exploratoryPlotsDE2.R

# Online Video Survey Data

# online video survey 2013
# http://pewinternet.org/Shared-Content/Data-Sets/2013/July-2013--Online-Video-%28onmibus%29.aspx
fileUrl2 <- "http://pewinternet.org/~/media/Files/Data%20Sets/2013/Omnibus_July_2013_Video_csv.csv"
download.file(fileUrl2, destfile = "onlineVideo.csv")
downloaded2On <- date()
OV <- read.csv("onlineVideo.csv")
dim(OV)
names(OV)
str(OV)


length(OV$sample[OV$sample == 8])
length(OV$act134[OV$act134 == 1])

head(OV[1:4, 1:4])

barplot(table(as.factor(weirdos$educ2)))
barplot(table(as.factor(weirdos$employ)))

nas <- is.na(OV$sample)
length(which(nas)) # 0

length(which(is.na(OV$sex))) # 0
length(which(is.na(OV$eminuse))) #0
length(which(is.na(OV$intmob))) #0

sample1 <- subset(OV, OV$sample == 1) # subset by sample
dim(sample1)
act <- subset(OV, OV$eminuse == 1 | OV$intmob == 1) # subset to explore act
dim(act)

barplot(table(as.factor(sample1$age)))

# trimmed age above 97
trimAgeAct1 <- subset(act, act$age <= 97)
trimAgeSample1 <- subset(sample1, sample1$age <= 97)

trimAgeAct1$age[1:5]

barplot(table(as.factor(trimAgeAct1$age)))

# distribution of age in sample 1 vs. that of act
par(mfrow = c(1, 2))
hist(trimAgeAct1$age, xlab = "eminuse+intmob", col = "blue", main = "")
hist(trimAgeSample1$age, xlab = "sample = 1", col = "blue", main = "")


table(trimAgeAct1$sex, trimAgeAct1$age)

# create char version of sex variable (act)
trimAgeAct1$sex <- as.character(trimAgeAct1$sex)
trimAge1Sex <- gsub("2", "female", trimAgeAct1$sex)
trimAge1Sex <- gsub("1", "male", trimAge1Sex)
levels(as.factor(trimAge1Sex))

# split age values into chunks (act)
library(Hmisc)
ageRanges<- cut2(trimAgeAct1$age, g=6)
ageRanges[1:10]
trimAgeAct1$sex_char <- trimAge1Sex
trimAgeAct1$ageRanges <- ageRanges

# make a proportion stacked barplot (act)
table1 <- table(trimAgeAct1$sex_char, trimAgeAct1$ageRanges)
proptab <- prop.table(table1, 2)
proptab

pdf(file="ageGender.pdf",height=4,width=6)
par(mfrow = c(1, 1))
barplot(proptab, col = c("green", "blue"),
        main = "gender/age proportion",
        xlab = "age")
legend("topright", legend = c("female", "male"), bg = "white", col = c("green", "blue"),
       pch = 15)
dev.off()

str(trimAgeAct1)

# create understandable values in education variable (act)
eduChar <- as.character(trimAgeAct1$educ2)
eduChar <- gsub("1", "lessThanHigh", eduChar)
eduChar <- gsub("2", "incompleteHigh", eduChar)
eduChar <- gsub("3", "gradHigh", eduChar)
eduChar <- gsub("4", "collNoDegree", eduChar)
eduChar <- gsub("5", "2YearsDegree", eduChar)
eduChar <- gsub("6", "4YearsDegree", eduChar)
eduChar <- gsub("7", "postGradNoDeg", eduChar)
eduChar <- gsub("8", "postGradDeg", eduChar)
eduChar <- gsub("9", "noReply", eduChar)

eduChar[1:10]
trimAgeAct1$eduChar <- eduChar
barplot(table(trimAgeAct1$eduChar))

par(mfrow = c(1, 2))
barplot(table(OV$educ2))
barplot(table(trimAgeAct1$educ2))

pdf(file="edu.pdf",height=4,width=6)
par(mfrow = c(1, 1))
dens1 <- density(OV$educ2)
dens2 <- density(trimAgeAct1$educ2)
plot(dens1,lwd=3, col="green")
lines(dens2, lwd=3, col="red")
legend("topright", pch = 19,
       col = c("green", "red"), legend = c("All", "INTMOB+EMINUSE"))
dev.off()

str(trimAgeAct1)

# char equivalent of race variable (act)
raceChar <- as.character(trimAgeAct1$race)
raceChar <- gsub("1", "White", raceChar)
raceChar <- gsub("2", "Black", raceChar)
raceChar <- gsub("3", "Asian", raceChar)
raceChar <- gsub("4", "Mixed", raceChar)
raceChar <- gsub("5", "AmerIndian", raceChar)
raceChar <- gsub("6", "Other", raceChar)
raceChar <- gsub("9", "noReply", raceChar)


trimAgeAct1$raceChar <- raceChar
trimAgeAct1$raceChar[1:5]

barplot(table(trimAgeAct1$raceChar))

# race/education prpportion barplot (act)
table2 <- table(trimAgeAct1$eduChar, trimAgeAct1$raceChar)
proptab <- prop.table(table2, 2)
proptab


par(mfrow = c(1, 1))
barplot(proptab, col = rainbow(9),
        main = "education/race proportion",
        xlab = "race")
legend("topright",
       col = rainbow(9),
       legend = c("2YearsDegree", "4YearsDegree", "collNoDegree", "gradHigh",
                  "incompleteHigh", "lessThanHigh", "noReply", "postGradDeg",
                  "postGradNoDeg"), pch = 15)

# char equivalent of race variable (general)
raceCharOV <- as.character(OV$race)
raceCharOV <- gsub("1", "White", raceCharOV)
raceCharOV <- gsub("2", "Black", raceCharOV)
raceCharOV <- gsub("3", "Asian", raceCharOV)
raceCharOV <- gsub("4", "Mixed", raceCharOV)
raceCharOV <- gsub("5", "AmerIndian", raceCharOV)
raceCharOV <- gsub("6", "Other", raceCharOV)
raceCharOV <- gsub("9", "noReply", raceCharOV)

OV$raceCharOV <- raceCharOV

# char equivalent of education variable (general)
eduCharOV <- as.character(OV$educ2)
eduCharOV <- gsub("1", "lessThanHigh", eduCharOV)
eduCharOV <- gsub("2", "incompleteHigh", eduCharOV)
eduCharOV <- gsub("3", "gradHigh", eduCharOV)
eduCharOV <- gsub("4", "collNoDegree", eduCharOV)
eduCharOV <- gsub("5", "2YearsDegree", eduCharOV)
eduCharOV <- gsub("6", "4YearsDegree", eduCharOV)
eduCharOV <- gsub("7", "postGradNoDeg", eduCharOV)
eduCharOV <- gsub("8", "postGradDeg", eduCharOV)
eduCharOV <- gsub("9", "noReply", eduCharOV)

OV$eduCharOV <- eduCharOV

# general education/race barplot
tableOV1 <- table(OV$eduCharOV, OV$raceCharOV)

barplot(tableOV1, beside = T, col = rainbow(9), main = "race/education overall")
legend("topleft",
       col = rainbow(9),
       legend = c("2YearsDegree", "4YearsDegree", "collNoDegree", "gradHigh",
                  "incompleteHigh", "lessThanHigh", "noReply", "postGradDeg",
                  "postGradNoDeg"), pch = 15, cex = 0.7)
# income variable char equivalent (general)
incCharOV <- as.character(OV$inc)
incCharOV <- gsub("1", ">$10k", incCharOV)
incCharOV <- gsub("2", "$10k-$20k", incCharOV)
incCharOV <- gsub("3", "$20k-$30k", incCharOV)
incCharOV <- gsub("4", "$30k-$40k", incCharOV)
incCharOV <- gsub("5", "$40k-$50k", incCharOV)
incCharOV <- gsub("6", "$50k-$75k", incCharOV)
incCharOV <- gsub("7", "$75k-$100k", incCharOV)
incCharOV <- gsub("7", "$100k-$150k", incCharOV)
incCharOV <- gsub("7", "<$150k", incCharOV)
OV$incCharOV <- incCharOV

# income variable char equivalent (act)
incChar <- as.character(trimAgeAct1$inc)
incChar <- gsub("1", ">$10k", incChar)
incChar <- gsub("2", "$10k-$20k", incChar)
incChar <- gsub("3", "$20k-$30k", incChar)
incChar <- gsub("4", "$30k-$40k", incChar)
incChar <- gsub("5", "$40k-$50k", incChar)
incChar <- gsub("6", "$50k-$75k", incChar)
incChar <- gsub("7", "$75k-$100k", incChar)
incChar <- gsub("8", "$100k-$150k", incChar)
incChar <- gsub("9", "<$150k", incChar)
trimAgeAct1$incChar <- incChar

# education/age barplot (act)
tab4 <- table(trimAgeAct1$eduChar, trimAgeAct1$ageRanges)
pdf(file="eduAge.pdf",height=4,width=8)
barplot(tab4, col = rainbow(9), xlab = "age",
        beside = T, main = "age / education")
par(mar=c(5, 4, 4, 9), xpd = T)
legend("topright", inset=c(-0.65,0),
       col = rainbow(9),
       legend = c("2YearsDegree", "4YearsDegree", "collNoDegree", "gradHigh",
                  "incompleteHigh", "lessThanHigh", "noReply", "postGradDeg",
                  "postGradNoDeg"), pch = 15, cex = 0.7)

dev.off()

# exploring 18-29 age group (general)
youngest <- OV$age <= 29
length(which(youngest))
hist(OV$age[youngest], col = "blue", xlab = "age",
     main = "Age distribution (18-29 y.o.)")