Extending Data Wrangling Verbs

Thursday, October 10

Today we will…

Debrief PA 3
Debrief Lab 2 & Challenge 2
Discuss Lab 2 Peer Review
Outline Grade Expectations for Syllabus
New Material
- Extend dplyr verbs to have more functionality
Lab 3: Teacher Evaluations

PA 3: Identify the Mystery College

Efficient Coding

For the final problem, many groups’ code looked like this:

colleges_clean |> 
  filter(REGION == 7) |>
  filter(ADM_RATE > median(ADM_RATE)) |>
  filter(TUITION_DIFF != 0) |>
  filter(SAT_AVG %% 2 == 1) |>
  filter(STABBR != "ID") |>
  filter(UGDS < 10000) |>
  filter(STABBR != "MT") |> 
  slice_min(order_by = ADM_RATE)

Is there a more “efficient” way do this?

A few words about `drop_na()`

Easy tool to remove missing values
Unilaterally removes any row with a missing value for any variable
But you can specify what columns it should look at for missing values!

colleges_clean |> 
  drop_na(REGION, 
          ADM_RATE, 
          TUITION_DIFF, 
          SAT_AVG, 
          STABBR, 
          UGDS)

Lab 2 & Challenge 2

Grading / Feedback & Revisions

Each question will earn a score of “Success” or “Growing”.
- Questions marked “Growing” will receive feedback on how to improve your solution.
- These questions can be resubmitted for additional feedback.

Earning a “Success” doesn’t necessarily mean your solution is without error.
- You may still receive feedback on how to improve your solution.
- These questions cannot be resubmitted for additional feedback.

Revisions for Lab 2 & Challenge 2 are due next Friday (October 18th)
- You must submit your revised HTML to the original Lab 2 / Challenge 2 assignment portal.
- You must include reflections on what you learned by completing the revisions.

Lab 2 Growing Points

Q1: Loading in data with the here() function
- In this class, we use a package oriented workflow to read in the data.
- We do not specify relative paths to read in our data.

Q5: Adding transparency
- Only variables being mapped to aesthetics are inserted into the aes() function!
- Hard coded values (e.g., alpha = 0.5) belong outside the aes() function.

Report formatting: No messages or warnings should be output in the HTML document!
- Use code chunk options and execute options to suppress these messages and warnings.

Don’t make people tilt their head

Peer Code Review

Each of you was assigned one student’s lab to provide feedback on their code formatting.

Peer Code Review

Your feedback is to be provided in the comment box!

What feedback would you give?

ggplot(data = surveys, mapping = aes(x=hindfoot_length,y= weight)) +  
  geom_jitter(alpha=.2,color='tomato')+ facet_wrap(~species)+geom_boxplot(outlier.shape = NA)+labs(
    title ='Weight to hindfoot comparison'
  )+ xlab('length (mm)')+ylab('Weight(g)')

Grade Expectations

Defining Grades in 331

A: Superior Attainment of Course Objectives
B: Good Attainment of Course Objectives
C: Acceptable Attainment of Course Objectives
D: Poor Attainment of Course Objectives

We need to define criteria for each of these grades based on the four objectives of this course—learning targets, revising thinking, extending thinking, collaborating with peers.

Extending `dplyr` verbs

Example Data set – Cereal

library(liver)
data(cereal)

glimpse(cereal)

Rows: 77
Columns: 16
$ name     <fct> 100% Bran, 100% Natural Bran, All-Bran, All-Bran with Extra F…
$ manuf    <fct> N, Q, K, K, R, G, K, G, R, P, Q, G, G, G, G, R, K, K, G, K, N…
$ type     <fct> cold, cold, cold, cold, cold, cold, cold, cold, cold, cold, c…
$ calories <int> 70, 120, 70, 50, 110, 110, 110, 130, 90, 90, 120, 110, 120, 1…
$ protein  <int> 4, 3, 4, 4, 2, 2, 2, 3, 2, 3, 1, 6, 1, 3, 1, 2, 2, 1, 1, 3, 3…
$ fat      <int> 1, 5, 1, 0, 2, 2, 0, 2, 1, 0, 2, 2, 3, 2, 1, 0, 0, 0, 1, 3, 0…
$ sodium   <int> 130, 15, 260, 140, 200, 180, 125, 210, 200, 210, 220, 290, 21…
$ fiber    <dbl> 10.0, 2.0, 9.0, 14.0, 1.0, 1.5, 1.0, 2.0, 4.0, 5.0, 0.0, 2.0,…
$ carbo    <dbl> 5.0, 8.0, 7.0, 8.0, 14.0, 10.5, 11.0, 18.0, 15.0, 13.0, 12.0,…
$ sugars   <int> 6, 8, 5, 0, 8, 10, 14, 8, 6, 5, 12, 1, 9, 7, 13, 3, 2, 12, 13…
$ potass   <int> 280, 135, 320, 330, -1, 70, 30, 100, 125, 190, 35, 105, 45, 1…
$ vitamins <int> 25, 0, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25…
$ shelf    <int> 3, 3, 3, 3, 3, 1, 2, 3, 1, 3, 2, 1, 2, 3, 2, 1, 1, 2, 2, 3, 2…
$ weight   <dbl> 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.33, 1.00, 1.00, 1…
$ cups     <dbl> 0.33, 1.00, 0.33, 0.50, 0.75, 0.75, 1.00, 0.75, 0.67, 0.67, 0…
$ rating   <dbl> 68.40297, 33.98368, 59.42551, 93.70491, 34.38484, 29.50954, 3…

Count with `count()`

How many cereals does each manuf have in this dataset?

cereal |> 
  group_by(manuf) |> 
  count()

manuf	n
A	1
G	22
K	23
N	6
P	9
Q	8
R	8

`group_by()` + `slice()`

For each manuf, find the cereal with the most fiber.

cereal |> 
  group_by(manuf) |> 
  slice_max(order_by = fiber)

name	manuf	type	calories	protein	fat	sodium	fiber	carbo	sugars	potass	vitamins	shelf	weight	cups	rating
Maypo	A	hot	100	4	1	0	0.0	16	3	95	25	2	1.00	1.00	54.85092
Total Raisin Bran	G	cold	140	3	1	190	4.0	15	14	230	100	3	1.50	1.00	28.59278
All-Bran with Extra Fiber	K	cold	50	4	0	140	14.0	8	0	330	25	3	1.00	0.50	93.70491
100% Bran	N	cold	70	4	1	130	10.0	5	6	280	25	3	1.00	0.33	68.40297
Post Nat. Raisin Bran	P	cold	120	3	1	200	6.0	11	14	260	25	3	1.33	0.67	37.84059
Quaker Oatmeal	Q	hot	100	5	2	0	2.7	-1	-1	110	0	1	1.00	0.67	50.82839
Bran Chex	R	cold	90	2	1	200	4.0	15	6	125	25	1	1.00	0.67	49.12025

Multiple Variables in `slice()`

Find the 3 cereals with the highest fiber and potass.

If you are ordering by multiple variables, wrap them in a data.frame!

cereal |> 
  slice_max(order_by = data.frame(fiber, potass),
            n = 3)

name	manuf	type	calories	protein	fat	sodium	fiber	carbo	sugars	potass	vitamins	shelf	weight	cups	rating
All-Bran with Extra Fiber	K	cold	50	4	0	140	14	8	0	330	25	3	1	0.50	93.70491
100% Bran	N	cold	70	4	1	130	10	5	6	280	25	3	1	0.33	68.40297
All-Bran	K	cold	70	4	1	260	9	7	5	320	25	3	1	0.33	59.42551

Discretize with `if_else()`

For each cereal, label the potass as “high” or “low”.

if_else(<CONDITION>, <TRUE OUTPUT>, <FALSE OUTPUT>)

cereal |> 
  mutate(po_category = if_else(potass <= 100, 
                               "low", 
                               "high"),
         .after = potass)

name	manuf	type	calories	protein	fat	sodium	fiber	carbo	sugars	potass	po_category	vitamins	shelf	weight	cups	rating
100% Bran	N	cold	70	4	1	130	10.0	5.0	6	280	high	25	3	1.00	0.33	68.40297
100% Natural Bran	Q	cold	120	3	5	15	2.0	8.0	8	135	high	0	3	1.00	1.00	33.98368
All-Bran	K	cold	70	4	1	260	9.0	7.0	5	320	high	25	3	1.00	0.33	59.42551
All-Bran with Extra Fiber	K	cold	50	4	0	140	14.0	8.0	0	330	high	25	3	1.00	0.50	93.70491
Almond Delight	R	cold	110	2	2	200	1.0	14.0	8	-1	low	25	3	1.00	0.75	34.38484
Apple Cinnamon Cheerios	G	cold	110	2	2	180	1.5	10.5	10	70	low	25	1	1.00	0.75	29.50954
Apple Jacks	K	cold	110	2	0	125	1.0	11.0	14	30	low	25	2	1.00	1.00	33.17409
Basic 4	G	cold	130	3	2	210	2.0	18.0	8	100	low	25	3	1.33	0.75	37.03856
Bran Chex	R	cold	90	2	1	200	4.0	15.0	6	125	high	25	1	1.00	0.67	49.12025
Bran Flakes	P	cold	90	3	0	210	5.0	13.0	5	190	high	25	3	1.00	0.67	53.31381
Cap'n'Crunch	Q	cold	120	1	2	220	0.0	12.0	12	35	low	25	2	1.00	0.75	18.04285
Cheerios	G	cold	110	6	2	290	2.0	17.0	1	105	high	25	1	1.00	1.25	50.76500
Cinnamon Toast Crunch	G	cold	120	1	3	210	0.0	13.0	9	45	low	25	2	1.00	0.75	19.82357
Clusters	G	cold	110	3	2	140	2.0	13.0	7	105	high	25	3	1.00	0.50	40.40021
Cocoa Puffs	G	cold	110	1	1	180	0.0	12.0	13	55	low	25	2	1.00	1.00	22.73645
Corn Chex	R	cold	110	2	0	280	0.0	22.0	3	25	low	25	1	1.00	1.00	41.44502
Corn Flakes	K	cold	100	2	0	290	1.0	21.0	2	35	low	25	1	1.00	1.00	45.86332
Corn Pops	K	cold	110	1	0	90	1.0	13.0	12	20	low	25	2	1.00	1.00	35.78279
Count Chocula	G	cold	110	1	1	180	0.0	12.0	13	65	low	25	2	1.00	1.00	22.39651
Cracklin' Oat Bran	K	cold	110	3	3	140	4.0	10.0	7	160	high	25	3	1.00	0.50	40.44877
Cream of Wheat (Quick)	N	hot	100	3	0	80	1.0	21.0	0	-1	low	0	2	1.00	1.00	64.53382
Crispix	K	cold	110	2	0	220	1.0	21.0	3	30	low	25	3	1.00	1.00	46.89564
Crispy Wheat & Raisins	G	cold	100	2	1	140	2.0	11.0	10	120	high	25	3	1.00	0.75	36.17620
Double Chex	R	cold	100	2	0	190	1.0	18.0	5	80	low	25	3	1.00	0.75	44.33086
Froot Loops	K	cold	110	2	1	125	1.0	11.0	13	30	low	25	2	1.00	1.00	32.20758
Frosted Flakes	K	cold	110	1	0	200	1.0	14.0	11	25	low	25	1	1.00	0.75	31.43597
Frosted Mini-Wheats	K	cold	100	3	0	0	3.0	14.0	7	100	low	25	2	1.00	0.80	58.34514
Fruit & Fibre Dates; Walnuts; and Oats	P	cold	120	3	2	160	5.0	12.0	10	200	high	25	3	1.25	0.67	40.91705
Fruitful Bran	K	cold	120	3	0	240	5.0	14.0	12	190	high	25	3	1.33	0.67	41.01549
Fruity Pebbles	P	cold	110	1	1	135	0.0	13.0	12	25	low	25	2	1.00	0.75	28.02576
Golden Crisp	P	cold	100	2	0	45	0.0	11.0	15	40	low	25	1	1.00	0.88	35.25244
Golden Grahams	G	cold	110	1	1	280	0.0	15.0	9	45	low	25	2	1.00	0.75	23.80404
Grape Nuts Flakes	P	cold	100	3	1	140	3.0	15.0	5	85	low	25	3	1.00	0.88	52.07690
Grape-Nuts	P	cold	110	3	0	170	3.0	17.0	3	90	low	25	3	1.00	0.25	53.37101
Great Grains Pecan	P	cold	120	3	3	75	3.0	13.0	4	100	low	25	3	1.00	0.33	45.81172
Honey Graham Ohs	Q	cold	120	1	2	220	1.0	12.0	11	45	low	25	2	1.00	1.00	21.87129
Honey Nut Cheerios	G	cold	110	3	1	250	1.5	11.5	10	90	low	25	1	1.00	0.75	31.07222
Honey-comb	P	cold	110	1	0	180	0.0	14.0	11	35	low	25	1	1.00	1.33	28.74241
Just Right Crunchy Nuggets	K	cold	110	2	1	170	1.0	17.0	6	60	low	100	3	1.00	1.00	36.52368
Just Right Fruit & Nut	K	cold	140	3	1	170	2.0	20.0	9	95	low	100	3	1.30	0.75	36.47151
Kix	G	cold	110	2	1	260	0.0	21.0	3	40	low	25	2	1.00	1.50	39.24111
Life	Q	cold	100	4	2	150	2.0	12.0	6	95	low	25	2	1.00	0.67	45.32807
Lucky Charms	G	cold	110	2	1	180	0.0	12.0	12	55	low	25	2	1.00	1.00	26.73451
Maypo	A	hot	100	4	1	0	0.0	16.0	3	95	low	25	2	1.00	1.00	54.85092
Muesli Raisins; Dates; & Almonds	R	cold	150	4	3	95	3.0	16.0	11	170	high	25	3	1.00	1.00	37.13686
Muesli Raisins; Peaches; & Pecans	R	cold	150	4	3	150	3.0	16.0	11	170	high	25	3	1.00	1.00	34.13976
Mueslix Crispy Blend	K	cold	160	3	2	150	3.0	17.0	13	160	high	25	3	1.50	0.67	30.31335
Multi-Grain Cheerios	G	cold	100	2	1	220	2.0	15.0	6	90	low	25	1	1.00	1.00	40.10596
Nut&Honey Crunch	K	cold	120	2	1	190	0.0	15.0	9	40	low	25	2	1.00	0.67	29.92429
Nutri-Grain Almond-Raisin	K	cold	140	3	2	220	3.0	21.0	7	130	high	25	3	1.33	0.67	40.69232
Nutri-grain Wheat	K	cold	90	3	0	170	3.0	18.0	2	90	low	25	3	1.00	1.00	59.64284
Oatmeal Raisin Crisp	G	cold	130	3	2	170	1.5	13.5	10	120	high	25	3	1.25	0.50	30.45084
Post Nat. Raisin Bran	P	cold	120	3	1	200	6.0	11.0	14	260	high	25	3	1.33	0.67	37.84059
Product 19	K	cold	100	3	0	320	1.0	20.0	3	45	low	100	3	1.00	1.00	41.50354
Puffed Rice	Q	cold	50	1	0	0	0.0	13.0	0	15	low	0	3	0.50	1.00	60.75611
Puffed Wheat	Q	cold	50	2	0	0	1.0	10.0	0	50	low	0	3	0.50	1.00	63.00565
Quaker Oat Squares	Q	cold	100	4	1	135	2.0	14.0	6	110	high	25	3	1.00	0.50	49.51187
Quaker Oatmeal	Q	hot	100	5	2	0	2.7	-1.0	-1	110	high	0	1	1.00	0.67	50.82839
Raisin Bran	K	cold	120	3	1	210	5.0	14.0	12	240	high	25	2	1.33	0.75	39.25920
Raisin Nut Bran	G	cold	100	3	2	140	2.5	10.5	8	140	high	25	3	1.00	0.50	39.70340
Raisin Squares	K	cold	90	2	0	0	2.0	15.0	6	110	high	25	3	1.00	0.50	55.33314
Rice Chex	R	cold	110	1	0	240	0.0	23.0	2	30	low	25	1	1.00	1.13	41.99893
Rice Krispies	K	cold	110	2	0	290	0.0	22.0	3	35	low	25	1	1.00	1.00	40.56016
Shredded Wheat	N	cold	80	2	0	0	3.0	16.0	0	95	low	0	1	0.83	1.00	68.23588
Shredded Wheat 'n'Bran	N	cold	90	3	0	0	4.0	19.0	0	140	high	0	1	1.00	0.67	74.47295
Shredded Wheat spoon size	N	cold	90	3	0	0	3.0	20.0	0	120	high	0	1	1.00	0.67	72.80179
Smacks	K	cold	110	2	1	70	1.0	9.0	15	40	low	25	2	1.00	0.75	31.23005
Special K	K	cold	110	6	0	230	1.0	16.0	3	55	low	25	1	1.00	1.00	53.13132
Strawberry Fruit Wheats	N	cold	90	2	0	15	3.0	15.0	5	90	low	25	2	1.00	1.00	59.36399
Total Corn Flakes	G	cold	110	2	1	200	0.0	21.0	3	35	low	100	3	1.00	1.00	38.83975
Total Raisin Bran	G	cold	140	3	1	190	4.0	15.0	14	230	high	100	3	1.50	1.00	28.59278
Total Whole Grain	G	cold	100	3	1	200	3.0	16.0	3	110	high	100	3	1.00	1.00	46.65884
Triples	G	cold	110	2	1	250	0.0	21.0	3	60	low	25	3	1.00	0.75	39.10617
Trix	G	cold	110	1	1	140	0.0	13.0	12	25	low	25	2	1.00	1.00	27.75330
Wheat Chex	R	cold	100	3	1	230	3.0	17.0	3	115	high	25	1	1.00	0.67	49.78744
Wheaties	G	cold	100	3	1	200	3.0	17.0	3	110	high	25	1	1.00	1.00	51.59219
Wheaties Honey Gold	G	cold	110	2	1	200	1.0	16.0	8	60	low	25	1	1.00	0.75	36.18756

What do you think .after does?

Re-level with `case_when()`

For each manufacturer, change the manuf code to the name of the manufacturer.

A series of if-else statements.

cereal |> 
  mutate(manuf = case_when(manuf == "A" ~ "American Home Food Products", 
                           manuf == "G" ~ "General Mills", 
                           manuf == "K" ~ "Kelloggs", 
                           manuf == "N" ~ "Nabisco", 
                           manuf == "P" ~ "Post", 
                           manuf == "Q" ~ "Quaker Oats", 
                           manuf == "R" ~ "Ralston Purina")
         )

Does this code create a new variable or change an existing variable?

name	manuf	type	calories	protein	fat	sodium	fiber	carbo	sugars	potass	vitamins	shelf	weight	cups	rating
100% Bran	Nabisco	cold	70	4	1	130	10.0	5.0	6	280	25	3	1.00	0.33	68.40297
100% Natural Bran	Quaker Oats	cold	120	3	5	15	2.0	8.0	8	135	0	3	1.00	1.00	33.98368
All-Bran	Kelloggs	cold	70	4	1	260	9.0	7.0	5	320	25	3	1.00	0.33	59.42551
All-Bran with Extra Fiber	Kelloggs	cold	50	4	0	140	14.0	8.0	0	330	25	3	1.00	0.50	93.70491
Almond Delight	Ralston Purina	cold	110	2	2	200	1.0	14.0	8	-1	25	3	1.00	0.75	34.38484
Apple Cinnamon Cheerios	General Mills	cold	110	2	2	180	1.5	10.5	10	70	25	1	1.00	0.75	29.50954
Apple Jacks	Kelloggs	cold	110	2	0	125	1.0	11.0	14	30	25	2	1.00	1.00	33.17409
Basic 4	General Mills	cold	130	3	2	210	2.0	18.0	8	100	25	3	1.33	0.75	37.03856
Bran Chex	Ralston Purina	cold	90	2	1	200	4.0	15.0	6	125	25	1	1.00	0.67	49.12025
Bran Flakes	Post	cold	90	3	0	210	5.0	13.0	5	190	25	3	1.00	0.67	53.31381
Cap'n'Crunch	Quaker Oats	cold	120	1	2	220	0.0	12.0	12	35	25	2	1.00	0.75	18.04285
Cheerios	General Mills	cold	110	6	2	290	2.0	17.0	1	105	25	1	1.00	1.25	50.76500
Cinnamon Toast Crunch	General Mills	cold	120	1	3	210	0.0	13.0	9	45	25	2	1.00	0.75	19.82357
Clusters	General Mills	cold	110	3	2	140	2.0	13.0	7	105	25	3	1.00	0.50	40.40021
Cocoa Puffs	General Mills	cold	110	1	1	180	0.0	12.0	13	55	25	2	1.00	1.00	22.73645
Corn Chex	Ralston Purina	cold	110	2	0	280	0.0	22.0	3	25	25	1	1.00	1.00	41.44502
Corn Flakes	Kelloggs	cold	100	2	0	290	1.0	21.0	2	35	25	1	1.00	1.00	45.86332
Corn Pops	Kelloggs	cold	110	1	0	90	1.0	13.0	12	20	25	2	1.00	1.00	35.78279
Count Chocula	General Mills	cold	110	1	1	180	0.0	12.0	13	65	25	2	1.00	1.00	22.39651
Cracklin' Oat Bran	Kelloggs	cold	110	3	3	140	4.0	10.0	7	160	25	3	1.00	0.50	40.44877
Cream of Wheat (Quick)	Nabisco	hot	100	3	0	80	1.0	21.0	0	-1	0	2	1.00	1.00	64.53382
Crispix	Kelloggs	cold	110	2	0	220	1.0	21.0	3	30	25	3	1.00	1.00	46.89564
Crispy Wheat & Raisins	General Mills	cold	100	2	1	140	2.0	11.0	10	120	25	3	1.00	0.75	36.17620
Double Chex	Ralston Purina	cold	100	2	0	190	1.0	18.0	5	80	25	3	1.00	0.75	44.33086
Froot Loops	Kelloggs	cold	110	2	1	125	1.0	11.0	13	30	25	2	1.00	1.00	32.20758
Frosted Flakes	Kelloggs	cold	110	1	0	200	1.0	14.0	11	25	25	1	1.00	0.75	31.43597
Frosted Mini-Wheats	Kelloggs	cold	100	3	0	0	3.0	14.0	7	100	25	2	1.00	0.80	58.34514
Fruit & Fibre Dates; Walnuts; and Oats	Post	cold	120	3	2	160	5.0	12.0	10	200	25	3	1.25	0.67	40.91705
Fruitful Bran	Kelloggs	cold	120	3	0	240	5.0	14.0	12	190	25	3	1.33	0.67	41.01549
Fruity Pebbles	Post	cold	110	1	1	135	0.0	13.0	12	25	25	2	1.00	0.75	28.02576
Golden Crisp	Post	cold	100	2	0	45	0.0	11.0	15	40	25	1	1.00	0.88	35.25244
Golden Grahams	General Mills	cold	110	1	1	280	0.0	15.0	9	45	25	2	1.00	0.75	23.80404
Grape Nuts Flakes	Post	cold	100	3	1	140	3.0	15.0	5	85	25	3	1.00	0.88	52.07690
Grape-Nuts	Post	cold	110	3	0	170	3.0	17.0	3	90	25	3	1.00	0.25	53.37101
Great Grains Pecan	Post	cold	120	3	3	75	3.0	13.0	4	100	25	3	1.00	0.33	45.81172
Honey Graham Ohs	Quaker Oats	cold	120	1	2	220	1.0	12.0	11	45	25	2	1.00	1.00	21.87129
Honey Nut Cheerios	General Mills	cold	110	3	1	250	1.5	11.5	10	90	25	1	1.00	0.75	31.07222
Honey-comb	Post	cold	110	1	0	180	0.0	14.0	11	35	25	1	1.00	1.33	28.74241
Just Right Crunchy Nuggets	Kelloggs	cold	110	2	1	170	1.0	17.0	6	60	100	3	1.00	1.00	36.52368
Just Right Fruit & Nut	Kelloggs	cold	140	3	1	170	2.0	20.0	9	95	100	3	1.30	0.75	36.47151
Kix	General Mills	cold	110	2	1	260	0.0	21.0	3	40	25	2	1.00	1.50	39.24111
Life	Quaker Oats	cold	100	4	2	150	2.0	12.0	6	95	25	2	1.00	0.67	45.32807
Lucky Charms	General Mills	cold	110	2	1	180	0.0	12.0	12	55	25	2	1.00	1.00	26.73451
Maypo	American Home Food Products	hot	100	4	1	0	0.0	16.0	3	95	25	2	1.00	1.00	54.85092
Muesli Raisins; Dates; & Almonds	Ralston Purina	cold	150	4	3	95	3.0	16.0	11	170	25	3	1.00	1.00	37.13686
Muesli Raisins; Peaches; & Pecans	Ralston Purina	cold	150	4	3	150	3.0	16.0	11	170	25	3	1.00	1.00	34.13976
Mueslix Crispy Blend	Kelloggs	cold	160	3	2	150	3.0	17.0	13	160	25	3	1.50	0.67	30.31335
Multi-Grain Cheerios	General Mills	cold	100	2	1	220	2.0	15.0	6	90	25	1	1.00	1.00	40.10596
Nut&Honey Crunch	Kelloggs	cold	120	2	1	190	0.0	15.0	9	40	25	2	1.00	0.67	29.92429
Nutri-Grain Almond-Raisin	Kelloggs	cold	140	3	2	220	3.0	21.0	7	130	25	3	1.33	0.67	40.69232
Nutri-grain Wheat	Kelloggs	cold	90	3	0	170	3.0	18.0	2	90	25	3	1.00	1.00	59.64284
Oatmeal Raisin Crisp	General Mills	cold	130	3	2	170	1.5	13.5	10	120	25	3	1.25	0.50	30.45084
Post Nat. Raisin Bran	Post	cold	120	3	1	200	6.0	11.0	14	260	25	3	1.33	0.67	37.84059
Product 19	Kelloggs	cold	100	3	0	320	1.0	20.0	3	45	100	3	1.00	1.00	41.50354
Puffed Rice	Quaker Oats	cold	50	1	0	0	0.0	13.0	0	15	0	3	0.50	1.00	60.75611
Puffed Wheat	Quaker Oats	cold	50	2	0	0	1.0	10.0	0	50	0	3	0.50	1.00	63.00565
Quaker Oat Squares	Quaker Oats	cold	100	4	1	135	2.0	14.0	6	110	25	3	1.00	0.50	49.51187
Quaker Oatmeal	Quaker Oats	hot	100	5	2	0	2.7	-1.0	-1	110	0	1	1.00	0.67	50.82839
Raisin Bran	Kelloggs	cold	120	3	1	210	5.0	14.0	12	240	25	2	1.33	0.75	39.25920
Raisin Nut Bran	General Mills	cold	100	3	2	140	2.5	10.5	8	140	25	3	1.00	0.50	39.70340
Raisin Squares	Kelloggs	cold	90	2	0	0	2.0	15.0	6	110	25	3	1.00	0.50	55.33314
Rice Chex	Ralston Purina	cold	110	1	0	240	0.0	23.0	2	30	25	1	1.00	1.13	41.99893
Rice Krispies	Kelloggs	cold	110	2	0	290	0.0	22.0	3	35	25	1	1.00	1.00	40.56016
Shredded Wheat	Nabisco	cold	80	2	0	0	3.0	16.0	0	95	0	1	0.83	1.00	68.23588
Shredded Wheat 'n'Bran	Nabisco	cold	90	3	0	0	4.0	19.0	0	140	0	1	1.00	0.67	74.47295
Shredded Wheat spoon size	Nabisco	cold	90	3	0	0	3.0	20.0	0	120	0	1	1.00	0.67	72.80179
Smacks	Kelloggs	cold	110	2	1	70	1.0	9.0	15	40	25	2	1.00	0.75	31.23005
Special K	Kelloggs	cold	110	6	0	230	1.0	16.0	3	55	25	1	1.00	1.00	53.13132
Strawberry Fruit Wheats	Nabisco	cold	90	2	0	15	3.0	15.0	5	90	25	2	1.00	1.00	59.36399
Total Corn Flakes	General Mills	cold	110	2	1	200	0.0	21.0	3	35	100	3	1.00	1.00	38.83975
Total Raisin Bran	General Mills	cold	140	3	1	190	4.0	15.0	14	230	100	3	1.50	1.00	28.59278
Total Whole Grain	General Mills	cold	100	3	1	200	3.0	16.0	3	110	100	3	1.00	1.00	46.65884
Triples	General Mills	cold	110	2	1	250	0.0	21.0	3	60	25	3	1.00	0.75	39.10617
Trix	General Mills	cold	110	1	1	140	0.0	13.0	12	25	25	2	1.00	1.00	27.75330
Wheat Chex	Ralston Purina	cold	100	3	1	230	3.0	17.0	3	115	25	1	1.00	0.67	49.78744
Wheaties	General Mills	cold	100	3	1	200	3.0	17.0	3	110	25	1	1.00	1.00	51.59219
Wheaties Honey Gold	General Mills	cold	110	2	1	200	1.0	16.0	8	60	25	1	1.00	0.75	36.18756

Calculate a Summary Statistic for Many Columns

For each type of cereal, calculate the mean nutrient levels.

cereal |> 
  group_by(type) |> 
  summarize(mean_cal = mean(calories), 
            mean_protein = mean(protein), 
            mean_fat = mean(fat), 
            mean_sodium = mean(sodium), 
            mean_fiber = mean(fiber), 
            mean_carbs = mean(carbo), 
            mean_sugars = mean(sugars), 
            mean_potassium = mean(potass)
            )

Does this seem like the most efficient way we could do this?

Summarize multiple columns with `across()`

Within the summarize() function, we use the across() function, with three arguments:

.cols – to specify the columns to apply functions to.
.fns – to specify the function(s) to apply.
.x – as a placeholder (alias) for the variables being passed into the function.

We use lambda functions: ~ <FUN_NAME>(.x, <ARGS>) to specify what function(s) to apply

Summarize multiple columns with `across()`

For each type of cereal, calculate the mean nutrient levels.

cereal |> 
  group_by(type) |> 
  summarise(
    across(.cols = calories:potass, 
           .fns = ~ mean(.x)
           )
    )

type	calories	protein	fat	sodium	fiber	carbo	sugars	potass
cold	107.1622	2.486486	1.013513	165.06757	2.189189	14.7027	7.1756757	97.21622
hot	100.0000	4.000000	1.000000	26.66667	1.233333	12.0000	0.6666667	68.00000

Summarize multiple columns with `across()`

If missing values were present, we would need to remove them when calculating the mean!

cereal |> 
  group_by(type) |> 
  summarise(
    across(.cols = calories:potass, 
           .fns = ~ mean(.x, na.rm = TRUE)
           )
    )

type	calories	protein	fat	sodium	fiber	carbo	sugars	potass
cold	107.6667	2.416667	0.8833333	170.08333	1.800000	14.66667	7.2000000	86.66667
hot	100.0000	4.000000	1.0000000	26.66667	1.233333	12.00000	0.6666667	68.00000

Getting Fancy with Names

.names

A glue specification that describes how to name the output columns. This can use {.col} to stand for the selected column name, and {.fn} to stand for the name of the function being applied. The default (NULL) is equivalent to "{.col}" for the single function case and "{.col}_{.fn}" for the case where a list is used for .fns.

cereal |> 
  group_by(type) |> 
  summarise(
    across(.cols = calories:potass, 
           .fns = ~ mean(.x), 
           .names = "mean_{.col}"
           )
    )

type	mean_calories	mean_protein	mean_fat	mean_sodium	mean_fiber	mean_carbo	mean_sugars	mean_potass
cold	107.1622	2.486486	1.013513	165.06757	2.189189	14.7027	7.1756757	97.21622
hot	100.0000	4.000000	1.000000	26.66667	1.233333	12.0000	0.6666667	68.00000

Piping into `ggplot()`

Plot the mean protein per cup for each manuf.

cereal |> 
  mutate(manuf = case_when(manuf == "A" ~ "American Home Food Products", 
                           manuf == "G" ~ "General Mills", 
                           manuf == "K" ~ "Kelloggs", 
                           manuf == "N" ~ "Nabisco", 
                           manuf == "P" ~ "Post", 
                           manuf == "Q" ~ "Quaker Oats", 
                           manuf == "R" ~ "Ralston Purina")) |> 
  filter(type == "cold") |> 
  mutate(pro_per_cup = protein / cups) |> 
  group_by(manuf) |> 
  summarise(mean_pro_per_cup = mean(pro_per_cup)) |> 
  ggplot(mapping = aes(y = manuf, 
                       x = mean_pro_per_cup, 
                       shape = manuf)) +
  geom_point(show.legend = FALSE,
             size = 6) +
  labs(subtitle = "for 77 different breakfast cereals",
       title = "Mean Grams of Protein per Cup", 
       x = "", 
       y = "") +
  theme_bw() +
  theme(plot.title = element_text(size = 24),
        plot.subtitle = element_text(size = 18),
        axis.text = element_text(size = 22)
        ) +
  scale_x_continuous(limits = c(0, 10))

Piping into `ggplot()`

Creating a Game Plan

Just like when creating graphics with ggplot, wrangling data with dplyr involves thinking through many steps and writing many layers of code.

To help you think through a wrangling problem, I strongly encourage you to create a game plan before we start writing code.

This might involve…

a sketch or flowchart.
a list of dplyr verbs and variable names.
annotating the head of the dataframe.

What is the median grams of sugars per shelf and the number of cereals per shelf, when we drop the missing values (coded as sugars = -1)?

The person with the nearest birthday: explain out loud to your neighbor how you would do this manipulation.

cereal |> 
  select(sugars, shelf) |> 
  filter(sugars != -1) |> 
  group_by(shelf) |> 
  summarise(med_sugars = median(sugars),
            num_shelf = n()
            )

# A tibble: 3 × 3
  shelf med_sugars num_shelf
  <int>      <dbl>     <int>
1     1          3        19
2     2         12        21
3     3          6        36

Lab 3 & Challenge 3

Exploring teacher evaluations during COVID-19

To do…

Lab 3: Student Evaluations of Teaching
- Due Sunday, 10/13 at 11:59pm
Challenge 3: Extending Teaching Evaluation Investigations
- Due Sunday, 10/13 at 11:59pm
Read Chapter 4: Data Joins and Transformations
- Check-in 4.1 + 4.2 due Tuesday 10/15 at 12pm

Extending Data Wrangling Verbs

Thursday, October 10

PA 3: Identify the Mystery College

Efficient Coding

A few words about drop_na()

Lab 2 & Challenge 2

Grading / Feedback & Revisions

Lab 2 Growing Points

Don’t make people tilt their head

Peer Code Review

Peer Code Review

Peer Code Review

What feedback would you give?

Grade Expectations

Defining Grades in 331

Extending dplyr verbs

Example Data set – Cereal

Count with count()

group_by() + slice()

Multiple Variables in slice()

Discretize with if_else()

Re-level with case_when()

Calculate a Summary Statistic for Many Columns

Summarize multiple columns with across()

Summarize multiple columns with across()

Summarize multiple columns with across()

Getting Fancy with Names

Piping into ggplot()

Piping into ggplot()

Creating a Game Plan

Creating a Game Plan

Creating a Game Plan

Lab 3 & Challenge 3

To do…

A few words about `drop_na()`

Extending `dplyr` verbs

Count with `count()`

`group_by()` + `slice()`

Multiple Variables in `slice()`

Discretize with `if_else()`

Re-level with `case_when()`

Summarize multiple columns with `across()`

Summarize multiple columns with `across()`

Summarize multiple columns with `across()`

Piping into `ggplot()`

Piping into `ggplot()`