How to use parsing inside a gtable and retain trailing zeros

Question

I want to add a table to my ggplot. Within that table I need to add a subscript to a few character values (for Winsorized means and standard deviations). This can be achieved when I specify parse = TRUE but then as a result I lose trailing zeros formatted using sprintf. Is there a way to use plotmath and keep trailing zeros in my table?

# exmple data
data(iris)

# packages
library(dplyr)
library(tidyr)
library(ggplot2)
library(psych)
library(gridExtra)
library(grid)
library(gtable)

# sumarise data
summary.df <- iris %>% 
  group_by(Species) %>% 
  summarise(mean_length = mean(Sepal.Length),
            meanw_length = winsor.mean(Sepal.Length),
            sd_length = sd(Sepal.Length),
            sdw_length = winsor.sd(Sepal.Length, trim=0.05)) %>% 
  gather(key='Metric', value='Value', 
         mean_length, meanw_length,
         sd_length, sdw_length) %>% 
  mutate(Value = sprintf("%2.1f", Value)) %>% 
  spread(key=Species, value=Value)

# rename metrics
# use plotmath notation for subsript
summary.df$Metric[summary.df$Metric == 'mean_length'] <- 'Mean'
summary.df$Metric[summary.df$Metric == 'meanw_length'] <- 'Mean[w]'
summary.df$Metric[summary.df$Metric == 'sd_length'] <- 'SD'
summary.df$Metric[summary.df$Metric == 'sdw_length'] <- 'SD[w]'

# regular theme
tt <- ttheme_default(core = list(fg_params=list(cex = 0.7)),
                     colhead = list(fg_params=list(cex = 0.7)),
                     rowhead = list(fg_params=list(cex = 0.7)))

# theme with parsing
tt_parse <- ttheme_default(core = list(fg_params=list(cex = 0.7,
                                                      parse=TRUE)), 
                           colhead = list(fg_params=list(cex = 0.7)),
                           rowhead = list(fg_params=list(cex = 0.7)))


# Graph with regular table theme
iris %>% 
  ggplot(aes(x=Sepal.Length, fill=Species)) +
  geom_density(alpha = 0.8) + 
  coord_cartesian(ylim = c(0, 1.5)) +
  labs(title = 'Regular Theme') +
  annotation_custom( grob=tableGrob(summary.df, theme=tt, rows=NULL), 
                     xmin=6.25, xmax=8,
                     ymin = 1, ymax=1.4)

# graph with table theme with parsing
iris %>% 
  ggplot(aes(x=Sepal.Length, fill=Species)) +
  geom_density(alpha = 0.8) + 
  coord_cartesian(ylim = c(0, 1.5)) +
  labs(title = 'Theme with Parsing') +
  annotation_custom( grob=tableGrob(summary.df, theme=tt_parse, rows=NULL), 
                     xmin=6.25, xmax=8,
                     ymin = 1, ymax=1.4)

Answer 1

Trailing zeros can be printed ensuring that 5.0 is interpreted as a character string, not a numeric value. Following the suggestions given here, the solution is based on the use of:

sprintf('"%2.1f"',5.0)
# [1] "\"5.0\""

Hence, the modified code is:

data(iris)
library(dplyr)
library(tidyr)
library(ggplot2)
library(psych)
library(gridExtra)
library(grid)
library(gtable)

summary.df <- iris %>% 
  group_by(Species) %>% 
  summarise(mean_length = mean(Sepal.Length),
            meanw_length = winsor.mean(Sepal.Length),
            sd_length = sd(Sepal.Length),
            sdw_length = winsor.sd(Sepal.Length, trim=0.05)) %>% 
  gather(key='Metric', value='Value', 
         mean_length, meanw_length,
         sd_length, sdw_length) %>% 
  mutate(Value = sprintf('"%2.1f"', Value)) %>% 
  spread(key=Species, value=Value)

summary.df$Metric[summary.df$Metric == 'mean_length'] <- 'Mean'
summary.df$Metric[summary.df$Metric == 'meanw_length'] <- 'Mean[w]'
summary.df$Metric[summary.df$Metric == 'sd_length'] <- 'SD'
summary.df$Metric[summary.df$Metric == 'sdw_length'] <- 'SD[w]'

tt_parse <- ttheme_default(core = list(fg_params=list(cex = 0.7,
                                                      parse=TRUE)), 
                           colhead = list(fg_params=list(cex = 0.7)),
                           rowhead = list(fg_params=list(cex = 0.7)))

iris %>% 
  ggplot(aes(x=Sepal.Length, fill=Species)) +
  geom_density(alpha = 0.8) + 
  coord_cartesian(ylim = c(0, 1.5)) +
  labs(title = 'Theme with Parsing') +
  annotation_custom( grob=tableGrob(summary.df, theme=tt_parse, rows=NULL), 
                     xmin=6.25, xmax=8,
                     ymin = 1, ymax=1.4)