Multiple facet normalized bar graph with bar color

Question

I have a data frame like so:

df<- data.frame(month= rep(c("Jan", "Feb", "Mar", "Apr", "May"), 3), 
       year= c(seq(2001:2003,5), rep(2002, 5), rep(2003, 5)), 
       clim_var= c(rep("precip_mm", 5), rep("tmin",5), rep("tmax", 5)), 
       anomaly= sample(-20:20, 15, replace = TRUE))
df<-df[-c(3,10),]
library("zoo")
df$date<- as.yearmon(paste(df$year, df$month), format= "%Y %b")

You will notice that some dates may be missing, but for the most part it is time series data for various climate variables. I would like each climate variable to be a facet. The y axis will plot anomaly for each level of of the clim_var column. Such that I should get a multifacted figure which each graph similar to this:

I have tried this code (modified from)

library(ggplot2)
gg<- ggplot(df, aes(x= seq_along(date), y = anomaly)) + 
geom_bar(stat = 'identity', aes(fill = anomaly>0), position = 'dodge', col = 
'transparent') + 
theme_bw() + scale_fill_discrete(guide = 'none') + 
labs(x = '', y = 'anomaly')
gg + facet_grid(clim_var~.)
gg+ scale_x_datetime(labels = date_format("%b %Y"))

The issue seems to be plotting the date. It's as though it is not being recognized as a date so data for each clim_var take up 1/3 of the plot area and the x-axis is continuous values rather than dates. I want the output to have axis label that include the month and year like so...

In my real dataset there are many years of data, so it may be most clean to specify labels for only Jan and then leave other months as tick marks without labels. Any insight to that would be appreciated.

Edit:

Corrected data frame such that each clim-var has multiple years of data

precip_mm<- data.frame(clim_var= rep("precip_mm",36),  month= rep(c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sept", "Oct", "Nov", "Dec" ), 3), 
                   year= c(rep(2001,12),rep(2002,12), rep(2003, 12)), 
                   anomaly= sample(-20:20, 36, replace = TRUE))
tmin<- data.frame(clim_var= rep("tmin",36),  month= rep(c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sept", "Oct", "Nov", "Dec" ), 3), 
                   year= c(rep(2001,12),rep(2002,12), rep(2003, 12)), 
                   anomaly= sample(-20:20, 36, replace = TRUE))
tmax<- data.frame(clim_var= rep("tmax",36),  month= rep(c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sept", "Oct", "Nov", "Dec" ), 3), 
                   year= c(rep(2001,12),rep(2002,12), rep(2003, 12)), 
                   anomaly= sample(-20:20, 36, replace = TRUE))
df<- rbind(precip_mm, tmin)
df<-rbind(df, tmax)
df<-df[-c(3,10, 50, 100),]

library("zoo")
df$date<- as.yearmon(paste(df$year, df$month), format= "%Y %b")

Adjustments suggested in comments

library(ggplot2)
gg<- ggplot(df, aes(x= date, y = anomaly)) + 
 geom_bar(stat = 'identity', aes(fill = anomaly>0), position = 'dodge', col = 
         'transparent') + 
theme_bw() + scale_fill_discrete(guide = 'none') + 
labs(x = '', y = 'anomaly')
gg + facet_grid(clim_var~.)
gg+ scale_x_yearmon()

Output does not facet each clim_var, however x-axis is correctly labeled.

Edit2:

labels_month <- format(seq(from = min(df$date), to = 
max(df$date), by = "1 months"), "%Y-%b")
labels_month[rep(c(FALSE, TRUE), c(1, 11))] <- ""
labels_month<- as.Date(labels_month, format= "%Y-%b")

x_breaks <- seq(min(df$date), max(df$date), by = "1 months")

p1 <- ggplot(df, aes(x = factor(date), y = df)) +
geom_col(aes(fill = anomoly > 0),
       position = "dodge",
       col = "transparent") +
theme_bw(base_size = 12) + 
scale_fill_discrete(guide = "none") +
labs(x = "", y = "") + 
scale_x_date(expand = c(0.015, 0.015),
           labels = labels_month, 
           breaks = x_breaks) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
facet_grid(climvar ~ ., 
         labeller = label_parsed,
         switch = "y",
         scales = 'free_y') 
p1

Answer 1

You might be better off with facet_wrap if you have a large number of facets and it solves your scale problem like this:

gg<- ggplot(df, aes(x= seq_along(date), y = anomaly)) + 
  geom_bar(stat = 'identity', aes(fill = anomaly>0), position = 'dodge', col = 
             'transparent') + 
  theme_bw() + scale_fill_discrete(guide = 'none') + 
  labs(x = '', y = 'anomaly')
gg + facet_wrap(clim_var~., scales= "free_x")

You can use the nrow and ncol of facet_wrap to shape it as you please.

So with the date format:

df$date<- as.Date(paste(df$year, df$month, "1", sep="-"), format="%Y-%b-%d")

library(ggplot2)
library(scales)
gg<- ggplot(df, aes(x= date, y = anomaly)) + 
  geom_bar(stat = 'identity', aes(fill = anomaly>0), position = 'dodge', col = 
             'transparent') + 
  theme_bw() + scale_fill_discrete(guide = 'none') + 
  labs(x = '', y = 'anomaly')
gg + facet_wrap(clim_var~., scales= "free")+ scale_x_date(breaks = date_breaks("2 months"),labels = date_format("%m/%y"))

I prefer this method as it gives me a lot of flexibility for my date axis.

Answer 2

Is this what you are looking for?

library(zoo)
library(ggplot2)

set.seed(123)
precip_mm <- data.frame(
  clim_var = rep("precip_mm", 36),
  month = rep(c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sept", "Oct", "Nov", "Dec"), 3),
  year = c(rep(2001, 12), rep(2002, 12), rep(2003, 12)),
  anomaly = sample(-20:20, 36, replace = TRUE))
tmin <- data.frame(
  clim_var = rep("tmin", 36),
  month = rep(c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sept", "Oct", "Nov", "Dec"), 3),
  year = c(rep(2001, 12), rep(2002, 12), rep(2003, 12)),
  anomaly = sample(-20:20, 36, replace = TRUE))
tmax <- data.frame(
  clim_var = rep("tmax", 36),
  month = rep(c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sept", "Oct", "Nov", "Dec"), 3),
  year = c(rep(2001, 12), rep(2002, 12), rep(2003, 12)),
  anomaly = sample(-20:20, 36, replace = TRUE))
df <- rbind(precip_mm, tmin)
df <- rbind(df, tmax)
df <- df[-c(3, 10, 50, 100), ]
df$date <- as.yearmon(paste(df$year, df$month), format = "%Y %b")


# Setup nice labels for T and P
df$clim_var <- factor(df$clim_var, 
                      levels = c("tmax", "tmin", "precip_mm"),
                      labels = c(expression("T"[max]~anomaly~(degree*C)),
                                 expression("T"[min]~anomaly~(degree*C)),
                                 expression("Precip anomaly ("*"mm)")))
# Plot
p1 <- ggplot(df, aes(x = factor(date), y = anomaly)) +
  geom_col(aes(fill = anomaly > 0),
           position = "dodge",
           col = "transparent") +
  theme_bw(base_size = 12) + 
  scale_fill_discrete(guide = "none") +
  labs(x = "", y = "") + 
  facet_grid(clim_var ~ ., 
             labeller = label_parsed,
             switch = "y",
             scales = 'free_y') +
  theme(strip.placement = 'outside',
        strip.background = element_blank(),
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) 
p1

If you want to modify the x-axis, check this answer

df$date2 <- as.Date(paste(df$year, df$month, "01", sep = "-"), format = "%Y-%b-%d")
p2 <- ggplot(df, aes(x = date2, y = anomaly)) +
  geom_col(aes(fill = anomaly > 0),
           position = "dodge",
           col = "transparent") +
  theme_bw(base_size = 12) + 
  scale_fill_discrete(guide = "none") +
  labs(x = "", y = "") + 
  facet_grid(clim_var ~ ., 
             labeller = label_parsed,
             switch = "y",
             scales = 'free_y') +
  theme(strip.placement = 'outside',
        strip.background = element_blank())  +
  scale_x_date(date_breaks = "12 months", date_labels = "%b-%Y")
p2