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
