| 1 |
Year.Release <- game$Year.Release |
| 2 |
counts <- data.frame(table(Year.Release)) |
| 3 |
p <- game %>% |
| 4 |
select(Year.Release, Global.Sales) %>% |
| 5 |
group_by(Year.Release) %>% |
| 6 |
summarise(Total.Sales = sum(Global.Sales)) |
| 7 |
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame |
| 8 |
names(q)[3] <- "count" |
| 9 |
q$count <- as.numeric(q$count) |
| 10 |
|
| 11 |
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) + |
| 12 |
geom_col(fill = "green") + |
| 13 |
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) + |
| 14 |
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year |
| 15 |
theme(axis.text.x = element_text(angle = 90), |
| 16 |
panel.background = element_rect(fill = "purple"), |
| 17 |
panel.grid.major = element_blank(), |
| 18 |
panel.grid.minor = element_blank()) + |
| 19 |
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release) |
| 20 |
|
| 21 |
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales |
| 22 |
|
