Day 3 of R workshop

1. Contingency
Table
Levels
Class
Conditional
Tree
RBootcamp
Day 3 - Part 2
Olga Scrivner and Jefferson Davis
Assistant Jivitesh Poojary
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2. Contingency
Table
Levels
Class
Conditional
Tree
Outline
1 Contingency Tables
2 Factors and Levels
3 Categorical vs Continuous Regression Models
4 Conditional Trees
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3. Contingency
Table
Levels
Class
Conditional
Tree
Review
Download Categoricaldata.csv from Bootcamp Day 3
or http://cl.indiana.edu/~obscrivn/docs/
categoricaldata.csv
Open a new script
mydata <- read.csv(file.choose())
colnames()
rownames()
nrow()
tail()
summary()
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4. Contingency
Table
Levels
Class
Conditional
Tree
Review
Download Categoricaldata.csv from Bootcamp Day 3
or http://cl.indiana.edu/~obscrivn/docs/
categoricaldata.csv
Open a new script
mydata <- read.csv(file.choose())
colnames()
rownames()
nrow()
tail()
summary()
NEW: str()
Type and Run str(mydata)3 / 22

5. Contingency
Table
Levels
Class
Conditional
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2x2 Contingency Tables
1 Let’s build a table for R.Use and Lexical.Item
attach(categoricaldata) (remember to run your
commands)
2 table(A,B): A - rows, B - columns
table(R.Use, Lexical.Item)
3 Let’s name it mytable
mytable <- table(R.Use, Lexical.Item)
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6. Contingency
Table
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Adding Totals
table(A,B): A - rows, B - columns
To add margins (sum) over columns/row:
addmargins(mytable)
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7. Contingency
Table
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Saving as CSV
Name your new table with margins:
mytable2 <- addmargins(mytable)
write.csv(mytable2, file=“table.csv”)
Open table.csv file (it should be in your working directory)
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8. Contingency
Table
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Percentages
prop.table(mytable): (1 - rows, 2 - columns)
To add row percentages:
prop.table(mytable, 1)
To add columns percentage:
margin.table(mytable, 2)
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9. Contingency
Table
Levels
Class
Conditional
Tree
3-way Table I
table(R.Use, Lexical.Item, Store)
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10. Contingency
Table
Levels
Class
Conditional
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3-way Table II
Let’s give a name:
mytable3 <- table(R.Use, Lexical.Item, Store)
ftable(mytable3)
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11. Contingency
Table
Levels
Class
Conditional
Tree
Subset
Syntax: subset(data, condition)
Let’s subset Kleins data - we need double equal sign here:
kleins <- subset(categoricaldata, Store==“Kleins”)
Run summary(kleins)
we need to drop empty levels:
kleins <- droplevels(kleins)
summary(kleins)
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12. Contingency
Table
Levels
Class
Conditional
Tree
Levels
Levels - factor’s values (e.g. male-female)
Let’s look what values we have for Store:
levels(Store)
Store has three levels
We can look at each level separately:
levels(Store)[1]
levels(Store)[2]
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13. Contingency
Table
Levels
Class
Conditional
Tree
Changing Levels Names
Let’s rename our levels (values)
levels(Store)[1] <-“Store1”
Check levels names: levels(Store)
Let’s change it back
levels(Store)[1] <-“Kleins”
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14. Contingency
Table
Levels
Class
Conditional
Tree
Changing Order
Default: deletion
Let’s change it to retention
labovdata$R.Use <- relevel(labovdata$R.Use,
ref=“retention”)
Attach labovdata attach(labovdata)
look at levels(R.Use)
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15. Contingency
Table
Levels
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Class - Factor
age <- c(1,2,3)
Class(age)
Let’s change numeric to factor
age <- as.factor(age)
class(age)
Change from factor to numeric
age <- as.integer(Age)
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16. Contingency
Table
Levels
Class
Conditional
Tree
Class - Factor
age <- c(1,2,3)
Class(age)
Let’s change numeric to factor
age <- as.factor(age)
class(age)
Change from factor to numeric
age <- as.integer(Age)
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17. Contingency
Table
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Recap: Regression Analysis
Type of Variables
a.) Binary/categorical (only two values)
b.) Continuous (numeric)
c.) Multinomial - categorical with more than two values -
mlogit
Regression Types
a.) Logistic regression - binary categorical -glm
b.) Linear - only continuous - lm and lme4
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18. Contingency
Table
Levels
Class
Conditional
Tree
Conditional Tree Regression
Conditional tree: a simple non-parametric regression analysis,
commonly used in social and psychological studies
Linear/logistic regression: all information is combined linearly
Conditional tree regression: splitting to capture interaction
between variables
Recursive splitting (tree branches)
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19. Contingency
Table
Levels
Class
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Conditional Tree
1 Polarity is relevant to the second group of individuals.
2 The variant were occurs significantly more often with negative
polarity
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20. Contingency
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Conditional Tree
1 Install packages: party, partykit
2 Activate packages or type and run:
library(party)
library(partykit)
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21. Contingency
Table
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Creating Conditional Tree
1 regression formula
2 ctree
3 plot
mytree<- ctree(R.Use∼Store+Lexical.Item+Style,
data=mydata)
plot(mytree, main=“Conditional Inference Tree”)
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22. Contingency
Table
Levels
Class
Conditional
Tree
Conditional Tree
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23. Contingency
Table
Levels
Class
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Conditional Tree
1 Store is the most significant factor for R-use
Kleins (working class store) - more R-deletion
2 R-use in Macy’s and Saks is conditioned by lexical item:
Floor shows more R-retention than Fourth
3 Style is not significant
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24. Contingency
Table
Levels
Class
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Changing Fonts
fontsize
col - colour
lwd - line width
plot(mytree, main=“Conditional Inference
Tree”,gp=gpar(fontsize=10))
Change font size to 5 and 15
plot(mytree, main=“Conditional Inference
Tree”,gp=gpar(fontsize=10,col=“red”,lwd=1.2))
Change color and line width
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25. Contingency
Table
Levels
Class
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Practice-DataCamp
1 RBootcamp day 3
2 Complete all practice to receive points!
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