Lesson 03: First Three Wrangling Verbs

This lesson is focused on the first three wrangling verbs: select(), filter(), and mutate().

Explanation

select() is used to specify which columns to use as opposed to default to all of the columns

filter() is used to subset data based on specified criteria

mutate() is used to make new columns of data

This will be our starting dataframe used in each of the examples below.

manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact
audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact
audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact

Selecting Columns

Syntax: select()

Think of it as selecting check boxes for each column to use. Any selected, whether it’s just one column or whether they’re many columns, will be available once this select() code is completed.

Scenario

Say we want this dataframe to the right where not all of the columns are needed, only the manufacturer, model, cty, and hwy columns. select() allows us to achieve this.

manufacturer	model	cty	hwy
audi	a4	18	29
audi	a4	21	29
audi	a4	20	31
audi	a4	21	30
audi	a4	16	26

Code Solution

mpg %>% 
    select(manufacturer, model, cty, hwy)

Filtering Rows

Syntax: filter()

Filtering is used in the scenarios like, “Only need students that are age 16 years old”, or “Only the the redwood trees in this forest that are taller than 30 ft”.

Scenario

Say we want this dataframe to the right where the cars are made by ford (manufacturer column) and the amount of highway miles per gallon is greater than 20 (hwy column). filter() allows allows us to achieve this.

manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
ford	mustang	3.8	1999	6	manual(m5)	r	18	26	r	subcompact
ford	mustang	3.8	1999	6	auto(l4)	r	18	25	r	subcompact
ford	mustang	4.0	2008	6	manual(m5)	r	17	26	r	subcompact
ford	mustang	4.0	2008	6	auto(l5)	r	16	24	r	subcompact
ford	mustang	4.6	1999	8	auto(l4)	r	15	21	r	subcompact
ford	mustang	4.6	1999	8	manual(m5)	r	15	22	r	subcompact

Code Solution

mpg %>%
    filter(manufacturer == "ford", hwy > 20)

Operators to use in Filter Statements

Basic Operators:

> greater than

< less than

== equals

>= greater than or equal to

<= less than or equal to

Advace Operators

!= is not equal to

%in% c() is in

Making New Columns

Syntax: mutate()

Think of the mutate() verb as starting with one column and change it to where you want it to become–mutating a column. You can make new columns not based on columns, based on one column, or based on multiple columns.

Scenario

Say we want this dataframe to the right (make sure to scroll to see the other columns) where there was an error in the data and the city miles per gallon needs 5 more miles than what it currently had (cty column) and we need the a column for the engine displacement (displ column) divided by the number of cylinders (cylcolumn). In reality this second task is not a useful but we’re doing it for practice sake.

manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class	new_col
audi	a4	1.8	1999	4	auto(l5)	f	23	29	p	compact	0.4500000
audi	a4	1.8	1999	4	manual(m5)	f	26	29	p	compact	0.4500000
audi	a4	2.0	2008	4	manual(m6)	f	25	31	p	compact	0.5000000
audi	a4	2.0	2008	4	auto(av)	f	26	30	p	compact	0.5000000
audi	a4	2.8	1999	6	auto(l5)	f	21	26	p	compact	0.4666667
audi	a4	2.8	1999	6	manual(m5)	f	23	26	p	compact	0.4666667

Code Solution

mpg %>%
    mutate(cty = cty + 5, new_col = displ/cyl)

Practice Exercises

New dataset in use for the practices exercises:

library(tidyverse)
dat <- read_csv("https://raw.githubusercontent.com/BrighamEaquinto/brighameaquinto.github.io/master/datasets/insurance.csv")

Question 1

Select the age, sex, and bmi columns

age	sex	bmi
19	female	27.900
18	male	33.770
28	male	33.000
33	male	22.705
32	male	28.880
31	female	25.740

Code Solution

dat %>% 
    select(age, sex, bmi)

Question 2

Select the charges, region, and age columns.

charges	region	age
16884.924	southwest	19
1725.552	southeast	18
4449.462	southeast	28
21984.471	northwest	33
3866.855	northwest	32
3756.622	southeast	31

Code Solution

dat %>% 
    select(charges, region, age)

Question 3

Filter the data where sex is only male and region is only northeast.

age	sex	bmi	children	smoker	region	charges
37	male	29.830	2	no	northeast	6406.411
25	male	26.220	0	no	northeast	2721.321
23	male	23.845	0	no	northeast	2395.172
35	male	36.670	1	yes	northeast	39774.276
38	male	37.050	1	no	northeast	6079.672
43	male	27.360	3	no	northeast	8606.217

Code Solution

dat %>% 
    filter(sex == "male", region == "northeast")

Question 4

Filter the data like this:

• age is younger than or equal to 40
• bmi is greater than 21

age	sex	bmi	children	smoker	region	charges
19	female	27.900	0	yes	southwest	16884.924
18	male	33.770	1	no	southeast	1725.552
28	male	33.000	3	no	southeast	4449.462
33	male	22.705	0	no	northwest	21984.471
32	male	28.880	0	no	northwest	3866.855
31	female	25.740	0	no	southeast	3756.622

Code Solution

dat %>% 
    filter(age <= 40, bmi > 21)

Question 5

Make a new column called bmi_modified that is the of the bmi - 12

age	sex	bmi	children	smoker	region	charges	bmi_modified
19	female	27.900	0	yes	southwest	16884.924	15.900
18	male	33.770	1	no	southeast	1725.552	21.770
28	male	33.000	3	no	southeast	4449.462	21.000
33	male	22.705	0	no	northwest	21984.471	10.705
32	male	28.880	0	no	northwest	3866.855	16.880
31	female	25.740	0	no	southeast	3756.622	13.740

Code Solution

dat %>% 
    mutate(bmi_modified = bmi - 12) 

Question 6

Make a new column called age_modified that is the current age divided by 2

age	sex	bmi	children	smoker	region	charges	age_modified
19	female	27.900	0	yes	southwest	16884.924	9.5
18	male	33.770	1	no	southeast	1725.552	9.0
28	male	33.000	3	no	southeast	4449.462	14.0
33	male	22.705	0	no	northwest	21984.471	16.5
32	male	28.880	0	no	northwest	3866.855	16.0
31	female	25.740	0	no	southeast	3756.622	15.5

Code Solution

dat %>% 
    mutate(age_modified = age/2) 

Question 7: Combining It All Together

• Only use the age, sex, and charges columns
• Make sure the age column is greater than 50 and there are only females
• Make a new column called real_charges which is the charges times 4

age	sex	charges	real_charges
60	female	28923.14	115692.55
62	female	27808.73	111234.90
56	female	11090.72	44362.87
52	female	10797.34	43189.34
60	female	13228.85	52915.39
59	female	14001.13	56004.54

Code Solution

dat %>% 
    select(age, sex, charges) %>% 
    filter(age > 50, sex == "female") %>% 
    mutate(real_charges = charges * 4)