how to plot summarized data in ggplot - r

Question

I have a dataset that I want to summarize by calculating the ratio of 2 columns. However, I also need to calculate this ratio by different ‘cuts’ of my data set. i.e, ratio of the overall data, ratio by year, ratio by type, etc.
I will also need to put each ratio calculation in a bar chart.
What I want to know is whether I can plot all these bar charts without having to create a separate summary grouping dataset first.
For example, right now, before I send it to ggplot, I use group_by/summarize to my data first to calculate the ratio. Then I send it to ggplot.
Chart1 <- data %>% group_by(cut1) %>% summarise(ratio=sum(column1)/sum(column2))
ggplot(Chart1, aes(x=cut1, y=ratio)) + geom_bar(stat='identity', fill = "tomato2")
for chart 2 and chart 3, I do the same thing again
Chart2 <- data %>% group_by(cut2) %>% summarise(ratio=sum(column1)/sum(column2))
ggplot(Chart2, aes(x=cut2, y=ratio)) + geom_bar(stat='identity', fill = "tomato2")
Chart3 <- data %>% group_by(cut3) %>% summarise(ratio=sum(column1)/sum(column2))
ggplot(Chart3, aes(x=cut3, y=ratio)) + geom_bar(stat='identity', fill = "tomato2")
Is there another way to do this? Initially, I was thinking there would be a way that I can just create the ratio once and then I can use it over many times (similar to a calculated field in excel pivot tables). Is there something better than the above method?
Also, if summarizing each ratio separately is the best way, how do I do a facet chart? For example, I may want to do a facet of ratio to cut 1 and cut 2
edit: more info with example using created data:
c1 <- c('a','a','a', 'b','b', 'b', 'c','c','c')
c2 <- c('aa','aa','aa', 'bb','bb', 'bb', 'cc','cc','cc')
v1 <-c(1,2,3,4,5,6,7,8,9)
v2<-c(9,8,7,6,5,4,3,2,1)
mydata <-data.frame(c1,c2,v1,v2)
Chart1 <- mydata %>% group_by(c1) %>% summarise(ratio=sum(v1)/sum(v2))
ggplot(Chart1, aes(x=c1, y=ratio)) + geom_bar(stat='identity', fill = "tomato2") + theme(axis.text.x=element_text(angle=90))
The outcome I want is to understand how to best summarize data before plotting it. Do I need to summarize each calculation by each grouping seperatly, or is there an easier way?
for the example above, if I wanted to calculate ratio and group it by c1, and then create another ratio chart and group by c2, and then another by c3....do I need to do 3 different aggregations.

Answer 1

Does this accomplish what you want?
library(tidyverse)
c1 <- c('a','a','a', 'b','b', 'b', 'c','c','c')
c2 <- c('aa','aa','aa', 'bb','bb', 'bb', 'cc','cc','cc')
v1 <-c(1,2,3,4,5,6,7,8,9)
v2<-c(9,8,7,6,5,4,3,2,1)
mydata <-data.frame(c1,c2,v1,v2)
Chart1 <- mydata %>%
gather(key = 'cuts', value = 'categories', -(v1:v2)) %>%
group_by(cuts, categories) %>%
summarise(ratio=sum(v1)/sum(v2))
# This lets you facet them onto the same chart,
# but that doesn't really make sense,
# since the cuts will have different x axes
ggplot(Chart1, aes(x=categories, y=ratio)) +
geom_bar(stat='identity', fill = "tomato2") +
facet_grid(cuts~.) +
theme(axis.text.x=element_text(angle=90))
# This lets you make each plot separately
Chart1 %>%
filter(cuts == 'c1') %>%
ggplot(aes(x=categories, y=ratio)) +
geom_bar(stat='identity', fill = "tomato2") +
theme(axis.text.x=element_text(angle=90))
# Use a for loop to save all of the plots to files
for(i in 1:(length(mydata)-2)){
p <-
Chart1 %>%
filter(cuts == names(mydata)[[i]]) %>%
ggplot(aes(x=categories, y=ratio)) +
geom_bar(stat='identity', fill = "tomato2") +
theme(axis.text.x=element_text(angle=90))
ggsave(paste0("myPlot",i,".png"), plot = p)
}
Only thing that I wasn't sure about, is how to facet the different cuts if they don't have the same values on the x-axis. If you just want to stack them on top of each other, you could use the gridExtra package:
library(gridExtra)
plot1 <- Chart1 %>%
filter(cuts == 'c1') %>%
ggplot(aes(x=categories, y=ratio)) +
geom_bar(stat='identity', fill = "tomato2") +
theme(axis.text.x=element_text(angle=90))
plot2 <- Chart1 %>%
filter(cuts == 'c2') %>%
ggplot(aes(x=categories, y=ratio)) +
geom_bar(stat='identity', fill = "tomato2") +
theme(axis.text.x=element_text(angle=90))
grid.arrange(plot1, plot2, ncol=1, nrow = 2)