I already found the solution.
@Raptor
#------> I just had to add this <------
gdp_count_parts <- data %>%
group_by(parts) %>%
summarize(count_parts=sum(number_of_parts))
#-------------------------------
the final result, it looks like this
library(dplyr)
#---------------------DATA---------------------#---------------------DATA---------------------#---------------------DATA---------------------
parts <- c('Part 1','Part 2','Part 1','Part 1','Part 3','Part 3','Part 1','Part 1','Part 3','Part 3','Part 2','Part 3','Part 3','Part 2','Part 4')
number_of_parts <- c(1,1,1,1,0,1,0,1,1,0,0,0,0,1,0)
date <- as.Date(c('2010-11-1','2008-3-25','2007-3-14','2010-11-1','2008-3-25','2007-3-14','2010-11-1','2008-3-25','2007-3-14','2010-11-1','2008-3-25','2007-3-14','2010-11-1','2008-3-25','2007-3-14'))
data <- data.frame(parts,number_of_parts,date)
data2 <- data%>%
group_by(parts)%>%
summarise(Count_type_parts = n())
data3 <- data %>%
group_by(parts) %>%
summarize(count_parts=sum(number_of_parts))
data <- merge(data,data2, by.x = 'parts',by.y = 'parts')
data <- merge(data,data3, by.x = 'parts',by.y = 'parts')
#---------------------DATA---------------------#---------------------DATA---------------------#---------------------DATA---------------------