7 - split-apply-combine(-review)

Author

Jacob Lahne

Published

June 24, 2026

Introduction

This week we are going to take a breath. We’ve covered a lot of material up to this point, and (writing this in the summer before the class has even begun) I will have sped through or even skipped over some material because I ran out of time. This week, we’re going to take the time to review that material and answer your questions.

We’re also going to leave time this week in our code session on Thursday to go over issues you’re having with your Data Analysis Report #1. Remember, it is due next Thursday!

Data Analysis Report #1

Remember that the rough draft for your data analysis report is due October 9, this Thursday. This means, like for any normal homework, that I want you to submit a good-faith first draft to the same assignment before class on Thursday.

Printing data nicely

For many of you, I’ve encouraged you to think about how to print data nicely once imported into R: not printing out 100+ lines of a data frame in a rendered file. Let’s look at a few ways to do that.

Today we’re going to work with the polyphenol data–again–and also take a look at the cider sorting data for some examples.

polyphenols <- read_csv("data/Week 4/polyphenol testing.csv")

Rows: 81 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (4): Catechin, PC B2, Chlorogenic acid, equivalents in gallic acid mg/L

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.

cider_demo <- readxl::read_excel("data/cider sorting.xlsx", range = "A1185:AE1250")

If we print out these data frame and knit the file, it’s kind of ugly. For a real report, this wouldn’t be acceptable.

knitr::kable(polyphenols)

Catechin	PC B2	Chlorogenic acid	equivalents in gallic acid mg/L
0.2	1	5	2.79
0.2	1	5	2.73
0.2	1	5	2.74
0.2	1	200	57.73
0.2	1	200	58.19
0.2	1	200	69.24
0.2	1	500	150.31
0.2	1	500	161.70
0.2	1	500	161.13
0.2	30	5	15.17
0.2	30	5	15.27
0.2	30	5	14.01
0.2	30	200	63.54
0.2	30	200	64.00
0.2	30	200	62.40
0.2	30	500	168.25
0.2	30	500	159.71
0.2	30	500	136.65
0.2	100	5	46.57
0.2	100	5	46.12
0.2	100	5	50.90
0.2	100	200	95.32
0.2	100	200	92.36
0.2	100	200	84.16
0.2	100	500	148.32
0.2	100	500	158.29
0.2	100	500	157.43
30.0	1	5	14.01
30.0	1	5	13.80
30.0	1	5	13.87
30.0	1	200	60.01
30.0	1	200	51.92
30.0	1	200	65.14
30.0	1	500	170.24
30.0	1	500	173.38
30.0	1	500	170.53
30.0	30	5	21.93
30.0	30	5	17.97
30.0	30	5	17.32
30.0	30	200	57.28
30.0	30	200	52.04
30.0	30	200	50.33
30.0	30	500	155.44
30.0	30	500	153.16
30.0	30	500	158.00
30.0	100	5	52.38
30.0	100	5	51.47
30.0	100	5	52.61
30.0	100	200	115.58
30.0	100	200	117.57
30.0	100	200	116.43
30.0	100	500	119.56
30.0	100	500	130.38
30.0	100	500	118.71
100.0	1	5	44.75
100.0	1	5	45.32
100.0	1	5	44.98
100.0	1	200	56.37
100.0	1	200	56.14
100.0	1	200	57.39
100.0	1	500	131.52
100.0	1	500	138.64
100.0	1	500	136.36
100.0	30	5	50.67
100.0	30	5	51.92
100.0	30	5	50.79
100.0	30	200	56.82
100.0	30	200	63.43
100.0	30	200	66.85
100.0	30	500	143.19
100.0	30	500	153.45
100.0	30	500	143.19
100.0	100	5	56.03
100.0	100	5	56.94
100.0	100	5	56.14
100.0	100	200	121.55
100.0	100	200	115.58
100.0	100	200	118.71
100.0	100	500	151.17
100.0	100	500	145.19
100.0	100	500	151.45

knitr::kable(cider_demo)

Test_Name	Section_Name	Section_Number	Sample_Set_Number	Session_Number	Session_Name	Panelist_Code	Panelist_Name	Panelist_Display_Name	Panelist_Email	Q1__Gender	Q1__Gender_COMMENTS	Q1__Gender_Time_Stamp	Q2__Age	Q2__Age_Time_Stamp	Q3__Purchase_cider	Q3__Purchase_cider_Time_Stamp	Q4__Cider_choice	Q4__Cider_choice_Time_Stamp	Q5__Drink_cider	Q5__Drink_cider_Time_Stamp	Q6__1__I_only_consume_hard_cider	Q6__2__Wine_(red__white__sparkling_etc)	Q6__3__Wine_coolers	Q6__4__Beer	Q6__5__Spirits_(whiskey__bourbon__vodka__rum__gin_etc)	Q6__6__Liqueurs_(fruit__flower__cream__honey__etc_)	Q6__7__Other(s):_please_indicate	Q6__7__Other(s):_please_indicate_COMMENTS	Q6___Time_Stamp	QG:Hard Cider Consumption
102918_VA Cider Sort	Second Section: Questionnaire	2	1	1	Session 1	cotrupi_vtu	Catherine Cotrupi	Catherine	cotrupi@vt.edu	2	NA	2018-10-29 10:15:08	2	2018-10-29 10:15:11	3	2018-10-29 10:15:17	Taste, I prefer sweet and smooth- not bitter or hoppy	2018-10-29 10:15:58	4	2018-10-29 10:16:03	0	1	0	0	1	0	0	NA	2018-10-29 10:16:18	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	2	1	Session 1	kessingervi_vtu	Vincent Kessinger	Vincent	kessingervi@outlook.com	1	NA	2018-10-29 10:17:27	2	2018-10-29 10:17:30	2	2018-10-29 10:17:36	Taste, sweetness and reputation of the cidery.	2018-10-29 10:18:30	3	2018-10-29 10:18:38	0	1	0	1	1	1	0	NA	2018-10-29 10:19:00	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	3	1	Session 1	kadieb_vtu	Kadie Britt	Kadie	kadieb@vt.edu	2	NA	2018-10-29 10:49:55	1	2018-10-29 10:49:59	3	2018-10-29 10:50:07	I prefer a drier cider, or just one that is not extremely sweet. I judge by brand as well. I prefer one that is not Reddís or Angry Orchard, for example. I typically go for Bold Rock.	2018-10-29 10:52:01	4	2018-10-29 10:52:06	0	1	0	1	0	0	0	NA	2018-10-29 10:52:14	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	4	1	Session 1	tutene_vtu	Evan Tuten	Evan	tutene@vt.edu	1	NA	2018-11-01 10:20:33	1	2018-11-01 10:20:35	1	2018-11-01 10:20:40	gotta be sweet	2018-11-01 10:21:01	2	2018-11-01 10:21:05	0	1	1	1	1	0	0	NA	2018-11-01 10:21:13	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	5	1	Session 1	hkissel2_vtu	Heather Kissel	Heather	hkissel2@vt.edu	2	NA	2018-10-29 11:12:59	1	2018-10-29 11:13:03	3	2018-10-29 11:13:15	I am not a beer drinker, and like alcoholic beverages that are low alcohol content but still taste sweet and I love regular cider	2018-10-29 11:14:16	3	2018-10-29 11:14:30	0	1	1	0	1	1	0	NA	2018-10-29 11:14:49	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	6	1	Session 1	merri96_vtu	Merrick Seltz	Merrick	merri96@vt.edu	1	NA	2018-10-29 11:12:04	1	2018-10-29 11:12:07	2	2018-10-29 11:12:15	Fruit aromas with no ìbarnyardî scent, effervescence and a dry finish	2018-10-29 11:13:20	2	2018-10-29 11:13:24	0	1	0	1	1	0	0	NA	2018-10-29 11:13:33	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	7	1	Session 1	camwalt_vtu	Cameron Walton	Cameron	camwalt@vt.edu	1	NA	2018-10-29 11:18:12	1	2018-10-29 11:18:15	2	2018-10-29 11:18:22	Brand name. Always willing to try new cider, but I normally look for brand.	2018-10-29 11:19:55	3	2018-10-29 11:20:00	0	1	0	1	1	1	0	NA	2018-10-29 11:20:19	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	8	1	Session 1	ann.sandbrook@vt.edu	Ann Sandbrook	Ann	ann.sandbrook@vt.edu	2	NA	2018-10-29 11:48:08	2	2018-10-29 11:48:15	2	2018-10-29 11:48:38	Type of cider: sparkling, dry, carbonated or sweet. If I want a cider more like a wine (still, dry or sparkling) or more like a beer (carbonated slightly sweet, lower ALC)	2018-10-29 11:50:19	3	2018-10-29 11:50:25	0	1	0	1	1	1	0	NA	2018-10-29 11:50:44	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	9	1	Session 1	Ksara1_vtu	Sara Klopf	Sara	Ksara1@vt.edu	2	NA	2018-10-29 12:32:59	2	2018-10-29 12:33:02	2	2018-10-29 12:33:10	Not too sweet, good fresh apple flavor, not too strong alcohol	2018-10-29 12:33:43	2	2018-10-29 12:33:47	0	1	0	1	1	0	0	NA	2018-10-29 12:33:54	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	10	1	Session 1	dalego_vtu	Dan Goerlich	Dan	dalego@vt.edu	1	NA	2018-10-29 12:24:39	3	2018-10-29 12:24:45	1	2018-10-29 12:24:57	I rarely consume hard cider. On the seldom occasions that I do, I look for a mellow taste, not bubbly, with a pleasant smell.	2018-10-29 12:27:11	2	2018-10-29 12:27:19	0	1	1	1	1	1	0	NA	2018-10-29 12:27:52	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	11	1	Session 1	mcrotto@vt.edu	Michelle Crotto	Michelle	mcrotto@vt.edu	2	NA	2018-10-29 12:30:17	2	2018-10-29 12:30:21	2	2018-10-29 12:30:27	Taste	2018-10-29 12:30:40	3	2018-10-29 12:30:50	0	1	0	1	1	0	0	NA	2018-10-29 12:31:10	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	12	1	Session 1	Ericjd_vtu	Eric Dinwiddie	Eric	Ericjd@vt.edu	1	NA	2018-10-29 12:55:51	2	2018-10-29 12:55:57	2	2018-10-29 12:56:06	Local, tradional	2018-10-29 12:56:45	2	2018-10-29 12:56:52	0	0	0	0	1	1	0	NA	2018-10-29 12:57:05	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	13	1	Session 1	paule19@vt.edu	Paulette Cairns	Paulette	paule19@vt.edu	2	NA	2018-10-29 13:11:31	1	2018-10-29 13:11:34	2	2018-10-29 13:11:45	That it has a nice flavor and is not too sweet or too acidic.	2018-10-29 13:12:35	3	2018-10-29 13:12:42	0	1	0	1	1	1	0	NA	2018-10-29 13:12:53	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	14	1	Session 1	karisb_vtu	Karis Boyd-Sinkler	Karis	karisb@vt.edu	2	NA	2018-10-29 13:11:15	1	2018-10-29 13:11:17	3	2018-10-29 13:11:23	Brand - I try to buy VA ciders based on the fact that I grew up in VA. Flavor type, cost, label	2018-10-29 13:12:51	3	2018-10-29 13:12:56	0	1	0	0	1	0	0	NA	2018-10-29 13:13:05	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	15	1	Session 1	Santosc_vtu	Claire Santos	Claire	Santosc@vt.edu	2	NA	2018-10-29 13:18:31	3	2018-10-29 13:18:34	4	2018-10-29 13:18:45	Taste. Canít be dry or bitter	2018-10-29 13:19:21	5	2018-10-29 13:19:27	0	1	0	0	0	0	0	NA	2018-10-29 13:19:34	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	16	1	Session 1	jacuff_vtu	Jennifer Acuff	Jennifer	jacuff@vt.edu	2	NA	2018-10-30 13:57:42	1	2018-10-30 13:57:47	2	2018-10-30 13:57:54	Variety of fruits (apple, pear) and less sweet flavor profiles, more crisp.	2018-10-30 13:59:14	2	2018-10-30 13:59:26	0	1	0	1	1	0	0	NA	2018-10-30 13:59:37	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	17	1	Session 1	Cherrie1_vtu	Cherrie Rose	Cherrie	Cherrie1@vt.edu	2	NA	2018-10-29 13:28:10	2	2018-10-29 13:28:13	3	2018-10-29 13:28:18	Taste	2018-10-29 13:28:38	3	2018-10-29 13:28:45	0	1	1	0	1	1	0	NA	2018-10-29 13:29:05	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	18	1	Session 1	kdebose_vtu	Kiri DeBose	Kiri	kdebose@vt.edu	2	NA	2018-10-29 14:04:18	3	2018-10-29 14:04:21	2	2018-10-29 14:04:27	I prefer those that are on the sweeter side with some noticeable apple flavor (prefer that taste to come at the end and linger a littleat the end). Like one that has a nice little kick to it as taste (as opppsed to flat) but not enough to throw one down and overwhelm the flavor itself.	2018-10-29 14:08:24	2	2018-10-29 14:08:30	0	1	0	0	1	0	0	NA	2018-10-29 14:08:52	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	19	1	Session 1	gibsonm@vt.edu	Monika Gibson	Monika	gibsonm@vt.edu	2	NA	2018-10-30 10:31:37	4	2018-10-30 10:31:41	4	2018-10-30 10:31:50	No sweeteners; fresh, dry, fruity taste; interesting flavor combos if not plain apple cider	2018-10-30 10:33:43	5	2018-10-30 10:33:49	0	1	0	0	1	0	0	NA	2018-10-30 10:34:22	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	20	1	Session 1	wlauren_vtu	Lauren Wind	Lauren	wlauren@vt.edu	2	NA	2018-10-30 10:21:41	1	2018-10-30 10:21:44	2	2018-10-30 10:21:49	Brewer location ie is it local	2018-10-30 10:22:27	3	2018-10-30 10:22:33	0	1	1	1	1	1	0	NA	2018-10-30 10:22:42	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	21	1	Session 1	mponder_vtu	Monica Ponder	Monica	mponder@vt.edu	2	NA	2018-11-01 10:29:06	3	2018-11-01 10:29:10	2	2018-11-01 10:29:18	Price; drier styles more appealing, is the label pretty?	2018-11-01 10:30:05	3	2018-11-01 10:30:19	0	1	0	1	1	0	0	NA	2018-11-01 10:30:30	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	22	1	Session 1	ldouglas_vtu	Laura Lawson	Laura	ldouglas@vt.edu	2	NA	2018-10-30 12:43:54	3	2018-10-30 12:43:58	2	2018-10-30 12:44:04	Pleasant smell with citrus and floral notes in taste	2018-10-30 12:45:13	3	2018-10-30 12:45:20	0	1	0	0	1	0	0	NA	2018-10-30 12:45:35	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	23	1	Session 1	ndbriggs_vtu	Nathan Briggs	Nathan	ndbriggs@vt.edu	1	NA	2018-10-30 12:32:12	1	2018-10-30 12:32:15	3	2018-10-30 12:32:23	Dryness, heritage apples, funky-ness	2018-10-30 12:32:56	3	2018-10-30 12:33:03	0	1	0	1	1	0	0	NA	2018-10-30 12:33:19	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	24	1	Session 1	rachael.m.stone_vtu	Rachael Stone	Rachael	rachael.m.stone@gmail.com	2	NA	2018-10-30 12:33:48	1	2018-10-30 12:33:52	2	2018-10-30 12:33:57	Sweet/dry	2018-10-30 12:34:18	3	2018-10-30 12:34:24	0	1	0	1	0	0	0	NA	2018-10-30 12:34:33	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	25	1	Session 1	babybug_vtu	Beck Hall	Beck	babybug@vt.edu	3	Nonbinary	2018-10-30 12:37:22	1	2018-10-30 12:37:26	3	2018-10-30 12:37:34	Love horseblanket smell and feel - usually find it through trial and error - labels rarely indicate the feeling of the cider	2018-10-30 12:38:47	3	2018-10-30 12:38:54	0	1	0	1	1	0	0	NA	2018-10-30 12:39:09	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	26	1	Session 1	najlamk_vtu	Najla Miranda Mouchrek	Najla	najlamk@vt.edu	2	NA	2018-10-30 13:22:39	3	2018-10-30 13:22:42	2	2018-10-30 13:22:50	I like it to be sweet, not too acid, and slightly sparkling.	2018-10-30 13:24:11	2	2018-10-30 13:24:17	0	1	0	1	0	0	0	NA	2018-10-30 13:24:29	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	27	1	Session 1	kswaby_vtu	Keri Swaby	Keri	kswaby@vt.edu	2	NA	2018-10-30 13:11:01	3	2018-10-30 13:11:07	1	2018-10-30 13:11:16	N/a	2018-10-30 13:11:38	2	2018-10-30 13:11:48	0	1	0	1	1	0	0	NA	2018-10-30 13:12:28	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	28	1	Session 1	robynes_vtu	Robyn Stuart	Robyn	robynes@vt.edu	2	NA	2018-10-30 13:05:09	2	2018-10-30 13:05:13	3	2018-10-30 13:05:23	Cost, source (i.e. local or craft), dry but flavorful	2018-10-30 13:06:32	5	2018-10-30 13:06:37	0	1	0	0	1	1	0	NA	2018-10-30 13:06:51	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	29	1	Session 1	cnproudfoot_vtu	Chad Proudfoot	Chad	cnproudfoot@vt.edu	1	NA	2018-10-30 13:17:28	2	2018-10-30 13:17:32	2	2018-10-30 13:17:40	Tart, appealing taste	2018-10-30 13:18:09	3	2018-10-30 13:18:13	0	1	1	1	1	0	0	NA	2018-10-30 13:18:26	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	30	1	Session 1	jamiemcr_vtu	James McReynolds	James	jamiemcr@vt.edu	1	NA	2018-10-30 13:35:27	5	2018-10-30 13:35:35	1	2018-10-30 13:35:48	Sweetness	2018-10-30 13:36:06	2	2018-10-30 13:36:12	0	1	1	1	1	1	0	NA	2018-10-30 13:36:26	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	31	1	Session 1	ahashimo_vtu	Amanda Hashimoto	Amanda	ahashimo@vt.edu	2	NA	2018-10-30 13:45:05	1	2018-10-30 13:45:08	2	2018-10-30 13:45:18	Sweetness and more alcohol content	2018-10-30 13:46:00	2	2018-10-30 13:46:06	0	0	0	1	1	1	0	NA	2018-10-30 13:46:17	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	32	1	Session 1	sopint8_vtu	Sophie Pinton	Sophie	sopint8@vt.edu	2	NA	2018-10-30 14:22:46	1	2018-10-30 14:22:50	1	2018-10-30 14:23:04	N/a	2018-10-30 14:24:30	3	2018-10-30 14:24:37	0	1	0	1	1	0	0	NA	2018-10-30 14:24:45	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	33	1	Session 1	aprussin_vtu	AJ Prussin	AJ	aprussin@vt.edu	1	NA	2018-10-30 14:44:34	2	2018-10-30 14:44:37	2	2018-10-30 14:44:41	Being dry. I hate any added sugar or sweetness.	2018-10-30 14:45:28	2	2018-10-30 14:45:32	0	1	0	1	0	0	0	NA	2018-10-30 14:45:37	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	34	1	Session 1	roxi3_vtu	Roxanne Smith	Roxanne	roxi3@vt.edu	2	NA	2018-10-30 14:22:28	1	2018-10-30 14:22:30	1	2018-10-30 14:22:36	Previous knowledge that itís not too sweet	2018-10-30 14:23:09	2	2018-10-30 14:23:15	0	1	0	1	1	0	0	NA	2018-10-30 14:23:30	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	35	1	Session 1	rachr15_vtu	Rachel Rupnow	Rachel	rachr15@vt.edu	2	NA	2018-10-30 14:39:18	1	2018-10-30 14:39:21	2	2018-10-30 14:39:27	Prefer semisweet or sweet	2018-10-30 14:40:11	2	2018-10-30 14:40:25	0	1	0	1	1	0	0	NA	2018-10-30 14:40:42	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	36	1	Session 1	millercd@vt.edu	Christina Miller	Christina	millercd@vt.edu	2	NA	2018-10-30 14:36:25	2	2018-10-30 14:36:30	2	2018-10-30 14:36:37	Interesting flavors, brand, price, flavors that arenít too sweet	2018-10-30 14:37:39	2	2018-10-30 14:37:43	0	1	0	1	1	0	0	NA	2018-10-30 14:37:53	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	37	1	Session 1	kwater_vtu	Kim Waterman	Kim	kwater@vt.edu	2	NA	2018-10-30 14:56:05	4	2018-10-30 14:56:09	2	2018-10-30 14:56:17	Not too sweet, but with a good fruity note. Pretty labels!	2018-10-30 14:57:45	2	2018-10-30 14:57:51	0	0	0	0	1	0	0	NA	2018-10-30 14:58:03	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	38	1	Session 1	bwd_vtu	Brett Driver	Brett	bwd@vt.edu	1	NA	2018-10-30 15:04:57	1	2018-10-30 15:05:03	1	2018-10-30 15:05:26	N/A	2018-10-30 15:05:47	2	2018-10-30 15:05:54	0	1	0	1	1	0	0	NA	2018-10-30 15:06:10	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	39	1	Session 1	trsjr_vtu	Thomas Saunders	Thomas	trsjr@vt.edu	1	NA	2018-10-30 15:36:59	4	2018-10-30 15:37:06	2	2018-10-30 15:37:14	Price	2018-10-30 15:38:30	2	2018-10-30 15:38:38	0	1	0	1	1	0	0	NA	2018-10-30 15:38:57	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	40	1	Session 1	mswright@vt.edu	Melissa Wright	Melissa	mswright@vt.edu	2	NA	2018-11-01 11:10:17	2	2018-11-01 11:10:21	2	2018-11-01 11:10:28	Bright, crisp flavor; light and refreshing; not too heavy or filling	2018-11-01 11:11:06	3	2018-11-01 11:11:11	0	1	0	1	1	0	0	NA	2018-11-01 11:11:22	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	41	1	Session 1	kaylalk_vtu	Kayla Kipps	Kayla	kaylalk@vt.edu	2	NA	2018-10-30 15:35:02	1	2018-10-30 15:35:06	4	2018-10-30 15:35:13	Semi-sweet, low calorie	2018-10-30 15:35:37	5	2018-10-30 15:35:43	0	1	0	1	0	0	0	NA	2018-10-30 15:35:49	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	42	1	Session 1	elsheba_vtu	Elsheba Abraham	Elsheba	elsheba@vt.edu	2	NA	2018-10-30 15:40:48	1	2018-10-30 15:40:50	2	2018-10-30 15:40:57	Taste more sweet and/or fruity	2018-10-30 15:41:36	2	2018-10-30 15:41:42	0	1	0	1	1	0	0	NA	2018-10-30 15:41:50	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	43	1	Session 1	plaura_vtu	Laura Palmer	Laura	plaura@vt.edu	2	NA	2018-10-30 15:45:03	1	2018-10-30 15:45:09	2	2018-10-30 15:45:15	Sweetness, aftertaste, local	2018-10-30 15:45:51	3	2018-10-30 15:46:00	0	1	1	0	0	0	0	NA	2018-10-30 15:46:11	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	44	1	Session 1	tuckj21_vtu	Jordan Tuck	Jordan	tuckj21@vt.edu	1	NA	2018-10-30 16:05:02	1	2018-10-30 16:05:07	3	2018-10-30 16:05:14	If I have had before (either to try something new or go with something I know I like) Price	2018-10-30 16:06:22	3	2018-10-30 16:06:36	0	0	0	1	1	0	0	NA	2018-10-30 16:06:46	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	45	1	Session 1	ihaugen@vt.edu	Inga Haugen	Inga	ihaugen@vt.edu	2	NA	2018-11-01 11:32:56	2	2018-11-01 11:33:02	3	2018-11-01 11:33:09	Taste. Not too sweet, NOT hoppy, more dry and crisp. Local is a priority after taste.	2018-11-01 11:34:29	3	2018-11-01 11:34:41	0	0	0	1	1	0	0	NA	2018-11-01 11:34:54	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	46	1	Session 1	sihuima_vtu	Sihui Ma	Sihui	sihuima@vt.edu	2	NA	2018-11-01 14:12:57	1	2018-11-01 14:13:00	2	2018-11-01 14:13:14	N/A	2018-11-01 14:13:33	2	2018-11-01 14:13:39	0	1	0	1	1	0	0	NA	2018-11-01 14:13:54	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	47	1	Session 1	ecoiley_vtu	Erin Coiley	Erin	ecoiley@vt.edu	2	NA	2018-11-01 11:45:54	1	2018-11-01 11:45:57	3	2018-11-01 11:46:07	Pricing, brand, ingredients	2018-11-01 11:46:48	3	2018-11-01 11:46:56	0	1	1	0	1	0	0	NA	2018-11-01 11:47:08	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	48	1	Session 1	hpatton_vtu	Hannah Patton	Hannah	hpatton@vt.edu	2	NA	2018-11-01 12:23:25	1	2018-11-01 12:23:28	3	2018-11-01 12:23:36	I like hard ciders in which you can really taste the fruit. I really enjoy apple and pineapple hard ciders for this reason. I also like when theyíre very carbonated and on the sour side as well.	2018-11-01 12:25:14	3	2018-11-01 12:25:19	0	1	0	0	1	1	0	NA	2018-11-01 12:25:31	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	49	1	Session 1	catie_cat_vtu	Catie Pannill	Catie	catie_cat@me.com	2	NA	2018-11-01 12:21:25	1	2018-11-01 12:21:29	2	2018-11-01 12:21:40	Flavor and then origin	2018-11-01 12:22:40	3	2018-11-01 12:22:49	0	1	0	1	0	0	0	NA	2018-11-01 12:23:08	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	50	1	Session 1	Ashsuei1_vtu	Ashley Iadonisi	Ashley	Ashsuei1@vt.edu	2	NA	2018-11-01 12:47:28	1	2018-11-01 12:47:31	2	2018-11-01 12:47:38	I like to go with a brand the someone recommended. I like ciders that taste like the fruit thatís advertised to be in them. I also tend to like more of a cinnamon flavor with them because I tend to drink them when itís cooler out.	2018-11-01 12:49:20	3	2018-11-01 12:49:25	0	1	0	1	1	1	0	NA	2018-11-01 12:49:35	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	51	1	Session 1	mgolusky_vtu	Mark Golusky	Mark	mgolusky@vtti.vt.edu	1	NA	2018-11-01 12:55:17	3	2018-11-01 12:55:20	1	2018-11-01 12:55:27	N/A	2018-11-01 12:55:44	2	2018-11-01 12:55:50	0	1	0	1	1	0	0	NA	2018-11-01 12:56:01	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	52	1	Session 1	llyang@vt.edu	Lily Yang	Lily	llyang@vt.edu	2	NA	2018-11-01 12:52:39	1	2018-11-01 12:52:41	2	2018-11-01 12:52:45	Style and brewery	2018-11-01 12:53:14	2	2018-11-01 12:53:20	0	1	0	1	1	1	0	NA	2018-11-01 12:53:32	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	53	1	Session 1	darya2_vtu	Darya Evans	Darya	darya2@vt.edu	2	NA	2018-11-01 13:15:36	1	2018-11-01 13:15:39	3	2018-11-01 13:15:48	Stronger crisp apple flavor. But also economically appealing.	2018-11-01 13:16:42	3	2018-11-01 13:16:46	0	1	0	0	1	1	0	NA	2018-11-01 13:16:56	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	54	1	Session 1	groover_vtu	Gordon groover	Gordon	groover@vt.edu	1	NA	2018-11-01 13:20:02	5	2018-11-01 13:20:07	2	2018-11-01 13:20:23	Taste	2018-11-01 13:20:47	4	2018-11-01 13:21:00	0	1	0	1	1	0	0	NA	2018-11-01 13:21:12	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	55	1	Session 1	rrboyer_vtu	Renee Boyer	Renee	rrboyer@vt.edu	2	NA	2018-11-01 13:30:18	3	2018-11-01 13:30:23	3	2018-11-01 13:30:29	Flavor. I like dry, or semi sweet cider	2018-11-01 13:31:07	3	2018-11-01 13:31:15	0	1	0	0	1	0	0	NA	2018-11-01 13:31:23	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	56	1	Session 1	bvon_vtu	Britannia Vondrasek	Britannia	bvon@vt.edu	2	NA	2018-11-01 13:37:06	1	2018-11-01 13:37:10	2	2018-11-01 13:37:17	Taste	2018-11-01 13:37:44	3	2018-11-01 13:37:52	0	1	0	0	1	1	0	NA	2018-11-01 13:38:12	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	57	1	Session 1	mike.zarella_vtu	Mike Zarella	Mike	mike.zarella@gmail.com	1	NA	2018-11-01 13:57:40	2	2018-11-01 13:57:46	2	2018-11-01 13:57:59	Quality, dry	2018-11-01 13:59:24	2	2018-11-01 13:59:30	0	1	0	1	1	0	0	NA	2018-11-01 13:59:45	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	58	1	Session 1	mknox15_vtu	Mackenzie Knox	Mackenzie	mknox15@vt.edu	2	NA	2018-11-01 14:20:01	1	2018-11-01 14:20:05	2	2018-11-01 14:20:12	The sweetness, the price, the flavor profile	2018-11-01 14:20:42	3	2018-11-01 14:20:47	0	1	0	0	1	1	0	NA	2018-11-01 14:20:59	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	59	1	Session 1	kaitr97_vtu	Kaitlin Rosenberger	Kaitlin	kaitr97@vt.edu	2	NA	2018-11-01 14:20:04	1	2018-11-01 14:20:07	2	2018-11-01 14:20:13	Price is the most important factor, then sweetness and a strong apple flavor	2018-11-01 14:20:45	3	2018-11-01 14:20:50	0	1	0	1	0	0	0	NA	2018-11-01 14:21:01	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	60	1	Session 1	juliah_vtu	Julia Horrocks	Julia	juliah@vt.edu	2	NA	2018-11-01 14:26:22	1	2018-11-01 14:26:25	3	2018-11-01 14:26:32	I like trying local ciders made from apples.	2018-11-01 14:27:20	3	2018-11-01 14:27:25	0	1	1	1	1	0	0	NA	2018-11-01 14:27:45	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	61	1	Session 1	atashi04_vtu	Atashi Sharma	Atashi	atashi04@vt.edu	2	NA	2018-11-01 15:04:16	2	2018-11-01 15:04:19	4	2018-11-01 15:04:24	Should be sweet, fruity, not too strong smelling.	2018-11-01 15:04:54	4	2018-11-01 15:05:02	0	1	1	0	1	1	0	NA	2018-11-01 15:05:13	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	62	1	Session 1	lkburns_vtu	Lisa Burns	Lisa	lkburns@vt.edu	2	NA	2018-11-01 15:12:21	3	2018-11-01 15:12:25	2	2018-11-01 15:12:31	Sweet flavor	2018-11-01 15:12:50	3	2018-11-01 15:12:55	1	1	0	1	0	0	0	NA	2018-11-01 15:13:05	NA
102918_VA Cider Sort	Second Section: Questionnaire	2	63	1	Session 1	lblanc_vtu	Lori Blanc	Lori	lblanc@vt.edu	2	NA	2018-11-01 15:22:49	3	2018-11-01 15:22:53	2	2018-11-01 15:23:01	It is rare that I buy cider, so I probably just go with a combination of what is available and the label (or recommendations by friends)	2018-11-01 15:24:25	2	2018-11-01 15:24:29	0	1	0	1	1	1	0	NA	2018-11-01 15:24:40	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	64	1	Session 1	velvag_vtu	Velva Groover	Velva	velvag@vt.edu	2	NA	2018-11-01 15:59:32	4	2018-11-01 15:59:35	2	2018-11-01 15:59:46	Less sweet cider that is fruity tasting	2018-11-01 16:00:33	4	2018-11-01 16:00:47	0	1	0	1	1	0	0	NA	2018-11-01 16:01:00	Yes
102918_VA Cider Sort	Second Section: Questionnaire	2	65	1	Session 1	wrightrc_vtu	Clay Wright	Clay	wrightrc@vt.edu	1	NA	2018-11-01 15:53:22	2	2018-11-01 15:53:26	2	2018-11-01 15:53:32	I normally purchase dry ciders by the pint when a local or small cider maker is on tap at a brewery/bar/restaurant	2018-11-01 15:55:44	3	2018-11-01 15:55:52	0	1	0	1	1	0	1	Mead, sake, kombucha	2018-11-01 15:56:38	Yes

By default, tibbles will behave well and print out only the first couple lines:

polyphenols

# A tibble: 81 × 4
   Catechin `PC B2` `Chlorogenic acid` `equivalents in gallic acid mg/L`
      <dbl>   <dbl>              <dbl>                             <dbl>
 1      0.2       1                  5                              2.79
 2      0.2       1                  5                              2.73
 3      0.2       1                  5                              2.74
 4      0.2       1                200                             57.7 
 5      0.2       1                200                             58.2 
 6      0.2       1                200                             69.2 
 7      0.2       1                500                            150.  
 8      0.2       1                500                            162.  
 9      0.2       1                500                            161.  
10      0.2      30                  5                             15.2 
# ℹ 71 more rows

But if we want this to look nice, we might want to get an actual kable out of the tibble without printing all too many lines.

There are a couple options: head(), slice_head(), or setting options(knitr.kable.max_rows = n) to some small n.

`head()`

As you know, head(n = ) returns the first n items of a data frame. We can pipe this directly into kable().

polyphenols |>
  head(n = 10) |>
  knitr::kable()

Catechin	PC B2	Chlorogenic acid	equivalents in gallic acid mg/L
0.2	1	5	2.79
0.2	1	5	2.73
0.2	1	5	2.74
0.2	1	200	57.73
0.2	1	200	58.19
0.2	1	200	69.24
0.2	1	500	150.31
0.2	1	500	161.70
0.2	1	500	161.13
0.2	30	5	15.17

This has a certain ease to it.

`slice_head()`

The slice_head() function is the tidyverse version of head(). It is nice because it works on grouped data.

BONUS MARKDOWN

~~strikethrough~~

Material from Week 4–“split-apply-combine” and convenience

Split-apply-combine: `group_by()`, `count()`, and `summarize()`

We are only scratching the surface of what we can do with tidyverse, and you will both be reading more about this and we’ll be returning throughout the semester as we learn more application. But here is an exploratory data analysis application that we will see here and will come back to us frequently.

The group_by() function takes a data frame and groups it by whatever variable is specified. It looks for distinct values, so it will work with even numeric variables (although not well, if they are not truly grouping variables, such as actual observed values).

polyphenols |>
  group_by(Catechin) # note no quotes

# A tibble: 81 × 4
# Groups:   Catechin [3]
   Catechin `PC B2` `Chlorogenic acid` `equivalents in gallic acid mg/L`
      <dbl>   <dbl>              <dbl>                             <dbl>
 1      0.2       1                  5                              2.79
 2      0.2       1                  5                              2.73
 3      0.2       1                  5                              2.74
 4      0.2       1                200                             57.7 
 5      0.2       1                200                             58.2 
 6      0.2       1                200                             69.2 
 7      0.2       1                500                            150.  
 8      0.2       1                500                            162.  
 9      0.2       1                500                            161.  
10      0.2      30                  5                             15.2 
# ℹ 71 more rows

By itself, this does not appear to do anything, although if you look closely at the output from printing the tibble you’ll notice that it now lists Groups: Catechin [3].

We can use this to split the tibble into groups and do things to each group. For example, the n() function gives the number of rows in a group within a tibble (when used in mutate(), etc) and so, if we have 3 groups, n() will tell us for each group how many rows are in that group:

polyphenols |>
  group_by(Catechin) |>
  mutate(number_of_rows = n())

# A tibble: 81 × 5
# Groups:   Catechin [3]
   Catechin `PC B2` `Chlorogenic acid` equivalents in gallic ac…¹ number_of_rows
      <dbl>   <dbl>              <dbl>                      <dbl>          <int>
 1      0.2       1                  5                       2.79             27
 2      0.2       1                  5                       2.73             27
 3      0.2       1                  5                       2.74             27
 4      0.2       1                200                      57.7              27
 5      0.2       1                200                      58.2              27
 6      0.2       1                200                      69.2              27
 7      0.2       1                500                     150.               27
 8      0.2       1                500                     162.               27
 9      0.2       1                500                     161.               27
10      0.2      30                  5                      15.2              27
# ℹ 71 more rows
# ℹ abbreviated name: ¹`equivalents in gallic acid mg/L`

…of course, this is very boring because this is a balanced factorial-design experiment with complete data, so each group of Catechin levels has 27 rows!

But this now allows us to use filter(), mutate(), and a host of other tidyverse functions in “grouped” mode, which means when we do all of those they will act WITHIN the group you have defined. So we can see the effect, we will first filter polyphenols, and then create a new variable with a grouped mutate() that is the average for that group.

polyphenols |>
  # this is purely for ease of printing this example
  filter(`Chlorogenic acid` == 5) |> 
  group_by(Catechin) |>
  mutate(average_eq_gc = mean(`equivalents in gallic acid mg/L`))

# A tibble: 27 × 5
# Groups:   Catechin [3]
   Catechin `PC B2` `Chlorogenic acid` equivalents in gallic aci…¹ average_eq_gc
      <dbl>   <dbl>              <dbl>                       <dbl>         <dbl>
 1      0.2       1                  5                        2.79          21.8
 2      0.2       1                  5                        2.73          21.8
 3      0.2       1                  5                        2.74          21.8
 4      0.2      30                  5                       15.2           21.8
 5      0.2      30                  5                       15.3           21.8
 6      0.2      30                  5                       14.0           21.8
 7      0.2     100                  5                       46.6           21.8
 8      0.2     100                  5                       46.1           21.8
 9      0.2     100                  5                       50.9           21.8
10     30         1                  5                       14.0           28.4
# ℹ 17 more rows
# ℹ abbreviated name: ¹`equivalents in gallic acid mg/L`

But that’s not all! Now that we have the idea of a group, we can use it for all kinds of useful things. For example, the count() function will just return the number of rows in each group. Let’s use that to check if our polyphenols was from a balanced design (e.g., has the same number of observations at every treatment level and intersection):

polyphenols |>
  # note that we can group by multiple variables
  group_by(Catechin, `Chlorogenic acid`, `PC B2`) |> 
  # looks balanced, with triplicates at every treatment factor level
  count()

# A tibble: 27 × 4
# Groups:   Catechin, Chlorogenic acid, PC B2 [27]
   Catechin `Chlorogenic acid` `PC B2`     n
      <dbl>              <dbl>   <dbl> <int>
 1      0.2                  5       1     3
 2      0.2                  5      30     3
 3      0.2                  5     100     3
 4      0.2                200       1     3
 5      0.2                200      30     3
 6      0.2                200     100     3
 7      0.2                500       1     3
 8      0.2                500      30     3
 9      0.2                500     100     3
10     30                    5       1     3
# ℹ 17 more rows

The count() function gets way more useful when we have large data that is unbalanced, and we want to be able to easily pull counts without knowing what they are beforehand.

Much more useful, but slightly more complex is the summarize() function, which applies arbitrary summary functions to the subsets defined by group_by(). So, say we want to find the average and standard deviation at each of the groups we defined before, instead of the count:

polyphenols |>
  group_by(Catechin, `PC B2`, `Chlorogenic acid`) |>
  summarize(mean_eq_gc = mean(`equivalents in gallic acid mg/L`),
            sd_eq_gc = sd(`equivalents in gallic acid mg/L`))

# A tibble: 27 × 5
# Groups:   Catechin, PC B2 [9]
   Catechin `PC B2` `Chlorogenic acid` mean_eq_gc sd_eq_gc
      <dbl>   <dbl>              <dbl>      <dbl>    <dbl>
 1      0.2       1                  5       2.75   0.0321
 2      0.2       1                200      61.7    6.52  
 3      0.2       1                500     158.     6.42  
 4      0.2      30                  5      14.8    0.700 
 5      0.2      30                200      63.3    0.824 
 6      0.2      30                500     155.    16.3   
 7      0.2     100                  5      47.9    2.64  
 8      0.2     100                200      90.6    5.78  
 9      0.2     100                500     155.     5.52  
10     30         1                  5      13.9    0.107 
# ℹ 17 more rows

Even I think that’s pretty cool, and I wrote the code! This can be a bit tricky to get your head around, so let’s talk through it.

The key intuition is that summarize() takes multiple rows, identified by the same grouping variable (here the triplicate measurement reps identified by the combinations of treatment variables) and applies a function to each group that returns a single number: remember that mean() gives the mean (average) of a set of numeric observations, and sd() gives the standard deviaiton.

NB: One final tip: you can always run part of a piped data flow by selecting just that part and hitting “run” in the right top corner of your RStudio coding panel or hitting ctrl/cmd + enter. This is useful for testing your code flow.

Utilities for data management

Honestly, the amount of power in tidyverse is way more than we can cover today, and is covered more comprehensively (obviously) by Wickham and Grolemund (2017), including your reading for this week. Instead, I want to draw attention to a few “quality of life” functions that tidyverse also provides that make tasks that are overly difficult in R a little easier.

`rename()`

A common need in data analysis is renaming your columns, because they are called something clear but not machine-readable/easily typable in your data file, and they turn into a mess in R. We have that problem in our polyphenols dataset:

names(polyphenols)

[1] "Catechin"                        "PC B2"                          
[3] "Chlorogenic acid"                "equivalents in gallic acid mg/L"

Even with tab-completion, those are a pain to deal with. Renaming these in base R is a huge pain, in fact. You can copy them to a new column by using the <- operator, then remove the original column using the [] operator with negative indices. Or you can use the names() <- operator. Neither is very easy to do on the fly.

In tidyverse, the rename() function is there to help. It works in two, equally useful ways.

# New names go on the left hand side, old names on the right
polyphenols |>
  rename(chlor = `Chlorogenic acid`,
         pcb2 = `PC B2`,
         cat = Catechin,
         eq_ga = `equivalents in gallic acid mg/L`)

# A tibble: 81 × 4
     cat  pcb2 chlor  eq_ga
   <dbl> <dbl> <dbl>  <dbl>
 1   0.2     1     5   2.79
 2   0.2     1     5   2.73
 3   0.2     1     5   2.74
 4   0.2     1   200  57.7 
 5   0.2     1   200  58.2 
 6   0.2     1   200  69.2 
 7   0.2     1   500 150.  
 8   0.2     1   500 162.  
 9   0.2     1   500 161.  
10   0.2    30     5  15.2 
# ℹ 71 more rows

# Alternatively, we can rename by INDEX, with new name on the left and column #
# on the right.  This is easier for programming, but is less safe because you
# have to be certain you're matching columns correctly.


# don't forget we have to actually save the changes if we want them to stick
polyphenols <- 
  polyphenols |> 
  rename(cat = 1, pcb2 = 2, chlor = 3, eq_ga = 4)

`arrange()`

Another thing we often want to do is sort the rows of a data table according to some criterion. Doing this in base R requires the use of sort(), which works only on vectors, not on data frames, and so requires a similar approach to the base R filtering approach we saw above.

The tidyverse version is very simple: arrange() just requires that you tell it a data frame and one or more columns to sort by.

# we'll use the soups data for this next bit
soups <- read_csv("data/Week 4/soup pilot data.csv")

Rows: 24 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (5): subject, minestrone1, minestrone2, hotandsour1, hotandsour2

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.

# show us the data in ascending order of liking for hotandsour1
soups |>
  arrange(hotandsour1)

# A tibble: 24 × 5
   subject minestrone1 minestrone2 hotandsour1 hotandsour2
     <dbl>       <dbl>       <dbl>       <dbl>       <dbl>
 1      12          75          25        -100        -100
 2      21          20         100        -100          60
 3      23          10         100        -100          85
 4       4          20          60         -80          65
 5      13         -25          50         -75         -10
 6      14          20          40         -75          60
 7       9          40          90         -60          20
 8      18          40          90         -60         -15
 9      22          20          60         -60         -10
10       1           0          50         -50          10
# ℹ 14 more rows

# writing "-" before the sorting column goes in descending order
soups |>
  arrange(-hotandsour1)

# A tibble: 24 × 5
   subject minestrone1 minestrone2 hotandsour1 hotandsour2
     <dbl>       <dbl>       <dbl>       <dbl>       <dbl>
 1       8          70          80          50         -10
 2       7          75          90          40          80
 3       5          65          96          37          99
 4      24          10          75          35          55
 5      17          15          90          30          50
 6      10          15          65          25          20
 7       3          80          97          10          37
 8      11         -50          75         -15          50
 9       2          70          80         -20          20
10      15          70          90         -25         -60
# ℹ 14 more rows

# this sorts first ascending by hotandsour1, then within that descending by hotandsour2
soups |>
  arrange(hotandsour1, -hotandsour2)

# A tibble: 24 × 5
   subject minestrone1 minestrone2 hotandsour1 hotandsour2
     <dbl>       <dbl>       <dbl>       <dbl>       <dbl>
 1      23          10         100        -100          85
 2      21          20         100        -100          60
 3      12          75          25        -100        -100
 4       4          20          60         -80          65
 5      14          20          40         -75          60
 6      13         -25          50         -75         -10
 7       9          40          90         -60          20
 8      22          20          60         -60         -10
 9      18          40          90         -60         -15
10      16          20          90         -50          30
# ℹ 14 more rows

`relocate()`

R doesn’t really care about the order of columns in a data frame, but we might, for example

If we have a lot of columns, they might not print properly in the console, requiring some work every time we want to check their values.
We might be outputting data to a knitr::kable() or other display mode

We can reorder columns using similar syntax to our base R select operations above, but this requires many steps. Again, the simple tidyverse solution is a single function: relocate():

# by default the column(s) are moved to the "front" of the table
soups |>
  relocate(hotandsour2)

# A tibble: 24 × 5
   hotandsour2 subject minestrone1 minestrone2 hotandsour1
         <dbl>   <dbl>       <dbl>       <dbl>       <dbl>
 1          10       1           0          50         -50
 2          20       2          70          80         -20
 3          37       3          80          97          10
 4          65       4          20          60         -80
 5          99       5          65          96          37
 6          20       6          30          80         -50
 7          80       7          75          90          40
 8         -10       8          70          80          50
 9          20       9          40          90         -60
10          20      10          15          65          25
# ℹ 14 more rows

# .before and .after (note the ".") can be used to move to specific spots
soups |>
  relocate(hotandsour2, .after = subject)

# A tibble: 24 × 5
   subject hotandsour2 minestrone1 minestrone2 hotandsour1
     <dbl>       <dbl>       <dbl>       <dbl>       <dbl>
 1       1          10           0          50         -50
 2       2          20          70          80         -20
 3       3          37          80          97          10
 4       4          65          20          60         -80
 5       5          99          65          96          37
 6       6          20          30          80         -50
 7       7          80          75          90          40
 8       8         -10          70          80          50
 9       9          20          40          90         -60
10      10          20          15          65          25
# ℹ 14 more rows

And on to Week 8–sprinting forward!

Session Info

sessionInfo()

R version 4.5.3 (2026-03-11)
Platform: aarch64-apple-darwin20
Running under: macOS Tahoe 26.5.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] tictoc_1.2.1    lubridate_1.9.5 forcats_1.0.1   stringr_1.6.0  
 [5] dplyr_1.2.1     purrr_1.2.2     readr_2.2.0     tidyr_1.3.2    
 [9] tibble_3.3.1    ggplot2_4.0.2   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] rematch_2.0.0      bit_4.6.0          gtable_0.3.6       jsonlite_2.0.0    
 [5] crayon_1.5.3       compiler_4.5.3     tidyselect_1.2.1   parallel_4.5.3    
 [9] scales_1.4.0       readxl_1.4.5       yaml_2.3.12        fastmap_1.2.0     
[13] R6_2.6.1           generics_0.1.4     knitr_1.51         htmlwidgets_1.6.4 
[17] pillar_1.11.1      RColorBrewer_1.1-3 tzdb_0.5.0         rlang_1.2.0       
[21] stringi_1.8.7      xfun_0.57          S7_0.2.1           bit64_4.6.0-1     
[25] otel_0.2.0         timechange_0.4.0   cli_3.6.6          withr_3.0.2       
[29] magrittr_2.0.5     digest_0.6.39      grid_4.5.3         vroom_1.7.1       
[33] rstudioapi_0.18.0  hms_1.1.4          lifecycle_1.0.5    vctrs_0.7.3       
[37] evaluate_1.0.5     glue_1.8.1         cellranger_1.1.0   farver_2.1.2      
[41] rmarkdown_2.31     tools_4.5.3        pkgconfig_2.0.3    htmltools_0.5.9

References

Wickham, Hadley, and Garrett Grolemund. 2017. R for Data Science. O’Reilly.