Geom_Boxplot() from Ggplot2:Forcing an Empty Level to Appear

ggplot2: forcing space for empty second-level category

Could coord_cartesian be a solution that you are looking for?

It will zoom in and will not try to "outsmart" the data like scale_y_continuous

library(dplyr)
library(ggplot2)

set.seed(42)
n <- 100
dat <- data.frame(x=runif(n),
                  cat1=sample(letters[1:4], size=n, replace=TRUE),
                  cat2=sample(LETTERS[1:3], size=n, replace=TRUE))

LARGE_VALUE <- 2

dat <- dat %>%
  mutate(x = ifelse(cat1 == 'b' & cat2 == 'B', 
                    LARGE_VALUE,
                    x))

ggplot(dat, aes(cat1, x)) + 
  geom_boxplot(aes(fill=cat2)) + 
  coord_cartesian(ylim = c(0,1))

ggplot2 keep unused levels barplot

You need to set drop=FALSE on both scales (fill and x) like this:

library(ggplot2)
df <- data.frame(type=c("A", "A", "A", "B", "B"), group=rep("group1", 5))
df1 <- data.frame(type=c("A", "A", "A", "B", "B", "A", "A", "C", "B", "B"), group=c(rep("group1", 5),rep("group2", 5)))
df$type <- factor(df$type, levels=c("A","B", "C"))
df1$type <- factor(df1$type, levels=c("A","B", "C"))

plt <-  ggplot(df, aes(x=type, fill=type)) + 
          geom_bar(position='dodge') + 
          scale_fill_discrete(drop=FALSE) +
          scale_x_discrete(drop=FALSE)
plt1 <- ggplot(df1, aes(x=type, fill=type)) + 
          geom_bar(position='dodge') + 
          scale_fill_discrete(drop=FALSE) +
          scale_x_discrete(drop=FALSE)

Edit:

I'm pretty sure this works. Forgot to change x to type instead of group and the position='dodge'! Just paste and test. The stat_bin deals with bins with zero counts. Check the docs.

Consistent width of boxplots if missing data by group?

We can make use of the preserve argument in position_dodge.

From ?position_dodge

preserve: Should dodging preserve the total width of all elements at a position, or the width of a single element?

ggplot(data.sub, aes(x=variety, y=note, fill=treatment)) + 
 geom_boxplot(position = position_dodge(preserve = "single"))

ggplot will not plot missing category

Here's a workaround you could use:

# generate dummy data 
set.seed(123)
df1 <- data.frame(lets = sample(letters[1:4], 20, replace = T),
                  y = rnorm(20), stringsAsFactors = FALSE)
# define factor, including the missing category as a level
df1$lets <- factor(df1$lets, levels = letters[1:5])
# make plot
ggplot(df1, aes(x = lets, y = y))+
    geom_boxplot(aes(fill = lets))+
    geom_point(data = NULL, aes(x = 'e', y = 0), pch = NA)+
    scale_fill_brewer(drop = F, palette = 'Set1')+
    theme_bw()

Basically, we plot an "empty" point (i.e. pch = NA) so that the category shows up on the x-axis, but has no visible geom associated with it. We also define our discrete variable, lets as a factor with five levels when only four are present in the data.frame. The missing category is the letter e.

NB: You'll have to adjust the positioning of this "empty" point so that it doesn't skew your y axis.

Otherwise, you could use the result from this answer to avoid having to plot an "empty" point.

# generate dummy data 
set.seed(123)
df1 <- data.frame(lets = sample(letters[1:4], 20, replace = T),
                  y = rnorm(20), stringsAsFactors = FALSE)
# define factor, including the missing category as a level
df1$lets <- factor(df1$lets, levels = letters[1:5])
# make plot
ggplot(df1, aes(x = lets, y = y)) +
    geom_boxplot(aes(fill = lets)) +
    scale_x_discrete(drop = F) +
    scale_fill_brewer(drop = F, palette = 'Set1') +
    theme_bw()

ggplot won't use levels for x-axis order when a reactive input is empty

I can't fully test this solution since the data you provided isn't available (so I can't run the app), but I suspect that the following should help.

First, by using ordered or factor(..., ordered = TRUE) you can tell the graph what order to put label in.

Second, in order to prevent the column from showing up on the graph you must remove all datapoints for that column INCLUDING NA.

control_list <- c("ABC_control", "GHI_control", "DEF_control", "CUSTOM_control1")

# Some data to try out
values_list <- data.frame(
  Control_types2 = ordered(rep(control_list, times = 4), levels =control_list),
  values = c(0.25,0.50,0.75,NA)
)

Mean_list <- data.frame(
  Control_types = ordered(control_list, levels =control_list),
  Mean_correlation = c(0.25,0.50,0.75,NA)
)

# Original plot code
ggplot() +
  geom_point(data = values_list,aes(x=Control_types2, y=values,size = 7, color = Control_types2), show.legend = FALSE, position=position_jitter(h=0, w=0.1), alpha = 0.7) +
  geom_boxplot(data = Mean_list, aes(Control_types, Mean_correlation), size = 0.5, colour = "black")

# New plot with NA values removed
ggplot() + 
  geom_point(data = values_list %>% filter(!is.na(values)),
             aes(x=Control_types2, y=values,size = 7, color = Control_types2), 
             show.legend = FALSE, 
             position=position_jitter(h=0, w=0.1), 
             alpha = 0.7) +
  geom_boxplot(data = Mean_list %>% filter(!is.na(Mean_correlation)), 
               aes(Control_types, Mean_correlation), 
               size = 0.5, 
               colour = "black")

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