R/final_figures_eps/frankenreiter_eps.R
In databrew/sfi:
# Packages
library(sfi)
library(webshot)
library(ggplot2)
library(dplyr)
library(plotly)
library(ggiraph)
library(scales)
library(tidyverse)
library(directlabels)
library(knitr)
library(Hmisc)
library(gridExtra)
library(RColorBrewer)
library(extrafont)
library(kableExtra)
library(grid)
# webshot::install_phantomjs()
loadfonts()
#### This markdown is for Frankenreiter figures.
# Frankenreiter 2 (version 1)
# # Get data
# data <- all_data$frankenreiter$f2
# data <- data %>%
# tidyr::gather(key = key,
# value = value,
# number_tokens: mean_sent_length)
#
# # Relabel
# key_dict <-
# data.frame(key = c("ave_syllabels",
# "mean_sent_length",
# "number_tokens",
# "perc_differentwords2"),
# new_key = c('Mean syllables per word',
# 'Mean sentence length',
# 'Tokens per opinion',
# 'Type-token ratio'))
# data <- left_join(data, key_dict, by= 'key')
#
#
# date_breaks <- as.Date(paste0(seq(1950,
# 2010,
# by = 10),
# '-01-01'))
# date_labels <- as.character(seq(1950,
# 2010,
# by = 10))
#
# g1 <- ggplot(data = data,
# aes(x = docdate,
# y = value)) +
# geom_point(size = 1,
# alpha = 1,
# color = 'black',
# pch = '.') +
# geom_smooth(se = TRUE,
# fill = 'darkgrey',
# alpha = 1,
# linetype = 0) +
# geom_vline(xintercept = date_breaks,
# alpha = 0.3) +
# facet_wrap(~new_key,
# ncol = 2,
# scales = 'free_y') +
# scale_x_date(name = 'Date',
# breaks = date_breaks,
# labels = date_labels) +
# theme_sfi(lp = 'none',
# y_axis_title_style = 'bold',
# x_axis_title_style = 'bold',
# title_style = 'bold') +
# labs(x = '',
# y = '',
# title = 'Figure 1',
# subtitle = paste0('Development over time of four measures of style over time.',
# '\n', 'Each opinion is represented by one point per panel'),
# caption = '*Lines smoothed using local regression') +
# theme(axis.text=element_text(size = 10, hjust = 1))
#
# g1
# Frankenreiter 2 (version 2)
# Get data
data <- all_data$frankenreiter$f2
data <- data %>%
tidyr::gather(key = key,
value = value,
number_tokens: mean_sent_length)
# Relabel
key_dict <-
data.frame(key = c("ave_syllabels",
"mean_sent_length",
"number_tokens",
"perc_differentwords2"),
new_key = c('Mean syllables per word',
'Mean sentence length',
'Tokens per opinion',
'Type-token ratio'))
data <- left_join(data, key_dict, by= 'key')
date_breaks <- as.Date(paste0(seq(1950,
2010,
by = 10),
'-01-01'))
date_labels <- as.character(seq(1950,
2010,
by = 10))
x <- data %>%
mutate(year = as.numeric(format(docdate, '%Y'))) %>%
mutate(val = value) %>%
group_by(year, new_key) %>%
summarise(value = mean(value, na.rm = TRUE),
p25 = quantile(val, na.rm = TRUE, 0.25),
p75 = quantile(val, na.rm = TRUE, 0.75)) %>%
ungroup %>%
mutate(year = as.Date(paste0(year, '-01-01')))
g2 <-
ggplot(data = x,
aes(x = year,
y = value)) +
geom_ribbon(aes(x = year,
ymin = p25,
ymax = p75),
alpha = 0.6) +
geom_point(size = 2,
alpha = 1) +
geom_vline(xintercept = date_breaks,
alpha = 0.3) +
facet_wrap(~new_key,
ncol = 2,
scales = 'free_y') +
scale_x_date(name = 'Date',
breaks = date_breaks,
labels = date_labels) +
theme_sfi(lp = 'none',
title_style = 'bold') +
labs(x = 'Year',
y = '',
title = '',
subtitle = paste0(''),
caption = '*Std error smoothed using local regression. Showing only mean points.') +
theme(axis.text=element_text(size = 8, hjust = 1))
g2
ggsave("image_files/Frakenreiter_Figure_2.eps", width = 6, height = 7, device=cairo_ps, fallback_resolution = 1000)
# Frankenreiter 3 (version 1)
#
# # Get data
# data <- all_data$frankenreiter$f3
#
# g1 <- ggplot(data, aes(diff, similarity)) +
# geom_point(size = 1,
# alpha = 0.9,
# color = 'black') +
# geom_smooth(method = 'loess',
# linetype = 0,
# fill = 'black',
# alpha = 0.3) +
# theme_sfi(lp = 'none',
# y_axis_title_style = 'bold',
# x_axis_title_style = 'bold',
# title_style = 'bold') +
# labs(x = '',
# y = '',
# title = 'Figure 3 Similarity and temporal distance',
# caption = '*Std error from local regression.') +
# theme(axis.text=element_text(size = 10, hjust = 1))
# g1
# Frankenreiter 3 (version 2)
#Get data
data <- all_data$frankenreiter$f3
# FILTERING OUT SOME DATA TO MATCH THEIRS
data <- data %>%
filter(!(similarity < 0.3 & diff < 25)) %>%
filter(diff <= 57)
liney <- data %>%
group_by(diff) %>%
summarise(similarity = mean(similarity, na.rm = TRUE)) %>%
ungroup
spiney <- data %>%
mutate(diff = round(diff, digits = -1)) %>%
group_by(diff) %>%
summarise(similarity = mean(similarity, na.rm = TRUE)) %>%
ungroup
quanty <- data %>%
mutate(diff = round(diff, digits = -1)) %>%
group_by(diff) %>%
summarise(avg = mean(similarity, na.rm = TRUE),
med = median(similarity, na.rm = TRUE),
q75 = quantile(similarity, 0.75, na.rm = TRUE),
q25 = quantile(similarity, 0.25, na.rm = TRUE)) %>%
ungroup
g2 <- ggplot(data = data %>%
mutate(diff = round(diff, digits = -1)),
aes(x = diff,
y = similarity)) +
geom_violin(aes(group = factor(diff)),
alpha = 1,
fill = 'darkgrey',
color = NA) +
ylim(0, 1) +
geom_jitter(alpha = 0.7,
# pch = 1,
size = 1) +
theme_sfi(lp = 'none',
title_style = 'bold') +
labs(x = '',
y = '',
title = '',
subtitle = '',
caption = paste0('*Distribution represented with mirrored normal density (violin plot).', '\n', 'Data points randomly "jittered" on the X axis and grouped by every 10 years.')) +
theme(axis.text=element_text(size = 10, hjust = 1))
g2
ggsave("image_files/Frakenreiter_Figure_3.eps", width = 6, height = 7, device=cairo_ps, fallback_resolution = 1000)
# Frankenreiter 4 (version 1)
#
# # Get data
# data <- all_data$frankenreiter$f4 %>%
# filter(diff >= 0) # we are making this modification to match the vis from the paper
#
# g1 <- ggplot(data, aes(diff, similarity)) +
# geom_smooth(method = 'loess',
# linetype = 0,
# fill = 'darkgrey',
# alpha = 1) +
# geom_point(size = 1,
# alpha = 0.9,
# color = 'black') +
# theme_sfi(lp = 'none',
# y_axis_title_style = 'bold',
# x_axis_title_style = 'bold',
# title_style = 'bold') +
# labs(x = '',
# y = '',
# title = paste0('Figure 4 Similarity between different', '\n' , 'judges as a function of time'),
# caption = '*Std error from local regression.') +
# theme(axis.text=element_text(size = 10, hjust = 1))
#
#
# g1
#
# Frankenreiter 4 (version 2)
# Get data
data <- all_data$frankenreiter$f4 %>%
filter(diff >= 0) # we are making this modification to match the vis from the paper
liney <- data %>%
group_by(diff) %>%
summarise(similarity = mean(similarity, na.rm = TRUE)) %>%
ungroup
spiney <- data %>%
mutate(diff = round(diff, digits = -1)) %>%
group_by(diff) %>%
summarise(similarity = mean(similarity, na.rm = TRUE)) %>%
ungroup
quanty <- data %>%
mutate(diff = round(diff, digits = -1)) %>%
group_by(diff) %>%
summarise(avg = mean(similarity, na.rm = TRUE),
med = median(similarity, na.rm = TRUE),
q75 = quantile(similarity, 0.75, na.rm = TRUE),
q25 = quantile(similarity, 0.25, na.rm = TRUE)) %>%
ungroup
g2 <- ggplot(data = data %>%
mutate(diff = round(diff, digits = -1)),
aes(x = diff,
y = similarity)) +
geom_violin(aes(group = factor(diff)),
alpha = 1,
fill = 'darkgrey',
color = NA) +
geom_jitter(alpha = 0.4,
# pch = 1,
size = 1) +
ylim(0, 1) +
theme_sfi(lp = 'none',
title_style = 'bold') +
labs(x = '',
y = '',
title = paste0(''),
subtitle = '',
caption = paste0('*Distribution represented with mirrored normal density (violin plot).', '\n', 'Data points randomly "jittered" on the X axis and grouped by every 10 years.')) +
theme(axis.text=element_text(size = 10, hjust = 1))
g2
ggsave("image_files/Frakenreiter_Figure_4.eps", width = 6, height = 7, device=cairo_ps, fallback_resolution = 1000)
# Frankenreiter 5
## 1955 - 2014
# Get data
data <- all_data$frankenreiter$f5
# # assign Metrix MDS (1955-2014) to source 1
# # assign Metrix MDS (1970-2014) to source 2
# data$source <- ifelse(data$source == '1', 'Metrix MDS (1955-2014)',
# 'Metrix MDS (1970-2014)')
# data$source <- as.factor(data$source)
data1 <- data[data$source == 1,]
# plot the 1955 to 2014 data
cols <- make_colors(length(unique(data1$year)), bw = TRUE)
data1$year <- as.numeric(data1$year)
g1 <- ggplot(data1,
aes(x = coord2,
y = coord1,
color = year)) +
geom_point(size = 4,
pch = 16,
alpha = 0.9) +
ggrepel::geom_text_repel(data=subset(data1, year== 1981 | year ==1995 | year == 2004 | year == 2007),
aes(coord2, coord1, label=year), vjust = 2, hjust = -2) +
ggrepel::geom_text_repel(data=subset(data1, year == 1973),
aes(coord2, coord1, label=year), vjust = -1.5, hjust = 0) +
ggrepel::geom_text_repel(data=subset(data1, year == 2013),
aes(coord2, coord1, label=year), vjust = -1.5, hjust = 0) +
ggrepel::geom_text_repel(data=subset(data1, year == 1986),
aes(coord2, coord1, label=year), vjust = 1, hjust = -1) +
theme_sfi(lp = 'bottom',
title_style = 'bold',
lkw = TRUE,
lkt = 'point',
legend_width = 40) +
labs(x = 'Coordinate 2',
y = 'Coordinate 1',
title = '',
subtitle = '') +
scale_color_gradient(name = 'Year', low = "#2C2C2C", high = "#ABABAB")
g1
ggsave("image_files/Frakenreiter_Figure_5a.eps", width = 7, height = 8, device=cairo_ps, fallback_resolution = 1000)
# Frankenreiter 5
## 1970 - 2014
# Get data
data <- all_data$frankenreiter$f5
data2 <- data[data$source == 2,]
cols <- make_colors(length(unique(data2$year)), bw = TRUE)
data2$year <- as.numeric(data2$year)
g2 <- ggplot(data2,
aes(x = coord2,
y = coord1,
color = year)) +
geom_point(size = 4,
pch = 16,
alpha = 0.9) +
ggrepel::geom_text_repel(data=subset(data2, year == 1970 | year == 1973 | year== 1981),
aes(coord2, coord1, label=year), vjust = -1, hjust = 1.5) +
ggrepel::geom_text_repel(data=subset(data2,year == 2000 | year == 2004 | year == 2007 | year== 2010 | year == 2014),
aes(coord2, coord1, label=year), vjust = 1, hjust = -1.5) +
ggrepel::geom_text_repel(data=subset(data2,year == 1995),
aes(coord2, coord1, label=year), vjust = 0, hjust = -1) +
ggrepel::geom_text_repel(data=subset(data2,year == 1986 | year == 1990),
aes(coord2, coord1, label=year), vjust = 0, hjust = .5) +
theme_sfi(lp = 'bottom',
title_style = 'bold',
lkw = TRUE,
lkt = 'point',
legend_width = 30) +
labs(x = 'Coordinate 2',
y = 'Coordinate 1',
title = '',
subtitle = '') +
scale_color_gradient(name = 'Year', low = "#2C2C2C", high = "#ABABAB") +
theme(axis.text=element_text(size = 10, hjust = 1))
g2
ggsave("image_files/Frakenreiter_Figure_5b.eps", width = 6, height = 7, device=cairo_ps, fallback_resolution = 1000)
# Frankenreiter 6 1973 enlargement (version 1)
#
# # Get data
# data <- all_data$frankenreiter$f6 %>%
# mutate(year = as.numeric(year))
#
# # separate data into two datasets based on key
# data_1 <- data[data$key == '1973 enlargement',]
#
# # capitalize data
# data_1$judgetrad <- Hmisc::capitalize(data_1$judgetrad)
#
# # recode data so the new judgetrad variable indicates if the judge is new
# data_1$judgetrad <- ifelse(data_1$new == 1, paste0(data_1$judgetrad, ' New'), data_1$judgetrad)
#
# # -----------------------
# # 1973 enlargement
#
# # Version 1 with lines at end
# g1 <- ggplot(data = data_1,
# aes(x = year,
# y = kl.dist3,
# group = interaction(judgetrad,judge),
# color = judgetrad)) +
# geom_line(size = 1) +
# xlim(c(1973, 1990)) +
# geom_dl(aes(label = judge),
# method = list(dl.combine("last.points"), hjust = -0.5,
# cex = 0.6,
# dl.move('DK1', vjust = -1)), alpha = 0.8, color = 'black') +
# scale_color_manual(name = '',
# values = c('black', '#414141', '#979797', '#C6C6C6')) +
# labs(x = 'Year',
# y = '',
# title = 'Figure 6 (1973 Enlargement)',
# subtitle = 'Development of writing style of ECJ in comparison to the writing of judges
# between 1973 and 1975') +
# theme_sfi(lp = 'bottom',
# y_axis_title_style = 'bold',
# x_axis_title_style = 'bold',
# title_style = 'bold') +
# theme(axis.text=element_text(size = 10, hjust = 1))
#
# g1
#
# # Frankenreiter 6 1973 enlargement (version 2)
#
#
# # Get data
# data <- all_data$frankenreiter$f6 %>%
# mutate(year = as.numeric(year))
#
# # separate data into two datasets based on key
# data_1 <- data[data$key == '1973 enlargement',]
#
# # capitalize data
# data_1$judgetrad <- Hmisc::capitalize(data_1$judgetrad)
#
# # recode data so the new judgetrad variable indicates if the judge is new
# data_1$judgetrad <- ifelse(data_1$new == 1, paste0(data_1$judgetrad, ' New'), data_1$judgetrad)
#
# # -----------------------
# # 1973 enlargement
#
# # Version 1 with lines at end
# g1 <- ggplot(data = data_1,
# aes(x = year,
# y = kl.dist3,
# group = interaction(judgetrad,judge),
# color = judgetrad)) +
# geom_line(size = 1) +
# geom_point(size = 1,
# alpha = 0.6,
# color = 'black') +
# xlim(c(1973, 1990)) +
# geom_dl(aes(label = judge),
# method = list(dl.combine("last.points"), hjust = -0.5,
# cex = 0.6,
# dl.move('DK1', vjust = -1)), alpha = 0.8, color = 'black') +
# scale_color_manual(name = '',
# values = c('black', '#414141', '#979797', '#C6C6C6')) +
# labs(x = 'Year',
# y = '',
# title = 'Figure 6 (1973 Enlargement)',
# subtitle = 'Development of writing style of ECJ in comparison to the writing of judges
# between 1973 and 1975') +
# theme_sfi(lp = 'bottom',
# y_axis_title_style = 'bold',
# x_axis_title_style = 'bold',
# title_style = 'bold') +
# theme(axis.text=element_text(size = 10, hjust = 1))
#
# g1
#
#
# # Frankenreiter 6 1995 enlargement (version 1)
#
#
# # Get data
# data <- all_data$frankenreiter$f6 %>%
# mutate(year = as.numeric(year))
# data_2 <- data[data$key == '1995 enlargement',]
# data_2$judgetrad <- Hmisc::capitalize(data_2$judgetrad)
# data_2$judgetrad <- ifelse(data_2$new == 1, paste0(data_2$judgetrad, ' New'), data_2$judgetrad)
#
#
# # version 5 barplot
# g2 <- ggplot(data = data_2,
# aes(x = year,
# y = kl.dist3,
# fill = judgetrad)) +
# geom_point(size = 0.5,
# color = 'black',
# alpha = 0.4) +
# geom_line(size = 0.5,
# alpha = 0.8) +
# geom_dl(aes(label = judgetrad),
# method = list(dl.combine("last.points"), cex = 0.7,
# vjust = -0.3,alpha = 0.8, color = 'black',
# dl.trans(x = x - 2.8))) +
# ylim(c(0, .035)) +
# labs(x = 'Year',
# y = '',
# title = 'Figure 6 (1995 Enlargement)',
# subtitle = 'Development of writing style of ECJ in comparison to the writing of judges
# between 1973 and 1975') +
# theme_sfi(lp = 'none',
# y_axis_title_style = 'bold',
# x_axis_title_style = 'bold',
# title_style = 'bold') +
# scale_fill_manual(name = '',
# values = c('black', 'black','black',"#595959", "#9C9C9C",'#D3D0D0')) +
# theme(axis.text=element_text(size = 10, hjust = 1)) +
# facet_wrap(~judge)
#
# g2
# Frankenreiter 6 1995 enlargement (version 2)
# Get data
data <- all_data$frankenreiter$f6 %>%
mutate(year = as.numeric(year))
# recode new variable
data$new <- as.factor(ifelse(data$new == 1, 'New judges', 'Old judges'))
# separate data into two datasets based on key
data_1 <- data[data$key == '1973 enlargement',]
# capitalize data
data_1$judgetrad <- as.factor(Hmisc::capitalize(data_1$judgetrad))
# keep only last name
data_1$judgename <- gsub('Mertens de Wilmars', 'de Wilmars', data_1$judgename)
data_1$judgename <- gsub('Mackenzie Stuart', 'Stuart', data_1$judgename)
# correct variable 'new' levles
data_1$new <- factor(data_1$new, levels = c('Old judges', 'New judges'))
data_1$judgetrad<- factor(data_1$judgetrad, levels = c('Roman', 'German','Nordic', 'Commonlaw'))
#
# # recode data so the new judgetrad variable indicates if the judge is new
# data_1$judgetrad <- ifelse(data_1$new == 1, paste0(data_1$judgetrad, ' New'), data_1$judgetrad)
# -----------------------
# 1973 enlargement
# Version 1 with lines at end
g1 <- ggplot(data = data_1,
aes(x = year,
y = kl.dist3,
group = interaction(judgetrad,judgename),
color = judgetrad)) +
geom_line(size = 1.5) +
geom_point(size = 1,
alpha = 1,
color = 'black') +
xlim(c(1973, 1992)) +
geom_dl(aes(label = judgename),
method = list(dl.combine("last.points"), cex = 1,hjust = -0.1, fontface = 'bold',
dl.move('Donner', cex = 1,vjust = -0.3)),
alpha = 1, color = 'black') +
scale_color_manual(name = '',
breaks = c('Roman', 'German', 'Nordic', 'Commonlaw'),
values = c('#000000', '#434343', '#7F7F7F', '#ABABAB')) +
labs(x = 'Year',
y = '',
title = '',
subtitle = '') +
theme_sfi(lp = 'bottom',
title_style = 'bold') +
theme(axis.text=element_text(size = 10, hjust = 1)) +
facet_wrap(~new)
g1
ggsave("image_files/Frakenreiter_Figure_6a.eps", width = 9, height = 7, device=cairo_ps, fallback_resolution = 600)
# Get data
data <- all_data$frankenreiter$f6 %>%
mutate(year = as.numeric(year))
# recode new variable
data$new <- ifelse(data$new == 1, 'New judges', 'Old judges')
data_2 <- data[data$key == '1995 enlargement',]
data_2$judgetrad <- Hmisc::capitalize(data_2$judgetrad)
# correct variable 'new' levles
data_2$new <- factor(data_2$new, levels = c('Old judges', 'New judges'))
data_2$judgetrad<- factor(data_2$judgetrad, levels = c('Roman', 'German','Nordic', 'Commonlaw'))
data_2$judgename <- gsub('Moitinho de Almeida', 'de Almeida', data_2$judgename)
data_2$judgename <- gsub('Rodriguez Iglesias', 'Iglesias', data_2$judgename)
data_2$judge_label <- ''
data_list <- list()
unique_names <- unique(data_2$judgename)
for(i in 1:length(unique_names)) {
this_name <- unique_names[i]
sub_dat <- data_2[data_2$judgename == this_name,]
sub_dat <- sub_dat[order(sub_dat$kl.dist3, decreasing = TRUE),]
sub_dat$judge_label[i] <- this_name
data_list[[i]] <- sub_dat
print(this_name)
}
data_2 <- do.call('rbind', data_list)
# judge names: Wathelet, Puissochet, Murray, de Almeida, Edward
# data_2$judgetrad <- ifelse(data_2$new == 1, paste0(data_2$judgetrad, ' New'), data_2$judgetrad)
library(ggrepel)
g3 <- ggplot(data = data_2,
aes(x = year,
y = kl.dist3,
group = interaction(judgetrad,judgename),
color = judgetrad)) +
ylim(c(0, .03)) +
geom_point(size = 1,
color = 'black',
alpha = 1) +
geom_line(size = 1) +
geom_dl(data = data_2[data_2$new == 'New judges',],
aes(label = judgename),
method = list(dl.combine("last.points"), hjust = -0.1, cex = 1,
dl.move('Ragnemalm', hjust = 0.1, vjust = -0.4, cex = 1),
dl.move('Wathelet', hjust = 2, vjust = -0.4, cex = 1)),
alpha = 1, color = 'black') +
geom_dl(data = data_2 %>% filter(judgename %in% c('Murray','Wathelet', 'Edward')),
aes(label = judgename),
method = list(dl.combine("first.points"), hjust = 1.1, cex = 1),
alpha = 1, color = 'black') +
geom_dl(data = data_2 %>% filter(judgename %in% c('Hirsch','Mancini', 'Iglesias', 'de Almeida','Kakouris', 'Kapteyn', 'Gulmann', 'Puissochet')),
aes(label = judgename),
method = list(dl.combine("last.points"), hjust = -0.1, cex = 1),
alpha = 1, color = 'black') +
scale_color_manual(name = '',
breaks = c('Roman', 'German', 'Nordic', 'Commonlaw'),
values = c('#000000', '#434343', '#7F7F7F', '#ABABAB')) +
xlim(c(1990, 2015)) +
labs(x = 'Year',
y = '',
title = '',
subtitle = '') +
theme_sfi(lp = 'bottom',
title_style = 'bold') +
theme(axis.text=element_text(size = 9, hjust = 1)) +
facet_wrap(~new)
g3
ggsave("image_files/Frakenreiter_Figure_6b.eps", width = 10, height = 10, device=cairo_ps, fallback_resolution = 2000)
databrew/sfi documentation built on May 29, 2019, 1:52 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# Packages
library(sfi)
library(webshot)
library(ggplot2)
library(dplyr)
library(plotly)
library(ggiraph)
library(scales)
library(tidyverse)
library(directlabels)
library(knitr)
library(Hmisc)
library(gridExtra)
library(RColorBrewer)
library(extrafont)
library(kableExtra)
library(grid)
# webshot::install_phantomjs()
loadfonts()
#### This markdown is for Frankenreiter figures.
# Frankenreiter 2 (version 1)
# # Get data
# data <- all_data$frankenreiter$f2
# data <- data %>%
# tidyr::gather(key = key,
# value = value,
# number_tokens: mean_sent_length)
#
# # Relabel
# key_dict <-
# data.frame(key = c("ave_syllabels",
# "mean_sent_length",
# "number_tokens",
# "perc_differentwords2"),
# new_key = c('Mean syllables per word',
# 'Mean sentence length',
# 'Tokens per opinion',
# 'Type-token ratio'))
# data <- left_join(data, key_dict, by= 'key')
#
#
# date_breaks <- as.Date(paste0(seq(1950,
# 2010,
# by = 10),
# '-01-01'))
# date_labels <- as.character(seq(1950,
# 2010,
# by = 10))
#
# g1 <- ggplot(data = data,
# aes(x = docdate,
# y = value)) +
# geom_point(size = 1,
# alpha = 1,
# color = 'black',
# pch = '.') +
# geom_smooth(se = TRUE,
# fill = 'darkgrey',
# alpha = 1,
# linetype = 0) +
# geom_vline(xintercept = date_breaks,
# alpha = 0.3) +
# facet_wrap(~new_key,
# ncol = 2,
# scales = 'free_y') +
# scale_x_date(name = 'Date',
# breaks = date_breaks,
# labels = date_labels) +
# theme_sfi(lp = 'none',
# y_axis_title_style = 'bold',
# x_axis_title_style = 'bold',
# title_style = 'bold') +
# labs(x = '',
# y = '',
# title = 'Figure 1',
# subtitle = paste0('Development over time of four measures of style over time.',
# '\n', 'Each opinion is represented by one point per panel'),
# caption = '*Lines smoothed using local regression') +
# theme(axis.text=element_text(size = 10, hjust = 1))
#
# g1
# Frankenreiter 2 (version 2)
# Get data
data <- all_data$frankenreiter$f2
data <- data %>%
tidyr::gather(key = key,
value = value,
number_tokens: mean_sent_length)
# Relabel
key_dict <-
data.frame(key = c("ave_syllabels",
"mean_sent_length",
"number_tokens",
"perc_differentwords2"),
new_key = c('Mean syllables per word',
'Mean sentence length',
'Tokens per opinion',
'Type-token ratio'))
data <- left_join(data, key_dict, by= 'key')
date_breaks <- as.Date(paste0(seq(1950,
2010,
by = 10),
'-01-01'))
date_labels <- as.character(seq(1950,
2010,
by = 10))
x <- data %>%
mutate(year = as.numeric(format(docdate, '%Y'))) %>%
mutate(val = value) %>%
group_by(year, new_key) %>%
summarise(value = mean(value, na.rm = TRUE),
p25 = quantile(val, na.rm = TRUE, 0.25),
p75 = quantile(val, na.rm = TRUE, 0.75)) %>%
ungroup %>%
mutate(year = as.Date(paste0(year, '-01-01')))
g2 <-
ggplot(data = x,
aes(x = year,
y = value)) +
geom_ribbon(aes(x = year,
ymin = p25,
ymax = p75),
alpha = 0.6) +
geom_point(size = 2,
alpha = 1) +
geom_vline(xintercept = date_breaks,
alpha = 0.3) +
facet_wrap(~new_key,
ncol = 2,
scales = 'free_y') +
scale_x_date(name = 'Date',
breaks = date_breaks,
labels = date_labels) +
theme_sfi(lp = 'none',
title_style = 'bold') +
labs(x = 'Year',
y = '',
title = '',
subtitle = paste0(''),
caption = '*Std error smoothed using local regression. Showing only mean points.') +
theme(axis.text=element_text(size = 8, hjust = 1))
g2
ggsave("image_files/Frakenreiter_Figure_2.eps", width = 6, height = 7, device=cairo_ps, fallback_resolution = 1000)
# Frankenreiter 3 (version 1)
#
# # Get data
# data <- all_data$frankenreiter$f3
#
# g1 <- ggplot(data, aes(diff, similarity)) +
# geom_point(size = 1,
# alpha = 0.9,
# color = 'black') +
# geom_smooth(method = 'loess',
# linetype = 0,
# fill = 'black',
# alpha = 0.3) +
# theme_sfi(lp = 'none',
# y_axis_title_style = 'bold',
# x_axis_title_style = 'bold',
# title_style = 'bold') +
# labs(x = '',
# y = '',
# title = 'Figure 3 Similarity and temporal distance',
# caption = '*Std error from local regression.') +
# theme(axis.text=element_text(size = 10, hjust = 1))
# g1
# Frankenreiter 3 (version 2)
#Get data
data <- all_data$frankenreiter$f3
# FILTERING OUT SOME DATA TO MATCH THEIRS
data <- data %>%
filter(!(similarity < 0.3 & diff < 25)) %>%
filter(diff <= 57)
liney <- data %>%
group_by(diff) %>%
summarise(similarity = mean(similarity, na.rm = TRUE)) %>%
ungroup
spiney <- data %>%
mutate(diff = round(diff, digits = -1)) %>%
group_by(diff) %>%
summarise(similarity = mean(similarity, na.rm = TRUE)) %>%
ungroup
quanty <- data %>%
mutate(diff = round(diff, digits = -1)) %>%
group_by(diff) %>%
summarise(avg = mean(similarity, na.rm = TRUE),
med = median(similarity, na.rm = TRUE),
q75 = quantile(similarity, 0.75, na.rm = TRUE),
q25 = quantile(similarity, 0.25, na.rm = TRUE)) %>%
ungroup
g2 <- ggplot(data = data %>%
mutate(diff = round(diff, digits = -1)),
aes(x = diff,
y = similarity)) +
geom_violin(aes(group = factor(diff)),
alpha = 1,
fill = 'darkgrey',
color = NA) +
ylim(0, 1) +
geom_jitter(alpha = 0.7,
# pch = 1,
size = 1) +
theme_sfi(lp = 'none',
title_style = 'bold') +
labs(x = '',
y = '',
title = '',
subtitle = '',
caption = paste0('*Distribution represented with mirrored normal density (violin plot).', '\n', 'Data points randomly "jittered" on the X axis and grouped by every 10 years.')) +
theme(axis.text=element_text(size = 10, hjust = 1))
g2
ggsave("image_files/Frakenreiter_Figure_3.eps", width = 6, height = 7, device=cairo_ps, fallback_resolution = 1000)
# Frankenreiter 4 (version 1)
#
# # Get data
# data <- all_data$frankenreiter$f4 %>%
# filter(diff >= 0) # we are making this modification to match the vis from the paper
#
# g1 <- ggplot(data, aes(diff, similarity)) +
# geom_smooth(method = 'loess',
# linetype = 0,
# fill = 'darkgrey',
# alpha = 1) +
# geom_point(size = 1,
# alpha = 0.9,
# color = 'black') +
# theme_sfi(lp = 'none',
# y_axis_title_style = 'bold',
# x_axis_title_style = 'bold',
# title_style = 'bold') +
# labs(x = '',
# y = '',
# title = paste0('Figure 4 Similarity between different', '\n' , 'judges as a function of time'),
# caption = '*Std error from local regression.') +
# theme(axis.text=element_text(size = 10, hjust = 1))
#
#
# g1
#
# Frankenreiter 4 (version 2)
# Get data
data <- all_data$frankenreiter$f4 %>%
filter(diff >= 0) # we are making this modification to match the vis from the paper
liney <- data %>%
group_by(diff) %>%
summarise(similarity = mean(similarity, na.rm = TRUE)) %>%
ungroup
spiney <- data %>%
mutate(diff = round(diff, digits = -1)) %>%
group_by(diff) %>%
summarise(similarity = mean(similarity, na.rm = TRUE)) %>%
ungroup
quanty <- data %>%
mutate(diff = round(diff, digits = -1)) %>%
group_by(diff) %>%
summarise(avg = mean(similarity, na.rm = TRUE),
med = median(similarity, na.rm = TRUE),
q75 = quantile(similarity, 0.75, na.rm = TRUE),
q25 = quantile(similarity, 0.25, na.rm = TRUE)) %>%
ungroup
g2 <- ggplot(data = data %>%
mutate(diff = round(diff, digits = -1)),
aes(x = diff,
y = similarity)) +
geom_violin(aes(group = factor(diff)),
alpha = 1,
fill = 'darkgrey',
color = NA) +
geom_jitter(alpha = 0.4,
# pch = 1,
size = 1) +
ylim(0, 1) +
theme_sfi(lp = 'none',
title_style = 'bold') +
labs(x = '',
y = '',
title = paste0(''),
subtitle = '',
caption = paste0('*Distribution represented with mirrored normal density (violin plot).', '\n', 'Data points randomly "jittered" on the X axis and grouped by every 10 years.')) +
theme(axis.text=element_text(size = 10, hjust = 1))
g2
ggsave("image_files/Frakenreiter_Figure_4.eps", width = 6, height = 7, device=cairo_ps, fallback_resolution = 1000)
# Frankenreiter 5
## 1955 - 2014
# Get data
data <- all_data$frankenreiter$f5
# # assign Metrix MDS (1955-2014) to source 1
# # assign Metrix MDS (1970-2014) to source 2
# data$source <- ifelse(data$source == '1', 'Metrix MDS (1955-2014)',
# 'Metrix MDS (1970-2014)')
# data$source <- as.factor(data$source)
data1 <- data[data$source == 1,]
# plot the 1955 to 2014 data
cols <- make_colors(length(unique(data1$year)), bw = TRUE)
data1$year <- as.numeric(data1$year)
g1 <- ggplot(data1,
aes(x = coord2,
y = coord1,
color = year)) +
geom_point(size = 4,
pch = 16,
alpha = 0.9) +
ggrepel::geom_text_repel(data=subset(data1, year== 1981 | year ==1995 | year == 2004 | year == 2007),
aes(coord2, coord1, label=year), vjust = 2, hjust = -2) +
ggrepel::geom_text_repel(data=subset(data1, year == 1973),
aes(coord2, coord1, label=year), vjust = -1.5, hjust = 0) +
ggrepel::geom_text_repel(data=subset(data1, year == 2013),
aes(coord2, coord1, label=year), vjust = -1.5, hjust = 0) +
ggrepel::geom_text_repel(data=subset(data1, year == 1986),
aes(coord2, coord1, label=year), vjust = 1, hjust = -1) +
theme_sfi(lp = 'bottom',
title_style = 'bold',
lkw = TRUE,
lkt = 'point',
legend_width = 40) +
labs(x = 'Coordinate 2',
y = 'Coordinate 1',
title = '',
subtitle = '') +
scale_color_gradient(name = 'Year', low = "#2C2C2C", high = "#ABABAB")
g1
ggsave("image_files/Frakenreiter_Figure_5a.eps", width = 7, height = 8, device=cairo_ps, fallback_resolution = 1000)
# Frankenreiter 5
## 1970 - 2014
# Get data
data <- all_data$frankenreiter$f5
data2 <- data[data$source == 2,]
cols <- make_colors(length(unique(data2$year)), bw = TRUE)
data2$year <- as.numeric(data2$year)
g2 <- ggplot(data2,
aes(x = coord2,
y = coord1,
color = year)) +
geom_point(size = 4,
pch = 16,
alpha = 0.9) +
ggrepel::geom_text_repel(data=subset(data2, year == 1970 | year == 1973 | year== 1981),
aes(coord2, coord1, label=year), vjust = -1, hjust = 1.5) +
ggrepel::geom_text_repel(data=subset(data2,year == 2000 | year == 2004 | year == 2007 | year== 2010 | year == 2014),
aes(coord2, coord1, label=year), vjust = 1, hjust = -1.5) +
ggrepel::geom_text_repel(data=subset(data2,year == 1995),
aes(coord2, coord1, label=year), vjust = 0, hjust = -1) +
ggrepel::geom_text_repel(data=subset(data2,year == 1986 | year == 1990),
aes(coord2, coord1, label=year), vjust = 0, hjust = .5) +
theme_sfi(lp = 'bottom',
title_style = 'bold',
lkw = TRUE,
lkt = 'point',
legend_width = 30) +
labs(x = 'Coordinate 2',
y = 'Coordinate 1',
title = '',
subtitle = '') +
scale_color_gradient(name = 'Year', low = "#2C2C2C", high = "#ABABAB") +
theme(axis.text=element_text(size = 10, hjust = 1))
g2
ggsave("image_files/Frakenreiter_Figure_5b.eps", width = 6, height = 7, device=cairo_ps, fallback_resolution = 1000)
# Frankenreiter 6 1973 enlargement (version 1)
#
# # Get data
# data <- all_data$frankenreiter$f6 %>%
# mutate(year = as.numeric(year))
#
# # separate data into two datasets based on key
# data_1 <- data[data$key == '1973 enlargement',]
#
# # capitalize data
# data_1$judgetrad <- Hmisc::capitalize(data_1$judgetrad)
#
# # recode data so the new judgetrad variable indicates if the judge is new
# data_1$judgetrad <- ifelse(data_1$new == 1, paste0(data_1$judgetrad, ' New'), data_1$judgetrad)
#
# # -----------------------
# # 1973 enlargement
#
# # Version 1 with lines at end
# g1 <- ggplot(data = data_1,
# aes(x = year,
# y = kl.dist3,
# group = interaction(judgetrad,judge),
# color = judgetrad)) +
# geom_line(size = 1) +
# xlim(c(1973, 1990)) +
# geom_dl(aes(label = judge),
# method = list(dl.combine("last.points"), hjust = -0.5,
# cex = 0.6,
# dl.move('DK1', vjust = -1)), alpha = 0.8, color = 'black') +
# scale_color_manual(name = '',
# values = c('black', '#414141', '#979797', '#C6C6C6')) +
# labs(x = 'Year',
# y = '',
# title = 'Figure 6 (1973 Enlargement)',
# subtitle = 'Development of writing style of ECJ in comparison to the writing of judges
# between 1973 and 1975') +
# theme_sfi(lp = 'bottom',
# y_axis_title_style = 'bold',
# x_axis_title_style = 'bold',
# title_style = 'bold') +
# theme(axis.text=element_text(size = 10, hjust = 1))
#
# g1
#
# # Frankenreiter 6 1973 enlargement (version 2)
#
#
# # Get data
# data <- all_data$frankenreiter$f6 %>%
# mutate(year = as.numeric(year))
#
# # separate data into two datasets based on key
# data_1 <- data[data$key == '1973 enlargement',]
#
# # capitalize data
# data_1$judgetrad <- Hmisc::capitalize(data_1$judgetrad)
#
# # recode data so the new judgetrad variable indicates if the judge is new
# data_1$judgetrad <- ifelse(data_1$new == 1, paste0(data_1$judgetrad, ' New'), data_1$judgetrad)
#
# # -----------------------
# # 1973 enlargement
#
# # Version 1 with lines at end
# g1 <- ggplot(data = data_1,
# aes(x = year,
# y = kl.dist3,
# group = interaction(judgetrad,judge),
# color = judgetrad)) +
# geom_line(size = 1) +
# geom_point(size = 1,
# alpha = 0.6,
# color = 'black') +
# xlim(c(1973, 1990)) +
# geom_dl(aes(label = judge),
# method = list(dl.combine("last.points"), hjust = -0.5,
# cex = 0.6,
# dl.move('DK1', vjust = -1)), alpha = 0.8, color = 'black') +
# scale_color_manual(name = '',
# values = c('black', '#414141', '#979797', '#C6C6C6')) +
# labs(x = 'Year',
# y = '',
# title = 'Figure 6 (1973 Enlargement)',
# subtitle = 'Development of writing style of ECJ in comparison to the writing of judges
# between 1973 and 1975') +
# theme_sfi(lp = 'bottom',
# y_axis_title_style = 'bold',
# x_axis_title_style = 'bold',
# title_style = 'bold') +
# theme(axis.text=element_text(size = 10, hjust = 1))
#
# g1
#
#
# # Frankenreiter 6 1995 enlargement (version 1)
#
#
# # Get data
# data <- all_data$frankenreiter$f6 %>%
# mutate(year = as.numeric(year))
# data_2 <- data[data$key == '1995 enlargement',]
# data_2$judgetrad <- Hmisc::capitalize(data_2$judgetrad)
# data_2$judgetrad <- ifelse(data_2$new == 1, paste0(data_2$judgetrad, ' New'), data_2$judgetrad)
#
#
# # version 5 barplot
# g2 <- ggplot(data = data_2,
# aes(x = year,
# y = kl.dist3,
# fill = judgetrad)) +
# geom_point(size = 0.5,
# color = 'black',
# alpha = 0.4) +
# geom_line(size = 0.5,
# alpha = 0.8) +
# geom_dl(aes(label = judgetrad),
# method = list(dl.combine("last.points"), cex = 0.7,
# vjust = -0.3,alpha = 0.8, color = 'black',
# dl.trans(x = x - 2.8))) +
# ylim(c(0, .035)) +
# labs(x = 'Year',
# y = '',
# title = 'Figure 6 (1995 Enlargement)',
# subtitle = 'Development of writing style of ECJ in comparison to the writing of judges
# between 1973 and 1975') +
# theme_sfi(lp = 'none',
# y_axis_title_style = 'bold',
# x_axis_title_style = 'bold',
# title_style = 'bold') +
# scale_fill_manual(name = '',
# values = c('black', 'black','black',"#595959", "#9C9C9C",'#D3D0D0')) +
# theme(axis.text=element_text(size = 10, hjust = 1)) +
# facet_wrap(~judge)
#
# g2
# Frankenreiter 6 1995 enlargement (version 2)
# Get data
data <- all_data$frankenreiter$f6 %>%
mutate(year = as.numeric(year))
# recode new variable
data$new <- as.factor(ifelse(data$new == 1, 'New judges', 'Old judges'))
# separate data into two datasets based on key
data_1 <- data[data$key == '1973 enlargement',]
# capitalize data
data_1$judgetrad <- as.factor(Hmisc::capitalize(data_1$judgetrad))
# keep only last name
data_1$judgename <- gsub('Mertens de Wilmars', 'de Wilmars', data_1$judgename)
data_1$judgename <- gsub('Mackenzie Stuart', 'Stuart', data_1$judgename)
# correct variable 'new' levles
data_1$new <- factor(data_1$new, levels = c('Old judges', 'New judges'))
data_1$judgetrad<- factor(data_1$judgetrad, levels = c('Roman', 'German','Nordic', 'Commonlaw'))
#
# # recode data so the new judgetrad variable indicates if the judge is new
# data_1$judgetrad <- ifelse(data_1$new == 1, paste0(data_1$judgetrad, ' New'), data_1$judgetrad)
# -----------------------
# 1973 enlargement
# Version 1 with lines at end
g1 <- ggplot(data = data_1,
aes(x = year,
y = kl.dist3,
group = interaction(judgetrad,judgename),
color = judgetrad)) +
geom_line(size = 1.5) +
geom_point(size = 1,
alpha = 1,
color = 'black') +
xlim(c(1973, 1992)) +
geom_dl(aes(label = judgename),
method = list(dl.combine("last.points"), cex = 1,hjust = -0.1, fontface = 'bold',
dl.move('Donner', cex = 1,vjust = -0.3)),
alpha = 1, color = 'black') +
scale_color_manual(name = '',
breaks = c('Roman', 'German', 'Nordic', 'Commonlaw'),
values = c('#000000', '#434343', '#7F7F7F', '#ABABAB')) +
labs(x = 'Year',
y = '',
title = '',
subtitle = '') +
theme_sfi(lp = 'bottom',
title_style = 'bold') +
theme(axis.text=element_text(size = 10, hjust = 1)) +
facet_wrap(~new)
g1
ggsave("image_files/Frakenreiter_Figure_6a.eps", width = 9, height = 7, device=cairo_ps, fallback_resolution = 600)
# Get data
data <- all_data$frankenreiter$f6 %>%
mutate(year = as.numeric(year))
# recode new variable
data$new <- ifelse(data$new == 1, 'New judges', 'Old judges')
data_2 <- data[data$key == '1995 enlargement',]
data_2$judgetrad <- Hmisc::capitalize(data_2$judgetrad)
# correct variable 'new' levles
data_2$new <- factor(data_2$new, levels = c('Old judges', 'New judges'))
data_2$judgetrad<- factor(data_2$judgetrad, levels = c('Roman', 'German','Nordic', 'Commonlaw'))
data_2$judgename <- gsub('Moitinho de Almeida', 'de Almeida', data_2$judgename)
data_2$judgename <- gsub('Rodriguez Iglesias', 'Iglesias', data_2$judgename)
data_2$judge_label <- ''
data_list <- list()
unique_names <- unique(data_2$judgename)
for(i in 1:length(unique_names)) {
this_name <- unique_names[i]
sub_dat <- data_2[data_2$judgename == this_name,]
sub_dat <- sub_dat[order(sub_dat$kl.dist3, decreasing = TRUE),]
sub_dat$judge_label[i] <- this_name
data_list[[i]] <- sub_dat
print(this_name)
}
data_2 <- do.call('rbind', data_list)
# judge names: Wathelet, Puissochet, Murray, de Almeida, Edward
# data_2$judgetrad <- ifelse(data_2$new == 1, paste0(data_2$judgetrad, ' New'), data_2$judgetrad)
library(ggrepel)
g3 <- ggplot(data = data_2,
aes(x = year,
y = kl.dist3,
group = interaction(judgetrad,judgename),
color = judgetrad)) +
ylim(c(0, .03)) +
geom_point(size = 1,
color = 'black',
alpha = 1) +
geom_line(size = 1) +
geom_dl(data = data_2[data_2$new == 'New judges',],
aes(label = judgename),
method = list(dl.combine("last.points"), hjust = -0.1, cex = 1,
dl.move('Ragnemalm', hjust = 0.1, vjust = -0.4, cex = 1),
dl.move('Wathelet', hjust = 2, vjust = -0.4, cex = 1)),
alpha = 1, color = 'black') +
geom_dl(data = data_2 %>% filter(judgename %in% c('Murray','Wathelet', 'Edward')),
aes(label = judgename),
method = list(dl.combine("first.points"), hjust = 1.1, cex = 1),
alpha = 1, color = 'black') +
geom_dl(data = data_2 %>% filter(judgename %in% c('Hirsch','Mancini', 'Iglesias', 'de Almeida','Kakouris', 'Kapteyn', 'Gulmann', 'Puissochet')),
aes(label = judgename),
method = list(dl.combine("last.points"), hjust = -0.1, cex = 1),
alpha = 1, color = 'black') +
scale_color_manual(name = '',
breaks = c('Roman', 'German', 'Nordic', 'Commonlaw'),
values = c('#000000', '#434343', '#7F7F7F', '#ABABAB')) +
xlim(c(1990, 2015)) +
labs(x = 'Year',
y = '',
title = '',
subtitle = '') +
theme_sfi(lp = 'bottom',
title_style = 'bold') +
theme(axis.text=element_text(size = 9, hjust = 1)) +
facet_wrap(~new)
g3
ggsave("image_files/Frakenreiter_Figure_6b.eps", width = 10, height = 10, device=cairo_ps, fallback_resolution = 2000)
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.