"dot plot" relative to the mean with standard deviation

Question

In the attached graphic the dots mean the average value of selectivity of each specie. In this plot, I would like

1) to have larger font sizes of the smaller dots referents to the smaller body mass (bodymass<9Kg). I wanted to reduce the scale of difference between the dots by placing the smaller ones a little larger, but keeping the others biggest (>9Kg) in the same size.

2) I would like too add a black border around the dots (this is just to highlight the points)

3) I would like to add the standard deviation (sd) of each average dot. The mean and standard deviation values are shown respectively in columns "media'' and ''sp''.

Could someone help me? thanks

library(tidyverse)
Dataset %>%
ggplot(aes(x = media, y = specie, 
             colour = energetic_level, size = log(bodymass))) +
  geom_point(alpha = .9) +
  scale_colour_continuous(low = 'green', high = 'red') +
  labs(x = 'Response rate', y = 'Species') +
  ggthemes::theme_few() +
  theme(axis.text.x = element_text(angle = 90, vjust = .5)) 

media   dp  specie  bodymass    energetic_level
4.063478961 3.627269468 AAChlor_cyn 5000.01 3.2
4.05537378  3.585436083 ABOtol_cras 1206.61 2.4
3.999309751 3.818689333 ACMiop_tal  1248.86 3
3.945049659 3.855743536 BACerc_mit  5041.29 2.5
3.862515658 3.687924328 BCThry_swin 4000    2.8
3.655056928 3.732785731 DAHys_afri  14936.02    2.8
3.56041853  3.478167947 DBLep_cap   1500    3
3.402431689 3.446995588 DCCivet_civ 12075.58    4.6
3.401743858 3.569716116 FGenet_gen  1756.17 6.1
3.39029097  3.414370313 GALept_serv 11999.96    7
3.39009097  1.552336764 GBPhil_mont 4896.05 2.6
3.32029097  1.920646552 HOryct_afer 56175.2 5
3.239734182 3.540636613 IHipp_amph  1536310.4   3
3.154474564 3.526089786 JBSylv_grim 15639.15    3.2
2.883544415 3.007873613 MAPota_larv 69063.79    3.3
2.719993477 1.308813082 MBTrag_scri 43250.39    3
2.718552867 3.080761281 MCPant_pa   52399.99    7
1.982822501 2.085016316 MDRed_aru   58059.24    3
1.529854402 1.814623348 MFSync_caf  592665.98   3
1.443776834 1.254052861 NLox_afric  3824539.93  3
1.402107786 1.637998721 OCan_mes    22000   5.2
1.164299734 1.397597868 PPant_le    158623.93   6.8
0.887732043 1.318886523 QLyc_pict   21999.99    7
0.82952687  0.789227213 UCroc_croc  63369.98    7
0.782973623 0.570878282 VTrag_oryx  562592.69   2.7
0.477482615 0.624782141 YHipp_eq    264173.96   3

Answer 1

Please note that all of your questions have been addressed in one form or the other here on SO. Therefore this seems like an extended multi-duplicate post, and a thorough SO search would've given you all the answers/deetails you needed.

Specifically:

1. Changing dot sizes can be achieved using one of scale_size, scale_size_area or scale_radius. I'm not entirely clear on what you're asking in point 1, but a look at these functions and playing around with some of the function's parameters should give you what you want.

   Relevant posts are: defining minimum point size in ggplot2 - geom_point , ggplot2: how to manually adjust scale_area

2. To have filled dots with a black outline use pch = 21 and then use a fill aesthetic with colour = "black".

   This is a duplicate of: Place a border around points

3. Horizontal error bars can be achieved with geom_errorbarh; vertical ones (if necessary) if geom_errorbar.

   Relevant posts are: ggplot2 : Adding two errorbars to each point in scatterplot, Remove endpoints from error bars in ggplot2

All up, you'd end up with something like this:

library(ggthemes)
library(ggplot2)
ggplot(df, aes(media, specie, fill = energetic_level)) +
    geom_errorbarh(aes(xmax = media + dp, xmin = media - dp)) +
    geom_point(aes(size = log(bodymass)), pch = 21, colour = "black", alpha = .9) +
    scale_fill_continuous(low = 'green', high = 'red') +
    scale_size_area() + 
    labs(x = 'Response rate', y = 'Species') +
    ggthemes::theme_few() +
    theme(axis.text.x = element_text(angle = 90, vjust = .5))

---

Sample data

df <- read.table(text =
    "media   dp  specie  bodymass    energetic_level
4.063478961 3.627269468 AAChlor_cyn 5000.01 3.2
4.05537378  3.585436083 ABOtol_cras 1206.61 2.4
3.999309751 3.818689333 ACMiop_tal  1248.86 3
3.945049659 3.855743536 BACerc_mit  5041.29 2.5
3.862515658 3.687924328 BCThry_swin 4000    2.8
3.655056928 3.732785731 DAHys_afri  14936.02    2.8
3.56041853  3.478167947 DBLep_cap   1500    3
3.402431689 3.446995588 DCCivet_civ 12075.58    4.6
3.401743858 3.569716116 FGenet_gen  1756.17 6.1
3.39029097  3.414370313 GALept_serv 11999.96    7
3.39009097  1.552336764 GBPhil_mont 4896.05 2.6
3.32029097  1.920646552 HOryct_afer 56175.2 5
3.239734182 3.540636613 IHipp_amph  1536310.4   3
3.154474564 3.526089786 JBSylv_grim 15639.15    3.2
2.883544415 3.007873613 MAPota_larv 69063.79    3.3
2.719993477 1.308813082 MBTrag_scri 43250.39    3
2.718552867 3.080761281 MCPant_pa   52399.99    7
1.982822501 2.085016316 MDRed_aru   58059.24    3
1.529854402 1.814623348 MFSync_caf  592665.98   3
1.443776834 1.254052861 NLox_afric  3824539.93  3
1.402107786 1.637998721 OCan_mes    22000   5.2
1.164299734 1.397597868 PPant_le    158623.93   6.8
0.887732043 1.318886523 QLyc_pict   21999.99    7
0.82952687  0.789227213 UCroc_croc  63369.98    7
0.782973623 0.570878282 VTrag_oryx  562592.69   2.7
0.477482615 0.624782141 YHipp_eq    264173.96   3", header = T)