The function get_tables of swissparl shows that the
Webservices contain a table called
BusinessRole. By using the get_glimpse function, we see that the
table itself contains data on the role councillors play in political
businesses.
glimpse_br <- swissparl::get_glimpse("BusinessRole", rows = 1000)
knitr::kable(table(glimpse_br$RoleName), col.names = c("RoleName", "Freq"))| RoleName | Freq |
|---|---|
| Bekämpfer(-in) | 4 |
| Mitunterzeichner(-in) | 904 |
| Sprecher(-in) | 8 |
| Urheber(-in) | 84 |
In the first 1000 rows we find four different roles: combatants, co-signers, speakers and authors. We want to use this kind of information to get an idea of what cooperation patterns can be found in the Council of States for the last term (December 2015 to November 2019).
As a first step, we have to find out which businesses and proposals were
submitted to the Council during this period. For this we use the
function get_data in such a way that only the corresponding businesses
are downloaded. We get the names (SubmissionCouncilAbbreviation and
SubmissionLegislativePeriod) and values (SR and 50) of the
necessary variables with get_glimpse("Business").
biz <- swissparl::get_data(
table = "Business",
SubmissionCouncilAbbreviation = "SR",
SubmissionLegislativePeriod = 50,
Language = "DE"
)We find 732 entries. Let’s get to know them better:
# Packages
library(dplyr)
library(ggplot2)
library(hrbrthemes)
# Plot by type
biz %>%
group_by(BusinessTypeAbbreviation) %>%
count() %>%
ungroup() %>%
ggplot(aes(reorder(BusinessTypeAbbreviation, n), n)) +
geom_col() +
coord_flip() +
labs(
title = "Who with Whom in the Council of States?",
subtitle = "Political Businesses During the 50th Legislative Period by Type",
caption = "Data: Parliamentary Services of the Federal Assembly, Bern"
) +
theme_ipsum_rc(grid="X") +
theme(axis.title = element_blank())
Interpellations and Motions were by far the most frequently used instruments in the Council of States.
# Plot by author
biz %>%
group_by(SubmittedBy) %>%
count() %>%
ungroup() %>%
top_n(10, n) %>%
mutate(SubmittedBy = stringr::str_remove_all(SubmittedBy, "\\s.*$")) %>%
ggplot(aes(reorder(SubmittedBy, n), n)) +
geom_col() +
coord_flip() +
labs(
title = "Who with Whom in the Council of States?",
subtitle = "Political Businesses During the 50th Legislative Period by Author",
caption = "Data: Parliamentary Services of the Federal Assembly, Bern"
) +
theme_ipsum_rc(grid="X") +
theme(axis.title = element_blank())
We see that not only individual councillors launched businesses, but also commissions. The missing values come from political businesses that the Federal Council, i.e. the executive branch, submitted to the Council.
Now that we have know all the businesses for the desired period, we can
download the associated business roles using get_data.
biz.roles <- swissparl::get_data(
table = "BusinessRole",
BusinessNumber = biz$ID,
Language = "DE"
)We find 4524 entries. Off to the next exploration:
# Plot by role
biz.roles %>%
group_by(RoleName) %>%
count() %>%
ungroup() %>%
ggplot(aes(reorder(RoleName, n), n)) +
geom_col() +
coord_flip() +
labs(
title = "Who with Whom in the Council of States?",
subtitle = "Roles in Political Businesses of the 50th Legislative Period",
caption = "Data: Parliamentary Services of the Federal Assembly, Bern"
) +
theme_ipsum_rc(grid="X") +
theme(axis.title = element_blank())
702 authorships are matched by 3804 co-signatures (on average
5.4). The table does not include neither the names of the authors nor
the co-signers. get_tables tells us that the Webservices contain named
MemberCouncil. We try our luck.
council.members <- swissparl::get_data(
table = "MemberCouncil",
ID = biz.roles$MemberCouncilNumber,
Language = "DE"
)Looks like we got the right idea. We join the data to our biz.role
data.
biz.roles <- left_join(
biz.roles,
council.members,
by = c("MemberCouncilNumber" = "ID")
) # Plot by role
biz.roles %>%
filter(Role %in% c(3, 7)) %>%
mutate(name_canton = paste0(LastName, " (", CantonAbbreviation, ")")) %>%
group_by(name_canton, RoleName) %>%
count() %>%
ungroup() %>%
mutate(RoleName = stringr::str_remove_all(RoleName, "\\(.*?\\)")) %>%
pivot_wider(names_from = RoleName, values_from = n) %>%
ggplot(aes(Mitunterzeichner, Urheber, label = name_canton)) +
geom_smooth(method = lm, color = "black", fill = "grey80") +
geom_point() +
ggrepel::geom_text_repel() +
labs(
title = "Who with Whom in the Council of States?",
subtitle = "Roles in Political Businesses of the 50th Legislative Period",
caption = "Data: Parliamentary Services of the Federal Assembly, Bern"
) +
scale_y_continuous(limits = c(0, 40)) +
theme_ipsum_rc()
There seems to be a great variance in the way council members “live” their mandate.
Now that we have the necessary data, we want to address the question of who collaborated with whom, and how often? We apply an approach from the field of social network analysis.
In order to do this, we must first find out who co-signed how often in whose proposals. We split the authors and the co-signers into two separate datasets, each including the business number and the councillor id, and then join them together to create 4021 author co-signer pairs.
# Business authors
authors <- biz.roles %>%
filter(Role == 7) %>%
filter(!is.na(MemberCouncilNumber)) %>%
select(BusinessNumber, MemberCouncilNumber) %>%
distinct(BusinessNumber, MemberCouncilNumber, .keep_all = T)
# Business cosigners
cosigners <- biz.roles %>%
filter(Role == 3) %>%
filter(!is.na(MemberCouncilNumber)) %>%
select(BusinessNumber, MemberCouncilNumber) %>%
distinct(BusinessNumber, MemberCouncilNumber, .keep_all = T)
# Author-cosigner-pair
acp <- left_join(
authors,
cosigners,
by = "BusinessNumber",
suffix = c(".author", ".cosigner")
)We can then use this dataset to define the links or edges of our network.
edges <- acp %>%
rename(
from = "MemberCouncilNumber.cosigner",
to = "MemberCouncilNumber.author"
) %>%
group_by(from, to) %>%
count() %>%
ungroup() %>%
filter(!is.na(from))edges %>%
complete(from, to, fill = list(n = 0)) %>%
ggplot(aes(n)) +
geom_bar() +
labs(
x = "Num. of\ncooperations",
y = "Freq.",
title = "Who with Whom in the Council of States?",
subtitle = "Frequency of Author-Co-Signatory Cooperations",
caption = "Data: Parliamentary Services of the Federal Assembly, Bern"
) +
theme_ipsum_rc() +
theme(panel.grid.minor = element_blank())
If we take into account the implicitly missing values in our data, it turns out that very often there was no cooperation (~41 percent). On the other hand, there also seem to have been a few councillors who very often support each other’s businesses.
In order to display the network graphically we have to create its nodes.
nodes <- left_join(
tibble::tibble(ID = unique(c(edges$from, edges$to))),
council.members %>%
mutate(ID = ID) %>%
select(ID, LastName, GenderAsString, CantonAbbreviation, PartyAbbreviation),
by = "ID"
)Now we create a network object using tidygraph, which is called a
tbl_graph. A tbl_graph consists of two tibbles: our edges tibble and
the nodes tibble we just created.
cs50 <- tidygraph::tbl_graph(
nodes = nodes %>% mutate(ID = as.character(ID)),
edges = edges %>% mutate_at(vars(from, to), as.character),
directed = F
)Finally We can plot our network with the awesome package ggraph - an
extension of ggplot2. We use the so-called eigenvector
centrality to
highlight the influence of a node/councillor.
cs50 %>%
mutate(importance = tidygraph::centrality_eigen(weights = n)) %>%
mutate(label = paste0(LastName, " (", CantonAbbreviation, ")")) %>%
ggraph() +
geom_edge_fan(aes(alpha = n, width = n)) +
geom_node_point(aes(size = importance)) +
geom_node_text(aes(label = label)) +
theme_graph() +
theme(legend.position = "none")
In this first simple network 3 councillors stand out: Verena Diener Lenz (glp, ZH), Benedikt Würth (CVP, SG) and Daniel Fässler (CVP, AI). After short research it turns out why: They all served only for a few months during the 50th legislative period. For this reason, they are excluded from the analysis.
nodes2 <- nodes %>%
filter(!ID %in% c(61, 4056, 4237)) %>%
mutate(ID = as.character(ID))
edges2 <- edges %>%
filter(!from %in% c(61, 4056, 4237)) %>%
filter(!to %in% c(61, 4056, 4237)) %>%
mutate_at(vars(from, to), as.character)
cs50_2 <- tidygraph::tbl_graph(nodes = nodes2, edges = edges2, directed = F)New try:
# For reproducibility reasons
set.seed(15)
# Network plot
cs50_2 %>%
mutate(party = factor(PartyAbbreviation, levels = c("SP", "SVP", "CVP", "FDP-Liberale", "GPS", "BDP", "-"))) %>%
mutate(importance = tidygraph::centrality_eigen(weights = n)) %>%
mutate(label = paste0(LastName, " (", CantonAbbreviation, ")")) %>%
ggraph() +
geom_edge_fan(aes(alpha = n, width = sqrt(n)), edge_colour = "gray60") +
scale_edge_width(range = c(0.001, 2)) +
scale_edge_alpha(range = c(0.001, 1)) +
geom_node_point(aes(size = importance^2, color = party)) +
geom_node_text(aes(label = label), size = 2, nudge_y = -0.08) +
scale_color_manual(values = colors) +
scale_size(range = c(0.1, 10)) +
labs(
title = "Who with Whom in the Council of States?",
subtitle = "Co-Signing Behaviour in the 50th Legislative Period",
caption = "Data: Parliamentary Services of the Federal Assembly, Bern\nNode Size: Eigenvector Centrality"
) +
theme_graph() +
theme_ipsum_rc() +
theme(
legend.position = "none",
panel.grid = element_blank(),
axis.text = element_blank(),
axis.title = element_blank()
)
As was to be expected, cooperation took place primarily within parties/political camps. But there were exceptions: For example, the liberal Raphaël Comte from the canton of Neuchâtel, who apparently often cooperated with council members from the nearby cantons of Fribourg and Jura, although they did not come from the same side of the political spectrum.
Also the measure of importance shows a geographical/regional pattern: Among the most influential council members (according to eigenvector centrality) were Claude Hêche, Anne Seydoux-Christe and Didier Berberat from French-speaking Switzerland and Damian Müller, Erich Ettlin and Joachim Eder all from central Swiss cantons.