A course in quantitative research workflow for students in the higher education administration program at the University of Florida
The data we’ve used so far in this course have been almost entirely
numerical. Even when the field represented an expected level of
education, for example, we didn’t see “complete a Bachelor’s degree.”
Instead, we saw the number 6 — sometimes with a label and
sometimes without (meaning we had to look up what 6 stood for). In
the few cases in which we’ve seen strings or dates, the values have
been very regular.
Much education-related data, however, are not this
uniform. Particularly when using administrative data files, you are
likely to read in columns that contain unstructured strings: names,
addresses, dates, etc. Why are they unstructured? Almost always the
answer is that person who initially keyed in the data neither had a
dropdown menu of options to choose from nor separate fields for each
part of the data element (e.g., first name, last name). Instead,
they have a blank field in which they type:
Enter name: Isaiah Berlin
Why is this a problem? With an open field, the variations are (often) unlimited:
I. Berlinisaiah berlinBerlin, IsaiahSimilarly, the same date can be written any number of ways:
Enter date: February 11, 2020
11 February 202011 Feb 2020Feb. 11, 20202/11/2020 (American)11/2/2020 (most everyone
else)2/11/20To be clear, this is not to impugn those who enter the data. Rather, it’s an acknowledgment that the original uses of the data we analyze may differ from our own, e.g., data input to meet compliance with an administrative task versus data input for statistical analysis.
Now, imagine gaining access to an administrative data set with these
two irregular columns, name and date, and thousands or even
millions of rows. To complete your analytic task, you must clean the
data such that for each observation, you keep only last names and need
to convert the date into a format that will allow you to easily
calculate time periods between dates. With so many observations, it’s
an impossible task to do by hand. But because the data are irregular,
you can’t, for example, just select the second word from the name
(what if the last name is first in some rows?) or the second number
after a forward slash, / (what about when the date uses hyphens,
month name, or a different order?).
You won’t always need to work with strings and dates, but when you do, having a few specialty tools in your toolbox will be greatly beneficial. Sometimes they can mean the difference between being able to answer your question and not. In this lesson, we’ll discuss two: regular expressions and dates.
As before, we’ll continue working within the tidyverse. We’ll focus, however, on using two specific libraries:
You may have noticed already that when we load the tidyverse library
with library(tidyverse), the stringr library is already
loaded. The lubridate library, though part of the tidyverse, is
not. We need to load it separately.
## ---------------------------
## libraries
## ---------------------------
## NB: The stringr library is loaded with tidyverse, but
## lubridate is not, so we need to load it separately
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.5 ✔ purrr 0.3.4
## ✔ tibble 3.1.6 ✔ dplyr 1.0.7
## ✔ tidyr 1.1.4 ✔ stringr 1.4.0
## ✔ readr 2.1.1 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(lubridate)
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
NB: As we have done in the past few lessons, we’ll run this script assuming that
our working directory is set to the scripts directory.
## ---------------------------
## directory paths
## ---------------------------
## assume we're running this script from the ./scripts subdirectory
dat_dir <- file.path("..", "data")
To practice working with strings, we’ll use data from Integrated Postsecondary Education Data System (IPEDS):
The National Center for Education Statistics (NCES) administers the Integrated Postsecondary Education Data System (IPEDS), which is a large-scale survey that collects institution-level data from postsecondary institutions in the United States (50 states and the District of Columbia) and other U.S. jurisdictions. IPEDS defines a postsecondary institution as an organization that is open to the public and has the provision of postsecondary education or training beyond the high school level as one of its primary missions. This definition includes institutions that offer academic, vocational and continuing professional education programs and excludes institutions that offer only avocational (leisure) and adult basic education programs. Definitions for other terms used in this report may be found in the IPEDS online glossary.
NCES annually releases national-level statistics on postsecondary institutions based on the IPEDS data. National statistics include tuition and fees, number and types of degrees and certificates conferred, number of students applying and enrolled, number of employees, financial statistics, graduation rates, student outcomes, student financial aid, and academic libraries.
You can find more information about IPEDS here. As higher education scholars, IPEDS data are a valuable resource that you may often turn to (I do).
We’ll use one file (which can be found here), that covers institutional characteristics for one year:
hd2007.csv)## ---------------------------
## input
## ---------------------------
## read in data and lower all names using rename_all(tolower)
df <- read_csv(file.path(dat_dir, "hd2007.csv")) %>%
rename_all(tolower)
## Rows: 7052 Columns: 59
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (16): INSTNM, ADDR, CITY, STABBR, ZIP, CHFNM, CHFTITLE, EIN, OPEID, WEBA...
## dbl (43): UNITID, FIPS, OBEREG, GENTELE, OPEFLAG, SECTOR, ICLEVEL, CONTROL, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
str_detect()So far, we’ve filtered data using dplyr’s filter() verb. When
matching a string, we have used == (or != for negative match). For
example, if we wanted to limit our data to only those institutions in
Florida, we could filter using the stabbr column:
## filter using state abbreviation (not saving, just viewing)
df %>%
filter(stabbr == "FL")
## # A tibble: 316 × 59
## unitid instnm addr city stabbr zip fips obereg chfnm chftitle gentele
## <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <dbl>
## 1 132268 Wyotech-… 470 … Ormo… FL 32174 12 5 Stev… Preside… 3.86e12
## 2 132338 The Art … 1799… Fort… FL 3331… 12 5 Char… Preside… 9.54e13
## 3 132374 Atlantic… 4700… Coco… FL 3306… 12 5 Robe… Director 7.54e 9
## 4 132408 The Bapt… 5400… Grac… FL 32440 12 5 Thom… Preside… 8.50e 9
## 5 132471 Barry Un… 1130… Miami FL 3316… 12 5 Sist… Preside… 8.01e 9
## 6 132523 Gooding … 615 … Pana… FL 32401 12 5 Dr. … CRNA Ph… 8.51e 9
## 7 132602 Bethune-… 640 … Dayt… FL 3211… 12 5 Dr T… Preside… 3.86e 9
## 8 132657 Lynn Uni… 3601… Boca… FL 3343… 12 5 Kevi… Preside… 5.61e 9
## 9 132666 Bradento… 5505… Brad… FL 34209 12 5 A. P… CEO 9.42e 9
## 10 132675 Bradford… 609 … Star… FL 32091 12 5 Rand… Director 9.05e 9
## # … with 306 more rows, and 48 more variables: ein <chr>, opeid <chr>,
## # opeflag <dbl>, webaddr <chr>, adminurl <chr>, faidurl <chr>, applurl <chr>,
## # sector <dbl>, iclevel <dbl>, control <dbl>, hloffer <dbl>, ugoffer <dbl>,
## # groffer <dbl>, fpoffer <dbl>, hdegoffr <dbl>, deggrant <dbl>, hbcu <dbl>,
## # hospital <dbl>, medical <dbl>, tribal <dbl>, locale <dbl>, openpubl <dbl>,
## # act <chr>, newid <dbl>, deathyr <dbl>, closedat <chr>, cyactive <dbl>,
## # postsec <dbl>, pseflag <dbl>, pset4flg <dbl>, rptmth <dbl>, ialias <chr>, …
This works well because the stabbr column, even though it uses
strings, is regular. But what happens when the strings aren’t so
regular? For example, let’s look the different titles chief college
administrators take.
## see first few rows of distinct chief titles
df %>%
distinct(chftitle)
## # A tibble: 556 × 1
## chftitle
## <chr>
## 1 Commandant
## 2 President
## 3 Chancellor
## 4 Interim President
## 5 CEO
## 6 Acting President
## 7 Director
## 8 President/CEO
## 9 Interim Chancellor
## 10 President/COO
## # … with 546 more rows
We find over 500 unique titles. Just looking at the first 10 rows, we see that some titles are pretty similar — President vs. CEO vs. President/CEO — but not exactly the same. Let’s look again, but this time get counts of each distinct title and arrange from most common to least.
## return the most common titles
df %>%
## get counts of each type
count(chftitle) %>%
## arrange in descending order so we see most popular at top
arrange(desc(n))
## # A tibble: 556 × 2
## chftitle n
## <chr> <int>
## 1 President 3840
## 2 Director 560
## 3 Chancellor 265
## 4 Executive Director 209
## 5 Owner 164
## 6 Campus President 116
## 7 Superintendent 105
## 8 CEO 90
## 9 <NA> 85
## 10 Interim President 75
## # … with 546 more rows
Quick exercise
What do you notice about the data frames returned by
distinct()andcount()? What’s the same? What doescount()do thatdistinct()does not?
Getting our counts and arranging, we can see that President is by far the most common title. That said, we also see Campus President and Interim President (and before we saw Acting President as well).
If your research question asked, how many chief administrators use the
title of “President”? regardless the various iterations, you can’t
really use a simple == filter any more. In theory, you could inspect
your data, find the unique versions, get counts of each of those using
==, and then sum them up — but that’s a lot of work and likely to
be error prone!
Instead, we can use the stringr function str_detect(), which looks
for a pattern in a vector of strings:
str_detect(< vector of strings >, < pattern >)
Going item by item in the vector, it compares what it sees to the
pattern. If it matches, then it returns TRUE; it not, then
FALSE. Here’s a toy example:
## string vector example
fruits <- c("green apple", "banana", "red apple")
## search for "apple", which should be true for the first and third item
str_detect(fruits, "apple")
## [1] TRUE FALSE TRUE
We can use str_detect() inside filter() to select only certain
rows in our data frame. In our case, we want only those observations
in which the title "President" occurs in the chftitle
column. Because we’re only detecting, as long as "President" occurs
anywhere in the title, we’ll get that row back.
## how many use some form of the title president?
df %>%
## still starting with our count
count(chftitle) %>%
## ...but keeping only those titles that contain "President"
filter(str_detect(chftitle, "President")) %>%
## arranging as before
arrange(desc(n))
## # A tibble: 173 × 2
## chftitle n
## <chr> <int>
## 1 President 3840
## 2 Campus President 116
## 3 Interim President 75
## 4 President/COO 47
## 5 President/CEO 46
## 6 School President 31
## 7 Vice President 29
## 8 President and CEO 17
## 9 College President 15
## 10 President & CEO 14
## # … with 163 more rows
Now we’re seeing many more versions. We can even more clearly see a few titles that are almost certainly the same title, but were just inputted differently — President/CEO vs. President and CEO vs. President & CEO.
Quick exercise
Ignoring the sub-counts of the various versions, how many chief administrators have the word “President” in their title?
Seeing the different versions of basically the same title should have us stopping to think: since it seems that this data column contains free form input (e.g. Input chief administrator title:), maybe we should allow for typos? The easiest: Is there any reason to assume that “President” will be capitalized?
Quick exercise
What happens if we search for “president” with a lowercase “p”?
Ah! We find a few stragglers. How can we restructure our filter so that we get these, too? There are at least two solutions.
Regular expressions (aka regex) are strings that use a special syntax to create patterns that can be used to match other strings. They are very useful when you need to match strings that have some general form, but may differ in specifics.
We already used this technique in the a prior lesson when we matched
columns in the all_schools_wide.csv with contains("19") so that we
could pivot_longer(). Instead of naming all the columns
specifically, we recognized that each column took the form of
<test>_19<YY>. This is a type of regular expression.
In the tidyverse some of the stringr and tidyselect helper functions abstract-away some of the nitty-gritty behind regular expressions. Knowing a little about regular expression syntax, particularly how it is used in R, can go a long way.
In our first case, we can match strings that have a capital P
President or lowercase p president using square brackets
([]). If we want either “P” or “p”, then we can use the regex,
[Pp], in place of the first character: "[Pp]resident". This will
match either "President" or "president".
## solution 1: look for either P or p
df %>%
count(chftitle) %>%
filter(str_detect(chftitle, "[Pp]resident")) %>%
arrange(desc(n))
## # A tibble: 175 × 2
## chftitle n
## <chr> <int>
## 1 President 3840
## 2 Campus President 116
## 3 Interim President 75
## 4 President/COO 47
## 5 President/CEO 46
## 6 School President 31
## 7 Vice President 29
## 8 President and CEO 17
## 9 College President 15
## 10 President & CEO 14
## # … with 165 more rows
Though we don’t see the new observations in the abbreviated output, we
note that the number of rows has increased by two. This means that
there are at least two title formats in which "president" is
lowercase and that we weren’t picking up when we only used the
uppercase version of "President" before.
Another solution, which is probably much easier in this particular
case, is to set all potential values in chftitle to the same case
and then match using that case. In many situations, this is preferable
since you don’t need to guess cases up front.
We won’t change the values in chftitle permanently — only while
filtering. To compare apples to apples (rather than "Apples" to
"apples"), we’ll wrap our column name with the function
str_to_lower(), which will make character lowercase, and match using
lowercase "president".
## solution 2: make everything lowercase so that case doesn't matter
df %>%
count(chftitle) %>%
filter(str_detect(str_to_lower(chftitle), "president")) %>%
arrange(desc(n))
## # A tibble: 177 × 2
## chftitle n
## <chr> <int>
## 1 President 3840
## 2 Campus President 116
## 3 Interim President 75
## 4 President/COO 47
## 5 President/CEO 46
## 6 School President 31
## 7 Vice President 29
## 8 President and CEO 17
## 9 College President 15
## 10 President & CEO 14
## # … with 167 more rows
We recover another two titles when using this second
solution. Clearly, our first solution didn’t account for other cases
(perhaps “PRESIDENT"?).
In general, I find it’s a good idea to try a solution like the second one before a more complicated one like the first. But because every problem is different, so too are the solutions. You may find yourself using a combination of the two.
Not-so-quick exercise
Another chief title that was high on the list was “Owner.” How many institutions have an “Owner” as their chief administrator? Of these, how many are private, for-profit institutions (
control == 3)? How many have the word “Beauty” in their name?
str_sub()In addition to filtering data, we sometimes need to create new variables from pieces of exiting variables. For example, let’s look at the zip code values that are included in the file.
## show first few zip code values
df %>%
select(unitid, zip)
## # A tibble: 7,052 × 2
## unitid zip
## <dbl> <chr>
## 1 100636 36112-6613
## 2 100654 35762
## 3 100663 35294-0110
## 4 100690 36117-3553
## 5 100706 35899
## 6 100724 36101-0271
## 7 100733 35401
## 8 100751 35487-0166
## 9 100760 35010
## 10 100812 35611
## # … with 7,042 more rows
We can see that we have both regular 5 digit zip codes as well as
those that include the extra 4 digits (ZIP+4). Let’s say we don’t need
those last four digits for our analysis (particularly because not
every school uses them anyway). Our task is to create a new column
that pulls out only the main part of the zip code. It is has to work
both for zip values that include the additional hyphen and 4 digits
as well as those that only have the primary 5 digits to begin with.
One solution in this case is to take advantage of the fact that zip
codes — minus the sometimes extra 4 digits — should be regular: 5
digits. If want the sub-part of a string and that sub-part is always
in the same spot, we can use the function, str_sub(), which takes a
string or column name first, and has arguments for the starting and
ending character that mark the sub-string of interest.
In our case, we want the first 5 digits so we should start == 1 and
end == 5:
## pull out first 5 digits of zip code
df <- df %>%
mutate(zip5 = str_sub(zip, start = 1, end = 5))
## show (use select() to subset so we can set new columns)
df %>%
select(unitid, zip, zip5)
## # A tibble: 7,052 × 3
## unitid zip zip5
## <dbl> <chr> <chr>
## 1 100636 36112-6613 36112
## 2 100654 35762 35762
## 3 100663 35294-0110 35294
## 4 100690 36117-3553 36117
## 5 100706 35899 35899
## 6 100724 36101-0271 36101
## 7 100733 35401 35401
## 8 100751 35487-0166 35487
## 9 100760 35010 35010
## 10 100812 35611 35611
## # … with 7,042 more rows
A quick visual inspection of the first few rows shows that our
str_sub() function performed as expected (for a real analysis,
you’ll want to do more formal checks).
str_replace()We can also use a more sophisticated regex pattern with the function
str_replace(). The pieces of our regex pattern,
"([0-9]+)(-[0-9]+)?", are translated as this:
[0-9] := any digit, 0 1 2 3 4 5 6 7 8 9+ := match the preceding one or more times? := match the preceding 0 or more times() := subexpressionPut together, we have:
([0-9]+) := first, look for 1 or more digits(-[0-9]+)? := second, look for a hyphen and one or more digits, but
you may not find any of thatBecause we used parentheses, (), to separate our subexpressions, we
can call them using their numbers (in order) in the last argument of
str_replace():
"\\1" := return the first subexpressionSo what’s happening? If given a zip code that is "32605", the regex
pattern will collect each digit — "3" "2" "6" "0" "5" —
into the first subexpression because it never sees a hyphen. That
first subexpression, "\\1", is returned: "32605". That’s what we
want.
If given "32605-1234", it will collect the first 5 digits in the
first subexpression, but will stop adding characters there when it
sees the hyphen. From then on out, it adds everything it sees the
second subexpression: "-" "1" "2" "3" "4". But because
str_replace() only returns the first subexpression, we still get the
same answer: "32605". This is what we want.
Let’s try it on the data.
## drop last four digits of extended zip code if they exist
df <- df %>%
mutate(zip5_v2 = str_replace(zip, "([0-9]+)(-[0-9]+)?", "\\1"))
## show (use select() to subset so we can set new columns)
df %>%
select(unitid, zip, zip5, zip5_v2)
## # A tibble: 7,052 × 4
## unitid zip zip5 zip5_v2
## <dbl> <chr> <chr> <chr>
## 1 100636 36112-6613 36112 36112
## 2 100654 35762 35762 35762
## 3 100663 35294-0110 35294 35294
## 4 100690 36117-3553 36117 36117
## 5 100706 35899 35899 35899
## 6 100724 36101-0271 36101 36101
## 7 100733 35401 35401 35401
## 8 100751 35487-0166 35487 35487
## 9 100760 35010 35010 35010
## 10 100812 35611 35611 35611
## # … with 7,042 more rows
Quick exercise
What if you wanted to the get the last 4 digits (after the hyphen)? What bit of two bits of code above would you change so that you can store the last 4 digits without including the hyphen? Make a new variable called
zip_plus4and store these values. HINT Look at the help file forstr_replace().
Let’s compare our two versions: do we get the same results?
## check if both versions of new zip column are equal
identical(df %>% select(zip5), df %>% select(zip5_v2))
## [1] FALSE
No! Let’s see where they are different:
## filter to rows where zip5 != zip5_v2 (not storing...just looking)
df %>%
filter(zip5 != zip5_v2) %>%
select(unitid, zip, zip5, zip5_v2)
## # A tibble: 4 × 4
## unitid zip zip5 zip5_v2
## <dbl> <chr> <chr> <chr>
## 1 108199 90015--350 90015 90015--350
## 2 113953 92113--191 92113 92113--191
## 3 431707 06360--709 06360 06360--709
## 4 435240 551012595 55101 551012595
Quick exercise
What happened? In this scenario, which string subsetting technique worked better?
Depending on the task, regular expressions can either feel like a blessing or a curse. To be honest, I’ve spent more time cursing than thanking them. That said, regular expressions are often the only way to perform a data wrangling task on unstructured string data. They are also a cornerstone of natural language processing techniques, which are increasingly of interest to education researchers.
We’ve only scratched the surface of what regular expressions can do. If you face string data in the future, taking a little time to craft a regular expression can be well worth it.
In opening section, we’ve seen that dates often come in many different formats. While you can format and clean them using regular expressions, you may also want to format them such that R knows they are dates.
Why?
When dealing with something straightforward like years, it’s easy
enough to store the years a regular numbers and then subtract the
recent year from the past year to get a duration: 2020 - 2002 equals
18 years.
But what if you have daily data for the school year and you want to know how many days a student had between a first and second test? What if the differences were more than a month of days and every student took the first and second tests on different days? What if you had a panel data set, with students across years, some of which were leap years? You can see how calculating the exact number days between tests for each student could quickly become difficult if trying to do it using regular numerical values.
R makes this easier by having special time-based data types that will keep track of these issues for us and allow us to work with dates almost as we do with regular numbers.
In our IPEDS data set, we can see that few institutions closed in 2007 and 2008. We’ll limit our next analyses to these institutions.
## subset to schools who closed during this period
df <- df %>%
filter(closedat != -2)
## show first few rows
df %>% select(unitid, instnm, closedat)
## # A tibble: 83 × 3
## unitid instnm closedat
## <dbl> <chr> <chr>
## 1 103440 Sheldon Jackson College 6/29/07
## 2 104522 DeVoe College of Beauty 3/29/08
## 3 105242 Mundus Institute Sep-07
## 4 105880 Long Technical College-East Valley 3/31/07
## 5 119711 New College of California Jan-08
## 6 136996 Ross Medical Education Center 7/31/07
## 7 137625 Suncoast II the Tampa Bay School of Massage Therapy LLC 5/31/08
## 8 141583 Hawaii Business College Sep-07
## 9 150127 Ball Memorial Hospital School of Radiologic Technology May-07
## 10 160144 Pat Goins Shreveport Beauty School 3/1/08
## # … with 73 more rows
We can see that closedat is stored as a string. Based on our domain
knowledge and context clues, we know that the dates are generally in a
MM/DD/YYYY (American) format.
We can use the lubridate command mdy() to make a new variable that
contains the same information, but in a format that R recognizes as a
date.
## create a new close date column
df <- df %>%
mutate(closedat_dt = mdy(closedat))
## Warning: 35 failed to parse.
## show
df %>% select(starts_with("close"))
## # A tibble: 83 × 2
## closedat closedat_dt
## <chr> <date>
## 1 6/29/07 2007-06-29
## 2 3/29/08 2008-03-29
## 3 Sep-07 NA
## 4 3/31/07 2007-03-31
## 5 Jan-08 NA
## 6 7/31/07 2007-07-31
## 7 5/31/08 2008-05-31
## 8 Sep-07 NA
## 9 May-07 NA
## 10 3/1/08 2008-03-01
## # … with 73 more rows
Well, we are part of the way there. It seems that mdy() didn’t
really work with dates like Sep-2007. What can we do?
One solution is to add in a fake day for the ones that didn’t parse
and then convert using mdy(). We’ll use regular expressions with an
str_replace().
## convert MON-YYYY to MON-01-YYYY
df <- df %>%
mutate(closedat_fix = str_replace(closedat, "-", "-01-"),
closedat_fix_dt = mdy(closedat_fix))
## Warning: 7 failed to parse.
## show
df %>% select(starts_with("close"))
## # A tibble: 83 × 4
## closedat closedat_dt closedat_fix closedat_fix_dt
## <chr> <date> <chr> <date>
## 1 6/29/07 2007-06-29 6/29/07 2007-06-29
## 2 3/29/08 2008-03-29 3/29/08 2008-03-29
## 3 Sep-07 NA Sep-01-07 2007-09-01
## 4 3/31/07 2007-03-31 3/31/07 2007-03-31
## 5 Jan-08 NA Jan-01-08 2008-01-01
## 6 7/31/07 2007-07-31 7/31/07 2007-07-31
## 7 5/31/08 2008-05-31 5/31/08 2008-05-31
## 8 Sep-07 NA Sep-01-07 2007-09-01
## 9 May-07 NA May-01-07 2007-05-01
## 10 3/1/08 2008-03-01 3/1/08 2008-03-01
## # … with 73 more rows
Quick exercise
We had 7 parsing errors. Can you figure out which rows failed to parse and guess why? HINT if
mdy()failed to parseclosedat, then the subsequent new columns are likely missing values.
Now that we’ve successfully converted the string date into a proper date type, it’s easy to pull out the pieces of that date, including:
year()month()day()wday()## add columns for
## - year
## - month
## - day
## - day of week (dow)
df <- df %>%
mutate(close_year = year(closedat_fix_dt),
close_month = month(closedat_fix_dt),
close_day = day(closedat_fix_dt),
close_dow = wday(closedat_fix_dt, label = TRUE))
## show
df %>%
select(closedat_fix_dt, close_year, close_month, close_day, close_dow)
## # A tibble: 83 × 5
## closedat_fix_dt close_year close_month close_day close_dow
## <date> <dbl> <dbl> <int> <ord>
## 1 2007-06-29 2007 6 29 Fri
## 2 2008-03-29 2008 3 29 Sat
## 3 2007-09-01 2007 9 1 Sat
## 4 2007-03-31 2007 3 31 Sat
## 5 2008-01-01 2008 1 1 Tue
## 6 2007-07-31 2007 7 31 Tue
## 7 2008-05-31 2008 5 31 Sat
## 8 2007-09-01 2007 9 1 Sat
## 9 2007-05-01 2007 5 1 Tue
## 10 2008-03-01 2008 3 1 Sat
## # … with 73 more rows
Quick exercise
Can we trust our
close_dowvariable? Why?
It’s also easy to calculate differences of time. We can use normal arithmetic — future date - past date — and R will take care of all the underlying calendar calculations for us (e.g., days in a given month, leap years, etc).
## how long since the institution closed
## - as of 1 January 2020
## - as of today
df <- df %>%
mutate(time_since_close_jan = ymd("2020-01-01") - closedat_fix_dt,
time_since_close_now = today() - closedat_fix_dt)
## show
df %>% select(starts_with("time_since_close"))
## # A tibble: 83 × 2
## time_since_close_jan time_since_close_now
## <drtn> <drtn>
## 1 4569 days 5329 days
## 2 4295 days 5055 days
## 3 4505 days 5265 days
## 4 4659 days 5419 days
## 5 4383 days 5143 days
## 6 4537 days 5297 days
## 7 4232 days 4992 days
## 8 4505 days 5265 days
## 9 4628 days 5388 days
## 10 4323 days 5083 days
## # … with 73 more rows
As with strings and regular expressions, we’ve only scratched the surface of working with dates in R. For example, you can also work with times (hours, minutes, seconds, etc). Now that you’ve been introduced, however, you should have a starting point for working with panel and administrative data that includes strings and dates that you need to process before conducting your analyses.