RHTML custom visual, data loss results in misleading plot

Hello,

has anyone encountered the problem where the visual looks significantly different than when compiled in R?

My output clearly missed out on alot of data, as many bars are missing from the column plot. Data consists of less than 2000 obs.

This is my R-code, if anyone are interested:

source('./r_files/flatten_HTML.r')

############### Library Declarations ###############
libraryRequireInstall("ggplot2");
libraryRequireInstall("plotly")
libraryRequireInstall("tidyverse")
libraryRequireInstall("scales")
libraryRequireInstall("lubridate")


################### Actual code ####################
# Prep maskin data --------------------------------------------------------
maskin = Values %>%
  mutate(Ferdig_Tid_Short = as.Date(Ferdig_Tid_Short),
         MaskinNr = as.character(MaskinNr)) %>% 
  mutate(
    year = year(Ferdig_Tid_Short),
    first = floor_date(x = today(),'year',),
    last = as.Date(ifelse(year(today())==year,
                          as.character(today()),
                          as.character((ceiling_date(x = today(),'year',)-days(1)))))
  ) %>%
  group_by(year,MaskinNr,MaskinGruppeNavn) %>% 
  summarise(
    maskintimer = sum(Reell_Maskintid),
    qyeartimer = as.numeric(max(last)-min(first))*24
  ) %>% 
  ungroup %>% 
  transmute(
    year = factor(year),
    MaskinNr = factor(MaskinNr, levels = unique(sort(as.numeric(Values$MaskinNr)))),
    maskingruppe = factor(MaskinGruppeNavn,
                          levels = c('65-90 tonn','120-400 tonn',
                                     '420-600 tonn',
                                     '1000-1500 tonn',
                                     'Pakningslegging')),
    diff = round(maskintimer/qyeartimer,3)
  );


# Lag plot ----------------------------------------------------------------
color_vec = c('#60D9BA','#287878','#01A001','#CFF06D','#00FF51','#FFFF00','#FF8000');


colors = maskin %>% 
  transmute(maskingruppe,
            MaskinNr = MaskinNr) %>%
  distinct() %>%
  arrange(MaskinNr) %>% 
  group_by(maskingruppe) %>% 
  mutate(color = map_chr(1:length(maskingruppe),function(x)color_vec[x])) %>% 
  pull(color);

g = maskin %>%
  filter(maskingruppe != 'Pakningslegging') %>% 
  ggplot(aes(year,diff,fill = MaskinNr)) +
  geom_col(position = position_dodge()) +
  facet_wrap(~maskingruppe) +
  theme_bw() +
  theme(plot.background = element_rect(fill = '#e7f0f0', color = '#e7f0f0'),
        legend.position="none") +
  xlab('') +
  ylab('') +
  scale_y_continuous (labels = scales::percent) +
  scale_fill_manual(values=colors);
####################################################

############# Create and save widget ###############
p = ggplotly(g);
internalSaveWidget(p, 'out.html');
####################################################

################ Reduce paddings ###################
ReadFullFileReplaceString('out.html', 'out.html', ',"padding":[0-9]*,', ',"padding":0,')
####################################################