How to show whiskers and points on violin plots?

I have a dataframe df with the following data. I want to plot the logCPM expression of the gene between two groups A and B.

Samples  Type   GeneA
Sample1    B    14.82995162
Sample2    B    12.90512275
Sample3    B    9.196524783
Sample4    A    19.42866012
Sample5    A    19.70386922
Sample6    A    16.22906914
Sample7    A    12.48966785
Sample8    B    15.53280377
Sample9    A    9.345795955
Sample10    B   9.196524783
Sample11    B   9.196524783
Sample12    B   9.196524783
Sample13    A   9.434355615
Sample14    A   15.27604692
Sample15    A   18.90867329
Sample16    B   11.71503095
Sample17    B   13.7632545
Sample18    A   9.793864295
Sample19    B   9.196524783
Sample20    A   14.52562066
Sample21    A   13.85116605
Sample22    A   9.958492229
Sample23    A   17.57075876
Sample24    B   13.04499079
Sample25    B   15.33577937
Sample26    A   13.95849295
Sample27    B   9.196524783
Sample28    A   18.20524388
Sample29    B   17.7058873
Sample30    B   14.0199393
Sample31    A   16.21499069
Sample32    A   14.171432
Sample33    B   9.196524783
Sample34    B   9.196524783
Sample35    B   15.16648035
Sample36    B   12.9435081
Sample37    B   13.81971106
Sample38    B   15.82901231

I tried making a violin plot using ggviolin.

library("ggpubr")
pdf("eg.pdf", width = 5, height = 5)
p <- ggviolin(df, x = "Type", y = "GeneA", fill = "Type",
          color = "Type", palette = c("#00AFBB", "#FC4E07"),
          add="boxplot",add.params = list(fill="white"),
          order = c("A", "B"),
          ylab = "GeneA (logCPM)", xlab = "Groups")
ggpar(p, ylim = c(5,25))
dev.off()

I got the violin plot like this .

1) In this I don't see any whiskers and any points on violin.

2) Is there a way to show which point is which sample? like giving a different color to the point (for eg: I'm interested in Sample 10. I want to give different color to that point because I'm interested to see the expression of that)

Thank you

May I suggest using elephant/raincloud or hybrid boxplot plots instead?

From the blog post linked above:

Violin plots mirror the data density in a totally uninteresting/uninformative way, simply repeating the same exact information for the sake of visual aesthetic.

In raincloud plot, we get basically everything we need: eyeballed statistical inference, assessment of data distributions (useful to check assumptions), and the raw data itself showing outliers and underlying patterns.

library(tidyverse)
library(ggrepel)

df <- read_table2(txt)

# create new variable for coloring & labeling `Sample10` pts
df <- df %>% 
  mutate(colSel = ifelse(Samples == 'Sample10', '#10', 'dummy'),
         labSel = ifelse(Samples == 'Sample10', '#10', ''))

# create summary statistics
sumld <- df %>%
  group_by(Type) %>%
  summarise(
    mean     = mean(GeneA, na.rm = TRUE),
    median   = median(GeneA, na.rm = TRUE),
    sd       = sd(GeneA, na.rm = TRUE),
    N        = n(),
    ci       = 1.96 * sd/sqrt(N),
    lower95  = mean - ci,
    upper95  = mean + ci,
    lower    = mean - sd,
    upper    = mean + sd) %>% 
  ungroup()
sumld
#> # A tibble: 2 x 10
#>   Type   mean median    sd     N    ci lower95 upper95 lower upper
#>   <chr> <dbl>  <dbl> <dbl> <int> <dbl>   <dbl>   <dbl> <dbl> <dbl>
#> 1 A      14.7   14.5  3.54    17  1.68    13.0    16.3 11.1   18.2
#> 2 B      12.4   12.9  2.85    21  1.22    11.2    13.6  9.54  15.2

raincloud plot

## get geom_flat_violin function
## https://gist.github.com/benmarwick/b7dc863d53e0eabc272f4aad909773d2
## mirror: https://pastebin.com/J9AzSxtF 
devtools::source_gist("2a1bb0133ff568cbe28d", filename = "geom_flat_violin.R")

pos <- position_jitter(width = 0.15, seed = 1)

p0 <- ggplot(data = df, aes(x = Type, y = GeneA, fill = Type)) +
  geom_flat_violin(position = position_nudge(x = .2, y = 0), alpha = .8) +
  guides(fill = FALSE) +
  guides(color = FALSE) +
  scale_color_brewer(palette = "Dark2") +
  scale_fill_brewer(palette = "Dark2") +
  theme_classic()

# raincloud plot
p1 <- p0 + 
  geom_point(aes(color = Type), 
             position = pos, size = 3, alpha = 0.8) +
  geom_boxplot(width = .1, show.legend = FALSE, outlier.shape = NA, alpha = 0.5)
p1

# coloring Sample10
p0 +
  geom_point(aes(color = colSel), 
             position = pos, size = 3, alpha = 0.8) +
  geom_text_repel(aes(label = labSel),
                  point.padding = 0.25,
                  direction = 'y',
                  position = pos) +
  geom_boxplot(width = .1, show.legend = FALSE, outlier.shape = NA, alpha = 0.5) +
  scale_color_manual(values = c('dummy' = 'grey50', '#10' = 'red'))

# errorbar instead of boxplot
p0 + 
  geom_point(aes(color = colSel), 
             position = pos, size = 3, alpha = 0.8) +
  geom_point(data = sumld, aes(x = Type, y = mean), 
             position = position_nudge(x = 0.3), size = 3.5) +
  geom_text_repel(aes(label = labSel),
                  point.padding = 0.25,
                  direction = 'y',
                  position = pos) +
  geom_errorbar(data = sumld, aes(ymin = lower95, ymax = upper95, y = mean), 
                position = position_nudge(x = 0.3), width = 0) +
  guides(fill = FALSE) +
  guides(color = FALSE) +
  scale_color_manual(values = c('dummy' = 'grey50', '#10' = 'red')) +
  scale_fill_brewer(palette = "Dark2") +
  theme_classic()

hybrid boxplot using geom_boxjitter() from the ggpol package

## https://stackoverflow.com/a/49338481/ 
library(ggpol)

half_box <- ggplot(df) + geom_boxjitter(aes(x = Type, y = GeneA, 
                                            fill = Type, color = Type),
                                        jitter.shape = 21, jitter.color = NA, 
                                        jitter.height = 0, jitter.width = 0.04,
                                        outlier.color = NA, errorbar.draw = TRUE) +
  scale_color_brewer(palette = "Dark2") +
  scale_fill_brewer(palette = "Dark2") +
  theme_classic()
half_box