Analyzing our Facebook and Google ad campaign data
First, lets try to find the campaigns with most clicks. To do this, we’ll filter only those rows that have clicks, then group the dataset by campaign (data.campaign column) and sum up the click count per campaign.
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)
top_10 <-
my_data %>%
filter(data.clicks > 0) %>%
group_by(data.campaign) %>%
summarise(n_clicks = sum(data.clicks)) %>%
ungroup %>%
arrange(desc(n_clicks)) %>%
slice_head(n = 10)
knitr::kable(top_10)| data.campaign | n_clicks |
|---|---|
| AO_Smart_Shopping_Running_Jogging | 56670 |
| city_display_awareness_hyd_april_2021 | 54711 |
| AO_Dynamic_Remarketing_Cart_Abandoners | 54089 |
| AO_Dynamic_Remarketing_Cart_Abandoners_Website | 39969 |
| AO_Smart_Shopping_Yoga | 27551 |
| AO_Search_Brand_Exact | 24535 |
| AO_UAC_app_install | 21958 |
| AO_UAC_app_install_Hyd | 20155 |
| AO_Shopping_Cycling | 18852 |
| AO_UAC_app_install_chennai | 16905 |
Thereafter we can quickly visualize our data using ggplot2
ggplot(top_10, aes(x = n_clicks, y = data.campaign)) +
geom_col()
We can gain further insight by grouping by other variables add mapping them to the plot aesthetics. For example, in this case we might want to keep track of the data source (linkedin, google, …). So we’ll modify our magrittr chain to add another grouping variable before tallying the clicks.
top_10 <-
my_data %>%
filter(data.clicks > 0) %>%
group_by(data.source, data.campaign) %>%
summarise(n_clicks = sum(data.clicks)) %>%
ungroup %>%
arrange(desc(n_clicks)) %>%
slice_head(n = 10)
#> `summarise()` has grouped output by 'data.source'. You can override using the `.groups` argument.
knitr::kable(top_10)| data.source | data.campaign | n_clicks |
|---|---|---|
| AO_Smart_Shopping_Running_Jogging | 56670 | |
| city_display_awareness_hyd_april_2021 | 54711 | |
| AO_Dynamic_Remarketing_Cart_Abandoners | 54089 | |
| AO_Dynamic_Remarketing_Cart_Abandoners_Website | 39969 | |
| AO_Smart_Shopping_Yoga | 27551 | |
| AO_Search_Brand_Exact | 24535 | |
| AO_UAC_app_install | 21958 | |
| AO_UAC_app_install_Hyd | 20155 | |
| AO_Shopping_Cycling | 18852 | |
| AO_UAC_app_install_chennai | 16905 |
ggplot(top_10, aes(x = n_clicks, y = data.campaign, fill = data.source)) +
geom_col()
In this case the vast majority of data (all of it for the top 10 ad campaigns) comes from Google, so only this source is labeled on the graph.
We can apply the same type of data manipulation and plotting to check the data.spend values.
my_data %>%
filter(data.clicks > 0) %>%
group_by(data.campaign) %>%
summarise(sum_spend = sum(as.numeric(data.spend))) %>%
ungroup %>%
arrange(desc(sum_spend)) %>%
slice_head(n = 10) %>%
ggplot(aes(x = sum_spend, y = data.campaign)) +
geom_col()
Finally, for a direct comparison, we can aggregate both the clicks and spending per ad campaign and plot them jointly:
library(tidyr)
my_data %>%
filter(data.clicks > 0) %>%
group_by(data.campaign) %>%
summarise(n_clicks = sum(data.clicks), sum_spend = sum(as.numeric(data.spend))) %>%
arrange(desc(sum_spend)) %>%
slice_head(n = 10) %>%
pivot_longer(cols = c("n_clicks", "sum_spend"), names_to = "aggreg", values_to = "values") %>%
ggplot(aes(x = values, y = data.campaign, fill = aggreg)) +
geom_col() +
facet_wrap("aggreg", ncol = 2) +
theme(legend.position="bottom")