Load libraries
library(ggplot2)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr)
Load data
Demographics
df <- data %>%
select(Condition, Køn) %>%
mutate(Køn = factor(Køn, levels = c("Male", "Female")))
# Create a histogram (or rather a bar plot, since Køn is categorical)
p <- ggplot(df, aes(x = Køn,fill = Condition)) +
geom_bar(position = "dodge") +
labs(title = "Gender by Condition (AI initiated vs User initiated)", x = "Gender", y = "Antal") +
scale_fill_manual(values = c(
"AI" = "steelblue",
"User" = "tomato")) +
theme_minimal()
# Show plot
p
# Save plot
ggsave(filename = paste0("plots/","Questionnaire_Gender",".png"), plot = p, width = 10, height = 6, dpi = 300, bg = "white")
df <- data %>%
select(Condition, Alder) %>%
mutate(Alder = factor(Alder, levels = c("21-25","26-30")))
# Create a histogram (or rather a bar plot, since Age is categorical)
p <- ggplot(df, aes(x = Alder,fill = Condition)) +
geom_bar(position = "dodge") +
labs(title = "Age by Condition (AI initiated vs User initiated)", x = "Age", y = "Antal") +
scale_fill_manual(values = c(
"AI" = "steelblue",
"User" = "tomato")) +
theme_minimal()
# Show plot
p
ggsave(filename = paste0("plots/","Questionnaire_Age",".png"), plot = p, width = 10, height = 6, dpi = 300, bg = "white")
# Load your data
data <- read.csv("./data/AI_usage_data.csv", dec = ",")
## Warning in read.table(file = file, header = header, sep = sep, quote = quote, :
## incomplete final line found by readTableHeader on './data/AI_usage_data.csv'
data
## X ChatGPT Google.Gemini Siri Microsoft.Copilot GitHub.Copilot GrammalyGO
## 1 AI 9 0 2 1 2 0
## 2 User 8 3 0 2 1 0
## CanvaAI I.do.not.use.AI Other
## 1 1 0 3
## 2 0 0 2
# Define your labels
my.labels <- c(
"ChatGPT", "Google Gemini", "Siri", "Microsoft Copilot", "GitHub Copilot", "GrammalyGO", "CanvaAI", "I do not use AI", "Other"
)
# Manually create the dataframe
df <- tibble::tibble(
Group = c("AI", "User"),
ChatGPT = c(9, 8),
Google_Gemini = c(0, 3),
Siri = c(2, 0),
Microsoft_Copilot = c(1, 2),
GitHub_Copilot = c(2, 1),
GrammarlyGO = c(0, 0),
CanvaAI = c(1, 0)
)
# Pivot longer for ggplot
df_long <- df %>%
pivot_longer(cols = -Group, names_to = "Tool", values_to = "Count")
# Reorder Tool levels based on total count (descending)
df_long <- df_long %>%
group_by(Tool) %>%
mutate(TotalCount = sum(Count)) %>%
ungroup() %>%
mutate(Tool = reorder(Tool, -TotalCount))
df_long
## # A tibble: 14 × 4
## Group Tool Count TotalCount
## <chr> <fct> <dbl> <dbl>
## 1 AI ChatGPT 9 17
## 2 AI Google_Gemini 0 3
## 3 AI Siri 2 2
## 4 AI Microsoft_Copilot 1 3
## 5 AI GitHub_Copilot 2 3
## 6 AI GrammarlyGO 0 0
## 7 AI CanvaAI 1 1
## 8 User ChatGPT 8 17
## 9 User Google_Gemini 3 3
## 10 User Siri 0 2
## 11 User Microsoft_Copilot 2 3
## 12 User GitHub_Copilot 1 3
## 13 User GrammarlyGO 0 0
## 14 User CanvaAI 0 1
# Plot
p <- ggplot(df_long, aes(x = Tool, y = Count, fill = Group)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8), width=0.8) +
labs(title = "AI Tools Usage by Group", x = "Tool", y = "Count") +
theme_minimal() +
scale_y_continuous(breaks = seq(0, 10, 1)) +
scale_fill_manual(values = c(
"AI" = "steelblue",
"User" = "tomato")) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "top",
panel.grid.major = element_line(color = "grey80"),
panel.grid.minor = element_line(color = "grey90")
)
# Save the plot
ggsave(filename = "plots/Questionnaire_AI_usage_by_group.png", plot = p, width = 10, height = 6, dpi = 300, bg = "white")
# Show plot
p
Questionnaire
library(ggplot2)
library(dplyr)
library(tidyr)
Questionnaire_plot <- function(data, questions, columns, title, title_offset) {
# Prepare the data
Engagement <- data %>%
select(Condition, all_of(columns)) %>%
mutate(Condition = factor(Condition, levels = c("AI", "User"))) %>%
mutate(across(2:ncol(.), as.numeric))
# Pivot longer
Engagement_long <- Engagement %>%
pivot_longer(cols = -Condition,
names_to = "Question",
values_to = "Score") %>%
mutate(Question = factor(Question, levels = unique(Question)))
# Plot
p <- ggplot(Engagement_long, aes(x = Question, y = Score, fill = Condition)) +
stat_summary(fun = mean, geom = "bar", position = position_dodge()) +
labs(title = title,
x = "", y = "Average Score") +
stat_summary(fun = mean,
geom = "text",
aes(label = round(..y.., 2)),
position = position_dodge(width = 0.9),
vjust = 0.4, hjust = 1.2, size = 3.5) +
scale_x_discrete(labels = questions) +
scale_fill_manual(values = c(
"AI" = "steelblue",
"User" = "tomato")) +
theme_minimal() +
theme(legend.position = "top",panel.grid.minor = element_line(color = "grey78")) +
coord_flip()
# Save plot
ggsave(filename = paste0("plots/","Questionnaire_",title, ".png"), plot = p, width = 10, height = 6, dpi = 300, bg = "white")
# Return plot
return(p)
}
# Load your data
data <- read.csv("./data/Questionnaire_data.csv", dec = ",")
# Define your labels
my.labels <- c(
"I hvilken grad følte du dig mentalt fordybet i oplevelsen? \n(1 = Slet ikke, 7 Rigtig meget)",
"Hvor involverende var oplevelsen? \n(1 = Slet ikke, 7 Rigtig meget)",
"Hvor fuldstændigt blev dine sanser engageret? \n(1 = Slet ikke, 7 Rigtig meget)",
"I hvilken grad oplevede du en følelse af virkelighed?\n(1 = Slet ikke, 7 Rigtig meget)",
"Hvor engagerende var samtalen? \n(1 = Slet ikke, 7 Rigtig meget)",
"Hvor afslappende eller spændende var oplevelsen? \n(1 = Meget afslappende, 7 Meget spændende)"
)
# Call the function
Questionnaire_plot(data, questions = my.labels, columns = 6:11, title = "Average Engagement per question by Condition",title_offset=0)
## Warning: The dot-dot notation (`..y..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(y)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Load your data
data <- read.csv("./data/Questionnaire_data.csv", dec = ",")
# Define your labels
my.labels <- c(
"Brugen af ROSIE i mit projekt \nville gøre mig i stand til at udføre opgaver hurtigere \n(1 = Meget usandsynligt, 7 = Meget sandsynligt)",
"Brugen af ROSIE ville kunne forbedre min arbejdspræstation \n(1 = Meget usandsynligt, 7 = Meget sandsynligt)",
"Brugen af ROSIE i mit arbejde ville øge min produktivitet. \n(1 = Meget usandsynligt, 7 = Meget sandsynligt)",
"Brugen af ROSIE ville forbedre min evne til at udføre \nmit arbejde godt. \n(1 = Meget usandsynligt, 7 = Meget sandsynligt)",
"Brugen af ROSIE ville gøre det \nlettere at udføre mit arbejde. \n(1 = Meget usandsynligt, 7 = Meget sandsynligt)",
"Jeg synes at ROSIE ville være nyttig i mit arbejde. \n(1 = Meget usandsynligt, 7 = Meget sandsynligt)"
)
# Call the function
Questionnaire_plot(data, questions = my.labels, columns = 6:11, title = "Perceived usefulness per question by Condition",title_offset=0)
# Load your data
data <- read.csv("./data/Questionnaire_data.csv", dec = ",")
# Define your labels
my.labels <- c(
"Det ville være nemt for mig at lære at betjene ROSIE \n(1 = Meget usandsynligt, 7 = Meget sandsynligt)",
"Jeg ville have nemt ved at få ROSIE \ntil at gøre hvad jeg ville have hende til at gøre \n(1 = Meget usandsynligt, 7 = Meget sandsynligt)",
"Min interaktion med ROSIE ville være tydelig og forståelig \n(1 = Meget usandsynligt, 7 = Meget sandsynligt)",
"Jeg kunne forestille mig, at ROSIE ville være tydelig og forståelig \n(1 = Meget usandsynligt, 7 = Meget sandsynligt)",
"Det ville være nemt for mig at blive dygtig til at bruge ROSIE \n(1 = Meget usandsynligt, 7 = Meget sandsynligt)",
"Jeg kunne forestille mig, at ROSIE ville være nem at bruge \n(1 = Meget usandsynligt, 7 = Meget sandsynligt)"
)
# Call the function
Questionnaire_plot(data, questions = my.labels, columns = 6:11, title = " Perceived Ease of Use (PEU) per question by Condition",title_offset=-0.6)
# Load your data
data <- read.csv("./data/Questionnaire_data.csv", dec = ",")
Intention_To_use_data <- data %>%
select(Condition, 27) %>%
mutate(Condition = factor(Condition, levels = c("AI", "User"))) %>%
mutate(across(2:ncol(.), as.numeric))
# Pivot longer
Intention_To_use_data_long <- Intention_To_use_data %>%
pivot_longer(cols = -Condition,
names_to = "Question",
values_to = "Score") %>%
mutate(Question = factor(Question, levels = unique(Question)))
Intention_To_use_data_long
## # A tibble: 18 × 3
## Condition Question Score
## <fct> <fct> <dbl>
## 1 AI Intention.of.use.Score 5.08
## 2 AI Intention.of.use.Score 3.83
## 3 AI Intention.of.use.Score 6.58
## 4 AI Intention.of.use.Score 5.75
## 5 AI Intention.of.use.Score 4.5
## 6 AI Intention.of.use.Score 5.17
## 7 AI Intention.of.use.Score 4.83
## 8 AI Intention.of.use.Score 5.5
## 9 AI Intention.of.use.Score 5.58
## 10 User Intention.of.use.Score 4.42
## 11 User Intention.of.use.Score 5.75
## 12 User Intention.of.use.Score 6.25
## 13 User Intention.of.use.Score 3.5
## 14 User Intention.of.use.Score 3.33
## 15 User Intention.of.use.Score 5.75
## 16 User Intention.of.use.Score 4.5
## 17 User Intention.of.use.Score 5.25
## 18 User Intention.of.use.Score 5.25
# T-test
t_test_result <- t.test(Intention_To_use_data_long$Score ~ Intention_To_use_data_long$Condition)
IntentiontoUsewithPvalueString <- paste("Intention to use (p =", round(t_test_result$p.value, 3), ")")
Engagement_data <- data %>%
select(Condition, 12) %>%
mutate(Condition = factor(Condition, levels = c("AI", "User"))) %>%
mutate(across(2:ncol(.), as.numeric))
# Pivot longer
Engagement_data_long <- Engagement_data %>%
pivot_longer(cols = -Condition,
names_to = "Question",
values_to = "Score") %>%
mutate(Question = factor(Question, levels = unique(Question)))
Engagement_data_long
## # A tibble: 18 × 3
## Condition Question Score
## <fct> <fct> <dbl>
## 1 AI Score...Engagement 4.83
## 2 AI Score...Engagement 3.33
## 3 AI Score...Engagement 4.5
## 4 AI Score...Engagement 5.5
## 5 AI Score...Engagement 3.67
## 6 AI Score...Engagement 4.83
## 7 AI Score...Engagement 3.67
## 8 AI Score...Engagement 3.83
## 9 AI Score...Engagement 4.33
## 10 User Score...Engagement 4.33
## 11 User Score...Engagement 4.5
## 12 User Score...Engagement 4.33
## 13 User Score...Engagement 4.33
## 14 User Score...Engagement 2.83
## 15 User Score...Engagement 4.5
## 16 User Score...Engagement 3.67
## 17 User Score...Engagement 3.83
## 18 User Score...Engagement 2.83
# T-test
t_test_result <- t.test(Engagement_data_long$Score ~ Engagement_data_long$Condition)
EngagementwithPvalueString <- paste("Engagement (p =", round(t_test_result$p.value, 3), ")")
# Define your labels
my.labels <- c(
EngagementwithPvalueString,
"Percieved usefulness",
"Perceived Ease of Use (PEU)",
IntentiontoUsewithPvalueString
)
# Call the function
Questionnaire_plot(data, questions = my.labels, columns = c(12,19,26,27), title = "Scores for each construct",title_offset = 0.4)
