#
#
####### Variable Conversions #######
#

# Import an example data file.

library(foreign)
het.trial <- read.spss("http://www.unt.edu/rss/class/Jon/R_SC/Module6/M6_TrialHet.sav", 
  use.value.labels=TRUE, max.value.labels=Inf, to.data.frame=TRUE)
attach(het.trial)
detach("package:foreign")

# Creating a smaller (more managable) example data frame.

subset1 <- data.frame(x1,x2,x3,extroversion,openness,agreeable,neuroticism)
detach(het.trial)
rm(het.trial)

# Notice, the first three variable (x1, x2, x3) of subset1 are factors; while 
# the other four variables are numeric.

summary(subset1)

is.numeric(subset1[,1])
is.factor(subset1[,1])

is.numeric(subset1$x2)
is.factor(subset1$x2)

is.numeric(subset1[,3])
is.factor(subset1[,3])

is.numeric(subset1[,4])
is.factor(subset1[,4])

# Convert a single variable from factor to numeric (there are several ways to do this).

new.x1 <- as.numeric(subset1[,1])
is.numeric(new.x1)

new.x2 <- as.numeric(subset1$x2)
is.numeric(new.x2)

new.x3 <- sapply(subset1[,3], as.numeric)
is.numeric(new.x3)

# Going from numeric to factor is the same. 

old.x3 <- as.factor(new.x3)
is.factor(old.x3)

# For those who favor Likert scales, you can also specify a variable as 'ordered' which 
# gives it the properties of an ordinal scaled factor; it is important to note that 
# 'ordered' is simply an attribute of a factor. A factor variable may be ordered (i.e. ordinal) 
# or not (i.e. categorical). 

ord.x3 <- ordered(old.x3)
is.factor(ord.x3)
is.numeric(ord.x3)
is.ordered(ord.x3)

# One can also use the 'as.factor' function to convert numeric to "ordered" factor 
# by simply using the optional argument 'ordered' in the 'as.factor' function. 

z <- 1:4
z <- as.numeric(sample(z, 100, replace = TRUE))
summary(z)
z
is.numeric(z)

z.ord <- factor(z, levels = c(1, 2, 3, 4), labels = c("strongly disagree", 
         "disagree", "agree", "strongly agree"), ordered = TRUE)

summary(z.ord)
z.ord

is.factor(z.ord)
is.ordered(z.ord)

rm(z, z.ord)

### Converting more than one variable from factor (or other type[s]) to numeric.

# One way is to use the 'data.matrix' function; the 'data.matrix' function 
# returns an object which contains numeric vectors (variables).

subset2 <- data.matrix(subset1)
summary(subset1)
summary(subset2)

is.numeric(subset2[,1])
is.factor(subset2[,1])

is.numeric(subset2[,2])
is.factor(subset2[,2])

is.numeric(subset2[,3])
is.factor(subset2[,3])

is.numeric(subset2[,4])
is.factor(subset2[,4])

# Another way to accomplish this is with an 'apply' statement. 

subset3 <- apply(subset1, 2, as.numeric)
summary(subset1)
summary(subset3)

is.numeric(subset3[,1])
is.factor(subset3[,1])

is.numeric(subset3[,2])
is.factor(subset3[,2])

is.numeric(subset3[,3])
is.factor(subset3[,3])

is.numeric(subset3[,4])
is.factor(subset3[,4])

# Going back to factors from numeric, first three columns of subset3 [1:3]. 
# First, convert the data matrix (subset3) to a data.frame.

subset3 <- data.frame(subset3)

subset4 <- apply(subset3[,1:3], 2, as.factor)
subset4 <- data.frame(subset4, subset3[,4:6])
summary(subset3)
summary(subset4)

is.numeric(subset4[,1])
is.factor(subset4[,1])

is.numeric(subset4[,2])
is.factor(subset4[,2])

is.numeric(subset4[,3])
is.factor(subset4[,3])

is.numeric(subset4[,4])
is.factor(subset4[,4])

# Cleaning up the workspace.

ls()
rm(new.x1, new.x2, new.x3, old.x3, ord.x3, subset1, subset2, subset3, subset4)
ls()





# END; Mar. 6, 2012.