Factors, dates, and strings

EVR 628- Intro to Environmental Data Science

Author

Affiliation

Juan Carlos Villaseñor-Derbez (JC)

Rosenstiel School of Marine, Atmospheric, and Earth Science and Institute for Data Science and Computing

Preamble:

Exercise 0 - Set up

Post-it up

1. Download this CSV file to your EVR 628 project folder. Save it into data/raw/.
2. Start a new script, call it factors_dates_and_strings.R and save it into your scripts/01_processing/ folder
3. Add a code outline and load the tidyverse and janitor packages at the top of your script

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## SET UP ######################################################################
# Load packages ----------------------------------------------------------------
library(janitor)
library(tidyverse)

Warning: package 'ggplot2' was built under R version 4.5.2

Warning: package 'tidyr' was built under R version 4.5.2

Warning: package 'dplyr' was built under R version 4.5.2

Exercise 1 - Dates

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1. Load your data into an object called tour_data <- and clean the column names
2. Inspect your data. How many columns / rows? What are these columns?
3. Create a new object called tour_data_clean <- and add a new column called date that contains the date. We will build a large pipeline starting here.
4. Remove the year, month, and day columns that we no longer need
5. Place the date column all the way to the left

# Load data  -------------------------------------------------------------------
tour_data <- read_csv("data/raw/tour_data.csv") |> 
  clean_names()

dim(tour_data)
colnames(tour_data)
head(tour_data)

## PROCESSING ##################################################################
# Add a date -------------------------------------------------------------------
tour_data_clean <- tour_data |>                 #3) New object
  mutate(date = make_date(year, month, day)) |> # 4) Build my date
  select(date, everything(), -c(year, month, day)) # 5) Remove and organize columns

[1] 60  6

[1] "year"       "month"      "day"        "vessel"     "passengers"
[6] "notes"     

# A tibble: 6 × 6
   year month   day vessel passengers notes                                     
  <dbl> <dbl> <dbl> <chr>       <dbl> <chr>                                     
1  2025     1     1 Condor         21 8 Sea lions; 5 Whales; 4 Whale shark      
2  2025     1     7 Falcon          6 5 whale; 7 Sea lions; 5 Sea turtle; 4 sea…
3  2025     1    13 Falcon          8 5 Dolphins                                
4  2025     1    19 Condor          6 4 Sea turtle; 4 sea turtle                
5  2025     1    25 Condor         29 2 sea turtle; 8 whale                     
6  2025     2     1 Falcon         19 3 Whale shark; 7 whale                    

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Exercise 2 - Strings

Part 1 - Data contained in a column with strings of text

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1. As demonstrated in class, use separate_longer_delim() to create one row per species mentioned in the data
2. Standardize the data so all strings appear in lowercase
3. Make sure they are all singular

## PROCESSING ##################################################################
# Add a date -------------------------------------------------------------------
tour_data_clean <- tour_data |>                        
  mutate(date = make_date(year, month, day)) |>        
  select(date, everything(), -c(year, month, day)) |>
  separate_longer_delim(cols = notes, delim = ";") |>  # 1) Separate column into new rows
  mutate(notes = str_to_lower(notes),                  # 2) All strings as lower case
         notes = str_remove(notes, "s$"))

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Part 2 - Data STILL contained in a column with strings of text!

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1. Look at the documentation for str_extract()
2. Run the first two examples at the bottom directly in your console. What seems to be going on?

pause to discuss

3. Using the str_extract() function, build a new column called fauna that contains the species observed¹
4. Print your data to the console. Does fauna look right?
5. Using the str_extract() function, build a new column called n that contains the number of organisms observed (make sure it’s numeric)
6. Remove the notes column

## PROCESSING ##################################################################
# Add a date -------------------------------------------------------------------
tour_data_clean <- tour_data |>                        
  mutate(date = make_date(year, month, day)) |>        
  select(date, everything(), -c(year, month, day)) |>
  separate_longer_delim(cols = notes, delim = ";") |>
  mutate(notes = str_to_lower(notes),
         notes = str_remove(notes, "s$"),
         fauna = str_extract(string = notes, pattern = "[a-z ]+$"), # 3) Extract fauna
         fauna = str_squish(fauna),                                 # 4) Remove white space
         n = str_extract(string = notes, pattern = "[:digit:]+"),   # 5) Extract n
         n = as.numeric(n)) |>                                            # and make sure it's numeric
  select(-notes)                                                      # 6) Remove notes

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Exercise 3 - Visualization (a bit of everything)

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1. Recreate the plot below

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## VISUALIZE ###################################################################
ggplot(data = tour_data_clean,
       mapping = aes(x = str_to_sentence(fauna), # This is the only str-relevant change here
                     y = n)) +
  stat_summary(geom = "col", fun = "sum") +
  facet_wrap(~vessel, ncol = 1) +
  labs(title = "Sigthings by vessel",
       x = "Fauna",
       y = "N") +
  theme_minimal(base_size = 14)

Footnotes

1. Hint: Look at the documentation for ?regex