R/final_figures_eps/chen_ash_eps.R
In databrew/sfi:
# Packages
library(sfi)
library(kmodR)
library(directlabels)
library(webshot)
library(ggplot2)
library(dplyr)
library(ggrepel)
library(plotly)
library(ggiraph)
library(scales)
library(tidyverse)
library(knitr)
library(Hmisc)
library(RColorBrewer)
library(extrafont)
library(kableExtra)
library(grid)
# webshot::install_phantomjs()
loadfonts()
## Global options
# # get data
data <- all_data$chen$f1
# get label numbers
data$text_label <- unlist(lapply(strsplit(data$circuit, '-', fixed = TRUE), function(x) x[length(x)]))
# remove 11 with FC and SC with 12, and 0 with dc
data$text_label <- gsub('^0$', 'DC', data$text_label)
data$text_label <- gsub('12', 'FC', data$text_label)
# randomly sample half of the data set for clarity
keep_index <- sample(1:nrow(data), nrow(data)/2)
data <- data[keep_index, ]
invisible(temp <- kmod(data[, c('x', 'y')], k = 14, l = 200, i_max = 300, conv_method = "delta_C",
conv_error = 0, allow_empty_c = TRUE))
dat_c <- as.data.frame(cbind(data, clust_dist = temp$XC_dist_sqr_assign))
outlier_index <- as.character(temp$L_index)
# create an variable that indicates outlier
dat_c$outlier <- ifelse(dat_c$clust_dist.dist_sqr > 50, 'Outlier', 'In Cluster')
dat_c$clust_dist.c <- as.factor(as.character(dat_c$clust_dist.c))
# # recode SC and FC as in cluster
dat_2 <- dat_c
dat_2$outlier <- as.factor(ifelse(dat_2$text_label == 'SC', 'In Cluster',
ifelse(dat_2$text_label == 'FC', 'In Cluster',
ifelse(dat_2$text_label == 'DC', 'In Cluster', dat_2$outlier))))
# version 11
g1 <- ggplot(dat_c,
aes(x = x,
y = y,
group = clust_dist.c,
shape = clust_dist.c,
color = outlier)) +
geom_point(size = 1,
alpha = 0.8) +
scale_color_manual(name = '',
values = c('black', 'grey')) +
scale_shape_manual(name = 'Cluster group',
breaks = sort(c('1', '10', '11', '12', '13', '14', '2', '3', '4', '5','6', '7', '8', '9')),
values = c(14, 20, 8, 19, 12, 1, 2, 3, 5, 6, 7, 10, 15, 17)) +
labs(x = '',
y = '',
title = paste0(''),
caption = paste0('Point shape represents belonging to a cluster' , '\n', '(K nearest neighbors clustering)')) +
theme_sfi(lp = '',
title_style = 'bold') +
theme(axis.text=element_text(size = 10, hjust = 1),
plot.title = element_text(size =12)) +
annotate(geom = 'text',
label = 'SC',
alpha = 0.8,
x = -28,
y = 42,
family = c("Computer modern"),
size =6) +
annotate(geom = 'text',
label = 'FC',
alpha = 0.8,
x = -32,
y = 30,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = 'DC',
alpha = 0.8,
x = -16,
y = 30,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '9',
alpha = 0.8,
x = -23,
y = 9,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '5',
alpha = 0.8,
x = -7,
y = -35.5,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '8',
alpha = 0.8,
x = 16,
y = -38,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '6',
alpha = 0.8,
x = 26,
y = 8,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '10',
alpha = 0.8,
x = 23,
y = -15,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '4',
alpha = 0.8,
x = 3,
y = -13,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '7',
alpha = 0.8,
x = 13,
y = 18,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '2',
x = 10,
y = 40,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '3',
alpha = 0.8,
x = -6,
y = 30,
family = 'CMU Bright',
size =6)
g1
ggsave("image_files/Chen_Ash_Figure_1_alt.eps", width = 10, height = 10, device=cairo_ps, fallback_resolution = 2000)
# version 11
g1 <- ggplot(dat_2,
aes(x = x,
y = y,
group = clust_dist.c,
shape = clust_dist.c,
color = outlier)) +
geom_point(size = 1,
alpha = 0.8) +
scale_color_manual(name = '',
values = c('black', 'grey')) +
scale_shape_manual(name = 'Cluster group',
breaks = sort(c('1', '10', '11', '12', '13', '14', '2', '3', '4', '5','6', '7', '8', '9')),
values = c(14, 20, 8, 19, 12, 1, 2, 3, 5, 6, 7, 10, 15, 17)) +
labs(x = '',
y = '',
title = paste0(''),
caption = paste0('')) +
theme_sfi(lp = 'none',
title_style = 'bold') +
theme(axis.text=element_text(size = 10, hjust = 1),
plot.title = element_text(size =12)) +
annotate(geom = 'text',
label = 'SC',
alpha = 0.8,
x = -28,
y = 42,
family = c("Computer modern"),
size = 6) +
annotate(geom = 'text',
label = 'FC',
alpha = 0.8,
x = -32,
y = 30,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = 'DC',
alpha = 0.8,
x = -16,
y = 30,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '9',
alpha = 0.8,
x = -23,
y = 9,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '5',
alpha = 0.8,
x = -7,
y = -35.5,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '8',
alpha = 0.8,
x = 16,
y = -38,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '6',
alpha = 0.8,
x = 26,
y = 8,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '10',
alpha = 0.8,
x = 23,
y = -15,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '4',
alpha = 0.8,
x = 3,
y = -13,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '7',
alpha = 0.8,
x = 13,
y = 18,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '2',
x = 10,
y = 40,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '3',
alpha = 0.8,
x = -6,
y = 30,
family = 'CMU Bright',
size =6)
g1
ggsave("image_files/Chen_Ash_Figure_1.eps", width = 10, height = 10, device=cairo_ps, fallback_resolution = 2000)
# Chen and Ash 1
### K-means clustering with outlier detection
# # no scientific notation
# options(scipen = '999')
#
# # get data
data <- all_data$chen$f1
# get label numbers
data$text_label <- unlist(lapply(strsplit(data$circuit, '-', fixed = TRUE), function(x) x[length(x)]))
# remove 11 with FC and SC with 12, and 0 with dc
data$text_label <- gsub('^0$', 'DC', data$text_label)
data$text_label <- gsub('12', 'FC', data$text_label)
# # Compute and plot wss for k = 2 to k = 15.
# length(unique(data$text_label))
# dat <- data[, c('x', 'y')]
# k.max <- 14
# wss <- sapply(1:k.max,
# function(k){kmeans(dat, k, nstart=50,iter.max = 15 )$tot.withinss})
# # wss
# plot(1:k.max, wss,
# type="b", pch = 19, frame = FALSE,
# xlab="Number of clusters K",
# ylab="Total within-clusters sum of squares")
#
#
# # K-Means Cluster Analysis
# fit <- kmeans(dat, 13) # 5 cluster solution
# # get cluster means
# aggregate(dat,by=list(fit$cluster),FUN=mean)
# # append cluster assignment
# dat_clust <- data.frame(dat, fit$cluster)
temp <- kmod(data[, c('x', 'y')], k = 13, l = 200, i_max = 300, conv_method = "delta_C",
conv_error = 0, allow_empty_c = TRUE)
dat_c <- as.data.frame(cbind(data, clust_dist = temp$XC_dist_sqr_assign))
outlier_index <- as.character(temp$L_index)
# create an variable that indicates outlier
dat_c$outlier <- ifelse(dat_c$clust_dist.dist_sqr > 50, 'Outlier', 'In Cluster')
dat_c$clust_dist.c <- as.factor(as.character(dat_c$clust_dist.c))
# recode SC and FC as in cluster
dat_c$outlier <- ifelse(dat_c$text_label == 'SC', 'In Cluster',
ifelse(dat_c$text_label == 'FC', 'In Cluster',
ifelse(dat_c$text_label == 'DC', 'In Cluster', dat_c$outlier)))
# find outliers based on
# version 11
g1 <- ggplot(dat_c,
aes(x = x,
y = y,
group = clust_dist.c,
color = outlier)) +
geom_point(size = 1,
alpha = 0.8) +
labs(x = '',
y = '',
title = paste0('Figure 1: Centered by Topic-Year', '\n', 'Averaged by Judge, Labeled by Court'),
caption = paste0('Circuit, CC Judge Vector', '\n',
'Demeaned by Year and Big Topic')) +
scale_color_manual(name = '',
values = c('black', 'grey'))+
theme_sfi(lp = 'bottom',
title_style = 'bold') +
theme(axis.text=element_text(size = 10, hjust = 1, family = 'Computer modern'),
plot.title = element_text(size =12)) +
annotate(geom = 'text',
label = 'SC',
alpha = 0.8,
x = -28,
y = 42,
family = c("Computer modern"),
size =6) +
annotate(geom = 'text',
label = 'FC',
alpha = 0.8,
x = -32,
y = 30,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = 'DC',
alpha = 0.8,
x = -16,
y = 30,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '9',
alpha = 0.8,
x = -23,
y = 9,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '5',
alpha = 0.8,
x = -7,
y = -35.5,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '8',
alpha = 0.8,
x = 16,
y = -40,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '6',
alpha = 0.8,
x = 26,
y = 8,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '10',
alpha = 0.8,
x = 27,
y = -16,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '4',
alpha = 0.8,
x = 3,
y = -13,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '7',
alpha = 0.8,
x = 13,
y = 21,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '2',
x = 10,
y = 40,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '3',
alpha = 0.8,
x = -9,
y = 30,
family = 'CMU Bright',
size =6)
g1
ggsave("image_files/Chen_Ash_Figure_1_alt2.eps", width = 10, height = 10, device=cairo_ps, fallback_resolution = 2000)
# Chen and Ash 2
# # get data
data <- all_data$chen$f2
# rename
names(data) <- c('x', 'y', 'circuit','decade', 'decades')
# remove s from decades so it can be numeric
data$decades <- gsub('s', '', data$decades, fixed = TRUE)
data$decades <- as.numeric(data$decades)
# create an empty label column
data$label <- ''
# create list to store loop results
data_list <- list()
unique_years <- unique(data$decades)
for(i in 1:length(unique_years)){
this_year <- unique_years[i]
sub_year <- data[data$decades == this_year,]
if(this_year == 1890 | this_year == 1950 | this_year == 2010) {
# randomly assign year label to one row
random_row <- sample(1:nrow(sub_year), 1)
sub_year$label[random_row] <- this_year
}
data_list[[i]] <- sub_year
print(this_year)
}
data <- do.call('rbind', data_list)
cols <- make_colors(length(unique(data$decade)), bw = TRUE)
# version 11
g1 <- ggplot(data,
aes(x = x,
y = y,
color = decades)) +
ylim(c(-7, 20)) +
geom_point(size = 2) +
# geom_dl(data = subset(data, decades == 1890),
# aes(label = decades), method = 'last.qp', hjust = 1) +
# geom_dl(data = subset(data, decades == 1950),
# aes(label = decades), method = 'smart.grid') +
# geom_dl(data = subset(data, decades == 2010),
# aes(label = decades), method = 'last.qp') +
geom_text_repel(aes(label = label),segment.size = 0) +
scale_color_gradient(name = 'Year', low = "#2C2C2C", high = "#ABABAB") +
labs(x = '',
y = '',
title = 'Figure 2.',
subtitle = 'Centered by Court Topic, Averaged by Court-Year, Labeled by Decade',
caption = 'Court Decade, SC & CC Court Decade Vector, Demeaned by Circuit and Big Topic') +
theme_sfi(lp = 'bottom',
lkw = TRUE,
lkt = 'point',
legend_width = 40,
title_style = 'bold') +
theme(axis.text=element_text(size = 10))
g1
ggsave("image_files/Chen_Ash_Figure_2.eps", width = 6, height = 6, device=cairo_ps, fallback_resolution = 2000)
# Chen and Ash 3
#
# # get data
data <- all_data$chen$f3
names(data) <- gsub('-', '.', names(data), fixed = TRUE)
data <- data[data$big.issue != 'Others',]
data <- data[data$big.issue != '9',]
data$big.issue <- as.factor(data$big.issue)
JY_levels <- levels(data$big.issue)
JY_labels <- c('1 - Criminal Appeal', '2 - Civil Rights', '3 - First Amendment','4 - Due Process',
'5 - Privacy', '6 - Labor','7 - Regulation')
JY_labels_n <- c('1', '2', '3','4','5', '6','7')
data$big.issue.n <- data$big.issue
data$big.issue <- factor(data$big.issue, levels=JY_levels, labels=JY_labels)
data$big.issue.n <- factor(data$big.issue.n, levels=JY_levels, labels=JY_labels_n)
# # remove 9 and others
# data <- data %>% filter(`big-issue` != '9') %>%
# filter(`big-issue` != 'Others')
#
# Y_labels <- c('1 - Criminal Appeal', '2 - Civil Rights', '3 - First Amendment','4 - Due Process',
# '5 - Privacy', '6 - Labor','7 - Regulation','9 - Miscellaneous', 'Others')
# JY_labels_n <- c('1', '2', '3','4','5', '6','7','9', 'Others')
#
#
# version 11
g1 <- ggplot(data,
aes(x = x,
y = y)) +
geom_point(size = 2,
color = 'black',
alpha = 0.4) +
scale_color_manual(name = 'Confidence elipse',
values = c('darkgrey','darkgrey', 'darkgrey','darkgrey','darkgrey','darkgrey',
'darkgrey','darkgrey','darkgrey','darkgrey','darkgrey','darkgrey','darkgrey')) +
labs(x = '',
y = '',
title = 'Figure 3.',
subtitle = 'Centered by Judge-Year, Averaged by Topic-Year, Labeled by Topic',
caption = paste0('Big Topics and Year, SC and CC Topic Year Vector, Demeaned by Judge & Year')) +
annotate(geom = 'text',
x = 0,
y = -50,
family = 'CMU Bright',
label = 'Regulation') +
annotate(geom = 'text',
family = 'CMU Bright',
x = 0,
y = 50,
label = 'Criminal Appeal') +
annotate(geom = 'text',
family = 'CMU Bright',
x = -38,
y = -19,
label = '1st Amendment') +
annotate(geom = 'text',
family = 'CMU Bright',
x = -40,
y = 28,
label = 'Civil Rights') +
annotate(geom = 'text',
family = 'CMU Bright',
x = 17,
y = 31,
label = 'Privacy') +
annotate(geom = 'text',
family = 'CMU Bright',
x = 38,
y = 13,
label = 'Labor') +
annotate(geom = 'text',
family = 'CMU Bright',
x = 30,
y = -2,
label = 'Due Process') +
theme_sfi(lp = 'none',
title_style = 'bold') +
theme(axis.text=element_text(size = 10, hjust = 1))
g1
ggsave("image_files/Chen_Ash_Figure_3.eps", width = 6, height = 6, device=cairo_ps, fallback_resolution = 2000)
# Chen and Ash 4
# get data
data <- all_data$chen$f4
# recode party affiliation
data <- data[data$party %in% c(1,0),]
data$party <- ifelse(data$party == 0, 'R', 'D')
#
# version 11
g1 <- ggplot(data,
aes(x = x,
y = y,
fill = party)) +
geom_point(size = 2,
alpha = 0.7,
pch = 21,
color = 'black') +
scale_fill_manual(name = '',
values = c('black', 'white')) +
labs(x = '',
y = '',
title = '',
subtitle = '',
caption = '') +
theme_sfi(lp = 'bottom',
title_style = 'bold') +
theme(axis.text=element_text(size = 10, hjust = 1)) + guides(col = FALSE)
g1
ggsave("image_files/Chen_Ash_Figure_4.eps", width = 6, height = 6, device=cairo_ps, fallback_resolution = 2000)
# figure 5 (use figure 4 data)
data <- all_data$chen$f1
# version 11
g1 <- ggplot(data,
aes(x = x,
y = y,
group = cohort,
shape = cohort)) +
geom_point(size = 2,
alpha = 0.7) +
scale_shape_manual(name = '',
breaks = c('1910s', '1920s', '1930s', '1940s', '1950s'),
values = c(0, 1, 2, 15, 16)) +
labs(x = '',
y = '',
title = paste0(''),
caption = paste0('')) +
theme_sfi(lp = 'right') +
theme(axis.text=element_text(size = 10, hjust = 1),
plot.title = element_text(size =12),
legend.text = element_text(size = 10))
g1
ggsave("image_files/Chen_Ash_Figure_5.eps", width = 8, height = 6, device=cairo_ps, fallback_resolution = 2000)
# figure 6 (use figure 4 data)
data <- all_data$chen$f1
unique(data$law_sch)[grepl('Texas', unique(data$law_sch))]
law_schools <-'University of Texas School of Law|University of Michigan Law School|University of Pennsylvania Law School|Columbia Law School|Harvard Law School|Stanford Law School|Yale Law School|University of Chicago Law School|University of Virginia School of Law'
# subset data by law school
dat_law <- data[grepl(law_schools, data$law_sch),]
# version 11
g1 <- ggplot(dat_law,
aes(x = x,
y = y,
group = law_sch,
shape = law_sch)) +
geom_point(size = 2,
alpha = 0.7) +
scale_shape_manual(name = '',
values = c(0, 1, 2, 3, 5, 6, 15, 16, 17)) +
labs(x = '',
y = '',
title = paste0(''),
caption = paste0('')) +
theme_sfi(lp = 'right') +
theme(axis.text=element_text(size = 10, hjust = 1),
plot.title = element_text(size =12),
legend.text = element_text(size = 10))
g1
ggsave("image_files/Chen_Ash_Figure_6.eps", width = 9, height = 6, device=cairo_ps, fallback_resolution = 2000)
databrew/sfi documentation built on May 29, 2019, 1:52 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# Packages
library(sfi)
library(kmodR)
library(directlabels)
library(webshot)
library(ggplot2)
library(dplyr)
library(ggrepel)
library(plotly)
library(ggiraph)
library(scales)
library(tidyverse)
library(knitr)
library(Hmisc)
library(RColorBrewer)
library(extrafont)
library(kableExtra)
library(grid)
# webshot::install_phantomjs()
loadfonts()
## Global options
# # get data
data <- all_data$chen$f1
# get label numbers
data$text_label <- unlist(lapply(strsplit(data$circuit, '-', fixed = TRUE), function(x) x[length(x)]))
# remove 11 with FC and SC with 12, and 0 with dc
data$text_label <- gsub('^0$', 'DC', data$text_label)
data$text_label <- gsub('12', 'FC', data$text_label)
# randomly sample half of the data set for clarity
keep_index <- sample(1:nrow(data), nrow(data)/2)
data <- data[keep_index, ]
invisible(temp <- kmod(data[, c('x', 'y')], k = 14, l = 200, i_max = 300, conv_method = "delta_C",
conv_error = 0, allow_empty_c = TRUE))
dat_c <- as.data.frame(cbind(data, clust_dist = temp$XC_dist_sqr_assign))
outlier_index <- as.character(temp$L_index)
# create an variable that indicates outlier
dat_c$outlier <- ifelse(dat_c$clust_dist.dist_sqr > 50, 'Outlier', 'In Cluster')
dat_c$clust_dist.c <- as.factor(as.character(dat_c$clust_dist.c))
# # recode SC and FC as in cluster
dat_2 <- dat_c
dat_2$outlier <- as.factor(ifelse(dat_2$text_label == 'SC', 'In Cluster',
ifelse(dat_2$text_label == 'FC', 'In Cluster',
ifelse(dat_2$text_label == 'DC', 'In Cluster', dat_2$outlier))))
# version 11
g1 <- ggplot(dat_c,
aes(x = x,
y = y,
group = clust_dist.c,
shape = clust_dist.c,
color = outlier)) +
geom_point(size = 1,
alpha = 0.8) +
scale_color_manual(name = '',
values = c('black', 'grey')) +
scale_shape_manual(name = 'Cluster group',
breaks = sort(c('1', '10', '11', '12', '13', '14', '2', '3', '4', '5','6', '7', '8', '9')),
values = c(14, 20, 8, 19, 12, 1, 2, 3, 5, 6, 7, 10, 15, 17)) +
labs(x = '',
y = '',
title = paste0(''),
caption = paste0('Point shape represents belonging to a cluster' , '\n', '(K nearest neighbors clustering)')) +
theme_sfi(lp = '',
title_style = 'bold') +
theme(axis.text=element_text(size = 10, hjust = 1),
plot.title = element_text(size =12)) +
annotate(geom = 'text',
label = 'SC',
alpha = 0.8,
x = -28,
y = 42,
family = c("Computer modern"),
size =6) +
annotate(geom = 'text',
label = 'FC',
alpha = 0.8,
x = -32,
y = 30,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = 'DC',
alpha = 0.8,
x = -16,
y = 30,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '9',
alpha = 0.8,
x = -23,
y = 9,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '5',
alpha = 0.8,
x = -7,
y = -35.5,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '8',
alpha = 0.8,
x = 16,
y = -38,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '6',
alpha = 0.8,
x = 26,
y = 8,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '10',
alpha = 0.8,
x = 23,
y = -15,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '4',
alpha = 0.8,
x = 3,
y = -13,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '7',
alpha = 0.8,
x = 13,
y = 18,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '2',
x = 10,
y = 40,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '3',
alpha = 0.8,
x = -6,
y = 30,
family = 'CMU Bright',
size =6)
g1
ggsave("image_files/Chen_Ash_Figure_1_alt.eps", width = 10, height = 10, device=cairo_ps, fallback_resolution = 2000)
# version 11
g1 <- ggplot(dat_2,
aes(x = x,
y = y,
group = clust_dist.c,
shape = clust_dist.c,
color = outlier)) +
geom_point(size = 1,
alpha = 0.8) +
scale_color_manual(name = '',
values = c('black', 'grey')) +
scale_shape_manual(name = 'Cluster group',
breaks = sort(c('1', '10', '11', '12', '13', '14', '2', '3', '4', '5','6', '7', '8', '9')),
values = c(14, 20, 8, 19, 12, 1, 2, 3, 5, 6, 7, 10, 15, 17)) +
labs(x = '',
y = '',
title = paste0(''),
caption = paste0('')) +
theme_sfi(lp = 'none',
title_style = 'bold') +
theme(axis.text=element_text(size = 10, hjust = 1),
plot.title = element_text(size =12)) +
annotate(geom = 'text',
label = 'SC',
alpha = 0.8,
x = -28,
y = 42,
family = c("Computer modern"),
size = 6) +
annotate(geom = 'text',
label = 'FC',
alpha = 0.8,
x = -32,
y = 30,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = 'DC',
alpha = 0.8,
x = -16,
y = 30,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '9',
alpha = 0.8,
x = -23,
y = 9,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '5',
alpha = 0.8,
x = -7,
y = -35.5,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '8',
alpha = 0.8,
x = 16,
y = -38,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '6',
alpha = 0.8,
x = 26,
y = 8,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '10',
alpha = 0.8,
x = 23,
y = -15,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '4',
alpha = 0.8,
x = 3,
y = -13,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '7',
alpha = 0.8,
x = 13,
y = 18,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '2',
x = 10,
y = 40,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '3',
alpha = 0.8,
x = -6,
y = 30,
family = 'CMU Bright',
size =6)
g1
ggsave("image_files/Chen_Ash_Figure_1.eps", width = 10, height = 10, device=cairo_ps, fallback_resolution = 2000)
# Chen and Ash 1
### K-means clustering with outlier detection
# # no scientific notation
# options(scipen = '999')
#
# # get data
data <- all_data$chen$f1
# get label numbers
data$text_label <- unlist(lapply(strsplit(data$circuit, '-', fixed = TRUE), function(x) x[length(x)]))
# remove 11 with FC and SC with 12, and 0 with dc
data$text_label <- gsub('^0$', 'DC', data$text_label)
data$text_label <- gsub('12', 'FC', data$text_label)
# # Compute and plot wss for k = 2 to k = 15.
# length(unique(data$text_label))
# dat <- data[, c('x', 'y')]
# k.max <- 14
# wss <- sapply(1:k.max,
# function(k){kmeans(dat, k, nstart=50,iter.max = 15 )$tot.withinss})
# # wss
# plot(1:k.max, wss,
# type="b", pch = 19, frame = FALSE,
# xlab="Number of clusters K",
# ylab="Total within-clusters sum of squares")
#
#
# # K-Means Cluster Analysis
# fit <- kmeans(dat, 13) # 5 cluster solution
# # get cluster means
# aggregate(dat,by=list(fit$cluster),FUN=mean)
# # append cluster assignment
# dat_clust <- data.frame(dat, fit$cluster)
temp <- kmod(data[, c('x', 'y')], k = 13, l = 200, i_max = 300, conv_method = "delta_C",
conv_error = 0, allow_empty_c = TRUE)
dat_c <- as.data.frame(cbind(data, clust_dist = temp$XC_dist_sqr_assign))
outlier_index <- as.character(temp$L_index)
# create an variable that indicates outlier
dat_c$outlier <- ifelse(dat_c$clust_dist.dist_sqr > 50, 'Outlier', 'In Cluster')
dat_c$clust_dist.c <- as.factor(as.character(dat_c$clust_dist.c))
# recode SC and FC as in cluster
dat_c$outlier <- ifelse(dat_c$text_label == 'SC', 'In Cluster',
ifelse(dat_c$text_label == 'FC', 'In Cluster',
ifelse(dat_c$text_label == 'DC', 'In Cluster', dat_c$outlier)))
# find outliers based on
# version 11
g1 <- ggplot(dat_c,
aes(x = x,
y = y,
group = clust_dist.c,
color = outlier)) +
geom_point(size = 1,
alpha = 0.8) +
labs(x = '',
y = '',
title = paste0('Figure 1: Centered by Topic-Year', '\n', 'Averaged by Judge, Labeled by Court'),
caption = paste0('Circuit, CC Judge Vector', '\n',
'Demeaned by Year and Big Topic')) +
scale_color_manual(name = '',
values = c('black', 'grey'))+
theme_sfi(lp = 'bottom',
title_style = 'bold') +
theme(axis.text=element_text(size = 10, hjust = 1, family = 'Computer modern'),
plot.title = element_text(size =12)) +
annotate(geom = 'text',
label = 'SC',
alpha = 0.8,
x = -28,
y = 42,
family = c("Computer modern"),
size =6) +
annotate(geom = 'text',
label = 'FC',
alpha = 0.8,
x = -32,
y = 30,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = 'DC',
alpha = 0.8,
x = -16,
y = 30,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '9',
alpha = 0.8,
x = -23,
y = 9,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '5',
alpha = 0.8,
x = -7,
y = -35.5,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '8',
alpha = 0.8,
x = 16,
y = -40,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '6',
alpha = 0.8,
x = 26,
y = 8,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '10',
alpha = 0.8,
x = 27,
y = -16,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '4',
alpha = 0.8,
x = 3,
y = -13,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '7',
alpha = 0.8,
x = 13,
y = 21,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '2',
x = 10,
y = 40,
family = 'CMU Bright',
size =6) +
annotate(geom = 'text',
label = '3',
alpha = 0.8,
x = -9,
y = 30,
family = 'CMU Bright',
size =6)
g1
ggsave("image_files/Chen_Ash_Figure_1_alt2.eps", width = 10, height = 10, device=cairo_ps, fallback_resolution = 2000)
# Chen and Ash 2
# # get data
data <- all_data$chen$f2
# rename
names(data) <- c('x', 'y', 'circuit','decade', 'decades')
# remove s from decades so it can be numeric
data$decades <- gsub('s', '', data$decades, fixed = TRUE)
data$decades <- as.numeric(data$decades)
# create an empty label column
data$label <- ''
# create list to store loop results
data_list <- list()
unique_years <- unique(data$decades)
for(i in 1:length(unique_years)){
this_year <- unique_years[i]
sub_year <- data[data$decades == this_year,]
if(this_year == 1890 | this_year == 1950 | this_year == 2010) {
# randomly assign year label to one row
random_row <- sample(1:nrow(sub_year), 1)
sub_year$label[random_row] <- this_year
}
data_list[[i]] <- sub_year
print(this_year)
}
data <- do.call('rbind', data_list)
cols <- make_colors(length(unique(data$decade)), bw = TRUE)
# version 11
g1 <- ggplot(data,
aes(x = x,
y = y,
color = decades)) +
ylim(c(-7, 20)) +
geom_point(size = 2) +
# geom_dl(data = subset(data, decades == 1890),
# aes(label = decades), method = 'last.qp', hjust = 1) +
# geom_dl(data = subset(data, decades == 1950),
# aes(label = decades), method = 'smart.grid') +
# geom_dl(data = subset(data, decades == 2010),
# aes(label = decades), method = 'last.qp') +
geom_text_repel(aes(label = label),segment.size = 0) +
scale_color_gradient(name = 'Year', low = "#2C2C2C", high = "#ABABAB") +
labs(x = '',
y = '',
title = 'Figure 2.',
subtitle = 'Centered by Court Topic, Averaged by Court-Year, Labeled by Decade',
caption = 'Court Decade, SC & CC Court Decade Vector, Demeaned by Circuit and Big Topic') +
theme_sfi(lp = 'bottom',
lkw = TRUE,
lkt = 'point',
legend_width = 40,
title_style = 'bold') +
theme(axis.text=element_text(size = 10))
g1
ggsave("image_files/Chen_Ash_Figure_2.eps", width = 6, height = 6, device=cairo_ps, fallback_resolution = 2000)
# Chen and Ash 3
#
# # get data
data <- all_data$chen$f3
names(data) <- gsub('-', '.', names(data), fixed = TRUE)
data <- data[data$big.issue != 'Others',]
data <- data[data$big.issue != '9',]
data$big.issue <- as.factor(data$big.issue)
JY_levels <- levels(data$big.issue)
JY_labels <- c('1 - Criminal Appeal', '2 - Civil Rights', '3 - First Amendment','4 - Due Process',
'5 - Privacy', '6 - Labor','7 - Regulation')
JY_labels_n <- c('1', '2', '3','4','5', '6','7')
data$big.issue.n <- data$big.issue
data$big.issue <- factor(data$big.issue, levels=JY_levels, labels=JY_labels)
data$big.issue.n <- factor(data$big.issue.n, levels=JY_levels, labels=JY_labels_n)
# # remove 9 and others
# data <- data %>% filter(`big-issue` != '9') %>%
# filter(`big-issue` != 'Others')
#
# Y_labels <- c('1 - Criminal Appeal', '2 - Civil Rights', '3 - First Amendment','4 - Due Process',
# '5 - Privacy', '6 - Labor','7 - Regulation','9 - Miscellaneous', 'Others')
# JY_labels_n <- c('1', '2', '3','4','5', '6','7','9', 'Others')
#
#
# version 11
g1 <- ggplot(data,
aes(x = x,
y = y)) +
geom_point(size = 2,
color = 'black',
alpha = 0.4) +
scale_color_manual(name = 'Confidence elipse',
values = c('darkgrey','darkgrey', 'darkgrey','darkgrey','darkgrey','darkgrey',
'darkgrey','darkgrey','darkgrey','darkgrey','darkgrey','darkgrey','darkgrey')) +
labs(x = '',
y = '',
title = 'Figure 3.',
subtitle = 'Centered by Judge-Year, Averaged by Topic-Year, Labeled by Topic',
caption = paste0('Big Topics and Year, SC and CC Topic Year Vector, Demeaned by Judge & Year')) +
annotate(geom = 'text',
x = 0,
y = -50,
family = 'CMU Bright',
label = 'Regulation') +
annotate(geom = 'text',
family = 'CMU Bright',
x = 0,
y = 50,
label = 'Criminal Appeal') +
annotate(geom = 'text',
family = 'CMU Bright',
x = -38,
y = -19,
label = '1st Amendment') +
annotate(geom = 'text',
family = 'CMU Bright',
x = -40,
y = 28,
label = 'Civil Rights') +
annotate(geom = 'text',
family = 'CMU Bright',
x = 17,
y = 31,
label = 'Privacy') +
annotate(geom = 'text',
family = 'CMU Bright',
x = 38,
y = 13,
label = 'Labor') +
annotate(geom = 'text',
family = 'CMU Bright',
x = 30,
y = -2,
label = 'Due Process') +
theme_sfi(lp = 'none',
title_style = 'bold') +
theme(axis.text=element_text(size = 10, hjust = 1))
g1
ggsave("image_files/Chen_Ash_Figure_3.eps", width = 6, height = 6, device=cairo_ps, fallback_resolution = 2000)
# Chen and Ash 4
# get data
data <- all_data$chen$f4
# recode party affiliation
data <- data[data$party %in% c(1,0),]
data$party <- ifelse(data$party == 0, 'R', 'D')
#
# version 11
g1 <- ggplot(data,
aes(x = x,
y = y,
fill = party)) +
geom_point(size = 2,
alpha = 0.7,
pch = 21,
color = 'black') +
scale_fill_manual(name = '',
values = c('black', 'white')) +
labs(x = '',
y = '',
title = '',
subtitle = '',
caption = '') +
theme_sfi(lp = 'bottom',
title_style = 'bold') +
theme(axis.text=element_text(size = 10, hjust = 1)) + guides(col = FALSE)
g1
ggsave("image_files/Chen_Ash_Figure_4.eps", width = 6, height = 6, device=cairo_ps, fallback_resolution = 2000)
# figure 5 (use figure 4 data)
data <- all_data$chen$f1
# version 11
g1 <- ggplot(data,
aes(x = x,
y = y,
group = cohort,
shape = cohort)) +
geom_point(size = 2,
alpha = 0.7) +
scale_shape_manual(name = '',
breaks = c('1910s', '1920s', '1930s', '1940s', '1950s'),
values = c(0, 1, 2, 15, 16)) +
labs(x = '',
y = '',
title = paste0(''),
caption = paste0('')) +
theme_sfi(lp = 'right') +
theme(axis.text=element_text(size = 10, hjust = 1),
plot.title = element_text(size =12),
legend.text = element_text(size = 10))
g1
ggsave("image_files/Chen_Ash_Figure_5.eps", width = 8, height = 6, device=cairo_ps, fallback_resolution = 2000)
# figure 6 (use figure 4 data)
data <- all_data$chen$f1
unique(data$law_sch)[grepl('Texas', unique(data$law_sch))]
law_schools <-'University of Texas School of Law|University of Michigan Law School|University of Pennsylvania Law School|Columbia Law School|Harvard Law School|Stanford Law School|Yale Law School|University of Chicago Law School|University of Virginia School of Law'
# subset data by law school
dat_law <- data[grepl(law_schools, data$law_sch),]
# version 11
g1 <- ggplot(dat_law,
aes(x = x,
y = y,
group = law_sch,
shape = law_sch)) +
geom_point(size = 2,
alpha = 0.7) +
scale_shape_manual(name = '',
values = c(0, 1, 2, 3, 5, 6, 15, 16, 17)) +
labs(x = '',
y = '',
title = paste0(''),
caption = paste0('')) +
theme_sfi(lp = 'right') +
theme(axis.text=element_text(size = 10, hjust = 1),
plot.title = element_text(size =12),
legend.text = element_text(size = 10))
g1
ggsave("image_files/Chen_Ash_Figure_6.eps", width = 9, height = 6, device=cairo_ps, fallback_resolution = 2000)
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