face book page analysis commands in R Language

Face book page analysis steps

install.packages("Rook")

Installing package into ‘E:/Users/cherub/Documents/R/win-library/3.1’

(as ‘lib’ is unspecified)

trying URL 'http://cran.rstudio.com/bin/windows/contrib/3.1/Rook_1.1-1.zip'

Content type 'application/zip' length 278588 bytes (272 Kb)

package ‘Rook’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in

E:\Users\cherub\AppData\Local\Temp\Rtmp4I7BWi\downloaded_packages

> install.packages("Rfacebook")

Installing package into ‘E:/Users/cherub/Documents/R/win-library/3.1’

(as ‘lib’ is unspecified)

trying URL 'http://cran.rstudio.com/bin/windows/contrib/3.1/Rfacebook_0.4.zip'

Content type 'application/zip' length 56670 bytes (55 Kb)

package ‘Rfacebook’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in

E:\Users\cherub\AppData\Local\Temp\Rtmp4I7BWi\downloaded_packages

> require("Rfacebook")

Loading required package: Rfacebook

Loading required package: httr

Loading required package: rjson

package ‘Rfacebook’ was built under R version 3.1.2

> page_name <- "forbes"

"CAACEdEose0cBABPMO40HvHNn0NZBxlXkRZBal0V2bgPGjRnZACznRzxwrqtT0DYDwGh36dbBpVL0

f5nTsL6ZBA7lgfGJrfkb08waKtRrEHKVjgRcDgE8S5oPC7VpSAFeYtgXAlaX3IquKADT5sn89f8CgYyV6k3I

gIaZBB8HT9cnqXwNzLGkMdt3ZBu9PwIO6bWfV7ZB46FufYoPxMp1SW6"

> page <- getPage(page_name, token, n = number_posts, feed = FALSE)

> data_frame_gender <-

data.frame(post=character(),male=numeric(),female=numeric(),etc=numeric(),likes=numeric(),type=chara

cter(),stringsAsFactors=FALSE)

> for(i in 1:length(posts))

+     post <- getPost(temp,token)

+     data_frame_gender[i,1] <- post$post$message

+     data_frame_gender[i,5] <- post$post$likes

+     data_frame_gender[i,6] <- post$post$type

+     gender_frame <- data.frame(gender=character(),stringsAsFactors=FALSE)

+     for(j in 1:length(post$likes$from_id))

+         likes <- post$likes$from_id

+         user_id <- likes[j]

+         user <- getUsers(user_id,token=token)

+         gender <- user$gender

+         gender_frame[nrow(gender_frame)+1,] <- gender

+     number_males <- nrow(subset(gender_frame, gender=="male"))

+     number_females <- nrow(subset(gender_frame, gender=="female"))

+     number_etc <- data_frame_gender[i,5] - (number_males+number_females)

+     data_frame_gender[i,2] <- number_males

+     data_frame_gender[i,3] <- number_females

+     data_frame_gender[i,4] <- number_etc

> for(i in 1:length(posts))

+     #dataframe values:

+     post <- getPost(temp,token)

+     data_frame_gender[i,1] <- post$post$message

+     data_frame_gender[i,5] <- post$post$likes

+     data_frame_gender[i,6] <- post$post$type

+     gender_frame <- data.frame(gender=character(),stringsAsFactors=FALSE)

+     for(j in 1:length(post$likes$from_id))

+         likes <- post$likes$from_id

+         user_id <- likes[j]

+         user <- getUsers(user_id,token=token)

+         gender <- user$gender

+         gender_frame[nrow(gender_frame)+1,] <- gender

+     number_males <- nrow(subset(gender_frame, gender=="male"))

+     number_females <- nrow(subset(gender_frame, gender=="female"))

+     number_etc <- data_frame_gender[i,5] - (number_males+number_females)

+     data_frame_gender[i,2] <- number_males

+     data_frame_gender[i,3] <- number_females

+     data_frame_gender[i,4] <- number_etc

c(sum(data_frame_gender$male),sum(data_frame_gender$female),sum(data_frame_gender$etc))

> pct <- round(slices/sum(slices)*100)

> lbls <- names(data_frame_gender[2:4])

> lbls <- paste(lbls, pct) # add percents to labels

> lbls <- paste(lbls,"%",sep="") # ad % to labels

> pie(slices, labels = lbls, main="Gender Distribution of all analyzed posts")









face book page analysis