In joebrew/vilaweb: Reproducible data analysis for www.vilaweb.cat
Comandos separatistas
# Basic knitr options
library(knitr)
opts_chunk$set(comment = NA,
echo = FALSE,
warning = FALSE,
message = FALSE,
error = TRUE,
cache = FALSE,
fig.path = 'figures/')
# Libraries
library(vilaweb)
library(rtweet)
library(tidyverse)
library(databrew)
# Set up
source('get_data.R')
# There is now an object named tl, with all tweets
# Ensure no duplicates
tl <- tl %>% dplyr::distinct(.keep_all = TRUE) %>%
filter(!duplicated(id))
# # Read temp data <-
# out_list <- list()
# peeps <- dir('data')
# # peeps <- gsub('_tweets', '', peeps)
# for(i in 1:length(peeps)){
# peep <- peeps[i]
# dat <- read_csv(paste0('data/', peep, '/tweets.csv'))
# out_list[[i]] <- dat
# }
# tl <-bind_rows(out_list)
# # Filter out low-posters
# removers <- c('eva_granados',
# 'carmencalvo_',
# 'meritxell_batet',
# 'alejandrotgn')
# Flag the posts
tl <- tl %>%
# filter(!username %in% removers) %>%
# Only keep those who have posts going back to Sep 2017
group_by(username) %>%
filter(min(date) <= '2017-09-01') %>%
ungroup %>%
# Identify comandos posts
mutate(rebelion = grepl('rebel', tolower(tweet))) %>%
mutate(comandos = grepl('comando', tolower(tweet)),
kale = grepl('kale borroka', tolower(tweet))) %>%
mutate(violence = grepl('violen', tolower(tweet))) %>%
mutate(genero = grepl('machis|masclis|mujer|dona|done|géner|gèner|genero|marido|marit|novio|esposa|novia|doméstic|domèstic', tolower(tweet))) %>%
mutate(violence_not_genero = violence & !genero) %>%
mutate(cent = grepl('155', tolower(tweet))) %>%
# Flag the newspaper posts
mutate(newspaper = tolower(username) %in% tolower(c(
'ElMundoEspana',
'elpais_espana',
'LaVanguardia',
'cronicaglobal',
'elespanolcom',
'elperiodico',
'elconfidencial',
'OKDIARIO',
'enoticiescat'))) %>%
mutate(party = tolower(username) %in% tolower(c(
'PSOE',
'socialistes_cat',
'CiutadansCs',
'PPopular',
'PPCatalunya',
'vox_es',
'CiudadanosCs',
'Societatcc'))) %>%
mutate(type = ifelse(newspaper, 'Newspaper',
ifelse(party, 'Party/group',
'Person')))
# By time
bt <-
tl %>%
mutate(year_month = date_truncate(date, level = 'month') + 15) %>%
group_by(type, date = year_month,
person = username) %>%
summarise(n_violence_not_genero = length(which(violence_not_genero)),
n_violence = length(which(violence)),
n_comandos = length(which(comandos)),
n_kale = length(which(kale)),
n_rebelion = length(which(rebelion)),
n_cent = length(which(cent)),
d = n()) %>%
ungroup %>%
mutate(p_comandos = n_comandos / d * 100,
p_kale = n_kale / d * 100,
p_violence = n_violence / d * 100,
p_cent = n_cent / d * 100)
bt_kale <- bt %>%
filter(date >= '2017-08-01',
date <= '2018-12-30')
bt <- bt %>%
filter(date >= '2017-08-01',
date <= '2018-11-30')
# Plot of use of comandos among ciudadanos
make_people_plot <- function(language = 'en'){
dater <- catalan_date(new_line = FALSE)
plot_data <- bt %>% filter(type %in% c('Person', 'Party/group'))
plot_data <- plot_data %>%
filter(person %in% c('albert_rivera',
'inesarrimadas',
'ciudadanoscs',
'ciutadanscs'))
gapper <- 5
date_breaks <- sort(unique(plot_data$date))
plot_data$person<- paste0('@', plot_data$person)
label_df <- data_frame(date = as.Date('2017-10-01'),
y = 15,
label = 'La "Rebelión"')
if(language == 'en'){
title = 'Tweets containing the word "comandos"'
subtitle = 'Monthly, August 2017-November 2018'
caption = 'Data extracted/processed and chart created on 8 December 2018. Joe Brew. @joethebrew.'
x = 'Month'
y = 'Tweets'
} else {
title = 'Tuits amb la paraula "comando(s)"'
subtitle = 'Mensual, Agost 2017-Nov 2018'
caption = 'Dades descarregades/processades i gràfic creat el 8 de desembre 2018. Joe Brew. @joethebrew.'
x = 'Mes'
y = 'Tuits'
}
g <- ggplot(data = plot_data,
aes(x = date,
y = n_comandos)) +
geom_bar(stat = 'identity',
aes(group = person,
fill = person)) +
theme_vilaweb() +
labs(title = title,
caption = caption,
subtitle = subtitle,
x = x,
y = y) +
scale_fill_manual(name = '',
values = databrew::make_colors(n = length(unique(plot_data$person)))) +
scale_y_continuous(breaks = seq(0, 100, 5)) +
scale_x_date(breaks = sort(unique(plot_data$date)),
labels = dater(sort(unique(plot_data$date)))) +
# facet_wrap(~person) +
theme(axis.text.x = element_text(angle = 90,
hjust = 1)) +
geom_label(data = label_df,
aes(x = date,
y = y,
label = label),
fill = 'white',
size = 6) +
geom_line(data = data.frame(date = rep(as.Date('2017-10-15'), 2),
y = c(12, 0)),
aes(x = date,
y = y)) +
geom_point(data = data.frame(date = rep(as.Date('2017-10-15'), 2),
y = c(12, 0)),
aes(x = date,
y = y)) +
theme(plot.title = element_text(size = 24),
plot.subtitle = element_text(size = 20),
legend.text = element_text(size = 18,
color = 'black')) +
guides(fill = guide_legend(ncol = 2,
keywidth=0.3,
keyheight=0.4,
default.unit="inch"))
# theme(legend.position = 'right')
return(g)
}
make_people_plot('ca')
# Plot of use of kale among ciudadanos
make_kale_plot <- function(language = 'en'){
dater <- catalan_date(new_line = FALSE)
plot_data <- bt_kale %>% filter(type %in% c('Person', 'Party/group'))
# plot_data <- plot_data %>%
# group_by(person) %>%
# filter(sum(n_kale) > 0) %>%
# ungroup
plot_data <- plot_data %>% filter(person == 'pablocasado_')
# Manually insert not yet gathered dec data
plot_data$n_kale[plot_data$date == '2018-12-16'] <- 2
# plot_data <- plot_data %>%
# filter(person %in% c('albert_rivera',
# 'inesarrimadas',
# 'ciudadanoscs',
# 'ciutadanscs'))
gapper <- 5
date_breaks <- sort(unique(plot_data$date))
plot_data$person<- paste0('@', plot_data$person)
label_df <- data_frame(date = as.Date('2017-10-01'),
y = 15,
label = 'La "Rebelión"')
if(language == 'en'){
title = 'Tweets from Pablo Casado containing the words "kale borroka"'
subtitle = 'Monthly, August 2017-December 2018'
caption = 'Data extracted/processed and chart created on 8 December 2018. Joe Brew. @joethebrew.'
x = 'Month'
y = 'Tweets'
} else {
title = 'Tuits de Pablo Casado amb les paraules "kale borroka"'
subtitle = 'Mensual, Agost 2017-Desesmbre 2018'
caption = 'Dades descarregades/processades i gràfic creat el 8 de desembre 2018. Joe Brew. @joethebrew.'
x = 'Mes'
y = 'Tuits'
}
g <- ggplot(data = plot_data,
aes(x = date,
y = n_kale)) +
geom_bar(stat = 'identity',
fill = '#ff6600',
color = 'black',
lwd = 0.3,
aes(group = person
# ,fill = person
)) +
theme_vilaweb() +
labs(title = title,
caption = caption,
subtitle = subtitle,
x = '',
y = y) +
# scale_fill_manual(name = '',
# values = databrew::make_colors(n = length(unique(plot_data$person)))) +
scale_y_continuous(breaks = seq(0, 100, 5)) +
scale_x_date(breaks = sort(unique(plot_data$date)),
labels = dater(sort(unique(plot_data$date)))) +
# facet_wrap(~person) +
theme(axis.text.x = element_text(angle = 90,
hjust = 1,
size = 15)) +
# geom_label(data = label_df,
# aes(x = date,
# y = y,
# label = label),
# fill = 'white',
# size = 6) +
# geom_line(data = data.frame(date = rep(as.Date('2017-10-15'), 2),
# y = c(12, 0)),
# aes(x = date,
# y = y)) +
# geom_point(data = data.frame(date = rep(as.Date('2017-10-15'), 2),
# y = c(12, 0)),
# aes(x = date,
# y = y)) +
theme(plot.title = element_text(size = 18),
plot.subtitle = element_text(size = 15),
legend.text = element_text(size = 10,
color = 'black')) +
geom_text(aes(label = n_kale),
nudge_y = 0.5,
size = 7,
alpha = 0.7)
# guides(fill = guide_legend(ncol = 3,
# keywidth=0.3,
# keyheight=0.2,
# default.unit="inch"))
# theme(legend.position = 'right')
return(g)
}
make_kale_plot('ca')
# Plot of use of violence among cronica global
make_cronica_plot <- function(language = 'en'){
dater <- catalan_date(new_line = FALSE)
plot_data <- bt
# plot_data <- plot_data %>%
# group_by(person) %>%
# filter(sum(n_kale) > 0) %>%
# ungroup
plot_data <- plot_data %>% filter(person == 'cronicaglobal')
# plot_data <- plot_data %>%
# filter(person %in% c('albert_rivera',
# 'inesarrimadas',
# 'ciudadanoscs',
# 'ciutadanscs'))
gapper <- 5
date_breaks <- sort(unique(plot_data$date))
plot_data$person<- paste0('@', plot_data$person)
label_df <- data_frame(date = as.Date('2017-10-01'),
y = 15,
label = 'La "Rebelión"')
if(language == 'en'){
title = 'Tweets from Cronica Global containing the word "violence"'
subtitle = 'Monthly, August 2017-December 2018'
caption = 'Data extracted/processed and chart created on 8 December 2018. Joe Brew. @joethebrew.'
x = 'Month'
y = 'Tweets'
} else {
title = 'Tuits de Cronica Global amb la paraula "violencia"'
subtitle = 'Mensual, Agost 2017-Desesmbre 2018'
caption = 'Dades descarregades/processades i gràfic creat el 8 de desembre 2018. Joe Brew. @joethebrew.'
x = 'Mes'
y = 'Tuits'
}
g <- ggplot(data = plot_data,
aes(x = date,
y = n_violence)) +
geom_bar(stat = 'identity',
fill = '#ff6600',
color = 'black',
lwd = 0.3,
aes(group = person
# ,fill = person
)) +
theme_vilaweb() +
labs(title = title,
caption = caption,
subtitle = subtitle,
x = '',
y = y) +
# scale_fill_manual(name = '',
# values = databrew::make_colors(n = length(unique(plot_data$person)))) +
scale_y_continuous(breaks = seq(0, 100, 5)) +
scale_x_date(breaks = sort(unique(plot_data$date)),
labels = dater(sort(unique(plot_data$date)))) +
# facet_wrap(~person) +
theme(axis.text.x = element_text(angle = 90,
hjust = 1,
size = 15)) +
# geom_label(data = label_df,
# aes(x = date,
# y = y,
# label = label),
# fill = 'white',
# size = 6) +
# geom_line(data = data.frame(date = rep(as.Date('2017-10-15'), 2),
# y = c(12, 0)),
# aes(x = date,
# y = y)) +
# geom_point(data = data.frame(date = rep(as.Date('2017-10-15'), 2),
# y = c(12, 0)),
# aes(x = date,
# y = y)) +
theme(plot.title = element_text(size = 18),
plot.subtitle = element_text(size = 15),
legend.text = element_text(size = 10,
color = 'black'))
# guides(fill = guide_legend(ncol = 3,
# keywidth=0.3,
# keyheight=0.2,
# default.unit="inch"))
# theme(legend.position = 'right')
return(g)
}
make_cronica_plot('ca')
# Plot of use of rebelion from casado
make_casado_plot <- function(language = 'en'){
dater <- catalan_date(new_line = FALSE)
plot_data <- bt
# plot_data <- plot_data %>%
# group_by(person) %>%
# filter(sum(n_kale) > 0) %>%
# ungroup
plot_data <- plot_data %>% filter(person == 'pablocasado_')
# plot_data <- plot_data %>%
# filter(person %in% c('albert_rivera',
# 'inesarrimadas',
# 'ciudadanoscs',
# 'ciutadanscs'))
gapper <- 1
date_breaks <- sort(unique(plot_data$date))
plot_data$person<- paste0('@', plot_data$person)
label_df <- data_frame(date = as.Date('2017-10-01'),
y = 2,
label = 'La "Rebelión"')
if(language == 'en'){
title = 'Tweets from Pablo Casado containing the word "rebellion"'
subtitle = 'Monthly, August 2017-December 2018'
caption = 'Data extracted/processed and chart created on 8 December 2018. Joe Brew. @joethebrew.'
x = 'Month'
y = 'Tweets'
} else {
title = 'Tuits de Pablo Casado amb la paraula "rebelión"'
subtitle = 'Mensual, Agost 2017-Desesmbre 2018'
caption = 'Dades descarregades/processades i gràfic creat el 8 de desembre 2018. Joe Brew. @joethebrew.'
x = 'Mes'
y = 'Tuits'
}
g <- ggplot(data = plot_data,
aes(x = date,
y = n_rebelion)) +
geom_bar(stat = 'identity',
fill = '#ff6600',
color = 'black',
lwd = 0.3,
aes(group = person
# ,fill = person
)) +
theme_vilaweb() +
labs(title = title,
caption = caption,
subtitle = subtitle,
x = '',
y = y) +
# scale_fill_manual(name = '',
# values = databrew::make_colors(n = length(unique(plot_data$person)))) +
scale_y_continuous(breaks = seq(0, 100, 5)) +
scale_x_date(breaks = sort(unique(plot_data$date)),
labels = dater(sort(unique(plot_data$date)))) +
# facet_wrap(~person) +
theme(axis.text.x = element_text(angle = 90,
hjust = 1,
size = 15)) +
geom_label(data = label_df,
aes(x = date,
y = y,
label = label),
fill = 'white',
size = 6) +
geom_line(data = data.frame(date = rep(as.Date('2017-10-15'), 2),
y = c(1.7, 0)),
aes(x = date,
y = y)) +
geom_point(data = data.frame(date = rep(as.Date('2017-10-15'), 2),
y = c(1.7, 0)),
aes(x = date,
y = y)) +
theme(plot.title = element_text(size = 18),
plot.subtitle = element_text(size = 15),
legend.text = element_text(size = 10,
color = 'black'))
# guides(fill = guide_legend(ncol = 3,
# keywidth=0.3,
# keyheight=0.2,
# default.unit="inch"))
# theme(legend.position = 'right')
return(g)
}
make_casado_plot('ca')
joebrew/vilaweb documentation built on Sept. 11, 2020, 3:42 a.m.
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Comandos separatistas
# Basic knitr options library(knitr) opts_chunk$set(comment = NA, echo = FALSE, warning = FALSE, message = FALSE, error = TRUE, cache = FALSE, fig.path = 'figures/')
# Libraries library(vilaweb) library(rtweet) library(tidyverse) library(databrew)
# Set up source('get_data.R') # There is now an object named tl, with all tweets # Ensure no duplicates tl <- tl %>% dplyr::distinct(.keep_all = TRUE) %>% filter(!duplicated(id))
# # Read temp data <- # out_list <- list() # peeps <- dir('data') # # peeps <- gsub('_tweets', '', peeps) # for(i in 1:length(peeps)){ # peep <- peeps[i] # dat <- read_csv(paste0('data/', peep, '/tweets.csv')) # out_list[[i]] <- dat # } # tl <-bind_rows(out_list) # # Filter out low-posters # removers <- c('eva_granados', # 'carmencalvo_', # 'meritxell_batet', # 'alejandrotgn') # Flag the posts tl <- tl %>% # filter(!username %in% removers) %>% # Only keep those who have posts going back to Sep 2017 group_by(username) %>% filter(min(date) <= '2017-09-01') %>% ungroup %>% # Identify comandos posts mutate(rebelion = grepl('rebel', tolower(tweet))) %>% mutate(comandos = grepl('comando', tolower(tweet)), kale = grepl('kale borroka', tolower(tweet))) %>% mutate(violence = grepl('violen', tolower(tweet))) %>% mutate(genero = grepl('machis|masclis|mujer|dona|done|géner|gèner|genero|marido|marit|novio|esposa|novia|doméstic|domèstic', tolower(tweet))) %>% mutate(violence_not_genero = violence & !genero) %>% mutate(cent = grepl('155', tolower(tweet))) %>% # Flag the newspaper posts mutate(newspaper = tolower(username) %in% tolower(c( 'ElMundoEspana', 'elpais_espana', 'LaVanguardia', 'cronicaglobal', 'elespanolcom', 'elperiodico', 'elconfidencial', 'OKDIARIO', 'enoticiescat'))) %>% mutate(party = tolower(username) %in% tolower(c( 'PSOE', 'socialistes_cat', 'CiutadansCs', 'PPopular', 'PPCatalunya', 'vox_es', 'CiudadanosCs', 'Societatcc'))) %>% mutate(type = ifelse(newspaper, 'Newspaper', ifelse(party, 'Party/group', 'Person'))) # By time bt <- tl %>% mutate(year_month = date_truncate(date, level = 'month') + 15) %>% group_by(type, date = year_month, person = username) %>% summarise(n_violence_not_genero = length(which(violence_not_genero)), n_violence = length(which(violence)), n_comandos = length(which(comandos)), n_kale = length(which(kale)), n_rebelion = length(which(rebelion)), n_cent = length(which(cent)), d = n()) %>% ungroup %>% mutate(p_comandos = n_comandos / d * 100, p_kale = n_kale / d * 100, p_violence = n_violence / d * 100, p_cent = n_cent / d * 100) bt_kale <- bt %>% filter(date >= '2017-08-01', date <= '2018-12-30') bt <- bt %>% filter(date >= '2017-08-01', date <= '2018-11-30')
# Plot of use of comandos among ciudadanos make_people_plot <- function(language = 'en'){ dater <- catalan_date(new_line = FALSE) plot_data <- bt %>% filter(type %in% c('Person', 'Party/group')) plot_data <- plot_data %>% filter(person %in% c('albert_rivera', 'inesarrimadas', 'ciudadanoscs', 'ciutadanscs')) gapper <- 5 date_breaks <- sort(unique(plot_data$date)) plot_data$person<- paste0('@', plot_data$person) label_df <- data_frame(date = as.Date('2017-10-01'), y = 15, label = 'La "Rebelión"') if(language == 'en'){ title = 'Tweets containing the word "comandos"' subtitle = 'Monthly, August 2017-November 2018' caption = 'Data extracted/processed and chart created on 8 December 2018. Joe Brew. @joethebrew.' x = 'Month' y = 'Tweets' } else { title = 'Tuits amb la paraula "comando(s)"' subtitle = 'Mensual, Agost 2017-Nov 2018' caption = 'Dades descarregades/processades i gràfic creat el 8 de desembre 2018. Joe Brew. @joethebrew.' x = 'Mes' y = 'Tuits' } g <- ggplot(data = plot_data, aes(x = date, y = n_comandos)) + geom_bar(stat = 'identity', aes(group = person, fill = person)) + theme_vilaweb() + labs(title = title, caption = caption, subtitle = subtitle, x = x, y = y) + scale_fill_manual(name = '', values = databrew::make_colors(n = length(unique(plot_data$person)))) + scale_y_continuous(breaks = seq(0, 100, 5)) + scale_x_date(breaks = sort(unique(plot_data$date)), labels = dater(sort(unique(plot_data$date)))) + # facet_wrap(~person) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + geom_label(data = label_df, aes(x = date, y = y, label = label), fill = 'white', size = 6) + geom_line(data = data.frame(date = rep(as.Date('2017-10-15'), 2), y = c(12, 0)), aes(x = date, y = y)) + geom_point(data = data.frame(date = rep(as.Date('2017-10-15'), 2), y = c(12, 0)), aes(x = date, y = y)) + theme(plot.title = element_text(size = 24), plot.subtitle = element_text(size = 20), legend.text = element_text(size = 18, color = 'black')) + guides(fill = guide_legend(ncol = 2, keywidth=0.3, keyheight=0.4, default.unit="inch")) # theme(legend.position = 'right') return(g) } make_people_plot('ca')
# Plot of use of kale among ciudadanos make_kale_plot <- function(language = 'en'){ dater <- catalan_date(new_line = FALSE) plot_data <- bt_kale %>% filter(type %in% c('Person', 'Party/group')) # plot_data <- plot_data %>% # group_by(person) %>% # filter(sum(n_kale) > 0) %>% # ungroup plot_data <- plot_data %>% filter(person == 'pablocasado_') # Manually insert not yet gathered dec data plot_data$n_kale[plot_data$date == '2018-12-16'] <- 2 # plot_data <- plot_data %>% # filter(person %in% c('albert_rivera', # 'inesarrimadas', # 'ciudadanoscs', # 'ciutadanscs')) gapper <- 5 date_breaks <- sort(unique(plot_data$date)) plot_data$person<- paste0('@', plot_data$person) label_df <- data_frame(date = as.Date('2017-10-01'), y = 15, label = 'La "Rebelión"') if(language == 'en'){ title = 'Tweets from Pablo Casado containing the words "kale borroka"' subtitle = 'Monthly, August 2017-December 2018' caption = 'Data extracted/processed and chart created on 8 December 2018. Joe Brew. @joethebrew.' x = 'Month' y = 'Tweets' } else { title = 'Tuits de Pablo Casado amb les paraules "kale borroka"' subtitle = 'Mensual, Agost 2017-Desesmbre 2018' caption = 'Dades descarregades/processades i gràfic creat el 8 de desembre 2018. Joe Brew. @joethebrew.' x = 'Mes' y = 'Tuits' } g <- ggplot(data = plot_data, aes(x = date, y = n_kale)) + geom_bar(stat = 'identity', fill = '#ff6600', color = 'black', lwd = 0.3, aes(group = person # ,fill = person )) + theme_vilaweb() + labs(title = title, caption = caption, subtitle = subtitle, x = '', y = y) + # scale_fill_manual(name = '', # values = databrew::make_colors(n = length(unique(plot_data$person)))) + scale_y_continuous(breaks = seq(0, 100, 5)) + scale_x_date(breaks = sort(unique(plot_data$date)), labels = dater(sort(unique(plot_data$date)))) + # facet_wrap(~person) + theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 15)) + # geom_label(data = label_df, # aes(x = date, # y = y, # label = label), # fill = 'white', # size = 6) + # geom_line(data = data.frame(date = rep(as.Date('2017-10-15'), 2), # y = c(12, 0)), # aes(x = date, # y = y)) + # geom_point(data = data.frame(date = rep(as.Date('2017-10-15'), 2), # y = c(12, 0)), # aes(x = date, # y = y)) + theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 15), legend.text = element_text(size = 10, color = 'black')) + geom_text(aes(label = n_kale), nudge_y = 0.5, size = 7, alpha = 0.7) # guides(fill = guide_legend(ncol = 3, # keywidth=0.3, # keyheight=0.2, # default.unit="inch")) # theme(legend.position = 'right') return(g) } make_kale_plot('ca')
# Plot of use of violence among cronica global make_cronica_plot <- function(language = 'en'){ dater <- catalan_date(new_line = FALSE) plot_data <- bt # plot_data <- plot_data %>% # group_by(person) %>% # filter(sum(n_kale) > 0) %>% # ungroup plot_data <- plot_data %>% filter(person == 'cronicaglobal') # plot_data <- plot_data %>% # filter(person %in% c('albert_rivera', # 'inesarrimadas', # 'ciudadanoscs', # 'ciutadanscs')) gapper <- 5 date_breaks <- sort(unique(plot_data$date)) plot_data$person<- paste0('@', plot_data$person) label_df <- data_frame(date = as.Date('2017-10-01'), y = 15, label = 'La "Rebelión"') if(language == 'en'){ title = 'Tweets from Cronica Global containing the word "violence"' subtitle = 'Monthly, August 2017-December 2018' caption = 'Data extracted/processed and chart created on 8 December 2018. Joe Brew. @joethebrew.' x = 'Month' y = 'Tweets' } else { title = 'Tuits de Cronica Global amb la paraula "violencia"' subtitle = 'Mensual, Agost 2017-Desesmbre 2018' caption = 'Dades descarregades/processades i gràfic creat el 8 de desembre 2018. Joe Brew. @joethebrew.' x = 'Mes' y = 'Tuits' } g <- ggplot(data = plot_data, aes(x = date, y = n_violence)) + geom_bar(stat = 'identity', fill = '#ff6600', color = 'black', lwd = 0.3, aes(group = person # ,fill = person )) + theme_vilaweb() + labs(title = title, caption = caption, subtitle = subtitle, x = '', y = y) + # scale_fill_manual(name = '', # values = databrew::make_colors(n = length(unique(plot_data$person)))) + scale_y_continuous(breaks = seq(0, 100, 5)) + scale_x_date(breaks = sort(unique(plot_data$date)), labels = dater(sort(unique(plot_data$date)))) + # facet_wrap(~person) + theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 15)) + # geom_label(data = label_df, # aes(x = date, # y = y, # label = label), # fill = 'white', # size = 6) + # geom_line(data = data.frame(date = rep(as.Date('2017-10-15'), 2), # y = c(12, 0)), # aes(x = date, # y = y)) + # geom_point(data = data.frame(date = rep(as.Date('2017-10-15'), 2), # y = c(12, 0)), # aes(x = date, # y = y)) + theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 15), legend.text = element_text(size = 10, color = 'black')) # guides(fill = guide_legend(ncol = 3, # keywidth=0.3, # keyheight=0.2, # default.unit="inch")) # theme(legend.position = 'right') return(g) } make_cronica_plot('ca')
# Plot of use of rebelion from casado make_casado_plot <- function(language = 'en'){ dater <- catalan_date(new_line = FALSE) plot_data <- bt # plot_data <- plot_data %>% # group_by(person) %>% # filter(sum(n_kale) > 0) %>% # ungroup plot_data <- plot_data %>% filter(person == 'pablocasado_') # plot_data <- plot_data %>% # filter(person %in% c('albert_rivera', # 'inesarrimadas', # 'ciudadanoscs', # 'ciutadanscs')) gapper <- 1 date_breaks <- sort(unique(plot_data$date)) plot_data$person<- paste0('@', plot_data$person) label_df <- data_frame(date = as.Date('2017-10-01'), y = 2, label = 'La "Rebelión"') if(language == 'en'){ title = 'Tweets from Pablo Casado containing the word "rebellion"' subtitle = 'Monthly, August 2017-December 2018' caption = 'Data extracted/processed and chart created on 8 December 2018. Joe Brew. @joethebrew.' x = 'Month' y = 'Tweets' } else { title = 'Tuits de Pablo Casado amb la paraula "rebelión"' subtitle = 'Mensual, Agost 2017-Desesmbre 2018' caption = 'Dades descarregades/processades i gràfic creat el 8 de desembre 2018. Joe Brew. @joethebrew.' x = 'Mes' y = 'Tuits' } g <- ggplot(data = plot_data, aes(x = date, y = n_rebelion)) + geom_bar(stat = 'identity', fill = '#ff6600', color = 'black', lwd = 0.3, aes(group = person # ,fill = person )) + theme_vilaweb() + labs(title = title, caption = caption, subtitle = subtitle, x = '', y = y) + # scale_fill_manual(name = '', # values = databrew::make_colors(n = length(unique(plot_data$person)))) + scale_y_continuous(breaks = seq(0, 100, 5)) + scale_x_date(breaks = sort(unique(plot_data$date)), labels = dater(sort(unique(plot_data$date)))) + # facet_wrap(~person) + theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 15)) + geom_label(data = label_df, aes(x = date, y = y, label = label), fill = 'white', size = 6) + geom_line(data = data.frame(date = rep(as.Date('2017-10-15'), 2), y = c(1.7, 0)), aes(x = date, y = y)) + geom_point(data = data.frame(date = rep(as.Date('2017-10-15'), 2), y = c(1.7, 0)), aes(x = date, y = y)) + theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 15), legend.text = element_text(size = 10, color = 'black')) # guides(fill = guide_legend(ncol = 3, # keywidth=0.3, # keyheight=0.2, # default.unit="inch")) # theme(legend.position = 'right') return(g) } make_casado_plot('ca')
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