Egocentric Networks
Tanner Williams and Brent Lund
1/30/21
##Introduction
Within our survey we are attempting to find a link between the influence of thought and behaviors to social media. At the beginning of the millennium television and radio were still the dominate means of disseminating information, (albeit waning) while the internet was burgeoning as the new means of gathering and disseminating, that same information. However, due to the fact that television and radio information were essentially controlled by big corporations, if those corporations had an agenda, they could lean their opinions towards that direction either left or right. However, as the rise of social media came about we can now see that the monopolistic hold of that information is essential gone. We now have all individuals from all aspects of life that give their opinions, and beliefs on certain matters going on in the world. Our survey question are based on where and how. Where do these individuals get their information, is it one particular website, twitter, MSNBC? Or do they still get their information from Larry King, newspapers, PBS, radio. We want to see if certain behaviors and attitudes are associated with a certain means of getting information. Do the individuals who are active on twitter, believe there is not right to the 1st amendment, or do the beliefs of individuals who watch Ben Shapiro all associate in someway with far right beliefs and views. Also do these same individuals gather with like minded individuals, do they go to the same restaurants, donate to the same politicians, by exploring the connection between social media, and media in general we can get a clearer picture of what is going on .
##Name Generator Approach
Our approach specifically to the name generator was to analyze the information based on the relationships of these individuals. For example, we can see that perhaps these individuals who answered these questions in a similar manner quite possibly could be related. We analyze if they are related kinfolk, and also to look at and see if they know one another somehow. We look at the relationship in regards to age and education rather than just some individual off the street. We also look to see if these individuals perhaps are related ethnically instead of family.
##Positional Generator Approach
For our positional generator survey we look at the social status of the individuals who answered our questions and look at their behaviors based on their contacts. We look at and see if the respondents hold a specific position, or have a specify social size. We examine if the respondents have a specific social stratification and status within society. We see if specific social positions have access to higher strata measure through contacts in professions. We look at if certain respondents have high status origins, and if those respondents also have family and friends within relatively the same social status.
##Resource Generator Approach For resource generator we analyze social resources in a more direct manner, we look to see if certain resources can be provided by these individuals. We look at what types of resources and instrumental support these individuals have access to. However these section just does not capture resources it also looks at other dimensions of capital, potentially overlapping in certain aspects with positional generators.
Data Input
We chose to input data manually into an array structure instead of messing with importing the file originally as we weren’t sure how many submissions we were going to have. We ended up using the file to import things like the names. We also initialize our packages.
#setwd("~/InClassWork1/440/CSV")
library(ggplot2)
library(network)## network: Classes for Relational Data
## Version 1.16.1 created on 2020-10-06.
## copyright (c) 2005, Carter T. Butts, University of California-Irvine
## Mark S. Handcock, University of California -- Los Angeles
## David R. Hunter, Penn State University
## Martina Morris, University of Washington
## Skye Bender-deMoll, University of Washington
## For citation information, type citation("network").
## Type help("network-package") to get started.library(ggnetwork)
library(GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2library(igraph)##
## Attaching package: 'igraph'## The following objects are masked from 'package:network':
##
## %c%, %s%, add.edges, add.vertices, delete.edges, delete.vertices,
## get.edge.attribute, get.edges, get.vertex.attribute, is.bipartite,
## is.directed, list.edge.attributes, list.vertex.attributes,
## set.edge.attribute, set.vertex.attribute## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## unionResponses <- read.csv("Responses.csv", header = TRUE, sep = ",")
#worldview <- c("Sometimes", "Never", "Most of the time")
#new_source <- c("social media platforms", "Tv, radio, newspaper", "Websites")
#censorship <- c("Yes", "No", "It depends")
#up_to_date <- c("It would not effect my knowledge of current events", "I would not know what is happening",
# "I wouldn't be as up to date, but I'd still know generally what is happening")
#time_social_media <- c("0 - 25 %", "25 - 75%", "75 - 100%")
#fads <- c("Mostly", "Somewhat, but I learn through other outlets as well",
#"Somewhat, but I learn through other outlets as well")
Q1Value <- c(3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 1, 1, 5, 3,
3, 1, 1, 3, 3, 1, 1, 1, 3, 3, 1, 3, 1, 3, 3, 1,
3, 1, 1, 1, 1, 5, 3, 3, 3, 3, 3, 3, 1, 3, 3, 1,
3, 3, 3, 3, 3, 1, 3, 3, 5, 3, 3, 5, 3, 3, 3, 3,
3, 3, 3, 3, 5, 3, 1, 1, 3, 3, 5, 3, 3, 1, 3, 3,
3, 3, 3, 3, 1, 3, 1, 3, 1, 3, 5, 3, 1, 3)
Q2Value <- c(4, 2, 4, 2, 3, 4, 2, 4, 3, 3, 3, 4, 4, 2, 4, 5,
4, 3, 3, 3, 4, 4, 3, 4, 4, 4, 4, 1, 4, 3, 3, 5,
3, 3, 4, 4, 3, 2, 4, 4, 3, 4, 3, 3, 3, 3, 4, 4,
2, 4, 4, 4, 3, 3, 4, 3, 3, 4, 4, 2, 3, 3, 4, 2,
5, 2, 2, 2, 4, 4, 3, 4, 3, 4, 4, 1, 3, 4, 4, 4,
4, 2, 5, 3, 4, 3, 3, 3, 5, 1, 2, 4, 4, 3)
Q3Value <- c(3, 2, 1, 5, 1, 2, 3, 2, 1, 1, 1, 5, 1, 1, 4, 3,
2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 5, 5,
2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 1, 1, 1,
5, 5, 5, 5, 5, 1, 5, 1, 5, 5, 5, 5, 1, 1, 1, 1,
5, 1, 5, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1, 5, 5,
1, 1, 5, 5, 1, 5, 1, 1, 1, 5, 1, 5, 5, 5)
Q4Value <- c(3, 3, 1, 3, 1, 1, 5, 1, 1, 5, 3, 1, 1, 3, 3,
1, 3, 1, 3, 5, 3, 3, 3, 1, 1, 3, 1, 5, 3, 1,
3, 3, 3, 3, 1, 3, 1, 5, 1, 3, 3, 3, 3, 5, 1,
1, 3, 1, 3, 1, 3, 3, 3, 5, 1, 3, 3, 3, 3, 3,
3, 3, 1, 1, 3, 3, 3, 5, 3, 3, 3, 3, 1, 3, 1,
5, 3, 1, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 3, 1,
3, 3, 1, 3)
Q5Value <- c(3, 1, 3, 1, 1, 1, 1, 1, 1, 3, 3, 1, 1, 3, 3, 1,
1, 1, 1, 1, 3, 1, 3, 1, 1, 3, 1, 3, 1, 3, 3, 3,
3, 3, 3, 1, 1, 1, 3, 1, 3, 1, 3, 3, 3, 1, 1, 3,
1, 3, 1, 1, 1, 3, 3, 1, 3, 3, 1, 1, 3, 3, 1, 1,
1, 3, 1, 3, 3, 1, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1,
3, 1, 3, 1, 1, 1, 1, 1, 3, 1, 1, 5, 3, 1)
#Q5Value <- c(3, 1, 3, 1, 1, 1, 1, 1, 1, 3, 3, 1, 1, 3, 3, 1,
# 1, 1, 1, 1, 3, 1, 3, 1, 1, 3, 1, 3, 1, 3, 3, 3,
# 3, 3, 3, 1, 1, 1, 3, 1, 3, 1, 3, 3, 3, 1, 1, 3,
# 1, 3, 1, 1, 1, 3, 3, 1, 3, 3, 1, 1, 3, 3, 1, 1,
# 1, 3, 1, 3, 3, 1, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1,
# 3, 1, 3, 1, 1, 1, 1, 1, 3, 1, 1, 5, 3, 1) #deleted a 1
#names <- c("Anon", "JF", "BH", "DC", "IA", "LC", "NL", "AF", "Anon", "Anon", "DS", "LF", "RW",
# "KL", "Anon", "MJ", "Anon", "Anon", "Anon", "JK", "CT", "AC", "RM", "RA", "CJ", "AH",
# "Anon", "ST", "LSK", "YW", "Anon", "MK", "Anon", "KA", "CH", "RH", "HR", "LC", "SK",
# "KJ", "AR", "HF", "LO", "SPN", "RL", "TD", "PS", "MI", "RS", "SA", "MY", "KG", "LW",
# "LL", "Anon", "SH", "BG", "Anon", "Anon", "RB", "VJ", "WL", "WS", "Anon", "MS", "RM",
# "Anon", "DMC", "CB", "DN", "K", "Anon", "LL", "MD", "MH", "ZP", "BJK", "JW", "ELM",
# "IE", "MH", "FK", "Anon", "MH", "JF", "W", "MF", "KN", "JW", "JP", "Anon", "AP", "Anon", "Anon") #Added Last Anon
names <- c("Anon", "JF", "BH", "DC", "IA", "LC", "NL", "AF", "Anon", "Anon", "DS", "LF", "RW",
"KL", "Anon", "MJ", "Anon", "Anon", "Anon", "JK", "CT", "AC", "RM", "RA", "CJ", "AH",
"Anon", "ST", "LSK", "YW", "Anon", "MK", "Anon", "KA", "CH", "RH", "HR", "LC", "SK",
"KJ", "AR", "HF", "LO", "SPN", "RL", "TD", "PS", "MI", "RS", "SA", "MY", "KG", "LW",
"LL", "Anon", "SH", "BG", "Anon", "Anon", "RB", "VJ", "WL", "WS", "Anon", "MS", "RM",
"Anon", "DMC", "CB", "DN", "K", "Anon", "LL", "MD", "MH", "ZP", "BJK", "JW", "ELM",
"IE", "MH", "FK", "Anon", "MH", "JF", "W", "MF", "KN", "JW", "JP", "Anon", "AP", "Anon", "a")
#names <- Responses[8]
#Q1 <- Responses[9]
#Q2 <- Responses[10]
#Q3 <- Responses[11]
#Q4 <- Responses[12]
Q5 <- Responses[13]
worldview <- Responses[2]
new_source <-Responses[3]
censorship <- Responses[4]
up_to_date <- Responses[5]
time_social_media <-Responses[6]
fads <- Responses[7]##Responses Bar Graph Implementation For each question on our survey we determined what value it fell on a scale of 1 - 5. This scale differed for each question. We then use this scale to analyze reliablility, bias, position and resource availability.
####################### Q1 ##################
net <- data.frame(Q1Value, names)
net## Q1Value names
## 1 3 Anon
## 2 3 JF
## 3 1 BH
## 4 3 DC
## 5 3 IA
## 6 1 LC
## 7 3 NL
## 8 3 AF
## 9 1 Anon
## 10 3 Anon
## 11 3 DS
## 12 1 LF
## 13 1 RW
## 14 1 KL
## 15 5 Anon
## 16 3 MJ
## 17 3 Anon
## 18 1 Anon
## 19 1 Anon
## 20 3 JK
## 21 3 CT
## 22 1 AC
## 23 1 RM
## 24 1 RA
## 25 3 CJ
## 26 3 AH
## 27 1 Anon
## 28 3 ST
## 29 1 LSK
## 30 3 YW
## 31 3 Anon
## 32 1 MK
## 33 3 Anon
## 34 1 KA
## 35 1 CH
## 36 1 RH
## 37 1 HR
## 38 5 LC
## 39 3 SK
## 40 3 KJ
## 41 3 AR
## 42 3 HF
## 43 3 LO
## 44 3 SPN
## 45 1 RL
## 46 3 TD
## 47 3 PS
## 48 1 MI
## 49 3 RS
## 50 3 SA
## 51 3 MY
## 52 3 KG
## 53 3 LW
## 54 1 LL
## 55 3 Anon
## 56 3 SH
## 57 5 BG
## 58 3 Anon
## 59 3 Anon
## 60 5 RB
## 61 3 VJ
## 62 3 WL
## 63 3 WS
## 64 3 Anon
## 65 3 MS
## 66 3 RM
## 67 3 Anon
## 68 3 DMC
## 69 5 CB
## 70 3 DN
## 71 1 K
## 72 1 Anon
## 73 3 LL
## 74 3 MD
## 75 5 MH
## 76 3 ZP
## 77 3 BJK
## 78 1 JW
## 79 3 ELM
## 80 3 IE
## 81 3 MH
## 82 3 FK
## 83 3 Anon
## 84 3 MH
## 85 1 JF
## 86 3 W
## 87 1 MF
## 88 3 KN
## 89 1 JW
## 90 3 JP
## 91 5 Anon
## 92 3 AP
## 93 1 Anon
## 94 3 aggplot(data=net, aes(x = reorder(Q1Value, names), y=Q1Value)) +
geom_bar(stat='identity') + labs(title = "How Much Does Social Media Influence Your Worldviews?") +
scale_x_discrete(limit = c("1","3","5"),
labels = c("Informed","Neutral","Easily Influenced"))## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
######################### Q2 ###########################
net <- data.frame(Q2Value, names)
net## Q2Value names
## 1 4 Anon
## 2 2 JF
## 3 4 BH
## 4 2 DC
## 5 3 IA
## 6 4 LC
## 7 2 NL
## 8 4 AF
## 9 3 Anon
## 10 3 Anon
## 11 3 DS
## 12 4 LF
## 13 4 RW
## 14 2 KL
## 15 4 Anon
## 16 5 MJ
## 17 4 Anon
## 18 3 Anon
## 19 3 Anon
## 20 3 JK
## 21 4 CT
## 22 4 AC
## 23 3 RM
## 24 4 RA
## 25 4 CJ
## 26 4 AH
## 27 4 Anon
## 28 1 ST
## 29 4 LSK
## 30 3 YW
## 31 3 Anon
## 32 5 MK
## 33 3 Anon
## 34 3 KA
## 35 4 CH
## 36 4 RH
## 37 3 HR
## 38 2 LC
## 39 4 SK
## 40 4 KJ
## 41 3 AR
## 42 4 HF
## 43 3 LO
## 44 3 SPN
## 45 3 RL
## 46 3 TD
## 47 4 PS
## 48 4 MI
## 49 2 RS
## 50 4 SA
## 51 4 MY
## 52 4 KG
## 53 3 LW
## 54 3 LL
## 55 4 Anon
## 56 3 SH
## 57 3 BG
## 58 4 Anon
## 59 4 Anon
## 60 2 RB
## 61 3 VJ
## 62 3 WL
## 63 4 WS
## 64 2 Anon
## 65 5 MS
## 66 2 RM
## 67 2 Anon
## 68 2 DMC
## 69 4 CB
## 70 4 DN
## 71 3 K
## 72 4 Anon
## 73 3 LL
## 74 4 MD
## 75 4 MH
## 76 1 ZP
## 77 3 BJK
## 78 4 JW
## 79 4 ELM
## 80 4 IE
## 81 4 MH
## 82 2 FK
## 83 5 Anon
## 84 3 MH
## 85 4 JF
## 86 3 W
## 87 3 MF
## 88 3 KN
## 89 5 JW
## 90 1 JP
## 91 2 Anon
## 92 4 AP
## 93 4 Anon
## 94 3 aggplot(data=net, aes(x = reorder(Q2Value, names), y=Q2Value)) +
geom_bar(stat='identity') + labs(title = "Where Do You Get Your News?") +
scale_x_discrete(limit = c("1","2","3","4","5"),
labels = c("Ignorant","Unifromed","Generally Aware","Up to Date","Very Informed"))## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
####################################### Q3 #####################
net3 <- data.frame(Q3Value, names)
net3## Q3Value names
## 1 3 Anon
## 2 2 JF
## 3 1 BH
## 4 5 DC
## 5 1 IA
## 6 2 LC
## 7 3 NL
## 8 2 AF
## 9 1 Anon
## 10 1 Anon
## 11 1 DS
## 12 5 LF
## 13 1 RW
## 14 1 KL
## 15 4 Anon
## 16 3 MJ
## 17 2 Anon
## 18 1 Anon
## 19 1 Anon
## 20 1 JK
## 21 1 CT
## 22 1 AC
## 23 1 RM
## 24 1 RA
## 25 1 CJ
## 26 1 AH
## 27 1 Anon
## 28 4 ST
## 29 1 LSK
## 30 1 YW
## 31 5 Anon
## 32 5 MK
## 33 2 Anon
## 34 1 KA
## 35 2 CH
## 36 1 RH
## 37 1 HR
## 38 1 LC
## 39 2 SK
## 40 2 KJ
## 41 1 AR
## 42 2 HF
## 43 1 LO
## 44 1 SPN
## 45 2 RL
## 46 1 TD
## 47 1 PS
## 48 1 MI
## 49 5 RS
## 50 5 SA
## 51 5 MY
## 52 5 KG
## 53 5 LW
## 54 1 LL
## 55 5 Anon
## 56 1 SH
## 57 5 BG
## 58 5 Anon
## 59 5 Anon
## 60 5 RB
## 61 1 VJ
## 62 1 WL
## 63 1 WS
## 64 1 Anon
## 65 5 MS
## 66 1 RM
## 67 5 Anon
## 68 1 DMC
## 69 1 CB
## 70 1 DN
## 71 1 K
## 72 5 Anon
## 73 1 LL
## 74 1 MD
## 75 1 MH
## 76 1 ZP
## 77 5 BJK
## 78 1 JW
## 79 5 ELM
## 80 5 IE
## 81 1 MH
## 82 1 FK
## 83 5 Anon
## 84 5 MH
## 85 1 JF
## 86 5 W
## 87 1 MF
## 88 1 KN
## 89 1 JW
## 90 5 JP
## 91 1 Anon
## 92 5 AP
## 93 5 Anon
## 94 5 aggplot(data=net3, aes(x = reorder(Q3Value, names), y=Q3Value)) +
geom_bar(stat='identity') + labs(title = "Excluding Anything Illegal, Should Speech on Social Media be Censored?") +
scale_x_discrete(limit = c("1","2","3","4","5"),
labels = c("Against it","Special Cases (Hate Speech)","Neutral","Mostly for Censorship","Pro-Censorship"))## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
####################################### Q4 #####################
net4 <- data.frame(Q4Value, names)
net4## Q4Value names
## 1 3 Anon
## 2 3 JF
## 3 1 BH
## 4 3 DC
## 5 1 IA
## 6 1 LC
## 7 5 NL
## 8 1 AF
## 9 1 Anon
## 10 5 Anon
## 11 3 DS
## 12 1 LF
## 13 1 RW
## 14 3 KL
## 15 3 Anon
## 16 1 MJ
## 17 3 Anon
## 18 1 Anon
## 19 3 Anon
## 20 5 JK
## 21 3 CT
## 22 3 AC
## 23 3 RM
## 24 1 RA
## 25 1 CJ
## 26 3 AH
## 27 1 Anon
## 28 5 ST
## 29 3 LSK
## 30 1 YW
## 31 3 Anon
## 32 3 MK
## 33 3 Anon
## 34 3 KA
## 35 1 CH
## 36 3 RH
## 37 1 HR
## 38 5 LC
## 39 1 SK
## 40 3 KJ
## 41 3 AR
## 42 3 HF
## 43 3 LO
## 44 5 SPN
## 45 1 RL
## 46 1 TD
## 47 3 PS
## 48 1 MI
## 49 3 RS
## 50 1 SA
## 51 3 MY
## 52 3 KG
## 53 3 LW
## 54 5 LL
## 55 1 Anon
## 56 3 SH
## 57 3 BG
## 58 3 Anon
## 59 3 Anon
## 60 3 RB
## 61 3 VJ
## 62 3 WL
## 63 1 WS
## 64 1 Anon
## 65 3 MS
## 66 3 RM
## 67 3 Anon
## 68 5 DMC
## 69 3 CB
## 70 3 DN
## 71 3 K
## 72 3 Anon
## 73 1 LL
## 74 3 MD
## 75 1 MH
## 76 5 ZP
## 77 3 BJK
## 78 1 JW
## 79 3 ELM
## 80 3 IE
## 81 1 MH
## 82 1 FK
## 83 1 Anon
## 84 1 MH
## 85 3 JF
## 86 3 W
## 87 3 MF
## 88 3 KN
## 89 3 JW
## 90 1 JP
## 91 3 Anon
## 92 3 AP
## 93 1 Anon
## 94 3 aggplot(data=net4, aes(x = reorder(Q4Value, names), y=Q4Value)) +
geom_bar(stat='identity') + labs(title = "Would you be as up to date on current events if you did not have a social media platform?") +
scale_x_discrete(limit = c("1","3","5"),
labels = c("No Idea","General Awareness","Up to Date"))## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
####################################### Q5 #####################
net5 <- data.frame(Q5, names)
net5## Q5Value names
## 1 3 Anon
## 2 1 JF
## 3 3 BH
## 4 1 DC
## 5 1 IA
## 6 1 LC
## 7 1 NL
## 8 1 AF
## 9 1 Anon
## 10 3 Anon
## 11 3 DS
## 12 1 LF
## 13 1 RW
## 14 3 KL
## 15 3 Anon
## 16 1 MJ
## 17 1 Anon
## 18 1 Anon
## 19 1 Anon
## 20 1 JK
## 21 3 CT
## 22 1 AC
## 23 3 RM
## 24 1 RA
## 25 1 CJ
## 26 3 AH
## 27 1 Anon
## 28 3 ST
## 29 1 LSK
## 30 3 YW
## 31 3 Anon
## 32 3 MK
## 33 3 Anon
## 34 3 KA
## 35 1 CH
## 36 1 RH
## 37 1 HR
## 38 3 LC
## 39 1 SK
## 40 3 KJ
## 41 1 AR
## 42 3 HF
## 43 3 LO
## 44 3 SPN
## 45 1 RL
## 46 1 TD
## 47 3 PS
## 48 1 MI
## 49 3 RS
## 50 1 SA
## 51 1 MY
## 52 1 KG
## 53 3 LW
## 54 3 LL
## 55 1 Anon
## 56 3 SH
## 57 3 BG
## 58 1 Anon
## 59 1 Anon
## 60 3 RB
## 61 3 VJ
## 62 1 WL
## 63 1 WS
## 64 1 Anon
## 65 3 MS
## 66 1 RM
## 67 3 Anon
## 68 3 DMC
## 69 1 CB
## 70 3 DN
## 71 3 K
## 72 3 Anon
## 73 1 LL
## 74 1 MD
## 75 3 MH
## 76 3 ZP
## 77 3 BJK
## 78 3 JW
## 79 1 ELM
## 80 3 IE
## 81 1 MH
## 82 3 FK
## 83 1 Anon
## 84 1 MH
## 85 1 JF
## 86 1 W
## 87 1 MF
## 88 3 KN
## 89 1 JW
## 90 1 JP
## 91 5 Anon
## 92 3 AP
## 93 1 Anon
## 94 1 aggplot(data=net5, aes(x = reorder(Q5Value, names), y=Q5Value)) +
geom_bar(stat='identity') + labs(title = "How Much of your free time do You Spend on Social Media?") +
scale_x_discrete(limit = c("1","3","5"),
labels = c("0-25%","25-75%","75-100%"))## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
#########################################################################Network of Question relatability
#Name Generator Analysis What we see when we analyze our results from the name generator is the individuals who answered question 1 on the higher aspect of our 1-5 scale tended to have relations more with nodes 2 and 4 and 5 according to the data. These individuals we see are have more of a relational aspect they the individuals who answered lower. Some of the individuals who took the survey are related which is why we would see such a connection. We can also state that many of the individuals that were served came from the same cultural background and with this tended to view events such as free speech, censorship, etc., in a similar manner. We can also see that these individuals quite possibly are very similar in age and education as well.
#Positional Generator Analysis For our positional generator survey we see that the individuals who answered higher on the scale for node 2 do not seem to have much of a connection with the other respondents from other questions. This means that the individuals who answered higher specifically to this particular question may be much higher in the social structure than all the other respondents. They may also afford more social influence and power as well, possible positions, may be doctors, engineers, or congressmen. More than likely we will see that these individuals have other family members and friends that enjoy the same access as these individuals.
#Resource Genertor Analysis For the resource generator we can see that the individuals who answered high on the 1-5 scale spectrum of node three have some relations with all the individuals who answered high on every other node with the exception of node 1. This demonstrates that these individuals are individuals who have access to some substantial resources. It also shows that theses individuals who have access to substantial resources also enjoy a higher status withing society. This would make sense due to the fact that the higher the status the more wealth and power that usually comes with it therefore granting you more access to resources. It also shows that the relationship of these individuals has a big chunk of individuals who are kin, and for the most part come from the same ethnical background.
surveyData <- as.matrix(data.frame(names))
surveyNetwork <- network(surveyData, directed = FALSE)
surveyNetwork$Q1Value <- Q1Value
surveyNetwork$Q2Value <- Q2Value
surveyNetwork$Q3Value <- Q3Value
surveyNetwork$Q4Value <- Q4Value
surveyNetwork$Q5Value <- Q5Value
net6 <- data.frame(surveyNetwork$Q1Value, surveyNetwork$Q2Value,
surveyNetwork$Q3Value, surveyNetwork$Q4Value,
surveyNetwork$Q5Value)
ggnet2(net =net6,
label = TRUE,
label.size = 3,
arrow.size = 7,
arrow.gap = .03,
color.palette = "Reds",
color.legend = "1-Not very influenced, 5-Extremely influenced")## Warning: Removed 21 rows containing missing values (geom_segment).
##Survey Name Network This is a network of relating an individual to how they answered a question. From here we are able to see the other individuals that answered the same way. These people are related in that their views on a subject might be similar overall or just on this specific issue. If we see a pattern emerge over multiple questions with the same few people being clustered together we might conclude that they hold similar values.
a <- Q4Value - Q5Value
a## [1] 0 2 -2 2 0 0 4 0 0 2 0 0 0 0 0 0 2 0 2 4 0 2 0 0 0
## [26] 0 0 2 2 -2 0 0 0 0 -2 2 0 4 -2 2 0 2 0 2 -2 0 2 -2 2 -2
## [51] 2 2 2 2 -2 2 0 0 2 2 0 0 0 0 2 0 2 2 0 2 0 0 -2 2 0
## [76] 2 0 -2 0 2 -2 0 -2 0 2 2 2 2 0 0 2 -2 -2 2surveyDataTable <- data.frame(names,fads)
print(surveyDataTable)## names
## 1 Anon
## 2 JF
## 3 BH
## 4 DC
## 5 IA
## 6 LC
## 7 NL
## 8 AF
## 9 Anon
## 10 Anon
## 11 DS
## 12 LF
## 13 RW
## 14 KL
## 15 Anon
## 16 MJ
## 17 Anon
## 18 Anon
## 19 Anon
## 20 JK
## 21 CT
## 22 AC
## 23 RM
## 24 RA
## 25 CJ
## 26 AH
## 27 Anon
## 28 ST
## 29 LSK
## 30 YW
## 31 Anon
## 32 MK
## 33 Anon
## 34 KA
## 35 CH
## 36 RH
## 37 HR
## 38 LC
## 39 SK
## 40 KJ
## 41 AR
## 42 HF
## 43 LO
## 44 SPN
## 45 RL
## 46 TD
## 47 PS
## 48 MI
## 49 RS
## 50 SA
## 51 MY
## 52 KG
## 53 LW
## 54 LL
## 55 Anon
## 56 SH
## 57 BG
## 58 Anon
## 59 Anon
## 60 RB
## 61 VJ
## 62 WL
## 63 WS
## 64 Anon
## 65 MS
## 66 RM
## 67 Anon
## 68 DMC
## 69 CB
## 70 DN
## 71 K
## 72 Anon
## 73 LL
## 74 MD
## 75 MH
## 76 ZP
## 77 BJK
## 78 JW
## 79 ELM
## 80 IE
## 81 MH
## 82 FK
## 83 Anon
## 84 MH
## 85 JF
## 86 W
## 87 MF
## 88 KN
## 89 JW
## 90 JP
## 91 Anon
## 92 AP
## 93 Anon
## 94 a
## Are.you.introduced.to.sub.culture.fads.through.social.media..such.as.movies..music..new.trends.
## 1 Mostly
## 2 Mostly
## 3 No too much, I use different outlets
## 4 Mostly
## 5 No too much, I use different outlets
## 6 Mostly
## 7 Mostly
## 8 No too much, I use different outlets
## 9 No too much, I use different outlets
## 10 Somewhat, but I learn through other outlets as well
## 11 Somewhat, but I learn through other outlets as well
## 12 No too much, I use different outlets
## 13 Somewhat, but I learn through other outlets as well
## 14 No too much, I use different outlets
## 15 Somewhat, but I learn through other outlets as well
## 16 Somewhat, but I learn through other outlets as well
## 17 Somewhat, but I learn through other outlets as well
## 18 No too much, I use different outlets
## 19 Somewhat, but I learn through other outlets as well
## 20 Somewhat, but I learn through other outlets as well
## 21 Somewhat, but I learn through other outlets as well
## 22 Somewhat, but I learn through other outlets as well
## 23 No too much, I use different outlets
## 24 No too much, I use different outlets
## 25 Somewhat, but I learn through other outlets as well
## 26 No too much, I use different outlets
## 27 Somewhat, but I learn through other outlets as well
## 28 Somewhat, but I learn through other outlets as well
## 29 Somewhat, but I learn through other outlets as well
## 30 Mostly
## 31 No too much, I use different outlets
## 32 Somewhat, but I learn through other outlets as well
## 33 Somewhat, but I learn through other outlets as well
## 34 Mostly
## 35 Mostly
## 36 Somewhat, but I learn through other outlets as well
## 37 No too much, I use different outlets
## 38 No too much, I use different outlets
## 39 Somewhat, but I learn through other outlets as well
## 40 Somewhat, but I learn through other outlets as well
## 41 Somewhat, but I learn through other outlets as well
## 42 Somewhat, but I learn through other outlets as well
## 43 No too much, I use different outlets
## 44 No too much, I use different outlets
## 45 No too much, I use different outlets
## 46 Somewhat, but I learn through other outlets as well
## 47 Somewhat, but I learn through other outlets as well
## 48 No too much, I use different outlets
## 49 Mostly
## 50 No too much, I use different outlets
## 51 Somewhat, but I learn through other outlets as well
## 52 Somewhat, but I learn through other outlets as well
## 53 Somewhat, but I learn through other outlets as well
## 54 Mostly
## 55 Mostly
## 56 No too much, I use different outlets
## 57 Somewhat, but I learn through other outlets as well
## 58 Somewhat, but I learn through other outlets as well
## 59 No too much, I use different outlets
## 60 Somewhat, but I learn through other outlets as well
## 61 Somewhat, but I learn through other outlets as well
## 62 Somewhat, but I learn through other outlets as well
## 63 Somewhat, but I learn through other outlets as well
## 64 Mostly
## 65 Mostly
## 66 Somewhat, but I learn through other outlets as well
## 67 Somewhat, but I learn through other outlets as well
## 68 Somewhat, but I learn through other outlets as well
## 69 Somewhat, but I learn through other outlets as well
## 70 No too much, I use different outlets
## 71 Somewhat, but I learn through other outlets as well
## 72 Somewhat, but I learn through other outlets as well
## 73 Somewhat, but I learn through other outlets as well
## 74 Somewhat, but I learn through other outlets as well
## 75 No too much, I use different outlets
## 76 Somewhat, but I learn through other outlets as well
## 77 Somewhat, but I learn through other outlets as well
## 78 No too much, I use different outlets
## 79 Somewhat, but I learn through other outlets as well
## 80
## 81 Somewhat, but I learn through other outlets as well
## 82 Somewhat, but I learn through other outlets as well
## 83 Somewhat, but I learn through other outlets as well
## 84 Somewhat, but I learn through other outlets as well
## 85 No too much, I use different outlets
## 86 Somewhat, but I learn through other outlets as well
## 87 Somewhat, but I learn through other outlets as well
## 88 Somewhat, but I learn through other outlets as well
## 89 No too much, I use different outlets
## 90 Somewhat, but I learn through other outlets as well
## 91 No too much, I use different outlets
## 92 Somewhat, but I learn through other outlets as well
## 93 Somewhat, but I learn through other outlets as well
## 94 MostlysurveyData <- as.matrix(data.frame(worldview, new_source, censorship, up_to_date,
time_social_media, fads, names))
ggnet2(net = surveyData,
label = TRUE,
label.size = 3,
arrow.size = 3,
arrow.gap = .03)## Warning in ggnet2(net = surveyData, label = TRUE, label.size = 3, arrow.size =
## 3, : network is undirected; arrow.size ignored## Warning in ggnet2(net = surveyData, label = TRUE, label.size = 3, arrow.size =
## 3, : network is undirected; arrow.gap ignored
ggnet2(surveyData,
label = TRUE,
label.size = 3,
arrow.size = 3,
arrow.gap = .03,
node.color = 'Overlapping Relations',
node.shape = 'Labels',
palette = 'Spectral')## Warning in ggnet2(surveyData, label = TRUE, label.size = 3, arrow.size = 3, :
## network is undirected; arrow.size ignored## Warning in ggnet2(surveyData, label = TRUE, label.size = 3, arrow.size = 3, :
## network is undirected; arrow.gap ignored
ggnet2(surveyData, size = 12, label = TRUE, label.size = 5, label.color = "white", color = "green", color.palette = "Reds")
#Resources Yang, S., Keller F., Zheng L., Social Network Analysis Methods and examples, social_network_analysis_methods_and_examples_1e | WebViewer (gcu.edu), retrieved 1/31/2021