# Drill 8 (April 4, 2024)

# new libraries for most of you
# install.packages("ez") 
# install.packages("lme4")

# Libraries ---------------------------------------------------------------
library(tidyverse)
library(ez)
library(lme4)

# options(digits = 2) # use only if you want to keep R from going wild with decimal places

# The data ----------------------------------------------------------------
df <- read_csv("http://whlevine.hosted.uark.edu/psyc5143/iguanas.csv")

head(df, 15) 

# Iguanas are thought to use their tongues to sample the environment to detect
# friend or foe. To experimentally test this, 10 iguanas are each exposed to
# five different kinds of sand: two control conditions: home and clean; three
# treatment conditions: iguana (fellow species members), lizard (a competitor
# species), and kangaroo rats (another competitor species). The number of times
# they extrude their tongue is counted to see if it varies across environments.

# The ez package ----------------------------------------------------------

# Descriptive stats

df %>%
        group_by(sand) %>% 
        summarise(n = n(), 
                  M = mean(extrusions),
                  SD = sd(extrusions))

ezStats(data = df,             # the data frame
        wid = ID,              # the ID variable for subjects
        within = sand,         # the within-subjects factor
        dv = extrusions)       # the DV

# Inferential stats (i.e. RM ANOVA)
ezANOVA(data = df,            # the data
        wid = ID,             # an identifier for the unit in which repeated measures occur
        within = sand,        # the within-subjects factor
        dv = extrusions,      # the DV
        detailed = T          # include if you want to get SSs 
)
# Note the result for the sand factor: F(4, 36) = 8.8 
# (we'll compare this with another model shortly)

ezPlot(data = df,              # a plot of results
       dv = extrusions, 
       wid = ID, 
       within = sand, 
       x = sand)            

# RM as a mixed-effects model ---------------------------------------------

# lmer(outcome ~ fixed_effect_1 * fixed_effect_2 + (random_effects | within_ID_variable))

df$id.f <- as.factor(df$ID)
df$sand <- as.factor(df$sand) # not necessary, strictly speaking; lmer treats 
                              # it as a factor by default, but this is a good habit


modelLMM <- lmer(extrusions ~  1 + factor(sand) + (1|id.f), df)

anova(modelLMM)

ezANOVA(data = df,            
        wid = ID,             
        within = sand,        
        dv = extrusions,      
        detailed = T          
)$ANOVA$F[2]

# paired-sample t-tests are good, too! (just keep FWER/FDR in mind)

df %>% 
  filter(sand == "clean" | sand == "iguana") %>% 
  t.test(extrusions ~ sand, paired = T, data = .)

df %>% 
  filter(sand == "clean" | sand == "lizard") %>% 
  t.test(extrusions ~ sand, paired = T, data = .)