A/B Testing for ShoeFly.com question #11

Hello everyone,

I believe I have done the exercise correctly, however I am in doubt because the hint of question 11 says that ad A outperforms ad B on all days except Tuesday and Sunday. However the results my code gives me in the end says that ad B outperforms A only on Tuesday.

Could someone confirm my findings?
https://www.codecademy.com/courses/learn-r/projects/r-aggregates-shoefly

ad A
day ad_clicked count percentage
1 - Monday TRUE 43 0.3805310
2 - Tuesday TRUE 43 0.3613445
3 - Wednesday TRUE 38 0.3064516
4 - Thursday TRUE 47 0.4051724
5 - Friday TRUE 51 0.3984375
6 - Saturday TRUE 45 0.3813559
7 - Sunday TRUE 43 0.3944954

ad B
day ad_clicked count percentage
1 - Monday TRUE 32 0.2831858
2 - Tuesday TRUE 45 0.3781513
3 - Wednesday TRUE 35 0.2822581
4 - Thursday TRUE 29 0.2500000
5 - Friday TRUE 38 0.2968750
6 - Saturday TRUE 42 0.3559322
7 - Sunday TRUE 34 0.3119266

If you want to have a quick look, here is the entire code of my exercise:

define views_by_utm here:

views_by_utm ← ad_clicks %>%
group_by(utm_source) %>%
summarize(count = n())
views_by_utm


```{r error=TRUE}
# define clicks_by_utm here:
clicks_by_utm <- ad_clicks %>%
group_by(utm_source, ad_clicked) %>%
summarize(count = n())
clicks_by_utm

# define percentage_by_utm here:
percentage_by_utm <- clicks_by_utm %>%
group_by(utm_source) %>%
mutate(percentage = count / sum(count)) %>%
filter(ad_clicked == TRUE)
percentage_by_utm

# define experiment_split here:
experiment_split <- ad_clicks %>%
  group_by(experimental_group) %>%
  summarize(count = n())
experiment_split

# define clicks_by_experiment here:
clicks_by_experiment <- ad_clicks %>%
  group_by(experimental_group, ad_clicked) %>%
  summarize(count = n())
clicks_by_experiment

# define a_clicks here:
a_clicks <- ad_clicks %>%
  filter(experimental_group == "A")
a_clicks

# define b_clicks here:
b_clicks <- ad_clicks %>%
  filter(experimental_group == "B")
b_clicks

# define a_clicks_by_day here:
a_clicks_by_day <- a_clicks %>%
  group_by(day, ad_clicked) %>%
  summarize(count = n())
a_clicks_by_day

# define b_clicks_by_day here:
b_clicks_by_day <- b_clicks %>%
  group_by(day, ad_clicked) %>%
  summarize(count = n())
b_clicks_by_day

# define a_percentage_by_day here:
a_percentage_by_day <- a_clicks_by_day %>%
  group_by(day) %>%
  mutate(percentage = count/sum(count)) %>%
  filter(ad_clicked == TRUE)
a_percentage_by_day

# define b_percentage_by_day here:
b_percentage_by_day <- b_clicks_by_day %>%
  group_by(day) %>%
  mutate(percentage = count/sum(count)) %>%
  filter(ad_clicked == TRUE)
b_percentage_by_day