Data Importation

Lecture 9

Katie Solarz

Duke University
STA 199 Summer 2026: Session I

May 27, 2026

Administrative Details

  • Midterm next Monday!

    • Practice + some review tomorrow in class

    • Practice questions for both the multiple choice & live coding portions of the exam will be posted tomorrow!

    • I will post solutions to the practice questions by Saturday morning

  • Lab 3 after lecture tomorrow

    • This will be a shorter than usual lab & is meant to serve as a recap of all we’ve learned so far (aka, helpful exam prep!)

    • Reminder: Lab 3 will be due by 12:00pm (noon) on Sunday; there will be no late window for this lab assignment

    • Solutions will posted to the website promptly thereafter

Administrative Details

  • Project work begins next week!

    • Since the midterm takes place during both of the formal lecture and lab timeslots next Monday, lecture time next Tuesday (9:30am - 10:45am) will function as a lab meeting

    • Both lab sessions next week (T, Th) will be dedicated worktime

    • You will receive your team assignments (2 groups of 3, 2 groups of 4) in lab next Tuesday

    • As a reminder, projects are completed in groups BUT grades are individual; your final project grade may ultimately differ from your teammates’ if there is unequal participation

    • Participation is measured by 1) lab attendance on project work days; 2) commit history in project repos on GitHub; 3) peer review forms

Let’s zoom out for a second

Data science and statistical thinking

Before Midterm 1…

  • Data science: the real-world art of transforming messy, imperfect, incomplete data into knowledge;

After Midterm 1…

  • Statistics: the mathematical discipline of quantifying our uncertainty about that knowledge.

Data science

Data science

  1. Collection: we won’t seriously study data collection (take an experimental design class if you are interested!); we will discuss data importation methods today
  • for the purposes of this class: accessing package data (library(); df <- library::df), data importation (read_csv, read_xlsx, read_xls)
  • in reality…: web-scraping, domain-specific issues of measurement, survey design, experimental design, etc.

Data science

  1. Collection: we won’t seriously study data collection; we will discuss data importation methods today
  1. Preparation: cleaning, wrangling, and otherwise tidying the data so we can actually work with it.
  • keywords: mutate, fct_relevel, pivot_*, *_join

Data science

  1. Collection: we won’t seriously study data collection; we will discuss data importation methods today

  2. Preparation: cleaning, wrangling, and otherwise tidying the data so we can actually work with it.

  1. Analysis: finally, transform the data into knowledge…
  • visual summaries: ggplot, geom_*, etc.
  • numerical summaries: summarize, group_by, count, mean, median, sd, quantile, IQR, cor, etc.
  • The visualizations and the summaries should complement one another!

Reading data into R

Package data

  • When data is neatly stored in a package, such as tidyverse, loading the package loads the dataset; you can explicitly save a packaged df to your RStudio environment by running df <- library::df

  • Most often, this is not the case

Reading in rectangular data

Reading rectangular data

  • Using readr: (in tidyverse)
    • Most commonly: read_csv() - file saved as .csv
    • Maybe also: read_tsv(), read_delim(), etc - other file formats
  • Using readxl:
    • read_excel() - R determines file type for you
    • read_xls(), read_xlsx() - use if you know whether you have a .xls or .xlsx file

  • Using googlesheets4: read_sheet() – we haven’t covered this in the videos, but might be useful for your projects

    • Fun fact: The “Schedule” page on the course website pulls information from an underlying Google sheets file

Using read_csv()

Generally, the format is:

df_name <- read_csv("path_to_file_name.csv")

Path to file

For example, recall we worked with durham-climate.csv in AE08. Where is durham-climate.csv?

When in our AE repo, we read in the data with the following code:

durham_climate <- read_csv("data/durham-climate.csv")

Path to file

  • use / to separate folder(s) + file names; file path in quotes

  • Answer: read_csv("data/durham-climate.csv")

Why not include ae-kgsolarz?

Where is durham-climate.csv?

We can also write files!

This allows us to save data for later usage, share data outside of R, etc.


Using write_csv():

write_csv(r_df_name, "path_to_file.csv")

Application exercise

Goal 1.1: Reading and writing CSV files

  • Read a CSV file with tidy data

  • Split it into subsets based on features of the data

  • Write out subsets as CSV files

Goal 1.2: Practice - Case When

  • case_when() is similar to if_else(), but allows multiple cases
  • case_when() is often used within mutate() to create a new column
df |>
  mutate(new_var = case_when(
    condition_1 ~ result_1,
    condition_2 ~ result_2,
    condition_3 ~ result_3,
    ...,
    .default = default_result
  ))

An aside - If Else

  • case_when() is similar to if_else(), but if_else() only allows for 2 cases / conditions

  • Long story short… use if_else() if you are only considering 2 cases / conditions; for > 2 conditions, use case_when()

  • In words, we’d read the code below as: “If logical_1 evaluates to TRUE (i.e., this condition is met), then choose result_1; else (i.e., this condition is not met), choose result 2

df |>
  mutate(new_var = if_else(logical_1, result_1, result_2))

## create a new column, "is_december" with a value 1 if the month is December and a value 0 otherwise

durham_climate |>
  mutate(is_december = if_else(month == "December", 1, 0))

Age gap in Hollywood relationships

Goal 2.1: Reading Excel files & non-tidy data

  • Read an Excel file with non-tidy data

  • Tidy it up!

Goal 2.2: String Functions

We’ve seen lots of functions that deal with numeric data (mean, median, sum, etc.) - what about characters?

  • stringr is a tidyverse package with lots of functions for dealing with character strings

  • today: str_detect in stringr

Goal 2.2: String Functions

  • str_detect() identifies if a character / sequence of characters is a substring within a longer string

  • useful in cases when you need to check some condition, for example:

    • in a filter()

    • in an if_else() or case_when()

Goal 2.2: String Functions

  • str_detect() identifies if a character / sequence of characters is a substring within a longer string

  • useful in cases when you need to check some condition, for example:

    • in a filter()

    • in an if_else() or case_when()

example: which classes in a list are in the stats department?

classes <- c("sta199", "dance122", "math185", "sta240", "pubpol202")
str_detect(classes, "sta")
[1]  TRUE FALSE FALSE  TRUE FALSE

Goal 2.2: String Functions

General form:

str_detect(character_var, "word_to_detect")

Sales data

Are these data tidy? Why or why not?

Sales data

What data tidying must be done to go from the original, non-tidy data to the below tidy version of these data?