Normalising data with dplyr mutate() brings inconsistencies - r

Question

I'm trying to reproduce the framework from this blogpost http://www.luishusier.com/2017/09/28/balance/ with the following code but it looks like I get inconsistent results
library(tidyverse)
library(magrittr)
ids <- c("1617", "1516", "1415", "1314", "1213", "1112", "1011", "0910", "0809", "0708", "0607", "0506")
data <- ids %>%
map(function(i) {read_csv(paste0("http://www.football-data.co.uk/mmz4281/", i ,"/F1.csv")) %>%
select(Date:AST) %>%
mutate(season = i)})
data <- bind_rows(data)
data <- data[complete.cases(data[ , 1:3]), ]
tmp1 <- data %>%
select(season, HomeTeam, FTHG:FTR,HS:AST) %>%
rename(BP = FTHG,
BC = FTAG,
TP = HS,
TC = AS,
TCP = HST,
TCC = AST,
team = HomeTeam)%>%
mutate(Pts = ifelse(FTR == "H", 3, ifelse(FTR == "A", 0, 1)),
Terrain = "Domicile")
tmp2 <- data %>%
select(season, AwayTeam, FTHG:FTR, HS:AST) %>%
rename(BP = FTAG,
BC = FTHG,
TP = AS,
TC = HS,
TCP = AST,
TCC = HST,
team = AwayTeam)%>%
mutate(Pts = ifelse(FTR == "A", 3 ,ifelse(FTR == "H", 0 , 1)),
Terrain = "Extérieur")
tmp3 <- bind_rows(tmp1, tmp2)
l1_0517 <- tmp3 %>%
group_by(season, team)%>%
summarise(j = n(),
pts = sum(Pts),
diff_but = (sum(BP) - sum(BC)),
diff_t_ca = (sum(TCP, na.rm = T) - sum(TCC, na.rm = T)),
diff_t = (sum(TP, na.rm = T) - sum(TC, na.rm = T)),
but_p = sum(BP),
but_c = sum(BC),
tir_ca_p = sum(TCP, na.rm = T),
tir_ca_c = sum(TCC, na.rm = T),
tir_p = sum(TP, na.rm = T),
tir_c = sum(TC, na.rm = T)) %>%
arrange((season), desc(pts), desc(diff_but))
Then I apply the framework mentioned above:
l1_0517 <- l1_0517 %>%
mutate(
# First, see how many goals the team scores relative to the average
norm_attack = but_p %>% divide_by(mean(but_p)) %>%
# Then, transform it into an unconstrained scale
log(),
# First, see how many goals the team concedes relative to the average
norm_defense = but_c %>% divide_by(mean(but_c)) %>%
# Invert it, so a higher defense is better
raise_to_power(-1) %>%
# Then, transform it into an unconstrained scale
log(),
# Now that we have normalized attack and defense ratings, we can compute
# measures of quality and attacking balance
quality = norm_attack + norm_defense,
balance = norm_attack - norm_defense
) %>%
arrange(desc(norm_attack))
When I look at the column norm_attack, I expect to find the same value for equivalent but_p values, which is not the case here:
head(l1_0517, 10)
for instance when but_p has value 83, row 5 and row 7, I get norm_attack at 0.5612738 and 0.5128357 respectively.
Is it normal? I would expect mean(l1_0517$but_p) to be fixed and therefore obtaining the same result when a value of l1_0517$but_p is log normalised?
UPDATE
I have tried to work on a simpler example but I can't reproduce this issue:
df <- tibble(a = as.integer(runif(200, 15, 100)))
df <- df %>%
mutate(norm_a = a %>% divide_by(mean(a)) %>%
log())

Answer 1

I found the solution after looking at the type of l1_0517
It is a grouped_df hence the different results.
The correct code is:
l1_0517 <- tmp3 %>%
group_by(season, team)%>%
summarise(j = n(),
pts = sum(Pts),
diff_but = (sum(BP) - sum(BC)),
diff_t_ca = (sum(TCP, na.rm = T) - sum(TCC, na.rm = T)),
diff_t = (sum(TP, na.rm = T) - sum(TC, na.rm = T)),
but_p = sum(BP),
but_c = sum(BC),
tir_ca_p = sum(TCP, na.rm = T),
tir_ca_c = sum(TCC, na.rm = T),
tir_p = sum(TP, na.rm = T),
tir_c = sum(TC, na.rm = T)) %>%
ungroup() %>%
arrange((season), desc(pts), desc(diff_but))