Make a Subset of the Data
diamonds2 <- diamonds %>% filter(color %in% c("D","E","F"))
diamonds.report <- diamonds2 %>%
group_by(cut,color) %>%
summarize(mean.depth = mean(depth))
## `summarise()` has grouped output by 'cut'. You can override using the `.groups` argument.
diamonds.report
## # A tibble: 15 x 3
## # Groups: cut [5]
## cut color mean.depth
## <ord> <ord> <dbl>
## 1 Fair D 64.0
## 2 Fair E 63.3
## 3 Fair F 63.5
## 4 Good D 62.4
## 5 Good E 62.2
## 6 Good F 62.2
## 7 Very Good D 61.8
## 8 Very Good E 61.7
## 9 Very Good F 61.7
## 10 Premium D 61.2
## 11 Premium E 61.2
## 12 Premium F 61.3
## 13 Ideal D 61.7
## 14 Ideal E 61.7
## 15 Ideal F 61.7
p <- ggplot(data=diamonds.report,
aes(x = cut,
y = mean.depth,
fill = color))
p+ geom_bar(stat="identity",position = "dodge") +
scale_fill_brewer(palette = "Greens" , direction = -1) +
ggtitle("Diamonds") +
ylab("Mean Depth") +
theme_bw() +
theme(axis.title.x = element_text(color="black", size=14, face="bold"),
axis.title.y = element_text(color="black", size=14, face="bold"))
p+ geom_bar(stat="identity",position = "stack") +
scale_fill_brewer(palette = "Greens" , direction = -1) +
ggtitle("Diamonds") +
ylab("Mean Depth") +
theme_bw() +
theme(axis.title.x = element_text(color="black", size=14, face="bold"),
axis.title.y = element_text(color="black", size=14, face="bold"))
