library(psych)
library(dplyr)
library(kableExtra)
library(nFactors)
library(corrplot)
library(ggplot2)
library(tidyr)
library(forcats)
library(RColorBrewer) # For nicer color palettesMinu’s Poster Analysis
Load Libraries
This is where we load the libraries that we use in our analysis.
Load Data
This is where we load the data and do a bit of cleaning. We remove the extra rows from Qualtrics, indicate which sample is which, and remove participants who didn’t reach the end of the survey. ANd then we combine both samples into a single file.
df1 <- read.csv(file="data/Navigating Psych (SONA)_December 12, 2024_08.15.csv", header=T)
labels1 <- data.frame(t(cbind(df1[1,],samp="Sample Source")))
df1 <- df1[-c(1,2), ]
df1 <- subset(df1, Progress == "100", select=-c(4,14:15))
df1$samp <- "SONA"
df2 <- read.csv(file="data/Navigating psych (Prolific Final)_December 5, 2024_07.40.csv", header=T)
labels2 <- data.frame(t(cbind(df2[1,],samp="Sample Source")))
df2 <- df2[-c(1,2), ]
df2_quality <- subset(df2, select=c(8,173:175))
df2 <- subset(df2, Progress == "100" & Q45 == 2 & Q46 < 4, select=-c(15:17,173:175))
colnames(df2)[169] <- "id"
df2$samp <- "Prolific"
df <- rbind.data.frame(df1,df2)Attention Checks
This is where we check responses to the attention check items and yeet anyone who did respond correctly.
attn <- subset(labels1, grepl("attention", X1))
attn_chk <- rownames(attn)
# 3, 2, 5, 1, 1
correct_responses <- c(3, 2, 5, 1, 1)
# Check if the values in df match the correct responses
correct_counts <- apply(df[attn_chk], 1, function(row) sum(row == correct_responses))
# Display results
table(correct_counts)correct_counts
0 1 2 3 4 5
11 13 31 37 21 402 df$attn <- correct_counts
df <- subset(df, attn > 3)
bad_ids <- c("663e9d84425d4157a22f3167",
"666f47d2c81ed466b97cd82d",
"634ef87aec0966557c825573",
"669d8f03a8742ea4d11b35d8",
"6701a4d42a2e8c434e0fc50a",
"671736c2a43ff993ff9a070b",
"67348c5f2fd2234d5e8f7d58")
df <- subset(df, !(id %in% bad_ids))Descriptives
Subset
Take analytical items and pysch topic items out of larger data set to analyze them separatley.
rm(df1, df2)
df2 <- subset(df, select = grep("^Q27.1|Q10|Q11|Q12|Q13|Q14|Q15|Q16|Q17", colnames(df), value = TRUE))
df2 <- subset(df2, select = setdiff(colnames(df2), attn_chk))Clean Analytical
We needed to turn the free response analytical thinking responses into yes/no coded variables. started in R but switched to Excel because it was easier.
# table(df2$Q10)
# df2$Q10_rc <- 0
# df2$Q10_rc[df2$Q10 == "200"] <- 1
# df2$Q10_rc[df2$Q10 == "200 "] <- 1
# df2$Q10_rc[df2$Q10 == "200 crate"] <- 1
# df2$Q10_rc[df2$Q10 == "200 crates"] <- 1
# df2$Q10_rc[df2$Q10 == "200 crates "] <- 1
# df2$Q10_rc[df2$Q10 == "200 Crates"] <- 1
# df2$Q10_rc[df2$Q10 == "200 crates of oranges"] <- 1
# df2$Q10_rc[df2$Q10 == "200 crates of oranges remained "] <- 1
# df2$Q10_rc[df2$Q10 == "200 crates were left"] <- 1
# df2$Q10_rc[df2$Q10 == "200 crates were left "] <- 1
# df2$Q10_rc[df2$Q10 == "200 were left"] <- 1
#
#
# table(df2$Q10,df2$Q10_rc)
#
# table(df$Q11)
df2$id <- 1:nrow(df2)
# write.csv(subset(df2, select=c(1:8,id)), file="analytic.csv", row.names = F)
analytic <- read.csv(file="analytic.csv", header=T)
analytic2 <- subset(analytic, select=c(id,grep("_rc", colnames(analytic))))
df3 <- merge(df2,analytic2,by="id")Calculate averages
calc simple and analytical thinking scores.
df3$simple <- rowSums(subset(df3, select=c(Q10_rc,Q11_rc,Q12_rc,Q13_rc)))
df3$logic <- rowSums(subset(df3, select=c(Q14_rc,Q15_rc,G16_rc,Q17_rc)))Describe
We had to recode our variables as numeric so we could calculate descriptive statistics. we used describe command to view descriotives and check skew and kurtosis.
# Convert all columns in df2 to numeric
df4 <- df3
df4[] <- lapply(df3, function(x) as.numeric(x))
df4 <- subset(df4, select=c(grep("Q27", colnames(df4)),simple,logic))
# Get descriptive statistics for df2
desc_stats <- describe(df4)
# Create a new column to identify rows with skew or kurtosis greater than 2
desc_stats <- desc_stats %>%
mutate(highlight = ifelse(abs(skew) > 2 | abs(kurtosis) > 2, "highlight", "no"))
# Create a table with highlighted rows for high skewness or kurtosis
desc_stats %>%
kable(format = "html", digits = 2) %>%
kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover", "condensed")) %>%
row_spec(which(desc_stats$highlight == "highlight"), background = "red")| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | highlight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Q27.1_1 | 1 | 416 | 2.41 | 1.06 | 2 | 2.33 | 1.48 | 1 | 5 | 4 | 0.77 | -0.04 | 0.05 | no |
| Q27.1_2 | 2 | 413 | 2.44 | 1.22 | 2 | 2.33 | 1.48 | 1 | 5 | 4 | 0.61 | -0.62 | 0.06 | no |
| Q27.1_3 | 3 | 414 | 2.66 | 1.17 | 2 | 2.60 | 1.48 | 1 | 5 | 4 | 0.40 | -0.72 | 0.06 | no |
| Q27.1_4 | 4 | 413 | 2.86 | 1.26 | 3 | 2.82 | 1.48 | 1 | 5 | 4 | 0.28 | -1.02 | 0.06 | no |
| Q27.1_5 | 5 | 413 | 2.43 | 1.22 | 2 | 2.33 | 1.48 | 1 | 5 | 4 | 0.50 | -0.77 | 0.06 | no |
| Q27.1_6 | 6 | 415 | 2.38 | 1.23 | 2 | 2.27 | 1.48 | 1 | 5 | 4 | 0.60 | -0.69 | 0.06 | no |
| Q27.1_7 | 7 | 411 | 3.16 | 1.19 | 3 | 3.17 | 1.48 | 1 | 5 | 4 | -0.04 | -0.97 | 0.06 | no |
| Q27.1_8 | 8 | 413 | 2.75 | 1.26 | 3 | 2.69 | 1.48 | 1 | 5 | 4 | 0.21 | -1.03 | 0.06 | no |
| Q27.1_9 | 9 | 415 | 2.22 | 1.16 | 2 | 2.09 | 1.48 | 1 | 5 | 4 | 0.84 | -0.18 | 0.06 | no |
| Q27.1_10 | 10 | 415 | 2.07 | 1.07 | 2 | 1.92 | 1.48 | 1 | 5 | 4 | 0.98 | 0.29 | 0.05 | no |
| Q27.1_11 | 11 | 416 | 2.33 | 1.16 | 2 | 2.22 | 1.48 | 1 | 5 | 4 | 0.72 | -0.31 | 0.06 | no |
| Q27.1_12 | 12 | 416 | 2.48 | 1.21 | 2 | 2.39 | 1.48 | 1 | 5 | 4 | 0.49 | -0.71 | 0.06 | no |
| Q27.1_13 | 13 | 415 | 2.11 | 1.13 | 2 | 1.96 | 1.48 | 1 | 5 | 4 | 0.90 | 0.04 | 0.06 | no |
| Q27.1_14 | 14 | 413 | 2.50 | 1.26 | 2 | 2.39 | 1.48 | 1 | 5 | 4 | 0.49 | -0.83 | 0.06 | no |
| Q27.1_16 | 15 | 414 | 2.80 | 1.15 | 3 | 2.77 | 1.48 | 1 | 5 | 4 | 0.21 | -0.84 | 0.06 | no |
| Q27.1_17 | 16 | 415 | 2.27 | 1.16 | 2 | 2.13 | 1.48 | 1 | 5 | 4 | 0.81 | -0.17 | 0.06 | no |
| Q27.1_18 | 17 | 416 | 2.65 | 1.19 | 2 | 2.59 | 1.48 | 1 | 5 | 4 | 0.36 | -0.87 | 0.06 | no |
| Q27.1_19 | 18 | 415 | 2.32 | 1.22 | 2 | 2.19 | 1.48 | 1 | 5 | 4 | 0.73 | -0.47 | 0.06 | no |
| Q27.1_20 | 19 | 416 | 2.24 | 1.14 | 2 | 2.10 | 1.48 | 1 | 5 | 4 | 0.84 | -0.04 | 0.06 | no |
| Q27.1_21 | 20 | 413 | 2.34 | 1.12 | 2 | 2.25 | 1.48 | 1 | 5 | 4 | 0.65 | -0.39 | 0.06 | no |
| Q27.1_22 | 21 | 414 | 2.65 | 1.16 | 2 | 2.59 | 1.48 | 1 | 5 | 4 | 0.45 | -0.67 | 0.06 | no |
| Q27.1_23 | 22 | 416 | 2.45 | 1.19 | 2 | 2.36 | 1.48 | 1 | 5 | 4 | 0.51 | -0.68 | 0.06 | no |
| Q27.1_24 | 23 | 416 | 2.88 | 1.21 | 3 | 2.84 | 1.48 | 1 | 5 | 4 | 0.20 | -0.93 | 0.06 | no |
| Q27.1_25 | 24 | 415 | 2.27 | 1.18 | 2 | 2.15 | 1.48 | 1 | 5 | 4 | 0.69 | -0.48 | 0.06 | no |
| simple | 25 | 420 | 3.69 | 0.62 | 4 | 3.82 | 0.00 | 0 | 4 | 4 | -2.66 | 9.41 | 0.03 | highlight |
| logic | 26 | 420 | 2.46 | 1.03 | 3 | 2.51 | 1.48 | 0 | 4 | 4 | -0.58 | -0.27 | 0.05 | no |
Histograms
hist(df3$simple)
hist(df3$logic)
Factor Analysis
used this to identify the latent variables
latent variables - show which variables are moving together because of an underlying cause
d <- na.omit(subset(df4, select=c(grep("Q27",colnames(df4)))))
names(d) [1] "Q27.1_1" "Q27.1_2" "Q27.1_3" "Q27.1_4" "Q27.1_5" "Q27.1_6"
[7] "Q27.1_7" "Q27.1_8" "Q27.1_9" "Q27.1_10" "Q27.1_11" "Q27.1_12"
[13] "Q27.1_13" "Q27.1_14" "Q27.1_16" "Q27.1_17" "Q27.1_18" "Q27.1_19"
[19] "Q27.1_20" "Q27.1_21" "Q27.1_22" "Q27.1_23" "Q27.1_24" "Q27.1_25"labs <- c("Depression",
"Love",
"Toxic People",
"Anxiety",
"Dating",
"Attraction",
"Health & Wellbeing",
"Self",
"Narcissism",
"Interpersonal Abuse",
"Intelligence",
"Trauma",
"Psychiatric Symptoms",
"Friendship",
"Helpful Advice",
"Introversion",
"Good & Bad Habits",
"Family",
"Language & Communication",
"Manipulativeness",
"Facts & Information",
"Childhood",
"Changing & Improving Your Life",
"Fakeness")
colnames(d) <- labs
ev <- eigen(cor(d)) # get eigenvalues
ap <- parallel(subject=nrow(d),var=ncol(d),rep=100,cent=.05) # run the parallel analysis, gives us another perspective on how many factors should be used in the model
nS <- nScree(x=ev$values, aparallel=ap$eigen$qevpea) # creates the scree plot
plotnScree(nS) # shows us the scree plot, look for the elbows
EFA <- factanal(d, factors = 3, rotation = "promax")
print(EFA, digits=3, cutoff=.4, sort=T)
Call:
factanal(x = d, factors = 3, rotation = "promax")
Uniquenesses:
Depression Love
0.661 0.356
Toxic People Anxiety
0.381 0.540
Dating Attraction
0.326 0.263
Health & Wellbeing Self
0.519 0.428
Narcissism Interpersonal Abuse
0.437 0.298
Intelligence Trauma
0.528 0.322
Psychiatric Symptoms Friendship
0.389 0.380
Helpful Advice Introversion
0.364 0.506
Good & Bad Habits Family
0.321 0.478
Language & Communication Manipulativeness
0.412 0.384
Facts & Information Childhood
0.482 0.422
Changing & Improving Your Life Fakeness
0.399 0.447
Loadings:
Factor1 Factor2 Factor3
Self 0.591
Intelligence 0.609
Helpful Advice 0.818
Introversion 0.653
Good & Bad Habits 0.946
Language & Communication 0.727
Facts & Information 0.700
Changing & Improving Your Life 0.858
Fakeness 0.509
Depression 0.519
Toxic People 0.680
Narcissism 0.754
Interpersonal Abuse 0.963
Trauma 0.862
Psychiatric Symptoms 0.879
Manipulativeness 0.619
Love 0.757
Dating 0.849
Attraction 0.821
Anxiety 0.422
Health & Wellbeing
Friendship 0.489
Family 0.470
Childhood 0.411 0.450
Factor1 Factor2 Factor3
SS loadings 5.526 5.027 2.429
Proportion Var 0.230 0.209 0.101
Cumulative Var 0.230 0.440 0.541
Factor Correlations:
Factor1 Factor2 Factor3
Factor1 1.000 0.657 0.772
Factor2 0.657 1.000 0.623
Factor3 0.772 0.623 1.000
Test of the hypothesis that 3 factors are sufficient.
The chi square statistic is 781.34 on 207 degrees of freedom.
The p-value is 1.37e-67 d <- subset(d, select=-c(Anxiety,`Health & Wellbeing`,Friendship,Family,Childhood))
EFA <- factanal(d, factors = 3, rotation = "promax")
print(EFA, digits=3, cutoff=.4, sort=T)
Call:
factanal(x = d, factors = 3, rotation = "promax")
Uniquenesses:
Depression Love
0.699 0.372
Toxic People Dating
0.389 0.319
Attraction Self
0.254 0.422
Narcissism Interpersonal Abuse
0.410 0.268
Intelligence Trauma
0.538 0.340
Psychiatric Symptoms Helpful Advice
0.380 0.344
Introversion Good & Bad Habits
0.509 0.322
Language & Communication Manipulativeness
0.433 0.387
Facts & Information Changing & Improving Your Life
0.477 0.371
Fakeness
0.469
Loadings:
Factor1 Factor2 Factor3
Self 0.584
Intelligence 0.568
Helpful Advice 0.816
Introversion 0.622
Good & Bad Habits 0.904
Language & Communication 0.673
Facts & Information 0.688
Changing & Improving Your Life 0.867
Toxic People 0.652
Narcissism 0.755
Interpersonal Abuse 0.957
Trauma 0.819
Psychiatric Symptoms 0.856
Manipulativeness 0.603
Love 0.722
Dating 0.823
Attraction 0.800
Depression 0.455
Fakeness 0.475
Factor1 Factor2 Factor3
SS loadings 4.548 4.157 2.045
Proportion Var 0.239 0.219 0.108
Cumulative Var 0.239 0.458 0.566
Factor Correlations:
Factor1 Factor2 Factor3
Factor1 1.000 -0.606 0.743
Factor2 -0.606 1.000 -0.584
Factor3 0.743 -0.584 1.000
Test of the hypothesis that 3 factors are sufficient.
The chi square statistic is 360.27 on 117 degrees of freedom.
The p-value is 1.39e-26 # 1,3,4,9,10,12,13,19,21
# 2,5,6
d <- d - 1
df4$selfhelp <- rowMeans(subset(df4, select=c("Q27.1_8","Q27.1_11","Q27.1_16","Q27.1_17","Q27.1_18","Q27.1_20","Q27.1_22","Q27.1_24","Q27.1_25")))
df4$dysfunction <- rowMeans(subset(df4, select=c("Q27.1_1","Q27.1_3","Q27.1_4","Q27.1_9","Q27.1_10","Q27.1_12","Q27.1_13","Q27.1_19","Q27.1_21")))
df4$love <- rowMeans(subset(df4, select=c("Q27.1_2","Q27.1_5","Q27.1_6")))Loadings Plot
# Convert loadings to a regular numeric matrix
loadings_matrix <- as.matrix(EFA$loadings)
# Compute distance between rows (items) and do hierarchical clustering
row_order <- hclust(dist(loadings_matrix))$order
# Reorder the matrix rows
loadings_matrix_ordered <- loadings_matrix[row_order, ]
colnames(loadings_matrix_ordered) <- c("Self-Help","Dysfunction","Love")
rownames(loadings_matrix_ordered) <- c("Interpersonal Abuse - Identifying signs of manipulation and emotional abuse, understanding different types of abuse and their impacts.",
"Narcissism - Recognizing narcissistic traits and understanding narcissistic abuse.",
"Trauma - Recognizing signs of unhealed trauma, understanding its effects on behavior and personality, and healing from emotional trauma.",
"Psychiatric Symptoms - Identifying symptoms of various psychiatric disorders.",
"Manipulativeness - Recognizing manipulative behaviors and understanding gaslighting.",
"Depression- Signs of depression, understanding its effects, identifying depression in yourself and others.",
"Toxic People - Recognizing toxic behaviors in relationships and understanding how toxicity manifests in different types of relationships.",
"Attraction - Understanding signs of attraction and enhancing attractiveness.",
"Love - Differentiating between infatuation and love, understanding romantic interest, signs of true love and effective flirting.",
"Dating - Practical advice for dating, recognizing interest from others, and strategies to enhance romantic interactions.",
"Helpful Advice - Strategies for personal growth, improving relationships, and overcoming challenges.",
"Good & Bad Habits - Identifying habits that affect relationships and personal well-being, and understanding how to develop positive habits.",
"Changing & Improving Your Life - Practical advice for life improvement, overcoming negative habits, and making positive changes.",
"Introversion - Understanding introverted personalities, their strengths, and unique characteristics.",
"Facts & Information - Interesting psychological facts, information about human behavior, and insights into mental processes.",
"Fakeness - Recognizing fake behaviors in relationships, identifying insincerity, and understanding how to detect untrustworthy people.",
"Language & Communication - Understanding body language, different types of communication, and interpreting cues.",
"Self-Concept - Exploring self-perception, self-love, and how personal habits reflect personality.",
"Intelligence - Understanding the challenges and traits of highly intelligent people, types of intelligence, and emotional intelligence.")
# Helper to insert line breaks into long strings
wrap_labels <- function(labels, width = 40) {
sapply(labels, function(x) paste(strwrap(x, width = width), collapse = "\n"))
}
# Apply to row names
rownames(loadings_matrix_ordered) <- wrap_labels(rownames(loadings_matrix_ordered), width = 50)
# Create heatmap
corrplot(loadings_matrix_ordered,
is.corr = FALSE,
method = "color",
tl.col = "black",
tl.cex = 0.8,
cl.pos = "r", # Color legend on the right
cl.ratio = 1, # Width of the color legend
cl.align.text = "l", # Align legend text to the left
cl.cex = 0.7, # Size of legend text
mar = c(0, 0, 0, 0)) # Adjust margins to make space for legend
Histograms - Dysfunction
d2 <- subset(d, select=c(`Interpersonal Abuse`, Narcissism, Trauma, `Psychiatric Symptoms`, Manipulativeness, Depression, `Toxic People`))
d2_long <- d2 %>%
pivot_longer(cols = everything(), names_to = "item", values_to = "value")
# Step 1: Calculate item means
item_means <- d2_long %>%
group_by(item) %>%
summarize(mean_value = mean(value, na.rm = TRUE))
# Step 2: Create new label with mean
d2_long <- d2_long %>%
left_join(item_means, by = "item") %>%
mutate(label = paste0(item, "\nMean = ", round(mean_value, 2))) %>%
mutate(label = fct_reorder(label, -mean_value)) # Reorder by descending mean
# Step 3: Plot
ggplot(d2_long, aes(x = value, fill = item)) +
geom_histogram(bins = 5, color = "black") +
facet_wrap(~ label, scales = "free", ncol = 2) +
scale_fill_brewer(palette = "YlOrRd", guide = "none") +
labs(
title = "Histograms of Dysfunction Topics Encountered\n(0 = Never, 4 = Always)",
x = "Score",
y = "Count"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 12, lineheight = 1.4, hjust = 0.5)
)
Histograms - Self-Help
d2 <- subset(d, select=c(`Helpful Advice`, `Good & Bad Habits`, `Changing & Improving Your Life`, Introversion, `Facts & Information`, Fakeness, `Language & Communication`, Self, Intelligence))
d2_long <- d2 %>%
pivot_longer(cols = everything(), names_to = "item", values_to = "value")
# Step 1: Calculate item means
item_means <- d2_long %>%
group_by(item) %>%
summarize(mean_value = mean(value, na.rm = TRUE))
# Step 2: Create new label with mean
d2_long <- d2_long %>%
left_join(item_means, by = "item") %>%
mutate(label = paste0(item, "\nMean = ", round(mean_value, 2))) %>%
mutate(label = fct_reorder(label, -mean_value)) # Reorder by descending mean
ggplot(d2_long, aes(x = value, fill = item)) +
geom_histogram(bins = 5, color = "black") +
facet_wrap(~ label, scales = "free", ncol = 2) +
scale_fill_brewer(palette = "PuBu", guide = "none") + # 'Set3' is colorful and soft
labs(
title = "Histograms of Dysfunction Topics Encountered\n(0 = Never, 4 = Always)",
x = "Score",
y = "Count"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 12, lineheight = 1.4, hjust = 0.5)
)
Histograms - Love
d2 <- subset(d, select=c(Attraction, Love, Dating))
d2_long <- d2 %>%
pivot_longer(cols = everything(), names_to = "item", values_to = "value")
# Step 1: Calculate item means
item_means <- d2_long %>%
group_by(item) %>%
summarize(mean_value = mean(value, na.rm = TRUE))
# Step 2: Create new label with mean
d2_long <- d2_long %>%
left_join(item_means, by = "item") %>%
mutate(label = paste0(item, "\nMean = ", round(mean_value, 2))) %>%
mutate(label = fct_reorder(label, -mean_value)) # Reorder by descending mean
ggplot(d2_long, aes(x = value, fill = item)) +
geom_histogram(bins = 5, color = "black") +
facet_wrap(~ label, scales = "free", ncol = 2) +
scale_fill_brewer(palette = "BuGn", guide = "none") + # 'Set3' is colorful and soft
labs(
title = "Histograms of Dysfunction Topics Encountered\n(0 = Never, 4 = Always)",
x = "Score",
y = "Count"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 12, lineheight = 1.4, hjust = 0.5)
)
Correlation Test
which variables correlate with the simple/analytical questions and between the topics
cor_results <- corr.test(subset(df4, select=c(logic,dysfunction,love,selfhelp)))
# Extract the correlation matrix
cor_matrix <- cor_results$r
# Extract the p-values matrix
p_matrix <- cor_results$p
rownames(cor_matrix) <- c("1 Analytical Thinking",
"2 Dysfunction Topics (Abuse, Disorders, and Trauma)",
"3 Love Topics (Dating, Love, and Attraction)",
"4 Self-Help Topics (Habits, Improvement, and Advice)")
colnames(cor_matrix) <- c("1",
"2",
"3",
"4")
corrplot(cor_matrix,
method = "color", # Use color for correlation visualization
type = "upper", # Display only upper triangle
addCoef.col = "black", # Add coefficients in black
tl.col = "black", # Set text label color
number.cex = 0.8, # Adjust text size
p.mat = p_matrix, # Provide p-values for significance
sig.level = 0.05, # Set significance level
insig = "blank") # Hide insignificant correlations
Scatterplots
ggplot(df4, aes(x = dysfunction, y = selfhelp, color = love)) +
geom_point(size = 3, alpha = 0.7) +
scale_color_gradient(low = "lightblue", high = "darkred") +
labs(
title = "Relationships between Psychology Topics Encountered Online",
x = "Dysfunction Topics (Abuse, Disorders, and Trauma)",
y = "Self-Help Topics (Habits, Improvement, and Advice)",
color = "Love Topics"
) +
theme_minimal()Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_point()`).
ggplot(df4, aes(x = logic, y = dysfunction)) +
geom_jitter(width = 0.1, height = 0.1, alpha = 0.6, color = "steelblue", size=3) +
geom_smooth(method = "lm", se = TRUE, color = "darkred") +
labs(
title = "Relationship between Analytic Thinking and Dysfunction Topics",
x = "Analytic Thinking",
y = "Dysfunction Topics (Abuse, Disorders, and Trauma)"
) +
theme_minimal()`geom_smooth()` using formula = 'y ~ x'Warning: Removed 13 rows containing non-finite outside the scale range
(`stat_smooth()`).Warning: Removed 13 rows containing missing values or values outside the scale range
(`geom_point()`).