Plotting individual estimates

plot_individual_estimates.R

if (!require("pacman")) install.packages("pacman")
pacman::p_load("ggplot2", "Rmisc")
#############################################################
# Preparing data (you can skip this and go to the plot : ) 
#############################################################
# Simulate some date by trial
parts = 30 # Our participants
trials = 60 # Our Trials
df=data.frame()

# Simulate some date by trial
parts = 30 # Our participants
trials = 60 # Our Trials
conds = 2 # How many conditions we have
df=data.frame()
for(i in 1:30 ){
  # As I'm not focusing on simulatng realistic data, the responses are totally trivial
  mean = rnorm(1, 0.5, 1)
  response1 = rnorm(trials/2, mean = mean, sd = sd)
  response2 = rnorm(trials/2, mean = mean - runif(1, 0.1, 0.4), sd = sd)
  response <- c(response1, response2)
  type = c(rep("a", 30), rep("b", 30))
  cond = "A"
  df2 = data.frame( part = rep(i, 60), cond = rep(cond, 60), type = type, RT = response)
  df=rbind(df, df2)
}
for(i in 30:60 ){
  mean = rnorm(1, 0.5, 3)
  sd = runif(1, 2, 5)
  response1 = rnorm(trials/2, mean = mean, sd = sd)
  response2 = rnorm(trials/2, mean = mean - runif(1, 0.5, 4), sd = sd)
  response <- c(response1, response2)
  type = c(rep("a", 30), rep("b", 30))
  cond = "B"
  df2 = data.frame( part = rep(i, 60), cond = rep(cond, 60), type = type, RT = response)
  df=rbind(df, df2)
}


#############################################################
# Prepare the data
#############################################################

tab = summarySEwithin(data = df, measurevar = "RT",
                      withinvars = c("type", "part"), betweenvars = "cond")
# Get the mean of each participant in both types. 
# this is just for ordering the data 
tab = tab %>% group_by(part) %>%
  summarise(mean_rt = mean(RT)) %>%
  left_join(tab, . )
# Ordering by mean RT
tab = arrange(tab, desc(mean_rt), part)
#############################################################
# Do the plot  
#############################################################
pd <- position_dodge(0.4)
# Change them values to do a clean one
theme_set(theme_bw())
my_theme <- theme(panel.grid.major.x = element_blank(),
                  panel.grid.minor.x= element_blank(),
                  panel.background = element_blank(),
                  panel.border = element_blank(),
                  strip.text.x = element_text(size=14), strip.text.y = element_text(size=16),
                  strip.background = element_rect(colour="black"),
                  axis.text.x = element_text(color="black", size=14),
                  axis.text.y = element_text(color="black", size=14),
                  axis.title.x = element_text(face="bold", size=18),
                  axis.title.y = element_text(face="bold",size=18),
                  axis.ticks = element_blank(),
                  legend.title = element_text(size=14), legend.text = element_text(size = 14),
                  plot.title = element_text(vjust = 1.3, face="bold", size=20),
                  plot.margin = unit(c(1.5, 1.5, 1.5, 1.5), "cm")
)

ggplot(tab, aes(x = reorder(part, -mean_rt), y = RT, group = type, col = type)) +
  geom_point(position=pd) +
  coord_flip() + facet_wrap(~cond) +
  ylab("RT") + xlab("Participant") + ggtitle("Reaction times") +
  geom_linerange(aes(ymin= RT-se, ymax= RT+se), size = 1, position = pd) +
  my_theme +
  theme(axis.text.y = element_blank(),
        legend.position = c(.9,.8))