Engaged Learning part 3
2026-04-14
Graph #1: not talked about in class
Likert scale plot:
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.2 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsdata <- read.csv("ChenData.csv")
ame_data <- data %>%
select(AME1, AME2, AME3, AME4, AME5) %>%
pivot_longer(cols = everything(),
names_to = "Question",
values_to = "Response")
ame_summary <- ame_data %>%
count(Question, Response) %>%
group_by(Question) %>%
mutate(percent = n / sum(n))
ame_summary <- ame_summary %>%
mutate(direction = case_when(
Response %in% c(1, 2) ~ -1,
TRUE ~ 1
),
plot_value = percent * direction)library(scales)##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
##
## discard## The following object is masked from 'package:readr':
##
## col_factorlibrary(dplyr)
library(ggplot2)
# 1. Clean + prepare data with proper AMES labels
ame_summary_clean <- ame_summary %>%
filter(!is.na(Response), !is.na(plot_value)) %>%
mutate(
# Convert Response to an ordered factor based on the paper's 6-point scale
Response = factor(Response,
levels = 1:6,
labels = c("Not at all",
"A little",
"Somewhat",
"Moderately so",
"Very much so",
"Completely so"
)),
# Map AME codes to actual AMES factors [cite: 149]
Question = case_match(Question,
"AME1" ~ "Confidence: I feel I can win all competitions",
"AME2" ~ "Concentration: Nothing distracts me",
"AME3" ~ "Motivation: I feel excited for competition",
"AME4" ~ "Tirelessness: I have endless energy",
"AME5" ~ "Vigor: I feel energized in sports",
.default = Question
)
) %>%
group_by(Question) %>%
mutate(percent = plot_value / sum(plot_value)) %>%
ungroup()## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Question = case_match(...)`.
## ℹ In group 1: `Question = "AME1"`.
## Caused by warning:
## ! `case_match()` was deprecated in dplyr 1.2.0.
## ℹ Please use `recode_values()` instead.ggplot(ame_summary_clean, aes(x = Question, y = percent, fill = Response)) +
geom_bar(stat = "identity", width = 0.7) +
coord_flip() +
scale_y_continuous(labels = percent_format(), expand = expansion(mult = c(0, 0.05))) +
scale_fill_brewer(palette = "Spectral", name = "Response Level", na.translate=FALSE) +
labs(
x = NULL,
y = "Percentage of Responses",
title = "AME1–AME5 Response Distribution",
subtitle = "AMES scale responses from 81 male volleyball players"
) +
theme_minimal() +
theme(
legend.position = "right",
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank()
)
ScatterPlot: talked about in class
library(tidyverse)
library(scales)
t## function (x)
## UseMethod("t")
## <bytecode: 0x64d11e081070>
## <environment: namespace:base>data <- read.csv("ChenData.csv")
head(data)## ID Gender Age School Position JerseyNO TraingHr AME1 AME2 AME3 AME4 AME5 AME6
## 1 6 1 20 2 3 18 12 3 4 4 5 4 3
## 2 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 7 1 22 2 1 2 10 4 4 5 5 3 3
## 5 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 8 1 19 2 1 17 10 4 5 4 6 6 6
## AME7 AME8 AME9 AME10 AME11 AME12 AME13 AME14 AME15 AME16 AME17 AME18 PlayTime
## 1 3 4 4 4 3 2 4 4 3 3 3 3 60
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 2 5 4 4 3 2 3 5 4 5 4 4 60
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 4 6 4 5 3 3 4 5 5 5 5 5 90
## PerfomanceS BSSSmean AMEmean GImean round spikeF spikeP spikeL blockF
## 1 90 2.777778 3.500000 4.785714 5 0 0 0 0
## 2 NA NA NA NA NA 0 0 0 0
## 3 NA NA NA NA 4 0 0 0 0
## 4 65 2.888889 3.833333 4.285714 2 4 1 0 2
## 5 NA NA NA NA 4 13 4 3 3
## 6 80 2.444444 4.722222 4.500000 5 13 8 2 12
## blockP blockL serveF serveP serveL receiveF receiveP receiveL defenseF
## 1 0 0 0 0 0 43 19 2 24
## 2 0 0 0 0 0 29 19 0 27
## 3 0 0 0 0 0 37 10 0 13
## 4 0 0 4 0 0 0 0 0 1
## 5 1 4 15 0 3 0 0 0 8
## 6 0 5 14 0 3 1 1 0 8
## defenseP defenseL liftingF liftingP liftingL point figure topScorers
## 1 7 5 5 2 0 0 11 0
## 2 11 5 7 0 0 0 NA 0
## 3 6 3 5 2 1 0 NA 0
## 4 1 0 0 0 0 1 13 1
## 5 4 3 1 1 0 5 NA 5
## 6 4 3 1 0 0 8 13 8
## topSpikers topServers topDiggers topRecevers topBlockers topSetters
## 1 NA 0 1.4 0.3953488 0.00 0.40
## 2 NA 0 NA 0.6551724 0.00 0.00
## 3 NA 0 1.5 0.2702703 0.00 0.50
## 4 0.2500000 0 0.5 NA 0.00 0.00
## 5 0.3076923 0 1.0 NA 0.25 0.25
## 6 0.6153846 0 0.8 1.0000000 0.00 0.00
## concentration tireless motivation confidence vigor calm AMES
## 1 11 8 7 12 11 6 55
## 2 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA
## 4 10 7 10 12 13 8 60
## 5 NA NA NA NA NA NA NA
## 6 17 10 11 13 13 10 74ggplot(data ,aes(x = TraingHr, y = PerfomanceS)) +
geom_point(color = "blue", alpha = 0.6) +
geom_smooth(method = "lm", color = "red") + # Adds a trend line
labs(title = "Training Hours vs. Performance Score",
x = "Weekly Training Hours", y = "Performance Score")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 188 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 188 rows containing missing values or values outside the scale range
## (`geom_point()`).
# Install packages if you haven't already:
# install.packages("ggplot2")
# install.packages("dplyr")
library(ggplot2)
library(dplyr)
data <- read.csv("ChenData.csv")
clean_data <- data %>%
filter(!is.na(PerfomanceS) & !is.na(AMES))
perf_median <- median(clean_data$PerfomanceS)
clean_data <- clean_data %>%
mutate(Performance_Group = ifelse(PerfomanceS >= perf_median,
"High Performer",
"Low Performer"))
ggplot(clean_data, aes(x = AMES, fill = Performance_Group)) +
geom_density(alpha = 0.6, color = "white") +
scale_fill_manual(values = c("High Performer" = "purple", "Low Performer" = "lightblue")) +
theme_minimal(base_size = 12) +
labs(
title = "Mental Energy Distribution by Performance Level",
subtitle = ("High vs. low performers based on a median split of performance scores"),
x = "Athletic Mental Energy Score (AMES)",
y = "Density",
fill = "Category"
) +
theme(
legend.position = "right",
plot.title = element_text(face = "bold"),
panel.grid.minor = element_blank()
)
#graph 4 #Raincloud plot
install.packages("ggdist") ## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.5'
## (as 'lib' is unspecified)install.packages("gghalves")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.5'
## (as 'lib' is unspecified)## Warning: package 'gghalves' is not available for this version of R
##
## A version of this package for your version of R might be available elsewhere,
## see the ideas at
## https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packageslibrary(tidyverse)
library(ggdist)
data <- read.csv("ChenData.csv")
clean_data <- data %>%
filter(!is.na(AME1), !is.na(PerfomanceS))
median_perf <- median(clean_data$PerfomanceS)
clean_data <- clean_data %>%
mutate(
PerformanceGroup = ifelse(PerfomanceS >= median_perf, "High Performance", "Low Performance"),
PerformanceGroup = factor(PerformanceGroup, levels = c("Low Performance", "High Performance"))
)
clean_data <- clean_data %>%
rename(Confidence = AME1)
ggplot(clean_data, aes(x = PerformanceGroup, y = Confidence, fill = PerformanceGroup)) +
# Half violin (distribution)
stat_halfeye(
adjust = 0.5,
width = 0.6,
justification = -0.2,
point_colour = NA
) +
# Boxplot (summary)
geom_boxplot(
width = 0.15,
outlier.shape = NA,
alpha = 0.5
) +
# Jittered points (raw data)
geom_jitter(
width = 0.1,
alpha = 0.4,
size = 1.5
) +
scale_fill_brewer(palette = "Set2") +
labs(
title = "Confidence Levels by Performance Group",
subtitle = "Comparison of AME Confidence between high and low performing volleyball players",
x = "Performance Group",
y = "Confidence (AMES Scale)"
) +
theme_minimal() +
theme(legend.position = "none")