R Gallery
L.J Zigerell
2024-10-24
This page contains code for R plots made using ggplot from the tidyverse package. The code often isn’t intended to be ideal, but to instead include more code than needed, because it’s easier to delete code than to add code. A thorough list to theme elements is available here.
Saving plots
You can add at the end of the code a ggsave command to save to your working directory, such as…
ggsave("plot1.svg", width = 10, height = 5)…or you can add the directory to the command, such as…
ggsave("G:/data/plot1.svg", width = 10, height = 5)The command getwd() will return the working directory, and the command setwd() can be used to set the working directory.
Sample theme
theme.z <- theme( axis.text.x.bottom = element_text(size = 15, color = "black", hjust = 0.5, vjust = 0.5, margin = margin(t = 5, r = 5, b = 5, l = 5)), axis.text.x.top = element_text(size = 15, color = "black", hjust = 0.5, vjust = 0.5, margin = margin(t = 5, r = 5, b = 5, l = 5)), axis.text.y.left = element_text(size = 15, color = "black", hjust = 0.5, vjust = 0.5, margin = margin(t = 5, r = 5, b = 5, l = 5)), axis.text.x.right = element_text(size = 15, color = "black", hjust = 0.5, vjust = 0.5, margin = margin(t = 5, r = 5, b = 5, l = 5)), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), axis.title.x = element_text(size = 15, color = "black", hjust = 0.5, vjust = 0.5, margin = margin(t = 5, r = 5, b = 5, l = 5)), axis.title.y = element_text(size = 15, color = "black", hjust = 0.5, vjust = 0.5, margin = margin(t = 5, r = 5, b = 5, l = 5)), axis.title.y.right = element_text(size = 15, color = "black", hjust = 0.5, vjust = 0.5, margin = margin(t = 5, r = 5, b = 5, l = 5), angle = 0), legend.position = "none", panel.background = element_rect(linewidth = 0.5, color = "black", linetype = "solid", fill = "gray90"), panel.border = element_rect(linewidth = 1.0, color = "black", linetype = "solid", fill = NA), panel.grid.major.x = element_blank(), panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank(), panel.spacing.x = unit(1, "lines"), panel.spacing.y = unit(1, "lines"), plot.background = element_rect(fill = "white"), plot.caption = element_text(size = 12, color = "black", hjust = 0, vjust = 0.5, margin = margin(t = 5, r = 5, b = 5, l = 5)), plot.margin = unit(c(t = 10, r = 10, b = 10, l = 10), "pt"), plot.subtitle = element_text(size = 15, color = "black", hjust = 0.5, vjust = 0.5, margin = margin(t = 5, r = 5, b = 5, l = 5)), plot.title = element_text(size = 20, color = "black", hjust = 0.5, vjust = 0.5, margin = margin(t = 5, r = 5, b = 5, l = 5), face = "bold"), strip.background = element_rect(linewidth = 1, color = "black", linetype = "solid", fill = "black"), strip.text.x = element_text(size = 18, color = "white", hjust = 0.5, vjust = 0.5, margin = margin(t = 5, r = 5, b = 5, l = 5), face = "bold"), strip.text.y = element_text(size = 18, color = "white", hjust = 0.5, vjust = 0.5, margin = margin(t = 5, r = 5, b = 5, l = 5), face = "bold") )
Regression plot: Categorical
Code
This next plot is a regression-type plot in which the analysis does not assume a constant association between the predictor and the outcome net of controls. The data for this plot are loaded using a tribble command. But, as used in some plots below, data can be loaded from an external file.
library(tidyverse)
DATA <- tribble(
~X, ~PE , ~CILO, ~CIHI, ~N,
0, 32.4, 26.5, 38.3, 297,
1, 41.4, 33.0, 49.9, 103,
2, 38.6, 29.2, 48.0, 97,
3, 45.8, 37.2, 54.4, 107,
4, 44.8, 37.0, 52.6, 236,
5, 57.3, 45.0, 69.5, 144,
6, 70.6, 55.0, 86.3, 129,
7, 58.0, 49.8, 66.1, 113,
8, 56.8, 47.7, 65.8, 163,
9, 70.6, 62.0, 79.2, 102,
10, 73.9, 66.8, 81.0, 145,
11, 72.0, 58.8, 85.3, 96,
12, 74.4, 64.4, 84.5, 78,
13, 75.9, 63.1, 88.8, 32,
14, 63.2, 40.3, 86.2, 18,
15, 55.1, 16.4, 94.0, 12,
16, 77.8, 46.7, 95.0, 18)
theme.z <- theme(
axis.text.x = element_text(size = 15, color = "black", hjust = 0.5, margin = margin(t = 8, b = 8)),
axis.text.y = element_text(size = 15, color = "black", vjust = 0.5, margin = margin(r = 7, l = 7)),
axis.text.y.right = element_text(size = 15, color = "black", vjust = 0.5, margin = margin(r = 7, l = 7)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_text(size = 15, color = "black", hjust = 0.5, margin = margin(t = 8, b = 8)),
axis.title.y = element_blank(),
axis.title.y.right = element_blank(),
panel.background = element_rect(linewidth = 0.5, fill = "gray90"),
panel.border = element_rect(linewidth = 1.0, fill = NA ),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 12, hjust = 0 , margin = margin(t = 10)),
plot.margin = unit(c(t = 10, r = 10, b = 10, l = 10),"pt"),
plot.subtitle = element_text(size = 15, hjust = 0.5, margin = margin(t = 0, b = 8)),
plot.title = element_text(size = 20, hjust = 0.5, margin = margin(t = 0, b = 16), face = "bold")
)
CAPTION <- str_wrap("Data source: XXX\nError bars indicate 95% confidence intervals.\nNumbers on the inside bottom (e.g., 297) are sample sizes for that level of the predictor.", width = 95)
ggplot(data = DATA, mapping = aes(x = X, y = PE)) +
geom_rect(data = DATA, mapping = aes(xmin = -Inf, xmax = Inf, ymin = min(PE), ymax = max(PE)), fill = "lightsteelblue3", col = "black") +
geom_errorbar(mapping = aes(ymin = CILO, ymax = CIHI), width = 0, size = 0.75) +
geom_point(size = 3.5) +
geom_text(x = DATA$X, y = 5, vjust = 0, label = DATA$N, size = 5) +
scale_x_continuous(limits = c(0,16), breaks = seq(0,16,1)) +
scale_y_continuous(limits = c(0,100), breaks = seq(0,100,10), labels = seq(0,100,10), expand = c(0,0),
sec.axis = dup_axis()) +
labs(title = "Predicted outcome", x = "x variable", caption = CAPTION) +
theme.zFigure
Estimates plot: Estimates on the right
Code
library(tidyverse)
DATA <- tribble(
~RESPONSE , ~PE , ~CILO , ~CIHI ,
"Cold to Whites but not Blacks" , 0.7733116, 0.7254376, 0.8211857,
"Residual colder to Whites than Blacks", 0.5888966, 0.5624021, 0.615391 ,
"Rated Whites equal to Blacks" , 0.3925516, 0.3580722, 0.427031 ,
"Did not rate Whites and/or Blacks" , 0.3239428, 0.2534674, 0.3944182,
"Residual colder to Blacks than Whites", 0.238854 , 0.2193636, 0.2583444,
"Cold to Blacks but not Whites" , 0.1205505, 0.0998559, 0.1412452)
DATA$RESPONSE <- factor(DATA$RESPONSE, levels = unique(DATA$RESPONSE))
theme.z <- theme(
axis.text.x = element_blank(),
axis.text.x.top = element_text(size = 15, color = "black", hjust = 1 , margin = margin(t = 8,b = 8)),
axis.text.y = element_text(size = 15, color = "black", hjust = 1 , margin = margin(l = 8,r = 8)),
axis.text.y.right = element_blank(),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.background = element_rect(linewidth = 0.5, color = "black", fill = "gray90", linetype = "solid"),
panel.border = element_rect(linewidth = 1.0, color = "black", fill = NA , linetype = "solid"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.spacing.x = unit(2, "lines"),
panel.spacing.y = unit(1, "lines"),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 12 , hjust = 0 , margin = margin(t = 10)),
plot.margin = unit(c(t = 10,r = 10,b = 10,l = 10),"pt"),
plot.subtitle = element_text(size = 15 , hjust = 0.5, margin = margin(b = 10)),
plot.title = element_text(size = 20 , hjust = 0.5, margin = margin(t = 0,b = 8), face = "bold")
)
CAPTION <- str_wrap("Note: Predicted probabilities, with the outcome coded 1 for\nstrongly approve and somewhat approve and 0 for\nsomewhat disapprove, strongly disapprove, don't know,\nand skipped. Controls only for participant race. Error bars are\n83.4% confidence intervals. Data source: 2017/8 waves of the\nDemocracy Fund Voter Study Group. 2021.\nViews of the Electorate Research Survey. Washington, D.C.\nhttps://www.voterstudygroup.org/.", width = 50)
ggplot(DATA, mapping = aes(x = PE, y = RESPONSE)) +
geom_rect(xmin = DATA$CILO[DATA$RESPONSE == "Rated Whites equal to Blacks"], xmax = DATA$CIHI[DATA$RESPONSE == "Rated Whites equal to Blacks"], ymin = -Inf, ymax = Inf, fill = "slategray3", color = NA) +
geom_errorbarh(mapping = aes(xmin = CILO, xmax = CIHI), height = 0) +
geom_point(color = "black", size = 3.5) +
scale_x_continuous(limits = c(0,1), breaks = seq(0,1,by = 0.2), expand = c(0,0)) +
geom_text(x = 0.975, y = DATA$RESPONSE, size = 5, hjust = 1, label = format(round(DATA$PE, 2), nsmall = 2)) +
labs(title = "Approve of anthem protests", caption = CAPTION) +
theme.zFigure
Estimates plot: Facet shading
Code
This next plot plots data across multiple facets. The lines with “filter” are used to make sure that particular elements appear only in certain facets.
Lines in the section that start with “DATA$max” or “DATA$min” are intended to reduce the length of later lines of code.
library(tidyverse)
DATA <- tribble(
~RESPONDENTS , ~TARGET , ~PE, ~CILO, ~CIHI,
"White respondents" , "Mean ratings of Whites" , 71.03, 70.47, 71.59,
"White respondents" , "Mean ratings of Blacks" , 70.14, 69.57, 70.72,
"White respondents" , "Mean ratings of Hispanics", 70.49, 69.93, 71.04,
"White respondents" , "Mean ratings of Asians" , 70.24, 69.68, 70.8 ,
"Black respondents" , "Mean ratings of Whites" , 62.2 , 60.24, 64.17,
"Black respondents" , "Mean ratings of Blacks" , 85.36, 83.93, 86.79,
"Black respondents" , "Mean ratings of Hispanics", 71.07, 69.37, 72.78,
"Black respondents" , "Mean ratings of Asians" , 66.2 , 64.32, 68.07,
"Hispanic respondents", "Mean ratings of Whites" , 65.17, 63.46, 66.89,
"Hispanic respondents", "Mean ratings of Blacks" , 72.47, 70.95, 73.99,
"Hispanic respondents", "Mean ratings of Hispanics", 80.55, 79.04, 82.05,
"Hispanic respondents", "Mean ratings of Asians" , 72.3 , 70.77, 73.84,
"Asian respondents" , "Mean ratings of Whites" , 67.4 , 64.54, 70.27,
"Asian respondents" , "Mean ratings of Blacks" , 70.54, 68.01, 73.07,
"Asian respondents" , "Mean ratings of Hispanics", 69.68, 67.16, 72.21,
"Asian respondents" , "Mean ratings of Asians" , 80.28, 78.1 , 82.46)
DATA$RESPONDENTS <- factor(DATA$RESPONDENTS, levels = unique(DATA$RESPONDENTS))
DATA$TARGET <- factor(DATA$TARGET , levels = rev(unique(DATA$TARGET)))
DATA$max.w <- max(DATA$PE[DATA$RESPONDENTS == "White respondents"] , na.rm = TRUE)
DATA$min.w <- min(DATA$PE[DATA$RESPONDENTS == "White respondents"] , na.rm = TRUE)
DATA$max.b <- max(DATA$PE[DATA$RESPONDENTS == "Black respondents"] , na.rm = TRUE)
DATA$min.b <- min(DATA$PE[DATA$RESPONDENTS == "Black respondents"] , na.rm = TRUE)
DATA$max.h <- max(DATA$PE[DATA$RESPONDENTS == "Hispanic respondents"], na.rm = TRUE)
DATA$min.h <- min(DATA$PE[DATA$RESPONDENTS == "Hispanic respondents"], na.rm = TRUE)
DATA$max.a <- max(DATA$PE[DATA$RESPONDENTS == "Asian respondents"] , na.rm = TRUE)
DATA$min.a <- min(DATA$PE[DATA$RESPONDENTS == "Asian respondents"] , na.rm = TRUE)
CAPTION <- str_wrap("Note: Error bars are 83.4% confidence intervals. Data source: American National Election Studies. 2021. ANES 2020 Time Series Study Preliminary Release: Combined Pre-Election and Post-Election Data [dataset and documentation]. March 24, 2021 version. www.electionstudies.org.", width = 92)
theme.z <- theme(
axis.text.x = element_text(size = 15, color = "black", margin = margin(t = 8,b = 8)),
axis.text.y = element_text(size = 15, color = "black", margin = margin(l = 8,r = 8)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.background = element_rect(linewidth = 0.5, color = "black", fill = "gray90", linetype = "solid"),
panel.border = element_rect(linewidth = 1.0, color = "black", fill = NA , linetype = "solid"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.spacing.x = unit(2, "lines"),
panel.spacing.y = unit(1, "lines"),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 10, hjust = 0, margin = margin(t = 8)),
plot.margin = unit(c(0.5,0.5,0.5,0.5),"cm"),
plot.subtitle = element_text(size = 15, hjust = 0.5, margin = margin(b = 7)),
plot.title = element_text(size = 20, hjust = 0.5, face = "bold", margin = margin(t = 0,b = 12)),
strip.background = element_rect(linewidth = 1, color = "black", fill = "black"),
strip.text.x = element_text(size = 17, color = "white", face = "bold", margin = margin(t = 7.5,b = 7.5))
)
ggplot(data = DATA, mapping = aes(x = PE, y = TARGET)) +
facet_wrap(~RESPONDENTS, nrow = 2, dir = "v") +
geom_rect(data = filter(DATA, RESPONDENTS == "White respondents") ,
mapping = aes(xmin = min.w, xmax = max.w, ymin = -Inf, ymax = Inf), fill = "lightsteelblue3", color = "black", inherit.aes = FALSE) +
geom_rect(data = filter(DATA, RESPONDENTS == "Black respondents") ,
mapping = aes(xmin = min.b, xmax = max.b, ymin = -Inf, ymax = Inf), fill = "lightsteelblue3", color = "black", inherit.aes = FALSE) +
geom_rect(data = filter(DATA, RESPONDENTS == "Hispanic respondents"),
mapping = aes(xmin = min.h, xmax = max.h, ymin = -Inf, ymax = Inf), fill = "lightsteelblue3", color = "black", inherit.aes = FALSE) +
geom_rect(data = filter(DATA, RESPONDENTS == "Asian respondents") ,
mapping = aes(xmin = min.a, xmax = max.a, ymin = -Inf, ymax = Inf), fill = "lightsteelblue3", color = "black", inherit.aes = FALSE) +
geom_point(size = 3.5) +
geom_errorbarh(mapping = aes(xmin = CILO, xmax = CIHI), height = 0, size = 0.75) +
scale_x_continuous(breaks = seq(0,100,25), limits = c(0,100), labels = scales::number_format(accuracy = 1)) +
labs(title = "How racial groups rate each other", caption = CAPTION) +
theme.zFigure
Estimates plot: 83.4% and 95% confidence intervals
Code
library(tidyverse)
DATA <- tribble(
~GROUP ,~PE,~CILO83,~CIHI83,~CILO95,~CIHI95,
"Racial resentment of 0 on 0-to-12 index (N = 66)" ,-0.2922794,-0.448513,-0.1360458,-0.515034,-0.0695248,
"Racial resentment of 0 or 1 on a 0-to-12 index (N = 107)" ,-0.2216749,-0.3462494,-0.0971004,-0.3987587,-0.0445911,
"Racial resentment of less than 6 on a 0-to-12 index (N = 298)" ,-0.243315,-0.3211188,-0.1655112,-0.3535838,-0.1330462,
"At least 1 negative stereotype of Whites compared to Blacks (N = 165)",-0.1875,-0.2945985,-0.0804015,-0.3394902,-0.0355098,
"At least 2 negative stereotypes of Whites compared to Blacks (N = 87)",-0.3123679,-0.4537431,-0.1709926,-0.5135549,-0.1111809,
"Three negative stereotypes of Whites compared to Blacks (N = 43)" ,-0.3549784,-0.552015,-0.1579417,-0.6371384,-0.0728183,
"Rated Whites < 50 on 0-to-100 thermo but Blacks 50 or above (N = 39)" ,-0.4603175,-0.6502958,-0.2703391,-0.732731,-0.1879039,
"Rated Whites lower than Blacks on 0-to-100 thermo (N = 205)" ,-0.2152381,-0.3090966,-0.1213796,-0.3483605,-0.0821157)
DATA$GROUP <- factor(DATA$GROUP, levels = DATA$GROUP)
theme.z <- theme(
plot.background = element_rect(fill = "white"),
strip.background = element_rect(linewidth = 1, color = "black", fill = "black"),
strip.text.x = element_text(color = "white", face = "bold", size = 15, margin = margin(t = 7.5, b = 7.5)),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.background = element_rect(fill = "gray90", color = "black", linewidth = 0.5, linetype = "solid"),
panel.border = element_rect(fill = NA, color = "black", linetype = "solid", linewidth = 1.0),
panel.spacing.x = unit(1, "lines"),
panel.spacing.y = unit(1, "lines"),
axis.title.y = element_blank(),
axis.title.x = element_text(size = 12, color = "black"),
axis.ticks.y = element_blank(),
axis.ticks.x = element_blank(),
axis.text.x = element_text(size = 12, color = "black", vjust = -1),
axis.text.y = element_text(size = 12, color = "black", margin = margin(r = 10)),
plot.margin = unit(c(0.5,0.5,0.5,0.5),"cm"),
plot.title = element_text(face = "bold", margin = margin(t = 0, b = 13), size = 15, hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5, size = 12),
plot.caption = element_text(hjust = 0, size = 9)
)
CAPTION <- str_wrap("Thick error bars are 83.4% confidence intervals. Thin error bars are 95% confidence\nintervals. Estimates are from unweighted analyses, indicating percentage point\ndifferences in rating the Black target guilty relative to the White target. Total\nN is 649 Whites with responses for the 'guilty' item. Data source: Rice et al. 2021", width = 80)
ggplot(DATA, mapping = aes(100*PE, GROUP)) +
geom_rect(mapping = aes(xmin = 0, xmax = Inf, ymin = -Inf, ymax = Inf), linewidth = 1, color = "black", fill = "slategray3") +
geom_errorbarh(mapping = aes(xmin = 100*CILO95, xmax = 100*CIHI95), height = 0, size = 0.5, color = "gray60") +
geom_errorbarh(mapping = aes(xmin = 100*CILO83, xmax = 100*CIHI83), height = 0, size = 1.5) +
geom_point(shape = 21, color = "black", fill = "slategray3", size = 4, stroke = 2) +
scale_x_continuous(name = "", breaks = seq(-80,20,20), limits = c(-80,20), labels = scales::number_format(accuracy = 1)) +
geom_text(mapping = aes(x = 20, label = scales::percent(PE, accuracy = 1L)), position = position_dodge(width = 0.7), hjust = 1, size = 4.5) +
labs(title = "Pro-Black Mock Juror Bias, among Whites", caption = CAPTION) +
theme.zFigure
Estimates plot: Comparison with shaded error bars
Code
library(tidyverse)
DATA <- tribble(
~GROUP , ~RESPONSE , ~PE , ~CILO , ~CIHI ,
"Population A", "Response 1", 0.2487805, 0.2316981, 0.2666853,
"Population A", "Response 2", 0.3466610, 0.3276461, 0.3661784,
"Population B", "Response 1", 0.3337802, 0.3079924, 0.3606019,
"Population B", "Response 2", 0.3705482, 0.3441236, 0.3977713,
"Population C", "Response 1", 0.1463591, 0.1274313, 0.1675585,
"Population C", "Response 2", 0.3190987, 0.2921480, 0.3473157,
"Population D", "Response 1", 0.2456283, 0.2256315, 0.2667868,
"Population D", "Response 2", 0.3202551, 0.2983380, 0.3429953,
"Population E", "Response 1", 0.1215903, 0.0810799, 0.1784114,
"Population E", "Response 2", 0.3515452, 0.2775312, 0.4334554,
"Population F", "Response 1", 0.3167555, 0.2472624, 0.3955174,
"Population F", "Response 2", 0.2819693, 0.2294461, 0.341192,
"Population G", "Response 1", 0.2613607, 0.2113523, 0.3184242,
"Population G", "Response 2", 0.4241733, 0.3645215, 0.4861194)
DATA$RESPONSE <- factor(DATA$RESPONSE, levels = rev(unique(DATA$RESPONSE)))
DATA$GROUP <- factor(DATA$GROUP , levels = rev(unique(DATA$GROUP)))
STR <- str_wrap("Note about the plot. Data source: American National Election Studies. 2022. ANES 2022 Pilot Study [dataset and documentation]. December 14, 2022 version. www.electionstudies.org.", width = 75)
theme.z <- theme(
axis.text.x = element_text(color = "black", size = 15, margin = margin(t = 5)),
axis.text.y = element_text(color = "black", size = 15, margin = margin(r = 5)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.background = element_rect(fill = "gray90"),
legend.box.background = element_rect(color = "black", linewidth = 1.5),
legend.key = element_rect(fill = "gray90"),
legend.justification = c("right", "top"),
legend.margin = margin(15,15,15,15),
legend.position = c(0.95,0.9),
legend.spacing.x = unit(10, "pt"),
legend.spacing.y = unit(10, "pt"),
legend.text = element_text(size = 15, margin = margin(t = 0)),
legend.title = element_text(size = 15, face = "bold"),
panel.background = element_rect(fill = "gray90"),
panel.border = element_rect(fill = NA, color = "black", linetype = "solid", linewidth = 1.0),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
plot.caption = element_text(size = 11, hjust = 0, margin = margin(15,0,0,0)),
plot.subtitle = element_text(hjust = 0.5),
plot.margin = unit(c(t = 15,r = 25,b = 15,l = 15),"pt"),
plot.title = element_text(face = "bold", margin = margin(t = 0, b = 6), size = 20, hjust = 0.5)
)
ggplot(DATA, mapping = aes(color = RESPONSE, x = 100*PE, y = GROUP)) +
geom_errorbarh(mapping = aes(xmin = 0, xmax = 100), height = 0, size = 10, color = "gray80", alpha = 0.5) +
geom_errorbarh(mapping = aes(xmin = 100*(CILO), xmax = 100*(CIHI)), height = 0, size = 10, color = "steelblue", alpha = 0.5) +
geom_point(size = 3, shape = 19) +
scale_color_manual(values = c("Response 2" = "black","Response 1" = "white")) +
scale_x_continuous(limits = c(0,100), breaks = seq(0,100,by = 10), expand = c(0,0)) +
labs(title = "Title for the plot", caption = STR) +
theme.zFigure
Column plot
Code
This first plot is a column plot from a draft figure from Zigerell 2022 “Introducing Political Science Students to Data Visualization Strategies” in the Journal of Political Science Education.
library(tidyverse)
DATA <- tribble(
~PID , ~PE , ~CILO , ~CIHI ,
"Strong\nDemocrat" , 0.4445886, 0.4089648, 0.4802125,
"Not strong\nDemocrat" , 0.2726622, 0.2290617, 0.3162628,
"Independent\nDemocrat" , 0.2509012, 0.218784 , 0.2830185,
"Independent" , 0.2135729, 0.1755062, 0.2516396,
"Independent\nRepublican", 0.2633814, 0.218896 , 0.3078668,
"Not strong\nRepublican" , 0.2074255, 0.1671848, 0.2476663,
"Strong\nRepublican" , 0.3374613, 0.308117 , 0.3668057)
DATA$PID <- factor(DATA$PID, levels = DATA$PID)
CAPTION <- str_wrap("Note: Error bars are 83.4% confidence intervals. Estimates are from a logit regression, with weights applied and with categorical controls for gender, race, age group, education, marital status, household income, and gun ownership set at their means. Data source: American National Election Studies 2020 Time Series Study (2021).", width = 125)
TITLE <- c("Predicted probability of responding\n\"extremely important\" about how important\nthe respondent considers the issue of the federal gun laws")
theme.z <- theme(
axis.text.x = element_text(size = 15, color = "black", hjust = 0.5, margin = margin(t = 8,b = 8)),
axis.text.y = element_blank(),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.background = element_rect(linewidth = 0.5, color = "black", fill = "gray90", linetype = "solid"),
panel.border = element_rect(linewidth = 1.0, color = "black", fill = NA , linetype = "solid"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 12 , hjust = 0 , margin = margin(t = 10)),
plot.margin = unit(c(t = 10,r = 10,b = 10,l = 10),"pt"),
plot.subtitle = element_text(size = 15 , hjust = 0.5, margin = margin(b = 10)),
plot.title = element_text(size = 20 , hjust = 0.5, margin = margin(t = 0,b = 15), face = "bold")
)
ggplot(data = DATA, mapping = aes(x = PID, y = PE)) +
geom_col(color = "black", fill = c(rep_len("blue3",2), rep_len("gray50",3), rep_len("red3",2)),
linewidth = 1.1, width = 0.8) +
geom_text(mapping = aes(y = CIHI, label = scales::percent(PE, accuracy = 1L)), size = 5, hjust = 0.5, vjust = -1) +
geom_errorbar(mapping = aes(ymin = CILO, ymax = CIHI), size = 0.75, width = 0.25) +
scale_x_discrete() +
scale_y_continuous(expand = c(0,0), limits = c(0,1), sec.axis = (dup_axis())) +
labs(title = TITLE, y = "Probability", caption = CAPTION) +
theme.zFigure
Column plot: Stacked with confidence intervals
Code
library(tidyverse)
DATA <- tribble(
~RESPONSE, ~GROUP,~PE,~CILO,~CIHI,~CILO.STACK,~CIHI.STACK,
"Rated only men cold","Strong feminist",0.2244495,0.1734468,0.2852736,0.1734468,0.2852736,
"Rated only men cold","Feminist",0.1369234,0.1121956,0.1660818,0.1121956,0.1660818,
"Rated only men cold","Not a feminist",0.0818824,0.0684379,0.0976912,0.0684379,0.0976912,
"Rated only men cold","Anti-feminist",0.1230189,0.0776477,0.189456,0.0776477,0.189456,
"Residual colder to men","Strong feminist",0.4957903,0.4315959,0.5601238,0.6560454,0.7845733,
"Residual colder to men","Feminist",0.548797,0.5072817,0.5896439,0.6442051,0.7265673,
"Residual colder to men","Not a feminist",0.4317206,0.4061719,0.4576378,0.4880543,0.5395202,
"Residual colder to men","Anti-feminist",0.2593538,0.1988233,0.3307057,0.3218422,0.4537246,
"Rated men equal to women","Strong feminist",0.1141982,0.0770669,0.1660018,0.7973067,0.8862416,
"Rated men equal to women","Feminist",0.1583753,0.1292627,0.1925943,0.8149831,0.8783147,
"Rated men equal to women","Not a feminist",0.2650397,0.2426491,0.2887089,0.7562521,0.8023119,
"Rated men equal to women","Anti-feminist",0.2801124,0.2121814,0.3598575,0.5945541,0.7422302,
"Residual colder to women","Strong feminist",0.1357016,0.0968408,0.186929,0.9312788,1.021367,
"Residual colder to women","Feminist",0.1267096,0.099491,0.1600512,0.9435867,1.0041469,
"Residual colder to women","Not a feminist",0.1944195,0.1744745,0.2160478,0.9531172,0.9946905,
"Residual colder to women","Anti-feminist",0.2635357,0.200313,0.338272,0.8627981,1.0007571,
"Rated only women cold","Strong feminist",0.0247997,0.0091943,0.0651505,0.9793339,1.0352901,
"Rated only women cold","Feminist",0.0291947,0.0176643,0.0478848,0.9884696,1.0186901,
"Rated only women cold","Not a feminist",0.0269377,0.0196127,0.0368957,0.9926749,1.0099579,
"Rated only women cold","Anti-feminist",0.0739792,0.0371087,0.142078,0.9631295,1.0680988)
DATA$RESPONSE <- factor(DATA$RESPONSE, levels = rev(unique(DATA$RESPONSE)))
DATA$GROUP <- factor(DATA$GROUP , levels = unique(DATA$GROUP[1:5]))
theme.z <- theme(
axis.text.x = element_text(color = "black", size = 15, margin = margin(t = 0)),
axis.text.y = element_text(color = "black", size = 15, margin = margin(r = 0)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none",
legend.spacing.x = unit(10, "pt"),
legend.spacing.y = unit(10, "pt"),
legend.text = element_text(size = 15, margin = margin(t = 5)),
legend.title = element_text(size = 15, face = "bold"),
panel.background = element_rect(fill = "white"),
panel.border = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
plot.caption = element_text(size = 11, hjust = 0, margin = margin(15,0,0,0)),
plot.subtitle = element_text(hjust = 0.5),
plot.margin = unit(c(t = 15,r = 210,b = 15,l = 15),"pt"),
plot.title = element_text(face = "bold", margin = margin(t = 0, b = 6), size = 20, hjust = 0.5)
)
CAPTION <- str_wrap("Note: Cold refers to a rating under 50. Colder refers to a lower rating. Estimates are weighted. Sample size 1,498 U.S. adults, 147 strong feminists, 366 feminists,\n889 non-feminists, and 96 anti-feminists, with 2 non-responses for the feminist item and 1 non-response for the feeling thermometer items.\n83.4% confidence intervals are for the uncertainty about the indicated category (e.g., Rated men equal to women, among non-feminists).\nData source: American National Election Studies. 2022. ANES 2022 Pilot Study [dataset and documentation]. December 14, 2022 version. www.electionstudies.org.", width = 100)
ggplot(DATA, mapping = aes(fill = RESPONSE, y = 100*PE, x = GROUP)) +
geom_col(color = "black", linewidth = 1.1, width = 0.85) +
scale_fill_manual(values = rev(c("Rated only men cold" = "steelblue1","Residual colder to men" = "powderblue","Rated men equal to women" = "white","Residual colder to women" = "lightpink1","Rated only women cold" = "deeppink1")), name = "Category") +
scale_y_continuous(limits = c(0,107), breaks = seq(0,100,by = 10)) +
scale_x_discrete(limits = c("Strong feminist","Feminist","Not a feminist","Anti-feminist")) +
labs(title = "Ratings about men and about women\non 0-to-100 feeling thermometers", caption = CAPTION) +
coord_cartesian(clip = "off") +
geom_errorbar(mapping = aes(ymin = 100*(CILO.STACK), ymax = 100*(CIHI.STACK)), width = 0.5, size = 0.75, position = position_dodge(c(0.75))) +
annotate("text",x = 4.65,y = 96,size = 5.5,hjust = 0,label = "Rated only women cold") +
annotate("text",x = 4.65,y = 79,size = 5.5,hjust = 0,label = "Residual colder to women") +
annotate("text",x = 4.65,y = 52,size = 5.5,hjust = 0,label = "Rated men equal to women") +
annotate("text",x = 4.65,y = 25,size = 5.5,hjust = 0,label = "Residual colder to men") +
annotate("text",x = 4.65,y = 6,size = 5.5,hjust = 0,label = "Rated only men cold") +
theme.zFigure
Regression plot: Categorical with facets and internal arrows
Code
This next plot illustrates why it’s not ideal for an analysis to assume a constant association between the predictor and the outcome net of controls. The plot also includes arrows to emphasize a point of the plot. This plot appeared in Zigerell 2022 “Introducing Political Science Students to Data Visualization Strategies” in the Journal of Political Science Education.
library(tidyverse)
DATA <- read_csv("Figure 9.csv")
print(DATA, n = Inf)## # A tibble: 36 × 5
## FACET LEVEL PE CILO CIHI
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Con Uniform 0 -0.172 -0.236 -0.107
## 2 Con Uniform 1 -0.137 -0.191 -0.0844
## 3 Con Uniform 2 -0.103 -0.146 -0.0605
## 4 Con Uniform 3 -0.0690 -0.103 -0.0352
## 5 Con Uniform 4 -0.0348 -0.0627 -0.00690
## 6 Con Uniform 5 -0.000583 -0.0275 0.0263
## 7 Con Uniform 6 0.0336 0.00237 0.0649
## 8 Con Uniform 7 0.0679 0.0285 0.107
## 9 Con Uniform 8 0.102 0.0528 0.151
## 10 Con Relaxed 0 -0.0414 -0.195 0.112
## 11 Con Relaxed 1 -0.0477 -0.156 0.0607
## 12 Con Relaxed 2 -0.175 -0.259 -0.0918
## 13 Con Relaxed 3 -0.112 -0.198 -0.0252
## 14 Con Relaxed 4 0.0803 0.0117 0.149
## 15 Con Relaxed 5 -0.104 -0.166 -0.0416
## 16 Con Relaxed 6 -0.130 -0.194 -0.0666
## 17 Con Relaxed 7 0.196 0.123 0.269
## 18 Con Relaxed 8 0.220 0.133 0.308
## 19 Lib Uniform 0 0.101 0.0395 0.162
## 20 Lib Uniform 1 0.0899 0.0394 0.140
## 21 Lib Uniform 2 0.0793 0.0385 0.120
## 22 Lib Uniform 3 0.0687 0.0362 0.101
## 23 Lib Uniform 4 0.0581 0.0311 0.0852
## 24 Lib Uniform 5 0.0476 0.0212 0.0739
## 25 Lib Uniform 6 0.0370 0.00641 0.0675
## 26 Lib Uniform 7 0.0264 -0.0118 0.0645
## 27 Lib Uniform 8 0.0158 -0.0318 0.0633
## 28 Lib Relaxed 0 0.0426 -0.0992 0.184
## 29 Lib Relaxed 1 0.218 0.112 0.324
## 30 Lib Relaxed 2 0.0411 -0.0413 0.124
## 31 Lib Relaxed 3 -0.0297 -0.104 0.0447
## 32 Lib Relaxed 4 0.0901 0.0238 0.156
## 33 Lib Relaxed 5 -0.00485 -0.0732 0.0635
## 34 Lib Relaxed 6 0.0708 0.00751 0.134
## 35 Lib Relaxed 7 0.0869 0.0144 0.159
## 36 Lib Relaxed 8 -0.0302 -0.109 0.0491DATA$FACET <- factor(DATA$FACET, levels = unique(DATA$FACET))
DATA$LEVEL <- factor(DATA$LEVEL, levels = 0:8)
ANNOTATE <- data.frame(FACET = c("Con Uniform","Con Relaxed","Lib Uniform","Lib Relaxed"),
label = c("p<0.05\nevidence\nof an effect","No p<0.05\nevidence\nof an effect",NA,NA))
ANNOTATE$FACET <- factor(ANNOTATE$FACET, levels = c("Con Uniform","Con Relaxed","Lib Uniform","Lib Relaxed"))
LABELS <- c("Con Uniform" = "Conservative Trump treatment\n\u2013 Uniform model \u2013", "Con Relaxed" = "Conservative Trump treatment\n\u2013 Non-uniform model \u2013", "Lib Uniform" = "Liberal Trump treatment\n\u2013 Uniform model \u2013", "Lib Relaxed" = "Liberal Trump treatment\n\u2013 Non-uniform model \u2013")
CAPTION <- str_wrap("Note: The figure reports point estimates and 95% confidence intervals from a linear regression predicting the estimated effect of a \"conservative Trump\" treatment (top panels) and a \"liberal Trump\" treatment (bottom panels) on participant responses about a policy (with a liberal response coded higher), at levels of political knowledge. Left panels depict results reported in Figure 2 of Barber and Pope (2019), which did not permit a non-uniform association. Right panels depict results that permitted a non-uniform association. Data source: Barber (2019). See Figure A14 of Barber and Pope (2019) for a plot of a different way to permit the treatment effect estimate to not be uniform.", width = 110)
theme.z <- theme(
axis.text.x = element_text(size = 15, color = "black", hjust = c(0,1), margin = margin(t = 7,r = 0,b = 5,l = 0)),
axis.text.x.top = element_blank(),
axis.text.y = element_text(size = 15, color = "black", margin = margin(r = 7, l = 7)),
axis.text.y.right = element_text(size = 15, color = "black", margin = margin(r = 7, l = 7)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none",
panel.background = element_rect(linewidth = 1.0, color = "black", fill = "gray95", linetype = "solid"),
panel.border = element_rect(linewidth = 1.0, color = "black", fill = NA , linetype = "solid"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.spacing.x = unit(1, "lines"),
panel.spacing.y = unit(1, "lines"),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 12, hjust = 0 , margin = margin(t = 10)),
plot.margin = unit(c(t = 5,r = 5,b = 5,l = 5),"pt"),
plot.subtitle = element_text(size = 15, hjust = 0.5, margin = margin(b = 7)),
plot.title = element_text(size = 20, hjust = 0.5, face = "bold", margin = margin(t = 0,b = 12)),
strip.background = element_rect(linewidth = 1.0, color = "black", fill = "black"),
strip.text.x = element_text(size = 18, color = "white", face = "bold", margin = margin(t = 10,r = 10,b = 10,l = 10))
)
ggplot(data = DATA, mapping = aes(x = LEVEL, y = PE)) +
facet_wrap(vars(FACET), ncol = 2, dir = "h", labeller = as_labeller(LABELS)) +
geom_segment(x = -Inf, y = 0, xend = Inf, yend = 0, color = "gray80") +
geom_rect(xmin = -Inf, xmax = Inf, ymin = 0, ymax = Inf, fill = "gray80") +
geom_errorbar(mapping = aes(ymin = CILO, ymax = CIHI), size = 1.25, width = 0) +
geom_point(data = filter(DATA, FACET == "Con Uniform" | FACET == "Con Relaxed"),
size = 4, shape = 21, stroke = 1.5, color = "black", fill = "red3") +
geom_point(data = filter(DATA, FACET == "Lib Uniform" | FACET == "Lib Relaxed"),
size = 4, shape = 21, stroke = 1.5, color = "black", fill = "blue3") +
geom_text(data = ANNOTATE, mapping = aes(x = 2.25, y = 0.3, label = label), size = 5, hjust = 0, lineheight = 0.9) +
geom_curve(data = filter(DATA, FACET == "Con Uniform"), mapping = aes(x = 2, y = 0.35, xend = 0.9, yend = -0.08),
arrow = arrow(length = unit(0.25, "cm")), size = 0.05, curvature = 0.45) +
geom_curve(data = filter(DATA, FACET == "Con Uniform"), mapping = aes(x = 2, y = 0.30, xend = 1.8, yend = -0.08),
arrow = arrow(length = unit(0.25, "cm")), size = 0.05, curvature = 0.50) +
geom_curve(data = filter(DATA, FACET == "Con Relaxed"), mapping = aes(x = 2, y = 0.35, xend = 1 , yend = 0.18),
arrow = arrow(length = unit(0.25, "cm")), size = 0.05, curvature = 0.40) +
geom_curve(data = filter(DATA, FACET == "Con Relaxed"), mapping = aes(x = 2, y = 0.30, xend = 1.8, yend = 0.10),
arrow = arrow(length = unit(0.25, "cm")), size = 0.05, curvature = 0.50) +
scale_x_discrete(breaks = c(0,8), labels = c("Lowest\nPolitical\nKnowledge", "Highest\nPolitical\nKnowledge")) +
scale_y_continuous(limits = c(-0.5,0.5), breaks = seq(-0.4,0.4,0.2), labels = seq(-0.4,0.4,0.2),
sec.axis = dup_axis()) +
labs(title = "Estimated effect of the...", caption = CAPTION) +
theme.zFigure
Regression plot: Uniform
Code
Below is a regression plot in which the analysis did assume a constant association between the predictor and the outcome net of controls. For an analysis that has that assumption, it does not make much sense to plot individual point predictions, because these points would not be independent predictions. This is a draft of a figure from Zigerell 2022 “Introducing Political Science Students to Data Visualization Strategies” in the Journal of Political Science Education.
library(tidyverse)
DATA <- read_csv("Figure 4.CSV")
print(DATA, n = Inf)## # A tibble: 40 × 5
## FACET LEVEL PE CILO CIHI
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Republicans 1 0.367 0.342 0.393
## 2 Republicans 2 0.371 0.350 0.393
## 3 Republicans 3 0.376 0.358 0.393
## 4 Republicans 4 0.380 0.365 0.395
## 5 Republicans 5 0.384 0.370 0.397
## 6 Republicans 6 0.388 0.375 0.402
## 7 Republicans 7 0.392 0.377 0.408
## 8 Republicans 8 0.397 0.378 0.415
## 9 Republicans 9 0.401 0.379 0.423
## 10 Republicans 10 0.405 0.379 0.432
## 11 McConnell 1 0.130 0.115 0.144
## 12 McConnell 2 0.129 0.116 0.141
## 13 McConnell 3 0.128 0.118 0.138
## 14 McConnell 4 0.127 0.118 0.136
## 15 McConnell 5 0.127 0.118 0.135
## 16 McConnell 6 0.126 0.117 0.134
## 17 McConnell 7 0.125 0.116 0.134
## 18 McConnell 8 0.124 0.114 0.135
## 19 McConnell 9 0.124 0.111 0.136
## 20 McConnell 10 0.123 0.109 0.137
## 21 Ryan 1 0.239 0.219 0.258
## 22 Ryan 2 0.242 0.226 0.259
## 23 Ryan 3 0.246 0.232 0.260
## 24 Ryan 4 0.250 0.238 0.261
## 25 Ryan 5 0.253 0.242 0.264
## 26 Ryan 6 0.257 0.246 0.268
## 27 Ryan 7 0.261 0.248 0.273
## 28 Ryan 8 0.265 0.250 0.280
## 29 Ryan 9 0.269 0.250 0.287
## 30 Ryan 10 0.272 0.251 0.294
## 31 Trump 1 0.271 0.246 0.295
## 32 Trump 2 0.296 0.274 0.318
## 33 Trump 3 0.323 0.304 0.342
## 34 Trump 4 0.351 0.334 0.368
## 35 Trump 5 0.380 0.365 0.396
## 36 Trump 6 0.410 0.395 0.426
## 37 Trump 7 0.441 0.424 0.459
## 38 Trump 8 0.472 0.451 0.493
## 39 Trump 9 0.504 0.479 0.529
## 40 Trump 10 0.535 0.505 0.565DATA$FACET <- factor(DATA$FACET, levels = c("Trump", "McConnell", "Ryan", "Republicans"))
LABELS <- c("Republicans" = "Republicans\nin 2017", "McConnell" = "Mitch McConnell\nin 2018", "Ryan" = "Paul Ryan\nin 2018", "Trump" = "Donald Trump\nin 2018")
CAPTION <- str_wrap("Note: The figure reports results from an unweighted logit regression. Each outcome is a favorable rating, about Republicans (above 50 on a 0-to-100 feeling thermometer, with the residual category containing respondents who reported a rating 50 or lower or did not provide a rating) or the indicated Republican (very favorable or somewhat favorable, with the residual category containing respondents who selected very unfavorable or somewhat unfavorable or did not select a response). The main predictor is a measure of animus against Democratic groups, measured as the mean 0-to-100 feeling thermometer rating about Blacks, Latinos, Muslims, and gay and lesbian people. The measure of animus was coded to have ten levels with about an equal number at each level, with sample sizes between 411 and 429 for each of the ten levels. Regressions controlled for gender, race, age, education, family income, partisanship, ideology, religious attendance, and political interest. Estimates were calculated with controls at their means. Sample limited to respondents with substantive responses to all predictors and all four outcomes. Data source: VOTER study (Democracy Fund Voter Study Group 2018). Based on code from Mason et al. (2021b) and an analysis in Mason et al. (2021a), with modifications.", width = 120)
theme.z <- theme(
axis.text.x = element_text(size = 15, color = "black", hjust = c(0,1), margin = margin(t = 7,b = 7)),
axis.text.x.top = element_blank(),
axis.text.y = element_text(size = 15, color = "black", margin = margin(l = 7,r = 7)),
axis.text.y.right = element_text(size = 15, color = "black", margin = margin(l = 7,r = 7)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_text(size = 15, color = "black", margin = margin(t = 7,r = 7,b = 7,l = 7)),
axis.title.y = element_blank(),
legend.position = "none",
panel.background = element_rect(linewidth = 1.0, color = "black", fill = "gray90", linetype = "solid"),
panel.border = element_rect(linewidth = 1.0, color = "black", fill = NA, linetype = "solid"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.spacing.x = unit(1, "lines"),
panel.spacing.y = unit(1, "lines"),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 12, hjust = 0 , margin = margin(t = 10)),
plot.margin = unit(c(t = 5,r = 5,b = 5,l = 5),"pt"),
plot.subtitle = element_text(size = 15, hjust = 0.5, margin = margin(b = 7)),
plot.title = element_text(size = 20, hjust = 0.5, face = "bold", margin = margin(t = 0,b = 12)),
strip.background = element_rect(linewidth = 1.0, color = "black", fill = "black"),
strip.text.x = element_text(size = 18, color = "white", face = "bold", margin = margin(t = 10,r = 10,b = 10,l = 10))
)
ggplot(data = DATA, mapping = aes(x = LEVEL, y = PE, group = 1)) +
facet_wrap(vars(FACET), ncol = 4, dir = "v", labeller = as_labeller(LABELS)) +
geom_rect(data = filter(DATA, FACET == "Ryan"), mapping = aes(xmin = -Inf, xmax = Inf,
ymin = min(DATA$PE[DATA$FACET == "Ryan"]), ymax = max(DATA$PE[DATA$FACET == "Ryan"])), fill = "lightsteelblue3", inherit.aes = FALSE) +
geom_rect(data = filter(DATA, FACET == "McConnell"), mapping = aes(xmin = -Inf, xmax = Inf,
ymin = min(DATA$PE[DATA$FACET == "McConnell"]), ymax = max(DATA$PE[DATA$FACET == "McConnell"])), fill = "lightsteelblue3", inherit.aes = FALSE) +
geom_rect(data = filter(DATA, FACET == "Republicans"), mapping = aes(xmin = -Inf, xmax = Inf, ymin = min(DATA$PE[DATA$FACET == "Republicans"]), ymax = max(DATA$PE[DATA$FACET == "Republicans"])), fill = "lightsteelblue3", inherit.aes = FALSE) +
geom_rect(data = filter(DATA, FACET == "Trump"), mapping = aes(xmin = -Inf, xmax = Inf,
ymin = min(DATA$PE[DATA$FACET == "Trump"]), ymax = max(DATA$PE[DATA$FACET == "Trump"])), fill = "lightsteelblue3", inherit.aes = FALSE) +
geom_line(linewidth = 1.25, color = "red3") +
scale_x_continuous(name = "Animus toward Democratic groups in 2011", expand = c(0,0), breaks = c(1,10),
labels = c("Low\nanimus","High\nanimus")) +
scale_y_continuous(expand = c(0,0), limits = c(0,1), breaks = seq(0,1),
labels = scales::number_format(accuracy = 1), sec.axis = dup_axis()) +
labs(title = "Predicted probability of a favorable rating about...", caption = CAPTION) +
theme.zFigure
Estimates plot: Combined with Patchwork
Code
library(patchwork)
library(tidyverse)
DATA <- read_csv("Rights of Man Means.csv")
print(DATA, n = Inf)## # A tibble: 63 × 9
## LIBERT STUDY GENDER ITEM ORIGINAL PE SE CILO CIHI
## <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 6 Study 1 Women's reproductive … Low … ABscale… 0.521 0.107 0.365 0.677
## 2 6 Study 1 Women's reproductive … Sing… ABscale… 0.546 0.099 0.403 0.69
## 3 6 Study 1 Women's reproductive … Marr… ABscale… 0.521 0.105 0.369 0.673
## 4 6 Study 1 Men's reproductive au… Fair… FinAb_2x 0.624 0.084 0.502 0.745
## 5 6 Study 1 Men's reproductive au… Men … FinAb_4x 0.624 0.089 0.494 0.753
## 6 6 Study 1 Men's reproductive au… Man … FinAb_3x 0.588 0.079 0.473 0.704
## 7 6 Study 1 Men's reproductive au… Fath… FinAb_1x 0.471 0.094 0.334 0.607
## 8 6 Study 2 Women's reproductive … Low … ABscale… 0.508 0.108 0.351 0.665
## 9 6 Study 2 Women's reproductive … Sing… ABscale… 0.452 0.11 0.294 0.611
## 10 6 Study 2 Women's reproductive … Marr… ABscale… 0.468 0.112 0.305 0.631
## 11 6 Study 2 Men's reproductive au… Fair… FinAb_2x 0.611 0.078 0.498 0.724
## 12 6 Study 2 Men's reproductive au… Men … FinAb_4x 0.467 0.078 0.354 0.579
## 13 6 Study 2 Men's reproductive au… Man … FinAb_3x 0.489 0.093 0.354 0.624
## 14 6 Study 2 Men's reproductive au… Fath… FinAb_1x 0.544 0.085 0.421 0.668
## 15 6 Pooled Women's reproductive … Low … ABscale… 0.514 0.075 0.408 0.621
## 16 6 Pooled Women's reproductive … Sing… ABscale… 0.498 0.073 0.394 0.602
## 17 6 Pooled Women's reproductive … Marr… ABscale… 0.494 0.076 0.386 0.601
## 18 6 Pooled Men's reproductive au… Fair… FinAb_2x 0.617 0.056 0.537 0.697
## 19 6 Pooled Men's reproductive au… Men … FinAb_4x 0.543 0.059 0.459 0.627
## 20 6 Pooled Men's reproductive au… Man … FinAb_3x 0.537 0.061 0.45 0.624
## 21 6 Pooled Men's reproductive au… Fath… FinAb_1x 0.509 0.063 0.42 0.597
## 22 56 Study 1 Women's reproductive … Low … ABscale… 0.602 0.0567 0.523 0.682
## 23 56 Study 1 Women's reproductive … Sing… ABscale… 0.627 0.0576 0.546 0.708
## 24 56 Study 1 Women's reproductive … Marr… ABscale… 0.615 0.0544 0.538 0.691
## 25 56 Study 1 Men's reproductive au… Fair… FinAb_2x 0.609 0.0452 0.545 0.672
## 26 56 Study 1 Men's reproductive au… Men … FinAb_4x 0.7 0.0468 0.634 0.766
## 27 56 Study 1 Men's reproductive au… Man … FinAb_3x 0.657 0.0447 0.594 0.719
## 28 56 Study 1 Men's reproductive au… Fath… FinAb_1x 0.513 0.0500 0.443 0.583
## 29 56 Study 2 Women's reproductive … Low … ABscale… 0.584 0.0654 0.492 0.676
## 30 56 Study 2 Women's reproductive … Sing… ABscale… 0.528 0.0679 0.432 0.624
## 31 56 Study 2 Women's reproductive … Marr… ABscale… 0.553 0.0692 0.455 0.650
## 32 56 Study 2 Men's reproductive au… Fair… FinAb_2x 0.535 0.0513 0.463 0.607
## 33 56 Study 2 Men's reproductive au… Men … FinAb_4x 0.448 0.0506 0.377 0.519
## 34 56 Study 2 Men's reproductive au… Man … FinAb_3x 0.417 0.0579 0.336 0.499
## 35 56 Study 2 Men's reproductive au… Fath… FinAb_1x 0.478 0.0544 0.402 0.555
## 36 56 Pooled Women's reproductive … Low … ABscale… 0.593 0.0430 0.533 0.653
## 37 56 Pooled Women's reproductive … Sing… ABscale… 0.578 0.0446 0.515 0.640
## 38 56 Pooled Women's reproductive … Marr… ABscale… 0.584 0.0439 0.523 0.645
## 39 56 Pooled Men's reproductive au… Fair… FinAb_2x 0.572 0.0342 0.524 0.620
## 40 56 Pooled Men's reproductive au… Men … FinAb_4x 0.574 0.0367 0.523 0.625
## 41 56 Pooled Men's reproductive au… Man … FinAb_3x 0.537 0.0385 0.483 0.591
## 42 56 Pooled Men's reproductive au… Fath… FinAb_1x 0.496 0.0368 0.444 0.547
## 43 456 Study 1 Women's reproductive … Low … ABscale… 0.649 0.0386 0.595 0.703
## 44 456 Study 1 Women's reproductive … Sing… ABscale… 0.637 0.0405 0.580 0.693
## 45 456 Study 1 Women's reproductive … Marr… ABscale… 0.637 0.0382 0.583 0.690
## 46 456 Study 1 Men's reproductive au… Fair… FinAb_2x 0.554 0.0342 0.507 0.602
## 47 456 Study 1 Men's reproductive au… Men … FinAb_4x 0.633 0.0368 0.581 0.684
## 48 456 Study 1 Men's reproductive au… Man … FinAb_3x 0.577 0.0369 0.526 0.629
## 49 456 Study 1 Men's reproductive au… Fath… FinAb_1x 0.504 0.0369 0.452 0.555
## 50 456 Study 2 Women's reproductive … Low … ABscale… 0.645 0.0447 0.583 0.708
## 51 456 Study 2 Women's reproductive … Sing… ABscale… 0.601 0.0466 0.536 0.666
## 52 456 Study 2 Women's reproductive … Marr… ABscale… 0.619 0.0467 0.553 0.684
## 53 456 Study 2 Men's reproductive au… Fair… FinAb_2x 0.479 0.0367 0.428 0.530
## 54 456 Study 2 Men's reproductive au… Men … FinAb_4x 0.360 0.0373 0.308 0.412
## 55 456 Study 2 Men's reproductive au… Man … FinAb_3x 0.354 0.0402 0.298 0.410
## 56 456 Study 2 Men's reproductive au… Fath… FinAb_1x 0.462 0.0379 0.409 0.514
## 57 456 Pooled Women's reproductive … Low … ABscale… 0.647 0.0298 0.606 0.689
## 58 456 Pooled Women's reproductive … Sing… ABscale… 0.618 0.0312 0.574 0.661
## 59 456 Pooled Women's reproductive … Marr… ABscale… 0.627 0.0306 0.584 0.669
## 60 456 Pooled Men's reproductive au… Fair… FinAb_2x 0.514 0.0254 0.479 0.549
## 61 456 Pooled Men's reproductive au… Men … FinAb_4x 0.487 0.0282 0.448 0.526
## 62 456 Pooled Men's reproductive au… Man … FinAb_3x 0.458 0.0287 0.418 0.498
## 63 456 Pooled Men's reproductive au… Fath… FinAb_1x 0.481 0.0265 0.444 0.518theme.z <- theme(
axis.text.x = element_text(size = 15, color = "black", margin = margin(t = 5,b = 5), hjust = c(0,1)),
axis.text.y = element_text(size = 15, color = "black", margin = margin(l = 5,r = 5)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.background = element_rect(linewidth = 1.0, color = "black", fill = "gray80", linetype = "solid"),
panel.border = element_rect(linewidth = 1.0, color = "black", fill = NA , linetype = "solid"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.spacing.x = unit(1, "lines"),
panel.spacing.y = unit(1, "lines"),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 10, hjust = 0, margin = margin(t = 8)),
plot.margin = unit(c(0.5,0.05,0,0.05),"cm"),
plot.subtitle = element_text(size = 15, hjust = 0.5, margin = margin(b = 7)),
plot.title = element_text(size = 18, hjust = 0.5, face = "bold", margin = margin(t = 0,b = 8)),
strip.background = element_rect(linewidth = 1.0, color = "black", fill = "black"),
strip.text.x = element_text(size = 15, color = "white", face = "bold", margin = margin(t = 5,b = 5)),
strip.text.y = element_blank()
)
DATA$ITEM <- factor(DATA$ITEM , levels = rev(unique(DATA$ITEM)))
DATA$STUDY <- factor(DATA$STUDY, levels = unique(DATA$STUDY))
DATA$GENDER <- factor(DATA$GENDER, levels = unique(DATA$GENDER))
DATA.6 <- filter(DATA, LIBERT == 6)
DATA.56 <- filter(DATA, LIBERT == 56)
DATA.456 <- filter(DATA, LIBERT == 456)
PLOT.6 <- ggplot(data = DATA.6, mapping = aes(x = PE, y = ITEM)) +
facet_grid(GENDER ~ STUDY, space = "free_y", scales = "free_y") +
geom_rect(data = NULL, mapping = aes(xmin = 0,xmax = 0.5, ymin = -Inf, ymax = Inf), fill = "gray90") +
geom_point(size = 3.5) +
geom_errorbarh(mapping = aes(xmin = CILO, xmax = CIHI), height = 0, size = 0.75) +
scale_x_continuous(breaks = c(0,1), limits = c(0,1), labels = c("Least\nSupport", "Most\nSupport"), expand = c(0,0)) +
labs(title = "Libertarian identification of 6") +
theme.z +
theme(axis.text.x = element_blank())
PLOT.56 <- ggplot(data = DATA.56, mapping = aes(x = PE, y = ITEM)) +
facet_grid(GENDER ~ STUDY, space = "free_y", scales = "free_y") +
geom_rect(data = NULL, mapping = aes(xmin = 0,xmax = 0.5, ymin = -Inf, ymax = Inf), fill = "gray90") +
geom_point(size = 3.5) +
geom_errorbarh(mapping = aes(xmin = CILO, xmax = CIHI), height = 0, size = 0.75) +
scale_x_continuous(breaks = c(0,1), limits = c(0,1), labels = c("Least\nSupport", "Most\nSupport"), expand = c(0,0)) +
labs(title = "Libertarian identification of 5 or 6") +
theme.z +
theme(axis.text.x = element_blank(),
strip.text.x = element_blank())
PLOT.456 <- ggplot(data = DATA.456, mapping = aes(x = PE, y = ITEM)) +
facet_grid(GENDER ~ STUDY, space = "free_y", scales = "free_y") +
geom_rect(data = NULL, mapping = aes(xmin = 0,xmax = 0.5, ymin = -Inf, ymax = Inf), fill = "gray90") +
geom_point(size = 3.5) +
geom_errorbarh(mapping = aes(xmin = CILO, xmax = CIHI), height = 0, size = 0.75) +
scale_x_continuous(breaks = c(0,1), limits = c(0,1), labels = c("Least\nSupport", "Most\nSupport"), expand = c(0,0)) +
labs(title = "Libertarian identification of 4, 5, or 6") +
theme.z +
theme(strip.text.x = element_blank())
PLOT.6 + PLOT.56 + PLOT.456 + plot_layout(ncol = 1)Figure
Column plot: Stacked without legend
Code
library(tidyverse)
DATA <- tribble(
~RESPONSE ,~GROUP , ~PE ,
"Rated only men cold" ,"U.S. adults" , 0.111665 ,
"Rated only men cold" ,"Biden voters", 0.1721875,
"Rated only men cold" ,"Trump voters", 0.0515194,
"Residual colder to men" ,"U.S. adults" , 0.4547139,
"Residual colder to men" ,"Biden voters", 0.5282067,
"Residual colder to men" ,"Trump voters", 0.410322,
"Rated men equal to women","U.S. adults" , 0.2260411,
"Rated men equal to women","Biden voters", 0.1299536,
"Rated men equal to women","Trump voters", 0.3142311,
"Residual colder to women","U.S. adults" , 0.176867,
"Residual colder to women","Biden voters", 0.1426348,
"Residual colder to women","Trump voters", 0.198812,
"Rated only women cold" ,"U.S. adults" , 0.0301996,
"Rated only women cold" ,"Biden voters", 0.0255463,
"Rated only women cold" ,"Trump voters", 0.0251155)
DATA$RESPONSE <- factor(DATA$RESPONSE, levels = rev(unique(DATA$RESPONSE)))
DATA$GROUP <- factor(DATA$GROUP , levels = unique(DATA$GROUP[1:3]))
theme.z <- theme(
axis.text.x = element_text(color = "black", size = 15, margin = margin(t = 0)),
axis.text.y = element_text(color = "black", size = 15, margin = margin(l = 0, r = 0)),
axis.text.y.right = element_text(color = "black", size = 15, margin = margin(l = 0, r = 0)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none",
legend.spacing.x = unit(10, "pt"),
legend.spacing.y = unit(10, "pt"),
legend.text = element_text(size = 15, margin = margin(t = 5)),
legend.title = element_text(size = 15, face = "bold"),
panel.background = element_rect(fill = "white"),
panel.border = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
plot.caption = element_text(size = 11, hjust = 0, margin = margin(15,0,0,0)),
plot.subtitle = element_text(hjust = 0.5),
plot.margin = unit(c(t = 10,r = 10,b = 10,l = 10),"pt"),
plot.title = element_text(face = "bold", margin = margin(t = 0, b = 6), size = 20, hjust = 0.5)
)
CAPTION <- str_wrap("Note: Cold refers to a rating under 50. Colder refers to a lower rating. Estimates are weighted. \nSample size 1,500 U.S. adults, 607 Biden voters, 508 Democrats, 428 Independents, and 430 Republicans, with a non-response from 1 Democrat / Biden voter.\nData source: American National Election Studies. 2022. ANES 2022 Pilot Study [dataset and documentation].\nDecember 14, 2022 version. www.electionstudies.org. Used presvote20post for the vote variable.", width = 100)
ggplot(DATA, mapping = aes(fill = RESPONSE, y = 100*PE, x = GROUP)) +
geom_col(color = "black", linewidth = 1.1, width = 0.85) +
scale_fill_manual(values = rev(c("Rated only men cold" = "steelblue1","Residual colder to men" = "powderblue","Rated men equal to women" = "white","Residual colder to women" = "lightpink1","Rated only women cold" = "deeppink1")), name = "Category") +
scale_y_continuous(limits = c(0,100.01), breaks = seq(0,100,by = 10), sec.axis = sec_axis(~.,breaks = c(3,27,62,86,98), label = c("Rated only men cold","Residual colder to men","Rated men equal to women","Residual colder to women","Rated only women cold"))) +
labs(title = "Ratings about men and women\non 0-to-100 feeling thermometers", caption = CAPTION) +
theme.zFigure
Column plot: Stacked with legend
Code
This next plot is a column plot but with categories stacked on top of each other.
library(tidyverse)
DATA <- tribble(
~GROUP , ~ITEM , ~PE ,
"Democrats" , "Not at all" , 0.775,
"Democrats" , "A little" , 0.098,
"Democrats" , "A moderate amount", 0.077,
"Democrats" , "A lot" , 0.026,
"Democrats" , "A great deal" , 0.018,
"Republicans" , "Not at all" , 0.893,
"Republicans" , "A little" , 0.03 ,
"Republicans" , "A moderate amount", 0.047,
"Republicans" , "A lot" , 0.012,
"Republicans" , "A great deal" , 0.015,
"Independents", "Not at all" , 0.829,
"Independents", "A little" , 0.072,
"Independents", "A moderate amount", 0.064,
"Independents", "A lot" , 0.01 ,
"Independents", "A great deal" , 0.013)
DATA$ITEM <- factor(DATA$ITEM, levels = c("Not at all", "A little", "A moderate amount", "A lot", "A great deal"))
CAPTION <- str_wrap("Data source: American National Election Studies. 2021.\nANES 2020 Time Series Study Preliminary Release:\nPre-Election Data [dataset and documentation]. February 11, 2021\nversion. www.electionstudies.org.\n\nSample sizes: Democrats 2,683. Independents 2,525. Republicans 2,563.", width = 97)
theme.z <- theme(
axis.text.x = element_text(size = 15, color = "black", hjust = 0.5, margin = margin(t = 8,b = 8)),
axis.text.y = element_text(size = 15, color = "black", vjust = 0.5, margin = margin(r = 8,l = 8)),
axis.text.y.right = element_text(size = 15, color = "black", vjust = 0.5, margin = margin(r = 8,l = 8)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 15, color = "black", vjust = 0.5, margin = margin(r = 4), angle = 0),
axis.title.y.right = element_text(size = 15, color = "black", vjust = 0.5, margin = margin(l = 4), angle = 0),
legend.position = "right",
legend.spacing.x = unit(10, "pt"),
legend.spacing.y = unit(10, "pt"),
legend.text = element_text(size = 15, margin = margin(t = 5, r = 5, b = 5, l = 5)),
legend.title = element_text(size = 15, face = "bold"),
panel.background = element_rect(fill = "gray90"),
panel.border = element_rect(linewidth = 1.0, color = "black", fill = NA),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(linewidth = 0.1, color = "gray70", linetype = "solid"),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
plot.caption = element_text(size = 12, hjust = 0 , margin = margin(t = 10)),
plot.margin = unit(c(t = 10,r = 10,b = 10,l = 10), "pt"),
plot.subtitle = element_text(size = 15, hjust = 0.5, margin = margin(b = 7)),
plot.title = element_text(size = 19, hjust = 0.5, face = "bold", margin = margin(t = 0,b = 15))
)
ggplot(DATA, mapping = aes(fill = ITEM, y = 100*PE, x = GROUP)) +
geom_col(color = "black", linewidth = 1.1, width = 0.85) +
scale_fill_manual(values = rev(c("red4","red3","red","pink","white")), name = "Response") +
scale_x_discrete(limits = c("Democrats","Independents","Republicans")) +
scale_y_continuous(limits = c(0,100), breaks = seq(0,100,by = 10), sec.axis = dup_axis()) +
labs(title = "How much do you feel it is justified\nfor people to use violence to pursue\ntheir political goals in this country?", y = "%", caption = CAPTION) +
theme.zFigure
Column plot: Back-to-back
Code
This plot provides an example of data loaded from an external file. The line “DATA <- read_csv(”Figure 6.CSV”)” gets the CSV file from the working directory, but the line “DATA <- read_csv(file.choose())” can be used to open a dialog box so that you can navigate to a folder to select the file. This is a figure from Zigerell 2022 “Introducing Political Science Students to Data Visualization Strategies” in the Journal of Political Science Education.
library(grid)
library(tidyverse)
DATA <- read_csv("Figure 6.CSV")
print(DATA, n = Inf)## # A tibble: 22 × 5
## EVENT DATE FACET PE N
## <chr> <chr> <chr> <dbl> <dbl>
## 1 Launches campaign 2015 Jun 16 Serious -7 57
## 2 First GOP debate 2015 Aug 6 Serious -5 89
## 3 Call for Muslim ban 2015 Dec 7 Serious -16 99
## 4 Loses Iowa 2016 Feb 1 Serious -10 115
## 5 Super Tuesday 2016 Mar 15 Serious -28 155
## 6 Cruz and Kasich exit 2016 May 3 Serious -45 170
## 7 Delegate threshold 2016 May 26 Serious -16 125
## 8 Attacks Gold Star family 2016 Aug 1 Serious -36 144
## 9 First presidential debate 2016 Sep 26 Serious -46 268
## 10 Hot mic tape released 2016 Oct 7 Serious -47 180
## 11 Comey's letter 2016 Oct 28 Serious -11 115
## 12 Launches campaign 2015 Jun 16 Clown 16 57
## 13 First GOP debate 2015 Aug 6 Clown 11 89
## 14 Call for Muslim ban 2015 Dec 7 Clown 9 99
## 15 Loses Iowa 2016 Feb 1 Clown 17 115
## 16 Super Tuesday 2016 Mar 15 Clown 16 155
## 17 Cruz and Kasich exit 2016 May 3 Clown 22 170
## 18 Delegate threshold 2016 May 26 Clown 26 125
## 19 Attacks Gold Star family 2016 Aug 1 Clown 21 144
## 20 First presidential debate 2016 Sep 26 Clown 69 268
## 21 Hot mic tape released 2016 Oct 7 Clown 20 180
## 22 Comey's letter 2016 Oct 28 Clown 12 115DATA$PCT <- DATA$PE / DATA$N
DATA$EVENT <- factor(DATA$EVENT, levels = DATA$EVENT[1:11])
DATA$FACET <- factor(DATA$FACET, levels = c("Serious","Clown"))
TEXT.SERIOUS <- textGrob(expression(bold("Serious")), gp = gpar(fontsize = 21))
TEXT.CLOWN <- textGrob(expression(bold("Clown" )), gp = gpar(fontsize = 21))
CAPTION <- str_wrap("Note: Respective samples sizes were 57, 89, 99, 115, 155, 170, 125, 144, 268, 180, and 115. Data source: Boydstun and Lawrence (2019).", width = 50)
theme.z <- theme(
axis.text.x = element_text(size = 15, color = "black", margin = margin(t = 10,r = 10,b = 10,l = 10)),
axis.text.x.top = element_blank(),
axis.text.x.bottom = element_text(size = 15, color = "black", margin = margin(t = 10,r = 10,b = 10,l = 10)),
axis.text.y = element_text(size = 15, color = "black", margin = margin(t = 10,r = 10,b = 10,l = 10)),
axis.text.y.right = element_text(size = 15, color = "black", margin = margin(t = 10,r = 10,b = 10,l = 10)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_text(size = 15, color = "black", margin = margin(t = 0,r = 10,b = 10,l = 10)),
axis.title.y = element_blank(),
legend.position = "none",
panel.background = element_rect(linewidth = 0.5, color = "black", fill = "gray90", linetype = "solid"),
panel.border = element_rect(linewidth = 1.2, color = "black", fill = NA , linetype = "solid"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 12, hjust = 0 , margin = margin(t = 10)),
plot.margin = unit(c(t = 50,r = 5,b = 5,l = 5),"pt"),
plot.subtitle = element_text(size = 15, hjust = 0.5, margin = margin(b = 7)),
plot.title = element_text(size = 20, hjust = 0.5, face = "bold", margin = margin(t = 0,b = 35)),
strip.background = element_rect(linewidth = 1, color = "black", fill = "black"),
strip.text.x = element_text(size = 18, color = "white", face = "bold", margin = margin(t = 7.5,b = 7.5))
)
ggplot(data = DATA, mapping = aes(x = rev(as.numeric(EVENT)), y = 100*PCT, fill = FACET)) +
coord_flip(clip = "off") +
scale_fill_manual(values = c("purple","orange"), breaks = c("Serious","Clown"), labels = c("Serious","Clown")) +
geom_col(linewidth = 0.85, width = 0.7, color = "black") +
scale_x_continuous(breaks = 1:11, labels = rev(DATA$EVENT[1:11]),
sec.axis = sec_axis(~.,breaks = 1:11, labels = rev(DATA$DATE[1:11]))) +
scale_y_continuous(limits = c(-30,30), breaks = seq(-30,30,10), labels = c("30%","20%","10%","0%","10%","20%","30%")) +
labs(y = "Percentage of all articles", caption = CAPTION) +
annotation_custom(TEXT.SERIOUS, xmin = 12.75, xmax = 12.75, ymin = -15, ymax = -15) +
annotation_custom(TEXT.CLOWN , xmin = 12.75, xmax = 12.75, ymin = 15, ymax = 15) +
theme.zFigure
Column plot: Back-to-back with estimates
Code
This is a figure from Zigerell 2022 “Introducing Political Science Students to Data Visualization Strategies” in the Journal of Political Science Education.
library(grid)
library(tidyverse)
DATA <- tribble(
~EVENT , ~DATE , ~PE.SERIOUS, ~PE.CLOWN, ~N ,
"Launches campaign" , "2015 Jun 16", -7, 16, 57,
"First GOP debate" , "2015 Aug 6" , -5, 11, 89,
"Call for Muslim ban" , "2015 Dec 7" , -16, 9, 99,
"Loses Iowa" , "2016 Feb 1" , -10, 17, 115,
"Super Tuesday" , "2016 Mar 15", -28, 16, 155,
"Cruz and Kasich exit" , "2016 May 3" , -45, 22, 170,
"Delegate threshold" , "2016 May 26", -16, 26, 125,
"Attacks Gold Star family" , "2016 Aug 1" , -36, 21, 144,
"First presidential debate", "2016 Sep 26", -46, 69, 268,
"Hot mic tape released" , "2016 Oct 7" , -47, 20, 180,
"Comey's letter" , "2016 Oct 28", -11, 12, 115)
DATA$PCT <- DATA$PE.SERIOUS / DATA$N
DATA$EVENT <- factor(DATA$EVENT, levels = DATA$EVENT[1:11])
EVENT.AN <- as.numeric(DATA$EVENT)
TEXT.SERIOUS <- textGrob(expression(bold("Serious")), gp = gpar(fontsize = 21))
TEXT.CLOWN <- textGrob(expression(bold("Clown" )), gp = gpar(fontsize = 21))
CAPTION <- str_wrap("Note: Respective samples sizes were 57, 89, 99, 115, 155, 170, 125, 144, 268, 180, and 115. Data source: Boydstun and Lawrence (2019).", width = 50)
theme.z <- theme(
axis.text.x = element_blank(),
axis.text.x.top = element_blank(),
axis.text.y = element_text(size = 15 , color = "black", margin = margin(t = 10,r = 10,b = 10,l = 10)),
axis.text.y.right = element_text(size = 15 , color = "black", margin = margin(t = 10,r = 10,b = 10,l = 10)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_text(size = 15 , color = "black", margin = margin(t = 10,r = 10,b = 10,l = 10)),
axis.title.y = element_blank(),
legend.position = "none",
panel.background = element_rect(linewidth = 0.5, color = "black", fill = "gray90", linetype = "solid"),
panel.border = element_rect(linewidth = 1.2, color = "black", fill = NA , linetype = "solid"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 12 , hjust = 0 , margin = margin(t = 10)),
plot.margin = unit(c(t = 50,r = 5,b = 5,l = 5),"pt"),
plot.subtitle = element_text(size = 15 , hjust = 0.5),
plot.title = element_text(size = 20 , hjust = 0.5, margin = margin(t = 0,b = 35), face = "bold"),
strip.background = element_rect(linewidth = 1, color = "black", fill = "black"),
strip.text.x = element_text(size = 18, color = "white", face = "bold", margin = margin(t = 7.5,b = 7.5))
)
ggplot(data = DATA, mapping = aes(x = rev(EVENT.AN), y = 100*PCT)) +
coord_flip(clip = "off") +
scale_fill_manual(values = c("purple")) +
geom_col(color = "black", linewidth = 0.85, width = 0.7, fill = "purple") +
geom_segment(x = rev(EVENT.AN), xend = rev(EVENT.AN), y = 0, yend = 100*DATA$PE.CLOWN/DATA$N) +
geom_point(y = 100*DATA$PE.CLOWN/DATA$N, x = rev(EVENT.AN), shape = 21, size = 4.5, color = "black", fill = "orange", stroke = 1.25) +
geom_text(mapping = aes(x = rev(EVENT.AN), y = c(100*PCT), label = scales::percent(abs(PCT), accuracy = 1L)) , hjust = 1, size = c(rep_len(5,11)), nudge_y = c(rep_len(-3,11))) +
geom_text(mapping = aes(x = rev(EVENT.AN), y = 45 , label = scales::percent(abs(PE.CLOWN/N), accuracy = 1L)), hjust = 1, size = c(rep_len(5,11))) +
scale_x_continuous(breaks = 1:11, labels = rev(DATA$EVENT[1:11]), sec.axis = sec_axis(~.,breaks = 1:11, labels = rev(DATA$DATE[1:11]))) +
scale_y_continuous(limits = c(-45,45)) +
annotation_custom(TEXT.SERIOUS, xmin = 12.75, xmax = 12.75, ymin = -22.5, ymax = -22.5) +
annotation_custom(TEXT.CLOWN , xmin = 12.75, xmax = 12.75, ymin = 22.5, ymax = 22.5) +
labs(y = "Percentage of all articles", caption = CAPTION) +
theme.zFigure
Histogram 1
Code
library(tidyverse)
DATA <- read_csv("VOTER immig.csv")
print(DATA, n = Inf)## # A tibble: 5,900 × 1
## ft_immig_2020Sep
## <dbl>
## 1 89
## 2 88
## 3 91
## 4 96
## 5 81
## 6 76
## 7 60
## 8 80
## 9 78
## 10 29
## 11 50
## 12 100
## 13 77
## 14 997
## 15 92
## 16 21
## 17 100
## 18 100
## 19 50
## 20 60
## 21 74
## 22 50
## 23 91
## 24 62
## 25 51
## 26 1
## 27 70
## 28 100
## 29 50
## 30 96
## 31 99
## 32 88
## 33 91
## 34 99
## 35 99
## 36 90
## 37 50
## 38 61
## 39 91
## 40 100
## 41 50
## 42 84
## 43 99
## 44 73
## 45 63
## 46 96
## 47 50
## 48 92
## 49 49
## 50 70
## 51 85
## 52 49
## 53 80
## 54 100
## 55 98
## 56 68
## 57 100
## 58 41
## 59 50
## 60 93
## 61 55
## 62 78
## 63 75
## 64 50
## 65 52
## 66 92
## 67 77
## 68 3
## 69 34
## 70 49
## 71 66
## 72 100
## 73 1
## 74 78
## 75 95
## 76 92
## 77 90
## 78 72
## 79 71
## 80 65
## 81 60
## 82 75
## 83 90
## 84 75
## 85 100
## 86 51
## 87 52
## 88 50
## 89 80
## 90 50
## 91 74
## 92 75
## 93 98
## 94 45
## 95 51
## 96 99
## 97 75
## 98 66
## 99 60
## 100 52
## 101 90
## 102 50
## 103 99
## 104 75
## 105 51
## 106 43
## 107 70
## 108 99
## 109 56
## 110 29
## 111 91
## 112 72
## 113 83
## 114 85
## 115 50
## 116 90
## 117 99
## 118 11
## 119 75
## 120 99
## 121 50
## 122 80
## 123 90
## 124 60
## 125 49
## 126 41
## 127 99
## 128 70
## 129 51
## 130 77
## 131 74
## 132 88
## 133 69
## 134 96
## 135 40
## 136 100
## 137 59
## 138 52
## 139 76
## 140 77
## 141 91
## 142 50
## 143 997
## 144 80
## 145 51
## 146 85
## 147 3
## 148 50
## 149 81
## 150 46
## 151 78
## 152 96
## 153 84
## 154 50
## 155 74
## 156 52
## 157 86
## 158 92
## 159 70
## 160 50
## 161 50
## 162 50
## 163 71
## 164 997
## 165 78
## 166 10
## 167 99
## 168 95
## 169 65
## 170 60
## 171 49
## 172 86
## 173 60
## 174 86
## 175 31
## 176 50
## 177 99
## 178 51
## 179 75
## 180 60
## 181 59
## 182 100
## 183 65
## 184 25
## 185 90
## 186 95
## 187 90
## 188 55
## 189 100
## 190 80
## 191 100
## 192 91
## 193 50
## 194 50
## 195 60
## 196 51
## 197 94
## 198 94
## 199 51
## 200 76
## 201 88
## 202 997
## 203 70
## 204 74
## 205 29
## 206 94
## 207 53
## 208 58
## 209 96
## 210 100
## 211 77
## 212 55
## 213 25
## 214 63
## 215 81
## 216 79
## 217 91
## 218 83
## 219 90
## 220 75
## 221 50
## 222 52
## 223 48
## 224 81
## 225 90
## 226 49
## 227 81
## 228 91
## 229 50
## 230 80
## 231 99
## 232 10
## 233 50
## 234 73
## 235 100
## 236 70
## 237 80
## 238 51
## 239 6
## 240 92
## 241 50
## 242 96
## 243 100
## 244 79
## 245 100
## 246 70
## 247 100
## 248 95
## 249 78
## 250 8
## 251 81
## 252 95
## 253 0
## 254 64
## 255 75
## 256 83
## 257 40
## 258 58
## 259 51
## 260 82
## 261 87
## 262 89
## 263 76
## 264 10
## 265 99
## 266 50
## 267 77
## 268 997
## 269 997
## 270 85
## 271 100
## 272 68
## 273 70
## 274 15
## 275 89
## 276 100
## 277 80
## 278 70
## 279 100
## 280 80
## 281 66
## 282 50
## 283 59
## 284 90
## 285 15
## 286 78
## 287 73
## 288 65
## 289 84
## 290 72
## 291 80
## 292 54
## 293 78
## 294 90
## 295 47
## 296 997
## 297 53
## 298 100
## 299 51
## 300 6
## 301 997
## 302 90
## 303 52
## 304 49
## 305 83
## 306 99
## 307 50
## 308 76
## 309 90
## 310 82
## 311 51
## 312 37
## 313 90
## 314 90
## 315 80
## 316 50
## 317 77
## 318 49
## 319 90
## 320 80
## 321 50
## 322 77
## 323 81
## 324 60
## 325 50
## 326 61
## 327 74
## 328 81
## 329 83
## 330 100
## 331 6
## 332 80
## 333 21
## 334 73
## 335 53
## 336 95
## 337 58
## 338 93
## 339 100
## 340 78
## 341 100
## 342 64
## 343 100
## 344 62
## 345 70
## 346 95
## 347 85
## 348 49
## 349 80
## 350 81
## 351 25
## 352 79
## 353 50
## 354 78
## 355 68
## 356 100
## 357 997
## 358 95
## 359 100
## 360 75
## 361 100
## 362 49
## 363 80
## 364 85
## 365 31
## 366 79
## 367 69
## 368 2
## 369 76
## 370 84
## 371 100
## 372 52
## 373 87
## 374 80
## 375 81
## 376 73
## 377 61
## 378 77
## 379 63
## 380 80
## 381 95
## 382 94
## 383 78
## 384 100
## 385 59
## 386 81
## 387 99
## 388 69
## 389 60
## 390 51
## 391 99
## 392 90
## 393 54
## 394 99
## 395 91
## 396 90
## 397 100
## 398 50
## 399 88
## 400 72
## 401 70
## 402 79
## 403 78
## 404 94
## 405 61
## 406 95
## 407 50
## 408 49
## 409 96
## 410 50
## 411 75
## 412 50
## 413 50
## 414 94
## 415 90
## 416 89
## 417 50
## 418 90
## 419 79
## 420 80
## 421 91
## 422 97
## 423 85
## 424 54
## 425 100
## 426 50
## 427 50
## 428 99
## 429 49
## 430 60
## 431 90
## 432 69
## 433 10
## 434 22
## 435 74
## 436 37
## 437 83
## 438 65
## 439 50
## 440 82
## 441 99
## 442 52
## 443 99
## 444 100
## 445 63
## 446 100
## 447 75
## 448 31
## 449 70
## 450 48
## 451 81
## 452 51
## 453 53
## 454 69
## 455 65
## 456 81
## 457 81
## 458 75
## 459 100
## 460 40
## 461 70
## 462 99
## 463 85
## 464 100
## 465 61
## 466 93
## 467 40
## 468 99
## 469 75
## 470 50
## 471 51
## 472 65
## 473 100
## 474 0
## 475 66
## 476 50
## 477 100
## 478 74
## 479 100
## 480 89
## 481 50
## 482 60
## 483 50
## 484 100
## 485 70
## 486 69
## 487 80
## 488 75
## 489 50
## 490 61
## 491 93
## 492 90
## 493 19
## 494 2
## 495 20
## 496 89
## 497 55
## 498 100
## 499 60
## 500 80
## 501 100
## 502 6
## 503 96
## 504 50
## 505 98
## 506 95
## 507 60
## 508 94
## 509 81
## 510 40
## 511 60
## 512 76
## 513 60
## 514 51
## 515 53
## 516 81
## 517 75
## 518 55
## 519 61
## 520 81
## 521 100
## 522 83
## 523 0
## 524 100
## 525 75
## 526 51
## 527 85
## 528 62
## 529 96
## 530 15
## 531 76
## 532 50
## 533 100
## 534 72
## 535 80
## 536 51
## 537 79
## 538 83
## 539 997
## 540 55
## 541 82
## 542 100
## 543 84
## 544 99
## 545 66
## 546 99
## 547 51
## 548 40
## 549 97
## 550 51
## 551 90
## 552 77
## 553 62
## 554 97
## 555 58
## 556 94
## 557 43
## 558 100
## 559 74
## 560 85
## 561 100
## 562 48
## 563 80
## 564 50
## 565 88
## 566 94
## 567 50
## 568 72
## 569 85
## 570 90
## 571 78
## 572 71
## 573 92
## 574 52
## 575 84
## 576 80
## 577 82
## 578 95
## 579 88
## 580 10
## 581 50
## 582 5
## 583 96
## 584 75
## 585 95
## 586 71
## 587 49
## 588 11
## 589 81
## 590 100
## 591 52
## 592 67
## 593 88
## 594 99
## 595 90
## 596 49
## 597 91
## 598 95
## 599 11
## 600 91
## 601 94
## 602 71
## 603 47
## 604 50
## 605 79
## 606 52
## 607 100
## 608 76
## 609 76
## 610 51
## 611 67
## 612 100
## 613 50
## 614 43
## 615 50
## 616 79
## 617 88
## 618 59
## 619 78
## 620 99
## 621 75
## 622 50
## 623 100
## 624 99
## 625 50
## 626 90
## 627 52
## 628 95
## 629 50
## 630 31
## 631 99
## 632 60
## 633 80
## 634 82
## 635 99
## 636 0
## 637 50
## 638 60
## 639 25
## 640 22
## 641 76
## 642 43
## 643 75
## 644 997
## 645 50
## 646 63
## 647 12
## 648 90
## 649 100
## 650 97
## 651 53
## 652 997
## 653 91
## 654 90
## 655 60
## 656 91
## 657 80
## 658 65
## 659 86
## 660 78
## 661 80
## 662 60
## 663 47
## 664 92
## 665 70
## 666 80
## 667 50
## 668 80
## 669 70
## 670 51
## 671 79
## 672 49
## 673 96
## 674 21
## 675 30
## 676 81
## 677 50
## 678 71
## 679 50
## 680 61
## 681 74
## 682 92
## 683 95
## 684 87
## 685 57
## 686 30
## 687 100
## 688 71
## 689 49
## 690 56
## 691 997
## 692 50
## 693 22
## 694 50
## 695 60
## 696 89
## 697 83
## 698 79
## 699 60
## 700 69
## 701 997
## 702 29
## 703 33
## 704 23
## 705 62
## 706 97
## 707 99
## 708 84
## 709 50
## 710 64
## 711 25
## 712 70
## 713 72
## 714 87
## 715 50
## 716 60
## 717 94
## 718 94
## 719 1
## 720 50
## 721 88
## 722 41
## 723 50
## 724 51
## 725 81
## 726 62
## 727 19
## 728 47
## 729 55
## 730 83
## 731 51
## 732 997
## 733 80
## 734 80
## 735 64
## 736 75
## 737 997
## 738 50
## 739 78
## 740 90
## 741 2
## 742 13
## 743 79
## 744 85
## 745 63
## 746 100
## 747 50
## 748 1
## 749 60
## 750 75
## 751 73
## 752 51
## 753 50
## 754 100
## 755 17
## 756 94
## 757 76
## 758 79
## 759 78
## 760 66
## 761 79
## 762 1
## 763 2
## 764 3
## 765 56
## 766 93
## 767 41
## 768 15
## 769 83
## 770 61
## 771 77
## 772 48
## 773 67
## 774 21
## 775 65
## 776 50
## 777 80
## 778 94
## 779 100
## 780 66
## 781 17
## 782 71
## 783 67
## 784 20
## 785 76
## 786 81
## 787 81
## 788 50
## 789 60
## 790 39
## 791 84
## 792 60
## 793 50
## 794 54
## 795 74
## 796 49
## 797 89
## 798 7
## 799 997
## 800 10
## 801 82
## 802 63
## 803 100
## 804 82
## 805 99
## 806 63
## 807 79
## 808 75
## 809 91
## 810 50
## 811 57
## 812 66
## 813 50
## 814 997
## 815 10
## 816 38
## 817 60
## 818 50
## 819 59
## 820 75
## 821 91
## 822 50
## 823 50
## 824 1
## 825 0
## 826 1
## 827 52
## 828 6
## 829 50
## 830 100
## 831 69
## 832 98
## 833 27
## 834 25
## 835 100
## 836 28
## 837 71
## 838 59
## 839 50
## 840 77
## 841 99
## 842 96
## 843 49
## 844 71
## 845 87
## 846 1
## 847 3
## 848 4
## 849 37
## 850 50
## 851 74
## 852 74
## 853 49
## 854 80
## 855 20
## 856 76
## 857 54
## 858 52
## 859 997
## 860 0
## 861 1
## 862 11
## 863 78
## 864 49
## 865 2
## 866 60
## 867 100
## 868 80
## 869 7
## 870 40
## 871 60
## 872 74
## 873 49
## 874 77
## 875 84
## 876 86
## 877 92
## 878 100
## 879 50
## 880 1
## 881 65
## 882 79
## 883 50
## 884 79
## 885 96
## 886 51
## 887 91
## 888 85
## 889 100
## 890 30
## 891 80
## 892 92
## 893 90
## 894 100
## 895 80
## 896 94
## 897 49
## 898 40
## 899 57
## 900 91
## 901 51
## 902 89
## 903 21
## 904 62
## 905 59
## 906 56
## 907 49
## 908 91
## 909 71
## 910 91
## 911 99
## 912 91
## 913 76
## 914 77
## 915 0
## 916 14
## 917 99
## 918 80
## 919 99
## 920 52
## 921 96
## 922 90
## 923 25
## 924 80
## 925 51
## 926 80
## 927 88
## 928 84
## 929 26
## 930 50
## 931 99
## 932 94
## 933 96
## 934 26
## 935 12
## 936 80
## 937 80
## 938 50
## 939 98
## 940 51
## 941 96
## 942 99
## 943 50
## 944 13
## 945 50
## 946 60
## 947 49
## 948 88
## 949 90
## 950 52
## 951 67
## 952 90
## 953 10
## 954 88
## 955 13
## 956 90
## 957 50
## 958 50
## 959 50
## 960 78
## 961 52
## 962 61
## 963 62
## 964 76
## 965 75
## 966 69
## 967 62
## 968 997
## 969 94
## 970 50
## 971 83
## 972 90
## 973 2
## 974 51
## 975 91
## 976 11
## 977 32
## 978 51
## 979 30
## 980 62
## 981 48
## 982 50
## 983 57
## 984 50
## 985 89
## 986 90
## 987 87
## 988 49
## 989 84
## 990 50
## 991 51
## 992 51
## 993 56
## 994 44
## 995 35
## 996 99
## 997 81
## 998 89
## 999 61
## 1000 56
## 1001 75
## 1002 79
## 1003 83
## 1004 81
## 1005 58
## 1006 49
## 1007 11
## 1008 49
## 1009 73
## 1010 93
## 1011 39
## 1012 50
## 1013 997
## 1014 0
## 1015 80
## 1016 61
## 1017 51
## 1018 42
## 1019 100
## 1020 50
## 1021 77
## 1022 95
## 1023 79
## 1024 66
## 1025 75
## 1026 82
## 1027 75
## 1028 90
## 1029 997
## 1030 57
## 1031 59
## 1032 81
## 1033 83
## 1034 89
## 1035 57
## 1036 75
## 1037 90
## 1038 90
## 1039 90
## 1040 62
## 1041 100
## 1042 51
## 1043 40
## 1044 50
## 1045 72
## 1046 80
## 1047 75
## 1048 53
## 1049 38
## 1050 12
## 1051 52
## 1052 100
## 1053 11
## 1054 97
## 1055 76
## 1056 96
## 1057 80
## 1058 82
## 1059 0
## 1060 2
## 1061 71
## 1062 59
## 1063 78
## 1064 75
## 1065 100
## 1066 71
## 1067 96
## 1068 83
## 1069 96
## 1070 8
## 1071 69
## 1072 100
## 1073 51
## 1074 51
## 1075 79
## 1076 100
## 1077 51
## 1078 82
## 1079 51
## 1080 50
## 1081 66
## 1082 91
## 1083 61
## 1084 74
## 1085 76
## 1086 82
## 1087 99
## 1088 82
## 1089 1
## 1090 58
## 1091 99
## 1092 61
## 1093 65
## 1094 48
## 1095 997
## 1096 13
## 1097 90
## 1098 49
## 1099 49
## 1100 80
## 1101 81
## 1102 81
## 1103 71
## 1104 83
## 1105 61
## 1106 50
## 1107 50
## 1108 997
## 1109 77
## 1110 56
## 1111 50
## 1112 9
## 1113 90
## 1114 90
## 1115 91
## 1116 93
## 1117 50
## 1118 71
## 1119 86
## 1120 78
## 1121 81
## 1122 99
## 1123 30
## 1124 36
## 1125 80
## 1126 90
## 1127 50
## 1128 100
## 1129 91
## 1130 88
## 1131 80
## 1132 52
## 1133 94
## 1134 90
## 1135 87
## 1136 31
## 1137 96
## 1138 59
## 1139 91
## 1140 97
## 1141 81
## 1142 92
## 1143 50
## 1144 50
## 1145 72
## 1146 94
## 1147 80
## 1148 71
## 1149 79
## 1150 99
## 1151 70
## 1152 80
## 1153 79
## 1154 76
## 1155 50
## 1156 63
## 1157 50
## 1158 100
## 1159 30
## 1160 96
## 1161 91
## 1162 80
## 1163 100
## 1164 72
## 1165 86
## 1166 997
## 1167 50
## 1168 98
## 1169 71
## 1170 91
## 1171 90
## 1172 66
## 1173 50
## 1174 99
## 1175 99
## 1176 86
## 1177 51
## 1178 50
## 1179 50
## 1180 80
## 1181 50
## 1182 82
## 1183 51
## 1184 51
## 1185 90
## 1186 51
## 1187 81
## 1188 51
## 1189 91
## 1190 26
## 1191 51
## 1192 77
## 1193 997
## 1194 81
## 1195 74
## 1196 22
## 1197 50
## 1198 50
## 1199 99
## 1200 59
## 1201 0
## 1202 100
## 1203 78
## 1204 80
## 1205 95
## 1206 100
## 1207 80
## 1208 75
## 1209 100
## 1210 83
## 1211 46
## 1212 96
## 1213 77
## 1214 96
## 1215 50
## 1216 51
## 1217 98
## 1218 50
## 1219 51
## 1220 52
## 1221 100
## 1222 79
## 1223 90
## 1224 30
## 1225 50
## 1226 50
## 1227 95
## 1228 71
## 1229 997
## 1230 99
## 1231 100
## 1232 98
## 1233 0
## 1234 77
## 1235 84
## 1236 100
## 1237 61
## 1238 97
## 1239 100
## 1240 51
## 1241 75
## 1242 80
## 1243 98
## 1244 997
## 1245 70
## 1246 50
## 1247 997
## 1248 51
## 1249 997
## 1250 76
## 1251 100
## 1252 91
## 1253 86
## 1254 50
## 1255 82
## 1256 78
## 1257 50
## 1258 60
## 1259 65
## 1260 60
## 1261 33
## 1262 997
## 1263 63
## 1264 20
## 1265 82
## 1266 90
## 1267 97
## 1268 40
## 1269 70
## 1270 79
## 1271 91
## 1272 78
## 1273 70
## 1274 86
## 1275 50
## 1276 84
## 1277 79
## 1278 64
## 1279 25
## 1280 27
## 1281 65
## 1282 89
## 1283 52
## 1284 60
## 1285 90
## 1286 50
## 1287 52
## 1288 50
## 1289 1
## 1290 50
## 1291 37
## 1292 1
## 1293 25
## 1294 100
## 1295 30
## 1296 77
## 1297 70
## 1298 21
## 1299 71
## 1300 18
## 1301 17
## 1302 83
## 1303 51
## 1304 77
## 1305 83
## 1306 40
## 1307 80
## 1308 50
## 1309 100
## 1310 50
## 1311 51
## 1312 98
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## 5900 62DATA$ft_immig_2020Sep[DATA$ft_immig_2020Sep > 100] <- NA
theme.z <- theme(
axis.text.x = element_text(color = "black", size = 15),
axis.text.y = element_text(color = "black", size = 15),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none",
panel.background = element_rect(fill = "gray90"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.1),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(linewidth = 0.1, color = "white"),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
plot.caption = element_text(size = 12 , hjust = 0, margin = margin(t = 10)),
plot.margin = unit(c(0.5,0.5,0.5,0.5), "cm"),
plot.title = element_text(face = "bold", size = 20, hjust = 0.5,margin = margin(b = 10)))
ggplot(data=DATA, mapping = aes(DATA$ft_immig_2020Sep)) +
geom_histogram(breaks = seq(-0.5,100.5, by = 1), mapping = aes(y = ..density..), col = "black", fill = "slategray4") +
labs(title = "Feeling thermometer ratings about immigrants\nall participants, unweighted", caption = "Data source: Sept 2020 wave of the Democracy Fund Voter Study Group. 2021.\nViews of the Electorate Research Survey. Washington, D.C. https://www.voterstudygroup.org/.") +
scale_y_continuous(labels=scales::percent_format(accuracy = 1), limits = c(-0.005,0.10), expand=c(0,0), breaks = seq(0,.10,.05), sec.axis = dup_axis()) +
scale_x_continuous(limits = c(-2.5,102.5), expand=c(0,0), breaks = seq(0,100,25)) +
theme.z Figure
Histogram 2
Code
library(tidyverse)
X <- rnorm(800, mean = 11, sd = 3)
DATA <- data.frame(X)
theme.z <- theme(
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_text(color = "black", size = 12),
legend.position = "none",
panel.background = element_rect(fill = "gray90"),
panel.border = element_rect(color = "black", fill = NA, size = 1.1),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(linewidth = 0.1, color = "gray80"),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
plot.caption = element_text(size = 12 , hjust = 0, margin = margin(t = 10)),
plot.margin = unit(c(0.5,0.5,0.5,0.5), "cm"),
plot.title = element_text(face = "bold", size = 14, hjust = 0.5,margin = margin(b = 10)))
ggplot(data = DATA, aes(X)) +
geom_histogram(breaks = seq(-0.5,20.5, by = 1), aes(y = ..count..), col = "black", fill = "dodgerblue", size = 1) +
scale_y_continuous(limits = c(0,120), expand = c(0,0), breaks = seq(0,120,10), sec.axis = sec_axis(trans = ~. /length(X), labels=scales::percent_format(accuracy = 1), name = "Frequency")) +
scale_x_continuous(limits = c(0, 25), expand = c(0,0), breaks = seq(1,24,1)) +
labs(x = "X", y = "Count") +
theme.zFigure
Scatterplot with regression line
Code
library(tidyverse)
DATA <- read_csv("states.csv")
theme.z <- theme(
axis.text.x = element_text(size = 15, color = "black", hjust = 0.5, margin = margin(t = 8, b = 8)),
axis.text.y = element_text(size = 15, color = "black", vjust = 0.5, margin = margin(r = 7, l = 7)),
axis.text.y.right = element_text(size = 15, color = "black", vjust = 0.5, margin = margin(r = 7, l = 7)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_text(size = 15, color = "black", hjust = 0.5, margin = margin(t = 8, b = 8)),
axis.title.y = element_text(size = 15, color = "black", hjust = 1 , vjust = 0.5, margin = margin(l = 8, r = 8), angle=0),
axis.title.y.right = element_blank(),
panel.background = element_rect(linewidth = 0.5, fill = "gray90"),
panel.border = element_rect(linewidth = 1.8, fill = NA ),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 12, hjust = 0 , margin = margin(t = 10)),
plot.margin = unit(c(t = 10, r = 10, b = 10, l = 10),"pt"),
plot.subtitle = element_text(size = 15, hjust = 0.5, margin = margin(t = 0, b = 8)),
plot.title = element_text(size = 20, hjust = 0.5, margin = margin(t = 0, b = 16), face = "bold")
)
summary(DATA$PctReligVery)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 16.00 31.00 36.50 37.16 43.75 59.00summary(DATA$PctEatProduceFreq)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 52.10 55.73 57.35 57.50 59.45 64.60summary(lm(DATA$PctReligVery ~ DATA$PctEatProduceFreq))##
## Call:
## lm(formula = DATA$PctReligVery ~ DATA$PctEatProduceFreq)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.998 -5.091 -1.438 4.140 19.056
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 143.8881 19.8256 7.258 2.96e-09 ***
## DATA$PctEatProduceFreq -1.8561 0.3443 -5.390 2.11e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.213 on 48 degrees of freedom
## Multiple R-squared: 0.3771, Adjusted R-squared: 0.3641
## F-statistic: 29.06 on 1 and 48 DF, p-value: 2.107e-06ggplot(data = DATA, mapping = aes(y = PctReligVery, x = PctEatProduceFreq)) +
geom_smooth(method="lm", color = "blue", se=FALSE, fullrange=TRUE) +
geom_point(size = 3.5) +
scale_x_continuous(limits = c(50,70), breaks = seq(50,70,5)) +
scale_y_continuous(limits = c(0,100), breaks = seq(0,100,10), labels = seq(0,100,10), expand = c(0,0),
sec.axis = dup_axis()) +
labs(x = "Percentage of state residents that\neat produce frequently", y = "Percentage of\nstate residents\nthat are\nvery religious") +
annotate("text", x = 70, y = 88, size = 4, hjust = 1, family = "mono", label = " Estimate p-value") +
annotate("text", x = 70, y = 80, size = 4, hjust = 1, family = "mono", label = " Intercept 143.89 <0.01") +
annotate("text", x = 70, y = 72, size = 4, hjust = 1, family = "mono", label = "% eat produce freq -1.86 <0.01") +
theme.zFigure
Map: United States
Code
library(tidyverse)
library(usmap)
DATA <- tribble(
~state, ~PCTBLACK,
"Alabama" , 26.2, "Alaska" , 3.3, "Arizona" , 4.1, "Arkansas" , 15.4,
"California" , 6.2, "Colorado" , 4 , "Connecticut" , 10.1, "Delaware" , 21.4,
"Florida" , 16 , "Georgia" , 30.5, "Hawaii" , 1.6, "Idaho" , 0.6,
"Illinois" , 14.5, "Indiana" , 9.1, "Iowa" , 2.9, "Kansas" , 5.9,
"Kentucky" , 7.8, "Louisiana" , 32 , "Maine" , 1.2, "Maryland" , 29.4,
"Massachusetts" , 6.6, "Michigan" , 14.2, "Minnesota" , 5.2, "Mississippi" , 37 ,
"Missouri" , 11.6, "Montana" , 0.4, "Nebraska" , 4.5, "Nevada" , 8.1,
"New Hampshire" , 1.1, "New Jersey" , 13.7, "New Mexico" , 2.1, "New York" , 15.9,
"North Carolina", 21.5, "North Dakota" , 1.2, "Ohio" , 12.2, "Oklahoma" , 7.4,
"Oregon" , 1.8, "Pennsylvania" , 10.8, "Rhode Island" , 5.7, "South Carolina", 27.9,
"South Dakota" , 1.3, "Tennessee" , 16.7, "Texas" , 11.8, "Utah" , 1.1,
"Vermont" , 1 , "Virginia" , 19.4, "Washington" , 3.6, "West Virginia" , 3.4,
"Wisconsin" , 6.3, "Wyoming" , 1 )
theme.z <- theme(
legend.position = "right",
legend.text = element_text(size = 15),
legend.title = element_text(size = 15, face = "bold"),
plot.margin = unit(c(t = 10,r = 10,b = 10,l = 10),"pt"),
plot.title = element_text(size = 20, hjust = 0.5, face = "bold", margin = margin(t = 0,b = 0))
)
plot_usmap(data = DATA, values = "PCTBLACK", color = "black", lwd = 0.75) +
scale_fill_continuous(low = "lightblue1", high = "darkblue", breaks = c(0,5,10,20,30,35)) +
guides(fill = guide_legend("Percent\nBlack")) +
labs(title = "Title") +
theme.zFigure
Map: World
Code
library(ggplot2)
library(maps)
library(rnaturalearth, rnaturalearthdata)
library(sf)
DATA <- read_csv("world map.csv")
print(DATA, n = Inf)## # A tibble: 222 × 3
## region CountryCode NUMBERS
## <chr> <chr> <dbl>
## 1 Afghanistan AFG 8
## 2 Albania ALB 6
## 3 Algeria DZA 6
## 4 American Samoa ASM 6
## 5 Andorra AND 8
## 6 Angola AGO 2
## 7 Antigua ATG 4
## 8 Argentina ARG 5
## 9 Armenia ARM 9
## 10 Aruba ABW 7
## 11 Australia AUS 5
## 12 Austria AUT 3
## 13 Azerbaijan AZE 1
## 14 Bahamas BHS 4
## 15 Bahrain BHR 3
## 16 Bangladesh BGD 9
## 17 Barbados BRB 0
## 18 Belarus BLR 9
## 19 Belgium BEL 3
## 20 Belize BLZ 4
## 21 Benin BEN 2
## 22 Bermuda BMU 4
## 23 Bhutan BTN 9
## 24 Bolivia BOL 9
## 25 Bosnia and Herzegovina BIH 4
## 26 Botswana BWA 3
## 27 Brazil BRA 6
## 28 Virgin Islands VGB 1
## 29 Brunei BRN 4
## 30 Bulgaria BGR 5
## 31 Burkina Faso BFA 5
## 32 Burundi BDI 0
## 33 Cabo Verde CPV 2
## 34 Cambodia KHM 0
## 35 Cameroon CMR 2
## 36 Canada CAN 2
## 37 Cayman Islands CYM 3
## 38 Central African Republic CAF 6
## 39 Central Europe and the Baltics CEB 2
## 40 Chad TCD 3
## 41 Channel Islands CHI 8
## 42 Chile CHL 2
## 43 China CHN 3
## 44 Colombia COL 1
## 45 Comoros COM 6
## 46 Democratic Republic of the Congo COD 0
## 47 Republic of Congo COG 1
## 48 Costa Rica CRI 8
## 49 Cote d'Ivoire CIV 1
## 50 Croatia HRV 9
## 51 Cuba CUB 5
## 52 Curacao CUW 3
## 53 Cyprus CYP 1
## 54 Czech Republic CZE 6
## 55 Denmark DNK 9
## 56 Djibouti DJI 8
## 57 Dominica DMA 6
## 58 Dominican Republic DOM 6
## 59 Ecuador ECU 4
## 60 Egypt EGY 8
## 61 El Salvador SLV 3
## 62 Equatorial Guinea GNQ 1
## 63 Eritrea ERI 6
## 64 Estonia EST 3
## 65 Eswatini SWZ 4
## 66 Ethiopia ETH 3
## 67 Faroe Islands FRO 5
## 68 Fiji FJI 0
## 69 Finland FIN 6
## 70 France FRA 9
## 71 French Polynesia PYF 9
## 72 Gabon GAB 3
## 73 Gambia GMB 2
## 74 Georgia GEO 2
## 75 Germany DEU 5
## 76 Ghana GHA 4
## 77 Gibraltar GIB 2
## 78 Greece GRC 4
## 79 Greenland GRL NA
## 80 Grenada GRD 2
## 81 Guam GUM 2
## 82 Guatemala GTM 0
## 83 Guinea GIN 3
## 84 Guinea-Bissau GNB 4
## 85 Guyana GUY 5
## 86 Haiti HTI 1
## 87 Honduras HND 7
## 88 Hong Kong SAR, China HKG 5
## 89 Hungary HUN 0
## 90 Iceland ISL 1
## 91 India IND 7
## 92 Indonesia IDN 7
## 93 Iran IRN 3
## 94 Iraq IRQ 3
## 95 Ireland IRL 5
## 96 Isle of Man IMN 6
## 97 Israel ISR 7
## 98 Italy ITA 7
## 99 Jamaica JAM 2
## 100 Japan JPN 8
## 101 Jordan JOR 4
## 102 Kazakhstan KAZ 7
## 103 Kenya KEN 0
## 104 Kiribati KIR 8
## 105 North Korea PRK 3
## 106 South Korea KOR 8
## 107 Kosovo XKX 0
## 108 Kuwait KWT 2
## 109 Kyrgyz Republic KGZ 5
## 110 Lao PDR LAO 9
## 111 Latvia LVA 8
## 112 Least developed countries: UN classification LDC 4
## 113 Lebanon LBN 9
## 114 Lesotho LSO 6
## 115 Liberia LBR 2
## 116 Libya LBY 2
## 117 Liechtenstein LIE 8
## 118 Lithuania LTU 1
## 119 Luxembourg LUX 6
## 120 Macao SAR, China MAC 4
## 121 Madagascar MDG 6
## 122 Malawi MWI 5
## 123 Malaysia MYS 0
## 124 Maldives MDV 2
## 125 Mali MLI 1
## 126 Malta MLT 4
## 127 Marshall Islands MHL 6
## 128 Mauritania MRT 4
## 129 Mauritius MUS 2
## 130 Mexico MEX 4
## 131 Micronesia FSM 6
## 132 Moldova MDA 7
## 133 Monaco MCO 7
## 134 Mongolia MNG 8
## 135 Montenegro MNE 9
## 136 Morocco MAR 9
## 137 Mozambique MOZ 0
## 138 Myanmar MMR 2
## 139 Namibia NAM 5
## 140 Nauru NRU 1
## 141 Nepal NPL 6
## 142 Netherlands NLD 6
## 143 New Caledonia NCL 8
## 144 New Zealand NZL 6
## 145 Nicaragua NIC 6
## 146 Niger NER 6
## 147 Nigeria NGA 7
## 148 North America NAC 0
## 149 North Macedonia MKD 2
## 150 Northern Mariana Islands MNP 6
## 151 Norway NOR 0
## 152 Not classified INX 2
## 153 Oman OMN 9
## 154 Pakistan PAK 1
## 155 Palau PLW 3
## 156 Panama PAN 5
## 157 Papua New Guinea PNG 5
## 158 Paraguay PRY 2
## 159 Peru PER 8
## 160 Philippines PHL 0
## 161 Poland POL 2
## 162 Portugal PRT 2
## 163 Puerto Rico PRI 1
## 164 Qatar QAT 6
## 165 Romania ROU 5
## 166 Russian Federation RUS 0
## 167 Rwanda RWA 8
## 168 Samoa WSM 3
## 169 San Marino SMR 4
## 170 Sao Tome and Principe STP 3
## 171 Saudi Arabia SAU 4
## 172 Senegal SEN 2
## 173 Serbia SRB 6
## 174 Seychelles SYC 2
## 175 Sierra Leone SLE 5
## 176 Singapore SGP 9
## 177 Sint Maarten SXM 3
## 178 Slovak Republic SVK 4
## 179 Slovenia SVN 9
## 180 Solomon Islands SLB 0
## 181 Somalia SOM 4
## 182 South Africa ZAF 5
## 183 South Sudan SSD 7
## 184 Spain ESP 5
## 185 Sri Lanka LKA 7
## 186 St. Kitts and Nevis KNA 6
## 187 St. Lucia LCA 0
## 188 St. Martin MAF 1
## 189 St. Vincent and the Grenadines VCT 9
## 190 Sudan SDN 5
## 191 Suriname SUR 7
## 192 Sweden SWE 2
## 193 Switzerland CHE 2
## 194 Syrian Arab Republic SYR 2
## 195 Tajikistan TJK 7
## 196 Tanzania TZA 8
## 197 Thailand THA 0
## 198 Timor-Leste TLS 7
## 199 Togo TGO 4
## 200 Tonga TON 9
## 201 Trinidad and Tobago TTO 6
## 202 Tunisia TUN 5
## 203 Turkey TUR 6
## 204 Turkmenistan TKM 7
## 205 Turks and Caicos Islands TCA 8
## 206 Tuvalu TUV 2
## 207 Uganda UGA 3
## 208 Ukraine UKR 8
## 209 United Arab Emirates ARE 2
## 210 United Kingdom GBR 2
## 211 Uruguay URY 1
## 212 USA USA 9
## 213 Uzbekistan UZB 2
## 214 Vanuatu VUT 0
## 215 Venezuela VEN 6
## 216 Vietnam VNM 1
## 217 Virgin Islands (U.S.) VIR 8
## 218 West Bank and Gaza PSE 7
## 219 World WLD 5
## 220 Yemen YEM 8
## 221 Zambia ZMB 7
## 222 Zimbabwe ZWE 5WORLD <- st_as_sf(map("world", plot = FALSE, fill = TRUE))
SUM <- merge(WORLD, DATA, by.x = c("ID"), by.y = c("region"))
theme.z <- theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
legend.box = "horizontal",
legend.position = "bottom",
panel.border = element_blank(),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
rect = element_blank()
)
ggplot(data = SUM) +
geom_sf(mapping = aes(fill = NUMBERS),color = "black", linewidth = 0.5) +
scale_fill_continuous(low = "lightsteelblue1", high = "royalblue4", limits = c(0,9), breaks = c(0,3,6,9)) +
guides(fill = guide_legend("Some data")) +
# coord_sf(xlim = c(60,100), ylim = c(0,50)) +
theme.zFigure
Map: Region
Code
library(maps)
library(rnaturalearth)
library(rnaturalearthdata)
library(tidyverse)
WORLD <- ne_countries(scale = "medium", returnclass = "sf")
IRAN <- ne_countries(country = "iran", type = "countries", scale = "medium", returnclass = "sf")
ggplot() +
geom_sf(data = WORLD) +
geom_sf(data = IRAN, linewidth = 1.2, color = "black", fill = "darkolivegreen3") +
coord_sf(xlim = c(40,68), ylim = c(22,46), expand = FALSE, datum = NA) +
geom_point(data = NULL, mapping = aes(x = 50.99155, y = 35.83266), shape = 21, color = "black", fill = "red", size = 4, stroke = 1.2) +
annotate("text", x = 50.99155, y = 35.2, label = "Karaj", size = 5 , color = "black", vjust = "top") +
annotate("text", x = 54 , y = 32 , label = "Iran" , size = 10, color = "black") +
theme(
axis.title = element_blank(),
panel.border = element_rect(linewidth = 1.0, color = "black", fill = NA)
)Figure
Waffle plot
Code
sort of as a pie chart (colors better in grayscale)
library(patchwork)
library(waffle)
theme.z <- theme(
legend.text = element_text(size = 15),
plot.caption = element_blank(),
plot.subtitle = element_text(face = "bold", margin = margin(t = 0, b = 6), size = 16, hjust = 0),
plot.title = element_text(face = "bold", margin = margin(t = 0, b = 6), size = 20, hjust = 0))
BIO.BWA <- c("Category 1 only (14%)" = 14, "Category 1 and 2 (11%)" = 11, "Category 2 only (24%)" = 24, "Neither 1 nor 2 (51%)" = 51)
BIO.BWA.BIO <- c("Category 1 only (20%)" = 20, "Category 1 and 2 (14%)" = 14, "Category 1 only (23%)" = 23, "Neither 1 nor 2 (43%)" = 43)
p1 <- waffle(BIO.BWA, rows = 5, size = 2, colors = c("slategray1","purple4","lightpink1","gray70")) +
labs(title = "Title of the first plot", subtitle = "Subtitle of the first plot") +
theme.z
p2 <- waffle(BIO.BWA.BIO, rows = 5, size = 2, colors = c("slategray1","purple4","lightpink1","gray70")) +
labs(title = "Title of the second plot", subtitle = "Subtitle of the second plot", caption = "Sample size 1,500 U.S. adults. Estimates are weighted.\nData source:") +
theme.z +
theme(plot.caption = element_text(size = 11, hjust = 0, margin = margin(10,0,0,0)),
plot.title = element_text(face = "bold", margin = margin(t = 10, b = 6), size = 20, hjust = 0)
)
p1 / p2Figure
Rotated text and external arrows
Code
library(tidyverse)
DATA <- tribble(
~MOD, ~X, ~Y,
0, 0, 30,
0, 1, 55,
1, 0, 50,
1, 1, 70)
theme.z <- theme(
plot.background = element_rect(fill = "white"),
plot.margin = unit(c(0.5,0.9,0.5,4.2),"cm"),
plot.title = element_text(face = "bold", margin = margin(t = 0, b = 12), size = 18, hjust = 0.5),
plot.subtitle = element_text(size = 12, hjust = 0.5),
plot.caption = element_text(size = 9, hjust = 1),
strip.background = element_rect(linewidth = 1.0, color = "black", fill = "black"),
strip.text.x = element_text(size = 14, color = "white", face = "bold"),
panel.background = element_rect(fill = "gray90", color = "black", linewidth = 0.5, linetype = "solid"),
panel.border = element_rect(fill = NA, color = "black", linetype = "solid", linewidth = 1.0),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.spacing.x = unit(2, "lines"),
panel.spacing.y = unit(1, "lines"),
axis.text.x = element_text(size = 15, color = "black", margin = margin(t = 7, b = 5)),
axis.text.y = element_text(size = 15, color = "black", margin = margin(r = 7)),
axis.ticks.y = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(size = 15, color = "black", angle = 0, vjust = 1),
axis.title.y.right = element_text(size = 15, color = "black", angle = 0, vjust = 1),
axis.title.x = element_text(size = 15, color = "black")
)
ggplot(DATA, mapping = aes(x = X, y = Y, group = MOD)) +
geom_smooth(data = subset(DATA, MOD == 0), method = "lm", formula = y~x, se = F, linewidth = 1, color = "blue") +
geom_smooth(data = subset(DATA, MOD == 1), method = "lm", formula = y~x, se = F, linewidth = 2, color = "pink") +
geom_point(pch = 21, size = 4, stroke = 1.5, color = "black", bg = "white") +
scale_x_continuous(name = "X", breaks = seq(0,1,1), expand = c(0,0), limits = c(-0.05,1.05)) +
scale_y_continuous(name = "Y", breaks = c(0,30,50), expand = c(0,0), limits = c(0,76), sec.axis = sec_axis(~., name = "Y", breaks = c(55,70))) +
annotate("text", x = 0.3, y = 26, label = "OLS Coefficients", hjust = 0, vjust = 1, size = 6, lineheight = 0.9, family = "mono", fontface = "bold") +
annotate("text", x = 0.3, y = 21, label = " 30 Intercept\n 20 Female\n 25 X\n -5 Female * X", hjust = 0, vjust = 1, size = 6, lineheight = 0.9, family = "mono") +
geom_text(x = 0.5, y = 41, label = "Slope for males of 25" , hjust = 0.5, vjust = 1, size = 5.6, angle = 19.5) +
geom_text(x = 0.5, y = 66, label = "Slope for females of 20", hjust = 0.5, vjust = 1, size = 5.6, angle = 16) +
geom_text(x = -0.25, y = 15, label = "Intercept\nof 30", hjust = 1, vjust = 0.5, size = 5.6) +
geom_text(x = -0.25, y = 40, label = "\"Female\"\ncoefficient\nof 20", hjust = 1, vjust = 0.5, size = 5.6) +
geom_segment(x = -0.2, y = 0, xend = -0.2, yend = 30, arrow = arrow(length = unit(10, "pt"), ends = "both")) +
geom_segment(x = -0.2, y = 50, xend = -0.2, yend = 30, arrow = arrow(length = unit(10, "pt"), ends = "both")) +
coord_cartesian(clip = "off") +
theme.zsummary(lm(Y ~ X*MOD, data = DATA))##
## Call:
## lm(formula = Y ~ X * MOD, data = DATA)
##
## Residuals:
## ALL 4 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 30 NaN NaN NaN
## X 25 NaN NaN NaN
## MOD 20 NaN NaN NaN
## X:MOD -5 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 3 and 0 DF, p-value: NAFigure
Brackets
Code
library(ggpubr)
library(tidyverse)
DATA <- tribble(
~HEALTH, ~VEGGIE, ~EXERCISE,
6, 0, 0,
5, 1, 0,
4, 2, 0,
51, 3, 1,
65, 4, 1,
65, 5, 1,
75, 6, 1,
66, 7, 1)
theme.z <- theme(
axis.text.x = element_text(size = 15, color = "black", margin = margin(t = 7,b = 7)),
axis.text.y = element_text(size = 15, color = "black", margin = margin(l = 7,r = 7)),
axis.text.y.right = element_text(size = 15, color = "black", margin = margin(l = 7,r = 7)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_text(size = 15, color = "black", margin = margin(t = 7,r = 7,b = 7,l = 7)),
axis.title.y = element_text(size = 15, color = "black", margin = margin(t = 7,r = 7,b = 7,l = 7)),
legend.position = "none",
panel.background = element_rect(linewidth = 1.0, color = "black", fill = "gray90", linetype = "solid"),
panel.border = element_rect(linewidth = 1.0, color = "black", fill = NA, linetype = "solid"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.spacing.x = unit(1, "lines"),
panel.spacing.y = unit(1, "lines"),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 12, hjust = 0 , margin = margin(t = 10)),
plot.margin = unit(c(t = 5,r = 5,b = 5,l = 5),"pt"),
plot.subtitle = element_text(size = 15, hjust = 0.5, margin = margin(b = 7)),
plot.title = element_text(size = 20, hjust = 0.5, face = "bold", margin = margin(t = 0,b = 12))
)
ggplot(data=DATA, mapping = aes(x = VEGGIE, y = HEALTH)) +
geom_smooth(data=subset(DATA, EXERCISE==1), formula = y ~ x, method="lm", se=FALSE, fullrange=TRUE, color = "green2", linewidth = 1.2) +
geom_smooth(data=subset(DATA, EXERCISE==0), formula = y ~ x, method="lm", se=FALSE, fullrange=TRUE, color = "red", linewidth = 1.2) +
geom_point(size = 4) +
geom_point(data=subset(DATA, EXERCISE==0), size = 4, pch=21, stroke = 1.2, fill = "red") +
geom_point(data=subset(DATA, EXERCISE==1), size = 4, pch=21, stroke = 1.2, fill = "green2") +
scale_x_continuous(name="VEGGIE\n(Number of days per week eating vegetables)", expand=c(0,0), limits = c(-0.5,7.5) , breaks = seq(0,7,1)) +
scale_y_continuous(name="HEALTH", limits = c(-5,100), breaks = seq(0,100,20)) +
geom_bracket(xmin=0, xmax = 2, label = "No exercise\nEXERCISE=0", y.position=15, label.size = 5.5, vjust = -0.2, tip.length=c(0.05 ,0.1)) +
geom_bracket(xmin=3, xmax = 7, label = "Exercises\nEXERCISE=1" , y.position=35, label.size = 5.5, vjust = 2.8, tip.length=c(-0.15, -0.35)) +
labs(title = "Health rating") +
theme.zFigure
Emphasis example 1
Code
library(tidyverse)
DATA <- tribble(
~AGE, ~ITEM, ~RACE, ~PCT, ~CILO, ~CIHI,
"Age 18-29","0 to 24","Whites",0.1686075,0.125517,0.2227235,
"Age 18-29","25 to 49","Whites",0.2258218,0.1745539,0.2869135,
"Age 18-29","50","Whites",0.0491381,0.027793,0.0854356,
"Age 18-29","51 to 75","Whites",0.2707414,0.2160958,0.3333297,
"Age 18-29","76 to 100","Whites",0.2841337,0.2276716,0.3482837,
"Age 18-29","0 to 24","Blacks",0.4298258,0.2903636,0.5813931,
"Age 18-29","25 to 49","Blacks",0.3173413,0.1963695,0.4693152,
"Age 18-29","50","Blacks",0.048522,0.0089412,0.2237587,
"Age 18-29","51 to 75","Blacks",0.1696017,0.0965342,0.2807865,
"Age 18-29","76 to 100","Blacks",0.0347092,0.0093621,0.1203453,
"Age 18-29","0 to 24","Hispanics",0.2210459,0.1426287,0.3261748,
"Age 18-29","25 to 49","Hispanics",0.1639248,0.1095114,0.2381445,
"Age 18-29","50","Hispanics",0.1007021,0.0496316,0.1936177,
"Age 18-29","51 to 75","Hispanics",0.2340969,0.1579551,0.3324516,
"Age 18-29","76 to 100","Hispanics",0.2802302,0.1932706,0.3875214,
"Age 18-29","0 to 24","Other",0.1799807,0.1005913,0.3010538,
"Age 18-29","25 to 49","Other",0.2835058,0.1587469,0.4534617,
"Age 18-29","50","Other",0.0279455,0.0065446,0.111475,
"Age 18-29","51 to 75","Other",0.3626855,0.2336636,0.5150665,
"Age 18-29","76 to 100","Other",0.1458826,0.0655658,0.2936658,
"Age 30-45","0 to 24","Whites",0.0880538,0.0688471,0.1119744,
"Age 30-45","25 to 49","Whites",0.1490723,0.1252224,0.1765479,
"Age 30-45","50","Whites",0.0528802,0.0363371,0.0763582,
"Age 30-45","51 to 75","Whites",0.2651047,0.2341556,0.2985497,
"Age 30-45","76 to 100","Whites",0.444889,0.4087117,0.48166,
"Age 30-45","0 to 24","Blacks",0.2473897,0.1837595,0.3242992,
"Age 30-45","25 to 49","Blacks",0.3215109,0.2441783,0.4100479,
"Age 30-45","50","Blacks",0.0686421,0.0413368,0.1118792,
"Age 30-45","51 to 75","Blacks",0.2180175,0.1547748,0.2979913,
"Age 30-45","76 to 100","Blacks",0.1178969,0.0727645,0.1854246,
"Age 30-45","0 to 24","Hispanics",0.1405747,0.0891829,0.2146035,
"Age 30-45","25 to 49","Hispanics",0.1712986,0.1257236,0.229066,
"Age 30-45","50","Hispanics",0.060349,0.0333786,0.1067063,
"Age 30-45","51 to 75","Hispanics",0.3072251,0.2453594,0.3768989,
"Age 30-45","76 to 100","Hispanics",0.3205526,0.2582513,0.3899815,
"Age 30-45","0 to 24","Other",0.1252955,0.0775243,0.1962415,
"Age 30-45","25 to 49","Other",0.1951634,0.1266456,0.2885051,
"Age 30-45","50","Other",0.0762974,0.0382879,0.1463,
"Age 30-45","51 to 75","Other",0.3186173,0.2348634,0.4159998,
"Age 30-45","76 to 100","Other",0.2846264,0.2003887,0.3871309,
"Age 46-59","0 to 24","Whites",0.0289146,0.0183031,0.045394,
"Age 46-59","25 to 49","Whites",0.0765872,0.0587539,0.0992624,
"Age 46-59","50","Whites",0.031878,0.0207482,0.0486813,
"Age 46-59","51 to 75","Whites",0.2623021,0.2280894,0.2996544,
"Age 46-59","76 to 100","Whites",0.6003181,0.5594169,0.6398667,
"Age 46-59","0 to 24","Blacks",0.2029044,0.1311466,0.3003523,
"Age 46-59","25 to 49","Blacks",0.2460165,0.171898,0.3390102,
"Age 46-59","50","Blacks",0.0785931,0.0413865,0.1442167,
"Age 46-59","51 to 75","Blacks",0.2879665,0.2003525,0.3949698,
"Age 46-59","76 to 100","Blacks",0.1845194,0.1238305,0.2659244,
"Age 46-59","0 to 24","Hispanics",0.0790971,0.0343998,0.1715537,
"Age 46-59","25 to 49","Hispanics",0.1759927,0.1123977,0.2648339,
"Age 46-59","50","Hispanics",0.0705534,0.0347834,0.1378541,
"Age 46-59","51 to 75","Hispanics",0.2658014,0.184284,0.3671479,
"Age 46-59","76 to 100","Hispanics",0.4085554,0.3102326,0.5147835,
"Age 46-59","0 to 24","Other",0.0394191,0.0110365,0.1311161,
"Age 46-59","25 to 49","Other",0.1522902,0.0886397,0.2491515,
"Age 46-59","50","Other",0.0303505,0.0095767,0.0920012,
"Age 46-59","51 to 75","Other",0.4064893,0.2907918,0.5335858,
"Age 46-59","76 to 100","Other",0.371451,0.2611008,0.4970653,
"Age 60+","0 to 24","Whites",0.0101233,0.0053504,0.0190725,
"Age 60+","25 to 49","Whites",0.0534469,0.0415213,0.0685529,
"Age 60+","50","Whites",0.013854,0.008874,0.0215679,
"Age 60+","51 to 75","Whites",0.2511503,0.2264746,0.2775499,
"Age 60+","76 to 100","Whites",0.6714254,0.6426687,0.698952,
"Age 60+","0 to 24","Blacks",0.0897313,0.0426382,0.1791068,
"Age 60+","25 to 49","Blacks",0.1742434,0.1083386,0.2681815,
"Age 60+","50","Blacks",0.043537,0.0206152,0.0896133,
"Age 60+","51 to 75","Blacks",0.4234502,0.3277676,0.5252404,
"Age 60+","76 to 100","Blacks",0.2562281,0.1733786,0.3613605,
"Age 60+","0 to 24","Hispanics",0.0609062,0.0219529,0.157825,
"Age 60+","25 to 49","Hispanics",0.0339614,0.0126179,0.0881835,
"Age 60+","50","Hispanics",0.0365783,0.013317,0.0964974,
"Age 60+","51 to 75","Hispanics",0.2722974,0.1807325,0.3882669,
"Age 60+","76 to 100","Hispanics",0.5962568,0.4774693,0.7047391,
"Age 60+","0 to 24","Other",0.0201068,0.0059742,0.0654697,
"Age 60+","25 to 49","Other",0.074297,0.0319437,0.1633308,
"Age 60+","50","Other",0.028154,0.0082708,0.0914298,
"Age 60+","51 to 75","Other",0.1951647,0.1240613,0.293371,
"Age 60+","76 to 100","Other",0.6822776,0.5659573,0.7795664)
theme.z <- theme(
panel.background = element_rect(fill = "black"),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.border = element_rect(color = "black", linewidth = 1, fill = NA),
panel.spacing.x = unit(1, "lines"),
panel.spacing.y = unit(0.5, "lines"),
strip.background = element_rect(linewidth = 1, color = "black", fill = "black"),
strip.text.x = element_text(color = "white", face = "bold", size = 15),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(color = "black", size = 12),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
plot.title = element_text(face = "bold", margin = margin(t = 0, b = 13), size = 16, hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0, margin = margin(10,0,0,0)),
legend.text = element_text(size = 12),
legend.position = "bottom"
)
DATA$AGE <- factor(DATA$AGE , levels = c("Age 18-29", "Age 30-45", "Age 46-59", "Age 60+"))
DATA$ITEM <- factor(DATA$ITEM, levels = rev(c("0 to 24", "25 to 49", "50", "51 to 75", "76 to 100")))
DATA$RACE <- factor(DATA$RACE, levels = c("Whites", "Blacks", "Hispanics", "Other"))
AGE.ORDER <- rev(c("Age 18-29", "Age 30-45", "Age 46-59", "Age 60+"))
RACE.ORDER <- c("Other", "Hispanics", "Blacks", "Whites")
CAPTION <- str_wrap("Plot indicates percentages in each group. Overall sample sizes: Whites 3,701. Blacks 539. Hispanics 647. Other 384.\nData source: American National Election Studies. 2021.\nANES 2020 Social Media Study: Pre-Election Data [dataset and documentation]. March 8, 2021 version. www.electionstudies.org", width = 100)
ggplot(DATA, mapping = aes(x = AGE, y = 100*PCT, fill = ITEM)) +
geom_col(color = "black", linewidth = 1.1, width = 0.8) +
scale_fill_manual(values = rev(c("red3", "red1", "white", "green1", "green3")), name = "Rating") +
coord_flip() +
facet_wrap(~RACE, ncol = 4, dir = "h") +
scale_x_discrete(limits = AGE.ORDER, labels = AGE.ORDER, name = "") +
scale_y_continuous(limits = c(0, 100.01)) +
labs(title = "Ratings of Police on 0-to-100 Feeling Thermometers", caption = CAPTION) +
theme.z + guides(fill = guide_legend(reverse = T))ggplot(DATA, mapping = aes(x = RACE, y = 100*PCT, fill = ITEM)) +
geom_col(color = "black", linewidth = 1.1, width = 0.8) +
scale_fill_manual(values = rev(c("red3", "red1", "white", "green1", "green3")), name = "Rating") +
coord_flip() +
facet_wrap(~AGE, ncol = 4, dir = "h") +
scale_x_discrete(limits = RACE.ORDER, labels = RACE.ORDER, name = "") +
scale_y_continuous(limits = c(0, 100.01)) +
labs(title = "Ratings of Police on 0-to-100 Feeling Thermometers", caption = CAPTION) +
theme.z + guides(fill = guide_legend(reverse = T))Figure
Emphasis example 2
Code
library(patchwork)
library(tidyverse)
DATA <- tribble(
~CONTROLS,~GROUP,~LEVEL,~PE,~CILO,~CIHI,
"Small controls","Ratings about Blacks","0 to 24",0.0461706,0.0270992,0.0652419,
"Small controls","Ratings about Blacks","25 to 49",0.0982854,0.0768214,0.1197495,
"Small controls","Ratings about Blacks","at 50",0.0754165,0.0575844,0.0932486,
"Small controls","Ratings about Blacks","51 to 85",0.1596652,0.146106,0.1732243,
"Small controls","Ratings about Blacks","86 to 99",0.2926404,0.2711568,0.3141241,
"Small controls","Ratings about Blacks","at 100",0.4549532,0.4144586,0.4954477,
"Small controls","Ratings about Whites","0 to 24",0.4226789,0.3650292,0.4803286,
"Small controls","Ratings about Whites","25 to 49",0.4011842,0.3655237,0.4368447,
"Small controls","Ratings about Whites","at 50",0.2425766,0.211447,0.2737062,
"Small controls","Ratings about Whites","51 to 85",0.2040362,0.1888231,0.2192493,
"Small controls","Ratings about Whites","86 to 99",0.1029744,0.0892483,0.1167005,
"Small controls","Ratings about Whites","at 100",0.0746247,0.057398,0.0918514,
"Full controls","Ratings about Blacks","0 to 24",0.0608033,0.0342018,0.0874048,
"Full controls","Ratings about Blacks","25 to 49",0.1260736,0.0973548,0.1547924,
"Full controls","Ratings about Blacks","at 50",0.0828104,0.0633727,0.102248,
"Full controls","Ratings about Blacks","51 to 85",0.1395332,0.1258388,0.1532275,
"Full controls","Ratings about Blacks","86 to 99",0.2051881,0.1857102,0.224666,
"Full controls","Ratings about Blacks","at 100",0.2983555,0.2607479,0.3359632,
"Full controls","Ratings about Whites","0 to 24",0.2897018,0.236501,0.3429027,
"Full controls","Ratings about Whites","25 to 49",0.2769997,0.2438245,0.3101748,
"Full controls","Ratings about Whites","at 50",0.1591508,0.1342149,0.1840868,
"Full controls","Ratings about Whites","51 to 85",0.1683675,0.1535977,0.1831373,
"Full controls","Ratings about Whites","86 to 99",0.1099687,0.0934504,0.126487,
"Full controls","Ratings about Whites","at 100",0.1013468,0.0774212,0.1252723)
DATA$GROUP <- factor(DATA$GROUP, levels = unique(DATA$GROUP))
DATA$LEVEL <- factor(DATA$LEVEL, levels = unique(DATA$LEVEL))
DATA.SMALL <- filter(DATA, DATA$CONTROLS == "Small controls")
DATA.FULL <- filter(DATA, DATA$CONTROLS == "Full controls")
theme.z <- theme(
axis.text.x = element_text(size = 15, color = "black", hjust = 0.5 , margin = margin(t = 10,b = 0)),
axis.text.x.top = element_blank(),
axis.text.y = element_text(size = 15, color = "black", hjust = 1 , margin = margin(l = 8,r = 8)),
axis.text.y.right = element_text(size = 15, color = "black", hjust = 0 , margin = margin(l = 8,r = 8)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.background = element_rect(linewidth = 0.5, color = "black", fill = "gray90", linetype = "solid"),
panel.border = element_rect(linewidth = 1.0, color = "black", fill = NA , linetype = "solid"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.spacing.x = unit(1, "lines"),
panel.spacing.y = unit(1, "lines"),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 12 , hjust = 0 , margin = margin(t = 10)),
plot.margin = unit(c(t = 10,r = 10,b = 10,l = 10),"pt"),
plot.subtitle = element_text(size = 15 , hjust = 0.5, margin = margin(b = 10)),
plot.title = element_text(size = 18 , hjust = 0.5, margin = margin(t = 0,b = 10), face = "bold"),
strip.background = element_rect(linewidth = 1, color = "black", fill = "black"),
strip.text.x = element_text(size = 16, color = "white", face = "bold", margin = margin(t = 7.5,b = 7.5))
)
plot.SMALL <- ggplot(DATA.SMALL, mapping = aes(x = PE, y = LEVEL)) +
facet_wrap(~GROUP, dir = "v", ncol = 1, scales = "free_y") +
geom_rect(data = filter(DATA.SMALL, GROUP == "Ratings about Blacks") , mapping = aes(xmin = DATA.SMALL$PE[DATA.SMALL$GROUP == "Ratings about Blacks" & DATA.SMALL$LEVEL == "0 to 24"], xmax = DATA.SMALL$PE[DATA.SMALL$GROUP == "Ratings about Blacks" & DATA.SMALL$LEVEL == "at 50"] , ymin = -Inf, ymax = Inf), fill = "red3" , color = "black", inherit.aes = FALSE) +
geom_rect(data = filter(DATA.SMALL, GROUP == "Ratings about Blacks") , mapping = aes(xmin = DATA.SMALL$PE[DATA.SMALL$GROUP == "Ratings about Blacks" & DATA.SMALL$LEVEL == "at 50"] , xmax = DATA.SMALL$PE[DATA.SMALL$GROUP == "Ratings about Blacks" & DATA.SMALL$LEVEL == "at 100"], ymin = -Inf, ymax = Inf), fill = "green3" , color = "black", inherit.aes = FALSE) +
geom_rect(data = filter(DATA.SMALL, GROUP == "Ratings about Whites") , mapping = aes(xmin = DATA.SMALL$PE[DATA.SMALL$GROUP == "Ratings about Whites" & DATA.SMALL$LEVEL == "0 to 24"], xmax = DATA.SMALL$PE[DATA.SMALL$GROUP == "Ratings about Whites" & DATA.SMALL$LEVEL == "at 50"] , ymin = -Inf, ymax = Inf), fill = "red3" , color = "black", inherit.aes = FALSE) +
geom_rect(data = filter(DATA.SMALL, GROUP == "Ratings about Whites") , mapping = aes(xmin = DATA.SMALL$PE[DATA.SMALL$GROUP == "Ratings about Whites" & DATA.SMALL$LEVEL == "at 50"] , xmax = DATA.SMALL$PE[DATA.SMALL$GROUP == "Ratings about Whites" & DATA.SMALL$LEVEL == "at 100"], ymin = -Inf, ymax = Inf), fill = "green3" , color = "black", inherit.aes = FALSE) +
geom_errorbarh(mapping = aes(xmin = CILO, xmax = CIHI), height = 0) +
geom_point(color = "black", size = 3.5) +
scale_x_continuous(limits = c(0,1), expand = c(0,0), breaks = 0.5, labels = "Controls for\ndemographics only") +
scale_y_discrete(position = "left") +
geom_text(x = 0.97, y = DATA.SMALL$LEVEL, size = 5, hjust = 1, label = format(round(DATA.SMALL$PE, 2), nsmall = 0)) +
theme.z
plot.FULL <- ggplot(DATA.FULL, mapping = aes(x = PE, y = LEVEL)) +
facet_wrap(~GROUP, dir = "v", ncol = 1, scales = "free_y") +
geom_rect(data = filter(DATA.FULL, GROUP == "Ratings about Blacks") , mapping = aes(xmin = DATA.FULL$PE[DATA.FULL$GROUP == "Ratings about Blacks" & DATA.FULL$LEVEL == "0 to 24"], xmax = DATA.FULL$PE[DATA.FULL$GROUP == "Ratings about Blacks" & DATA.FULL$LEVEL == "at 50"] , ymin = -Inf, ymax = Inf), fill = "red3" , color = "black", inherit.aes = FALSE) +
geom_rect(data = filter(DATA.FULL, GROUP == "Ratings about Blacks") , mapping = aes(xmin = DATA.FULL$PE[DATA.FULL$GROUP == "Ratings about Blacks" & DATA.FULL$LEVEL == "at 50"] , xmax = DATA.FULL$PE[DATA.FULL$GROUP == "Ratings about Blacks" & DATA.FULL$LEVEL == "at 100"], ymin = -Inf, ymax = Inf), fill = "green3" , color = "black", inherit.aes = FALSE) +
geom_rect(data = filter(DATA.FULL, GROUP == "Ratings about Whites") , mapping = aes(xmin = DATA.FULL$PE[DATA.FULL$GROUP == "Ratings about Whites" & DATA.FULL$LEVEL == "0 to 24"], xmax = DATA.FULL$PE[DATA.FULL$GROUP == "Ratings about Whites" & DATA.FULL$LEVEL == "at 50"] , ymin = -Inf, ymax = Inf), fill = "red3" , color = "black", inherit.aes = FALSE) +
geom_rect(data = filter(DATA.FULL, GROUP == "Ratings about Whites") , mapping = aes(xmin = DATA.FULL$PE[DATA.FULL$GROUP == "Ratings about Whites" & DATA.FULL$LEVEL == "at 50"] , xmax = DATA.FULL$PE[DATA.FULL$GROUP == "Ratings about Whites" & DATA.FULL$LEVEL == "at 100"], ymin = -Inf, ymax = Inf), fill = "green3" , color = "black", inherit.aes = FALSE) +
geom_errorbarh(mapping = aes(xmin = CILO, xmax = CIHI), height = 0) +
geom_point(color = "black", size = 3.5) +
scale_x_continuous(limits = c(0,1), expand = c(0,0), breaks = 0.5, labels = "Controls for demographics,\npartisanship, and ideology") +
scale_y_discrete(position = "right") +
geom_text(x = 0.97, y = DATA.FULL$LEVEL, size = 5, hjust = 1, label = format(round(DATA.FULL$PE, 2), nFULL = 0)) +
theme.z
plot.SMALL + plot.FULL +
plot_annotation(title = "Support for Reparations") & theme(plot.title = element_text(face = "bold", size = 18, hjust = 0.5))Figure
Double plot
Code
library(patchwork)
library(tidyverse)
DATA <- tribble(
~GENDERFAVOR,~GROUP,~PE,~CILO,~CIHI,
"Cold only to women","U.S. residents",0.0385036,0.0340647,0.0434949,
"Cold only to women","Believed Blasey Ford",0.1641699,0.1020244,0.2263155,
"Residual colder to women","U.S. residents",0.1580191,0.1496165,0.166801,
"Residual colder to women","Believed Blasey Ford",0.1979061,0.16163,0.2341823,
"Rated men equal to women","U.S. residents",0.1869231,0.1777329,0.1964749,
"Rated men equal to women","Believed Blasey Ford",0.308813,0.2644603,0.3531658,
"Did not rate men and/or women","U.S. residents",0.1068526,0.097831,0.1165985,
"Did not rate men and/or women","Believed Blasey Ford",0.311967,0.2394761,0.3844578,
"Residual colder to men","U.S. residents",0.3991769,0.3872212,0.4112541,
"Residual colder to men","Believed Blasey Ford",0.3985176,0.3594298,0.4376055,
"Cold only to men","U.S. residents",0.1105247,0.1030948,0.1184195,
"Cold only to men","Believed Blasey Ford",0.5188692,0.4589192,0.5788192)
theme.z <- theme(
axis.text.x = element_text(color = "black", size = 15, margin = margin(t = 5, b = 5)),
axis.text.y = element_text(color = "black", size = 15, margin = margin(l = 0, r = 0)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none",
panel.background = element_blank(),
panel.border = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
plot.margin = unit(c(t = 10,r = 0,b = 10,l = 0), "pt"))
DATA.DIST <- filter(DATA, GROUP == "U.S. residents")
DATA.DIST$GENDERFAVOR <- factor(DATA.DIST$GENDERFAVOR, levels = unique(DATA.DIST$GENDERFAVOR))
UCM <- DATA$PE[DATA$GROUP == "U.S. residents" & DATA$GENDERFAVOR == "Cold only to men"]
URM <- DATA$PE[DATA$GROUP == "U.S. residents" & DATA$GENDERFAVOR == "Residual colder to men"]
UDN <- DATA$PE[DATA$GROUP == "U.S. residents" & DATA$GENDERFAVOR == "Did not rate men and/or women"]
UEQ <- DATA$PE[DATA$GROUP == "U.S. residents" & DATA$GENDERFAVOR == "Rated men equal to women"]
URW <- DATA$PE[DATA$GROUP == "U.S. residents" & DATA$GENDERFAVOR == "Residual colder to women"]
UCW <- DATA$PE[DATA$GROUP == "U.S. residents" & DATA$GENDERFAVOR == "Cold only to women"]
BREAKS <- c(UCM/2, UCM+URM/2, UCM+URM+UDN/2, UCM+URM+UDN+UEQ/2, UCM+URM+UDN+UEQ+URW/2, UCM+URM+UDN+UEQ+URW+UCW/2)
p1 <- ggplot(DATA.DIST, mapping = aes(y = 100*PE, x = GROUP, fill = GENDERFAVOR)) +
geom_col(color = "black", linewidth = 1.1, width = 1) +
scale_fill_manual(values=c("Cold only to men"="lightskyblue3", "Residual colder to men"="powderblue", "Did not rate men and/or women"="gray90", "Rated men equal to women"="white", "Residual colder to women"="lightpink1", "Cold only to women"="lightpink3"), name="Category") +
scale_y_continuous(limits = c(0,100.01), breaks = 100*BREAKS, labels=unique(rev(DATA$GENDERFAVOR))) +
scale_x_discrete(position="top") +
coord_cartesian(clip="off") +
theme.z
DATA.KF <- filter(DATA, GROUP == "Believed Blasey Ford")
RCM <- DATA$PE[DATA$GROUP == "Republicans" & DATA$GENDERFAVOR == "Cold only to men"]
RRM <- DATA$PE[DATA$GROUP == "Republicans" & DATA$GENDERFAVOR == "Residual colder to men"]
RDN <- DATA$PE[DATA$GROUP == "Republicans" & DATA$GENDERFAVOR == "Did not rate men and/or women"]
REQ <- DATA$PE[DATA$GROUP == "Republicans" & DATA$GENDERFAVOR == "Rated men equal to women"]
RRW <- DATA$PE[DATA$GROUP == "Republicans" & DATA$GENDERFAVOR == "Residual colder to women"]
RCW <- DATA$PE[DATA$GROUP == "Republicans" & DATA$GENDERFAVOR == "Cold only to women"]
p2 <- ggplot(data=DATA.KF, mapping = aes(x = PE, y = rev(BREAKS))) +
geom_segment(mapping = aes(x = 0, y = rev(BREAKS), xend=1, yend=rev(BREAKS)), color = "gray80") +
geom_point(color = "black", size = 5) +
geom_errorbarh(mapping = aes(xmin=CILO, xmax = CIHI), size = 1.25, height = 0) +
scale_x_continuous(expand=c(0,0), limits = c(0,1), breaks = c(0.5), labels=c("Predicted probability of believing\nBlasey Ford, net of controls"), position="top") +
scale_y_continuous(limits = c(0,1)) +
theme.z +
theme(
axis.text.y = element_blank(),
plot.margin = unit(c(t = 10, r = 15, b = 10, l = 0), "pt"))
p1 + p2 + plot_layout(ncol = 2, widths = c(1,1.5))Figure
Grid
Code
library(patchwork)
library(tidyverse)
DATA <- read_csv("Grid.csv")
print(DATA, n = Inf)## # A tibble: 24 × 10
## FACET THERMO OUTCOME ...4 PE ...6 ...7 ...8 CILO CIHI
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 No controls Text … Outcom… 1 0.415 0.219 1.9 0.058 0.112 0.718
## 2 No controls Text … Outcom… 2 0.414 0.0615 6.74 0 0.329 0.499
## 3 No controls Text … Outcom… 3 0.540 0.0622 8.68 0 0.453 0.626
## 4 No controls Text … Outcom… 1 0.0814 0.0315 2.58 0.01 0.0377 0.125
## 5 No controls Text … Outcom… 2 0.500 0.0465 10.8 0 0.436 0.565
## 6 No controls Text … Outcom… 3 0.419 0.0455 9.2 0 0.355 0.482
## 7 No controls Text … Outcom… 1 0.140 0.0180 7.77 0 0.115 0.165
## 8 No controls Text … Outcom… 2 0.0743 0.0447 1.66 0.097 0.0124 0.136
## 9 No controls Text … Outcom… 3 0.481 0.167 2.89 0.004 0.250 0.712
## 10 No controls Text … Outcom… 1 0.163 0.0177 9.24 0 0.139 0.188
## 11 No controls Text … Outcom… 2 0.131 0.0692 1.89 0.059 0.0347 0.226
## 12 No controls Text … Outcom… 3 0.513 0.168 3.06 0.002 0.281 0.746
## 13 Demographic con… Text … Outcom… 1 0.0666 0.0156 4.27 0 0.0450 0.0883
## 14 Demographic con… Text … Outcom… 2 0.583 0.0278 21.0 0 0.545 0.622
## 15 Demographic con… Text … Outcom… 3 0.350 0.0265 13.2 0 0.313 0.387
## 16 Demographic con… Text … Outcom… 1 0.548 0.0760 7.21 0 0.443 0.654
## 17 Demographic con… Text … Outcom… 2 0.531 0.0422 12.6 0 0.472 0.589
## 18 Demographic con… Text … Outcom… 3 0.412 0.0413 9.98 0 0.355 0.469
## 19 Demographic con… Text … Outcom… 1 0.150 0.0761 1.97 0.049 0.0446 0.255
## 20 Demographic con… Text … Outcom… 2 0.199 0.0950 2.1 0.036 0.0674 0.331
## 21 Demographic con… Text … Outcom… 3 0.106 0.0641 1.65 0.098 0.0172 0.195
## 22 Demographic con… Text … Outcom… 1 0.147 0.0236 6.24 0 0.114 0.180
## 23 Demographic con… Text … Outcom… 2 0.106 0.0641 1.65 0.098 0.0172 0.195
## 24 Demographic con… Text … Outcom… 3 0.199 0.0950 2.1 0.036 0.0674 0.331theme.z <- theme(
axis.text.x = element_blank(),
axis.text.x.top = element_blank(),
axis.text.y = element_text(size = 15, color = "black", hjust = 1 , margin = margin(l = 8, r = 8)),
axis.text.y.right = element_text(size = 15, color = "black", hjust = 0 , margin = margin(l = 8, r = 8)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_text(size = 15, color = "black", hjust = 0.5, margin = margin(t = 8,b = 0)),
axis.title.y = element_blank(),
legend.position = "none",
panel.background = element_rect(linewidth = 0.5, color = "black", fill = "gray90", linetype = "solid"),
panel.border = element_rect(linewidth = 1.0, color = "black", fill = NA , linetype = "solid"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.spacing.x = unit(1, "lines"),
panel.spacing.y = unit(1, "lines"),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 12 , hjust = 0 , margin = margin(t = 10)),
plot.margin = unit(c(t = 10,r = 0,b = 10,l = 0),"pt"),
plot.subtitle = element_text(size = 15 , hjust = 0.5, margin = margin(b = 10)),
plot.title = element_text(size = 17 , hjust = 0.5, margin = margin(t = 0,b = 10), face = "bold"),
strip.background = element_rect(linewidth = 1.0, color = "black", fill = "black"),
strip.text.x = element_text(size = 18, color = "white", face = "bold", margin = margin(t = 7.5, b = 7.5, l = 25, r = 25)),
strip.text.y = element_text(size = 18, color = "white", face = "bold", margin = margin(t = 7.5, b = 7.5, l = 7.5, r = 7.5))
)
DATA$FACET <- factor(DATA$FACET , levels = unique(DATA$FACET))
DATA$OUTCOME <- factor(DATA$OUTCOME, levels = unique(DATA$OUTCOME))
DATA$THERMO <- factor(DATA$THERMO , levels = rev(unique(DATA$THERMO)))
DATA.NEGNC <- filter(DATA, OUTCOME == "Outcome 1" & DATA$FACET == "No controls")
DATA.POSNC <- filter(DATA, OUTCOME == "Outcome 2" & DATA$FACET == "No controls")
DATA.NEGPC <- filter(DATA, OUTCOME == "Outcome 1" & DATA$FACET == "Demographic controls")
DATA.POSPC <- filter(DATA, OUTCOME == "Outcome 2" & DATA$FACET == "Demographic controls")
ggplot(DATA, mapping = aes(PE, THERMO)) +
facet_grid(FACET ~ OUTCOME, switch="y", space = "free_y", scales = "free_y", labeller = labeller(FACET = label_wrap_gen(width = 15), OUTCOME = label_wrap_gen(width = 15))) +
geom_rect(data = DATA.NEGNC, mapping = aes(xmin = min(DATA.NEGNC$PE), xmax = max(DATA.NEGNC$PE), ymin = -Inf, ymax = Inf), color = "black", fill = "lightsteelblue3", inherit.aes = FALSE) +
geom_rect(data = DATA.POSNC, mapping = aes(xmin = min(DATA.POSNC$PE), xmax = max(DATA.POSNC$PE), ymin = -Inf, ymax = Inf), color = "black", fill = "lightsteelblue3", inherit.aes = FALSE) +
geom_rect(data = DATA.NEGPC, mapping = aes(xmin = min(DATA.NEGPC$PE), xmax = max(DATA.NEGPC$PE), ymin = -Inf, ymax = Inf), color = "black", fill = "lightsteelblue3", inherit.aes = FALSE) +
geom_rect(data = DATA.POSPC, mapping = aes(xmin = min(DATA.POSPC$PE), xmax = max(DATA.POSPC$PE), ymin = -Inf, ymax = Inf), color = "black", fill = "lightsteelblue3", inherit.aes = FALSE) +
geom_errorbarh(mapping = aes(xmin = CILO, xmax = CIHI), height = 0, size = 0.5) +
geom_point(size = 4) +
geom_text(x = 0.95, y = DATA$THERMO, size = 5, hjust = 1, label = format(round(DATA$PE, 2), nsmall = 2)) +
scale_x_continuous(breaks = seq(0,1), limits = c(0,1), expand = c(0,0)) +
scale_y_discrete(position = "right") +
labs(x = "Predicted probability") +
theme.zFigure
Ticks
Code
library(patchwork)
library(tidyverse)
theme.z <- theme(
axis.text.x = element_text(size = 15, color = "black", hjust = 0.5, margin = margin(t = 8, b = 0)),
axis.text.x.top = element_text(size = 15, color = "black", hjust = 0.5, margin = margin(t = 8, b = 0)),
axis.text.y = element_text(size = 15, color = "black", hjust = 1 , margin = margin(l = 8, r = 8)),
axis.text.y.right = element_text(size = 15, color = "black", hjust = 0 , margin = margin(l = 8, r = 8)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none",
panel.background = element_blank(),
panel.border = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.spacing.x = unit(2, "lines"),
panel.spacing.y = unit(1, "lines"),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 12 , hjust = 0 , margin = margin(t = 10)),
plot.margin = unit(c(t = 10,r = 5,b = 10,l = 5),"pt"),
plot.subtitle = element_text(size = 15 , hjust = 0.5, margin = margin(b = 10)),
plot.title = element_text(size = 17 , hjust = 0.5, margin = margin(t = 0, b = 10), face = "bold"),
)
DATA <- read_csv("Ticks.csv")
print(DATA, n = Inf)## # A tibble: 35 × 8
## SECTION LABELY GROUP ...4 PE ...6 CILO CIHI
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Rated immigrants 0 to 25 Rated 0 t… Rati… 0 0.0841 0.00433 0.0783 0.0903
## 2 Rated immigrants 26 to 49 Rated 26 … Rati… 1 0.104 0.00466 0.0973 0.110
## 3 Rated immigrants at 50 Rated at … Rati… 2 0.0899 0.00420 0.0843 0.0959
## 4 Did not rate immigrants Did not r… Rati… 3 0.0556 0.00384 0.0505 0.0612
## 5 Rated immigrants 51 to 75 Rated 51 … Rati… 4 0.258 0.00641 0.249 0.267
## 6 Rated immigrants 76 to 99 Rated 76 … Rati… 5 0.331 0.00679 0.322 0.341
## 7 Rated immigrants at 100 Rated at … Rati… 6 0.0776 0.00400 0.0722 0.0833
## 8 Rated Whites 0 to 25 Rated 0 t… Rati… 0 0.0550 0.00371 0.0501 0.0604
## 9 Rated Whites 26 to 49 Rated 26 … Rati… 1 0.108 0.00481 0.101 0.115
## 10 Rated Whites at 50 Rated at … Rati… 2 0.107 0.00461 0.101 0.114
## 11 Did not rate Whites Did not r… Rati… 3 0.0390 0.00333 0.0346 0.0439
## 12 Rated Whites 51 to 75 Rated 51 … Rati… 4 0.251 0.00642 0.242 0.260
## 13 Rated Whites 76 to 99 Rated 76 … Rati… 5 0.361 0.00690 0.352 0.371
## 14 Rated Whites at 100 Rated at … Rati… 6 0.0788 0.00396 0.0735 0.0845
## 15 Rated Blacks 0 to 25 Rated 0 t… Rati… 0 0.0562 0.00365 0.0514 0.0615
## 16 Rated Blacks 26 to 49 Rated 26 … Rati… 1 0.0808 0.00421 0.0751 0.0868
## 17 Rated Blacks at 50 Rated at … Rati… 2 0.0846 0.00408 0.0792 0.0905
## 18 Did not rate Blacks Did not r… Rati… 3 0.0383 0.00320 0.0341 0.0430
## 19 Rated Blacks 51 to 75 Rated 51 … Rati… 4 0.239 0.00629 0.230 0.247
## 20 Rated Blacks 76 to 99 Rated 76 … Rati… 5 0.386 0.00710 0.376 0.396
## 21 Rated Blacks at 100 Rated at … Rati… 6 0.115 0.00471 0.109 0.122
## 22 Rated Hispanics 0 to 25 Rated 0 t… Rati… 0 0.0428 0.00310 0.0387 0.0473
## 23 Rated Hispanics 26 to 49 Rated 26 … Rati… 1 0.0755 0.00403 0.0701 0.0812
## 24 Rated Hispanics at 50 Rated at … Rati… 2 0.0859 0.00412 0.0804 0.0918
## 25 Did not rate Hispanics Did not r… Rati… 3 0.0514 0.00369 0.0465 0.0567
## 26 Rated Hispanics 51 to 75 Rated 51 … Rati… 4 0.254 0.00643 0.245 0.263
## 27 Rated Hispanics 76 to 99 Rated 76 … Rati… 5 0.384 0.00709 0.374 0.394
## 28 Rated Hispanics at 100 Rated at … Rati… 6 0.107 0.00460 0.100 0.113
## 29 Rated Asians 0 to 25 Rated 0 t… Rati… 0 0.0403 0.00332 0.0360 0.0452
## 30 Rated Asians 26 to 49 Rated 26 … Rati… 1 0.0738 0.00420 0.0682 0.0799
## 31 Rated Asians at 50 Rated at … Rati… 2 0.0903 0.00417 0.0846 0.0962
## 32 Did not rate Asians Did not r… Rati… 3 0.0594 0.00391 0.0542 0.0651
## 33 Rated Asians 51 to 75 Rated 51 … Rati… 4 0.245 0.00643 0.236 0.254
## 34 Rated Asians 76 to 99 Rated 76 … Rati… 5 0.391 0.00705 0.382 0.401
## 35 Rated Asians at 100 Rated at … Rati… 6 0.0996 0.00435 0.0937 0.106DATA$GROUP <- factor(DATA$GROUP , levels = unique(DATA$GROUP))
DATA$SECTION <- factor(DATA$SECTION, levels = rev(unique(DATA$SECTION)))
LABELS <- c("Ratings about immigrants" = "Ratings\nabout\nimmigrants", "Ratings about Whites" = "Ratings\nabout\nWhites", "Ratings about Blacks" = "Ratings\nabout\nBlacks", "Ratings about Hispanics" = "Ratings\nabout\nHispanics", "Ratings about Asians" = "Ratings\nabout\nAsians")
ggplot(DATA, aes(fill = SECTION, y = 100*PE, x = GROUP)) +
geom_col(color = "black", linewidth = 1, width = 1) +
geom_rect(aes(xmin = 0.5, xmax = 5.5, ymin = 0, ymax = 100), fill = NA, col = "black", linewidth = 1.5) +
scale_fill_manual(values = rep.int(c("green3", "green1", "lightgreen", "white", "gray90", "pink2", "red3"), 8)) +
scale_y_continuous(limits = c(0,100.1), position = "left", breaks = c(4.2,13.6,23.3,30.5,46.2,75.7,96.1), label = unique(DATA$LABELY), expand=c(0,0), sec.axis = sec_axis(~.,breaks = seq(0,100,10), label = c("\u2014",rep.int("\u2013",4),"\u2014",rep.int("\u2013",4),"\u2014"))) +
scale_x_discrete(labels=LABELS, expand = c(0,0)) +
theme.z +
theme(plot.margin = unit(c(t = 10, r = 5, b = 15, l = 5), "pt"))Figure
Array 2
Code
library(patchwork)
library(tidyverse)
DATA <- read_csv("Array.csv")
print(DATA, n = Inf)## # A tibble: 8 × 7
## GROUP PE ...3 ...4 ...5 CILO CIHI
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Rated immigrants cold and at least 25 u… 0.137 0.0554 2.47 0.014 0.0600 0.214
## 2 Rated immigrants cold and at least 5 un… 0.301 0.0594 5.07 0 0.219 0.383
## 3 Rated immigrants cold and colder than e… 0.294 0.0537 5.48 0 0.220 0.369
## 4 Rated immigrants colder than each of WB… 0.370 0.0404 9.16 0 0.314 0.426
## 5 Rated immigrants equal to each of WBHA 0.514 0.0662 7.77 0 0.422 0.605
## 6 Rated immigrants within 5 or fewer unit… 0.552 0.0432 12.8 0 0.492 0.611
## 7 Rated immigrants warm and at least 5 un… 0.768 0.0780 9.84 0 0.660 0.876
## 8 Rated immigrants at 100 but did not rat… 0.763 0.0887 8.6 0 0.640 0.886theme.z <- theme(
axis.text.x = element_blank(),
axis.text.x.top = element_text(size = 15, color = "black", hjust = 1 , margin = margin(t = 8,b = 0)),
axis.text.y = element_text(size = 15, color = "black", hjust = 1 , margin = margin(l = 8,r = 8)),
axis.text.y.right = element_text(size = 15, color = "black", hjust = 0 , margin = margin(l = 8,r = 8)),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_text(size = 15, color = "black", hjust = 0.5, margin = margin(t = 8,b = 0)),
axis.title.y = element_blank(),
legend.position = "none",
panel.background = element_rect(linewidth = 0.5, color = "black", fill = "gray90", linetype = "solid"),
panel.border = element_rect(linewidth = 1.2, color = "black", fill = NA , linetype = "solid"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.spacing.x = unit(1, "lines"),
panel.spacing.y = unit(1, "lines"),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 12 , hjust = 0 , margin = margin(t = 10)),
plot.margin = unit(c(t = 10,r = 40,b = 10,l = 0),"pt"),
plot.subtitle = element_text(size = 15 , hjust = 0.5, margin = margin(b = 10)),
plot.title = element_text(size = 17 , hjust = 0.5, margin = margin(t = 0,b = 10), face = "bold"),
strip.background = element_rect(color = "black", fill = "black"),
strip.text.x = element_text(size = 18, color = "white", face = "bold", margin = margin(t = 7.5, b = 7.5))
)
DATA$GROUP <- factor(DATA$GROUP, levels = unique(rev(DATA$GROUP)))
ggplot(DATA, aes(PE, GROUP)) +
geom_rect(data = DATA, aes(xmin = min(DATA$PE), xmax = DATA$PE[DATA$GROUP == "Rated immigrants equal to each of WBHA"], ymin = -Inf, ymax = Inf), color = "black", fill = "red3" , inherit.aes = FALSE) +
geom_rect(data = DATA, aes(xmax = max(DATA$PE), xmin = DATA$PE[DATA$GROUP == "Rated immigrants within 5 or fewer units of each of WBHA"], ymin = -Inf, ymax = Inf), color = "black", fill = "green3", inherit.aes = FALSE) +
geom_errorbarh(aes(xmin = CILO, xmax = CIHI), height = 0, size = 0.5) +
geom_point(size = 4) +
geom_text(x = 1.05, y = DATA$GROUP, size = 5, hjust = 0, label = format(round(DATA$PE, 2), nsmall = 2)) +
coord_cartesian(clip = "off") +
scale_x_continuous(limits = c(0,1), expand = c(0,0)) +
labs(x = "Predicted probability of\nan intended vote\nfor Joe Biden\nover Donald Trump") +
theme.zFigure
DAG
Code
library(ggdag)
library(patchwork)
library(tidyverse)
TOL <- 0.25
theme.z <- theme(
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.title.y.right = element_blank(),
panel.background = element_rect(linewidth = 0.5, color = "black", fill = "gray95", linetype = "solid"),
panel.border = element_rect(linewidth = 1.0, color = "black", fill = NA , linetype = "solid"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 14, hjust = 0 , margin = margin(t = 10)),
plot.margin = unit(c(t = 10,r = 10,b = 10,l = 10),"pt"),
plot.subtitle = element_text(size = 15, hjust = 0.5, margin = margin(t = 0,b = 8)),
plot.title = element_text(size = 20, hjust = 0.5, face = "bold", margin = margin(t = 0,b = 12)))
DAG.COORDS <- list(
x = c(X = 0 , Z = 0.5, Y = 1),
y = c(X = 0 , Z = 1 , Y = 0))
CAPTION.CNF <- str_wrap("Control for Z to help isolate the effect of the \"treatment\" X on Y. Controlling for Z helps get all-else-equal before the \"treatment\" X and the outcome Y.", width = 42)
CAPTION.COL <- str_wrap("Do not control for Z. Controlling for Z would help get all-else-equal *after* the \"treatment\" X and the outcome Y, but there's no need to do that.", width = 42)
CAPTION.MED <- str_wrap("To estimate the total effect of X on Y, do not control for Z. Control for Z to isolate the effect of X on Y that does not run through Z.", width = 42)
CNF <- ggdag(dagify(
Y ~ X,
X ~ Z,
Y ~ Z,
coords=DAG.COORDS
)) +
geom_dag_point(shape = 21, color = "black", fill = "slategray3", stroke = 1.2) +
geom_dag_edges(edge_color = "black", edge_width = 1) +
geom_dag_text(color = "black") +
scale_x_continuous(limits = c(0 - TOL, 1 + TOL)) +
scale_y_continuous(limits = c(0 - TOL, 1 + TOL)) +
labs(title = "Confounder", caption = CAPTION.CNF) + theme.z
COL <- ggdag(dagify(
Y ~ X,
Z ~ X,
Z ~ Y,
coords=DAG.COORDS
)) +
geom_dag_point(shape = 21, color = "black", fill = "slategray3", stroke = 1.2) +
geom_dag_edges(edge_color = "black", edge_width = 1) +
geom_dag_text(color = "black") +
scale_x_continuous(limits = c(0 - TOL, 1 + TOL)) +
scale_y_continuous(limits = c(0 - TOL, 1 + TOL)) +
labs(title = "Collider", caption = CAPTION.COL) + theme.z
MED <- ggdag(dagify(
Y ~ X,
Z ~ X,
Y ~ Z,
coords=DAG.COORDS
)) +
geom_dag_point(shape = 21, color = "black", fill = "slategray3", stroke = 1.2) +
geom_dag_edges(edge_color = "black", edge_width = 1) +
geom_dag_text(color = "black") +
scale_x_continuous(limits = c(0 - TOL, 1 + TOL)) +
scale_y_continuous(limits = c(0 - TOL, 1 + TOL)) +
labs(title = "Mediator", caption = CAPTION.MED) + theme.z
CNF + COL + plot_layout(nrow=1)Figure
Bars
Code
library(tidyverse)
theme.z <- theme(
axis.text.x = element_text(color = "black", size = 15),
axis.text.y = element_text(color = "black", size = 15),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_text(color = "black", size = 15),
axis.title.y = element_text(color = "black", size = 15),
legend.position = "none",
panel.background = element_rect(fill = "gray90"),
panel.border = element_rect(color = "black", fill = NA, size = 1.1),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
plot.caption = element_text(size = 12 , hjust = 0, margin = margin(t = 10)),
plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), "cm"),
plot.title = element_text(face = "bold", size = 18, hjust = 0.5,margin = margin(b = 10)))
SAMPLE <- seq(10, 600, 1)
QT <- qt(0.975, SAMPLE - 1)
DATA <- data.frame(SAMPLE, QT)
ggplot(data=DATA, aes(x = SAMPLE,y = QT)) +
geom_rect(data = DATA, aes(xmin = -Inf, xmax = Inf, ymin = 1.95, ymax = 1.96), fill = "gray85") +
geom_rect(data = DATA, aes(xmin = -Inf, xmax = Inf, ymin = 1.97, ymax = 1.98), fill = "gray85") +
geom_rect(data = DATA, aes(xmin = -Inf, xmax = Inf, ymin = 1.99, ymax = 2.00), fill = "gray85") +
geom_rect(data = DATA, aes(xmin = -Inf, xmax = Inf, ymin = 2.01, ymax = 2.02), fill = "gray85") +
geom_rect(data = DATA, aes(xmin = -Inf, xmax = Inf, ymin = 2.03, ymax = 2.04), fill = "gray85") +
geom_rect(data = DATA, aes(xmin = -Inf, xmax = Inf, ymin = 2.05, ymax = 2.06), fill = "gray85") +
geom_rect(data = DATA, aes(xmin = -Inf, xmax = Inf, ymin = 2.07, ymax = 2.08), fill = "gray85") +
geom_rect(data = DATA, aes(xmin = -Inf, xmax = Inf, ymin = 2.09, ymax = 2.10), fill = "gray85") +
geom_point(size = 1) +
scale_x_continuous(limits = c(0,600), expand=c(0,0), breaks = seq(0,600,50)) +
scale_y_continuous(limits = c(1.95,2.1), breaks = seq(1.95,2.1,0.01), sec.axis = dup_axis()) +
labs(y = "Multiplier", x = "Sample size", title = "Multiplier in a formula for\nthe margin of error for a measurement\nfrom a random sample, at the 95% confidence level") +
theme.zFigure
Arrows
Code
library(patchwork)
library(tidyverse)
theme.z <- theme(
axis.text.x = element_text(size = 15, color = "black", margin = margin(t = 8, b = 8)),
axis.text.y = element_blank(),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.background = element_rect(linewidth = 0.5, color = "black", fill = "gray90", linetype = "solid"),
panel.border = element_rect(linewidth = 1.0, color = "black", fill = NA , linetype = "solid"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.spacing.x = unit(2, "lines"),
panel.spacing.y = unit(0, "lines"),
plot.background = element_rect(fill = "white"),
plot.caption = element_text(size = 10, hjust = 0, margin = margin(t = 8)),
plot.margin = unit(c(0.15,0.5,0.15,0.5), "cm"),
plot.subtitle = element_text(size = 15, hjust = 0.5, margin = margin(b = 7)),
plot.title = element_text(size = 20, hjust = 0.5, face = "bold", margin = margin(t = 0, b = 12)),
strip.background = element_rect(linewidth = 1, color = "black", fill = "black"),
strip.text.x = element_text(size = 17, color = "white", face = "bold", margin = margin(t = 7.5, b = 7.5))
)
DATA <- tribble(
~FACET,~X, ~Y,
"Standard deviation of 2.12", -5,1,
"Standard deviation of 2.12", -2,1)
ggplot(data = DATA, aes(x = X, y = Y)) +
facet_wrap(~FACET, ncol = 1, dir = "v", scales = "free") +
geom_curve(data = filter(DATA, FACET == "Standard deviation of 1.41"), aes(x = -5, y = 1, xend = -3.5, yend = 1), size = 1, curvature = -0.30, color = "slategray3") +
geom_curve(data = filter(DATA, FACET == "Standard deviation of 1.41"), aes(x = -3.5, y = 1, xend = -2, yend = 1), size = 1, curvature = -0.30, color = "slategray3") +
geom_point(size = 3.5) +
scale_y_continuous(limits = c(0.5,2.5)) +
scale_x_continuous(breaks = seq(-5,-2,1), limits = c(-5.2,-1.8), labels = scales::number_format(accuracy = 1)) +
theme.z