# load packages
library(tidyverse) # used to clean, manipulate and visualise data
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── 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 errors
library(yarrr) # for plot
## Loading required package: jpeg
## Loading required package: BayesFactor
## Loading required package: coda
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
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## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack
##
## ************
## Welcome to BayesFactor 0.9.12-4.7. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
##
## Type BFManual() to open the manual.
## ************
## Loading required package: circlize
## ========================================
## circlize version 0.4.16
## CRAN page: https://cran.r-project.org/package=circlize
## Github page: https://github.com/jokergoo/circlize
## Documentation: https://jokergoo.github.io/circlize_book/book/
##
## If you use it in published research, please cite:
## Gu, Z. circlize implements and enhances circular visualization
## in R. Bioinformatics 2014.
##
## This message can be suppressed by:
## suppressPackageStartupMessages(library(circlize))
## ========================================
##
## yarrr v0.1.5. Citation info at citation('yarrr'). Package guide at yarrr.guide()
## Email me at Nathaniel.D.Phillips.is@gmail.com
##
## Attaching package: 'yarrr'
##
## The following object is masked from 'package:ggplot2':
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## diamonds
# Read the raw data file
data8 <- read_csv(file = "/cloud/project/Study 8 data.csv")
## Rows: 373 Columns: 340
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (340): StartDate, EndDate, Status, IPAddress, Progress, Duration (in sec...
##
## 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.
# Remove row one and two for clarity
data8 <- data8[-c(1, 2), ]
# Apply exclusion critera
# remove participants who responded twice and keep only first response
# Use Prolific_PID variable
duplicates <- data8 %>%
count(Prolific_PID) %>%
filter(n > 1) %>%
pull(Prolific_PID) # Will identify P's that appear more than once
data8 <- data8 %>%
group_by(Prolific_PID) %>%
slice(1) %>%
ungroup() # Keep only the first occurrence that appears
# Remove participants who did not consent
data8 <- data8 %>%
filter(Consent == 1, na.rm = TRUE)
# remove participants who were not serious
data8 <- data8 %>%
filter(Serious_check == 1, na.rm = TRUE)
# remove participants who did not complete
data8 <- data8 %>%
filter(Finished == 1, na.rm = TRUE)
# remove participants who failed attention check
data8 <- data8 %>%
filter(SC0 >= 4)
# rename condition variable
colnames(data8)[colnames(data8)=="FL_10_DO"] <- "condition"
# make dataframe for FIGURE 2 HISTOGRAM
figure2 <- data8 %>%
group_by(condition, advancement) %>%
summarise(n=n())
## `summarise()` has grouped output by 'condition'. You can override using the
## `.groups` argument.
# recode condition titles
figure2 <- figure2 %>%
mutate(condition = recode(condition,
'Block_1_Generic_Conflict' = 'Conflicting/Generic',
'Block_3_Qualified_Conflict' = 'Conflicting/Qualified',
'Block_2_Generic_Consistent' = 'Non-conflicing/Generic',
'Block_4_Qualified_Consistent' = 'Non-conflicting/Qualified'))
# recode advancement titles
figure2 <- figure2 %>%
mutate(advancement = recode(advancement,
'-1' = 'Less',
'0' = 'Same',
'1' = 'More'))
# Set the factor levels to ensure correct order
figure2$advancement <- factor(figure2$advancement, levels = c("Less", "Same", "More"))
# Plot the histogram
plot <- ggplot(figure2, aes(x = advancement, y = n, fill = condition)) +
geom_bar(stat = "identity", position = "dodge") +
scale_fill_manual(values = c("#333333", "#818181", "#ababab", "#cccccc")) +
labs(x = "Advancement", y = "Number of Participants", fill = "Condition") +
theme(axis.title = element_text(size = 7), # Adjust axis titles size
axis.text = element_text(size = 6), # Adjust axis text size
legend.title = element_text(size = 7), # Adjust legend title size
legend.text = element_text(size = 6)) # Adjust legend text size
print(plot)
# FIGURE 1 PLOT
#sum the contradiction scores
#create a new variable called 'contradiction' that is the sum of the six contradition ratings
# Convert columns to numeric (since they are not already numeric)
data8[, c("contradiction_1", "contradiction_2", "contradiction_3", "contradiction_4", "contradiction_5", "contradiction_6")] <- lapply(data8[, c("contradiction_1", "contradiction_2", "contradiction_3", "contradiction_4", "contradiction_5", "contradiction_6")], as.numeric)
# Calculate row sums
data8$contradiction <- rowSums(data8[, c("contradiction_1", "contradiction_2", "contradiction_3", "contradiction_4", "contradiction_5", "contradiction_6")], na.rm = TRUE)
# make dataframe for FIGURE 1 PLOT
figure1 <- data8 %>%
group_by(contradiction, condition, advancement, confusion) %>%
summarise(n=n())
## `summarise()` has grouped output by 'contradiction', 'condition',
## 'advancement'. You can override using the `.groups` argument.
# seperate condition coloumn and rename
figure1 <- figure1 %>%
separate(col = condition, into = c("block", "number", "Format", "Conflict")) %>%
mutate(Conflict = ifelse(Conflict == 'Conflict', 'Conf.',
ifelse(Conflict == 'Consistent', 'Non.Conf.', Conflict)))
# fix non-numeric variables
figure1$advancement <- as.numeric(figure1$advancement)
figure1 <- na.omit(figure1)
figure1$confusion <- as.numeric(figure1$confusion)
figure1 <- na.omit(figure1)
# Set up the plotting area
par(mfrow = c(2, 2)) # 2 rows, 2 columns
# First plot: Contradiction
pirateplot(formula = contradiction ~ Conflict * Format,
data = figure1,
inf.method = 'ci',
yaxt = "n",
ylim = c(0, 30),
theme = 1,
main = "Contradiction",
ylab = "Perceived Contradiction",
cex.names = 0.75,
cex.lab = 0.9)
axis(2, at = seq(from = 0, to = 30, by = 10))
# Second plot: Advancement
pirateplot(formula = advancement ~ Conflict * Format,
data = figure1,
inf.method = 'ci',
yaxt = "n",
theme = 1,
main = "Advancement",
ylab = "Perceived Scientific Advancement",
cex.names = 0.75,
cex.lab = 0.9)
axis(2, at = seq(from = -1, to = 1, by = 1))
# Third plot: Confusion
pirateplot(formula = confusion ~ Conflict * Format,
data = figure1,
inf.method = 'ci',
yaxt = "n",
ylim = c(1, 5),
theme = 1,
main = "Confusion",
ylab = "Perceived Confusion",
cex.names = 0.75,
cex.lab = 0.9)
axis(2, at = seq(from = 1, to = 5, by = 1))
# Leave the fourth plot empty
plot.new()
