Today’s challenge is to
read in a dataset, and
create a network object
Read in one (or more) of the following data sets, using the correct R package and command.
tidyr package)Find the _data folder, located inside the posts folder. Then you can read in the data, using base read.csv or read_csv standard tidy read command to read Comma Separated Values files or, alternatively, read the data in directly from loading a package.
#load in DF
got.marriages <- read_csv('_data/got/got_marriages.csv')
head(got.marriages)# A tibble: 6 × 5
From To Type Notes Generation
<chr> <chr> <chr> <chr> <chr>
1 Targaryen Stark Married R+L=J Current
2 Baratheon Martell Engaged died Current
3 Baratheon Stark Engaged broken Current
4 Martell Essos Married <NA> Current
5 Martell Reach Affair <NA> Current
6 Martell Essos Affair <NA> Current table(got.marriages$Type)
Affair Engaged Married
36 2 217 g.got.marriages <- graph_from_data_frame(got.marriages)
table(got.marriages$Type)
Affair Engaged Married
36 2 217 plot(g.got.marriages,edge.arrow.size=0)
#color edges based on link
got.marriages$Type.Color <- as.factor(got.marriages$Type)
levels(got.marriages$Type.Color)[1] "Affair" "Engaged" "Married"#coloring
levels(got.marriages$Type.Color) <- c('gray', 'blue', 'green')
E(g.got.marriages)$color <- got.marriages$Type.Color
plot(g.got.marriages, edge.arrow.size = 0)
head(fish_encounters)# A tibble: 6 × 3
fish station seen
<fct> <fct> <int>
1 4842 Release 1
2 4842 I80_1 1
3 4842 Lisbon 1
4 4842 Rstr 1
5 4842 Base_TD 1
6 4842 BCE 1g.fish <- graph_from_data_frame(fish_encounters)
V(g.fish)$name [1] "4842" "4843" "4844" "4845" "4847" "4848" "4849"
[8] "4850" "4851" "4854" "4855" "4857" "4858" "4859"
[15] "4861" "4862" "4863" "4864" "4865" "Release" "I80_1"
[22] "Lisbon" "Rstr" "Base_TD" "BCE" "BCW" "BCE2" "BCW2"
[29] "MAE" "MAW" V(g.fish)$color[is.na(as.numeric(V(g.fish)$name))] <- 'lightblue'
plot(g.fish, edge.arrow.size = .5)
got.likes <- read.csv('_data/got/got_like_dislike.csv')
head(got.likes) Current.house Former.house Name Lysa.Arryn Petyr.Baelish
1 Arryn Tully Lysa Arryn NA 3
2 Baelish Petyr Baelish 3 NA
3 Lannister Baratheon Joffrey Baratheon 0 -1
4 Tyrell Baratheon Margaery Tyrell 0 1
5 Baratheon Renly Baratheon 0 -2
6 Baratheon Robert Baratheon 2 1
Joffrey.Baratheon Margaery.Tyrell Renly.Baratheon Robert.Baratheon
1 0 0 0 1
2 -1 1 -2 1
3 NA 4 -3 2
4 3 NA 5 0
5 -5 5 NA 3
6 4 0 3 NA
Stannis.Baratheon Brienne.of.Tarth Bronn Gregor.Clegane Sandor.Clegane
1 0 1 0 0 0
2 -1 0 -1 -1 -1
3 -5 -3 -1 3 4
4 -5 -4 0 0 1
5 -5 2 -1 -2 -2
6 2 0 0 -1 0
Xaro.Xhoan.Daxos Gendry Balon.Greyjoy Theon.Greyjoy Jaqen.H.ghar Hodor
1 0 0 -1 -1 0 2
2 0 0 0 0 0 0
3 0 -5 -2 -3 0 -1
4 0 -5 -1 -2 0 0
5 0 -4 0 -3 0 0
6 0 4 -1 4 0 1
Khal.Drogo Cersei.Lannister Jaime.Lannister Lancel.Lannister Tyrion.Lannister
1 0 -1 -1 -1 -1
2 0 -2 -2 0 -1
3 -3 5 4 1 -2
4 0 2 1 0 -1
5 0 -4 -3 -1 -2
6 -4 -2 -1 0 1
Tywin.Lannister Maester.Luwin Melisandre Jeor.Mormont Jorah.Mormont Osha
1 -1 1 0 0 0 0
2 -2 0 0 0 0 0
3 3 -1 -4 0 -3 -2
4 3 0 -4 0 -2 0
5 -5 1 -5 0 -2 0
6 -1 2 0 0 -3 -2
Podrick.Payne Pyat.Pree Grand.Maester.Pycelle Ros Davos.Seaworth Shae
1 0 0 0 0 0 0
2 -1 0 -1 -2 0 -1
3 -1 0 -1 -5 -2 -1
4 0 0 0 0 -2 -1
5 -1 0 -1 0 -3 -1
6 0 0 1 2 0 0
Jon.Snow Arya.Stark Bran.Stark Catelyn.Stark Robb.Stark Sansa.Stark
1 1 2 2 3 2 2
2 0 1 1 5 0 3
3 -1 -2 -2 -3 -5 0
4 0 -1 -1 -2 -5 0
5 0 2 2 3 2 2
6 2 4 4 5 4 4
Daenerys.Targaryen Viserys.Targaryen Samwell.Tarly Loras.Tyrell Varys
1 0 0 0 0 0
2 -1 0 0 1 -1
3 -5 -3 0 2 -1
4 -5 -3 0 5 0
5 -5 -3 0 5 -1
6 -5 -4 0 0 -2
Eddard.Stark
1 3
2 -3
3 -5
4 0
5 1
6 5g.likes <- graph_from_adjacency_matrix(as.matrix(got.likes[ , - c(1 : 3)]), mode = 'directed', weighted = TRUE)
kableExtra :: kable(as.matrix(got.likes[ , - c(1 : 3)]))| Lysa.Arryn | Petyr.Baelish | Joffrey.Baratheon | Margaery.Tyrell | Renly.Baratheon | Robert.Baratheon | Stannis.Baratheon | Brienne.of.Tarth | Bronn | Gregor.Clegane | Sandor.Clegane | Xaro.Xhoan.Daxos | Gendry | Balon.Greyjoy | Theon.Greyjoy | Jaqen.H.ghar | Hodor | Khal.Drogo | Cersei.Lannister | Jaime.Lannister | Lancel.Lannister | Tyrion.Lannister | Tywin.Lannister | Maester.Luwin | Melisandre | Jeor.Mormont | Jorah.Mormont | Osha | Podrick.Payne | Pyat.Pree | Grand.Maester.Pycelle | Ros | Davos.Seaworth | Shae | Jon.Snow | Arya.Stark | Bran.Stark | Catelyn.Stark | Robb.Stark | Sansa.Stark | Daenerys.Targaryen | Viserys.Targaryen | Samwell.Tarly | Loras.Tyrell | Varys | Eddard.Stark |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NA | 3 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 0 | 2 | 0 | -1 | -1 | -1 | -1 | -1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 2 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 3 |
| 3 | NA | -1 | 1 | -2 | 1 | -1 | 0 | -1 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -2 | -2 | 0 | -1 | -2 | 0 | 0 | 0 | 0 | 0 | -1 | 0 | -1 | -2 | 0 | -1 | 0 | 1 | 1 | 5 | 0 | 3 | -1 | 0 | 0 | 1 | -1 | -3 |
| 0 | -1 | NA | 4 | -3 | 2 | -5 | -3 | -1 | 3 | 4 | 0 | -5 | -2 | -3 | 0 | -1 | -3 | 5 | 4 | 1 | -2 | 3 | -1 | -4 | 0 | -3 | -2 | -1 | 0 | -1 | -5 | -2 | -1 | -1 | -2 | -2 | -3 | -5 | 0 | -5 | -3 | 0 | 2 | -1 | -5 |
| 0 | 1 | 3 | NA | 5 | 0 | -5 | -4 | 0 | 0 | 1 | 0 | -5 | -1 | -2 | 0 | 0 | 0 | 2 | 1 | 0 | -1 | 3 | 0 | -4 | 0 | -2 | 0 | 0 | 0 | 0 | 0 | -2 | -1 | 0 | -1 | -1 | -2 | -5 | 0 | -5 | -3 | 0 | 5 | 0 | 0 |
| 0 | -2 | -5 | 5 | NA | 3 | -5 | 2 | -1 | -2 | -2 | 0 | -4 | 0 | -3 | 0 | 0 | 0 | -4 | -3 | -1 | -2 | -5 | 1 | -5 | 0 | -2 | 0 | -1 | 0 | -1 | 0 | -3 | -1 | 0 | 2 | 2 | 3 | 2 | 2 | -5 | -3 | 0 | 5 | -1 | 1 |
| 2 | 1 | 4 | 0 | 3 | NA | 2 | 0 | 0 | -1 | 0 | 0 | 4 | -1 | 4 | 0 | 1 | -4 | -2 | -1 | 0 | 1 | -1 | 2 | 0 | 0 | -3 | -2 | 0 | 0 | 1 | 2 | 0 | 0 | 2 | 4 | 4 | 5 | 4 | 4 | -5 | -4 | 0 | 0 | -2 | 5 |
| 0 | -1 | -5 | -4 | -5 | 1 | NA | -2 | -2 | -3 | -4 | 0 | -5 | -2 | 0 | 0 | -1 | 0 | -5 | -4 | -2 | -3 | -5 | 0 | 5 | 0 | -4 | -2 | -2 | 0 | -3 | 0 | 5 | 0 | -2 | -2 | -2 | -4 | -5 | -2 | -5 | -3 | 0 | -5 | -2 | 0 |
| 1 | -1 | -3 | -3 | 5 | 0 | -4 | NA | -2 | -2 | -3 | 0 | 0 | -4 | -5 | 0 | 3 | 0 | -5 | -5 | -2 | -5 | -5 | 3 | -5 | 0 | -3 | 2 | -1 | 0 | -1 | 0 | -4 | -3 | 1 | 4 | 4 | 5 | 3 | 4 | -5 | 0 | 0 | 3 | -1 | 0 |
| 0 | -1 | -2 | -1 | -1 | 0 | -2 | -1 | NA | -3 | -5 | 0 | 0 | 0 | -1 | 0 | 0 | -1 | -3 | -3 | -1 | 5 | -4 | 0 | -2 | 0 | -1 | 0 | 4 | 0 | -3 | 0 | -2 | 5 | 0 | 0 | 0 | -2 | -2 | 1 | -2 | -2 | 0 | -1 | -2 | 0 |
| 0 | -1 | 3 | -1 | -2 | -2 | -3 | -2 | -2 | NA | -5 | 0 | -3 | 0 | 0 | -5 | 0 | -4 | 2 | 2 | 0 | -2 | 5 | 0 | -2 | 0 | -2 | 0 | -1 | 0 | 0 | 0 | -1 | -1 | -1 | -3 | -3 | -4 | -4 | 0 | -4 | 0 | 0 | -3 | 0 | -1 |
| 0 | -2 | 3 | 0 | -2 | 0 | -4 | -3 | -5 | -5 | NA | 0 | -2 | 0 | 0 | 0 | 0 | -3 | 2 | 1 | 0 | 0 | 3 | 0 | -2 | 0 | -1 | 0 | -2 | 0 | 0 | 0 | -1 | -1 | 0 | 0 | 0 | -3 | -3 | 5 | -5 | 0 | 0 | 0 | -1 | 0 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -3 | 0 | 0 | 0 | 0 | 0 |
| 0 | 0 | -5 | -5 | -4 | 5 | -5 | 0 | -1 | -3 | -2 | 0 | NA | 0 | 0 | 3 | 2 | 0 | -5 | -4 | -2 | 0 | -5 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | -3 | 0 | 0 | 0 | 0 | 5 | 2 | 0 | 0 | 0 | -2 | 0 | 0 | -1 | 0 | 3 |
| -1 | 0 | -2 | -1 | -1 | -1 | -2 | -5 | 0 | 0 | -1 | 0 | 0 | NA | 3 | 0 | 0 | 0 | -3 | -2 | -1 | -2 | -3 | 0 | -2 | 0 | -1 | 0 | -1 | 0 | -1 | 0 | 0 | -1 | -2 | -1 | -1 | -4 | -5 | -2 | -3 | 0 | 0 | -1 | 0 | -5 |
| -1 | 0 | -3 | -2 | -3 | 4 | -5 | -5 | -1 | 0 | -2 | 0 | 0 | 3 | NA | 0 | -5 | 0 | -4 | -3 | -1 | -3 | -4 | -5 | -2 | 0 | -1 | -5 | -2 | 0 | -1 | 3 | 0 | -1 | -2 | -4 | -5 | -4 | -4 | -2 | -3 | -1 | 0 | -1 | -1 | 3 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -5 | 0 | 0 | 3 | 0 | 0 | NA | 3 | 0 | -1 | -1 | -1 | 0 | -3 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | -3 | 0 | 0 | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 2 | 0 | -5 | 3 | NA | 0 | -3 | -2 | 0 | -1 | -3 | 5 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 5 | 5 | 5 | 5 | 4 | 0 | 0 | 0 | 0 | 0 | 4 |
| 0 | 0 | -1 | 0 | -5 | -5 | 0 | 0 | -1 | -4 | -3 | 0 | 0 | 0 | 0 | 0 | 0 | NA | -3 | -2 | 0 | -1 | -3 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | -4 | 0 | 0 | 0 | 0 |
| -1 | -2 | 5 | -5 | -5 | 3 | -5 | -5 | -3 | 2 | 2 | 0 | -5 | -3 | -4 | -1 | -3 | -3 | NA | 5 | 5 | -4 | 4 | 0 | -3 | 0 | -3 | -2 | 0 | 0 | 2 | 1 | -2 | -3 | -2 | -1 | -1 | -4 | -5 | 1 | -5 | -3 | 0 | -2 | 2 | -4 |
| -1 | -2 | 3 | -3 | -4 | -1 | -5 | -5 | -3 | 2 | 1 | 0 | -4 | -2 | -3 | -1 | -2 | -2 | 5 | NA | 2 | 2 | 4 | 0 | -1 | 0 | -1 | 0 | 0 | 0 | 2 | 0 | -1 | 0 | 0 | -1 | -2 | -5 | -5 | 0 | -4 | -2 | 0 | -1 | 0 | -5 |
| -1 | 0 | 1 | -1 | -1 | 0 | -2 | -2 | -1 | 0 | 0 | 0 | -2 | -1 | -1 | -1 | 0 | 0 | 5 | 2 | NA | -1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -2 | -2 | 0 | -1 | 0 | 0 | 0 | 0 | 0 |
| -5 | -1 | -3 | 0 | -2 | 1 | -5 | -4 | 5 | -3 | -2 | 0 | 0 | -2 | -3 | 0 | -1 | -1 | -4 | -3 | -1 | NA | -5 | 0 | -3 | 0 | -1 | 0 | 5 | 0 | 1 | 5 | 0 | 5 | 0 | 0 | 0 | -1 | -1 | 3 | -1 | -1 | 0 | -1 | -1 | 0 |
| -1 | -2 | 3 | -5 | -5 | 1 | -5 | -5 | -4 | 5 | 3 | 0 | -5 | -3 | -4 | -3 | -3 | -3 | 4 | 4 | 2 | -5 | NA | 0 | -4 | 0 | -3 | 0 | 0 | 0 | 2 | 0 | -2 | -2 | -1 | -1 | -1 | -5 | -5 | 0 | -5 | 0 | 0 | -3 | 1 | 0 |
| 1 | 0 | -1 | 0 | 0 | 2 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | -5 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 5 |
| 0 | 0 | -5 | -5 | -5 | 0 | 5 | -4 | -1 | -2 | -2 | 0 | 0 | -2 | -2 | 0 | 0 | 0 | -3 | -1 | 0 | 0 | -4 | 0 | NA | 0 | -3 | 0 | 0 | 0 | -1 | 0 | -2 | 0 | 0 | -1 | -1 | -5 | -5 | -2 | -4 | 0 | 0 | -3 | -3 | -5 |
| 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 | NA | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 1 |
| 0 | 0 | -3 | -2 | -2 | -3 | -5 | -3 | -1 | -2 | -1 | 0 | 0 | -1 | -1 | 0 | 0 | 4 | -3 | -1 | 0 | -1 | -3 | 0 | -3 | 3 | NA | 0 | 0 | -5 | 0 | 0 | -2 | 0 | 0 | -1 | -1 | -3 | -3 | -1 | 5 | -1 | 0 | -1 | 0 | -2 |
| 0 | 0 | -2 | 0 | 0 | -2 | -2 | 0 | 0 | 0 | 0 | 2 | 0 | -5 | 1 | 5 | 0 | -2 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | NA | 0 | 0 | -1 | 0 | -1 | 0 | 2 | 2 | 5 | 3 | 2 | 1 | -2 | -1 | 0 | -3 | -2 | 2 |
| 0 | 0 | -1 | -1 | -1 | 0 | -2 | -1 | 5 | -1 | 0 | 0 | 0 | -1 | -2 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | -2 | 1 | -3 | 4 | 0 | 0 | 0 | -2 | -2 | 3 | -3 | 0 | 0 | -1 | -2 | 0 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -5 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -5 | 0 | 0 | 0 | 0 | 0 |
| 0 | -1 | -1 | -1 | -1 | 1 | -3 | -1 | -3 | 0 | 0 | 0 | -3 | -1 | -1 | 0 | 0 | 0 | 2 | 2 | 0 | 2 | 2 | 0 | -1 | 0 | 0 | -1 | -2 | 0 | NA | 1 | -1 | -1 | 0 | 0 | 0 | -2 | -3 | 0 | -3 | -1 | 0 | -1 | -3 | -1 |
| 0 | -2 | -5 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | NA | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 0 | 0 | -2 | -2 | -3 | -1 | 5 | -3 | -2 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -2 | -1 | 0 | 0 | -2 | 0 | -2 | 0 | -2 | -1 | -3 | 0 | -1 | 0 | NA | -1 | -1 | -2 | -2 | -3 | -3 | -1 | -3 | 0 | 0 | -1 | -2 | 0 |
| 0 | -1 | -1 | -1 | -1 | 0 | 0 | -3 | 5 | -1 | 0 | 0 | 0 | -1 | -1 | 0 | 0 | 0 | -3 | 0 | 0 | 5 | -2 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | -1 | 3 | -1 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 0 | -1 | -1 | 0 | 2 | -2 | 1 | 0 | -1 | 0 | 0 | 0 | -2 | -2 | 1 | 4 | 0 | -2 | 0 | 0 | 0 | -1 | 5 | 0 | 5 | 0 | 2 | 0 | 0 | 0 | 0 | -1 | 0 | NA | 5 | 5 | -1 | 5 | 5 | 0 | 0 | 5 | 0 | 0 | 5 |
| 2 | 1 | -1 | -1 | 0 | 4 | -2 | 4 | 0 | -3 | 0 | 0 | 5 | -1 | -4 | 5 | 5 | 0 | -1 | -1 | 0 | 0 | -1 | 5 | -1 | 0 | -1 | 2 | 0 | 0 | 0 | 0 | -2 | 0 | 5 | NA | 5 | 5 | 5 | 4 | -1 | -1 | 1 | -1 | -5 | 5 |
| 2 | 1 | -1 | -1 | 0 | 4 | -2 | 4 | 0 | -3 | 0 | 0 | 2 | -1 | -5 | 1 | 5 | 0 | -1 | -2 | 0 | 0 | -1 | 5 | -1 | 0 | -1 | 5 | 0 | 0 | 0 | 0 | -2 | 0 | 5 | 5 | NA | 5 | 5 | 5 | -1 | -1 | 1 | -1 | -5 | 5 |
| 3 | 2 | -5 | -2 | 3 | 5 | -5 | 5 | -3 | -4 | -2 | 0 | 0 | -4 | -4 | 1 | 4 | 0 | -4 | -5 | -1 | -5 | -5 | 5 | -5 | 1 | -3 | 3 | -2 | 0 | -2 | 0 | -3 | 0 | -1 | 5 | 5 | NA | 4 | 5 | -5 | -1 | 0 | -1 | -5 | 5 |
| 2 | 0 | -5 | -1 | -4 | 5 | -5 | 3 | -2 | -4 | -3 | 0 | 0 | -5 | -4 | 1 | 5 | 0 | -5 | -5 | -2 | -1 | -5 | 5 | -5 | 0 | -3 | 2 | -2 | 0 | -3 | 0 | -3 | 0 | 5 | 5 | 5 | 3 | NA | 5 | -5 | -3 | 1 | -4 | -5 | 5 |
| 1 | 2 | -1 | 0 | -3 | 4 | -2 | 4 | 1 | 0 | 1 | 0 | 0 | -2 | -3 | 1 | 4 | 0 | 1 | 0 | 0 | 3 | 0 | 5 | -2 | 0 | -1 | 1 | 1 | 0 | 0 | 0 | -1 | 0 | 5 | 5 | 5 | 5 | 5 | NA | -3 | -1 | 1 | -1 | -1 | 5 |
| 0 | -2 | -5 | -5 | -5 | -5 | -5 | -5 | -2 | -4 | -5 | -3 | -2 | -3 | -3 | 0 | 0 | 5 | -5 | -4 | -1 | -1 | -5 | 0 | -4 | 0 | 5 | -2 | -3 | -5 | -3 | 0 | -3 | 0 | 0 | -1 | -1 | -4 | -5 | -3 | NA | 2 | 0 | 0 | -3 | -2 |
| 0 | 0 | -3 | -3 | -4 | -3 | -3 | 0 | -2 | 0 | 0 | 0 | 0 | 0 | -1 | 0 | 0 | -4 | -3 | -2 | 0 | -1 | 0 | 0 | 0 | 0 | -1 | -1 | 0 | 0 | -1 | 0 | 0 | 0 | 0 | -1 | -1 | -1 | -3 | -1 | -2 | NA | 0 | 0 | -1 | 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 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | NA | 0 | 0 | 1 |
| 0 | 1 | -2 | 5 | 5 | 0 | -5 | -3 | -1 | -3 | 0 | 0 | -1 | -1 | -1 | 0 | 0 | 0 | -2 | -1 | 0 | -1 | -3 | 0 | -3 | 0 | -1 | -3 | -1 | 0 | -1 | 0 | -1 | 0 | 0 | -1 | -1 | -1 | -4 | -1 | 0 | 0 | 0 | NA | -1 | 0 |
| 0 | -1 | -1 | 0 | -1 | -2 | -2 | -1 | -2 | 0 | -1 | 0 | 0 | 0 | -1 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 1 | 0 | -3 | 0 | 0 | -2 | -2 | 0 | -3 | 0 | -2 | 0 | 0 | -5 | -5 | -5 | -5 | -1 | -3 | -1 | 0 | -1 | NA | -5 |
| 3 | -1 | -5 | 0 | 1 | 5 | 0 | 0 | -1 | -1 | 0 | 0 | 3 | -5 | 3 | 0 | 4 | 0 | -4 | -5 | 0 | -1 | 0 | 5 | -5 | 1 | -2 | 2 | 0 | 0 | -1 | 0 | 0 | 0 | 5 | 5 | 5 | 5 | 5 | 5 | -2 | 0 | 1 | 0 | -5 | NA |
hist(E(g.likes)$weight)
E(g.likes)$color <- colorRampPalette(c("yellow", "blue"))(11)[E(g.likes)$weight + 6]
plot(g.likes, edge.arrow.size = 0.5) 
Add any comments or documentation as needed. More challenging data sets may require additional code chunks and documentation.
Load the package igraph and create an igraph object (i.e. a graph or network) in the form of an edge list. The command may vary whether the data is given as a list of connections or an adjacency matrix. Is the network directed or undirected; weighted or unweighted; unimodal or bipartite? Can you plot it?