R + Ggplot2: How to Hide Missing Dates from X-Axis

R + ggplot2: how to hide missing dates from x-axis?

Turn the date data into a factor then. At the moment, ggplot is interpreting the data in the sense you have told it the data are in - a continuous date scale. You don't want that scale, you want a categorical scale:

require(ggplot2)
dts <- as.Date( c('2011-01-10', '2011-01-11', '2011-01-15', '2011-01-16'))
df <- data.frame(dt = dts, val = seq_along(dts)) 
ggplot(df, aes(dt,val)) + geom_point() + 
        scale_x_date(format = '%d%b', major='days')

versus

df <- data.frame(dt = factor(format(dts, format = '%d%b')), 
                  val = seq_along(dts)) 
ggplot(df, aes(dt,val)) + geom_point()

which produces:

Is that what you wanted?

How can I tell ggplot to avoid plotting the missing dates

library(tidyverse)

value = c(-2.224791, -2.203189, -2.216392, -2.259517, -2.252137, -2.200599, -2.229062, -2.258825, -2.288452)
date = as.Date(c("2000-01-31","2000-04-30","2000-07-31","2000-10-31","2001-01-31","2015-01-31","2015-04-30","2015-07-31","2015-10-31"))
data = data.frame(value,date)

data_2001 = data %>%
  filter(date <= "2001-12-31")

data_2015 = data %>%
  filter(date >= "2015-01-01")

plot = ggplot() +
  geom_line(data = data_2001
            , aes(x=date,y=value)) +
  geom_line(data = data_2015
            , aes(x=date,y=value))

print(plot)

Remove date columns with no data in ggplot2 geom_bar

or we could set scale_x_date(..., expand = c(0,0)):

ggplot(small_data) +
    geom_bar(aes(date)) +
    scale_x_date(date_labels = "%d - %b", date_breaks = "1 day",
                 expand = c(0,0)) +
    theme(axis.text.x = element_text(angle = 65, hjust = 1))

Removing connetion lines between missing Dates in ggplot

One solution would be to specify the group aesthetics to match the groups you want to have connected by lines.

In your case this is the year:

ggplot(data = testsample, aes(x = Date, group = year(Date))) +
  geom_line(aes(y = Value1), color = "black", size = 1, alpha=0.5) +
  geom_line(aes(y = Value2), color = "red", size = 1, alpha=0.5) +
  labs(subtitle="testplot", 
       x = "year", 
       y = "values") +
  scale_x_date(date_labels = "%y", date_breaks  ="1 year")

Building on Gregors comment we can also change implicit missing values to explicit missing values, e.g. using tidyr::complete:

testsample2 <- tidyr::complete(testsample, Date = seq(min(Date), max(Date), by = "day"))
ggplot(data = testsample2, aes(x = Date)) +
  geom_line(aes(y = Value1), color = "black", size = 1, alpha=0.5) +
  geom_line(aes(y = Value2), color = "red", size = 1, alpha=0.5) +
  labs(subtitle="testplot", 
       x = "year", 
       y = "values") +
  scale_x_date(date_labels = "%y", date_breaks  ="1 year")

Removing the non-business days from ggplot axis

The package bdscale was designed for this purpose, once added, you can substitute your scale_x_date line with:

scale_x_bd(
  business.dates = df_1$date,
  max.major.breaks = 10,
  labels = date_format("%y-%m-%d")
)

To produce this plot...

Fully reproducible code

library(tidyverse)
library(quantmod)
library(bdscale)
library(scales)

getSymbols("BTCUSD=X;^DJI;^VIX;^GDAXI", from = "2017-01-01")

df <- merge(`BTCUSD=X`,`DJI`, all = TRUE) %>%
  merge(`VIX`, all = TRUE) %>%
  merge(`GDAXI`, all = TRUE)

df <- data.frame(date = index(df), coredata(df))

df_1 <- df %>%
  select(date, contains("Close")) %>%
  na.omit %>%
  filter(date > "2018-01-25") %>%
  gather(var, closing,2:5)

ggplot(data = df_1, aes(x = date, y = closing)) +
  geom_line() +
  facet_wrap(~var, scales = "free") +
  scale_x_bd(business.dates = df_1$date,
             max.major.breaks = 10,
             labels = date_format("%y-%m-%d")) +
  theme(text = element_text(size = 7),
        axis.text.x = element_text(angle = 90, hjust = 1))

Make x-axis continous ggplot R for missing values

You could probably complete the x-axis of the plot by expanding the data.frame with the missing years. Assuming your dataframe is called df, you might do:

library(tidyverse)
df969798 <- data.frame(year = c(1996:1998))
df <- bind_rows(df, df969798) %>% arrange(year)

Make sure the year column is of the same class, probably numeric or integer, as in df. The other variables, like station and yearly_mean, will then be filled with NAs. Then you can use the same code for your plot.

Setting x-axis limits with scale_x_date in ggplot, missing boundaries

What happens with out-of-bounds values in a scale is determined by the oob argument. Because you have bars that are parameterised by xmin/xmax/ymin/ymax values internally, the first xmin and last xmax fall outside your limits (so these are considered out-of-bounds). You can keep out-of-bounds values with scales::oob_keep().

library(ggplot2)

df = data.frame(
  date = as.Date(c("2021-10-10","2021-10-11","2021-10-12","2021-10-13",
                   "2021-10-14","2021-10-15","2021-10-16",
                   "2021-10-17","2021-10-18","2021-10-19")),
  count = c(1,2,3,4,1,2,2,1,2,3)
)

p <- ggplot(df, aes(x=date, y=count)) +
  geom_col(colour="black", fill="steelblue")

p +
  scale_x_date(limits = c(df$date[5], df$date[10]),
               oob = scales::oob_keep)

However, you most likely want to accommodate the first and last bars by expanding the limits to include their xmin/xmax values.

p +
  scale_x_date(limits = c(df$date[5] - 0.5, df$date[10] + 0.5))
#> Warning: Removed 4 rows containing missing values (position_stack).

^{Created on 2021-10-20 by the reprex package (v2.0.1)}

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