BasicStepsFBAPI_Answers.R

## This code was written for the Pre-Workshop of the IUSSP Research workshop on Digital Demography in the Era 
## of Big Data by Sofia Gil-Clavel. 05.06.2019. 
## You can check the Markdown format of this code and more information in 
## https://github.com/SofiaG1l/Using_Facebook_API

#### 1. Basic URL ####
# 
# First lets try using a browser, replace your data in the next URL:

# https://graph.facebook.com/<<vX.X>>/act_<<ACT>>/delivery_estimate?access_token=<<TOKEN>>&include_headers=false&method=get&pretty=0&suppress_http_code=1&method=get&optimization_goal=REACH&pretty=0&suppress_http_code=1&targeting_spec={"geo_locations":{"countries":["MX"]},"genders":[1] ,"age_min":16, "age_max":24}


#### 2. Retrieving in a Programmatic Way ####
# 
# In order to retrieve and transform the data to a data frame we will use the packages **tidyverse** and **jsonlite**.

rm(list=ls())
gc()

library(tidyverse)
library(jsonlite)

# The way we will pass our credentials to Facebook is through the string that we will save in **Credentials**, so save 
# your token into the variable *token* and your creation act into *act*:
  
token="Your Token"

act="Your Creation Act"

version="vX.X" # Change the Xs with your version and delete <<>>

Credentials=paste0('https://graph.facebook.com/',version,'/act_',act,'/delivery_estimate?access_token=',token,'&include_headers=false&method=get&optimization_goal=REACH&pretty=0&suppress_http_code=1')

#### 3. Total Population broken down by age, gender and country ####
# 
# Let's set up our initial variables, they will be save in R and then we will concatenate them in a string.

Age1=25
Age2=55

g=1 # 1:men and 2:women

C='"DE"' # Country code

# The parameters we will use are in a JSON(https://www.w3schools.com/js/js_json_intro.asp) format, but we will handle 
# them in R through a string:
#   
# * age_min: is a value
# * age_max: is a value
# * genders: is an array
# * geo_locations: is a JSON object where *country* is an array

query <- paste0(Credentials,'&
                targeting_spec={
                "age_min":',Age1,',
                "age_max":',Age2,',
                "genders":[',g,'],
                "geo_locations":{"countries":[',C,'],"location_types":["home"]},
                "facebook_positions":["feed","instant_article","instream_video","marketplace"],
                "device_platforms":["mobile","desktop"],
                "publisher_platforms":["facebook","messenger"],
                "messenger_positions":["messenger_home"]}')


(query_val<-url(query)%>%fromJSON)

query_val$data['estimate_dau'][[1]]
query_val$data['estimate_mau'][[1]]


# Since **age_min** and **age_max** are *JSON values* their input is always a single value, 
# in this case an integer value between 16 and 65, where 65 means *65 and over*.
# 
# In the case of **genders**, it is an array, that means that it can receive more than one value, 
# but the values must be the same type (integer, float, character, etc). So, if we want to query the 
# number of either women or men that use Facebook, we would have to set **genders** to $[1,2]$.
# 
# Finally, **geo_locations** is a JSON object, therefore, it can contain all the JSON objects already 
# described. In this case, we are specifying **countries** and **location_types** and both are arrays.
# 
# You can find more information about these and other parameters in 
# https://developers.facebook.com/docs/marketing-api/targeting-specs.
 

#### 3.1 Exercise 1 ####

# Change the parameters in the code in order to retrieve the next data:
# *The number of women and men between 20 and 55 years old that live in Spain and Germany and are Facebook users.*

#### Your code
Age1=20
Age2=55
g="1,2"
C='"ES","DE"'

query <- paste0(Credentials,'&
                targeting_spec={
                "age_min":',Age1,',
                "age_max":',Age2,',
                "genders":[',g,'],
                "geo_locations":{"countries":[',C,'],"location_types":["home"]},
                "facebook_positions":["feed","instant_article","instream_video","marketplace"],
                "device_platforms":["mobile","desktop"],
                "publisher_platforms":["facebook","messenger"],
                "messenger_positions":["messenger_home"]}')

(query_val<-url(query)%>%fromJSON)

query_val$data['estimate_dau'][[1]]
query_val$data['estimate_mau'][[1]]

#### 4. Total Population that match certain characteristics broken down by age, gender and country ####
   
# The first step is to know the name of all the possible variables that we can query. There are three 
# different classes:
# * demographics
# * interests
# * behaviors
 
# Let's retrieve all the *demographics* variables: 

library(httr)

DF_CHARTICS<-GET(
  
  "https://graph.facebook.com/<<vX.X>>/search", # Change the Xs with your version and delete <<>>
  
  query=list(
    
    type='adTargetingCategory',
    
    class='demographics',
    
    access_token=token,
    
    limit=2000
    
  )) %>%content(as="text")%>%fromJSON%>%.[[1]]

View(DF_CHARTICS)

# Now we will prepare a basic query, for this you just need to choose one variable and save the next information:

ROW=1

(TYPE=DF_CHARTICS$type[ROW])
(ID=DF_CHARTICS$id[ROW])
(NAME=DF_CHARTICS$name[ROW])

# For targeting populations that match specific characteristics we will use the parameter *flexible_spec* from 
# the Facebook Marketing API, this parameter is a JSON object. In order to incorporate it to our initial string, 
# we will save the string in the variable **CHARTICS**.

CHARTICS<-paste0(',"flexible_spec":[{"',TYPE,'":[{"id":"',ID,'","name":"',NAME,'"}]}]')

# A basic query including this parameter is:
  
query <- paste0(Credentials,'&
targeting_spec={"age_min":',Age1,',
                "age_max":',Age2,',
                "genders":[',g,']',
                CHARTICS,',
                "geo_locations":{"countries":[',C,'],"location_types":["home"]},
                "facebook_positions":["feed","instant_article","instream_video","marketplace"],
                "device_platforms":["mobile","desktop"],
                "publisher_platforms":["facebook","messenger"],
                "messenger_positions":["messenger_home"]}')


(query_val<-url(query)%>%fromJSON)

query_val$data['estimate_dau'][[1]]
query_val$data['estimate_mau'][[1]]


# In the case of the specific characteristics, you can make the next type of queries: 
   
#* *one characteristics **and** other*:
  
'"flexible_spec":[{
                    "TYPE_1":[{"id":"ID_1","name":"NAME_1"}]
                  },
                  {
                    "TYPE_2":[{"id":"ID_2","name":"NAME_2"}]
                  }]'

# * *one characteristics **or** other*:
  
'"flexible_spec":[{
    "TYPE_1":[{"id":"ID_1","name":"NAME_1"}],
    "TYPE_2":[{"id":"ID_2","name":"NAME_2"}]
  }]'

# In the case of OR we need to group by TYPE. Check the next example:
# *People that are travelers OR like soccer OR movies.*

'"flexible_spec": [{ 
    "behaviors": [
          {"id":6002714895372,"name":"All travelers"}
        ], 
    "interests": [ 
          {"id":6003107902433,"name":"Association football (Soccer)"}, 
          {"id":6003139266461,"name":"Movies"} 
        ] 
  }]'

# More info here: https://developers.facebook.com/docs/marketing-api/targeting-specs#broadcategories

#### 4.1 Exercise 2 ####
 
# Code the next query:
#   *The number of women between 50 and 60 years old that live in Spain that 
#    are "Away from hometown" and "Close friends of people with birthdays in a month" and are Facebook users.*

#### Your code:

Age1=50
Age2=60
g="2"
C='"ES"'

# Checking where the variables are and save their info:

ROW=which(DF_CHARTICS$name=="Away from hometown")

(TYPE_1=DF_CHARTICS$type[ROW])
(ID_1=DF_CHARTICS$id[ROW])
(NAME_1=DF_CHARTICS$name[ROW])

ROW=which(DF_CHARTICS$name=="Close friends of people with birthdays in a month")

(TYPE_2=DF_CHARTICS$type[ROW])
(ID_2=DF_CHARTICS$id[ROW])
(NAME_2=DF_CHARTICS$name[ROW])

# Preparing string of characteristics:
CHARTICS<-paste0(',"flexible_spec":[{"',TYPE_1,'":[{"id":"',ID_1,'","name":"',NAME_1,'"}]},
                 {"',TYPE_2,'":[{"id":"',ID_2,'","name":"',NAME_2,'"}]}]')

# Preparing query:
query <- paste0(Credentials,'&
targeting_spec={"age_min":',Age1,',
                "age_max":',Age2,',
                "genders":[',g,']',
                CHARTICS,',
                "geo_locations":{"countries":[',C,'],"location_types":["home"]},
                "facebook_positions":["feed","instant_article","instream_video","marketplace"],
                "device_platforms":["mobile","desktop"],
                "publisher_platforms":["facebook","messenger"],
                "messenger_positions":["messenger_home"]}')

# Retrieving:
(queryAND_val<-url(query)%>%fromJSON)

queryAND_val$data['estimate_dau'][[1]]
queryAND_val$data['estimate_mau'][[1]]


#### 4.1 Exercise 3 ####
   
# Code the next query:
#   *The number of women between 50 and 60 years old that live in Spain that 
#    are either "Away from hometown" or "Close friends of people with birthdays in a month" and are Facebook users.*
  
#### Your code

# Preparing string of characteristics:
CHARTICS<-paste0(',"flexible_spec":[{"',TYPE_1,'":[{"id":"',ID_1,'","name":"',NAME_1,'"},{"id":"',ID_2,'","name":"',NAME_2,'"}]}]')

# Preparing query:
query <- paste0(Credentials,'&
                targeting_spec={"age_min":',Age1,',
                "age_max":',Age2,',
                "genders":[',g,']',
                CHARTICS,',
                "geo_locations":{"countries":[',C,'],"location_types":["home"]},
                "facebook_positions":["feed","instant_article","instream_video","marketplace"],
                "device_platforms":["mobile","desktop"],
                "publisher_platforms":["facebook","messenger"],
                "messenger_positions":["messenger_home"]}')

# Retrieving:
(queryOR_val<-url(query)%>%fromJSON)

queryOR_val$data['estimate_dau'][[1]]
queryOR_val$data['estimate_mau'][[1]]



#### So Far, So Good? ####

# Let's challenge your understanding on retrieving data. In the next steps you will recreate part of the code that was 
# used for the paper Demographic Diferentials in Facebook Usage Around the World, but just for some of the countries 
# in Country_Codes.csv.


# **Basic Demographic Information**
#
# 1. Upload Country_Codes.csv into the R environment.
# 2. Create a data frame where you will save all the information.
# 3. Create a nest loop where you can change the next variables in your queries:
#     Country: each country in Country_Codes.csv.
#     Age: 16-24, 25-54, 55-64
#     Gender: female and male

# 1. Upload Country_Codes.csv into the R environment.

setwd("Path to the Data base") 

Country_Codes<-read.csv("Country_Codes.csv",stringsAsFactors = FALSE)

# 2. Create a data frame where you will save all the information.
total=nrow(Country_Codes)*2*3 # nrow(Country_Codes): number of countries, 2 genders, 3 ages group 
DB<-data.frame("CODE"=rep(0,total))
DB$GENDER=0
DB$AGE1=0
DB$AGE2=0
DB$estimate_dau=0
DB$estimate_mau=0
DB$estimate_ready=0

# 3. Create a nest loop where you can change the next variables in your queries:

CODE=Country_Codes$alpha.2
AGE=list(c(16,24),c(25,54),c(55,65))
SEX=c(1,2)

i=1
print(total)
for(c in CODE){
  for(a in AGE){
    for(s in SEX){

      query <- paste0(Credentials,'&
                targeting_spec={
                      "age_min":',a[1],',
                      "age_max":',a[2],',
                      "genders":[',s,'],
                      "geo_locations":{"countries":["',c,'"],"location_types":["home"]},
                      "facebook_positions":["feed","instant_article","instream_video","marketplace"],
                      "device_platforms":["mobile","desktop"],
                      "publisher_platforms":["facebook","messenger"],
                      "messenger_positions":["messenger_home"]}')
      
      # Retrieving:
      query_val<-url(query)%>%fromJSON
      
      DB$CODE[i]=c
      DB$GENDER[i]=s
      DB$AGE1[i]=a[1]
      DB$AGE2[i]=a[2]
      DB$estimate_dau[i]=query_val$data['estimate_dau'][[1]]
      DB$estimate_mau[i]=query_val$data['estimate_mau'][[1]]
      DB$estimate_ready[i]=ifelse(query_val$data['estimate_ready'][[1]]==1,"TRUE","FALSE")
      
      # Pause for 5 seconds
      Sys.sleep(5)
      print(paste("Processed:",round(100*i/total,digits = 2),"%"))
      i=i+1
    }
  }
}

View(DB)


# If you already have the steps 1 to 3, then you will notice a problem. What problem are you encountering?

# That you have to wait for 5 seonds before starting the next iteration
Sys.sleep(5)


# **Specific Characteristics**
# 
# Now we are going to restrict the population to those that match specific characteristics:
#   Away from hometown
#   Close friend of users with birthdays in a month

total=nrow(Country_Codes)*2*3*2 # nrow(Country_Codes): number of countries, 2 genders, 3 ages group, 2 specific caracteristics
DB<-data.frame("CODE"=rep(0,total))
DB$GENDER=0
DB$AGE1=0
DB$AGE2=0
DB$VARIABLE=0
DB$estimate_dau=0
DB$estimate_mau=0
DB$estimate_ready=0

CODE=Country_Codes$alpha.2
AGE=list(c(16,24),c(25,54),c(55,65))
SEX=c(1,2)
# The variables
VARIABLES=DF_CHARTICS[DF_CHARTICS$name%in%c("Away from hometown","Close friends of people with birthdays in a month"),]


i=1
print(total)
for(c in CODE){
  for(v in 1:nrow(VARIABLES)){
    for(a in AGE){
      for(s in SEX){
        
        CHARTICS<-paste0(',"flexible_spec":[{"',VARIABLES$type[v],'":[{"id":"',VARIABLES$id[v],'","name":"',VARIABLES$name[v],'"}]}]')
        
        # A basic query including this parameter is:
        
        query <- paste0(Credentials,'&
                        targeting_spec={"age_min":',a[1],',
                        "age_max":',a[2],',
                        "genders":[',s,']',
                        CHARTICS,',
                        "geo_locations":{"countries":["',c,'"],"location_types":["home"]},
                        "facebook_positions":["feed","instant_article","instream_video","marketplace"],
                        "device_platforms":["mobile","desktop"],
                        "publisher_platforms":["facebook","messenger"],
                        "messenger_positions":["messenger_home"]}')
        
        # Retrieving:
        query_val<-url(query)%>%fromJSON
        
        DB$CODE[i]=c
        DB$GENDER[i]=s
        DB$AGE1[i]=a[1]
        DB$AGE2[i]=a[2]
        DB$VARIABLE[i]=VARIABLES$name[v]
        DB$estimate_dau[i]=query_val$data['estimate_dau'][[1]]
        DB$estimate_mau[i]=query_val$data['estimate_mau'][[1]]
        DB$estimate_ready[i]=ifelse(query_val$data['estimate_ready'][[1]]==1,"TRUE","FALSE")
        
        # Pause for 5 seconds
        Sys.sleep(5)
        
        print(paste("Processed:",round(100*i/total,digits = 2),"%"))       
        i=i+1
      }
    }
  }
}

View(DB)