In databrew/sfi:
# Packages
library(sfi)
library(webshot)
library(ggplot2)
library(dplyr)
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
options(max.print="75")
opts_chunk$set(echo=FALSE,
cache=FALSE,
prompt=FALSE,
tidy=TRUE,
comment=NA,
message=FALSE,
warning=FALSE,
dpi = 300,
# dev = "cairo_pdf",
dev = c("png", "cairo_pdf"),
fig.pos="!h",
fig.path = 'figures/')
opts_knit$set(width=75)
options(xtable.comment = FALSE)
This markdown is for Dumas figures.
\newpage
# Dumas 1a (Version 1)
# Get data
data <- all_data$dumas$f1
# create a variable to represent the colors
data$run <- ifelse(grepl('1', data$label), 'Run 1',
ifelse(grepl('2', data$label), 'Run 2',
'Run 3'))
data$filter <- ifelse(grepl('Restrictive', data$label),
'Restrictive', 'Permissive')
# recode x and y to fit document
# names(data) <- c('label', 'True negative rate', 'True positive rate', 'run', 'filter')
# --------------------------------------------------
# g4
# facet wrap
g1 <-
ggplot(data,
aes(x = `True negative rate`,
y =`True positive rate`,
col = filter)) +
geom_point(size = 1,
alpha = 0.8) +
geom_line(size = 0.5,
alpha = 1) +
xlim(c(1, 0)) +
ylim(c(0, 1)) +
labs(title = 'Appeals level',
subtitle = 'Restrictive vs Permissive Filters',
caption = paste0('The ROC curves of the language model')) +
scale_color_manual(name = 'Filter type',
values = c('black', 'darkgrey')) +
geom_abline(intercept = 1,
color = 'black',
slope = 1,
size = 1,
alpha = 0.45) +
facet_wrap(~run, ncol = 3) +
theme_sfi(lp = 'bottom') +
theme(axis.text=element_text(size = 10, hjust = 1))
# Dumas 1a (Version 2)
g1 <-
ggplot(data,
aes(x = `True negative rate`,
y =`True positive rate`,
color = filter)) +
geom_line(size = 1,
alpha = 0.8,
show.legend = FALSE) +
xlim(c(1, 0)) +
ylim(c(0, 1)) +
labs(title = 'Appeals level',
subtitle = 'Restrictive vs Permissive Filters',
caption = paste0('The ROC curves of the language model', '\n',
'*lines smoothed by a local regression')) +
# scale_color_manual(name = 'Filter type',
# values = c('black', 'grey')) +
scale_color_manual(name = '',
values = c('black', 'darkgrey')) +
geom_abline(intercept = 1,
color = 'black',
# linetype = 'dashed',
slope = 1,
size = .7,
alpha = 0.6) +
facet_wrap(~run, ncol = 3) +
theme_sfi(lp = 'bottom') +
theme(axis.text=element_text(size = 10, hjust = 1))
Dumas 1a (Version 3)
# Get data
data <- all_data$dumas$f1
# create a variable to represent the colors
data$run <- ifelse(grepl('1', data$label), 'Run 1',
ifelse(grepl('2', data$label), 'Run 2',
'Run 3'))
data$filter <- ifelse(grepl('Restrictive', data$label),
'Restrictive', 'Permissive')
# recode x and y to fit document
# names(data) <- c('label', 'True negative rate', 'True positive rate', 'run', 'filter')
# --------------------------------------------------
# g4
# facet wrap
g1 <-
ggplot(data,
aes(x = `True negative rate`,
y =`True positive rate`,
col = filter)) +
geom_point(size = 1,
alpha = 0.6) +
geom_line(size = 0.5,
alpha = 1) +
xlim(c(1, 0)) +
ylim(c(0, 1)) +
labs(title = 'Appeals level',
subtitle = 'Restrictive vs Permissive Filters',
caption = paste0('The ROC curves of the language model')) +
scale_color_manual(name = 'Filter type',
values = c('black', 'darkgrey')) +
geom_abline(intercept = 1,
color = 'black',
slope = 1,
size = 1,
alpha = 0.45) +
facet_wrap(~run, nrow = 3) +
theme_sfi(lp = 'bottom') +
theme(axis.text=element_text(size = 10, hjust = 1))
g1
# Dumas 1b (Version 1)
# Get data
data <- all_data$dumas$f2
# create a variable to represent the colors
data$run <- ifelse(grepl('1', data$label), 'Run 1',
ifelse(grepl('2', data$label), 'Run 2',
'Run 3'))
data$filter <- ifelse(grepl('Restrictive', data$label, ignore.case = TRUE),
'Restrictive', 'Permissive')
# --------------------------------------------------
# g1
# # facet wrap
# g1 <-
# ggplot(data,
# aes(x = `True negative rate`,
# y =`True positive rate`,
# col = filter)) +
# geom_point(size = 0.5,
# alpha = 0.8) +
# geom_line(size = 0.5,
# alpha = 1) +
# xlim(c(1, 0)) +
# ylim(c(0, 1)) +
# labs(title = 'District Level',
# subtitle = 'Restrictive vs Permissive Filters',
# caption = paste0('The ROC curves of the language model')) +
# scale_color_manual(name = 'Filter type',
# values = c('black', 'grey')) +
# geom_abline(intercept = 1,
# color = 'black',
# slope = 1,
# size = 1,
# alpha = 0.6) +
# facet_wrap(~run, ncol = 3) +
# theme_sfi(lp = 'bottom') +
# theme(axis.text=element_text(size = 10, hjust = 1))
#
#
# g1
# Get data
# Dumas 1b (Version 2)
data <- all_data$dumas$f2
# create a variable to represent the colors
data$run <- ifelse(grepl('1', data$label), 'Run 1',
ifelse(grepl('2', data$label), 'Run 2',
'Run 3'))
data$filter <- ifelse(grepl('Restrictive', data$label, ignore.case = TRUE),
'Restrictive', 'Permissive')
# --------------------------------------------------
# g1
# # facet wrap
# g1 <-
# ggplot(data,
# aes(x = `True negative rate`,
# y =`True positive rate`,
# col = filter)) +
# geom_line(size = 0.5,
# alpha = 1) +
# xlim(c(1, 0)) +
# ylim(c(0, 1)) +
# labs(title = 'District Level',
# subtitle = 'Restrictive vs Permissive Filters',
# caption = paste0('The ROC curves of the language model')) +
# scale_color_manual(name = 'Filter type',
# values = c('black', 'grey')) +
# geom_abline(intercept = 1,
# color = 'black',
# slope = 1,
# size = 0.7,
# alpha = 0.6) +
# facet_wrap(~run, ncol = 3) +
# theme_sfi(lp = 'bottom') +
# theme(axis.text=element_text(size = 10, hjust = 1))
#
#
Dumas 1b (Version 3)
# Get data
data <- all_data$dumas$f2
# create a variable to represent the colors
data$run <- ifelse(grepl('1', data$label), 'Run 1',
ifelse(grepl('2', data$label), 'Run 2',
'Run 3'))
data$filter <- ifelse(grepl('Restrictive', data$label, ignore.case = TRUE),
'Restrictive', 'Permissive')
# --------------------------------------------------
# g1
# facet wrap
g1 <-
ggplot(data,
aes(x = `True negative rate`,
y =`True positive rate`,
col = filter)) +
geom_point(size = 1,
alpha = 0.6) +
geom_line(size = 0.5,
alpha = 1) +
xlim(c(1, 0)) +
ylim(c(0, 1)) +
labs(title = 'District Level',
subtitle = 'Restrictive vs Permissive Filters',
caption = paste0('The ROC curves of the language model')) +
scale_color_manual(name = 'Filter type',
values = c('black', 'grey')) +
geom_abline(intercept = 1,
color = 'black',
slope = 1,
size = 0.7,
alpha = 0.6) +
facet_wrap(~run, nrow = 3) +
theme_sfi(lp = 'bottom') +
theme(axis.text=element_text(size = 10, hjust = 1))
g1
# Dumas 2a (Verion 1)
# Get data
data <- all_data$dumas$f3
# create a variable to represent the colors
data$run <- ifelse(grepl('1', data$label), 'Run 1',
ifelse(grepl('2', data$label), 'Run 2',
'Run 3'))
data$filter <- ifelse(grepl('with embeddings', data$label),
'Permissive embeddings', 'Permisive')
# --------------------------------------------------
# g4
# facet wrap
g1 <-
ggplot(data,
aes(x = `True negative rate`,
y =`True positive rate`,
color = filter)) +
geom_point(size = 1,
alpha = 0.8) +
geom_line(size = 1,
alpha = 0.6) +
xlim(c(1, 0)) +
ylim(c(0, 1)) +
scale_color_manual(name = 'Filter type',
values = c('black', 'grey')) +
labs(title = 'Appeals level',
subtitle = 'Permissive vs Permissive Filters with embeddings',
caption = paste0('The ROC curves of the language model')) +
geom_abline(intercept = 1,
color = 'black',
slope = 1,
size = 1,
alpha = 0.6) +
facet_wrap(~run, ncol = 3) +
theme_sfi(lp = 'bottom',
x_axis_title_style = 'bold',
y_axis_title_style = 'bold') +
theme(axis.text=element_text(size = 10, hjust = 1))
# Dumas 2a (Verion 2)
# Get data
data <- all_data$dumas$f3
# create a variable to represent the colors
data$run <- ifelse(grepl('1', data$label), 'Run 1',
ifelse(grepl('2', data$label), 'Run 2',
'Run 3'))
data$filter <- ifelse(grepl('with embeddings', data$label),
'Permissive embeddings', 'Permisive')
# --------------------------------------------------
# g4
# # facet wrap
# g1 <-
# ggplot(data,
# aes(x = `True negative rate`,
# y =`True positive rate`,
# color = filter)) +
# geom_line(size = 1,
# alpha = 0.6) +
# xlim(c(1, 0)) +
# ylim(c(0, 1)) +
# scale_color_manual(name = 'Filter type',
# values = c('black', 'grey')) +
# labs(title = 'Appeals level',
# subtitle = 'Permissive vs Permissive Filters with embeddings',
# caption = paste0('The ROC curves of the language model')) +
# geom_abline(intercept = 1,
# color = 'black',
# slope = 1,
# size = 0.7,
# alpha = 0.6) +
# facet_wrap(~run, ncol = 3) +
# theme_sfi(lp = 'bottom',
# x_axis_title_style = 'bold',
# y_axis_title_style = 'bold') +
# theme(axis.text=element_text(size = 10, hjust = 1))
#
# g1
Dumas 2a (Version 3)
# Get data
data <- all_data$dumas$f3
# create a variable to represent the colors
data$run <- ifelse(grepl('1', data$label), 'Run 1',
ifelse(grepl('2', data$label), 'Run 2',
'Run 3'))
data$filter <- ifelse(grepl('with embeddings', data$label),
'Permissive embeddings', 'Permisive')
## --------------------------------------------------
# g1
# facet wrap
g1 <-
ggplot(data,
aes(x = `True negative rate`,
y =`True positive rate`,
color = filter)) +
geom_point(size = 1,
alpha = 0.6) +
geom_line(size = 1,
alpha = .8) +
xlim(c(1, 0)) +
ylim(c(0, 1)) +
scale_color_manual(name = 'Filter type',
values = c('black', 'grey')) +
labs(title = 'District Level',
subtitle = 'Permissive vs Permissive Filters with embeddings',
caption = paste0('The ROC curves of the language model')) +
geom_abline(intercept = 1,
color = 'black',
slope = 1,
size = 0.7,
alpha = 0.6) +
facet_wrap(~run, nrow = 3) +
theme_sfi(lp = 'bottom',
x_axis_title_style = 'bold',
y_axis_title_style = 'bold') +
theme(axis.text=element_text(size = 10, hjust = 1))
g1
# Dumas 2b (Verion 1)
# Get data
data <- all_data$dumas$f4
# create a variable to represent the colors
data$run <- ifelse(grepl('1', data$label), 'Run 1',
ifelse(grepl('2', data$label), 'Run 2',
'Run 3'))
data$filter <- ifelse(grepl('with embeddings', data$label),
'Permissive embeddings', 'Permisive')
# --------------------------------------------------
# g4
# facet wrap
# g1 <-
# ggplot(data,
# aes(x = `True negative rate`,
# y =`True positive rate`,
# color = filter)) +
# geom_point(size = 1,
# alpha = 0.8) +
# geom_line(size = 1,
# alpha = 0.6) +
# xlim(c(1, 0)) +
# ylim(c(0, 1)) +
# scale_color_manual(name = 'Filter type',
# values = c('black', 'grey')) +
# labs(title = 'Appeals level',
# subtitle = 'Permissive vs Permissive Filters with embeddings',
# caption = paste0('The ROC curves of the language model')) +
# geom_abline(intercept = 1,
# color = 'black',
# slope = 1,
# size = 1,
# alpha = 0.6) +
# facet_wrap(~run, ncol = 3) +
# theme_sfi(lp = 'bottom',
# x_axis_title_style = 'bold',
# y_axis_title_style = 'bold') +
# theme(axis.text=element_text(size = 10, hjust = 1))
#
# Dumas 2b (Version 2)
# Get data
data <- all_data$dumas$f4
# create a variable to represent the colors
data$run <- ifelse(grepl('1', data$label), 'Run 1',
ifelse(grepl('2', data$label), 'Run 2',
'Run 3'))
data$filter <- ifelse(grepl('with embeddings', data$label),
'Permissive embeddings', 'Permisive')
## --------------------------------------------------
# g1
# facet wrap
# g1 <-
# ggplot(data,
# aes(x = `True negative rate`,
# y =`True positive rate`,
# color = filter)) +
# geom_line(size = 1,
# alpha = 0.6) +
# xlim(c(1, 0)) +
# ylim(c(0, 1)) +
# scale_color_manual(name = 'Filter type',
# values = c('black', 'grey')) +
# labs(title = 'District Level',
# subtitle = 'Permissive vs Permissive Filters with embeddings',
# caption = paste0('The ROC curves of the language model')) +
# geom_abline(intercept = 1,
# color = 'black',
# slope = 1,
# size = 1,
# alpha = 0.6) +
# facet_wrap(~run, ncol = 3) +
# theme_sfi(lp = 'bottom',
# x_axis_title_style = 'bold',
# y_axis_title_style = 'bold') +
# theme(axis.text=element_text(size = 10, hjust = 1))
Dumas 2b (Version 3)
# Get data
data <- all_data$dumas$f4
# create a variable to represent the colors
data$run <- ifelse(grepl('1', data$label), 'Run 1',
ifelse(grepl('2', data$label), 'Run 2',
'Run 3'))
data$filter <- ifelse(grepl('with embeddings', data$label),
'Permissive embeddings', 'Permisive')
## --------------------------------------------------
# g1
# facet wrap
g1 <-
ggplot(data,
aes(x = `True negative rate`,
y =`True positive rate`,
color = filter)) +
geom_point(size = 1,
alpha = 0.8) +
geom_line(size = 1,
alpha = 0.6) +
xlim(c(1, 0)) +
ylim(c(0, 1)) +
scale_color_manual(name = 'Filter type',
values = c('black', 'grey')) +
labs(title = 'District Level',
subtitle = 'Permissive vs Permissive Filters with embeddings',
caption = paste0('The ROC curves of the language model')) +
geom_abline(intercept = 1,
color = 'black',
slope = 1,
size = 1,
alpha = 0.6) +
facet_wrap(~run, nrow = 3) +
theme_sfi(lp = 'bottom',
x_axis_title_style = 'bold',
y_axis_title_style = 'bold') +
theme(axis.text=element_text(size = 10, hjust = 1))
g1
databrew/sfi documentation built on May 29, 2019, 1:52 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# Packages library(sfi) library(webshot) library(ggplot2) library(dplyr) library(plotly) library(ggiraph) library(scales) library(tidyverse) library(knitr) library(Hmisc) library(RColorBrewer) library(extrafont) library(kableExtra) library(grid) # webshot::install_phantomjs() loadfonts() ## Global options options(max.print="75") opts_chunk$set(echo=FALSE, cache=FALSE, prompt=FALSE, tidy=TRUE, comment=NA, message=FALSE, warning=FALSE, dpi = 300, # dev = "cairo_pdf", dev = c("png", "cairo_pdf"), fig.pos="!h", fig.path = 'figures/') opts_knit$set(width=75) options(xtable.comment = FALSE)
This markdown is for Dumas figures.
\newpage
# Dumas 1a (Version 1) # Get data data <- all_data$dumas$f1 # create a variable to represent the colors data$run <- ifelse(grepl('1', data$label), 'Run 1', ifelse(grepl('2', data$label), 'Run 2', 'Run 3')) data$filter <- ifelse(grepl('Restrictive', data$label), 'Restrictive', 'Permissive') # recode x and y to fit document # names(data) <- c('label', 'True negative rate', 'True positive rate', 'run', 'filter') # -------------------------------------------------- # g4 # facet wrap g1 <- ggplot(data, aes(x = `True negative rate`, y =`True positive rate`, col = filter)) + geom_point(size = 1, alpha = 0.8) + geom_line(size = 0.5, alpha = 1) + xlim(c(1, 0)) + ylim(c(0, 1)) + labs(title = 'Appeals level', subtitle = 'Restrictive vs Permissive Filters', caption = paste0('The ROC curves of the language model')) + scale_color_manual(name = 'Filter type', values = c('black', 'darkgrey')) + geom_abline(intercept = 1, color = 'black', slope = 1, size = 1, alpha = 0.45) + facet_wrap(~run, ncol = 3) + theme_sfi(lp = 'bottom') + theme(axis.text=element_text(size = 10, hjust = 1))
# Dumas 1a (Version 2) g1 <- ggplot(data, aes(x = `True negative rate`, y =`True positive rate`, color = filter)) + geom_line(size = 1, alpha = 0.8, show.legend = FALSE) + xlim(c(1, 0)) + ylim(c(0, 1)) + labs(title = 'Appeals level', subtitle = 'Restrictive vs Permissive Filters', caption = paste0('The ROC curves of the language model', '\n', '*lines smoothed by a local regression')) + # scale_color_manual(name = 'Filter type', # values = c('black', 'grey')) + scale_color_manual(name = '', values = c('black', 'darkgrey')) + geom_abline(intercept = 1, color = 'black', # linetype = 'dashed', slope = 1, size = .7, alpha = 0.6) + facet_wrap(~run, ncol = 3) + theme_sfi(lp = 'bottom') + theme(axis.text=element_text(size = 10, hjust = 1))
Dumas 1a (Version 3)
# Get data data <- all_data$dumas$f1 # create a variable to represent the colors data$run <- ifelse(grepl('1', data$label), 'Run 1', ifelse(grepl('2', data$label), 'Run 2', 'Run 3')) data$filter <- ifelse(grepl('Restrictive', data$label), 'Restrictive', 'Permissive') # recode x and y to fit document # names(data) <- c('label', 'True negative rate', 'True positive rate', 'run', 'filter') # -------------------------------------------------- # g4 # facet wrap g1 <- ggplot(data, aes(x = `True negative rate`, y =`True positive rate`, col = filter)) + geom_point(size = 1, alpha = 0.6) + geom_line(size = 0.5, alpha = 1) + xlim(c(1, 0)) + ylim(c(0, 1)) + labs(title = 'Appeals level', subtitle = 'Restrictive vs Permissive Filters', caption = paste0('The ROC curves of the language model')) + scale_color_manual(name = 'Filter type', values = c('black', 'darkgrey')) + geom_abline(intercept = 1, color = 'black', slope = 1, size = 1, alpha = 0.45) + facet_wrap(~run, nrow = 3) + theme_sfi(lp = 'bottom') + theme(axis.text=element_text(size = 10, hjust = 1)) g1
# Dumas 1b (Version 1) # Get data data <- all_data$dumas$f2 # create a variable to represent the colors data$run <- ifelse(grepl('1', data$label), 'Run 1', ifelse(grepl('2', data$label), 'Run 2', 'Run 3')) data$filter <- ifelse(grepl('Restrictive', data$label, ignore.case = TRUE), 'Restrictive', 'Permissive') # -------------------------------------------------- # g1 # # facet wrap # g1 <- # ggplot(data, # aes(x = `True negative rate`, # y =`True positive rate`, # col = filter)) + # geom_point(size = 0.5, # alpha = 0.8) + # geom_line(size = 0.5, # alpha = 1) + # xlim(c(1, 0)) + # ylim(c(0, 1)) + # labs(title = 'District Level', # subtitle = 'Restrictive vs Permissive Filters', # caption = paste0('The ROC curves of the language model')) + # scale_color_manual(name = 'Filter type', # values = c('black', 'grey')) + # geom_abline(intercept = 1, # color = 'black', # slope = 1, # size = 1, # alpha = 0.6) + # facet_wrap(~run, ncol = 3) + # theme_sfi(lp = 'bottom') + # theme(axis.text=element_text(size = 10, hjust = 1)) # # # g1
# Get data # Dumas 1b (Version 2) data <- all_data$dumas$f2 # create a variable to represent the colors data$run <- ifelse(grepl('1', data$label), 'Run 1', ifelse(grepl('2', data$label), 'Run 2', 'Run 3')) data$filter <- ifelse(grepl('Restrictive', data$label, ignore.case = TRUE), 'Restrictive', 'Permissive') # -------------------------------------------------- # g1 # # facet wrap # g1 <- # ggplot(data, # aes(x = `True negative rate`, # y =`True positive rate`, # col = filter)) + # geom_line(size = 0.5, # alpha = 1) + # xlim(c(1, 0)) + # ylim(c(0, 1)) + # labs(title = 'District Level', # subtitle = 'Restrictive vs Permissive Filters', # caption = paste0('The ROC curves of the language model')) + # scale_color_manual(name = 'Filter type', # values = c('black', 'grey')) + # geom_abline(intercept = 1, # color = 'black', # slope = 1, # size = 0.7, # alpha = 0.6) + # facet_wrap(~run, ncol = 3) + # theme_sfi(lp = 'bottom') + # theme(axis.text=element_text(size = 10, hjust = 1)) # #
Dumas 1b (Version 3)
# Get data data <- all_data$dumas$f2 # create a variable to represent the colors data$run <- ifelse(grepl('1', data$label), 'Run 1', ifelse(grepl('2', data$label), 'Run 2', 'Run 3')) data$filter <- ifelse(grepl('Restrictive', data$label, ignore.case = TRUE), 'Restrictive', 'Permissive') # -------------------------------------------------- # g1 # facet wrap g1 <- ggplot(data, aes(x = `True negative rate`, y =`True positive rate`, col = filter)) + geom_point(size = 1, alpha = 0.6) + geom_line(size = 0.5, alpha = 1) + xlim(c(1, 0)) + ylim(c(0, 1)) + labs(title = 'District Level', subtitle = 'Restrictive vs Permissive Filters', caption = paste0('The ROC curves of the language model')) + scale_color_manual(name = 'Filter type', values = c('black', 'grey')) + geom_abline(intercept = 1, color = 'black', slope = 1, size = 0.7, alpha = 0.6) + facet_wrap(~run, nrow = 3) + theme_sfi(lp = 'bottom') + theme(axis.text=element_text(size = 10, hjust = 1)) g1
# Dumas 2a (Verion 1) # Get data data <- all_data$dumas$f3 # create a variable to represent the colors data$run <- ifelse(grepl('1', data$label), 'Run 1', ifelse(grepl('2', data$label), 'Run 2', 'Run 3')) data$filter <- ifelse(grepl('with embeddings', data$label), 'Permissive embeddings', 'Permisive') # -------------------------------------------------- # g4 # facet wrap g1 <- ggplot(data, aes(x = `True negative rate`, y =`True positive rate`, color = filter)) + geom_point(size = 1, alpha = 0.8) + geom_line(size = 1, alpha = 0.6) + xlim(c(1, 0)) + ylim(c(0, 1)) + scale_color_manual(name = 'Filter type', values = c('black', 'grey')) + labs(title = 'Appeals level', subtitle = 'Permissive vs Permissive Filters with embeddings', caption = paste0('The ROC curves of the language model')) + geom_abline(intercept = 1, color = 'black', slope = 1, size = 1, alpha = 0.6) + facet_wrap(~run, ncol = 3) + theme_sfi(lp = 'bottom', x_axis_title_style = 'bold', y_axis_title_style = 'bold') + theme(axis.text=element_text(size = 10, hjust = 1))
# Dumas 2a (Verion 2) # Get data data <- all_data$dumas$f3 # create a variable to represent the colors data$run <- ifelse(grepl('1', data$label), 'Run 1', ifelse(grepl('2', data$label), 'Run 2', 'Run 3')) data$filter <- ifelse(grepl('with embeddings', data$label), 'Permissive embeddings', 'Permisive') # -------------------------------------------------- # g4 # # facet wrap # g1 <- # ggplot(data, # aes(x = `True negative rate`, # y =`True positive rate`, # color = filter)) + # geom_line(size = 1, # alpha = 0.6) + # xlim(c(1, 0)) + # ylim(c(0, 1)) + # scale_color_manual(name = 'Filter type', # values = c('black', 'grey')) + # labs(title = 'Appeals level', # subtitle = 'Permissive vs Permissive Filters with embeddings', # caption = paste0('The ROC curves of the language model')) + # geom_abline(intercept = 1, # color = 'black', # slope = 1, # size = 0.7, # alpha = 0.6) + # facet_wrap(~run, ncol = 3) + # theme_sfi(lp = 'bottom', # x_axis_title_style = 'bold', # y_axis_title_style = 'bold') + # theme(axis.text=element_text(size = 10, hjust = 1)) # # g1
Dumas 2a (Version 3)
# Get data data <- all_data$dumas$f3 # create a variable to represent the colors data$run <- ifelse(grepl('1', data$label), 'Run 1', ifelse(grepl('2', data$label), 'Run 2', 'Run 3')) data$filter <- ifelse(grepl('with embeddings', data$label), 'Permissive embeddings', 'Permisive') ## -------------------------------------------------- # g1 # facet wrap g1 <- ggplot(data, aes(x = `True negative rate`, y =`True positive rate`, color = filter)) + geom_point(size = 1, alpha = 0.6) + geom_line(size = 1, alpha = .8) + xlim(c(1, 0)) + ylim(c(0, 1)) + scale_color_manual(name = 'Filter type', values = c('black', 'grey')) + labs(title = 'District Level', subtitle = 'Permissive vs Permissive Filters with embeddings', caption = paste0('The ROC curves of the language model')) + geom_abline(intercept = 1, color = 'black', slope = 1, size = 0.7, alpha = 0.6) + facet_wrap(~run, nrow = 3) + theme_sfi(lp = 'bottom', x_axis_title_style = 'bold', y_axis_title_style = 'bold') + theme(axis.text=element_text(size = 10, hjust = 1)) g1
# Dumas 2b (Verion 1) # Get data data <- all_data$dumas$f4 # create a variable to represent the colors data$run <- ifelse(grepl('1', data$label), 'Run 1', ifelse(grepl('2', data$label), 'Run 2', 'Run 3')) data$filter <- ifelse(grepl('with embeddings', data$label), 'Permissive embeddings', 'Permisive') # -------------------------------------------------- # g4 # facet wrap # g1 <- # ggplot(data, # aes(x = `True negative rate`, # y =`True positive rate`, # color = filter)) + # geom_point(size = 1, # alpha = 0.8) + # geom_line(size = 1, # alpha = 0.6) + # xlim(c(1, 0)) + # ylim(c(0, 1)) + # scale_color_manual(name = 'Filter type', # values = c('black', 'grey')) + # labs(title = 'Appeals level', # subtitle = 'Permissive vs Permissive Filters with embeddings', # caption = paste0('The ROC curves of the language model')) + # geom_abline(intercept = 1, # color = 'black', # slope = 1, # size = 1, # alpha = 0.6) + # facet_wrap(~run, ncol = 3) + # theme_sfi(lp = 'bottom', # x_axis_title_style = 'bold', # y_axis_title_style = 'bold') + # theme(axis.text=element_text(size = 10, hjust = 1)) #
# Dumas 2b (Version 2) # Get data data <- all_data$dumas$f4 # create a variable to represent the colors data$run <- ifelse(grepl('1', data$label), 'Run 1', ifelse(grepl('2', data$label), 'Run 2', 'Run 3')) data$filter <- ifelse(grepl('with embeddings', data$label), 'Permissive embeddings', 'Permisive') ## -------------------------------------------------- # g1 # facet wrap # g1 <- # ggplot(data, # aes(x = `True negative rate`, # y =`True positive rate`, # color = filter)) + # geom_line(size = 1, # alpha = 0.6) + # xlim(c(1, 0)) + # ylim(c(0, 1)) + # scale_color_manual(name = 'Filter type', # values = c('black', 'grey')) + # labs(title = 'District Level', # subtitle = 'Permissive vs Permissive Filters with embeddings', # caption = paste0('The ROC curves of the language model')) + # geom_abline(intercept = 1, # color = 'black', # slope = 1, # size = 1, # alpha = 0.6) + # facet_wrap(~run, ncol = 3) + # theme_sfi(lp = 'bottom', # x_axis_title_style = 'bold', # y_axis_title_style = 'bold') + # theme(axis.text=element_text(size = 10, hjust = 1))
Dumas 2b (Version 3)
# Get data data <- all_data$dumas$f4 # create a variable to represent the colors data$run <- ifelse(grepl('1', data$label), 'Run 1', ifelse(grepl('2', data$label), 'Run 2', 'Run 3')) data$filter <- ifelse(grepl('with embeddings', data$label), 'Permissive embeddings', 'Permisive') ## -------------------------------------------------- # g1 # facet wrap g1 <- ggplot(data, aes(x = `True negative rate`, y =`True positive rate`, color = filter)) + geom_point(size = 1, alpha = 0.8) + geom_line(size = 1, alpha = 0.6) + xlim(c(1, 0)) + ylim(c(0, 1)) + scale_color_manual(name = 'Filter type', values = c('black', 'grey')) + labs(title = 'District Level', subtitle = 'Permissive vs Permissive Filters with embeddings', caption = paste0('The ROC curves of the language model')) + geom_abline(intercept = 1, color = 'black', slope = 1, size = 1, alpha = 0.6) + facet_wrap(~run, nrow = 3) + theme_sfi(lp = 'bottom', x_axis_title_style = 'bold', y_axis_title_style = 'bold') + theme(axis.text=element_text(size = 10, hjust = 1)) g1
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