In AmandaPMurphy/SemesterProjectLabII:

knitr::opts_chunk$set(echo = TRUE)

library(tibble)
romeo_juliet <- tibble(subjects = 1:20,
                       Group = rep(c("No Context",
                                 "Context Before",
                                 "Context After",
                                 "Partial Context"), each = 5),
                       Comprehension = c(3,3,2,4,3,
                                         5,9,8,4,9,
                                         2,4,5,4,1,
                                         5,4,3,5,4
                                   )
                          )

romeo_juliet$Group <- factor(romeo_juliet$Group,
                             levels = c("No Context",
                                 "Context Before",
                                 "Context After",
                                 "Partial Context"))

knitr::kable(romeo_juliet)

using mutate function to create new columns in the data set:: mututate(name_your_column <- function_you_want (dataset$column you want))

library(dplyr)
# get grand mean
grand_mean <- mean(romeo_juliet$Comprehension)

# get squared deviations from grand mean
SS_total_table <- romeo_juliet %>%
  mutate(grand_mean = mean(romeo_juliet$Comprehension)) %>%
  mutate(deviations = Comprehension - grand_mean,
         sq_deviations = (Comprehension - grand_mean)^2)

#sum them
SS_total <- sum(SS_total_table$sq_deviations)

# get group means
group_means <- romeo_juliet %>%
  group_by(Group) %>%
  summarize(mean_Comprehension = mean(Comprehension),.groups = 'drop')

# get squared deviations between grand mean and group means
SS_between_table <- romeo_juliet %>%
  mutate(grand_mean = mean(romeo_juliet$Comprehension),
         group_means = rep(group_means$mean_Comprehension, each = 5)) %>%
  mutate(deviations = group_means - grand_mean,
         sq_deviations = (group_means - grand_mean)^2)

SS_between <- sum(SS_between_table$sq_deviations)

# get group means
group_means <- romeo_juliet %>%
  group_by(Group) %>%
  summarize(mean_Comprehension = mean(Comprehension), .groups = 'drop')

# get squared deviations between group means and original data points
SS_within_table <- romeo_juliet %>%
  mutate(group_means = rep(group_means$mean_Comprehension, each = 5)) %>%
  mutate(deviations = group_means - Comprehension,
         sq_deviations = (group_means - Comprehension)^2)

SS_within <- sum(SS_within_table$sq_deviations)

library(tibble)
example_data <- tibble(Group = rep(c("A","B"), each = 5),
                       DV = c(2,4,3,5,4,7,6,5,6,7))

t_test <- t.test(DV~Group, var.equal=TRUE,data = example_data)
my_aov <- summary(aov(DV~Group, data = example_data))

t_test$p.value
my_aov[[1]]$`Pr(>F)`[1]

t_test$statistic
round(t_test$p.value, digits = 5) == round(my_aov[[1]]$`Pr(>F)`[1], digits=5)

my_aov[[1]]$`F value`[1]

t_test$statistic^2

library(data.table)
library(readr)
all_data <- read_csv("data/Jamesetal.csv")

find IV = condition and label columns

all_data$Condition <- as.factor(all_data$Condition)
levels(all_data$Condition) <- c("Control",
                                "Reactivation+Tetris", 
                                "Tetris_only",
                                "Reactivation_only")

library(ggplot2)
ggplot(all_data, aes(x=Condition, y=Days_One_to_Seven_Number_of_Intrusions,
                     fill= Condition))+
  geom_bar(stat="summary",fun="mean", position="dodge")

my_aov2 <- aov(Days_One_to_Seven_Number_of_Intrusions~Condition, data= all_data)

print(my_aov2)

library(papaja)

apa_print(my_aov2)

apa_printout <- papaja::apa_print(my_aov2)

summary(my_aov2)

my_aov

Using a one factor between subjects ANOVA, group means were used as the four interventions: Control(no intervention), Reactivation Plus Tetris, Tetris only, and Reactivation only. An effect between interventions types was found significant at r apa_printout$full_result$Condition.

AmandaPMurphy/SemesterProjectLabII documentation built on May 23, 2022, 3:16 p.m.