in one column and the total is in another).

Call the new columns Species and Total.

# Add to the line below

owner_percent %>%

owner_percent %>%
  gather("Species", "Total", 2:3)

Join Data

Consider these two data sets:

• owner_percent

• owner_total

Join these two data sets and call it owner_total_percent.

Start with owner_percent and have it joined with owner_total in a left direction.

# Add to the line below

owner_percent_total <- _____join(________, ________)
  
owner_percent_total

owner_percent_total <- left_join(owner_percent, owner_total)
  
owner_percent_total

Lower case

Let’s use the functions in it to manipulate the strings in this data frame.

Turn the names in Name to lower case.

bo$Name <- ________(bo$Name)

bo

bo$Name <- str_to_lower(bo$Name)

bo

Title case

Convert the variables in Sex to title case.

bo$Name <- ________(bo$Name)

bo

bo$Sex <- str_to_title(bo$Sex)

bo2 <- bo
bo2

Combine string

Please create a new variable called sex_species that combines Sex and Species columns with a space in between.

bo3 <- mutate(bo2, 
             _________=_____(_______________________))
bo3

bo3 <- mutate(bo2,
             sex_species=str_c(Sex, " ", Species))

Substitute string

Now, replace all mentions of " Killer Whale;" and " Whale" from the sex_species column with a blank.

So “Male Killer Whale; Orca” turns in to “Male Orca” and “Male Beluga Whale” turns into “Male Beluga”

bo4 <- mutate(bo3, 
             sex_species=________(_______________________)) %>%
      mutate(sex_species=________(_______________________))

bo4$sex_species

bo4 <- mutate(bo3,
             sex_species=str_replace(sex_species, " Killer Whale;", "")) %>%
      mutate(sex_species=str_replace(sex_species, " Whale", ""))

bo4$sex_species

Detect strings

Let’s look at the first six rows of the bo data frame again.

Create a new data frame called seaworlds and use filter to keep only the rows that mention “Sea World” in the Facility column.

seaworlds <- filter(bo,
                    ______(_____________))

seaworlds

seaworlds <- filter(bo,
             str_detect(Facility, "Sea World"))

seaworlds

Convert this date

Turn Captivity.Date into a date data R can recognize.

We’ll use the lubridate package.

bo_dates <- bo %>%
  mutate(Captivity.Date=____(_______))

bo_dates

bo_dates <- bo %>%
  mutate(Captivity.Date=dmy(Captivity.Date))

bo_dates

Extract the month

Create a new column called month_number and extract the month (as a number) from the Captivity.Date.

bo_dates2 <- bo_dates %>%
  mutate(month_number=____(_______))

bo_dates2

bo_dates2 <- bo_dates %>%
  mutate(month_number=month(Captivity.Date))

bo_dates2

Create a new column called month_number and extract the month (as a label) from the Captivity.Date.

bo_dates3 <- bo_dates2 %>%
  mutate(month_label=____(_______, ______))

bo_dates3

bo_dates3 <- bo_dates2  %>%
  mutate(month_label=month(Captivity.Date, label=T))

bo_dates3

Time difference

Consider the modified bo_dates3 data frame we’ve created.

Based on available data, can you figure out the average age (in years) of Beluga Whales and Orca Whales (separately) when they join a facility (aka Facility.Date)?

Summarize your dataframe (group by Facility and Species) and create a new column called average_years.

bo_dates3 %>%

bo_dates3 %>%
  mutate(Birth.Date=dmy(Birth.Date), 
         Facility.Date=dmy(Facility.Date),
         age=difftime(Facility.Date, Birth.Date),
         age_years=as.numeric(age)/60/60/24/365.25) %>% 
  group_by(Facility, Species) %>% 
  summarize(average_years=mean(age_years, na.rm=T))

Alright, we can’t really tell the ages because of negative years? It looks like this data is pretty messy. Probably human error.