How to remove 0s from histogram using ggplot in R but still include them in the calculation of perce-CodePudding

I am trying to make a histogram using ggplot, where over 95% of the data is 0 and the rest of it is between 1 - 55. I do not want to show the 0s on the histogram - but I do want them accounted for in the total percentage, that way the other %s remain low. I've taken two approaches for this -- but what happens is the percentages for the rest of the data get messed up and the 0s aren't included in the calculation.

My first approach was this:

set1 %>% filter(total>0)%>%
  ggplot(aes(x=total, fill=lowcost)) 
  geom_histogram(binwidth=1,aes(y = (..count..)/sum(..count..)),col=I("black")) 
  scale_color_grey() scale_fill_grey(start = .85,
                                     end = .85,)  
  theme_linedraw() 
  guides(fill = "none", cols='none') 
  geom_vline(aes(xintercept=10, size='Low target'),
             color="black", linetype=5) 
  geom_vline(aes(xintercept=50, size='High target'),
             color="black", linetype="dotted") 
  scale_size_manual(values = c(.5, 0.5), guide=guide_legend(title = "Target", override.aes = list(linetype=c(3,5), color=c('black', 'black')))) 
  scale_y_continuous(labels=scales::percent) 
  scale_x_continuous(breaks = c(seq(0,50,10), 55), labels = c(seq(0, 50, 10), '>55'), limits = c(0, 60)) 
  facet_grid(cols = vars(lowcost)) 
  ggtitle("Ask Set 1 ") 
  theme(plot.title = element_text(hjust = 0.5)) 
  xlab("Total donation ($)") 
  ylab("Percent")

My second approach was not filtering out the 0s, but instead limiting the X axis to not include them, but this didn't work either:

set1 %>% 
  ggplot(aes(x=total, fill=lowcost)) 
  geom_histogram(binwidth=1,aes(y = (..count..)/sum(..count..)),col=I("black")) 
  scale_color_grey() scale_fill_grey(start = .85,
                                     end = .85,)  
  theme_linedraw() 
  guides(fill = "none", cols='none') 
  geom_vline(aes(xintercept=10, size='Low target'),
             color="black", linetype=5) 
  geom_vline(aes(xintercept=50, size='High target'),
             color="black", linetype="dotted") 
  scale_size_manual(values = c(.5, 0.5), guide=guide_legend(title = "Target", override.aes = list(linetype=c(3,5), color=c('black', 'black')))) 
  scale_y_continuous(labels=scales::percent) 
  scale_x_continuous(breaks = c(seq(0,50,10), 55), labels = c(seq(0, 50, 10), '>55'), limits = c(0.01, 60)) 
  facet_grid(cols = vars(lowcost)) 
  ggtitle("Ask Set 1 ") 
  theme(plot.title = element_text(hjust = 0.5)) 
  xlab("Total donation ($)") 
  ylab("Percent")

Both result in histograms like look like this: The tallest bar on the left histogram should actually be 1.19%

The percents should be the following in the histogram on the left:

The percents should be the following in the histogram on the right:

CodePudding user response：

I think you can do what you want using "clipping" with coord_cartesian. Try this (untested):

set1 %>%
  # filter(total>0) %>%                   # comment this out, do not filter
  ggplot(aes(x=total, fill=lowcost))  
  coord_cartesian(xlim = c(1, NA))        # start at 1, extend to the normal limit
  geom_histogram(binwidth=1, aes(y = (..count..)/sum(..count..)), col=I("black"))  
  ...                                     # rest unchanged

CodePudding user response：

Perhaps try something like this:

# Test data   expected outcome
set1 <- tibble(total=c(rep(0,10), rep(1,5), rep(2,5)))
set1 %>% count(total) %>% mutate(percent = n/sum(n))

# First, count the percentage and store it in a temporary variable
# Then, use the percentage variable with "identity" option for the histogram
# You can then either filter out the total first, or change the limit
set1 %>% 
    count(total) %>% 
    mutate(percent = n/sum(n)) %>%
    filter(total>0) %>%
    ggplot(aes(x=total,y=percent))   
    geom_histogram(stat="identity")  
    scale_x_continuous(limits = c(0, 3))  
    scale_y_continuous(labels=scales::percent)  
    ylab("Percent")