I have been a big fan of Kastellec and Leoni (2007) since it came out, and by putting their code for creating graphs from actually-published tables online, the authors allowed me and many others to adapt it for our own needs. But there can be no doubt that the code is cumbersome and that it takes a lot of fiddly work to get it to produce the desired results, work that likely contributes to the continued reliance in political science on tables to present regression results. As they acknowledged then, “it simply takes more work to produce graphs” than tables (Kastellec and Leoni 2007, 757). Now (just eight-plus years later!), the dotwhisker package, building on many other developments in the R ecosystem, makes producing effective plots of regression results nearly effortless.

Below, I present their original code for their three examples of plotting regression results, along with similar plots done using dotwhisker. I hope you’ll agree that the latter plots are not only much easier to make, but also—thanks to Hadley Wickham’s (2009) ggplot—better looking (and more easily further customizable).

### Figure 6: Presenting a Single Regression Model Using a Dot Plot with Error Bars.

Original, with very light edits, via Kastellec and Leoni’s http://svn.tables2graphs.com/tables2graphs/Rcode/Final%20Code%20for%20Website/figure_6_stevens_table_2.R

#Create vectors for coefficients, standard errors and variable names
#we place coefficient as last element in each vector rather than 1st
#since it is least important predictor, and thus we place it at the bottom of the graph
#note: we exclude the constant, since it is substantively meaningless

coef.vec <- c( 1.31, .93, 1.46, .07, .96, .2, .22, -.21, -.32, -.27,.23,
0, -.03, .13, .15, .31, -.10)
se.vec <- c( .33, .32, .32, .37, .37, .13, .12, .12, .12, .07, .07, .01, .21,
.14, .29, .25, .27)

var.names <- c("Argentina", "Chile", "Colombia", "Mexico", "Venezuela", #for longer names, we split into 2 lines using "\n" function
"Retrospective Egocentric", "Prospective Egocentric",
"Retrospective Sociotropic", "Prospective Sociotropic",
"Distance from President", "Ideology", "Age", "Female", "Education",

y.axis <- c(length(coef.vec):1)#create indicator for y.axis, descending so that R orders vars from top to bottom on y-axis

par(mar=c(2, 13, 0, 0))#set margins for plot, leaving lots of room on left-margin (2nd number in margin command) for variable names
plot(coef.vec, y.axis, type = "p", axes = F, xlab = "", ylab = "", pch = 19, cex = 1.2,#plot coefficients as points, turning off axes and labels.
xlim = c(-2,2.5), xaxs = "r", main = "") #set limits of x-axis so that they include mins and maxs of
#coefficients + .95% confidence intervals and plot is symmetric; use "internal axes", and leave plot title empty
#the 3 lines below create horiztonal lines for 95% confidence intervals, and vertical ticks for 90% intervals
segments(coef.vec-qnorm(.975)*se.vec, y.axis, coef.vec+qnorm(.975)*se.vec, y.axis, lwd =  1.5)#coef +/-1.96*se = 95% interval, lwd adjusts line thickness

axis(1, at = seq(-2,2,by=.5), labels = NA, tick = T,#draw x-axis and labels with tick marks
cex.axis = 1.2, mgp = c(2,.7,0))#reduce label size, moves labels closer to tick marks
axis(1, at = seq(-2,2,by=1), labels =  c(-2,  -1,  0, 1,2), tick = T,#draw x-axis and labels with tick marks
cex.axis = 1.2, mgp = c(2,.7,0))#reduce label size, moves labels closer to tick marks

axis(2, at = y.axis, label = var.names, las = 1, tick = T, mgp = c(2,.6,0),
cex.axis = 1.2) #draw y-axis with tick marks, make labels perpendicular to axis and closer to axis
segments(0,0,0,17,lty=2) # draw dotted line through 0
#box(bty = "l") #place box around plot

#use following code to place model info into plot region
x.height <- .57
text(x.height, 10, expression(R^{2} == .15), adj = 0, cex = 1) #add text for R-squared
text(x.height, 8, "n = 500", adj = 0, cex = 1)#add text for sample size

Redone using dotwhisker:

#install.packages("dotwhisker") # uncomment to install from CRAN
library(dplyr)
library(dotwhisker)
library(dplyr)

# Format data as tidy dataframe
results_df <- data.frame(term=var.names, estimate=coef.vec,
std.error=se.vec)

# Draw dot-and-whisker plot
results_df %>% dwplot + theme_bw() + theme(legend.position="none") +
ggtitle("Determinants of Authoritarian Aggression\nStevens, Bishin, and Barr (2006)\nvia Kastellec and Leoni (2007)") +
geom_vline(xintercept = 0, colour = "grey60", linetype = 2) +
annotate("text", x = 1.05, y = 10, size = 4, hjust = 0,
label = "R^2 == .15", parse = TRUE) +
annotate("text", x = 1.05, y = 9, size = 4, hjust = 0,
label = "Adjusted~R^2 == .12", parse = TRUE) +
annotate("text", x = 1.05, y = 8, size = 4, hjust = 0,
label = "n = 500") 

### Figure 7: Using Parallel Dot Plots with Error Bars to Present Two Regression Models.

Original, with very light edits, via Kastellec and Leoni’s http://svn.tables2graphs.com/tables2graphs/Rcode/Final%20Code%20for%20Website/figure_7_pekkanen_table_1.R

#Create Vectors for coefs and standard errors for each model, and variable names
#note that we  exclude "margin squared" since it doesn't appear in either model

coef.matrix <- matrix(c(-.039, NA, .048, -.133, .071, -.795, 1.47,
-.036, NA, .036, -.142, .07, -.834, 1.70,
-.051, NA, .017, .05, .011, -.532, .775,
-.037, -.02, .047, -.131,.072, -.783, 1.45,
-.034, -.018, -.035, -.139, .071, -.822, 1.68,
-.05, -.023, .016,-.049, .013, -.521, .819),nr=7)
## R2 of the models
R2<-  c(0.910,  0.910,  0.940,  0.910,  0.910,  0.940)

##standard error matrix, n.variables x n.models
se.matrix <- matrix(c(.003, NA, .011, .013, .028, .056, .152, .003, NA, .012, .014, .029, .059, .171, .003, NA,
.01, .013, .024, .044, .124, .003, .005, .011, .013, .028, .055, .152, .003, .005, .021, .014,
.029, .059, .17, .003,.006, .01, .013, .024, .044, .127),nr=7)

##variable names
coef.vec.1<- c(0.18, -0.19,-0.39,-0.09, NA, 0.04,-0.86, 0.39,-3.76, -1.61,
-0.34, -1.17, -1.15,-1.52, -1.66, -1.34,-2.89,-1.88,-1.08, 0.20)
se.vec.1 <-  c(0.22, 0.22, 0.18,.29, NA, 0.08,0.26,0.29,0.36,.19,0.19, 0.22,
0.22,0.25,0.28,0.32,0.48, 0.43,0.41, 0.20)
coef.vec.2 <-  c(0.27,-0.19, NA, NA, 0.005, 0.04,-0.98,-.36,-3.66, -1.59,
-0.45, -1.24, -1.04, -1.83, -1.82, -1.21, -2.77, -1.34, -0.94, 0.13)
se.vec.2 <- c(0.22,0.24, NA, NA, 0.004, 0.09 , .31 , .30 , .37 , .21 , .21 , .24 , .24,
.29 , .32 , .33 , .49 , .46 , .49 , .26)
var.names <- c("Zombie" , "SMD Only", "PR Only", "Costa Rican in PR",
"Vote Share Margin", "Urban-Rural Index","No Factional\nMembership",
"Legal Professional", "1st Term", "2nd Term", "4th Term",
"5th Term","6th Term","7th Term","8th Term","9th Term","10th Term",
"11th Term","12th Term", "Constant")

y.axis <- length(var.names):1#create indicator for y.axis, descending so that R orders vars from top to bottom on y-axis
adjust <- .15 #create object that we will use to adjust points and lines up and down to distinguish between models

layout(matrix(c(2,1),1,2), #in order to add variable categories and braces to left side of plot,
widths = c(1.5, 5))#we use layout command, create a small second panel on left side.
#using c(2,1) in matrix command tells R to create right panel 1st
#layout.show(2) #can use this command to check results of layout command (but it must be commented out when creating PDF).

par(mar=c(2,8,.5,1), lheight = .8)#set margins for regression plot
plot(coef.vec.1, y.axis+adjust, type = "p", axes = F, xlab = "", ylab = "", pch = 19, cex = 1.2, #plot model 1 coefs using black points (pch = 19, default = black), adding the "adjust amount" to the y.axis indicator to move points up
xlim = c(min((coef.vec.1-qnorm(.975)*se.vec.1 -.1), (coef.vec.2-qnorm(.975)*se.vec.2 -.1), na.rm = T), #set xlims at mins and maximums (from both models) of confidence intervals, plus .1 to leave room at ends of plots
max((coef.vec.1+qnorm(.975)*se.vec.1 -.1), (coef.vec.2+qnorm(.975)*se.vec.2 -.1), na.rm = T)),  #use na.rm=T since vectors have missing values
ylim = c(min(y.axis), max(y.axis)), main = "")
axis(1,at = seq(-4,1, by = 1), label = seq(-4,1, by = 1), mgp = c(2,.8,1), cex.axis = 1.3)#add x-axis and labels; "pretty" creates a sequence of  equally spaced nice values that cover the range of the values in 'x'-- in this case, integers
axis(2, at = y.axis, label = var.names, las = 1, tick = T, cex.axis =1.3)#add y-axis and labels; las = 1 makes labels perpendicular to y-axis
#axis(3,pretty(coef.vec.1, 3))#same as x-axis, but on top axis
abline(h = y.axis, lty = 2, lwd = .5, col = "light grey")#draw light dotted line at each variable for dotplot effect
#box(bty="l")#draw box around plot
segments(coef.vec.1-qnorm(.975)*se.vec.1, y.axis+adjust, coef.vec.1+qnorm(.975)*se.vec.1, y.axis+adjust, lwd =  1.3)#draw lines connecting 95% confidence intervals

abline(v=0, lty = 2) # draw dotted line through 0 for reference line for null significance hypothesis testing

#because we are using white points and do want the lines to go "through" points rather than over them
#we draw lines first and the overlay points
segments(coef.vec.2-qnorm(.975)*se.vec.2, y.axis-adjust, coef.vec.2+qnorm(.975)*se.vec.2, y.axis-adjust, lwd =  1.3)#draw lines connecting 95% confidence intervals
points(coef.vec.2, y.axis-adjust, pch = 21, cex = 1.2, bg = "white" ) #add point estimates for 2nd model; pch = 21 uses for overlay points, and "white" for white color

#add legend (manually) to identify which dots denote model 1 and which denote model 2
#legend(-4.5, 20, c("Model 1", "Model 2"), pch = c(19,21),bty = "n")
points(-4, 19.5, pch = 19, cex  = 1.2)
text(-3.7, 19.5, "Model 1", adj = 0,cex  = 1.2)#left-justify text using adj  = 0
points(-4, 18.5, pch = 21,cex  = 1.2)
text(-3.7, 18.5, "Model 2", adj = 0,cex  = 1.2)#left-justify text using adj  = 0

#Create Variable Categories and Braces to go in 2nd plot

par(mar=c(2,0,.5,0)) #set margins--- bottom (1st number) and top (3rd number) must be the same as in 1st plot
plot(seq(0,1,length=length(var.names)), y.axis, type = "n", axes = F,  xlab = "", ylab = "")#call empty plot using type="n"
#use a sequence of length 20 so that x and y have same length

left.side <- .55#use this to manipulate how far segments are from y-axis
#note:  getting braces and text in proper place requires much trial and error
segments(left.side,20.2,left.side,16.5) #add brackets around MP Type vars
segments(left.side,20.2,left.side+.15,20.2) #1 segment at a time
segments(left.side,16.5,left.side+.15,16.5)
text(.4, 18.5, "MP Type", srt = 90, font = 3, cex  = 1.5)#Add text; "srt" rotates to 90 degrees, font = 3 == italics

#don't add "Electoral Strength" since it's only 1 variable

segments(left.side,15.5,left.side,12.3) #add brackets around "Misc Controls"
segments(left.side,15.5,left.side+.15,15.5) #one segment at a time
segments(left.side,12.3,left.side+.15,12.3)
text(.3, 14, "Misc\nControls", srt = 90, font = 3, cex  = 1.5)#Add text; "srt" rotates to 90 degrees, font = 3 == italics

segments(left.side,12.15,left.side+.15,12.15)   #one segment at a time
segments(left.side,1.8,left.side+.15,1.8)
text(.4, 7, "Seniority", srt = 90, font = 3, cex  = 1.5)#Add text; "srt" rotates to 90 degrees, font = 3 == italics

Redone using dotwhisker:

# Format data as tidy dataframe
results_df <- data.frame(term = rep(var.names, times = 2),
estimate = c(coef.vec.1, coef.vec.2),
std.error = c(se.vec.1, se.vec.2),
model = c(rep("Model 1", 20), rep("Model 2", 20)),
stringsAsFactors = FALSE)

# Draw dot-and-whisker plot
p <- results_df %>% dwplot + theme_bw() +
theme(legend.justification=c(0, 1), legend.position=c(0, 1),
legend.title = element_blank(), legend.background =
element_rect(color="gray90")) +
xlab("Logit Coefficients") +
geom_vline(xintercept = 0, colour = "grey60", linetype = 2) +
ggtitle("Electoral Incentives and LDP Post Allocation\nPekkanen, Nyblade, and Krause (2006)\nvia Kastellec and Leoni (2007)")

grid.draw(p %>% add_brackets(list(c("MP Type", "Zombie", "Costa Rican in PR"),
c("Misc Controls", "Urban-Rural Index", "Legal Professional"),
c("Seniority", "1st Term", "12th Term"))))

### Figure 8: Using “Small Multiple” Plots to Present Regression Results from Several Models.

Original, with light edits, via Kastellec and Leoni’s http://svn.tables2graphs.com/tables2graphs/Rcode/Final%20Code%20for%20Website/figure_8_ansolabehere_table_4.R

library(grid)

##point estimates, in a n.variables, n.variables x n.models
coef.matrix <- matrix(c(-.039, NA, .048, -.133, .071, -.795, 1.47,
-.036, NA, .036, -.142, .07, -.834, 1.70,
-.051, NA, .017, .05, .011, -.532, .775,
-.037, -.02, .047, -.131,.072, -.783, 1.45,
-.034, -.018, -.035, -.139, .071, -.822, 1.68,
-.05, -.023, .016,-.049, .013, -.521, .819),nr=7)
## R2 of the models
R2<-  c(0.910,  0.910,  0.940,  0.910,  0.910,  0.940)

##standard error matrix, n.variables x n.models
se.matrix <- matrix(c(.003, NA, .011, .013, .028, .056, .152, .003, NA, .012, .014, .029, .059, .171, .003, NA,
.01, .013, .024, .044, .124, .003, .005, .011, .013, .028, .055, .152, .003, .005, .021, .014,
.029, .059, .17, .003,.006, .01, .013, .024, .044, .127),nr=7)

##variable names
varnames<- c("% of county\nregistration", "Law change", "Log population", "Log median\nfamily income",
"% population with\nh.s. education" ,"% population\nAfrican American" ,"Constant")

##exclude intercept
coef.matrix<-coef.matrix[-(7),]
se.matrix<-se.matrix[-7,]

## each panel has at most six models, plotted in pairs.
## in each pair, solid circles will be the models with "law change" in the specification
## empty circles, those without "law change"

##we are making a list, define it first as empty
Y1 <- vector(length=0,mode="list")
#estimates with law change (in the 4th to 6th columns)
Y1$estimate <- coef.matrix[,4:6] ##95% confidence intervals Y1$lo <- coef.matrix[,4:6]-qnorm(0.975)*se.matrix[,4:6]
Y1$hi <- coef.matrix[,4:6]+qnorm(0.975)*se.matrix[,4:6] ##90% confidence intervals Y1$lo1 <- coef.matrix[,4:6]-qnorm(0.95)*se.matrix[,4:6]
Y1$hi1 <- coef.matrix[,4:6]+qnorm(0.95)*se.matrix[,4:6] ##name the rows of Y1 estimate rownames(Y1$estimate) <- varnames[-7] ##no intercept

#estimates without law change
Y2 <- vector(length=0,mode="list")
Y2$estimate <- coef.matrix[,1:3] Y2$lo <- coef.matrix[,1:3]-qnorm(.975)*se.matrix[,1:3]
Y2$hi <- coef.matrix[,1:3]+qnorm(.975)*se.matrix[,1:3] Y2$lo1 <- coef.matrix[,1:3]-qnorm(.95)*se.matrix[,1:3]
Y2$hi1 <- coef.matrix[,1:3]+qnorm(.95)*se.matrix[,1:3] rownames(Y2$estimate) <- varnames[-7]

##Plot estimates in a single columns
source2 <- function(file, start, end) {
file.lines <- scan(file, what=character(),
skip=start - 1,
nlines=end - start + 1, sep='\n')
file.lines.collapsed <- paste(file.lines, collapse='\n')
source(textConnection(file.lines.collapsed))
}

source2("http://svn.tables2graphs.com/tables2graphs/Rcode/plotReg.R", 15, 372)

## create the graph (do not print it yet)
tmp <- plot.reg(Y1,Y2,#the lists
#the model labels
label.x=c("Full Sample","Excluding counties\nw. partial registration",
"Full sample w. \nstate year dummies"),
## reference lines
refline=c(0,0,0,0,0,0),
## space left in the bottom (for the x-axis labels)
hlast=.15,
## print the graph?
print=FALSE,
## line width / character size multiplier
lwd.fact=1.3,
## length of the cross- hairs
length.arrow=unit(0,"mm"),
## legend
##legend=c("without law change","with law change"),
## widths: variable names, plot size, y-axis
widths=c(.6,.4,.3),
## rotation of the variable name labes
rot.label.y=0,
## justification of the variable name labels
just.label.y="right",
## position (x-axis) of the variable name labels)
pos.label.y=0.95,
## size of the symbol
pch.size=0.6,expand.factor=.2,expand.factor2=0.1,
##legend
legend=c("With law\nchange dummy","Without law\nchange dummy"),leg.mult=.7,
##legend font size
leg.fontsize=11,
v.grid=TRUE,
yaxis.at=list(
NULL,
NULL,
seq(-.1,.1,.05),
seq(-.2,.2,.1),
seq(-.2,.2,.1),
NULL##seq(-1,1,.5)
)
)
## we rotate the labels of the x-axis 45 degrees. The grid utilities allow
## this modification "on the fly", and it is easy if you are careful at naming the paths
tmp <- editGrob(tmp,gPath("xaxis","labels"),rot=45,just="right",gp=gpar(lineheight=.75))
##tmp is the object we have just created,"xaxis" is the name of element in the object with the x-axis
##elements, and "labels" is the actual object in xaxis that we want to rotate
##just is the justification of the text
grid.draw(tmp) ## print the graph

Redone using dotwhisker:

# Format data as tidy dataframe
model_names <- c("Full Sample\n",
"Excluding counties\nw/ partial registration\n",
"Full sample w/\nstate year dummies\n")
submodel_names <- c("With","Without")

model_order <- c(4, 1, 5, 2, 6, 3)

results_df <- data.frame(term = rep(varnames[1:6], times = 6),
estimate = as.vector(coef.matrix[, model_order]),
std.error = as.vector(se.matrix[, model_order]),
model = rep(model_names, each = 12),
submodel = rep(rep(submodel_names, each = 6), times = 3),
stringsAsFactors = FALSE)

small_multiple(results_df) +
theme_bw() + ylab("Coefficient Estimate") +
geom_hline(yintercept = 0, colour = "grey60", linetype = 2) +
theme(axis.text.x  = element_text(angle = 45, hjust = 1),
legend.position=c(1, 1), legend.justification=c(1, 1),
legend.title = element_text(size=9),
legend.background = element_rect(color="gray90"),
legend.margin = unit(-3, "pt"),
legend.key.size = unit(10, "pt")) +
scale_colour_hue(name = "Law Change\nDummy") +
ggtitle("Registration Effects on Turnout\nAnsolabehere and Konisky (2006)\nvia Kastellec and Leoni (2007)")

## Affiliation

Frederick Solt

Department of Political Science,

University of Iowa,

324 Schaeffer Hall,

20 E Washington St, Iowa City, IA, 52242

Email: frederick-solt@uiowa.edu

Website: http://myweb.uiowa.edu/fsolt

## References

Kastellec, Jonathan P., and Eduardo L. Leoni. 2007. “Using Graphs Instead of Tables in Political Science.” Perspectives on Politics 5 (4): 755–71.

Wickham, Hadley. 2009. ggplot2: Elegant Graphics for Data Analysis. Springer New York. http://had.co.nz/ggplot2/book.