library(readr)
motivation <- read_csv("~/Downloads/motivation.csv")
## Rows: 14 Columns: 30
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): Gender, Afdeling_coschap, Previous_Ed, Failed, relevance_coschap, ...
## dbl (24): User_ID, Intrinsic Motivation_Med, Identified Regulation_Med, Intr...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
library(readr)
motivatie <- read_csv("~/Downloads/motivatie.csv")
## Rows: 14 Columns: 30
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): Gender, Afdeling_coschap, Previous_Ed, Failed, relevance_coschap, ...
## dbl (24): User_ID, Intrinsic Motivation_Med, Identified Regulation_Med, Intr...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
motivation_eng<-na.omit(motivatie)
library(ggplot2)
ggplot(motivation, aes(x = RAM_Med, y = RAM_KNO, color = as.factor(row.names(motivation)))) +
geom_point(size = 5) +
geom_vline(xintercept = 0, color = "#A9A9A9") + # Add vertical line at x = 0
geom_hline(yintercept = 0, color = "#A9A9A9") + # Add horizontal line at y = 0
scale_color_discrete() +
labs(title = "RAM Medicine vs RAM KNO",
x = "RAM Medicine",
y = "RAM KNO") +
xlim(-3, 15) +
ylim(-3, 15) +
theme_minimal() +
theme(legend.position = "none")
## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_point()`).
library(ggplot2)
library(gridExtra)
# Convert 'relevance_coschap' to factor
motivation$relevance_coschap <- as.factor(motivation$relevance_coschap)
# Box plot for AM_KNO with scatter plot overlay
p1 <- ggplot(data = motivation, aes(x = relevance_coschap, y = AM_KNO)) +
geom_boxplot() +
geom_jitter(aes(color = Gender, shape = Gender), width = 0.4) + # Add scatter plot with jitter, color, and shape by Gender
labs(
title = "Effect of Relevance Internship \non AM_KNO",
x = "Relevance Internship",
y = "Autonomous Motivation for KNO"
) +
scale_color_manual(values = c("vrouw" = "lightpink", "man" = "lightblue")) + # Set colors for Gender
scale_shape_manual(values = c("vrouw" = 16, "man" = 15)) + # Set shapes for Gender (16 = circle, 15 = square)
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylim(0, 7)
# Box plot for CM_KNO with scatter plot overlay
p2 <- ggplot(data = motivation, aes(x = relevance_coschap, y = CM_KNO)) +
geom_boxplot() +
geom_jitter(aes(color = Gender, shape = Gender), width = 0.4) + # Add scatter plot with jitter, color, and shape by Gender
labs(
title = "Effect of Relevance Internship \non CM_KNO",
x = "Relevance Internship",
y = "Controlled Motivation for KNO"
) +
scale_color_manual(values = c("vrouw" = "lightpink", "man" = "lightblue")) + # Set colors for Gender
scale_shape_manual(values = c("vrouw" = 16, "man" = 15)) + # Set shapes for Gender (16 = circle, 15 = square)
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylim(0, 7)
# Display plots side by side
grid.arrange(p1, p2, ncol = 2)
p3 <- ggplot(data = motivation, aes(x = relevance_career, y = AM_KNO)) +
geom_boxplot() +
geom_jitter(aes(color = Gender, shape = Gender), width = 0.4) + # Add scatter plot with jitter, color, and shape by Gender
labs(
title = "Effect of Relevance Career \non AM_KNO",
x = "Relevance Career",
y = "Autonomous Motivation for KNO"
) +
scale_color_manual(values = c("vrouw" = "lightpink", "man" = "lightblue")) + # Set colors for Gender
scale_shape_manual(values = c("vrouw" = 16, "man" = 15)) + # Set shapes for Gender (16 = circle, 15 = square)
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylim(0, 7)
# Box plot for CM_KNO with scatter plot overlay
p4 <- ggplot(data = motivation, aes(x = relevance_career, y = CM_KNO)) +
geom_boxplot() +
geom_jitter(aes(color = Gender, shape = Gender), width = 0.4) + # Add scatter plot with jitter, color, and shape by Gender
labs(
title = "Effect of Relevance Career \non CM_KNO",
x = "Relevance Career",
y = "Controlled Motivation for KNO"
) +
scale_color_manual(values = c("vrouw" = "lightpink", "man" = "lightblue")) + # Set colors for Gender
scale_shape_manual(values = c("vrouw" = 16, "man" = 15)) + # Set shapes for Gender (16 = circle, 15 = square)
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylim(0, 7)
# Display plots side by side
grid.arrange(p3, p4, ncol = 2)
# Create a factor variable for User_ID
motivation$User_ID <- as.factor(seq_len(nrow(motivation)))
# Convert relevance_coschap and relevance_career to factors
motivation$relevance_coschap <- as.factor(motivation$relevance_coschap)
motivation$relevance_career <- as.factor(motivation$relevance_career)
# Box plot for AM_KNO with scatter plot overlay
p1 <- ggplot(data = motivation, aes(x = relevance_coschap, y = AM_KNO)) +
geom_boxplot() +
geom_jitter(aes(color = User_ID), width = 0.4) + # Add scatter plot with jitter, color, and shape by Gender
labs(
title = "Effect of Relevance Internship \non AM_KNO",
x = "Relevance Internship",
y = "Autonomous Motivation for KNO"
) + # Set shapes for Gender (16 = circle, 15 = square)
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylim(0, 7)
# Box plot for CM_KNO with scatter plot overlay
p2 <- ggplot(data = motivation, aes(x = relevance_coschap, y = CM_KNO)) +
geom_boxplot() +
geom_jitter(aes(color = User_ID), width = 0.4) + # Add scatter plot with jitter, color, and shape by Gender
labs(
title = "Effect of Relevance Internship \non CM_KNO",
x = "Relevance Internship",
y = "Controlled Motivation for KNO"
) + # Set shapes for Gender (16 = circle, 15 = square)
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylim(0, 7)
# Display plots side by side
grid.arrange(p1, p2, ncol = 2)
## Warning: Removed 6 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 6 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_point()`).
p3 <- ggplot(data = motivation, aes(x = relevance_career, y = AM_KNO)) +
geom_boxplot() +
geom_jitter(aes(color = User_ID), width = 0.4) + # Add scatter plot with jitter, color, and shape by Gender
labs(
title = "Effect of Relevance Career \non AM_KNO",
x = "Relevance Career",
y = "Autonomous Motivation for KNO"
) + # Set shapes for Gender (16 = circle, 15 = square)
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylim(0, 7)
# Box plot for CM_KNO with scatter plot overlay
p4 <- ggplot(data = motivation, aes(x = relevance_career, y = CM_KNO)) +
geom_boxplot() +
geom_jitter(aes(color = User_ID), width = 0.4) + # Add scatter plot with jitter, color, and shape by Gender
labs(
title = "Effect of Relevance Career \non CM_KNO",
x = "Relevance Career",
y = "Controlled Motivation for KNO"
) + # Set shapes for Gender (16 = circle, 15 = square)
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylim(0, 7)
# Display plots side by side
grid.arrange(p3, p4, ncol = 2)
## Warning: Removed 6 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Removed 6 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 6 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_point()`).
install.packages("ggbeeswarm", repos = "https://cloud.r-project.org/")
##
## The downloaded binary packages are in
## /var/folders/3g/ln4t2cvs3250dv7p12p13c_c0000gn/T//RtmpbIl1uG/downloaded_packages
library(ggbeeswarm)
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))+xlim(0,7)
# 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))+xlim(0,7)
# 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)) +xlim(0,7)
# 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))+xlim(0,7)
# Display plots side by side
grid.arrange(p3, p4, ncol = 2)
motivatie<- na.omit(motivatie)
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
p5 <- ggplot(data = motivatie, aes(x = relevance_career, y = Level_Completed, color = RowID)) +
geom_quasirandom(width = 0.2) +
labs(
x = "relevance career",
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
p6 <- ggplot(data = motivatie, aes(x = relevance_coschap, y = Level_Completed, color = RowID)) +
geom_quasirandom(width = 0.2) +
labs(
x = "Relevance for Internship",
y = "Level Comppleted",
color = "User ID"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Display plots side by side
grid.arrange(p5, p6, ncol = 2, top="Effect of relevance for KNO on Engagement")
p7 <- ggplot(data = motivatie, aes(x = relevance_career, y = Questions_Attemped, color = RowID)) +
geom_quasirandom(width = 0.2) +
labs(
x = "relevance career",
y = "Questions Attempted",
color = "User ID"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
p8 <- ggplot(data = motivatie, aes(x = relevance_coschap, y = Questions_Attemped, color = RowID)) +
geom_quasirandom(width = 0.2) +
labs(
x = "relevance for internship",
y = "Questions Attempted",
color = "User ID"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Display plots side by side
grid.arrange(p7, p8, ncol = 2, top="Effect of relevance for KNO on Engagement")
library(ggplot2)
library(ggbeeswarm)
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)
library(gridExtra)
motivation_eng <- na.omit(motivatie[, c("relevance_coschap", "RAM_Med", "RAM_KNO", "AM_KNO", "CM_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")))
## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `relevance_coschap = fct_collapse(...)`.
## Caused by warning:
## ! Unknown levels in `f`: Niet
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
motivation_eng$RowID <- as.factor(seq_len(nrow(motivation_eng)))
p5 <- ggplot(data = motivation_eng, aes(x = relevance_career, y = Level_Completed, color = RowID)) +
geom_quasirandom(width = 0.2) +
labs(
x = "relevance career",
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
p6 <- ggplot(data = motivation_eng, aes(x = relevance_coschap, y = Level_Completed, color = RowID)) +
geom_quasirandom(width = 0.2) +
labs(
x = "Relevance for Internship",
y = "Level Comppleted",
color = "User ID"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Display plots side by side
grid.arrange(p5, p6, ncol = 2, top="Effect of relevance for KNO on Engagement")
p7 <- ggplot(data = motivation_eng, aes(x = relevance_career, y = Questions_Attemped, color = RowID)) +
geom_quasirandom(width = 0.2) +
labs(
x = "relevance career",
y = "Questions Attempted",
color = "User ID"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
p8 <- ggplot(data = motivation_eng, aes(x = relevance_coschap, y = Questions_Attemped, color = RowID)) +
geom_quasirandom(width = 0.2) +
labs(
x = "relevance for internship",
y = "Questions Attempted",
color = "User ID"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Display plots side by side
grid.arrange(p7, p8, ncol = 2, top="Effect of relevance for KNO on Engagement")
# Create a factor variable for User_ID
motivation_eng$User_ID <- as.factor(seq_len(nrow(motivation_eng)))
# Convert relevance_coschap and relevance_career to factors
motivation_eng$relevance_coschap <- as.factor(motivation_eng$relevance_coschap)
motivation_eng$relevance_career <- as.factor(motivation_eng$relevance_career)
# Box plot for AM_KNO with scatter plot overlay
p1 <- ggplot(data = motivation_eng, aes(x = relevance_coschap, y = AM_KNO)) +
geom_boxplot() +
geom_jitter(aes(color = User_ID), width = 0.4) + # Add scatter plot with jitter, color, and shape by Gender
labs(
title = "Effect of Relevance Internship \non AM_KNO",
x = "Relevance Internship",
y = "Autonomous Motivation for KNO"
) + # Set shapes for Gender (16 = circle, 15 = square)
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylim(0, 7)
# Box plot for CM_KNO with scatter plot overlay
p2 <- ggplot(data = motivation_eng, aes(x = relevance_coschap, y = CM_KNO)) +
geom_boxplot() +
geom_jitter(aes(color = User_ID), width = 0.4) + # Add scatter plot with jitter, color, and shape by Gender
labs(
title = "Effect of Relevance Internship \non CM_KNO",
x = "Relevance Internship",
y = "Controlled Motivation for KNO"
) + # Set shapes for Gender (16 = circle, 15 = square)
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylim(0, 7)
# Display plots side by side
grid.arrange(p1, p2, ncol = 2)
p3 <- ggplot(data = motivation_eng, aes(x = relevance_career, y = AM_KNO)) +
geom_boxplot() +
geom_jitter(aes(color = User_ID), width = 0.4) + # Add scatter plot with jitter, color, and shape by Gender
labs(
title = "Effect of Relevance Career \non AM_KNO",
x = "Relevance Career",
y = "Autonomous Motivation for KNO"
) + # Set shapes for Gender (16 = circle, 15 = square)
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylim(0, 7)
# Box plot for CM_KNO with scatter plot overlay
p4 <- ggplot(data = motivation_eng, aes(x = relevance_career, y = CM_KNO)) +
geom_boxplot() +
geom_jitter(aes(color = User_ID), width = 0.4) + # Add scatter plot with jitter, color, and shape by Gender
labs(
title = "Effect of Relevance Career \non CM_KNO",
x = "Relevance Career",
y = "Controlled Motivation for KNO"
) + # Set shapes for Gender (16 = circle, 15 = square)
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylim(0, 7)
# Display plots side by side
grid.arrange(p3, p4, ncol = 2)
