Características de los perfiles de la AGE
Introducción
Estas gráficas fueron creadas para una clase en el INAP (Instituto Nacional de Administraciones Públicas).
Se analizó la estructura de perfiles de la Administración General del Estado (AGE) en Twitter a partir de una lista de 631 perfiles elaborada por David Álvarez (@dalvarez37)
Librerías necesarias
library(readr)
library(dplyr)
library(tidyr)
library(ggrepel)
locale(date_names = "es", date_format = "%AD", time_format = "%AT",
decimal_mark = ",", grouping_mark = ".", tz = "UTC",
encoding = "UTF-8", asciify = FALSE)## <locale>
## Numbers: 123.456,78
## Formats: %AD / %AT
## Timezone: UTC
## Encoding: UTF-8
## <date_names>
## Days: domingo (dom.), lunes (lun.), martes (mar.), miércoles (mié.), jueves
## (jue.), viernes (vie.), sábado (sáb.)
## Months: enero (ene.), febrero (feb.), marzo (mar.), abril (abr.), mayo (may.),
## junio (jun.), julio (jul.), agosto (ago.), septiembre (sept.),
## octubre (oct.), noviembre (nov.), diciembre (dic.)
## AM/PM: a. m./p. m.Plantilla de gráficos
theme_a <- function(base_size = 12,
base_family = "sans"
)
{
tema <-
theme_bw(base_size=base_size) +
theme(legend.position="top") +
theme(legend.text = element_text(size=base_size,family = base_family)) +
theme(plot.title=element_text(size=base_size+2,
vjust=1.25,
family=base_family,
hjust = 0.5
)) +
theme(plot.subtitle=element_text(size=base_size-1, family = base_family)) +
theme(text = element_text(size=base_size-1,family = base_family)) +
theme(axis.text.x=element_text(size=base_size,family = base_family)) +
theme(axis.text.y=element_text(size=base_size, family = base_family)) +
theme(axis.title.x=element_text(size=base_size, vjust=0, family = base_family)) +
theme(axis.title.y=element_text(size=base_size, vjust=1.25, family = base_family)) +
theme(plot.caption=element_text(size=base_size-2, family = base_family)) +
theme(strip.text = element_text(size=base_size+2, family = base_family)) +
theme(strip.text.x = element_text(size=base_size+1, family = base_family)) +
theme(strip.text.y = element_text(size=base_size+1,, family = base_family))
return (tema)
}Leemos y unimos datos
- Perfiles incluidos en la lista lista https://twitter.com/i/lists/1408835183260864521
- Clasificación de los Perfiles incluidos en la lista lista https://twitter.com/i/lists/1408835183260864521 por análisis de red
`%notin%` <- Negate(`%in%`)
#leemos datos
list_members <- read_csv("https://raw.githubusercontent.com/congosto/congosto.github.io/master/AGE_perfiles.csv")
list_nodos <- read_csv("https://raw.githubusercontent.com/congosto/congosto.github.io/master/AGE_nodos.csv")
# Color y nombre de los grupos
grupos <- c("0","2","3","1")
grupos_nombres <- c("Gobierno","Exteriores",
"Cultura","Ciencia")
grupos_color <- c("0" = "#D433FF","2" = "#078C20",
"3" = "#04A4FA" ,"1" = "#EF634A")
# cambios de formato
list_members$since <- as.Date(list_members$since)
list_nodos$modularity_class <- as.character(list_nodos$modularity_class)
list_nodos$modularity_class <- factor(list_nodos$modularity_class, levels = grupos)
#Unimos datos
perfiles <- left_join(list_members,list_nodos, by = c("screen_name" = "Label") ) %>%
select (screen_name,followers,following,statuses,since, modularity_class,
indegree,outdegree) %>%
mutate (since_dias = as.numeric( Sys.Date() - since ),
tweets_dia = statuses / since_dias)Clasificamos por seguidores/seguidos
top_perfiles <- perfiles %>%
filter (followers > 500000 |
following > 10000)
ggplot () +
geom_point(data = perfiles , aes (x= following, y= followers,
color= modularity_class)) +
labs(x = "Seguidos ", y = "Seguidores ",
title = "Seguidores vs. seguidos de perfiles de AGE",
color ="")+
geom_text_repel (data = top_perfiles, aes (x= following, y= followers,
label = screen_name, color= modularity_class),
show.legend = FALSE, size = 5) +
scale_color_manual (values = grupos_color,
labels = grupos_nombres) +
scale_x_continuous(labels = scales::label_number_si())+
scale_y_continuous(labels = scales::label_number_si())+
theme_a()+
guides(color = guide_legend(override.aes = list(size = 4) ) )
Clasificamos por seguidores/actividad
top_perfiles <- perfiles %>%
filter (followers > 500000 |
tweets_dia > 30)
ggplot () +
geom_point(data = perfiles , aes (x = tweets_dia, y = followers ,
color= modularity_class)) +
labs(x = "Tweets/día", y = "Seguidores",
title = "Seguidores vs. actividad de perfiles de AGE",
color ="")+
geom_text_repel (data = top_perfiles, aes (x = tweets_dia, y = followers ,
label = screen_name, color= modularity_class),
show.legend = FALSE,size = 5) +
scale_color_manual (values = grupos_color,
labels = grupos_nombres) +
scale_y_continuous(labels = scales::label_number_si())+
theme_a()+
guides(color = guide_legend(override.aes = list(size = 4) ) )
Clasificamos por seguidores/antigüedad
top_perfiles <- perfiles %>%
filter (followers > 500000 )
ggplot () +
geom_point(data = perfiles , aes (x = since, y = followers,
color= modularity_class)) +
labs(x = "Antigüedad", y = "Seguidores",
title = "Seguidores vs. antigüedad de perfiles de AGE",
color ="")+
geom_text_repel (data = top_perfiles, aes (x= since , y= followers,
label = screen_name, color= modularity_class),
max.overlaps=18,force=10,
show.legend = FALSE, size = 5) +
scale_color_manual (values = grupos_color,
labels = grupos_nombres) +
scale_y_continuous(labels = scales::label_number_si())+
theme_a()+
guides(color = guide_legend(override.aes = list(size = 4) ) )
Clasificamos por seguidores/seguidos dentro de la AGE
top_perfiles <- perfiles %>%
filter (indegree > 200 )
ggplot () +
geom_point(data = perfiles , aes (x = outdegree, y = indegree,
color= modularity_class)) +
labs(x = "Seguidos", y = "Seguidores",
title = "Seguidores vs. seguidos entre perfiles de AGE",
color ="")+
geom_text_repel (data = top_perfiles, aes (x = outdegree, y = indegree,
label = screen_name, color= modularity_class),
show.legend = FALSE,size = 4.5) +
scale_color_manual (values = grupos_color,
labels = grupos_nombres) +
scale_y_continuous(labels = scales::label_number_si())+
theme_a()+
guides(color = guide_legend(override.aes = list(size = 4) ) )