Data Import &
Dates/Times

Day 11

Prof Amanda Luby

Carleton College
Stat 220 - Winter 2026

Today

Importing data into R
Working with dates and times

`desserts` data

Contains results from series 1-8 of The Great British Bake Off
Case defined by series, episode and baker

# A tibble: 549 × 16
   series episode baker     technical result uk_airdate     us_season us_airdate
    <dbl>   <dbl> <chr>     <chr>     <chr>  <chr>              <dbl> <date>    
 1      1       1 Annetha   2nd       IN     17 August 2010        NA NA        
 2      1       1 David     3rd       IN     17 August 2010        NA NA        
 3      1       1 Edd       1st       IN     17 August 2010        NA NA        
 4      1       1 Jasminder N/A       IN     17 August 2010        NA NA        
 5      1       1 Jonathan  9th       IN     17 August 2010        NA NA        
 6      1       1 Louise    N/A       IN     17 August 2010        NA NA        
 7      1       1 Miranda   8th       IN     17 August 2010        NA NA        
 8      1       1 Ruth      N/A       IN     17 August 2010        NA NA        
 9      1       1 Lea       10th      OUT    17 August 2010        NA NA        
10      1       1 Mark      N/A       OUT    17 August 2010        NA NA        
# ℹ 539 more rows
# ℹ 8 more variables: showstopper_chocolate <chr>, showstopper_dessert <chr>,
#   showstopper_fruit <chr>, showstopper_nut <chr>, signature_chocolate <chr>,
#   signature_dessert <chr>, signature_fruit <chr>, signature_nut <chr>

Basic syntax

All readr functions share a common syntax

df <- read_csv(file = "path/to/file.csv", ...)

`readr` functions

function	reads
read_csv()	Comma separated values
read_csv2()	Semi-colon separated values
read_delim()	General delimited files
read_fwf()	Fixed width files
read_log()	Apache log files
read_table()	Space separated
read_tsv()	Tab delimited values

`desserts` data

Contains results from series 1-8 of The Great British Bake Off
Case defined by series, episode and baker

# A tibble: 549 × 16
   series episode baker     technical result uk_airdate     us_season us_airdate
    <dbl>   <dbl> <chr>     <chr>     <chr>  <chr>              <dbl> <date>    
 1      1       1 Annetha   2nd       IN     17 August 2010        NA NA        
 2      1       1 David     3rd       IN     17 August 2010        NA NA        
 3      1       1 Edd       1st       IN     17 August 2010        NA NA        
 4      1       1 Jasminder N/A       IN     17 August 2010        NA NA        
 5      1       1 Jonathan  9th       IN     17 August 2010        NA NA        
 6      1       1 Louise    N/A       IN     17 August 2010        NA NA        
 7      1       1 Miranda   8th       IN     17 August 2010        NA NA        
 8      1       1 Ruth      N/A       IN     17 August 2010        NA NA        
 9      1       1 Lea       10th      OUT    17 August 2010        NA NA        
10      1       1 Mark      N/A       OUT    17 August 2010        NA NA        
# ℹ 539 more rows
# ℹ 8 more variables: showstopper_chocolate <chr>, showstopper_dessert <chr>,
#   showstopper_fruit <chr>, showstopper_nut <chr>, signature_chocolate <chr>,
#   signature_dessert <chr>, signature_fruit <chr>, signature_nut <chr>

Warm up {.smaller}

Use read_csv() to import the desserts data set from
https://stat220-w26.github.io/data/desserts.csv
Store the data in the desserts object
Our goal is to make the following plot. Will this data allow us to do so?

06:00

Did it work as expected?

desserts <- read_csv("https://stat220-w26.github.io/data/desserts.csv")
glimpse(desserts)

Rows: 549
Columns: 16
$ series                <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ episode               <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, …
$ baker                 <chr> "Annetha", "David", "Edd", "Jasminder", "Jonatha…
$ technical             <chr> "2nd", "3rd", "1st", "N/A", "9th", "N/A", "8th",…
$ result                <chr> "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", …
$ uk_airdate            <chr> "17 August 2010", "17 August 2010", "17 August 2…
$ us_season             <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ us_airdate            <date> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ showstopper_chocolate <chr> "chocolate", "chocolate", "no chocolate", "no ch…
$ showstopper_dessert   <chr> "other", "other", "other", "other", "other", "ca…
$ showstopper_fruit     <chr> "no fruit", "no fruit", "no fruit", "no fruit", …
$ showstopper_nut       <chr> "no nut", "no nut", "no nut", "no nut", "almond"…
$ signature_chocolate   <chr> "no chocolate", "chocolate", "no chocolate", "no…
$ signature_dessert     <chr> "cake", "cake", "cake", "cake", "cake", "cake", …
$ signature_fruit       <chr> "no fruit", "fruit", "fruit", "fruit", "fruit", …
$ signature_nut         <chr> "no nut", "no nut", "no nut", "no nut", "no nut"…

A couple issues…

technical is character, not numeric
uk_airdate is character, not date

The `col_types` argument

By default, read_csv looks at first 1000 rows to guess variable data types (guess_max), but we can also tell R how to read column types

desserts <- read_csv( "https://stat220-w26.github.io/data/desserts.csv",
  col_types = list( 
    technical = col_number(), 
    uk_airdate = col_date()   
  ) 
)

glimpse(desserts)

Rows: 549
Columns: 16
$ series                <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ episode               <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, …
$ baker                 <chr> "Annetha", "David", "Edd", "Jasminder", "Jonatha…
$ technical             <dbl> 2, 3, 1, NA, 9, NA, 8, NA, 10, NA, 8, 6, 2, 1, 3…
$ result                <chr> "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", …
$ uk_airdate            <date> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ us_season             <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ us_airdate            <date> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ showstopper_chocolate <chr> "chocolate", "chocolate", "no chocolate", "no ch…
$ showstopper_dessert   <chr> "other", "other", "other", "other", "other", "ca…
$ showstopper_fruit     <chr> "no fruit", "no fruit", "no fruit", "no fruit", …
$ showstopper_nut       <chr> "no nut", "no nut", "no nut", "no nut", "almond"…
$ signature_chocolate   <chr> "no chocolate", "chocolate", "no chocolate", "no…
$ signature_dessert     <chr> "cake", "cake", "cake", "cake", "cake", "cake", …
$ signature_fruit       <chr> "no fruit", "fruit", "fruit", "fruit", "fruit", …
$ signature_nut         <chr> "no nut", "no nut", "no nut", "no nut", "no nut"…

Looking for `problems`

List of potential problems parsing the file

problems(desserts)

# A tibble: 556 × 5
     row   col expected        actual         file 
   <int> <int> <chr>           <chr>          <chr>
 1     2     6 date in ISO8601 17 August 2010 ""   
 2     3     6 date in ISO8601 17 August 2010 ""   
 3     4     6 date in ISO8601 17 August 2010 ""   
 4     5     4 a number        N/A            ""   
 5     5     6 date in ISO8601 17 August 2010 ""   
 6     6     6 date in ISO8601 17 August 2010 ""   
 7     7     4 a number        N/A            ""   
 8     7     6 date in ISO8601 17 August 2010 ""   
 9     8     6 date in ISO8601 17 August 2010 ""   
10     9     4 a number        N/A            ""   
# ℹ 546 more rows

Date formatting woes

# A tibble: 556 × 5
    row   col expected        actual         file 
  <int> <int> <chr>           <chr>          <chr>
1     2     6 date in ISO8601 17 August 2010 ""   
2     3     6 date in ISO8601 17 August 2010 ""   
3     4     6 date in ISO8601 17 August 2010 ""   
4     5     4 a number        N/A            ""   
5     5     6 date in ISO8601 17 August 2010 ""   
# ℹ 551 more rows

ISO8601 format: 2010-08-17

What we have: 17 August 2010

Adding `format` instructions

desserts <- read_csv(
  "https://stat220-w26.github.io/data/desserts.csv",
  col_types = list(
    technical = col_number(), 
    uk_airdate = col_date(format = "%d %B %Y")  
  ) 
)

Year: "%Y" (4 digits). "%y" (2 digits)
Month: "%m" (2 digits), "%b" (abbreviated name in current locale), "%B" (full name in current locale).
Day: "%d" (2 digits), "%e" (optional leading space)

Looking for `problems`

List of potential problems parsing the file

problems(desserts)

# A tibble: 7 × 5
    row   col expected actual file 
  <int> <int> <chr>    <chr>  <chr>
1     5     4 a number N/A    ""   
2     7     4 a number N/A    ""   
3     9     4 a number N/A    ""   
4    11     4 a number N/A    ""   
5    35     4 a number N/A    ""   
6    36     4 a number N/A    ""   
7    37     4 a number N/A    ""

Addressing missing values

By default na = c("", "NA") are the recognized missing values

desserts <- read_csv(
  "https://stat220-w26.github.io/data/desserts.csv",
  col_types = list(
    technical = col_number(), 
    uk_airdate = col_date(format = "%d %B %Y")
  ),
  na = c("", "NA", "N/A") 
)

Looking for `problems`

List of potential problems parsing the file

# A tibble: 0 × 5
# ℹ 5 variables: row <int>, col <int>, expected <chr>, actual <chr>, file <chr>

desserts

# A tibble: 549 × 16
   series episode baker     technical result uk_airdate us_season us_airdate
    <dbl>   <dbl> <chr>         <dbl> <chr>  <date>         <dbl> <date>    
 1      1       1 Annetha           2 IN     2010-08-17        NA NA        
 2      1       1 David             3 IN     2010-08-17        NA NA        
 3      1       1 Edd               1 IN     2010-08-17        NA NA        
 4      1       1 Jasminder        NA IN     2010-08-17        NA NA        
 5      1       1 Jonathan          9 IN     2010-08-17        NA NA        
 6      1       1 Louise           NA IN     2010-08-17        NA NA        
 7      1       1 Miranda           8 IN     2010-08-17        NA NA        
 8      1       1 Ruth             NA IN     2010-08-17        NA NA        
 9      1       1 Lea              10 OUT    2010-08-17        NA NA        
10      1       1 Mark             NA OUT    2010-08-17        NA NA        
# ℹ 539 more rows
# ℹ 8 more variables: showstopper_chocolate <chr>, showstopper_dessert <chr>,
#   showstopper_fruit <chr>, showstopper_nut <chr>, signature_chocolate <chr>,
#   signature_dessert <chr>, signature_fruit <chr>, signature_nut <chr>

Column casting functions

Type	`dplyr::glimpse()`	`readr::col_*()`
logical	`<lgl>`	`col_logical`
numeric	`<int>` or `<dbl>`	`col_number`
character	`<chr>`	`col_character`
factor	`<fct>`	`col_factor`
date	`<date>`	`col_date`

`?read_csv`

read_csv(file, 
         col_names = TRUE,
         col_types = NULL,
         locale = default_locale(),
         na = c("", "NA"), 
         quoted_na = TRUE,
         quote = "\"", 
         comment = "",
         trim_ws = TRUE,
         skip = 0,
         n_max = Inf,
         guess_max = min(1000, n_max),
         progress = show_progress())

Your turn

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

data-4.csv
tricky-1.csv

The full URLs are in the 11-import.Rmd activity. (It might be helpful to look at the data file before trying to import it into R)

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

read_sheet

Get data out of google sheets and into R
Part of the tidyverse
Need to load separately

`read_sheet` works like `read_csv`

df <- read_sheet(ss = "url_to_googlesheet", ...)

sales data

Are these data tidy? Why or why not?

Sales data

What “data moves” do we need to go from the original, non-tidy data, to this tidy one?

# 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

Try it

Read in the file at 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

Stretch goal: Fully tidy the data. This is tricky! See the hints in the activity file.

# 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

05:00

What if I already imported data and have dates/times as character strings or numeric vectors?

Ultramarathon results

Ultra marathon = anything longer than 26.2 miles
The dates and times imported as character strings!

## Rows: 392
## Columns: 11
## $ state            <chr> "Minnesota", "Minnesota", "Minnesota", "Minnesota", "…
## $ Event            <chr> "Zumbro", "Zumbro", "Zumbro", "Zumbro", "Zumbro", "Zu…
## $ City             <chr> "Theilman", "Theilman", "Theilman", "Theilman", "Thei…
## $ race_url         <chr> "https://calendar.ultrarunning.com/event/zumbro", "ht…
## $ date             <chr> "04/08/21", "04/11/20", "04/13/19", "04/13/18", "04/0…
## $ Finishers        <chr> "121", "Race Cancelled", "Race Cancelled", "49", "149…
## $ top_result_m     <chr> "5:23:55", "Race Cancelled", "Race Cancelled", "9:13:…
## $ top_result_f     <chr> "6:30:42", "Race Cancelled", "Race Cancelled", "10:15…
## $ distance         <chr> "34 Miles", "50 Miles", "50 Miles", "50 Miles", "50 M…
## $ elevation_rating <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,…
## $ surface_rating   <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,…

Functions for working with dates and time spans
Part of the tidyverse
Need to load separately

Parsing dates

{lubridate} functions are intuitively named

library(lubridate)
mdy("01/29/25")

[1] "2025-01-29"

dmy("29-01-2025")

[1] "2025-01-29"

ymd("2025-01-29")

[1] "2025-01-29"

ymd_hm("2025-01-29 09:55")

[1] "2025-01-29 09:55:00 UTC"

Ultramarathon example

Dates are in the form 04/08/21, so use mdy() to parse

mn_ultras <- mn_ultras %>% 
  mutate(date = mdy(date))

glimpse(mn_ultras)

Rows: 392
Columns: 11
$ state            <chr> "Minnesota", "Minnesota", "Minnesota", "Minnesota", "…
$ Event            <chr> "Zumbro", "Zumbro", "Zumbro", "Zumbro", "Zumbro", "Zu…
$ City             <chr> "Theilman", "Theilman", "Theilman", "Theilman", "Thei…
$ race_url         <chr> "https://calendar.ultrarunning.com/event/zumbro", "ht…
$ date             <date> 2021-04-08, 2020-04-11, 2019-04-13, 2018-04-13, 2017…
$ Finishers        <chr> "121", "Race Cancelled", "Race Cancelled", "49", "149…
$ top_result_m     <chr> "5:23:55", "Race Cancelled", "Race Cancelled", "9:13:…
$ top_result_f     <chr> "6:30:42", "Race Cancelled", "Race Cancelled", "10:15…
$ distance         <chr> "34 Miles", "50 Miles", "50 Miles", "50 Miles", "50 M…
$ elevation_rating <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,…
$ surface_rating   <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,…

Extract info from a date/time

{lubridate} functions are intuitively named

function	action
`year()`, `month()`	extract year/month
`week()`	extract week of the year
`day()`, `wday()`	extract day of month/day of week
`hour()`, `minute()`, `second()`	extract hour/minute/second

Adding label = TRUE creates an ordered factor (for month or wday)

Extract info from a date/time

The most recent race in the data set was on 2022-01-31

month()
day()
wday()

What month was that in?

month("2022-01-31", label = TRUE)

[1] Jan
12 Levels: Jan < Feb < Mar < Apr < May < Jun < Jul < Aug < Sep < ... < Dec

What day of the month was it on?

day("2022-01-31")

[1] 31

What day of the week was it on?

wday("2022-01-31", label = TRUE)

[1] Mon
Levels: Sun < Mon < Tue < Wed < Thu < Fri < Sat

Measuring time

How long ago was the last race?

race <- ymd("2022-01-31")
today() - race

Time difference of 1458 days

Differences in date/time objects are difftime objects
difftimes use inconsistent units (sometimes weeks, days, hours, minutes, or seconds)

Math with date-times

Math with date-times relies on the timeline, which behaves inconsistently:

Measuring time: durations

Track the passage of physical time; always measured in seconds

Plus, a better display

as.duration(today() - race)

[1] "125971200s (~3.99 years)"

Measuring time: periods

periods track changes in clock times, ignoring timeline irregularities

ymd_hms("2025-01-29 09:55:00", 
        tz = "America/Chicago") + 
  days(1)

[1] "2025-01-30 09:55:00 CST"

ymd_hms("2023-02-28 09:55:00", 
        tz = "America/Chicago") + 
  days(1)

[1] "2023-03-01 09:55:00 CST"

Try it

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

05:00

If time

Get as far as you can in replicating this plot of series 8 data:

Data Import & Dates/Times

Today

desserts data

Basic syntax

readr functions

desserts data

Warm up {.smaller}

Did it work as expected?

The col_types argument

Looking for problems

Date formatting woes

Adding format instructions

Looking for problems

Addressing missing values

Looking for problems

Column casting functions

?read_csv

Your turn

read_sheet

read_sheet works like read_csv

sales data

Sales data

Try it

What if I already imported data and have dates/times as character strings or numeric vectors?

Ultramarathon results

Parsing dates

Ultramarathon example

Extract info from a date/time

Extract info from a date/time

Measuring time

Math with date-times

Measuring time: durations

Measuring time: periods

Try it

If time

Data Import &
Dates/Times

`desserts` data

`readr` functions

`desserts` data

The `col_types` argument

Looking for `problems`

Adding `format` instructions

Looking for `problems`

Looking for `problems`

`?read_csv`

`read_sheet` works like `read_csv`