Data import and dates/times

Author

Affiliation

Prof Amanda Luby

Carleton College
Stat 220 - Winter 2026

library(tidyverse)
library(ggthemes)
library(readxl)

Click the “code” button above to copy and paste the source code into RStudio

Warm Up

Use read_csv() to import the desserts data set from

https://stat220-w26.github.io/data/desserts.csv

# your code here

`readr` practice

Use the appropriate read_<type>() function to import the following data sets:

https://stat220-w26.github.io/data/data-4.csv
https://stat220-w26.github.io/data/tricky-1.csv

If you hit any errors/problems, be sure to explore them and identify the issue, even if you can’t “fix” it.

data-4.csv

# your code here

tricky-1.csv

# your code here

`read_sheet` practice

Use the read_sheet function from {googlesheets4} to import the following data set:

https://docs.google.com/spreadsheets/d/1NN2S7bHSQbiHgd6gJnMUoJ6_eB-YgqLi_JXckFzTkbc/edit?usp=sharing

Step 1: read in the data so it looks like the following:

# A tibble: 9 × 2
  id      n    
  <chr>   <chr>
1 Brand 1 n    
2 1234    8    
3 8721    2    
4 1822    3    
5 Brand 2 n    
6 3333    1    
7 2156    3    
8 3987    6    
9 3216    5

library(googlesheets4)
# your code here

Step 2 (Stretch goal): Manipulate the data so that it looks like the following:

# A tibble: 7 × 3
  brand   id    n    
  <chr>   <chr> <chr>
1 Brand 1 1234  8    
2 Brand 1 8721  2    
3 Brand 1 1822  3    
4 Brand 2 3333  1    
5 Brand 2 2156  3    
6 Brand 2 3987  6    
7 Brand 2 3216  5

# your code here

Hints

We need to create a brand column. Start by creating this column, filling in the value from the “id” column if n == "n" and NA otherwise
Next, we want to “fill in” all of the NA’s in our “brand” column. Look at the help page for the fill function
Use filter to remove the redundant rows and select to remove the redundant columns

`lubridate` practice

Task 1

Create a new copy of the desserts dataset, but do not parse the uk_airdate within read_csv. Instead, leave it as a character vector and parse the date using {lubridate} functions. Which approach do you prefer?

Then, create a new column called how_long_ago that measures the time between today and the UK airdate of the episode. Can you format this column:

in years
in months
in weeks
in days

(Hint: see ?time_length)

# your code here

--- title: "Data import and dates/times" --- ```{r} library(tidyverse) library(ggthemes) library(readxl) ``` Click the "code" button above to copy and paste the source code into RStudio # Warm Up Use `read_csv()` to import the `desserts` data set from https://stat220-w26.github.io/data/desserts.csv ```{r} # your code here ``` # `readr` practice Use the appropriate `read_<type>()` function to import the following data sets: - `https://stat220-w26.github.io/data/data-4.csv` - `https://stat220-w26.github.io/data/tricky-1.csv` If you hit any errors/problems, be sure to explore them and identify the issue, even if you can't "fix" it. ## data-4.csv ```{r} # your code here ``` ## tricky-1.csv ```{r} # your code here ``` # `read_sheet` practice Use the `read_sheet` function from {googlesheets4} to import the following data set: - `https://docs.google.com/spreadsheets/d/1NN2S7bHSQbiHgd6gJnMUoJ6_eB-YgqLi_JXckFzTkbc/edit?usp=sharing` Step 1: read in the data so it looks like the following: ``` # A tibble: 9 × 2 id n <chr> <chr> 1 Brand 1 n 2 1234 8 3 8721 2 4 1822 3 5 Brand 2 n 6 3333 1 7 2156 3 8 3987 6 9 3216 5 ``` ```{r} library(googlesheets4) # your code here ``` Step 2 (Stretch goal): Manipulate the data so that it looks like the following: ``` # A tibble: 7 × 3 brand id n <chr> <chr> <chr> 1 Brand 1 1234 8 2 Brand 1 8721 2 3 Brand 1 1822 3 4 Brand 2 3333 1 5 Brand 2 2156 3 6 Brand 2 3987 6 7 Brand 2 3216 5 ``` ```{r} # your code here ``` ::: callout-tip ## Hints (1) We need to create a `brand` column. Start by creating this column, filling in the value from the "id" column if `n == "n"` and NA otherwise (2) Next, we want to "fill in" all of the NA's in our "brand" column. Look at the help page for the `fill` function (3) Use `filter` to remove the redundant rows and `select` to remove the redundant columns ::: # `lubridate` practice ## Task 1 Create a new copy of the `desserts` dataset, but do *not* parse the `uk_airdate` within `read_csv`. Instead, leave it as a character vector and parse the date using {lubridate} functions. Which approach do you prefer? Then, create a new column called `how_long_ago` that measures the time between today and the UK airdate of the episode. Can you format this column: - in years - in months - in weeks - in days (*Hint:* see `?time_length`) ```{r} # your code here ```