library(car)
install.packages("car")  # for ANOVA
install.packages("car")
setwd("C:\Users\local_admin\OneDrive - Lancaster University\Projects\DogHead\Analysis Codes\Stats Files")
ancast
in\One
source("~/.active-rstudio-document")
setwd("C:\Users\local_admin\OneDrive - Lancaster University\Projects\DogHead\Analysis Codes\Stats Files")
setwd("C:\\Users\\local_admin\\OneDrive - Lancaster University\\Projects\\DogHead\\Analysis Codes\\Stats Files")
source("~/.active-rstudio-document")
data1 <- read_csv('./HeadDifference.csv',show_col_types = FALSE)
library(readr)
setwd("C:\\Users\\local_admin\\OneDrive - Lancaster University\\Projects\\DogHead\\Analysis Codes\\Stats Files")
data1 <- read_csv('./HeadDifference.csv',show_col_types = FALSE)
View(data1)
head(HeadMovement, 5)
HeadMovement <- read_csv('./HeadDifference.csv',show_col_types = FALSE)
head(HeadMovement, 5)
View(HeadMovement)
HeadMovement <- read_csv('./HeadDifference.csv',show_col_types = TRUE)
HeadMovement <- read_csv('./HeadDifference.csv',show_col_types = FALSE)
head(HeadMovement, 5)
my_data <- read.csv(file.choose())
shapiro.test(HeadMovement$1)
shapiro.test(HeadMovement$Head_amp_7_width_1)
# shapiro.test(HeadMovement$Head_amp_7_width_1)
normality_results <- lapply(HeadMovement, function(x) shapiro.test(x))
normality_results
View(normality_results)
<- lapply(HeadMovement, function(x) shapiro.test(x))
names(shapiro_results) <- names(HeadMovement)
normality_results
# HeadMovement <- read_csv('./HeadDifference.csv',show_col_types = FALSE)
HeadMovement <- read.csv(file.choose())
View(HeadMovement)
aov(errorRate ~ Cursor * Amp * Width, data = df_ErrorRate)
model <- aov(errorRate ~ Cursor * Amp * Width, data = df_ErrorRate)
# HeadMovement <- read_csv('./HeadDifference.csv',show_col_types = FALSE)
df_ErrorRate <- read.csv(file.choose())
model <- aov(errorRate ~ Cursor * Amp * Width, data = df_ErrorRate)
View(df_ErrorRate)
names(df)
names(df_ErrorRate)
model <- aov(ART.ErrorRate..for.Cursor.Amp.Width ~ Cursor * Amp * Width, data = df_ErrorRate)
summary(model)
setwd("C:\\Users\\local_admin\\OneDrive - Lancaster University\\Projects\\Participant Data")
setwd("C:\\Users\\local_admin\\OneDrive - Lancaster University\\Projects\\Participant Data")
setwd("C:\\Users\\local_admin\\OneDrive - Lancaster University\\Projects\\DOG Head\\Participant Data")
setwd("C:\\Users\\local_admin\\OneDrive - Lancaster University\\Projects\\DOGHead\\Participant Data")
participant_csv_files <- list.files(path = ".", pattern = "Trial.*\\.csv$", recursive = TRUE, full.names = TRUE)
participant_csv_files
participant_csv_files <- list.files(path = ".", pattern = "Trail.*\\.csv$", recursive = TRUE, full.names = TRUE)
participant_csv_files
participant_csv_files <- list.files(path = ".", pattern = "Trail.*\\.csv$", recursive = TRUE, full.names = TRUE)
df_list <- list()
for (csv_file in participant_csv_files)
{
df <- read.csv(csv_file)
df_list <- append(df_list, list(df))
}
# Combine all data frames into one
df_trial_raw <- do.call(rbind, df_list)
View(df_trial_raw)
ls()
setwd("C:\\Users\\local_admin\\OneDrive - Lancaster University\\Projects\\DOGHead\\Participant Data")
participant_csv_files <- list.files(path = ".", pattern = "Trail.*\\.csv$", recursive = TRUE, full.names = TRUE)
df_trial_raw <- NULL
for (csv_file in csv_files) {
combined_df <- rbind(combined_df, read.csv(csv_file))
}
ls()
setwd("C:\\Users\\local_admin\\OneDrive - Lancaster University\\Projects\\DOGHead\\Participant Data")
participant_csv_files <- list.files(path = ".", pattern = "Trail.*\\.csv$", recursive = TRUE, full.names = TRUE)
df_trial_raw <- NULL
for (csv_file in participant_csv_files) {
combined_df <- rbind(combined_df, read.csv(csv_file))
}
ls()
setwd("C:\\Users\\local_admin\\OneDrive - Lancaster University\\Projects\\DOGHead\\Participant Data")
participant_csv_files <- list.files(path = ".", pattern = "Trail.*\\.csv$", recursive = TRUE, full.names = TRUE)
df_trial_raw <- NULL
for (csv_file in participant_csv_files) {
df_trial_raw <- rbind(df_trial_raw, read.csv(csv_file))
}
ls
ls()
rm(ls())
rm(ls
)
rm(ls)
rm(list = ls())
rm(list = ls())
setwd("C:\\Users\\local_admin\\OneDrive - Lancaster University\\Projects\\DOGHead\\Participant Data")
participant_csv_files <- list.files(path = ".", pattern = "Trail.*\\.csv$", recursive = TRUE, full.names = TRUE)
df_trial_raw <- NULL
for (csv_file in participant_csv_files) {
df_trial_raw <- rbind(df_trial_raw, read.csv(csv_file))
}
df_trial_raw
View(df_trial_raw)
df_trial_raw.names
df_trial_raw.columns
names(df_trial_raw)
df_trial <- df_trial_raw[df_trial_raw$TrialInBlock != 1,]
participant_csv_files
list.files(path = ".", pattern = "Trail PID-P9.*\\.csv$", recursive = TRUE, full.names = TRUE)
mean_MT <- mean(df_trial$MovementTime)
std_MT <- sd(df_trial$MovementTime)
df_trial <- df_trial[df_trial$MovementTime <= mean_MT+3*std_MT,]
df_trial <- df_trial[(df_trial$MovementTime <= mean_MT + 3*std_MT) & (df_trial$MovementTime >= mean_MT - 3*std_MT),]
library(dplyr)
df_trial %>%
group_by(PID, CursorType, Amplitude, TargetSize) %>%
summarise(
movementTime = mean(df_trial$MovementTime)
)
x <- c(1, 2, NA, 4, 5)
mean_value <- mean(x)
df_mt <- df_trial %>%
group_by(PID, CursorType, Amplitude, TargetSize) %>%
summarise(
movementTime = mean(df_trial$MovementTime)
)
View(df_mt)
df_mt <- df_trial %>%
group_by(PID, CursorType, Amplitude, TargetSize) %>%
summarise(
# movementTime = mean(df_trial$MovementTime)
)
View(df_mt)
df_mt <- df_trial %>%
group_by(PID, CursorType, Amplitude, TargetSize) %>%
summarise(
movementTime = mean(MovementTime)
)
df_trial$CursorType <- gsub("Head Cursor|Dynmaic Gain Head Cursor|DOGHead1|DOGHead2", "NoGain|Slow|Mid|Fast", df_trial$CursorType)
df_mt <- df_trial %>%
group_by(PID, CursorType, Amplitude, TargetSize) %>%
summarise(
movementTime = mean(MovementTime)
)
pattern_replacements <- c("Head Cursor" = "NoGain",
"Dynmaic Gain Head Cursor" = "Slow",
"DOGHead1" = "Mid",
"DOGHead2" = "Fast")
df_trial$CursorType <- gsub(paste(names(pattern_replacements), collapse = "|"),
pattern_replacements,
df_trial$CursorType)
df_mt <- df_trial %>%
group_by(PID, CursorType, Amplitude, TargetSize) %>%
summarise(
movementTime = mean(MovementTime)
)
pattern_replacements <- c("Head Cursor" = "NoGain",
"Dynmaic Gain Head Cursor" = "Slow",
"DOGHead1" = "Mid",
"DOGHead2" = "Fast")
for (pattern in names(pattern_replacements)) {
df_trial$CursorType <- gsub(pattern, pattern_replacements[pattern], df_trial$CursorType)
}
df_mt <- df_trial %>%
group_by(PID, CursorType, Amplitude, TargetSize) %>%
summarise(
movementTime = mean(MovementTime)
)
df_trial$CursorType <- sub("Head Cursor", "Head", df_trial$CursorType)
df_trial$CursorType <- sub("Dynmaic Gain Head Cursor", "Slow", df_trial$CursorType)
df_trial$CursorType <- sub("DOGHead1", "Mid", df_trial$CursorType)
df_trial$CursorType <- sub("DOGHead2", "Fast", df_trial$CursorType)
df_mt <- df_trial %>%
group_by(PID, CursorType, Amplitude, TargetSize) %>%
summarise(
movementTime = mean(MovementTime)
)
rm(list = ls())
setwd("C:\\Users\\local_admin\\OneDrive - Lancaster University\\Projects\\DOGHead\\Participant Data")
participant_csv_files <- list.files(path = ".", pattern = "Trail.*\\.csv$", recursive = TRUE, full.names = TRUE)
df_trial_raw <- NULL
for (csv_file in participant_csv_files) {
df_trial_raw <- rbind(df_trial_raw, read.csv(csv_file))
}
df_trial <- df_trial_raw[df_trial_raw$TrialInBlock != 1,]
mean_MT <- mean(df_trial$MovementTime)
std_MT <- sd(df_trial$MovementTime)
df_trial <- df_trial[(df_trial$MovementTime <= mean_MT + 3*std_MT) & (df_trial$MovementTime >= mean_MT - 3*std_MT),]
df_trial$CursorType <- sub("Head Cursor", "Head", df_trial$CursorType)
df_trial$CursorType <- sub("Dynmaic Gain Head Cursor", "Slow", df_trial$CursorType)
df_trial$CursorType <- sub("DOGHead1", "Mid", df_trial$CursorType)
df_trial$CursorType <- sub("DOGHead2", "Fast", df_trial$CursorType)
df_mt <- df_trial %>%
group_by(PID, CursorType, Amplitude, TargetSize) %>%
summarise(
movementTime = mean(MovementTime)
)
df_trial$CursorType <- sub("Dynmaic Gain Head", "Slow", df_trial$CursorType)
df_mt <- df_trial %>%
group_by(PID, CursorType, Amplitude, TargetSize) %>%
summarise(
movementTime = mean(MovementTime)
)
model <- aov(movementTime ~ CursorType * Amplitude * TargetSize, data = df_mt)
summary(model)
df_mt %>%
group_by(PID, CursorType, Amplitude, TargetSize) %>%
get_summary_stats(movementTime, type='mean_sd')
library(rstatix)
install.packages("rstatix", repos = "https://cloud.r-project.org")
library(rstatix)
df_mt %>%
group_by(PID, CursorType, Amplitude, TargetSize) %>%
get_summary_stats(movementTime, type='mean_sd')
df_mt %>%
group_by(CursorType, Amplitude, TargetSize) %>%
get_summary_stats(movementTime, type='mean_sd')
df_mt %>%
group_by(CursorType, Amplitude, TargetSize) %>%
group_by(CursorType, Amplitude) %>%
get_summary_stats(movementTime, type='mean_sd')
