7. Mutate

Video Tutorial

Mutate

Mutate is an incredibly powerful tool to create new columns and new variables.

Let’s grab our code to read in the clean dataframe

fhv_clean <- read_csv(file = "For_Hire_Vehicles__FHV__-_Active.csv") %>% 
  clean_names() %>% 
  rename(hybrid = veh)

We create a new column with mutate by setting the name of our new column and a new value

fhv_clean %>% 
  mutate(city = "New York City")

You can create multiple new columns (...) at once

fhv_clean %>% 
  mutate(city = "New York City",
         active = TRUE) #I can overwrite column names too. I've made this active column boolean (true or false)

Mutate with logical expressions

Where mutate gets powerful is when you use it with logical expressions. Here we use if_else()

fhv_rideshare <- fhv_clean %>% 
  mutate(rideshare = if_else(
    condition = base_name == "UBER USA, LLC",
    true = "rideshare",
    false = "limo"
  )) #if it's an uber call it rideshare, if its a limo call it something else
#notice I named the arguments here! A good practice when the argument is not ...

Tabulate the variable we made with the count() funtion

fhv_rideshare %>% 
  count(rideshare)

What if we have more than one logical expression we care about? Check out case_when.

fhv_blackcar <- fhv_clean %>% 
  mutate(
    ride_type = case_when(
      base_name == "UBER USA, LLC" & base_type == "BLACK-CAR" ~ "BLACK CAR RIDESHARE",
      base_name != "UBER USA, LLC" & base_type == "BLACK-CAR" ~ "BLACK CAR NON-RIDESHARE",
      TRUE ~ base_type #if it doesn't meet either condition, return the base_type
    ))

Use & and | for and and or logical expressions with multiple conditions

fhv_blackcar %>% 
  count(ride_type)#now we have four categories!

Normalizing with Mutate

You can use statistical functions like mean to normalize data with mutate. mean will return the average of all the vehicle years. You can use mutate to generate a new variable that takes the distance from each observation to the mean.

fhv_clean %>% 
  mutate(year_norm = vehicle_year/mean(vehicle_year, na.rm = T),
         year_pct = percent_rank(vehicle_year)) %>% 
  select(vehicle_license_number, vehicle_year, year_norm, year_pct)