Chapter 9 Network Visualization
Visualizations of social networks are cool. I am often impressed if I see them in papers. They seem to signal that the authors are technically savvy.
If you know me a little bit you will also know that I think that the importance of a good description of your data cannot be downplayed: describing before (and over) explanation; simplicity (e.g. univariate or bivatiate statistics) trumps complexity. What is a better ‘description’ of your network data than a graphical summary of your network? What could I possibly have against network visualizations?
Unfortunately, more often than not the story behind the (cool, dynamic, interactive) network picture is not clear. I do not see selection and influence processes. We have have to guess what the node and tie attributes are. And, importantly, typical network structures remain elusive.
A good example of a bad network visualization is Smallworld, introduced in section 1.4.1.3. After this tutorial, you will be able to make a much better visualization of Smallworld, see Assignment 9.4.
Paradoxically, one of the reasons why oftentimes it is not worth the time to look at network visualizations is because scholars did not think it was worth the time to look at the data.
The take home message of this chapter is that before you can make a successful network visualization you need to answer the following question:
What story do I want my network visualization to tell?
The answer to this question depends on:
- your research question
- descriptive statistics of your data
- how preliminary network plots are able to convey your message.
In this chapter we will take on three case studies:
- Zachary’s karate club: We will first look for the story within the data.
- Twittersphere: The visualization of the three-layer twitter network in the Dutch House of Parliament should tell the story that twitter networks are segregated along party affiliations.
- Co-author:
Where for our analyses we heavily relay on the R package
lavaan(Rosseel 2012) andRSiena(Snijders et al. 2022), for network description and visualisation we will mainly useigraph(Csardi and Nepusz 2020).
In this tutorial you will learn how to:
- Change node characteristics (including their coordinates)
- Change tie characteristics (depending on tie and dyad characteristics)
- Visualize subgroups (communities/cliques) in the network
- How to add a title, legend, etc. to the plot
A very good starting point on the web for network visualizations is the open access work of Katherina Ognyanova (Ognyanova 2016): https://kateto.net/networks.
Naturally also have a look at the manual of igraph for R.
To have the list of parameters at hand when playing with igraph, run ?igraph.plotting.
9.1 Zachary’s karate club
We will start with a classical example: Zachary’s karate club (Zachary 1977). We will pretend that we know close to nothing about this network beforehand. I will demonstrate you can (sometimes) discover the story behind the data by plots and descriptive statistics.
As stated above, we will mainly rely on the igraph package. This means we have to make an igraph graph object before we can start doing anything. Luckily igraph comes with Zachary’s network build-in. But don’t worry, we will start from scratch later on. Let us have a first look at the data.
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17]
#> [1,] 0 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0
#> [2,] 1 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0
#> [3,] 1 1 0 1 0 0 0 1 1 1 0 0 0 1 0 0 0
#> [4,] 1 1 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0
#> [5,] 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0
#> [6,] 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1
#> [7,] 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1
#> [8,] 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [9,] 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [10,] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [11,] 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0
#> [12,] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [13,] 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [14,] 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [15,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [16,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [17,] 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0
#> [18,] 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [20,] 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [22,] 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [23,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [24,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [25,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [26,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [27,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [28,] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [29,] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [30,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [31,] 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
#> [32,] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [33,] 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0
#> [34,] 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 0
#> [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32]
#> [1,] 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1
#> [2,] 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0
#> [3,] 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0
#> [4,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [5,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [6,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [7,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [8,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [9,] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
#> [10,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [11,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [12,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [13,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [14,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [15,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [16,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [17,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [18,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [23,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [24,] 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0
#> [25,] 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1
#> [26,] 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1
#> [27,] 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
#> [28,] 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0
#> [29,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
#> [30,] 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0
#> [31,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [32,] 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0
#> [33,] 0 1 0 1 0 1 1 0 0 0 0 0 1 1 1
#> [34,] 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1
#> [,33] [,34]
#> [1,] 0 0
#> [2,] 0 0
#> [3,] 1 0
#> [4,] 0 0
#> [5,] 0 0
#> [6,] 0 0
#> [7,] 0 0
#> [8,] 0 0
#> [9,] 1 1
#> [10,] 0 1
#> [11,] 0 0
#> [12,] 0 0
#> [13,] 0 0
#> [14,] 0 1
#> [15,] 1 1
#> [16,] 1 1
#> [17,] 0 0
#> [18,] 0 0
#> [19,] 1 1
#> [20,] 0 1
#> [21,] 1 1
#> [22,] 0 0
#> [23,] 1 1
#> [24,] 1 1
#> [25,] 0 0
#> [26,] 0 0
#> [27,] 0 1
#> [28,] 0 1
#> [29,] 0 1
#> [30,] 1 1
#> [31,] 1 1
#> [32,] 1 1
#> [33,] 0 1
#> [34,] 1 09.1.1 Descriptive statistics
Size
#> [1] 34
#> [1] 78We discussed several network structure in section 4.3.2. Decide for yourself at which structures you want to look at. I give three examples below.
transitivity
# be aware that directed graphs are considered as undirected. but g is undirected.
igraph::transitivity(g, type = c("localundirected"), isolates = c("NaN", "zero"))#> [1] 0.1500000 0.3333333 0.2444444 0.6666667 0.6666667 0.5000000 0.5000000 1.0000000 0.5000000
#> [10] 0.0000000 0.6666667 NaN 1.0000000 0.6000000 1.0000000 1.0000000 1.0000000 1.0000000
#> [19] 1.0000000 0.3333333 1.0000000 1.0000000 1.0000000 0.4000000 0.3333333 0.3333333 1.0000000
#> [28] 0.1666667 0.3333333 0.6666667 0.5000000 0.2000000 0.1969697 0.1102941betweenness
#> [1] 231.0714286 28.4785714 75.8507937 6.2880952 0.3333333 15.8333333 15.8333333 0.0000000
#> [9] 29.5293651 0.4476190 0.3333333 0.0000000 0.0000000 24.2158730 0.0000000 0.0000000
#> [17] 0.0000000 0.0000000 0.0000000 17.1468254 0.0000000 0.0000000 0.0000000 9.3000000
#> [25] 1.1666667 2.0277778 0.0000000 11.7920635 0.9476190 1.5428571 7.6095238 73.0095238
#> [33] 76.6904762 160.5515873
Which nodes attract your attention?
9.1.3 triad-census
igraph::triad.census(g)
# I will use sna because it shows the names of the triads as well.
sna::triad.census(gmat)
unloadNamespace("sna") #I will detach this package again, otherwise it will interfere with all kind of functions from igraph, and my students will hate me for that.#> [1] 3971 0 1575 0 0 0 0 0 0 0 393 0 0 0 0 45
#> 003 012 102 021D 021U 021C 111D 111U 030T 030C 201 120D 120U 120C 210 300
#> [1,] 3971 0 1575 0 0 0 0 0 0 0 393 0 0 0 0 45Based on the above triad.census, please calculate the global transitivity of the network and check your answer with igraph or sna.
Even if you did not read-up on Zachary’s Karate club (Zachary 1977; Girvan and Newman 2002; Kadushin 2012) our initial plot and the above descriptive network statistics start to tell a story. There are two very central nodes (based on degree and betweenness) but their local transitivity index is relatively low (compared to other nodes). Are these two nodes connected? Well gmat[1,34] returns: 0. Thus NO!
9.1.4 Network visualisation
Let’s make size proportional to betweenness score:
# changing V
V(g)$size = betweenness(g, normalized = T, directed = FALSE) * 60 + 10 #after some trial and error
plot(g, mode = "undirected")
It would be nice if we could place the nodes 1 and 34 further apart. Preferably based on some nice algoritm. After some browsing in the igraph manual, I came up with this.
set.seed(2345)
l <- layout_with_mds(g) #https://igraph.org/r/doc/layout_with_mds.html
plot(g, layout = l)
It does more or less what we want, but it would be great to place nodes 1 and 34 even further apart.
#> [,1] [,2]
#> [1,] 1.070931935 -0.172458113
#> [2,] 0.732844464 0.754023309
#> [3,] 0.100582299 0.397693607
#> [4,] 0.708246655 0.570205545
#> [5,] 1.816293170 -0.120778206
#> [6,] 1.881329566 -0.135518854
#> [7,] 1.881329566 -0.135518854
#> [8,] 0.812606714 0.472619437
#> [9,] -0.003769996 0.615513628
#> [10,] -0.685680315 0.621065149
#> [11,] 1.816293170 -0.120778206
#> [12,] 1.621247830 -0.065820692
#> [13,] 1.637845123 0.001789972
#> [14,] 0.067317230 0.681421148
#> [15,] -1.796316404 0.351417630
#> [16,] -1.796316404 0.351417630
#> [17,] 2.775260452 -0.124317652
#> [18,] 1.616210024 0.182510197
#> [19,] -1.796316404 0.351417630
#> [20,] 0.048362858 0.566654982
#> [21,] -1.796316404 0.351417630
#> [22,] 1.616210024 0.182510197
#> [23,] -1.796316404 0.351417630
#> [24,] -1.891240567 -0.799574907
#> [25,] -0.258345165 -2.006346563
#> [26,] -0.360530857 -2.131642875
#> [27,] -1.865177401 0.128596564
#> [28,] -0.760226022 -0.529392331
#> [29,] -0.710979936 -0.299960128
#> [30,] -1.898426916 -0.149398746
#> [31,] -0.568691923 0.804189411
#> [32,] -0.048136037 -0.870967614
#> [33,] -1.023681000 -0.035802363
#> [34,] -1.146442924 -0.037605192
It now more or less looks like nodes 1 and 34 are ripping the network in two. Hey, that is a funny coincidence, let that now be exactly what the original paper was all about.
CAUTION!!:
In the last step we manipulated our data. The coordinates of two nodes have been altered manually and are no longer determined by some ‘objective’ algorithm. Is this not the same as slightly changing an inconvenient correlation in a correlation matrix or as removing some outliers from our dataset? At the very minimum, make sure to tell your readers what you have done! Add a note/legend to your graph.
plot(g, layout = l, margin = c(0, 0, 0, 0))
legend(x = -2, y = -1.5, c("Note: the position of nodes 1 and 34 have been set by Jochem Tolsma \n for visualisation purposes only and do not reflect network properties"),
bty = "n", cex = 0.8)
9.2 Twittersphere in the Dutch HoP
We are going to play with Twitter Networks among Dutch MPs.
Download twitter_20190919.Rdata
Load the Robject and have a look at it. Save the list elements in separate objects. We will use the same R object in the chapters on complete networks.
load("twitter_20190919.RData") #change to your working directory
str(twitter_20190919, 1)
keyf <- twitter_20190919[[1]]
mydata <- twitter_20190919[[2]]
seats <- twitter_20190919[[3]]#> List of 3
#> $ keyf :'data.frame': 147 obs. of 41 variables:
#> $ mydata:List of 8
#> ..- attr(*, "higher")= Named logi [1:9] FALSE FALSE FALSE FALSE FALSE FALSE ...
#> .. ..- attr(*, "names")= chr [1:9] "fnet,fnet" "atmnet,fnet" "rtnet,fnet" "fnet,atmnet" ...
#> ..- attr(*, "disjoint")= Named logi [1:9] FALSE FALSE FALSE FALSE FALSE FALSE ...
#> .. ..- attr(*, "names")= chr [1:9] "fnet,fnet" "atmnet,fnet" "rtnet,fnet" "fnet,atmnet" ...
#> ..- attr(*, "atLeastOne")= Named logi [1:9] FALSE FALSE FALSE FALSE FALSE FALSE ...
#> .. ..- attr(*, "names")= chr [1:9] "fnet,fnet" "atmnet,fnet" "rtnet,fnet" "fnet,atmnet" ...
#> ..- attr(*, "class")= chr "siena"
#> $ seats :'data.frame': 150 obs. of 5 variables:So, what do we have?
keyf: a data.frame on 147 Dutch MPs.mydata: This an object which is ready to analyze in RSiena. It is actually a quite complicated object. For now three things are important:- The nodes in
mydataare the same as inkeyfand inseats.
- It contains the twitter data at three timepoints (in
mydata$depvars). We have three layers:- fnet: who follows whom
- atmnet: who atmentions whom
- rtnet: who retweats whom
- It also contains time invariant information on the nodes (in
mydata$cCovars)
- The nodes in
seats: a dataset which contains the coordinates of the seats in the House of Parliament in the Netherlands.
We are going to focus on the atmentions of politicians. This is most closely related to political discussion. Thus who is having discussions with whom on Twitter?
Let us go fishing for some data. At this point it is not very important you understand what is going on. You will learn more about RSiena data objects in Chapter 7. For now, simply do as you are told! 😉
fnet <- mydata$depvars$fnet
atmnet <- mydata$depvars$atmnet
rtnet <- mydata$depvars$rtnet
vrouw <- mydata$cCovars$vrouw
partij <- mydata$cCovars$partij
ethminz <- mydata$cCovars$ethminz
lft <- mydata$cCovars$lft
# if you construct an object for RSiena, covariates are mean centered by default. I would like to
# have the original values again.
ethminz <- ethminz + attributes(ethminz)$mean
partij <- partij + attributes(partij)$mean
vrouw <- vrouw + attributes(vrouw)$mean
lft <- lft + attributes(lft)$meanHave a look at the network data. What are we a looking at?
#> 'sienaDependent' num [1:147, 1:147, 1:3] 0 0 0 1 0 1 0 1 1 1 ...
#> - attr(*, "type")= chr "oneMode"
#> - attr(*, "sparse")= logi FALSE
#> - attr(*, "nodeSet")= chr "Actors"
#> - attr(*, "netdims")= int [1:3] 147 147 3
#> - attr(*, "allowOnly")= logi TRUE
#> - attr(*, "uponly")= logi [1:2] TRUE FALSE
#> - attr(*, "downonly")= logi [1:2] FALSE FALSE
#> - attr(*, "distance")= int [1:2] 527 277
#> - attr(*, "vals")=List of 3
#> ..$ : 'table' int [1:4(1d)] 15781 5389 292 147
#> .. ..- attr(*, "dimnames")=List of 1
#> .. .. ..$ mymat: chr [1:4] "0" "1" "10" NA
#> ..$ : 'table' int [1:4(1d)] 15254 5916 292 147
#> .. ..- attr(*, "dimnames")=List of 1
#> .. .. ..$ mymat: chr [1:4] "0" "1" "10" NA
#> ..$ : 'table' int [1:3(1d)] 15457 6005 147
#> .. ..- attr(*, "dimnames")=List of 1
#> .. .. ..$ mymat: chr [1:3] "0" "1" NA
#> - attr(*, "nval")= int [1:3] 21462 21462 21462
#> - attr(*, "noMissing")= num [1:3] 0 0 0
#> - attr(*, "noMissingEither")= num [1:2] 0 0
#> - attr(*, "nonMissingEither")= num [1:2] 21462 21462
#> - attr(*, "balmean")= num 0.347
#> - attr(*, "structmean")= num 0.321
#> - attr(*, "simMean")= logi NA
#> - attr(*, "symmetric")= logi FALSE
#> - attr(*, "missing")= logi FALSE
#> - attr(*, "structural")= logi TRUE
#> - attr(*, "range2")= num [1:2] 0 1
#> - attr(*, "ones")= Named int [1:3] 5389 5916 6005
#> ..- attr(*, "names")= chr [1:3] "1" "1" "1"
#> - attr(*, "density")= Named num [1:3] 0.251 0.276 0.28
#> ..- attr(*, "names")= chr [1:3] "1" "1" "1"
#> - attr(*, "degree")= Named num [1:3] 36.7 40.2 40.9
#> ..- attr(*, "names")= chr [1:3] "1" "1" "1"
#> - attr(*, "averageOutDegree")= num 39.3
#> - attr(*, "averageInDegree")= num 39.3
#> - attr(*, "maxObsOutDegree")= num [1:3] 137 137 137
#> - attr(*, "missings")= num [1:3] 0 0 0
#> - attr(*, "name")= chr "fnet"It is just a ‘sienaDependent’ something [1:147,1:147,1:3] but with a lot of attributes which we may ignore for now. It is an array. In this array our nominations are stored in adjacency matrices.
Let us select the friendship (more precisely, the follower relations) relations and the atmention relations.
fnet1 <- fnet[, , 1] #friendship network wave 1
atmnet1 <- atmnet[, , 1] #atmention network wave 1
# we will use wave 2 and 3 later.
atmnet2 <- atmnet[, , 2]
atmnet3 <- atmnet[, , 3]You may wonder why we only have 147 nodes (of MPs) in our data. Well that is because at the time of writing three MPs did not have a twitter account or at least we could not find it.
One final thing before we can go and play with the data. We have to replace the missing values of RSiena 10 (structural zeros) into 0 (or NA) as well.
# table(fnet1, useNA='always') #uncomment if you want
fnet1[fnet1 == 10] <- 0
# table(fnet1, useNA='always') #uncomment if you want
atmnet1[atmnet1 == 10] <- 0
atmnet2[atmnet2 == 10] <- 0
atmnet3[atmnet3 == 10] <- 09.2.1 Descriptive statistics {des.twit}
We describe this dataset in quite some detail in Chapter 7. But I think it is good to show an important observation here as well:
| friends w1 | friends w2 | friends w3 | atmentions w1 | atmentions w2 | atmentions w3 | retweets w1 | retweets w2 | retweets w3 | |
|---|---|---|---|---|---|---|---|---|---|
| total | 0.25 | 0.28 | 0.28 | 0.05 | 0.04 | 0.01 | 0.05 | 0.03 | 0.03 |
| same sex | 0.26 | 0.29 | 0.29 | 0.05 | 0.04 | 0.01 | 0.05 | 0.03 | 0.03 |
| different sex | 0.24 | 0.27 | 0.27 | 0.04 | 0.03 | 0.01 | 0.05 | 0.03 | 0.03 |
| same party | 0.71 | 0.73 | 0.74 | 0.20 | 0.14 | 0.06 | 0.34 | 0.25 | 0.25 |
| different party | 0.19 | 0.22 | 0.22 | 0.03 | 0.02 | 0.01 | 0.01 | 0.01 | 0.01 |
| same ethnicity | 0.27 | 0.29 | 0.29 | 0.05 | 0.04 | 0.01 | 0.05 | 0.03 | 0.03 |
| different ethnicity | 0.21 | 0.24 | 0.24 | 0.05 | 0.03 | 0.01 | 0.04 | 0.03 | 0.03 |
| both minority | 0.25 | 0.28 | 0.28 | 0.05 | 0.03 | 0.01 | 0.05 | 0.03 | 0.03 |
| same age (<6) | 0.29 | 0.31 | 0.31 | 0.06 | 0.04 | 0.01 | 0.05 | 0.04 | 0.04 |
| different age (>5) | 0.24 | 0.26 | 0.26 | 0.04 | 0.03 | 0.01 | 0.04 | 0.03 | 0.03 |
From Table 9.1 we learn that intra-party densities are much higher than inter-party densities. This is an indication of segregation of Twittersphere along party division lines. This is the message we want our plots to get across.
9.2.2 First plots
The first step is to make a ‘graph object’.
G1 <- igraph::graph_from_adjacency_matrix(atmnet1, mode = "directed", weighted = NULL, diag = TRUE, add.colnames = NA,
add.rownames = NA)Suppose you would like to add the data to this graph.
# we need to retrieve the edges.
edges <- igraph::as_data_frame(G1, what = "edges")
# the first variable of the data we can attach needs to be some id, thus reorder columns of keyf
keyf <- cbind(keyf$EGOid, keyf[, names(keyf) != "EGOid"])
# the name has been changed as well. Lets correct this
names(keyf)[1] <- "EGOid"
# rebuild the graph.
G1 <- graph_from_data_frame(edges, directed = TRUE, vertices = keyf)
# let us examine the attributes
vertex_attr(G1)
# thus to find the names of our MPs we could now do this:
V(G1)$Naam#> $name
#> [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15"
#> [16] "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
#> [31] "31" "32" "33" "34" "35" "36" "37" "38" "39" "40" "41" "42" "43" "44" "45"
#> [46] "46" "47" "48" "49" "50" "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
#> [61] "61" "62" "63" "64" "65" "66" "67" "68" "69" "70" "71" "72" "73" "74" "75"
#> [76] "76" "77" "78" "79" "80" "81" "82" "83" "84" "85" "86" "87" "88" "89" "90"
#> [91] "91" "92" "93" "94" "95" "96" "97" "98" "99" "100" "101" "102" "103" "104" "105"
#> [106] "106" "107" "108" "109" "110" "111" "112" "113" "114" "115" "116" "117" "118" "119" "120"
#> [121] "121" "122" "123" "124" "125" "126" "127" "128" "129" "130" "131" "132" "133" "134" "135"
#> [136] "136" "137" "138" "139" "140" "141" "142" "143" "144" "145" "146" "147"
#>
#> $ZetelSegment20170315
#> [1] 1 2 6 3 3 1 4 4 3 1 3 3 2 6 3 4 3 2 4 1 1 5 1 2 3 2 1 1 2 5 2 6 4 5 6 5 6 3 1 4 1 6 4 6 2 3 5
#> [48] 4 6 6 2 2 1 3 2 2 4 3 3 5 2 4 4 3 2 5 4 5 4 1 4 4 3 3 6 3 6 6 6 3 3 1 2 6 6 6 6 6 6 6 6 4 4 4
#> [95] 4 4 4 4 4 5 2 2 2 2 2 2 4 3 3 4 3 3 3 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 6 6 5 5 5 5 6 6 1 1 1 1
#> [142] 1 1 1 1 1 1
#>
#> $ZetelRij20170315
#> [1] 1 3 6 2 3 4 4 3 4 2 4 2 3 6 4 2 5 5 5 4 4 3 4 1 2 2 3 4 1 1 2 6 2 5 3 6 1 3 3 4 5 5 6 3 2 5 6
#> [48] 1 2 1 5 6 5 5 4 4 3 4 5 4 6 1 2 4 5 2 6 5 3 1 5 4 1 1 6 3 4 3 4 5 3 6 5 2 2 3 4 4 5 5 6 3 5 5
#> [95] 4 6 6 5 6 6 5 4 4 3 3 4 6 6 6 4 6 6 6 6 6 6 6 5 6 6 4 1 2 3 3 2 4 4 4 5 6 3 5 5 6 4 5 5 6 2 2
#> [142] 5 5 6 6 6 6
#>
#> $ZetelKolom20170315
#> [1] 2 1 3 1 2 1 3 4 3 1 5 2 3 6 2 1 3 4 1 4 2 1 3 2 3 1 4 5 1 1 2 4 3 5 2 2 2 1 2 5 5 2 2 4 3 2 4
#> [48] 2 2 1 2 1 1 5 4 5 1 1 4 2 2 1 2 4 5 2 3 2 3 1 2 1 2 1 2 4 2 1 3 1 3 6 3 1 3 3 4 5 4 5 5 2 3 4
#> [95] 4 5 4 5 6 1 1 3 1 4 2 2 1 6 5 2 4 3 1 6 5 4 3 4 5 6 1 2 3 3 4 1 4 5 3 1 1 2 1 3 3 1 3 4 5 3 2
#> [142] 2 3 1 2 3 4
#>
#> $EGOhandle
#> [1] 101 201 301 401 501 701 801 1001 1101 1501 1701 1901 2001
#> [14] 2701 2901 3001 3101 3201 3401 4101 4201 4301 4401 4701 4801 5001
#> [27] 5101 5201 5801 6001 6401 6501 6601 6801 6901 7001 7401 7601 7801
#> [40] 8101 8801 9001 9101 9201 9301 9501 9601 9701 9901 10101 10201 10301
#> [53] 10402 10501 10701 10901 11201 11401 12001 12101 12301 12601 12801 13101 13401
#> [66] 13501 13801 14101 14201 14401 14601 14901 15001 15101 15501 15601 15801 16101
#> [79] 16701 16801 17001 18901 23001 30901 31101 31201 31401 31501 31601 31701 31901
#> [92] 35501 36001 36401 36501 36601 36801 36901 37001 37101 40501 41001 41301 41401
#> [105] 42001 44901 56601 56901 57101 57201 57401 57701 58101 58301 58401 58801 58901
#> [118] 63701 63801 63901 70401 70601 70701 71001 71101 71201 71301 71401 71501 71701
#> [131] 72201 74401 74801 75001 81801 83901 84201 103801 106102 109001 109101 109401 109501
#> [144] 109701 109801 109901 110001
#>
#> $IDActor
#> [1] 1 2 3 4 5 7 8 10 11 15 17 19 20 27 29 30 31 32 34
#> [20] 41 42 43 44 47 48 50 51 52 58 60 64 65 66 68 69 70 74 76
#> [39] 78 81 88 90 91 92 93 95 96 97 99 101 102 103 104 105 107 109 112
#> [58] 114 120 121 123 126 128 131 134 135 138 141 142 144 146 149 150 151 155 156
#> [77] 158 161 167 168 170 189 230 309 311 312 314 315 316 317 319 355 360 364 365
#> [96] 366 368 369 370 371 405 410 413 414 420 449 566 569 571 572 574 577 581 583
#> [115] 584 588 589 637 638 639 704 706 707 710 711 712 713 714 715 717 722 744 748
#> [134] 750 818 839 842 1038 1061 1090 1091 1094 1095 1097 1098 1099 1100
#>
#> $Handle.
#> [1] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [3] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [5] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [7] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [9] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [11] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [13] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [15] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [17] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [19] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [21] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [23] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [25] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [27] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [29] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [31] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [33] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [35] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [37] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [39] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [41] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [43] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [45] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [47] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [49] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [51] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [53] "2e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [55] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [57] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [59] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [61] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [63] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [65] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [67] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [69] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [71] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [73] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [75] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [77] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [79] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [81] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [83] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [85] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [87] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [89] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [91] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [93] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [95] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [97] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [99] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [101] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [103] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [105] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [107] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [109] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [111] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [113] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [115] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [117] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [119] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [121] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [123] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [125] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [127] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [129] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [131] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [133] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [135] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [137] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [139] "2e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [141] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [143] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [145] "1e (mogelijke) account van actor" "1e (mogelijke) account van actor"
#> [147] "1e (mogelijke) account van actor"
#>
#> $Twitterhandle
#> [1] "FleurAgemaPVV " "MustafaAmhaouch" "khadijaArib " "tamaravanark " "MalikAzmani "
#> [6] "harmbeertema " "SalimaBelhaj " "Vera_Bergkamp " "BisschopRoelof " "Martinbosma_pvv"
#> [11] "andrebosman " "HanTenBroeke " "HankeBruinsSlot" "JaspervanDijkSP" "elbertdijkgraaf"
#> [16] "piadijkstra " "remcovvd " "carladikfaber " "PDuisenberg " "JacoGeurts "
#> [21] "GraafdeMachiel " "rikgrashoff " "RidderDionGraus" "sybrandbuma " "markharbers "
#> [26] "PieterHeerma " "lilianhelderpvv" "Martijncda " "MonaKeijzer " "jesseklaver "
#> [31] "RaymondKnops " "NineKooiman " "wkoolmees " "HenkKrol " "attjekuiken "
#> [36] "tunahankuzu " "RenskeLeijten " "HelmaLodders " "BarryMadlener " "Paul_van_Meenen"
#> [41] "AgnesMulderCDA " "henknijboer " "cnijkerken " "MichielvNispen " "PieterOmtzigt "
#> [46] "foortvanoosten " "selcukozturknl " "APechtold " "ronaldvanraak " "emileroemer "
#> [51] "michelrog " "ErikRonnes " "rderoonpvv " "arnorutte " "carolaschouten "
#> [56] "gertjansegers " "swsjoerdsma " "keesvdstaaij " "ockjetellegen " "mariannethieme "
#> [61] "MvanToorenburg " "SvVeldhoven " "KeesVee " "BarbaraVVD " "JoelVoordewind "
#> [66] "lindavoortman " "aukjedevries " "WassenbergFrank" "svanweyenberg " "geertwilderspvv"
#> [71] "bvantwout " "erikziengs " "HalbeZijlstra " "MinPres " "ministerPloumen"
#> [76] "JeanineHennis " "J_Dijsselbloem " "LodewijkA " "sharon_dijksma " "SanderDekker "
#> [81] "dijkhoff " "thierrybaudet " "eppobruins " "MarijnissenL " "SadetKarabulut "
#> [86] "SandraBeckerman" "peterkwint " "bartvankent " "CemLacin " "FrankFutselaar "
#> [91] "MaartenHijink " "ivanengelshoven" "jpaternotte " "RobJetten " "JessicaVanEijs "
#> [96] "GroothuizenD66 " "RensRaemakers " "achrafbouali66 " "AntjeDiertens " "TjeerdNZO "
#> [101] "RenePetersOss " "harryvdmolen " "AnneKuik " "ChrisvanDamCDA " "JobavdBerg "
#> [106] "mvonmartels " "DennisWiersma " "bentebecker " "Sophie_Hermans " "a_mulder "
#> [111] "DilanYesilgoz " "danielkoerhuis " "ZelYassini " "Worsdorfer " "ArneWeverling "
#> [116] "svenplanken " "Jan_Middendorp " "LeonieSazias " "mj_vanrooijen " "CvanBrenk "
#> [121] "estherouwehand " "kathalijne " "TomvanderLee " "CorinneEllemeet" "ZihniOzdil "
#> [126] "bartsnels " "suzanne_GL " "BramvanOjikGL " "NevinOzutok " "Lisawesterveld "
#> [131] "IsabelleDiks " "GroenLiesbeth " "LammertvanRaan " "FemkeMerel " "F_azarkan "
#> [136] "dijkvangijs " "kirstenvdhul " "KarenGerbrands1" "THiddema " "VickyMaeijer "
#> [141] "GidiMarkuszower" "Danai_PVV " "edgarmulder1 " "leondejong " "gjfpopken "
#> [146] "KopsPVV " "rvanaalst "
#>
#> $Naam
#> [1] "Agema, Fleur " "Amhaouch, Mustafa "
#> [3] "Arib, Khadija " "v. Ark, Tamara "
#> [5] "Azmani, Malik " "Beertema, Harm "
#> [7] "Belhaj, Salima " "Bergkamp, Vera "
#> [9] "Bisschop, Roelof " "Bosma, Martin "
#> [11] "Bosman, Andre " "ten Broeke, Han "
#> [13] "Bruins Slot, Hanke " "Van Dijk, Jasper "
#> [15] "Dijkgraaf, Elbert " "Dijkstra, Pia "
#> [17] "Dijkstra, Remco " "Dik-Faber, Carla "
#> [19] "Duisenberg, Pieter " "Geurts, Jaco "
#> [21] "De Graaf, Machiel " "Grashoff, Rik "
#> [23] "Graus, Dion " "Van Haersma Buma, Sybrand "
#> [25] "Harbers, Mark " "Heerma, Pieter "
#> [27] "Helder, Lilian " "Van Helvert, Martijn "
#> [29] "Keijzer, Mona " "Klaver, jesse "
#> [31] "Knops, Raymond " "Kooiman, Nine "
#> [33] "Koolmees, Wouter " "Krol, Henk "
#> [35] "Kuiken, Attje " "Kuzu, Tunahan "
#> [37] "Leijten, Renske " "Lodders, Helma "
#> [39] "Madlener, Barry " "Van Meenen, Paul "
#> [41] "Mulder, Agnes " "Nijboer, Henk "
#> [43] "Nijkerken-de Haan, Chantal " "Van Nispen, Michiel "
#> [45] "Omtzigt, Pieter " "Van Oosten, Foort "
#> [47] "Ozturk, Selcuk " "Pechtold, Alexander "
#> [49] "Van Raak, Ronald " "Roemer, Emile "
#> [51] "Rog, Michel " "Ronnes, Erik "
#> [53] "De Roon, Raymond " "Rutte, Arno "
#> [55] "Schouten, Carola " "Segers, Gert-Jan "
#> [57] "Sjoerdsma, Sjoerd " "Van der Staaij, Kees "
#> [59] "Tellegen, Ockje " "Thieme, Marianne "
#> [61] "Van Toorenburg, Madeleine " "Van Veldhoven, Stientje "
#> [63] "Verhoeven, Kees " "Visser, Barbara "
#> [65] "Voordewind, Joel " "Voortman, Linda "
#> [67] "De Vries, Aukje " "Wassenberg, Frank "
#> [69] "Van Weyenberg, Steven " "Wilders, Geert "
#> [71] "Van t'Wout, Bas " "Ziengs, Erik "
#> [73] "Zijlstra, Halbe " "Rutte, Mark "
#> [75] "Ploumen, Lilianne " "Hennis-Plasschaert, Jeanine "
#> [77] "Dijsselbloem, Jeroen " "Asscher, Lodewijk "
#> [79] "Dijksma, Sharon " "Dekker, Sander "
#> [81] "Dijkhoff, Klaas " "Thierry Baudet "
#> [83] "Eppo Bruins " "LILIAN MARIJNISSEN "
#> [85] "SADET KARABULUT " "SANDRA BECKERMAN "
#> [87] "PETER KWINT " "BART VAN KENT "
#> [89] "CEM LACIN " "FRANK FUTSELAAR "
#> [91] "MAARTEN HIJINK " "Ingrid van Engelshoven "
#> [93] "Jan Paternotte " "Rob Jetten "
#> [95] "Jessica van Eijs " "Maarten Groothuizen "
#> [97] "Rens Raemakers " "Achraf Bouali "
#> [99] "Antje Diertens " "Tjeerd de Groot "
#> [101] "René Peters " "Harry van der Molen "
#> [103] "Anne Kuik " "Chris van Dam "
#> [105] "Joba van den Berg-Jansen " "Maurits von Martels "
#> [107] "Dennis Wiersma " "Bente Becker "
#> [109] "Sophie Hermans " "Anne Mulder "
#> [111] "Dilan Yesilgöz-Zegerius " "Daniel Koerhuis "
#> [113] "Zohair el Yassini " "Martin Wörsdörfer "
#> [115] "Arne Weverling " "Sven Koopmans "
#> [117] "Jan Middendorp " "Léonie Sazias "
#> [119] "Martin van Rooijen " "Corrie van Brenk "
#> [121] "Esther Ouwehand " "Kathalijne Buitenweg "
#> [123] "Tom van der Lee " "Corinne Ellemeet "
#> [125] "Zihni Özdil " "Bart Snels "
#> [127] "Suzanne Kröger " "Bram van Oijk "
#> [129] "Nevin Özütok " "Lisa Westerveld "
#> [131] "Isabelle Diks " "Liesbeth van Tongeren "
#> [133] "Lammert van Raan " "Femke Merel Arissen "
#> [135] "Farid Azarkan " "Gijs van Dijk "
#> [137] "Kirsten van den Hul " "Gerbrands, Karen "
#> [139] "Theo Hiddema " "Vicky Maeijer "
#> [141] "Gidi Markuszower " "Danai van Weerdenburg "
#> [143] "Edgar Mulder " "Léon de Jong "
#> [145] "Gabriëlle Popken " "Alexander Kops "
#> [147] "Roy van Aalst "
#>
#> $Kandidaat2017
#> [1] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [6] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [11] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [16] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [21] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [26] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [31] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [36] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [41] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [46] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [51] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [56] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [61] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [66] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [71] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [76] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [81] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [86] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [91] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [96] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [101] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [106] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [111] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [116] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [121] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [126] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [131] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [136] "wel kandidaat" "wel kandidaat" "geen kandidaat" "wel kandidaat" "wel kandidaat"
#> [141] "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat" "wel kandidaat"
#> [146] "wel kandidaat" "wel kandidaat"
#>
#> $Elected20170315
#> [1] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [6] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [11] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [16] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [21] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [26] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [31] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [36] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [41] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [46] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [51] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [56] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [61] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [66] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [71] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [76] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [81] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [86] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [91] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [96] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [101] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [106] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [111] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [116] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [121] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [126] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [131] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [136] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [141] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [146] "yes, verkozen" "yes, verkozen"
#>
#> $Kamerlid20171031
#> [1] "yes, verkozen" "yes, verkozen" "yes, verkozen" "no, niet verkozen"
#> [5] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [9] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [13] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [17] "yes, verkozen" "yes, verkozen" "no, niet verkozen" "yes, verkozen"
#> [21] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [25] "no, niet verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [29] "no, niet verkozen" "yes, verkozen" "no, niet verkozen" "yes, verkozen"
#> [33] "no, niet verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [37] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [41] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [45] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [49] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [53] "yes, verkozen" "yes, verkozen" "no, niet verkozen" "yes, verkozen"
#> [57] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [61] "yes, verkozen" "no, niet verkozen" "yes, verkozen" "no, niet verkozen"
#> [65] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [69] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [73] "no, niet verkozen" "no, niet verkozen" "yes, verkozen" "yes, verkozen"
#> [77] "no, niet verkozen" "yes, verkozen" "yes, verkozen" "no, niet verkozen"
#> [81] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [85] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [89] "yes, verkozen" "yes, verkozen" "yes, verkozen" "no, niet verkozen"
#> [93] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [97] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [101] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [105] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [109] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [113] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [117] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [121] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [125] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [129] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [133] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [137] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [141] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [145] "yes, verkozen" "yes, verkozen" "yes, verkozen"
#>
#> $PlekLijst
#> [1] 2 15 2 4 10 10 14 6 3 6 24 9 9 14 2 4 27 4 15 10 11 5 12 1 8 7 9 16 2 1 6
#> [32] 12 3 1 6 1 2 12 7 8 13 7 30 7 4 32 3 1 4 1 14 17 13 20 2 1 11 1 21 1 5 2
#> [63] 7 7 3 4 18 4 10 1 13 23 3 1 10 2 3 1 4 6 5 1 5 3 5 6 8 9 10 11 13 5 9
#> [94] 12 13 15 17 16 18 19 3 8 11 12 18 44 11 14 16 17 19 22 26 28 29 33 34 2 3 4 2 2 3 7
#> [125] 8 9 11 10 12 14 19 6 3 5 2 5 8 16 2 3 4 14 15 17 18 19 20
#>
#> $Partij
#> [1] "PVV" "CDA" "PvdA" "VVD" "VVD" "PVV" "D66"
#> [8] "D66" "SGP" "PVV" "VVD" "VVD" "CDA" "SP"
#> [15] "SGP" "D66" "VVD" "CU" "VVD" "CDA" "PVV"
#> [22] "GroenLinks" "PVV" "CDA" "VVD" "CDA" "PVV" "CDA"
#> [29] "CDA" "GroenLinks" "CDA" "SP" "D66" "50Plus" "PvdA"
#> [36] "DENK" "SP" "VVD" "PVV" "D66" "CDA" "PvdA"
#> [43] "VVD" "SP" "CDA" "VVD" "DENK" "D66" "SP"
#> [50] "SP" "CDA" "CDA" "PVV" "VVD" "CU" "CU"
#> [57] "D66" "SGP" "VVD" "PvdDieren" "CDA" "D66" "D66"
#> [64] "VVD" "CU" "GroenLinks" "VVD" "PvdDieren" "D66" "PVV"
#> [71] "VVD" "VVD" "VVD" "VVD" "PvdA" "VVD" "PvdA"
#> [78] "PvdA" "PvdA" "VVD" "VVD" "FvD" "CU" "SP"
#> [85] "SP" "SP" "SP" "SP" "SP" "SP" "SP"
#> [92] "D66" "D66" "D66" "D66" "D66" "D66" "D66"
#> [99] "D66" "D66" "CDA" "CDA" "CDA" "CDA" "CDA"
#> [106] "CDA" "VVD" "VVD" "VVD" "VVD" "VVD" "VVD"
#> [113] "VVD" "VVD" "VVD" "VVD" "VVD" "50Plus" "50Plus"
#> [120] "50Plus" "PvdDieren" "GroenLinks" "GroenLinks" "GroenLinks" "GroenLinks" "GroenLinks"
#> [127] "GroenLinks" "GroenLinks" "GroenLinks" "GroenLinks" "GroenLinks" "GroenLinks" "PvdDieren"
#> [134] "PvdDieren" "DENK" "PvdA" "PvdA" "PVV" "FvD" "PVV"
#> [141] "PVV" "PVV" "PVV" "PVV" "PVV" "PVV" "PVV"
#>
#> $Geslacht
#> [1] "vrouw" "man" "vrouw" "vrouw" "man" "man" "vrouw" "vrouw" "man" "man" "man"
#> [12] "man" "vrouw" "man" "man" "vrouw" "man" "vrouw" "man" "man" "man" "man"
#> [23] "man" "man" "man" "man" "vrouw" "man" "vrouw" "man" "man" "vrouw" "man"
#> [34] "man" "vrouw" "man" "vrouw" "vrouw" "man" "man" "vrouw" "man" "vrouw" "man"
#> [45] "man" "man" "man" "man" "man" "man" "man" "man" "man" "man" "vrouw"
#> [56] "man" "man" "man" "vrouw" "vrouw" "vrouw" "vrouw" "man" "vrouw" "man" "vrouw"
#> [67] "vrouw" "man" "man" "man" "man" "man" "man" "man" "vrouw" "vrouw" "man"
#> [78] "man" "vrouw" "man" "man" "man" "man" "vrouw" "vrouw" "vrouw" "man" "man"
#> [89] "man" "man" "man" "vrouw" "man" "man" "vrouw" "man" "man" "man" "vrouw"
#> [100] "man" "man" "man" "vrouw" "man" "vrouw" "man" "man" "vrouw" "vrouw" "man"
#> [111] "vrouw" "man" "man" "man" "man" "man" "man" "vrouw" "man" "vrouw" "vrouw"
#> [122] "vrouw" "man" "vrouw" "man" "man" "vrouw" "man" "vrouw" "vrouw" "vrouw" "vrouw"
#> [133] "man" "vrouw" "man" "man" "vrouw" "vrouw" "man" "vrouw" "man" "vrouw" "man"
#> [144] "man" "vrouw" "man" "man"
#>
#> $PartijAccount
#> [1] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [5] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [9] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [13] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [17] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [21] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [25] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [29] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [33] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [37] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [41] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [45] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [49] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [53] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [57] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [61] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [65] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [69] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [73] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [77] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [81] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [85] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [89] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [93] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [97] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [101] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [105] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [109] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [113] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [117] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [121] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [125] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [129] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [133] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [137] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [141] "geen partijaccount" "geen partijaccount" "geen partijaccount" "geen partijaccount"
#> [145] "geen partijaccount" "geen partijaccount" "geen partijaccount"
#>
#> $NotesTwitter
#> [1] " "
#> [2] " "
#> [3] " "
#> [4] " "
#> [5] " "
#> [6] " "
#> [7] " "
#> [8] " "
#> [9] " "
#> [10] " "
#> [11] " "
#> [12] " "
#> [13] " "
#> [14] " "
#> [15] " "
#> [16] " "
#> [17] " "
#> [18] " "
#> [19] " "
#> [20] " "
#> [21] " "
#> [22] " "
#> [23] " "
#> [24] " "
#> [25] " "
#> [26] " "
#> [27] " "
#> [28] " "
#> [29] " "
#> [30] " "
#> [31] " "
#> [32] " "
#> [33] " "
#> [34] " "
#> [35] " "
#> [36] " "
#> [37] " "
#> [38] " "
#> [39] " "
#> [40] " "
#> [41] " "
#> [42] " "
#> [43] " "
#> [44] " "
#> [45] " "
#> [46] " "
#> [47] " "
#> [48] " "
#> [49] " "
#> [50] " "
#> [51] " "
#> [52] " "
#> [53] " "
#> [54] " "
#> [55] " "
#> [56] " "
#> [57] " "
#> [58] " "
#> [59] " "
#> [60] " "
#> [61] " "
#> [62] " "
#> [63] " "
#> [64] " "
#> [65] " "
#> [66] " "
#> [67] " "
#> [68] " "
#> [69] " "
#> [70] " "
#> [71] " "
#> [72] " "
#> [73] " "
#> [74] " "
#> [75] " "
#> [76] " "
#> [77] " "
#> [78] " "
#> [79] " "
#> [80] " "
#> [81] " "
#> [82] " "
#> [83] " "
#> [84] " "
#> [85] " "
#> [86] " "
#> [87] " "
#> [88] " "
#> [89] " "
#> [90] " "
#> [91] " "
#> [92] " "
#> [93] " "
#> [94] " "
#> [95] " "
#> [96] " "
#> [97] " "
#> [98] " "
#> [99] " "
#> [100] " "
#> [101] " "
#> [102] " "
#> [103] " "
#> [104] "heeft na inventarisatie accounts een account geopend "
#> [105] " "
#> [106] " "
#> [107] " "
#> [108] " "
#> [109] " "
#> [110] " "
#> [111] " "
#> [112] " "
#> [113] " "
#> [114] " "
#> [115] " "
#> [116] " "
#> [117] " "
#> [118] " "
#> [119] " "
#> [120] " "
#> [121] " "
#> [122] " "
#> [123] " "
#> [124] " "
#> [125] " "
#> [126] " "
#> [127] " "
#> [128] " "
#> [129] " "
#> [130] " "
#> [131] " "
#> [132] " "
#> [133] " "
#> [134] " "
#> [135] " "
#> [136] " "
#> [137] " "
#> [138] " "
#> [139] " "
#> [140] " "
#> [141] " "
#> [142] " "
#> [143] " "
#> [144] " "
#> [145] " "
#> [146] " "
#> [147] " "
#>
#> $Transgender
#> [1] "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no"
#> [20] "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no"
#> [39] "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no"
#> [58] "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no"
#> [77] "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no"
#> [96] "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no"
#> [115] "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no"
#> [134] "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no"
#>
#> $EthMinZ
#> [1] 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1
#> [48] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0
#> [95] 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1
#> [142] 0 0 0 0 0 0
#>
#> $EtnMinStat
#> [1] "Majority status" "EThnic minority status" "EThnic minority status"
#> [4] "Majority status" "EThnic minority status" "Majority status"
#> [7] "EThnic minority status" "Majority status" "Majority status"
#> [10] "Majority status" "Majority status" "Majority status"
#> [13] "Majority status" "Majority status" "Majority status"
#> [16] "Majority status" "Majority status" "Majority status"
#> [19] "Majority status" "Majority status" "Majority status"
#> [22] "Majority status" "Majority status" "Majority status"
#> [25] "Majority status" "Majority status" "Majority status"
#> [28] "Majority status" "Majority status" "EThnic minority status"
#> [31] "Majority status" "Majority status" "Majority status"
#> [34] "Majority status" "Majority status" "EThnic minority status"
#> [37] "Majority status" "Majority status" "Majority status"
#> [40] "Majority status" "Majority status" "Majority status"
#> [43] "Majority status" "Majority status" "Majority status"
#> [46] "Majority status" "EThnic minority status" "Majority status"
#> [49] "Majority status" "Majority status" "Majority status"
#> [52] "Majority status" "Majority status" "Majority status"
#> [55] "Majority status" "Majority status" "Majority status"
#> [58] "Majority status" "Majority status" "Majority status"
#> [61] "Majority status" "Majority status" "Majority status"
#> [64] "Majority status" "Majority status" "Majority status"
#> [67] "Majority status" "Majority status" "Majority status"
#> [70] "Majority status" "Majority status" "Majority status"
#> [73] "Majority status" "Majority status" "Majority status"
#> [76] "Majority status" "Majority status" "Majority status"
#> [79] "Majority status" "Majority status" "Majority status"
#> [82] "Majority status" "Majority status" "Majority status"
#> [85] "EThnic minority status" "Majority status" "Majority status"
#> [88] "Majority status" "EThnic minority status" "Majority status"
#> [91] "Majority status" "Majority status" "Majority status"
#> [94] "Majority status" "Majority status" "Majority status"
#> [97] "Majority status" "EThnic minority status" "Majority status"
#> [100] "Majority status" "Majority status" "Majority status"
#> [103] "Majority status" "Majority status" "Majority status"
#> [106] "EThnic minority status" "Majority status" "Majority status"
#> [109] "Majority status" "Majority status" "EThnic minority status"
#> [112] "Majority status" "EThnic minority status" "EThnic minority status"
#> [115] "Majority status" "Majority status" "Majority status"
#> [118] "Majority status" "Majority status" "Majority status"
#> [121] "Majority status" "Majority status" "Majority status"
#> [124] "Majority status" "EThnic minority status" "Majority status"
#> [127] "EThnic minority status" "Majority status" "EThnic minority status"
#> [130] "Majority status" "Majority status" "Majority status"
#> [133] "Majority status" "Majority status" "EThnic minority status"
#> [136] "Majority status" "Majority status" "Majority status"
#> [139] "Majority status" "Majority status" "EThnic minority status"
#> [142] "Majority status" "Majority status" "Majority status"
#> [145] "Majority status" "Majority status" "Majority status"
#>
#> $EtnMinStat2
#> [1] "Geen visible minority" "Visible minority" "Visible minority"
#> [4] "Geen visible minority" "Visible minority" "Geen visible minority"
#> [7] "Visible minority" "Geen visible minority" "Geen visible minority"
#> [10] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [13] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [16] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [19] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [22] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [25] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [28] "Geen visible minority" "Geen visible minority" "Visible minority"
#> [31] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [34] "Geen visible minority" "Geen visible minority" "Visible minority"
#> [37] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [40] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [43] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [46] "Geen visible minority" "Visible minority" "Geen visible minority"
#> [49] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [52] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [55] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [58] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [61] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [64] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [67] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [70] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [73] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [76] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [79] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [82] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [85] "Visible minority" "Geen visible minority" "Geen visible minority"
#> [88] "Geen visible minority" "Visible minority" "Geen visible minority"
#> [91] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [94] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [97] "Geen visible minority" "Visible minority" "Geen visible minority"
#> [100] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [103] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [106] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [109] "Geen visible minority" "Geen visible minority" "Visible minority"
#> [112] "Geen visible minority" "Visible minority" "Geen visible minority"
#> [115] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [118] "Visible minority" "Geen visible minority" "Geen visible minority"
#> [121] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [124] "Geen visible minority" "Visible minority" "Geen visible minority"
#> [127] "Geen visible minority" "Geen visible minority" "Visible minority"
#> [130] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [133] "Geen visible minority" "Geen visible minority" "Visible minority"
#> [136] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [139] "Geen visible minority" "Geen visible minority" "Visible minority"
#> [142] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#> [145] "Geen visible minority" "Geen visible minority" "Geen visible minority"
#>
#> $GebJaar
#> [1] 1976 1970 1960 1974 1976 1952 1979 1971 1956 1964 1965 1969 1977 1971 1970 1954 1972 1971 1967
#> [20] 1970 1969 1961 1967 1965 1969 1977 1973 1978 1968 1986 1971 1980 1977 1950 1977 1981 1979 1968
#> [39] 1969 1956 1973 1983 1973 1982 1974 1977 1972 1965 1969 1962 1973 1967 1952 1972 1977 1969 1981
#> [58] 1968 1974 1972 1986 1973 1976 1977 1965 1979 1964 1966 1973 1963 1979 1960 1969 1967 1962 1973
#> [77] 1966 1974 1971 1975 1981 1983 1969 1985 1975 1983 1984 1983 1986 1979 1983 1966 1984 1987 1981
#> [96] 1976 1991 1974 1958 1968 1975 1980 1987 1963 1958 1960 1986 1985 1981 1969 1977 1981 1979 1972
#> [115] 1974 1973 1975 1957 1942 1960 1976 1970 1964 1976 1981 1966 1977 1954 1960 1981 1965 1958 1962
#> [134] 1983 1971 1980 1976 1967 1944 1986 1977 1976 1961 1982 1983 1984 1983
#>
#> $Kabinet20161123
#> [1] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [3] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [5] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [7] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [9] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [11] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [13] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [15] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [17] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [19] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [21] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [23] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [25] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [27] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [29] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [31] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [33] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [35] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [37] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [39] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [41] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [43] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [45] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [47] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [49] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [51] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [53] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [55] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [57] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [59] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [61] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [63] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [65] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [67] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [69] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [71] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [73] "Geen kabinetslid op genoemde datum" "Kabinetslid op genoemde datum"
#> [75] "Kabinetslid op genoemde datum" "Kabinetslid op genoemde datum"
#> [77] "Kabinetslid op genoemde datum" "Kabinetslid op genoemde datum"
#> [79] "Kabinetslid op genoemde datum" "Kabinetslid op genoemde datum"
#> [81] "Kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [83] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [85] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [87] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [89] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [91] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [93] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [95] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [97] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [99] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [101] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [103] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [105] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [107] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [109] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [111] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [113] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [115] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [117] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [119] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [121] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [123] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [125] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [127] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [129] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [131] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [133] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [135] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [137] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [139] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [141] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [143] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [145] "Geen kabinetslid op genoemde datum" "Geen kabinetslid op genoemde datum"
#> [147] "Geen kabinetslid op genoemde datum"
#>
#> $Kamerlid20161123
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [48] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0
#> [95] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
#> [142] 0 0 0 0 0 0
#>
#> $voorkeursstemmen
#> [1] 71229 4006 48440 13557 3127 515 13833 26094 1570 6430 2079
#> [12] 12447 7330 1343 13573 120557 1381 6508 2022 17058 619 3094
#> [23] 3725 788073 2846 1284 3130 19106 165384 651483 12876 4919 11606
#> [34] 233179 5949 129025 57956 5459 987 2961 12013 5151 12761 2261
#> [45] 97638 575 10367 863887 4378 675763 1823 5567 269 4713 33192
#> [56] 260999 2726 196205 3444 261658 20194 92700 6586 9220 6166 21098
#> [67] 5795 2773 1280 1258989 1072 5180 20307 1760117 21990 148431 51695
#> [78] 353777 35836 2810 146226 124991 1643 124626 13540 15575 1011 957
#> [89] 3069 2264 649 10941 2991 4903 19594 1145 7435 3489 24691
#> [100] 1825 11270 8636 15906 1441 3551 21510 2414 4288 4417 1293
#> [111] 5643 441 947 1370 8588 704 600 51736 9096 5091 18936
#> [122] 99157 2679 6626 5747 852 8086 6759 12428 17828 28390 25856
#> [133] 2529 6131 61876 1860 2253 545 44934 6751 1101 639 1127
#> [144] 291 390 679 1524
#>
#> $Kamerlid20170908
#> [1] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [5] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [9] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [13] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [17] "yes, verkozen" "yes, verkozen" "no, niet verkozen" "yes, verkozen"
#> [21] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [25] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [29] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [33] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [37] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [41] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [45] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [49] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [53] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [57] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [61] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [65] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [69] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [73] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [77] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [81] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [85] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [89] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [93] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [97] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [101] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [105] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [109] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [113] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [117] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [121] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [125] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [129] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [133] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [137] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [141] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [145] "yes, verkozen" "yes, verkozen" "yes, verkozen"
#>
#> $ZetelSegment20170908
#> [1] 1 2 6 3 3 1 4 4 3 1 3 3 2 6 3 4 3 2 NA 1 1 5 1 2 3 2 1 1 2 5 2
#> [32] 6 4 5 6 5 6 3 1 4 1 6 4 6 2 3 5 4 6 6 2 2 1 4 2 2 4 3 3 5 2 4
#> [63] 4 3 2 5 3 5 4 1 4 4 3 3 6 3 6 6 6 3 3 1 2 6 6 6 6 6 6 6 6 4 4
#> [94] 4 4 4 4 4 4 5 2 2 2 2 2 2 4 3 3 4 3 3 3 2 2 2 2 5 5 5 5 5 5 5
#> [125] 5 5 5 5 5 6 6 5 5 5 5 6 6 1 1 1 1 1 1 1 1 1 1
#>
#> $ZetelRij20170908
#> [1] 1 3 6 2 3 4 4 3 4 2 4 2 3 6 4 2 5 5 NA 4 4 3 4 1 2 2 3 4 1 1 2
#> [32] 6 2 5 3 6 1 3 3 4 5 5 6 3 2 5 6 1 2 1 5 6 5 5 4 4 3 4 5 4 6 1
#> [63] 2 4 5 2 5 5 3 1 5 4 1 1 6 3 4 3 4 5 3 6 5 2 2 3 4 4 5 5 6 3 5
#> [94] 5 4 6 6 5 6 6 5 4 4 3 3 4 6 6 6 4 6 6 6 6 6 6 6 5 6 6 4 1 2 3
#> [125] 3 2 4 4 4 5 6 3 5 5 6 4 5 5 6 2 2 5 5 6 6 6 6
#>
#> $ZetelKolom20170908
#> [1] 2 1 3 1 2 1 3 4 3 1 5 2 3 6 2 1 4 4 NA 4 2 1 3 2 3 1 4 5 1 1 2
#> [32] 4 3 5 2 2 2 1 2 5 5 2 2 4 3 3 4 2 2 1 2 1 1 1 4 5 1 1 5 2 2 1
#> [63] 2 4 5 2 1 2 3 1 2 1 2 1 2 4 2 1 3 2 3 6 3 1 3 3 4 5 4 5 5 2 3
#> [94] 4 4 5 4 5 6 1 1 3 1 4 2 2 1 6 5 2 4 3 1 6 5 4 3 4 5 6 1 2 3 3
#> [125] 4 1 4 5 3 1 1 2 1 3 3 1 3 4 5 3 2 2 3 1 2 3 4
#>
#> $NotesLucas
#> [1] " "
#> [2] " "
#> [3] " "
#> [4] " "
#> [5] " "
#> [6] " "
#> [7] " "
#> [8] " "
#> [9] " "
#> [10] " "
#> [11] " "
#> [12] " "
#> [13] " "
#> [14] " "
#> [15] " "
#> [16] " "
#> [17] " "
#> [18] " "
#> [19] " "
#> [20] " "
#> [21] " "
#> [22] " "
#> [23] " "
#> [24] " "
#> [25] " "
#> [26] " "
#> [27] " "
#> [28] " "
#> [29] " "
#> [30] " "
#> [31] " "
#> [32] " "
#> [33] " "
#> [34] " "
#> [35] " "
#> [36] " "
#> [37] " "
#> [38] " "
#> [39] " "
#> [40] " "
#> [41] " "
#> [42] " "
#> [43] " "
#> [44] " "
#> [45] " "
#> [46] " "
#> [47] " "
#> [48] " "
#> [49] " "
#> [50] " "
#> [51] " "
#> [52] " "
#> [53] " "
#> [54] " "
#> [55] " "
#> [56] " "
#> [57] " "
#> [58] " "
#> [59] " "
#> [60] " "
#> [61] " "
#> [62] " "
#> [63] " "
#> [64] " "
#> [65] " "
#> [66] " "
#> [67] " "
#> [68] " "
#> [69] " "
#> [70] " "
#> [71] " "
#> [72] " "
#> [73] " "
#> [74] " "
#> [75] " "
#> [76] " "
#> [77] " "
#> [78] " "
#> [79] " "
#> [80] " "
#> [81] " "
#> [82] " "
#> [83] " "
#> [84] " "
#> [85] " "
#> [86] " "
#> [87] " "
#> [88] " "
#> [89] " "
#> [90] " "
#> [91] " "
#> [92] " "
#> [93] " "
#> [94] " "
#> [95] " "
#> [96] " "
#> [97] " "
#> [98] " "
#> [99] " "
#> [100] "staat niet aangemerkt als kamerlid maar is het wel, want staat in debat app "
#> [101] " "
#> [102] " "
#> [103] " "
#> [104] " "
#> [105] " "
#> [106] " "
#> [107] " "
#> [108] " "
#> [109] " "
#> [110] " "
#> [111] " "
#> [112] " "
#> [113] " "
#> [114] " "
#> [115] " "
#> [116] " "
#> [117] " "
#> [118] " "
#> [119] " "
#> [120] " "
#> [121] " "
#> [122] " "
#> [123] " "
#> [124] " "
#> [125] " "
#> [126] " "
#> [127] " "
#> [128] " "
#> [129] " "
#> [130] " "
#> [131] " "
#> [132] " "
#> [133] " "
#> [134] " "
#> [135] " "
#> [136] " "
#> [137] " "
#> [138] " "
#> [139] " "
#> [140] " "
#> [141] " "
#> [142] " "
#> [143] " "
#> [144] " "
#> [145] " "
#> [146] " "
#> [147] " "
#>
#> $Kabinet20171031
#> [1] 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [48] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0
#> [95] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [142] 0 0 0 0 0 0
#>
#> $ZetelSegment20171031
#> [1] 1 2 6 NA 3 1 4 4 3 1 3 3 2 6 3 4 3 2 NA 1 1 5 1 2 NA 2 1 1 NA 5 NA
#> [32] 6 NA 5 6 5 6 3 1 4 1 6 3 6 2 3 5 4 6 6 2 2 1 4 NA 2 4 3 3 5 2 NA
#> [63] 4 NA 2 5 3 5 4 1 3 3 NA NA 6 3 NA 6 6 NA 3 1 2 6 6 6 6 6 6 6 6 NA 4
#> [94] 4 4 4 4 4 4 5 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 2 5 5 5 5 5 5 5
#> [125] 5 5 5 5 5 6 6 5 5 5 5 6 6 1 1 1 1 1 1 1 1 1 1
#>
#> $ZetelRij20171031
#> [1] 1 3 6 NA 3 4 4 3 4 2 4 2 3 6 4 2 4 5 NA 4 4 3 4 1 NA 2 3 4 NA 1 NA
#> [32] 6 NA 5 3 6 1 3 3 4 5 5 5 3 2 4 6 1 2 1 5 6 5 5 NA 4 3 4 2 4 1 NA
#> [63] 2 NA 4 2 5 5 3 1 1 3 NA NA 6 2 NA 3 4 NA 1 6 5 2 2 3 4 4 5 5 6 NA 5
#> [94] 5 4 6 6 5 6 6 5 4 4 3 3 4 5 6 6 3 6 6 6 6 6 6 6 5 6 6 4 1 2 3
#> [125] 3 2 4 4 4 5 6 3 5 5 6 4 5 5 6 2 2 5 5 6 6 6 6
#>
#> $ZetelKolom20171031
#> [1] 2 1 3 NA 4 1 3 4 3 1 5 2 3 6 2 1 5 3 NA 4 2 1 3 2 NA 1 4 5 NA 1 NA
#> [32] 4 NA 5 2 2 2 3 2 5 5 2 3 4 3 4 4 2 2 1 2 1 1 1 NA 5 1 1 3 2 1 NA
#> [63] 2 NA 4 2 1 2 3 1 2 1 NA NA 2 1 NA 1 3 NA 1 6 4 1 3 3 4 5 4 5 5 NA 3
#> [94] 4 4 5 4 5 6 1 1 3 1 4 2 2 1 6 5 2 4 6 4 3 2 1 6 4 5 6 1 2 3 3
#> [125] 4 1 4 5 3 1 1 2 1 3 3 1 3 4 5 3 2 2 3 1 2 3 4
#>
#> $alter_elected
#> [1] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [6] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [11] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [16] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [21] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [26] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [31] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [36] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [41] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [46] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [51] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [56] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [61] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [66] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [71] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [76] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [81] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [86] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [91] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [96] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [101] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [106] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [111] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [116] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [121] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [126] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [131] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [136] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [141] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [146] "yes, verkozen" "yes, verkozen"
#>
#> $ego_elected
#> [1] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [6] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [11] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [16] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [21] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [26] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [31] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [36] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [41] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [46] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [51] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [56] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [61] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [66] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [71] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [76] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [81] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [86] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [91] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [96] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [101] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [106] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [111] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [116] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [121] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [126] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [131] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [136] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [141] "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen" "yes, verkozen"
#> [146] "yes, verkozen" "yes, verkozen"
#>
#> $ALTERhandle
#> [1] 101 201 301 401 501 701 801 1001 1101 1501 1701 1901 2001
#> [14] 2701 2901 3001 3101 3201 3401 4101 4201 4301 4401 4701 4801 5001
#> [27] 5101 5201 5801 6001 6401 6501 6601 6801 6901 7001 7401 7601 7801
#> [40] 8101 8801 9001 9101 9201 9301 9501 9601 9701 9901 10101 10201 10301
#> [53] 10402 10501 10701 10901 11201 11401 12001 12101 12301 12601 12801 13101 13401
#> [66] 13501 13801 14101 14201 14401 14601 14901 15001 15101 15501 15601 15801 16101
#> [79] 16701 16801 17001 18901 23001 30901 31101 31201 31401 31501 31601 31701 31901
#> [92] 35501 36001 36401 36501 36601 36801 36901 37001 37101 40501 41001 41301 41401
#> [105] 42001 44901 56601 56901 57101 57201 57401 57701 58101 58301 58401 58801 58901
#> [118] 63701 63801 63901 70401 70601 70701 71001 71101 71201 71301 71401 71501 71701
#> [131] 72201 74401 74801 75001 81801 83901 84201 103801 106102 109001 109101 109401 109501
#> [144] 109701 109801 109901 110001
#>
#> $Naam2
#> [1] "FA" "MA" "KA" "TvA" "MA" "HB" "SB" "VB" "RB" "MB" "AB"
#> [12] "HtB" "HBS" "JvD" "ED" "PD" "RD" "CDF" "PD" "JG" "MdG" "RG"
#> [23] "DG" "SVHB" "MH" "PH" "LH" "MvH" "MK" "JK" "RK" "NK" "WK"
#> [34] "HK" "AK" "TK" "RL" "HL" "BM" "PvM" "AM" "HN" "CNdH" "MvN"
#> [45] "PO" "FvO" "SO" "AP" "RvR" "ER" "MR" "ER" "Rdr" "AR" "CS"
#> [56] "GJS" "SS" "KvdS" "OT" "MT" "Mvt" "SvV" "KV" "BV" "JV" "LV"
#> [67] "AdV" "FW" "SvW" "GW" "BvtW" "EZ" "HZ" "MR" "LP" "JHP" "JD"
#> [78] "LA" "SD" "SD" "KD" "TB" "EB" "LM" "SK" "SB" "PK" "BvK"
#> [89] "CL" "FF" "MH" "IvE" "JP" "RJ" "JvE" "MG" "RR" "AB" "AD"
#> [100] "TdG" "RP" "HvdM" "AK" "CvD" "JvdBJ" "MvM" "DW" "BB" "SH" "AM"
#> [111] "DYZ" "DK" "ZeY" "MW" "AW" "SK" "JM" "LS" "MvR" "CvB" "EO"
#> [122] "KB" "TvdL" "CE" "ZO" "BS" "SK" "BvO" "NO" "LW" "ID" "LvT"
#> [133] "LvR" "FMA" "FA" "GvD" "KvdH" "KG" "TH" "VM" "GM" "DvW" "EM"
#> [144] "LdJ" "GP" "AK" "RvA"
#>
#> $Partij_col
#> [1] "#1A2952" "#018A38" "#A4231D" "#EA851B" "#EA851B" "#1A2952" "#05A539" "#05A539" "#E25E1E"
#> [10] "#1A2952" "#EA851B" "#EA851B" "#018A38" "#DC331E" "#E25E1E" "#05A539" "#EA851B" "#0391DB"
#> [19] "#EA851B" "#018A38" "#1A2952" "#5FAF32" "#1A2952" "#018A38" "#EA851B" "#018A38" "#1A2952"
#> [28] "#018A38" "#018A38" "#5FAF32" "#018A38" "#DC331E" "#05A539" "#801D77" "#A4231D" "#18ADB3"
#> [37] "#DC331E" "#EA851B" "#1A2952" "#05A539" "#018A38" "#A4231D" "#EA851B" "#DC331E" "#018A38"
#> [46] "#EA851B" "#18ADB3" "#05A539" "#DC331E" "#DC331E" "#018A38" "#018A38" "#1A2952" "#EA851B"
#> [55] "#0391DB" "#0391DB" "#05A539" "#E25E1E" "#EA851B" "#005832" "#018A38" "#05A539" "#05A539"
#> [64] "#EA851B" "#0391DB" "#5FAF32" "#EA851B" "#005832" "#05A539" "#1A2952" "#EA851B" "#EA851B"
#> [73] "#EA851B" "#EA851B" "#A4231D" "#EA851B" "#A4231D" "#A4231D" "#A4231D" "#EA851B" "#EA851B"
#> [82] "#7D3536" "#0391DB" "#DC331E" "#DC331E" "#DC331E" "#DC331E" "#DC331E" "#DC331E" "#DC331E"
#> [91] "#DC331E" "#05A539" "#05A539" "#05A539" "#05A539" "#05A539" "#05A539" "#05A539" "#05A539"
#> [100] "#05A539" "#018A38" "#018A38" "#018A38" "#018A38" "#018A38" "#018A38" "#EA851B" "#EA851B"
#> [109] "#EA851B" "#EA851B" "#EA851B" "#EA851B" "#EA851B" "#EA851B" "#EA851B" "#EA851B" "#EA851B"
#> [118] "#801D77" "#801D77" "#801D77" "#005832" "#5FAF32" "#5FAF32" "#5FAF32" "#5FAF32" "#5FAF32"
#> [127] "#5FAF32" "#5FAF32" "#5FAF32" "#5FAF32" "#5FAF32" "#5FAF32" "#005832" "#005832" "#18ADB3"
#> [136] "#A4231D" "#A4231D" "#1A2952" "#7D3536" "#1A2952" "#1A2952" "#1A2952" "#1A2952" "#1A2952"
#> [145] "#1A2952" "#1A2952" "#1A2952"
#>
#> $X
#> [1] 7.9 9.5 -16.1 3.9 3.7 13.6 -3.3 -4.6 3.6 10.0 1.2 2.6 7.8 -16.9 4.7
#> [16] -1.1 4.0 9.9 -1.2 12.8 13.5 -6.8 13.2 5.3 1.3 7.9 10.8 12.5 6.4 -5.1
#> [31] 7.1 -16.4 -3.7 -12.4 -11.0 -10.3 -8.0 4.8 11.6 -5.5 14.0 -14.3 -2.3 -11.5 6.1
#> [46] 5.4 -12.2 -2.6 -9.5 -7.7 11.8 14.1 15.4 1.3 8.8 7.9 -1.0 5.7 2.6 -8.6
#> [61] 13.3 -1.3 -2.4 2.3 8.8 -6.9 -3.6 -9.6 -3.6 8.2 -2.4 -1.1 1.4 3.0 -15.7
#> [76] 1.3 -12.6 -10.6 -13.0 6.5 2.4 15.4 10.9 -9.0 -9.8 -11.4 -13.2 -13.3 -15.0 -15.1
#> [91] -16.6 -2.3 -3.7 -5.1 -4.5 -5.9 -4.8 -6.3 -7.2 -9.4 12.7 9.6 11.1 6.8 8.7
#> [106] 10.4 -1.0 1.3 2.6 -2.2 3.7 5.0 7.3 9.5 10.6 11.6 12.5 -11.5 -13.1 -13.9
#> [121] -7.7 -6.1 -7.7 -8.6 -9.3 -5.9 -10.4 -11.0 -9.5 -13.8 -15.2 -7.7 -8.6 -10.7 -11.3
#> [136] -12.2 -14.6 14.4 15.9 9.3 9.8 15.2 14.9 17.1 16.9 16.7 16.3
#>
#> $Y
#> [1] -2.9 1.3 -0.8 3.5 5.5 -4.5 7.4 5.0 7.4 -4.4 7.8 4.0 2.9 -4.4 7.0 4.2 9.1 6.1 9.6
#> [20] -1.0 -3.2 3.8 -2.1 0.5 4.2 0.3 -0.9 0.0 -0.4 0.5 1.4 -1.9 3.5 3.1 -1.9 7.5 -4.3 5.0
#> [39] -3.2 6.5 0.8 -0.4 11.0 -4.5 2.1 8.6 5.8 2.1 -3.1 -2.9 4.1 3.8 -4.4 9.6 4.7 5.4 6.0
#> [58] 6.5 9.3 4.7 4.8 2.4 4.0 7.7 6.8 1.4 10.8 6.1 5.5 -4.3 9.3 7.8 2.4 2.1 0.6 6.0
#> [77] -1.0 -0.9 -2.1 8.2 5.9 1.6 5.1 -1.8 -4.4 -3.2 -3.2 -4.5 -3.2 -4.4 -3.2 5.9 9.1 8.6 7.0
#> [96] 10.1 10.5 8.2 9.6 8.3 3.1 4.0 2.2 3.8 2.2 3.0 11.2 11.2 11.0 7.7 10.8 10.5 9.6 8.3
#> [115] 7.5 6.8 5.8 4.1 4.8 3.8 5.4 -0.4 0.3 2.2 1.3 2.1 3.0 2.2 4.0 0.8 1.6 2.9 6.8
#> [134] 5.1 6.8 0.0 -1.7 -0.4 0.6 -1.8 -3.1 -3.2 -1.7 -4.4 -3.2 -1.9 -0.8
#>
#> [1] "Agema, Fleur " "Amhaouch, Mustafa "
#> [3] "Arib, Khadija " "v. Ark, Tamara "
#> [5] "Azmani, Malik " "Beertema, Harm "
#> [7] "Belhaj, Salima " "Bergkamp, Vera "
#> [9] "Bisschop, Roelof " "Bosma, Martin "
#> [11] "Bosman, Andre " "ten Broeke, Han "
#> [13] "Bruins Slot, Hanke " "Van Dijk, Jasper "
#> [15] "Dijkgraaf, Elbert " "Dijkstra, Pia "
#> [17] "Dijkstra, Remco " "Dik-Faber, Carla "
#> [19] "Duisenberg, Pieter " "Geurts, Jaco "
#> [21] "De Graaf, Machiel " "Grashoff, Rik "
#> [23] "Graus, Dion " "Van Haersma Buma, Sybrand "
#> [25] "Harbers, Mark " "Heerma, Pieter "
#> [27] "Helder, Lilian " "Van Helvert, Martijn "
#> [29] "Keijzer, Mona " "Klaver, jesse "
#> [31] "Knops, Raymond " "Kooiman, Nine "
#> [33] "Koolmees, Wouter " "Krol, Henk "
#> [35] "Kuiken, Attje " "Kuzu, Tunahan "
#> [37] "Leijten, Renske " "Lodders, Helma "
#> [39] "Madlener, Barry " "Van Meenen, Paul "
#> [41] "Mulder, Agnes " "Nijboer, Henk "
#> [43] "Nijkerken-de Haan, Chantal " "Van Nispen, Michiel "
#> [45] "Omtzigt, Pieter " "Van Oosten, Foort "
#> [47] "Ozturk, Selcuk " "Pechtold, Alexander "
#> [49] "Van Raak, Ronald " "Roemer, Emile "
#> [51] "Rog, Michel " "Ronnes, Erik "
#> [53] "De Roon, Raymond " "Rutte, Arno "
#> [55] "Schouten, Carola " "Segers, Gert-Jan "
#> [57] "Sjoerdsma, Sjoerd " "Van der Staaij, Kees "
#> [59] "Tellegen, Ockje " "Thieme, Marianne "
#> [61] "Van Toorenburg, Madeleine " "Van Veldhoven, Stientje "
#> [63] "Verhoeven, Kees " "Visser, Barbara "
#> [65] "Voordewind, Joel " "Voortman, Linda "
#> [67] "De Vries, Aukje " "Wassenberg, Frank "
#> [69] "Van Weyenberg, Steven " "Wilders, Geert "
#> [71] "Van t'Wout, Bas " "Ziengs, Erik "
#> [73] "Zijlstra, Halbe " "Rutte, Mark "
#> [75] "Ploumen, Lilianne " "Hennis-Plasschaert, Jeanine "
#> [77] "Dijsselbloem, Jeroen " "Asscher, Lodewijk "
#> [79] "Dijksma, Sharon " "Dekker, Sander "
#> [81] "Dijkhoff, Klaas " "Thierry Baudet "
#> [83] "Eppo Bruins " "LILIAN MARIJNISSEN "
#> [85] "SADET KARABULUT " "SANDRA BECKERMAN "
#> [87] "PETER KWINT " "BART VAN KENT "
#> [89] "CEM LACIN " "FRANK FUTSELAAR "
#> [91] "MAARTEN HIJINK " "Ingrid van Engelshoven "
#> [93] "Jan Paternotte " "Rob Jetten "
#> [95] "Jessica van Eijs " "Maarten Groothuizen "
#> [97] "Rens Raemakers " "Achraf Bouali "
#> [99] "Antje Diertens " "Tjeerd de Groot "
#> [101] "René Peters " "Harry van der Molen "
#> [103] "Anne Kuik " "Chris van Dam "
#> [105] "Joba van den Berg-Jansen " "Maurits von Martels "
#> [107] "Dennis Wiersma " "Bente Becker "
#> [109] "Sophie Hermans " "Anne Mulder "
#> [111] "Dilan Yesilgöz-Zegerius " "Daniel Koerhuis "
#> [113] "Zohair el Yassini " "Martin Wörsdörfer "
#> [115] "Arne Weverling " "Sven Koopmans "
#> [117] "Jan Middendorp " "Léonie Sazias "
#> [119] "Martin van Rooijen " "Corrie van Brenk "
#> [121] "Esther Ouwehand " "Kathalijne Buitenweg "
#> [123] "Tom van der Lee " "Corinne Ellemeet "
#> [125] "Zihni Özdil " "Bart Snels "
#> [127] "Suzanne Kröger " "Bram van Oijk "
#> [129] "Nevin Özütok " "Lisa Westerveld "
#> [131] "Isabelle Diks " "Liesbeth van Tongeren "
#> [133] "Lammert van Raan " "Femke Merel Arissen "
#> [135] "Farid Azarkan " "Gijs van Dijk "
#> [137] "Kirsten van den Hul " "Gerbrands, Karen "
#> [139] "Theo Hiddema " "Vicky Maeijer "
#> [141] "Gidi Markuszower " "Danai van Weerdenburg "
#> [143] "Edgar Mulder " "Léon de Jong "
#> [145] "Gabriëlle Popken " "Alexander Kops "
#> [147] "Roy van Aalst "But now let us start plotting.
I cant see anything!! |:-(
Would simplify help?
Still way too dense. What is the density of the network??
#> [1] 0.04962259Actually, not very high at all.
9.2.3 From directed to reciprocated ties
But let us try to plot only the reciprocated ties.
# define undirected network
atmnet1_un <- atmnet1 == 1 & t(atmnet1) == 1
G2 <- graph_from_adjacency_matrix(atmnet1_un, mode = "undirected", weighted = NULL, diag = TRUE, add.colnames = NA,
add.rownames = NA)
# attach data if you want
edges <- igraph::as_data_frame(G2, what = "edges")
G2 <- graph_from_data_frame(edges, directed = FALSE, vertices = keyf)
plot(G2)
Mmm, It looks like MPs do like to mention themselves! Let simplify again.
9.2.4 Select nodes to plot
Suppose we want to remove the isolates.
# first make sure we don't end up with MPS who only mention themselves
diag(atmnet1_un) <- 0
# lets find the noisolates
noisolates <- rowSums(atmnet1_un, na.rm = T) > 0
# length(noisolates) sum(noisolates) if you select, select both correct nomination network as ego
# characteristics
atmnet1_un_sel <- atmnet1_un[noisolates, noisolates]
# if you are going to use the dataset keyf to add characteristics to the plot later, make sure to
# run the correct selection as well!!!
keyf_sel <- keyf[noisolates, ]
G2_sel <- graph_from_adjacency_matrix(atmnet1_un_sel, mode = "undirected", weighted = NULL, diag = TRUE,
add.colnames = NA, add.rownames = NA)
G2_sel <- simplify(G2_sel)
plot(G2_sel, mode = "undirected")
The same logic of course applies if you would like to select on node attributes (e.g. gender, party).
# option 1: see above. only select MPs from the liberal party
selection <- keyf$Partij == "VVD"
# build new adjacency matrix
atmnet1_un_sel2 <- atmnet1_un[selection, selection]
# etc.
# option 2. Suppose we have attached our dataset to our graph object. only select MPs from the
# liberal party
selection <- V(G2)$Partij == "VVD"
selection_id <- which(selection) # this gives us a numeric variable
G_sel <- induced_subgraph(G2, v = selection_id)
plot(G_sel)
9.2.5 Changing vertices
9.2.6 Changing edges I
You could use two different notations:
E(graph)$parameter <- ...
plot(graph, edge.parameter = ... )
9.2.7 Add a legend
# adding legend because I am working in Rmarkdown I need some {}
plot.igraph(G2, margin = 0, mode = "udirected")
legend(x = -1, y = -1, c("Female", "Male"), pch = 21, col = "#777777", pt.bg = c("red", "green"), pt.cex = 2,
cex = 0.8, bty = "n", ncol = 1)
9.2.8 Coordinates
Lets put the MPs where they belong.
Inspect coordinates.
The colors refer to the different parties.
knitr::include_graphics("TK.jpg")
plot(keyf$X, keyf$Y, xlim = c(-18, 18), ylim = c(-18, 18), col = keyf$Partij_col, pch = 16)
In case you wonder. The empty seats are the MPs without a Twitter account.
Lets assign these coordinates to our MPs
# it really depends on your plotting window (size, resolution etc.) to get consistent results you
# need to define this beforehand. won't do that now.
# give nodes coler of their party
V(G2)$color <- keyf$Partij_col
# change node size a bit
V(G2)$size = igraph::degree(G2) * 1.05 + 6
# remove the labels
V(G2)$label = ""
# less curvature
E(G2)$curved = 0.1
owncoords <- cbind(keyf$X, keyf$Y)
owncoords <- owncoords/8
owncoords[, 1] <- (owncoords[, 1] - mean(owncoords[, 1]))
owncoords[, 2] <- (owncoords[, 2] - mean(owncoords[, 2]))
plot.igraph(G2, mode = "undirected", layout = owncoords, rescale = F, margin = c(0, 0, 0, 0), xlim = c(min(owncoords[,
1]), max(owncoords[, 1])), ylim = c(min(owncoords[, 2]), max(owncoords[, 2])))
9.2.9 Changing edges II
preperation
We can change the edges based on dyad characteristics but if we have a weighted adjacency matrix also on the weights of the edges. To demonstrate this I first make a weighted atmention network. I simply sum whether MPs have mentioned each other in t1, t2 and t3.
# construct adjacency matrix first define the recipricated atmentions in each wave
atmnet1_un <- atmnet1 == 1 & t(atmnet1) == 1
atmnet2_un <- atmnet2 == 1 & t(atmnet2) == 1
atmnet3_un <- atmnet3 == 1 & t(atmnet3) == 1
atmnet_weighted <- atmnet1_un + atmnet2_un + atmnet3_un
# contstruct graph / let us keep the loops note that the parameter 'weighted' is set to true.
G_w <- igraph::graph_from_adjacency_matrix(atmnet_weighted, mode = "undirected", weighted = TRUE, diag = TRUE,
add.colnames = NA, add.rownames = NA)
# attach data
edges <- igraph::as_data_frame(G_w, what = "edges")
# inspect the weight.
edges$weight #not a lot of variation.
# rebuild the graph.
G_w <- graph_from_data_frame(edges, directed = FALSE, vertices = keyf)
# add changes as above
V(G_w)$color <- keyf$Partij_col
V(G_w)$size = igraph::degree(G_w) * 1.05 + 6
V(G_w)$label = ""
E(G_w)$curved = 0.1
# add the weights
E(G_w)$width <- E(G_w)$weight
plot.igraph(G_w, mode = "undirected", layout = owncoords, rescale = F, margin = c(0, 0, 0, 0), xlim = c(min(owncoords[,
1]), max(owncoords[, 1])), ylim = c(min(owncoords[, 2]), max(owncoords[, 2])))
#> [1] 1 1 1 1 2 1 1 2 1 1 1 1 2 1 1 1 2 1 1 2 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 3 1 1 1 1 2 3 1 1 1
#> [48] 2 1 1 2 1 1 1 1 1 1 1 2 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1
#> [95] 1 1 1 1 3 2 1 1 1 1 1 1 1 1 1 1 2 1 2 1 2 1 1 2 1 1 3 1 1 1 1 2 2 1 1 3 1 3 2 1 1 1 1 1 1 2 1
#> [142] 1 1 1 1 1 1 1 1 1 2 2 3 1 1 3 2 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2 3 2 1 1 1 1 1 1
#> [189] 1 2 1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 3 1 1 2 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [236] 1 2 1 1 2 1 2 1 1 1 1 1 1 1 3 1 3 1 1 1 2 2 1 1 1 1 1 1 2 1 2 3 1 1 1 1 1 1 1 2 1 3 1 2 1 1 1
#> [283] 1 2 1 1 1 1 1 3 1 3 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 1 2 3 3 1 1 1 1 1 1 3 2
#> [330] 1 1 1change edge based on dyad charactersitics
# let us make them the color of the nodes if it is between nodes from same party. let us make them
# red if between parties
edges <- get.adjacency(G_w)
edges_mat <- matrix(as.numeric(edges), nrow = nrow(edges))
# edges_mat
# because we have undirected, we only need the edges once ...I know ...
edges_mat[lower.tri(edges_mat)] <- 0
# table(keyf$Geslacht)
teller <- 1
coloredges <- NA
# we will loop over egos
for (i in 1:nrow(edges)) {
# We then loop over alters
for (j in 1:ncol(edges)) {
# we check if there is a tie between ego and alter
if (edges_mat[i, j] == 1) {
# if so, we check ego and alter are from the same party if so, we give the tie the
# color of the party
if (keyf$Partij_col[i] == keyf$Partij_col[j]) {
coloredges[teller] <- keyf$Partij_col[i]
}
# if so, we check ego and alter are from the same party if not, we give the tie a
# transparent grey color
if (keyf$Partij_col[i] != keyf$Partij_col[j]) {
coloredges[teller] <- "#0000004B"
}
teller <- teller + 1
}
}
}
E(G_w)$color = coloredges
# prepare a legend
Party_names <- unique(keyf$Partij)
Party_cols <- unique(keyf$Partij_col)
# reorder
Party_names <- Party_names[c(7, 3, 9, 10, 12, 11, 5, 4, 6, 2, 8, 1, 13)]
Party_cols <- Party_cols[c(7, 3, 9, 10, 12, 11, 5, 4, 6, 2, 8, 1, 13)]
# save the plot png('MPplotv2.png',width = 900, height= 900)
{
plot.igraph(G_w, mode = "undirected", layout = owncoords, rescale = F, margin = c(0, 0, 0, 0), xlim = c(min(owncoords[,
1]), max(owncoords[, 1])), ylim = c(min(owncoords[, 2]), max(owncoords[, 2])))
legend("topleft", legend = Party_names, pch = 21, col = "#777777", pt.bg = Party_cols, pt.cex = 2,
cex = 0.8, bty = "n", ncol = 3)
text(-2.2, -1.2, "Note 1: Node size based on degree", adj = 0, cex = 0.8)
text(-2.2, -1.3, "Note 2: Edge color based on Party of MPs, black if MPs from different party", adj = 0,
cex = 0.8)
text(-2.2, -1.4, "Note 3: Edge width based on number of @mentions", adj = 0, cex = 0.8)
}
# dev.off()
Figure 9.1: Reciprocated at-mention relations between Dutch MPs (2017)
9.3 Co-author networks
Remember the sociology staff at Radboud University? Let’s plot the 1.5 degree co-authorship network of the sociology staff! Perhaps we can even mark those staff members that are actually at Radboud University’s department of sociology? Or perhaps we can draw nodes proportional to some ego information? Do you remember that we went “one deep” in the collaboration networks? Well, that automatically means that those who do not work at RU sociology are also included in these data. And because we look at directed ties, it may mean that there are some isolated clusters. For instance, Hofstra lists McFarland as co-author, but not vice versa: this means that looking through Hofstra at his co-authors, McFarland is put into the data as well as his co-authors. What we first want is to look whether he lists any co-authors Bas his collaborators as well. If we succeed in that, we have the 1.5 degree network.
If you did not yet downloaded the data, please do so now.
soc_df
soc_collabs
collabs_1deep
# load the necessary datasets
load("addfiles/soc_df.RData")
load("addfiles/soc_collabs1.RData")
load("addfiles/soc_collabs2.RData")# get unique collaborators of soc staff first
soc_collabs_unique <- unique(soc_collabs[, 4]) # so 229 unique collaborators for RU staff?
soc_collabs_unique <- c(soc_collabs_unique, soc_df$gs_id) # add the soc staff themselves.
soc_collabs_unique <- data.frame(soc_collabs_unique)
soc_collabs_unique$v1 <- 1 # convenient to select on after the merge
soc_collabs_unique <- unique(soc_collabs_unique)
# so this is a very important step, we join the unique soc collaborators to the collaborators of
# collaborators
require(tidyverse)
onefivedegree <- left_join(collabs_1deep, soc_collabs_unique, by = c(coauth_id = "soc_collabs_unique"))
# Then, we drop those that are not among sociology collaborators and who don't lsit coauthors
# regardless
onefivedegree <- onefivedegree[!is.na(onefivedegree$v1), ]
onefivedegree <- onefivedegree[!is.na(onefivedegree$coauth), ]
# we pick those columns and have an edgelist of soc collaborators and whether they collaborate with
# those same collaborators the 1.5 degree network
onefivedegree <- onefivedegree[, c("name", "coauth")]
names(onefivedegree) <- c("from", "to")
# we get soc collaborators and add those to the data above and end up with a nice edgelist!
socc <- soc_collabs[!is.na(soc_collabs$coauth), ]
socc <- socc[, c("name", "coauth")]
names(socc) <- c("from", "to")
onefivedegree <- rbind(socc, onefivedegree)
save(onefivedegree, file = "addfiles/soc_onefivedegree.RData")So let’s try to plot this now.
onefivedegree <- as.matrix(onefivedegree) # matrix because igraph wants that
library(igraph) # Notice that we call igraph here, in anticipation of the viz tutorial.
net1 <- graph_from_edgelist(onefivedegree, directed = TRUE) # Notice the igraph function here!
plot(net1) # pretty ugly!
Ouch, that looks pretty rough… Can we make that prettier? With some simple steps?
plot(net1 ,
vertex.color = "gold", # nice color for the vertices
vertex.size = 4, # we'll vertices a bit smaller
vertex.frame.color = "gray", # we'll put a gray frame around vertices
vertex.label.color = "black", # not that ugly blue color for the labels (names)
vertex.label.family = "Helvetica", # not a fan of times new roman in figures
vertex.label.cex = 0.4, # make the label a bit smaller too
vertex.label.dist = 0.5, # we'll pull the labels a bit away from the vertices
edge.curved = 0.2, # curved edges is always a nice touch
edge.arrow.size = 0.1) # make arrow size (direction of edge) smaller
Now, let’s try to extract the names in this network and see whether we can single out the staff members. First we gather the vertices in the network object as data frame through a nice function in iGraph, namely as_ids(V(net1)). And then we will label the column with a nice variable name. We’ll collect the Radboud sociology nodes from the soc_df data frame. We then join with the in_network dataset and put some different color label on those nodes who were in the soc_df and those who were not. Do you understand the code below on extracting and attaching node attributes?
in_network <- data.frame(as_ids(V(net1)))
names(in_network)[1] <- "name"
#soc_df$total_cites <- soc_df$total_cites.x
ru_nodes <- soc_df[, c("name", "total_cites")]
in_network <- left_join(in_network, ru_nodes, by = c("name" = "name"))
in_network$vcol <- ifelse(is.na(in_network$total_cites), "#E69F00", "#56B4E9")
plot(net1,
vertex.color = in_network$vcol, #THIS WAS WHAT WE DID THE LAST CODEBLOCK FOR!
vertex.size = 4, # we'll make them a bit smaller
vertex.frame.color = "gray", # we'll put a frame around it
vertex.label.color = "black", # not that ugly blue color for the names
vertex.label.family = "Helvetica", # not a fan of times in figures
vertex.label.cex = 0.4, # a bit smaller too
vertex.label.dist = 0.5, # we'll pull the labels a bit away from the vertices
edge.curved = 0.2, # curved edges is always a nice tough
edge.arrow.size = 0.1) # arrow size smaller
We can redo this trick for other things as well. Show only the labels of RU sociology staff perhaps?
plot(net1,
vertex.color = in_network$vcol,
#NOTICE THESE CONDITIONAL STATEMENTS BELOW
vertex.label = ifelse(!is.na(in_network$total_cites), in_network$name, NA),
vertex.size = 4, # we'll make them a bit smaller
vertex.frame.color = "gray", # we'll put a frame around it
vertex.label.color = "black", # not that ugly blue color for the names
vertex.label.family = "Helvetica", # not a fan of times in figures
vertex.label.cex = 0.65, # a bit smaller too
vertex.label.dist = 0.5, # we'll pull the labels a bit away from the vertices
edge.curved = 0.2, # curved edges is always a nice tough
edge.arrow.size = 0.1) # arrow size smaller
Vertex size adjusted for citation? We can go on and on and on!
plot(net1,
vertex.color = in_network$vcol,
vertex.label = ifelse(!is.na(in_network$total_cites), in_network$name, NA),
# SAME HERE, TRY TO SMOOTH THE TOTAL_CITES A BIT WITH LOGSCALE
vertex.size = ifelse(!is.na(in_network$total_cites), log(in_network$total_cites), 2),
vertex.frame.color = "gray", # we'll put a frame around it
vertex.label.color = "black", # not that ugly blue color for the names
vertex.label.family = "Helvetica", # not a fan of times in figures
vertex.label.cex = 0.65, # a bit smaller too
vertex.label.dist = 0.5, # we'll pull the labels a bit away from the vertices
edge.curved = 0.2, # curved edges is always a nice tough
edge.arrow.size = 0.1) # arrow size smaller
And so on and so forth! You can play around with different algorithms that put these nodes on the grid differently too.
Let us know focus on the staff members of the sociology department of Radboud University Nijmegen. We thus once again need to a selection of our graph. Here, we will use the induced.subgraph function of the igraph package. Above we changed the color of the nodes based on the info in our in_network data object. We could use that to identify the nodes.
net1_mat <- igraph::as_adjacency_matrix(net1, sparse = FALSE)
net1_un <- (net1_mat + t(net1_mat)) > 0
net1_un <- graph_from_adjacency_matrix(net1_un, mode = "undirected")
net_sel <- induced.subgraph(net1_un, vids = (in_network$vcol == "#56B4E9"))
plot(net_sel)
9.4 Assignment
- Improve the plot of Zachary’s karate club:
- remove the labels
- add the relevant names for the persons in the network
- add communities (either based on network characteristics or based on what happened in reality to this network)
- add an informative title and legend
- remove the labels
- Improve the plot of Twittersphere in the Dutch House of Parliament:
- Perhaps change the size of nodes based on ties that cross party boundaries?
- Try to make a plot where information on the different layers of twitter (friendships, atmentions and retweets) are combined. Thus combine in one plot, or plot three networks next to each other.
- Perhaps change the size of nodes based on ties that cross party boundaries?
- A good example of a bad network visualization is Smallworld 1.4.1.3. The visualization of Smallworld should tell the story that a typical smallworld network has a relatively low density, high level of clustering and low average path length, see Chapter 6. Make a good example of a good network visualization of Smallworld. Please download the igraph object of Smalworld here.
References
Csardi, Gabor, and Tamas Nepusz. 2020. Igraph: Network Analysis and Visualization. https://igraph.org.
Girvan, Michelle, and Mark EJ Newman. 2002. “Community Structure in Social and Biological Networks.” Proceedings of the National Academy of Sciences 99 (12): 7821–26.
Kadushin, Charles. 2012. Understanding Social Networks: Theories, Concepts, and Findings. Oup Usa.
Ognyanova, Katherine. 2016. “Network Analysis with r and Igraph: NetSci x Tutorial.” https://www.kateto.net/networks-r-igraph.
Rosseel, Yves. 2012. “lavaan: An R Package for Structural Equation Modeling.” Journal of Statistical Software 48 (2): 1–36. https://doi.org/10.18637/jss.v048.i02.
Snijders, Tom A. B., Ruth Ripley, Krists Boitmanis, Christian Steglich, Johan Koskinen, Nynke Niezink, and Viviana Amati. 2022. RSiena: Siena - Simulation Investigation for Empirical Network Analysis. http://www.stats.ox.ac.uk/~snijders/siena/.
Zachary, Wayne W. 1977. “An Information Flow Model for Conflict and Fission in Small Groups.” Journal of Anthropological Research 33 (4): 452–73.