-
Notifications
You must be signed in to change notification settings - Fork 129
/
r_facebook_gender.r
64 lines (40 loc) · 1.86 KB
/
r_facebook_gender.r
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
require(Rfacebook)
# Change for your need
page_name <- "forbes"
number_posts <- 2
token <- "XXX"
#Get the general page info
page <- getPage(page_name, token, n = number_posts, feed = FALSE)
#Extract the post ids
posts <- page$id
data_frame_gender <- data.frame(post=character(),male=numeric(),female=numeric(),etc=numeric(),likes=numeric(),type=character(),stringsAsFactors=FALSE)
#process each post and analyze the gender distribution of the likes
for(i in 1:length(posts))
{
temp <- posts[i]
post <- getPost(temp,token)
data_frame_gender[i,1] <- post$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
}
slices <- 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")