Year.Release <- game$Year.Release counts <- data.frame(table(Year.Release)) p <- game %>% select(Year.Release, Global.Sales) %>% group_by(Year.Release) %>% summarise(Total.Sales = sum(Global.Sales)) q <- cbind.data.frame(p, counts[2]) # Add counts to data frame names(q)[3] <- "count" q$count <- as.numeric(q$count) ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) + geom_col(fill = "green") + geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) + geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year theme(axis.text.x = element_text(angle = 90), panel.background = element_rect(fill = "purple"), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release) # From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
