senadoRES

The goal of senadoRES is to provide information about the Senate of Spain. It uses the Open Data available and some other XML files I found while exploring the website.

Installation

You can install the released version of senadoRES with:

remotes::install_github("llrs/senadoRES")

Example

To make it easier to work and show some data I’ll use some packages:

library("senadoRES")
library("dplyr")
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library("ggplot2")

Senadores

This function return the members of the Senate:

ts <- senadores()
head(ts)
#>         nombre       apellidos legislatura ultCredencial procedTipo
#> 1     FERNANDO    ABAD BÉCQUER           5           260          D
#> 2      ENRIQUE  ABAD BENEDICTO          10           034          E
#> 3     ELISABET     ABAD GIRALT          12           051          D
#> 4 JOSÉ ANTONIO ABAD LÓPEZ-BREA           3           039          E
#> 5     PATRICIA ABASCAL JIMÉNEZ          14           199          E
#> 6       ADOLFO   ABEJÓN ORTEGA           9           081          E
#>   procedLiteral            procedLugar               grupoCod grupoSiglas
#> 1     Designado     Asamblea de Madrid     Asamblea de Madrid         800
#> 2        Electo                Sevilla                Sevilla         800
#> 3     Designada Parlamento de Cataluña Parlamento de Cataluña         807
#> 4        Electo                 Zamora                 Zamora          CP
#> 5        Electa     Eivissa-Formentera     Eivissa-Formentera         800
#> 6        Electo             Valladolid             Valladolid         801
#>   grupoNombre    sex
#> 1         GPS   male
#> 2         GPS   male
#> 3         GPN female
#> 4        GPCP   male
#> 5         GPS female
#> 6         GPP   male

We can see the increase of females on the Senate along the legislatures:

ts %>%
  group_by(legislatura) %>%
  count(sex) %>%
  mutate(total = sum(n)) %>%
  filter(!is.na(sex)) %>%
  mutate(ratio = n/total) %>%
  filter(sex != "male") %>% 
  ggplot() +
  geom_point(aes(legislatura, ratio), size = 5) +
  geom_hline(yintercept = 0.5, linetype = 2, col = "red") +
  scale_x_continuous(breaks = seq_len(15)) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1), 
                     breaks = seq(from = 0, to = 1, by = .1),
                     expand = expansion(add = c(0.01, 0.01)), limits = c(0, NA)) +
  theme_minimal() +
  labs(title = "Ratio of women", x  = "Legislatura", y = "% of women") +
  guides(col = FALSE, shape = FALSE) +
  theme(panel.grid.minor.x = element_blank())

Or see what has been done:

b <- boletin(boletin_csv(14, 1))
b[, c("FECHA", "DISP", "apartado", "subapartado", "TITULO", "document")]
#>        FECHA DISP                      apartado       subapartado
#> 1 2019-12-05    1 VI. Administración del Senado Personal eventual
#> 2 2019-12-05    2 VI. Administración del Senado Personal eventual
#> 3 2019-12-05    3 VI. Administración del Senado Personal eventual
#>                                                                                                                                              TITULO
#> 1                        Personal eventual del Senado como Asistentes de los señores Senadores de los Grupos Parlamentarios de la XIII Legislatura.
#> 2 Personal eventual adscrito a la Presidencia, Vicepresidencias Primera y Segunda y Secretarías Primera y Cuarta del Senado de la XIII Legislatura.
#> 3                                                                                       Personal eventual al servicio del ex-Presidente del Senado.
#>        document
#> 1 BOCG_D_14_1_1
#> 2 BOCG_D_14_1_2
#> 3 BOCG_D_14_1_3

So, on the first session of the 14th legislature they dealt with human resources. We can check one of those documents, to retrieve the text with documento.