barToDotplot.R

library(ggplot2)
library(dplyr)
library(tidyr)

##DATA
WM <- structure(list(ID = structure(1:32, .Label = c("10843_20151015", 
"11228_20150309", "11339_20141104", "11340_20141031", "11341_20141118", 
"11348_20141119", "11349_20141124", "11351_20141202", "11352_20141230", 
"11354_20141205", "11355_20141230", "11356_20150105", "11357_20150122", 
"11358_20150129", "11359_20150203", "11360_20150129", "11363_20150310", 
"11365_20150407", "11367_20150430", "11368_20150505", "11369_20150519", 
"11374_20150529", "11386_20150619", "11390_20150721", "11407_20150716", 
"11418_20151201", "11423_20150916", "11424_20150908", "11432_20150922", 
"11433_20150924", "11454_20151019", "11466_20151125"), class = "factor"), 
    Cohort = structure(c(2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 
    1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L), .Label = c("Clinical", "Control"
    ), class = "factor"), condition = structure(c(1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "0[delay_ld1]", class = "factor"), 
    CB_ld1 = c(-16.281676, -5.017022, -5.227623, 3.343006, -8.728013, 
    -1.673829, -2.75081, 8.145376, 2.876325, -0.918119, 7.712771, 
    -7.031173, -4.108706, -0.983761, -1.270249, -5.455893, 10.909173, 
    -4.585588, 0.217045, -0.777112, -5.737579, -4.112495, 0.951513, 
    -7.242032, -1.584525, 3.626982, 0.24173, 2.941472, 2.201748, 
    -3.817421, 9.010105, -0.619158), R_DLPFC_ld1 = c(-14.062443, 
    12.302224, 9.5302, 7.125673, -4.699015, 8.689309, 6.595875, 
    22.019142, 1.856274, -5.466109, 16.465215, -19.421602, 4.765969, 
    -5.260434, 3.913552, -11.227274, 34.114333, -4.050198, 16.788266, 
    6.188547, 19.822619, 6.039495, 12.00768, 10.942528, 17.668428, 
    12.03592, 28.771312, -8.235222, 30.612336, -0.401151, 21.059559, 
    1.263492), CB_ld3 = c(-0.321061, 3.125329, 5.64455, 1.283899, 
    -2.637401, -0.953091, -1.279139, -1.536689, 1.100666, -3.279125, 
    -6.739991, 3.772425, -2.765257, 3.725869, -0.157239, -10.725254, 
    -8.323062, -0.134436, -4.7262, -2.810531, -4.83956, 2.507507, 
    -4.606195, 8.076474, -7.005166, -2.298958, -1.281827, -11.093254, 
    2.375286, -0.834906, -3.126058, -2.295052), R_DLPFC_ld3 = c(16.879131, 
    15.386749, 37.125275, 23.745324, 15.742828, 10.105206, 4.72799, 
    -11.066886, 5.702247, 3.33002, 18.208506, 31.60593, 6.52933, 
    65.439944, 12.56645, -8.036658, -13.942518, 9.507799, 5.284815, 
    9.549544, -1.01616, 15.026034, -18.944697, 42.337117, -26.115444, 
    -1.401052, 25.448017, -22.440274, 22.527186, 10.789422, -2.151482, 
    -10.630512)), .Names = c("ID", "Cohort", "condition", "CB_ld1", 
"R_DLPFC_ld1", "CB_ld3", "R_DLPFC_ld3"), class = "data.frame", row.names = c(NA, 
-32L))


### Summarise Data

  names(WM)[c(5,7)] <- c('low','high')
  graph_data <- WM %>% 
     # gather BOLD value in columns 'low' and 'high' into one column called 'BOLD' 
     #  with a new 'load' column indicating if the BOLD value came from 'high' or 'low'
     gather(load,BOLD,low,high) %>% 
     # group by cohort and load
     group_by(Cohort,load) %>%
     # get mean and se
     summarise(mu=mean(BOLD),
               se=sd(BOLD)/sqrt(n()) ) %>%
     # get se bounds for errorbar
     mutate(ymin=mu-se,ymax=mu+se)

  

### Plot Data

  p <- ggplot(data=graph_data) +

       # load by mean, color and shape by group
       aes(x=load, y=mu, 
           color=Cohort,
           linetype=Cohort,
           fill=Cohort,
           shape=Cohort,group=Cohort,
           ymax=ymax, ymin=ymin) +

       # line first so shapes are on top
       geom_line()+
       geom_errorbar(width=.25)+
       geom_point(size=15)+

       # style points:
       #  filled square and diamond of two gray colors 
       scale_shape_manual(values = c(22, 23))+
       scale_fill_manual(values=c("#404040","#ACACAC")) +
       scale_color_manual(values=c("#404040","#ACACAC")) +

       # labels
       xlab("Load") + 
       ylab("% BOLD Chage") + 

       # graph display settings
       coord_cartesian(ylim=c(-20,20))+
       theme_bw(base_size=20)+
       theme(legend.position="none")+
       theme(panel.border = element_blank(), 
             panel.grid.major = element_blank(),
             panel.grid.minor = element_blank(),
             axis.line = element_line(colour = "black"))

print(p)