White gaps/blank space with stacked area graph (ggplot)

I have followed the guidance here to generate a stacked area graph for four groups within a categorical variable. The code works without errors but for some reason the graph contains white gaps, as you can see below. And I have checked those specific periods such as Oct and Nov and the data are complete for both months, and thus it should not be displaying white gaps. I am also open to other graph recommendations for the same data?
#install packages:

library(tidyverse)
library(stringr)
library(ggplot2)
library(zoo)
library(ggthemes)
library(writexl)
library(viridis)
library(hrbrthemes)
library(textclean)
library(lubridate)

Here is a data example:

dput(stackedgraph[1:20,c(1,2,4)])

output

structure(list(month_year = structure(c(2011.91666666667, 2011.91666666667, 
2011.91666666667, 2011.91666666667, 2012, 2012, 2012, 2012, 2012.08333333333, 
2012.08333333333, 2012.08333333333, 2012.08333333333, 2012.16666666667, 
2012.16666666667, 2012.16666666667, 2012.25, 2012.25, 2012.25, 
2012.25, 2012.33333333333), class = "yearmon"), directed_to_whom = c("MoE", 
"MoL", "Non-critical", "Private employers", "MoE", "MoL", "Non-critical", 
"Private employers", "MoE", "MoL", "Non-critical", "Private employers", 
"MoE", "Non-critical", "Private employers", "MoE", "MoL", "Non-critical", 
"Private employers", "MoE"), directed_to_whom_percentage = c(0.0923076923076923, 
0.107692307692308, 0.430769230769231, 0.369230769230769, 0.0666666666666667, 
0.0833333333333333, 0.45, 0.4, 0.0606060606060606, 0.121212121212121, 
0.287878787878788, 0.53030303030303, 0.184210526315789, 0.342105263157895, 
0.473684210526316, 0.131578947368421, 0.105263157894737, 0.210526315789474, 
0.552631578947368, 0.108108108108108)), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -20L), groups = structure(list(
    month_year = structure(c(2011.91666666667, 2012, 2012.08333333333, 
    2012.16666666667, 2012.25, 2012.33333333333), class = "yearmon"), 
    .rows = structure(list(1:4, 5:8, 9:12, 13:15, 16:19, 20L), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -6L), .drop = TRUE))

Here is the code to create the graph:

ggplot(stackedgraph, aes(x = as.Date(month_year),y = directed_to_whom_percentage)) + 
  geom_area(aes(fill=directed_to_whom,group = directed_to_whom), position='stack') +
  scale_fill_manual(values = c("MoL" = "light green",
                              "MoE" = "red",
                               "Private employers" = "light blue",
                              "Non-critical" = "black")) +
          scale_x_date(date_breaks = "months" , date_labels = "%b-%y") +
  theme_economist_white() +
    theme(plot.title = element_text(size = 5, face = "bold"),
          axis.text.x = element_text(angle =90, vjust = 0.5)) +
  theme(axis.title.x=element_blank(),
                      axis.ticks.x=element_blank()) +
    scale_y_continuous(labels = percent_format(accuracy = 1)) 

output:

I really appreciate the advice from Andy, I ran the code with a small change:

stackedgraph <- stackedgraph %>%
  ungroup() %>% # I used ungroup to avoid this [error][3] which I was receiving.
  complete(month_year, directed_to_whom, fill = list(directed_to_whom_percentage = 0))

Then the graph:

ggplot(stackedgraph, aes(x = as.Date(month_year), y = directed_to_whom_percentage)) +
  geom_area(aes(fill = directed_to_whom, group = directed_to_whom),
            position = 'stack') +
  scale_fill_manual(
    values = c(
      "MoL" = "light green",
      "MoE" = "red",
      "Private employers" = "light blue",
      "Non-critical" = "black"
    )
  ) +
  scale_x_date(date_breaks = "months" , date_labels = "%b-%y") +
  theme(
    plot.title = element_text(size = 5, face = "bold"),
    axis.text.x = element_text(angle = 90, vjust = 0.5)
  ) +
  theme(axis.title.x = element_blank(),
        axis.ticks.x = element_blank()) +
  scale_y_continuous(labels = percent_format(accuracy = 1))

output:

Ideally you would need a row with a 0 for every category in months when it has a value of zero - the area chart is extrapolating for missing months thereby adding over 100% in some cases. You can do a quick fix with a pivot_wider/pivot_longer combo to fill in missing 0s:

library(tidyverse)
library(zoo)

# Creating sample data to test
stackedgraph <- tibble::tribble(
                  ~month_year,   ~directed_to_whom, ~directed_to_whom_percentage,
                   "Dec 2011",               "MoE",           0.0923076923076923,
                   "Dec 2011",               "MoL",            0.107692307692308,
                   "Dec 2011",      "Non-critical",            0.430769230769231,
                   "Dec 2011", "Private employers",            0.369230769230769,
                   "Jan 2012",               "MoE",           0.0666666666666667,
                   "Jan 2012",               "MoL",           0.0833333333333333,
                   "Jan 2012",      "Non-critical",                         0.45,
                   "Jan 2012", "Private employers",                          0.4,
                   "Feb 2012",               "MoE",           0.0606060606060606,
                   "Feb 2012",               "MoL",            0.121212121212121,
                   "Feb 2012",      "Non-critical",            0.287878787878788,
                   "Feb 2012", "Private employers",             0.53030303030303,
                   "Mar 2012",               "MoE",            0.184210526315789,
                   "Mar 2012",      "Non-critical",            0.342105263157895,
                   "Mar 2012", "Private employers",            0.473684210526316,
                   "Apr 2012",               "MoE",            0.131578947368421,
                   "Apr 2012",               "MoL",            0.105263157894737,
                   "Apr 2012",      "Non-critical",            0.210526315789474,
                   "Apr 2012", "Private employers",            0.552631578947368
                  ) |> 
  mutate(month_year = as.yearmon(month_year))

# Run this code on your data

stackedgraph |>
  pivot_wider(names_from = directed_to_whom,
              values_from = directed_to_whom_percentage,
              values_fill = 0) |>
  pivot_longer(-month_year, 
               names_to = "directed_to_whom",
               values_to = "directed_to_whom_percentage") |>
  ggplot(aes(x = as.Date(month_year), y = directed_to_whom_percentage)) +
  geom_area(aes(fill = directed_to_whom, group = directed_to_whom),
            position = 'stack') +
  scale_fill_manual(
    values = c(
      "MoL" = "light green",
      "MoE" = "red",
      "Private employers" = "light blue",
      "Non-critical" = "black"
    )
  ) +
  scale_x_date(date_breaks = "months" , date_labels = "%b-%y") +
  theme(
    plot.title = element_text(size = 5, face = "bold"),
    axis.text.x = element_text(angle = 90, vjust = 0.5)
  ) +
  theme(axis.title.x = element_blank(),
        axis.ticks.x = element_blank()) +
  scale_y_continuous(labels = percent_format(accuracy = 1))

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Edit - a slightly tidier solution

The tidyr::complete function can do the steps for you in a bit more of a natural/explicit way. This will create every month_year*directed_to_whom combo in the dataframe and fill missing values with 0s:

stackedgraph <- stackedgraph %>%
  complete(month_year,
           directed_to_whom,
           fill = list(directed_to_whom_percentage = 0))

This does the same as the two pivots but is the nicer, proper way of doing it. This will then give the tidied, expanded dataframe which should pass to your ggplot code giving the right results.