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plots.R
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plots.R
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library(tidyverse)
library(magrittr)
library(broom.mixed) # Model results
library(insight) # Model predictions
library(ggtext)
library(ggrepel)
library(patchwork)
load("all-analyses.RData") # Includes plots
# Response by first name gender rating, with by-name means
# Experiment 1----
exp1_d_long <- exp1_d_FF %>%
pivot_longer(
cols = c(He, She, Other),
names_to = "Pronoun",
values_to = "Response"
)
exp1_d_long$Pronoun %<>% as.factor() %>% relevel("Other")
levels(exp1_d_long$Condition) <- c("First", "Full")
exp1_d_itemMeans <- exp1_d_FF %>%
group_by(Condition, GenderRatingCentered) %>%
summarise(
He = mean(He),
She = mean(She),
Other = mean(Other)
) %>%
pivot_longer(
cols = c(He, She, Other),
names_to = "Pronoun",
values_to = "Mean"
)
exp1_d_itemMeans$Pronoun %<>% as.factor() %>% relevel("Other")
levels(exp1_d_itemMeans$Condition) <- c("First", "Full")
exp1_p <- ggplot(exp1_d_long,
aes(x = GenderRatingCentered, color = Pronoun, fill = Pronoun)) +
geom_vline(xintercept = -0.21, linetype = "dashed") +
geom_point(data = exp1_d_itemMeans, aes(y = Mean)) + # points are item means
geom_smooth(aes(y = Response)) + # but smooth is on full data
facet_wrap(~Condition) +
theme_classic() +
scale_x_continuous(
limits = c(-3, 3), expand = c(0.02, 0.02),
breaks = c(-3, -2, -1, 0, 1, 2, 3),
labels = c("\u20133", "\u20132", "\u20131", "0", "1", "2", "3")
) +
scale_y_continuous(expand = c(0, 0), n.breaks = 5) +
scale_color_manual(values = c("grey70", "blue", "red")) +
scale_fill_manual(values = c("grey70", "blue", "red")) +
theme(
axis.text = element_text(size = 10),
axis.title = element_text(size = 11),
strip.text = element_text(size = 11),
legend.margin = margin(l = -10),
legend.text = element_text(size = 11),
legend.title = element_text(size = 11),
plot.title = element_text(size = 12, face = "bold"),
plot.title.position = "plot"
) +
labs(
title = "Experiment 1: Pronoun Used by Name Condition",
x = "Masculine \u2013 Feminine",
y = "Prop Gendered Pronoun"
) +
guides(color = guide_legend(byrow = TRUE))
exp1_p
# Experiment 2----
exp2_d_long <- exp2_d_FF %>%
pivot_longer(
cols = c(Male, Female, Other),
names_to = "Gender",
values_to = "Response"
)
exp2_d_long$Gender %<>% as.factor() %>% relevel("Other")
levels(exp2_d_long$Condition) <- c("First", "Full")
exp2_d_itemMeans <- exp2_d_FF %>%
group_by(Condition, GenderRatingCentered) %>%
summarise(
Male = mean(Male),
Female = mean(Female),
Other = mean(Other)
) %>%
pivot_longer(
cols = c(Male, Female, Other),
names_to = "Gender",
values_to = "Mean"
)
exp2_d_itemMeans$Gender %<>% as.factor() %>%
relevel("Male") %>%
relevel("Other")
levels(exp2_d_itemMeans$Condition) <- c("First", "Full")
exp2_p <- ggplot(exp2_d_long,
aes(x = GenderRatingCentered, color = Gender, fill = Gender)) +
geom_point(data = exp2_d_itemMeans, aes(y = Mean)) + # points are item means
geom_smooth(aes(y = Response)) + # but smooth is on full data
geom_vline(xintercept = -0.21, linetype = "dashed") +
facet_wrap(~Condition) +
theme_classic() +
scale_x_continuous(
limits = c(-3.1, 3), expand = c(0.02, 0.02),
breaks = c(-3, -2, -1, 0, 1, 2, 3),
labels = c("\u20133", "\u20132", "\u20131", "0", "1", "2", "3")
) +
scale_y_continuous(expand = c(0, 0), limits = c(-0.05, 1), n.breaks = 5) +
scale_color_manual(values = c("grey70", "blue", "red")) +
scale_fill_manual(values = c("grey70", "blue", "red")) +
theme(
axis.text = element_text(size = 10),
axis.title = element_text(size = 11),
strip.text = element_text(size = 11),
legend.margin = margin(l = -10),
legend.text = element_text(size = 11),
legend.title = element_text(size = 11),
plot.title = element_text(size = 12, face = "bold"),
plot.title.position = "plot"
) +
labs(
title = "Experiment 2: Gender Recalled by Name Condition",
x = "Masculine \u2013 Feminine",
y = "Prop Gender Recalled",
color = "Gender \nRecalled",
fill = "Gender \nRecalled"
) +
guides(color = guide_legend(byrow = TRUE))
exp2_p
# Experiment 3----
exp3_d_long <- exp3_d %>%
pivot_longer(
cols = c(He, She, Other),
names_to = "Pronoun",
values_to = "Response"
)
exp3_d_long$Pronoun %<>% as.factor() %>% relevel("Other")
levels(exp3_d_long$Condition) <- c("First", "Full", "Last")
exp3_d_itemMeans <- exp3_d %>%
group_by(Condition, GenderRatingCentered) %>%
summarise(
He = mean(He),
She = mean(She),
Other = mean(Other)
) %>%
pivot_longer(
cols = c(He, She, Other),
names_to = "Pronoun",
values_to = "Mean"
)
exp3_d_itemMeans$Pronoun %<>% as.factor() %>% relevel("Other")
levels(exp3_d_itemMeans$Condition) <- c("First", "Full", "Last")
exp3_p <- ggplot(exp3_d_long,
aes(x = GenderRatingCentered, color = Pronoun, fill = Pronoun)) +
geom_point(data = exp3_d_itemMeans, aes(y = Mean)) + # points are item means
geom_smooth(aes(y = Response)) + # but smooth is on full data
geom_vline(xintercept = -0.21, linetype = "dashed") +
facet_wrap(~Condition) +
theme_classic() +
scale_x_continuous(
limits = c(-3.1, 3), expand = c(0.02, 0.02),
breaks = c(-3, -2, -1, 0, 1, 2, 3),
labels = c("\u20133", "\u20132", "\u20131", "0", "1", "2", "3")
) +
scale_y_continuous(limits = c(-0.05, 1), expand = c(0, 0), n.breaks = 5) +
scale_color_manual(values = c("grey70", "blue", "red")) +
scale_fill_manual(values = c("grey70", "blue", "red")) +
theme(
axis.text = element_text(size = 10),
axis.title = element_text(size = 11),
strip.text = element_text(size = 11),
legend.margin = margin(l = -10),
legend.text = element_text(size = 11),
legend.title = element_text(size = 11),
plot.title = element_text(size = 12, face = "bold"),
plot.title.position = "plot"
) +
labs(
title = "Experiment 3: Pronoun Used by Name Condition",
x = "Masculine \u2013 Feminine",
y = "Prop Gendered Pronoun"
) +
guides(color = guide_legend(byrow = TRUE))
exp3_p
# Experiment 4----
exp4_d_long <- exp4_d %>%
pivot_longer(
cols = c(Male, Female, Other),
names_to = "Gender",
values_to = "Response"
)
exp4_d_long$Gender %<>% as.factor() %>% relevel("Other")
levels(exp4_d_long$Condition) <- c("First", "Full", "Last")
exp4_d_itemMeans <- exp4_d %>%
group_by(Condition, GenderRatingCentered) %>%
summarise(
Male = mean(Male),
Female = mean(Female),
Other = mean(Other)
) %>%
pivot_longer(
cols = c(Male, Female, Other),
names_to = "Gender",
values_to = "Mean"
)
exp4_d_itemMeans$Gender %<>% as.factor() %>%
relevel("Male") %>%
relevel("Other")
levels(exp4_d_itemMeans$Condition) <- c("First", "Full", "Last")
exp4_p <- ggplot(exp4_d_long,
aes(x = GenderRatingCentered, color = Gender, fill = Gender)) +
geom_point(data = exp4_d_itemMeans, aes(y = Mean)) + # points are item means
geom_smooth(aes(y = Response)) + # but smooth is on full data
geom_vline(xintercept = -0.21, linetype = "dashed") +
facet_wrap(~Condition) +
theme_classic() +
scale_x_continuous(
limits = c(-3.1, 3), expand = c(0.02, 0.02),
breaks = c(-3, -2, -1, 0, 1, 2, 3),
labels = c("\u20133", "\u20132", "\u20131", "0", "1", "2", "3")
) +
scale_y_continuous(limits = c(-0.05, 1), expand = c(0, 0), n.breaks = 5) +
scale_color_manual(values = c("grey70", "blue", "red")) +
scale_fill_manual(values = c("grey70", "blue", "red")) +
theme(
axis.text = element_text(size = 10),
axis.title = element_text(size = 11),
strip.text = element_text(size = 11),
legend.margin = margin(l = -10),
legend.text = element_text(size = 11),
legend.title = element_text(size = 11),
plot.title = element_text(size = 12, face = "bold"),
plot.title.position = "plot"
) +
labs(
title = "Experiment 4: Gender Recalled by Name Condition",
x = "Masculine \u2013 Feminine",
y = "Prop Gender Recalled",
color = "Gender \nRecalled",
fill = "Gender \nRecalled"
) +
guides(color = guide_legend(byrow = TRUE))
exp4_p
# Odds ratios----
## Setup----
all_odds <- purrr::map_df( # Pull beta estimates and SE from models
.x = list(
# Main models (just Condition models in Exp1&2 to get all conditions)
"Exp 1_Main" = exp1_m_cond, "Exp 2_Main" = exp2_m_cond,
"Exp 3_Main" = exp3_m_all, "Exp 4_Main" = exp4_m_all,
# Dummy coded models to get response bias just in Last
"Exp 1_Last" = exp1_m_L, "Exp 2_Last" = exp2_m_L,
"Exp 3_Last" = exp3_m_cond_L, "Exp 4_Last" = exp4_m_all_L,
# Dummy coded models to get response bias just in First + Full
"Exp 1_FF" = exp1_m_FF, "Exp 2_FF" = exp2_m_FF,
"Exp 3_FF" = exp3_m_cond_FF, "Exp 4_FF" = exp4_m_cond_FF,
# Equivalents excluding other responses
"Exp 1_Main_No Other" = exp1_m_cond_noOther,
"Exp 2_Main_No Other" = exp2_m_cond_noOther,
"Exp 3_Main_No Other" = exp3_m_noOther,
"Exp 4_Main_No Other" = exp4_m_noOther,
"Exp 1_Last_No Other" = exp1_m_L_noOther,
"Exp 2_Last_No Other" = exp2_m_L_noOther,
"Exp 3_Last_No Other" = exp3_m_noOther_L,
"Exp 4_Last_No Other" = exp4_m_noOther_L,
"Exp 1_FF_No Other" = exp1_m_FF_noOther,
"Exp 2_FF_No Other" = exp2_m_FF_noOther,
"Exp 3_FF_No Other" = exp3_m_noOther_FF,
"Exp 4_FF_No Other" = exp4_m_noOther_FF
),
.f = tidy,
.id = "exp"
) %>%
filter(effect == "fixed") %>% # just keep fixed effects
separate_wider_delim( # get model variables from variable name
exp,
delim = "_",
names = c("Experiment", "Conditions", "Other"),
too_few = "align_start"
) %>%
mutate(Other = case_when( # labels for other/no other
Other == "No Other" ~ "Without Other Responses",
is.na(Other) ~ "With Other Responses"
)) %>%
mutate(.after = Other, Outcome = case_when( # outcome variables
str_detect(Experiment, "1|3") ~ "She | He",
str_detect(Experiment, "2|4") ~ "Female | Male"
)) %>%
mutate(Outcome = ifelse(
Other == TRUE, str_c(Outcome, " + Other"), Outcome
)) %>%
mutate(.after = std.error, # calculate CI
CI_lower = estimate - std.error,
CI_upper = estimate + std.error,
logOdds = exp(estimate)
) %>%
mutate(.after = std.error, # convert log-odds beta estimate to odds ratio
OddsRatio = exp(estimate)) %>%
rename("LogOdds" = "estimate") %>%
filter( # keep intercepts, both condition contrasts, gender rating
!(Conditions != "Main" & str_detect(term, "Condition") |
str_detect(term, "Condition2|first vs full") |
str_detect(term, "GenderRating"))
) %>%
mutate(.after = term, Label = case_when( # label estimates
term == "(Intercept)" & Conditions == "Main" ~
"All\nConditions", # intercepts in main model = mean across conditions
term == "Conditionlast vs first/full" | term == "Condition1" ~
"Last vs\nFirst + Full", # last vs first+full contrast
Conditions == "Last" ~ # last name only (dummy coded intercept)
"Last",
Conditions == "FF" ~ # first + full names only (dummy coded intercept)
"First + Full"
)) %>%
select(-effect, -group, -term, -statistic, -p.value, -std.error) %>%
mutate(.after = OddsRatio,
OddsRatio_Split = OddsRatio >= 1.35) # split for plot axes
# Labels for plots
all_odds$Label %<>% factor(
levels = c("Last vs\nFirst + Full", "First + Full", "Last", "All\nConditions")
)
all_odds$Other %<>% as.factor() %>% fct_rev()
## OR w/o other responses----
all_p_oddsRatio <- ggplot(
data = all_odds %>% filter(Other == "With Other Responses"),
aes(y = Label, x = OddsRatio, color = Experiment, fill = Experiment)) +
geom_point(size = 2, key_glyph = "rect") +
facet_wrap(~OddsRatio_Split, scales = "free_x") +
scale_color_brewer(palette = "Spectral") +
scale_fill_brewer(palette = "Spectral") +
scale_x_continuous(
breaks = c(0, 0.25, 0.5, 0.75, 1, 1.25, 7, 10, 13, 16),
expand = c(0.05, 0.05)
) +
guides(color = guide_none(), fill = guide_legend(byrow = TRUE)) +
theme_classic() +
theme(
axis.text.x = element_text(size = 10),
axis.text.y = element_text(size = 11),
axis.ticks.y = element_blank(),
legend.position = c(0.80, 0.70),
legend.spacing.y = unit(10, "pt"),
legend.text = element_text(size = 11),
panel.spacing.x = unit(25, "pt"),
plot.title = element_markdown(size = 12, face = "bold"),
strip.text = element_blank(),
plot.title.position = "plot"
) +
labs(
title = paste("Odds Ratio of a <i>She</i>/<i>Female</i> vs. ",
"<i>He</i>/<i>Male</i> or <i>Other</i> Response",
"Across Experiments"),
x = "Odds Ratio",
y = element_blank(),
fill = element_blank()
)
all_p_oddsRatio
## Comparing analysis w/ and w/o other responses----
all_p_withOther <- ggplot(all_odds,
aes(
x = LogOdds, xmin = CI_lower, xmax = CI_upper,
y = Label,
color = Experiment,
group = Other, shape = Other
)) +
geom_pointrange(
size = 0.75, linewidth = 0.75,
position = position_dodge(width = 0.9)
) +
geom_vline(xintercept = 0) +
scale_color_brewer(palette = "Spectral") +
scale_x_continuous(limits = c(-4, 4)) +
guides(shape = guide_legend(reverse = TRUE)) +
theme_classic() +
theme(
text = element_text(size = 16),
axis.ticks.y = element_blank(),
legend.background = element_rect(fill = NA),
legend.box.background = element_rect(fill = "grey90", color = "grey90"),
legend.margin = margin(t = -8, b = 2, l = 5, r = 5),
legend.position = c(0.80, 0.70),
legend.spacing = unit(0, "pt"),
plot.title = element_markdown(),
plot.title.position = "plot"
) +
labs(title =
"Likelihood of a <i>She</i>/<i>Female</i> Response Across Experiments",
x = "Model Estimate",
color = element_blank(), shape = element_blank(), y = element_blank()
)
all_p_withOther
# Supplementary----
## Norming study----
p_norming <- read.csv("data/exp0_data_norming.csv",
stringsAsFactors = TRUE) %>%
select(-gender) %>%
pivot_longer( # pivot to have one row per name, not one column per name
cols = c(-ResponseId),
names_to = "Name",
values_to = "GenderRating"
) %>% # names used in study
filter(str_detect(Name, paste(sep = "",
"Matthew|Brian|James|Chris|Tommie|Emerson|Stevie|Quinn|Reese|",
"Taylor|Riley|Jessie|Kerry|Blair|Jackie|Jody|Elisha|Ashley|Mary|",
"Rebecca|Emily"
))) %>%
filter(Name != "Christopher") %>%
group_by(Name) %>% # get means for each name
summarise(MeanGenderRating = mean(GenderRating)) %>%
arrange(desc(MeanGenderRating)) %>%
left_join(read.csv("data/exp0_data_census.csv"), # join census data
by = "Name") %>%
ggplot(aes(
x = MeanGenderRating,
y = Census_ProbFemale,
color = Name, label = Name
)) +
geom_point(size = 2.5) +
geom_text_repel(min.segment.length = 0, force = 60) +
annotate(
geom = "text", label = "italic(r) == 0.92", parse = TRUE,
x = 6, y = 0.20, size = 4
) +
scale_color_manual(values = pals::ocean.phase(21)) +
scale_x_continuous(n.breaks = 7) +
guides(color = guide_none()) +
theme_classic() +
theme(
axis.text = element_text(size = 10),
axis.title = element_text(size = 11),
plot.title = element_text(size = 12, face = "bold"),
plot.title.position = "plot"
) +
labs(
title = "Norming Study",
x = "Very Masculine \u2013 Very Feminine",
y = "Proportion AFAB in Census Data"
)
p_norming
## Types of other responses in Experiment 1----
exp1_p_other <- read.csv("data/exp1_data.csv") %>%
filter(OtherType != "") %>%
select(Condition, SubjID, OtherType) %>%
mutate(across(c(Condition, OtherType), str_to_title)) %>%
ggplot(aes(x = OtherType, fill = OtherType)) +
geom_bar() +
facet_wrap(~Condition) +
scale_fill_brewer(palette = "Spectral") +
scale_y_continuous(expand = c(0, 0), limits = c(0, 175)) +
theme_classic() +
theme(
axis.text.x = element_blank(),
axis.text.y = element_text(size = 10),
axis.ticks.x = element_blank(),
axis.title = element_text(size = 11),
legend.text = element_text(size = 11),
legend.title = element_markdown(size = 11),
plot.title = element_markdown(size = 12, face = "bold"),
plot.title.position = "plot",
strip.text = element_text(size = 11)
) +
labs(
title = "Experiment 1: Types of *Other* Responses",
x = "Response Type",
y = "Number of Responses",
fill = "*Other* Type"
) +
guides(fill = guide_legend(byrow = TRUE))
exp1_p_other
# Save----
# Main
ggsave(
plot = exp1_p, path = "plots/",
filename = "exp1_gender-rating-itemMeans.png",
width = 6.5, height = 3.25, unit = "in", device = "png"
)
ggsave(
plot = exp2_p, path = "plots/",
filename = "exp2_gender-rating-itemMeans.png",
width = 6.5, height = 3.25, unit = "in", device = "png"
)
ggsave(
plot = exp3_p, path = "plots/",
filename = "exp3_gender-rating-itemMeans.png",
width = 6.5, height = 3.25, unit = "in", device = "png"
)
ggsave(
plot = exp4_p, path = "plots/",
filename = "exp4_gender-rating-itemMeans.png",
width = 6.5, height = 3.25, unit = "in", device = "png"
)
# Supplementary
ggsave(
plot = p_norming, path = "plots/",
filename = "norming-names.png",
width = 5, height = 5, units = "in", device = "png"
)
ggsave(
plot = exp1_p_other, path = "plots/",
filename = "exp1_otherTypes.png",
width = 6.5, height = 3.25, unit = "in", device = "png"
)
ggsave(
plot = all_p_oddsRatio, path = "plots/",
filename = "all_oddsRatio.png",
width = 6.5, height = 3.25, unit = "in", device = "png"
)
# Extras
ggsave(
plot = all_p_withOther, path = "extras/",
filename = "plot_OR_other.png",
width = 8, height = 4, unit = "in", device = "png"
)