R Course 2014: Lecture 7

8/10/2019 R Course 2014: Lecture 7

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Lecture 7:Merges and functions

Ben Fanson

Simeon Lisovski


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Lecture Outline1) concatenating data.frames

2) merges/joins

3) functions


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Quick review of last we

1) if-then...if( trt == 'a') {

print('yes')

}else{

print('no') }


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Quick review of last we

2) for loops...

for( trt in c('a','b','c') ) {

print(trt)

}


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AppendsBird_id treatment growth_rate

1 t1 12.3

2 t2 10.3

3 t3 14.5

Bird_id treatment growth_ra

4 t1 14.3

5 t2 9.3

6 t3 15.6


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Bird_id treatment growth_rate

1 t1 12.3

2 t2 10.3

3 t3 14.5


4 t1 14.3

5 t2 9.3

6 t3 15.6


1 t1 12.3

2 t2 10.3

3 t3 14.5

4 t1 14.3

5 t2 9.3

6 t3 15.6

Appends


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Bird_id lifespan

1 45

2 34

3 40

Bird_id growth_rate

1 14.3

2 9.3

3 15.6

Bird_id lifespan growth_rate

1 45 14.3

2 34 9.3

3 40 15.6

Merges (aka joins)unique identifier


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treatment

t1

t1

growth_rate

12.3

14.3

treatment

t2

t2

growth_rate

10.3

9.3

treatment

t3

t3

growth_rate

14.5

15.6

append: rbind()

= ds1

= ds2

= ds3

rbind(ds1, ds2treatment

t1

t1

t2

t2

t3

t3

growt

1

14

1

9

14

1


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treatment

t1

t1

growth_rate

12.3

14.3

treatment

t2

t2

growth_rate

10.3

9.3

rbind.fill()

= ds1

= ds2

rbind.fill(ds1treatment

t1

t1

t2

t2

growth

12.

14.

10.

9.3comments

good

delete

rbind.fill() is in dplyr [technically plyr package]


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combine columns: cbin

= ds1

= ds2

cbind(ds1, treatment

t1

t2

t3

t1t2

t3

growt

1

1

14

149

1

treatment

t1

t2t3

t1

t2

t3

growth_rate12.3

10.3

14.5

14.3

9.3

15.6

T f C M


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Types of Common Mergesjoins)

Inner Join Left Outer Join Full Oute

Method:

One-to-One, One-to-Many, or Many-to-Many

ds1 ds2 ds1 ds2 ds1 d


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id var2 var3

1 a b

2 a b

3 a b

id var4 var5

1 c d

2 c d

3 c d

jargon: left and right datase

Left Right

left is called 'x' in R right is called 'y' in R


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Inner Joins

Bird_id lifespan

1 45

2 34

3 40

4 50

Bird_id growth_rate

1 14.3

2 9.3

5 12.3

merge( left, right, by='Bi

left

right

Bird_id lifespan growth_r

1 45 14.3

2 34 9.3


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Inner Joins

Bird_id trt lifespan

1 A 45

2 A 34

3 B 40

4 B 50

Bird_id trt growth_rate

1 A 14.3

2 A 9.3

5 B 12.3

merge( left, right by=c('B

left

right

Bird_id trt lifespan

1 A 45

2 A 34


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left outer join

Bird_id lifespan

1 45

2 34

3 40

4 50Bird_id lifespan growth_ra

1 45 14.3

2 34 9.3

3 40 NA

4 50 NA

merge(left,right, by='Bird

left

right

Bird_id growth_rate

1 14.3

2 9.3

5 12.3


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full outer join

Bird_id lifespan

1 45

2 34

3 40

4 50Bird_id lifespan growth_ra

1 45 14.3

2 34 9.3

3 40 NA

4 50 NA

5 NA 12.3

merge(left,right, by='Bird

left

right

Bird_id growth_rate

1 14.3

2 9.3

5 12.3


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id var2 var3

1 a b

2 a b

3 a b

id var4 var5

1 c d

2 c d

3 c d

One-to-One Merge

left right


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One-to-Many Merge

id trt value

1 t1 123

1 t2 32

2 t1 35

3 t1 34

3 t2 12

3 t3 10

id age

1 11

2 9

3 4

leftright


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One-to-Many Merge

id trt value

1 t1 123

1 t2 32

2 t1 35

3 t1 34

3 t2 12

3 t3 10

id age

1 11

2 9

3 4

leftright


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One-to-Many Merge

id trt value

1 t1 123

1 t2 32

2 t1 35

3 t1 34

3 t2 12

3 t3 10

id age

1 11

2 9

3 4

leftright


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Many-to-Many Merge

id trt value

1 t1 123

1 t2 32

2 t1 35

2 t2 23

id age

1 9

1 11

2 42 5

left right


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Many-to-Many Merge

id trt value

1 t1 123

1 t2 32

2 t1 35

2 t2 23

id age

1 9

1 11

2 42 5

left right


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Many-to-Many Merge

id trt value

1 t1 123

1 t2 32

2 t1 35

2 t2 23

id age

1 9

1 11

2 42 5

left right

Wh d it tt t thi k b


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Why does it matter to think aboto-one, one-to-many, ....?

1) Merges can indicate that something is not quite right in yo

datasets

2) For instance,...

d li t i d t


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Bird_id lifespan

1 45

2 343 40

Bird_id growth_rate

1 14.3

1 14.32 9.3

3 15.6

Bird_id lifespan growth_rate

1 45 14.3

1 45 14.3

2 34 9.3

3 40 15.6

e.g. duplicates in a data

rule for inner join one-to-one: nrow(ds3) min( nrow(ds1), nro

nrow(ds1)=3 nrow

nrow(ds3)=4

merge(ds1,ds2, by='Bird_id')


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1) not using a 'by=' option [best practice is always use or R gu

Common Merge Mistak


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2) Duplicates in the 'unique' identifier, leading to a many-to-mmerge when expecting a one-to-many

e.g. which(duplicated(ds$id))

Common Merge Mistak


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3) unique identifiers are not exactly the samee.g. 'Burt' 'burt' [make sure your dataset is clean]

Common Merge Mistak


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3) unique identifiers are not exactly the samee.g. 'Burt' 'burt' [make sure your dataset is clean]

4) failing to check your nrow(output_ds) to see if it is doing wthink

Common Merge Mistak


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Writing functions


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making user-defined functions is a R strength

so far, we have seen lots of pre-defined functions

e.g. mean(), sum(), select(), summarise()

writing your own

ownFunction


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e.g. mean(), sum(), select(), rnorm()

writing your own

ownFunction


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e.g. mean(), sum(), select(), rnorm()

writing your own

ownFunction


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multiple arguments [ function(argument1, argument2

printResult


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default arguments

printResult


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default arguments

printResult


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'...' argument [generic argument]

printResult


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Global vs. Local variables

- any object created outside a function is global

- any object created within a function is localand will bedeleted after the function is run

Functions

F ti


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Local variables

addAmounts


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Global vs. Local variables

total_amount


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use return() to get a local variable

addAmounts


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Modularization

script 1

script 2

script 3

funcPlotting

funcStats

funcGeneric

.Rprofile

source('funcPlotting')

source('funcStats')

source('funcGeneric')

Next Week


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R plotting

1) overview of plotting in R2) introduction to ggplot [aka grammar of graphics ]

3) Week 9 and 10 will be introduction to base plot (by Sime

Next Week


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Lecture 7: Hands on Sectio

Lecture 7 files


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1) get Lecture7.Rfrom github

2) get all data files in data/lecture7/

3) open up Lecture7.Rin Rcourse_proj.Rpoj

4) start working through the example and then try the exercis

Lecture 7 files

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R Course 2014: Lecture 7

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