install.packages("ggbeeswarm", repos = "https://cloud.r-project.org/")
##
## The downloaded binary packages are in
## /var/folders/3g/ln4t2cvs3250dv7p12p13c_c0000gn/T//RtmpG4lyPZ/downloaded_packages
library(ggbeeswarm)
## Loading required package: ggplot2
library(ggplot2)
library(gridExtra)
motivatie$RowID <- as.factor(seq_len(nrow(motivatie)))
motivatie$relevance_coschap <- as.factor(motivatie$relevance_coschap)
motivatie$relevance_career <- as.factor(motivatie$relevance_career)
# Bee swarm plot for AM_KNO with relevance_coschap
p1 <- ggplot(data = motivatie, aes(x = AM_KNO, y = Level_Completed, color = RowID)) +
geom_quasirandom(width = 0.2) +
labs(
title = "Effect of AM KNO \non Engagement",
x = "Autonomous Motivation for KNO",
y = "Level Completed",
color = "User ID"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Bee swarm plot for CM_KNO with relevance_coschap
p2 <- ggplot(data = motivatie, aes(x = CM_KNO, y = Level_Completed, color = RowID)) +
geom_quasirandom(width = 0.2) +
labs(
title = "Effect of CM KNO \non Engagement",
x = "Controlled Motivation for KNO",
y = "Level Completed",
color = "User ID"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Display plots side by side
grid.arrange(p1, p2, ncol = 2)
# Bee swarm plot for AM_KNO with relevance_career
p3 <- ggplot(data = motivatie, aes(x = AM_KNO, y = Questions_Attemped, color = RowID)) +
geom_quasirandom(width = 0.2) +
labs(
title = "Effect AM KNO \non Engagement",
x = "Autonomous Motivation for KNO",
y = "Questions Attempted",
color = "User ID"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Bee swarm plot for CM_KNO with relevance_career
p4 <- ggplot(data = motivatie, aes(x = CM_KNO, y = Questions_Attemped, color = RowID)) +
geom_quasirandom(width = 0.2) +
labs(
title = "Effect of CM KNO \non Engagement",
x = "Controlled Motivatiin for KNO",
y = "Questions Attempted",
color = "User ID"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Display plots side by side
grid.arrange(p3, p4, ncol = 2)
motivatie$RowID <- as.factor(seq_len(nrow(motivatie)))
motivatie$relevance_coschap <- as.factor(motivatie$relevance_coschap)
motivatie$relevance_career <- as.factor(motivatie$relevance_career)
# Bee swarm plot for AM_KNO with relevance_coschap
p1 <- ggplot(data = motivatie, aes(x = Amotivation_KNO, y = Level_Completed, color = RowID)) +
geom_quasirandom(width = 0.2) +
labs(
x = "Amotivation for KNO",
y = "Level Completed",
color = "User ID"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Bee swarm plot for CM_KNO with relevance_coschap
p2 <- ggplot(data = motivatie, aes(x = Amotivation_KNO, y = Questions_Attemped, color = RowID)) +
geom_quasirandom(width = 0.2) +
labs(
x = "Amotivation for KNO",
y = "Questions Attempted",
color = "User ID"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Display plots side by side
grid.arrange(p1, p2, ncol = 2, top="Effect of Amotivation for KNO on Engagement")
# Load the necessary libraries
library(dplyr)
##
## Attaching package: 'dplyr'
## The following object is masked from 'package:gridExtra':
##
## combine
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(forcats)
motivation_eng <- na.omit(motivatie[, c("relevance_coschap", "RAM_Med", "RAM_KNO", "relevance_career","Questions_Attemped", "Level_Completed")])
# Simplify categorical variables if necessary
motivation_eng <- motivation_eng %>%
mutate(relevance_coschap = fct_collapse(relevance_coschap,
"No" = c("Niet", "Waarschijnlijk niet"),
"Yes" = c("Waarschijnlijk wel", "Zeker wel")),
relevance_career = fct_collapse(relevance_career,
"No" = c("Niet", "Waarschijnlijk niet"),
"Yes" = c("Waarschijnlijk wel", "Zeker wel")))
# Check the distribution after combining
table(motivation_eng$relevance_coschap)
##
## Geen idee/neutraal No Yes
## 1 4 3
table(motivation_eng$relevance_career)
##
## Geen idee/neutraal No Yes
## 2 3 3
# Simplified regression model for Level_Completed
model_level <- lm(Level_Completed ~ RAM_Med + RAM_KNO + relevance_coschap + relevance_career, data = motivation_eng)
summary(model_level)
##
## Call:
## lm(formula = Level_Completed ~ RAM_Med + RAM_KNO + relevance_coschap +
## relevance_career, data = motivation_eng)
##
## Residuals:
## 1 2 3 4 5 6 7
## -1.693e+00 3.142e-01 -3.814e-01 -1.693e+00 2.074e+00 9.098e-16 1.693e+00
## 8
## -3.142e-01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -13.3765 12.4328 -1.076 0.477
## RAM_Med 0.2073 0.7577 0.274 0.830
## RAM_KNO 0.1021 0.6222 0.164 0.896
## relevance_coschapNo 11.6411 5.3429 2.179 0.274
## relevance_coschapYes 3.0943 5.2648 0.588 0.662
## relevance_careerNo 11.9838 7.2776 1.647 0.347
## relevance_careerYes 7.6167 5.7163 1.332 0.410
##
## Residual standard error: 3.639 on 1 degrees of freedom
## Multiple R-squared: 0.9047, Adjusted R-squared: 0.3327
## F-statistic: 1.582 on 6 and 1 DF, p-value: 0.5432
model_questions <- lm(Questions_Attemped ~ RAM_Med + RAM_KNO + relevance_coschap + relevance_career, data = motivation_eng)
summary(model_questions)
##
## Call:
## lm(formula = Questions_Attemped ~ RAM_Med + RAM_KNO + relevance_coschap +
## relevance_career, data = motivation_eng)
##
## Residuals:
## 1 2 3 4 5 6 7
## -2.407e+01 4.469e+00 -5.425e+00 -2.407e+01 2.950e+01 1.563e-14 2.407e+01
## 8
## -4.469e+00
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -242.992 176.826 -1.374 0.400
## RAM_Med 4.623 10.776 0.429 0.742
## RAM_KNO 4.160 8.849 0.470 0.720
## relevance_coschapNo 198.735 75.990 2.615 0.233
## relevance_coschapYes 57.339 74.878 0.766 0.584
## relevance_careerNo 216.824 103.506 2.095 0.284
## relevance_careerYes 123.119 81.301 1.514 0.372
##
## Residual standard error: 51.75 on 1 degrees of freedom
## Multiple R-squared: 0.9315, Adjusted R-squared: 0.5206
## F-statistic: 2.267 on 6 and 1 DF, p-value: 0.4687
# Bee swarm plot for AM_KNO with relevance_coschap
p1 <- ggplot(data = motivatie, aes(x = RAM_KNO, y = Level_Completed, color = RowID)) +
geom_quasirandom(width = 0.2) +
labs(
x = "RAM for KNO",
y = "Level Completed",
color = "User ID"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Bee swarm plot for CM_KNO with relevance_coschap
p2 <- ggplot(data = motivatie, aes(x = RAM_KNO, y = Questions_Attemped, color = RowID)) +
geom_quasirandom(width = 0.2) +
labs(
x = "RAM for KNO",
y = "Questions Attempted",
color = "User ID"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Display plots side by side
grid.arrange(p1, p2, ncol = 2, top="Effect of RAM for KNO on Engagement")
