Problem of plotting graph when I use simplify, in graph

Question

I want to plot my graph. I provided a sample data set. I used simplify to remove loop, but it completely affect the structure of data. When I plot the data without using simplify, I have the correct colouring of vertex and edges but there is loop.

I used simplify to remove the loop and the colouring after it, is wrong, because each node and edge from it should have the same colour as I defined in the code.

does anyone know how to remove loop in plot to not affect the structure of data?

plot 1 : correct colouring but with loop

plot 2: wrong colouring after using simplify

The code:

      X user.screen_name           child       parent in_reply_to_screen_name vaccine_type label
1     0   TweeetLorraine 1.392218e+18 1.392218e+18                       1  AstraZeneca     0
2     1       phldenault 1.393259e+18 1.392218e+18          TweeetLorraine  AstraZeneca     2
41   40  ElizabethDuncan 1.392297e+18 1.392218e+18          TweeetLorraine  AstraZeneca     2
42   41          7Rose75 1.392294e+18 1.392218e+18          TweeetLorraine  AstraZeneca     1
43   42      wh0careswh0 1.392336e+18 1.392294e+18                 7Rose75  AstraZeneca     0
44   43   T_ProudVeteran 1.392330e+18 1.392294e+18                 7Rose75  AstraZeneca     2
45   44   TweeetLorraine 1.392294e+18 1.392294e+18                 7Rose75  AstraZeneca     2
46   45         Norlaine 1.392288e+18 1.392218e+18          TweeetLorraine  AstraZeneca     2
47   46    elham95264575 1.393212e+18 1.392288e+18                Norlaine  AstraZeneca     1
48   47      soyfreemike 1.392387e+18 1.392288e+18                Norlaine  AstraZeneca     0
49   48          KMTCarr 1.392288e+18 1.392218e+18          TweeetLorraine  AstraZeneca     2
50   49     angela_petta 1.392283e+18 1.392218e+18          TweeetLorraine  AstraZeneca     2
51   50     lhoneyimhome 1.392272e+18 1.392218e+18          TweeetLorraine  AstraZeneca     2

net1 <- graph_from_data_frame(df %>% select("child","parent")) 
rel = get.adjacency(graph, sparse = FALSE) 

graph = simplify(net1, remove.loops=TRUE)
graph
summary(graph)


vertex_attr(graph, "label") <- df$label
#Set edge attribute:
edge_attr(graph, "label") <- df$label




E(graph)$color[E(graph)$label == 2] <- '#B3DE69' #green
E(graph)$color[E(graph)$label == 1] <- '#80B1D3' #yellow
E(graph)$color[E(graph)$label == 0] <- '#FB8072'#purple

V(graph)$color[V(graph)$label == 2] <- '#B3DE69'
V(graph)$color[V(graph)$label == 1] <- '#80B1D3'
V(graph)$color[V(graph)$label == 0] <- '#FB8072'
g<-c('#B3DE69','#80B1D3','#FB8072')
plot(graph,layout=layout.fruchterman.reingold,
     vertex.frame.color=NA,vertex.label.color="black",
     edge.label = NA,
     vertex.size=3, usecurve=TRUE,
     edge.lwd=0.02,
     vertex.dist=10,vertex.label.dist=2,vertex.label.cex=0.9,
     pad=0.9,alpha=80,
     edge.arrow.size=.1)


legend("bottomleft",legend= c("Positive","Neutral","Negative"),
       col=g,pch=19,pt.cex=1.5,bty="n",
       title="Label category")

title(main="Visualization ", cex.main=1)

Answer 1

Typically it's easier to manipulate data in the data frame than in igraph attributes. I would suggest to prepare everything in the data frame before converting it to a graph. Then simplify will do it's job as it's supposed to do, and you can plot or analyse your graph as you wish. To preserve the edge attributes in simplify if remove.multiple is TRUE, you need to define the edge.attr.comb parameter. Below I used dplyr::first, meaning that we pick the first value at combining multiple edges.

Edit: using OP data & preserving edge attributes in simplify

library(igraph)
library(dplyr)
library(magrittr)
library(tibble)
library(rlang)
library(readr)

df <- read_tsv('so_user142_data.tsv', col_types = cols())

color_map <- c(
    '0' = '#B3DE69', # green
    '1' = '#80B1D3', # blue
    '2' = '#FB8072'  # salmon
)

df %<>%
    mutate(label = recode(label, !!!color_map)) %>%
    rename(color = label) %>%
    select(
        child = user.screen_name,
        parent = in_reply_to_screen_name,
        color
    )


vertex_colors <-
    bind_rows(
        df %>% select(name = child, color),
        df %>% select(name = parent, color)
    ) %>%
    group_by(name) %>%
    summarize_all(first) %>%
    ungroup

g <-
    df %>%
    graph_from_data_frame(vertices = vertex_colors) %>%
    simplify(edge.attr.comb = first)

png('so_user142_graph.png', 800, 800)

    plot(
        g,
        layout = layout.fruchterman.reingold,
        vertex.frame.color = NA,
        vertex.label.color = 'black',
        vertex.size = 7,
        edge.curved = TRUE,
        edge.lwd = 0.4,
        vertex.dist = 10,
        vertex.label.dist = 1.2,
        vertex.label.cex = 1.2,
        pad = 0.9,
        alpha = 80,
        edge.arrow.size = 1.
    )

dev.off()