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tutorial

library(facebookorganicR)

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(ggplot2)

Goal

The goal here is to outline in a couple of paragraphs and few lines of code some simple ways in which we can use the Windsor.ai API and R package facebookorganicR to gain insights into marketing campaign performance using data from Facebook Organic. The nice thing about Windsor.ai is that you can have all of your marketing channels aggregating in a single place and then access all data at once using this package. In this case, however, the package is focused on getting data from Facebook Organic campaigns. Of course, once the data is in R you can do much more than the examples below, and work on analysis, predictions or dashboards.

Getting data from Facebook Organic into R

After we create an account at Windsor.ai and obtain an API key, collecting our data from Windsor to R is as easy as:


my_facebookorganic_data <-
  fetch_facebookorganic(api_key = "your api key",
           date_from = Sys.Date()-100,
           date_to = Sys.Date(),
           fields = c("campaign", "clicks",
                      "spend", "impressions", "date"))

This code will collect data for the last 100 days. Lets take a look at the data we just downloaded to get a better idea about the structure and type of information included.

str(my_facebookorganic_data)
#> 'data.frame':    14 obs. of  5 variables:
#>  $ campaign   : chr  "retageting APAC" "retargeting UK&CO" "retageting APAC" "retargeting UK&CO" ...
#>  $ clicks     : num  4 0 5 7 0 0 4 2 3 0 ...
#>  $ spend      : num  2.57 2.48 2.39 2.54 0.94 0.71 2.59 2.12 2.43 0.13 ...
#>  $ impressions: num  806 693 819 689 299 190 682 688 822 135 ...
#>  $ date       : chr  "2022-09-28" "2022-09-28" "2022-09-29" "2022-09-29" ...

Analyzing our Facebook Organic data

Now we can analyze our Facebook Organic data. For instance, let’s compare the two campaigns we have to see which one performed better the last 100 days.

ggplot(my_facebookorganic_data, aes(y = clicks, fill = campaign)) + geom_boxplot()

It looks like APAC campaign is performing better than UK&CO in number of clicks. Now let’s see if this difference is statistically significant by using generalized linear models, as our variable response is number of clicks, which have a Poisson distribution.

lmod <- glm(clicks ~ campaign, data = my_facebookorganic_data, family = "poisson")
summary(lmod)
#> 
#> Call:
#> glm(formula = clicks ~ campaign, family = "poisson", data = my_facebookorganic_data)
#> 
#> Deviance Residuals: 
#>     Min       1Q   Median       3Q      Max  
#> -2.3905  -1.6036  -0.7599   0.6372   3.5065  
#> 
#> Coefficients:
#>                           Estimate Std. Error z value Pr(>|z|)    
#> (Intercept)                 1.0498     0.2236   4.695 2.67e-06 ***
#> campaignretargeting UK&CO  -0.7985     0.4014  -1.989   0.0467 *  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for poisson family taken to be 1)
#> 
#>     Null deviance: 43.735  on 13  degrees of freedom
#> Residual deviance: 39.456  on 12  degrees of freedom
#> AIC: 66.147
#> 
#> Number of Fisher Scoring iterations: 6

We can see that differences among campaigns are statistically significant and that the campaign UK&CO have a mean that is 0.79 lower than the APAC campaign.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.