sna/node_attributes_pnet.R at master · aterhorst/sna · GitHub

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#####################################################
#                                                   #
#            R script to read in and clean          #
#               node attribute data                 #
#                                                   #
#####################################################

library(plyr)
library(dplyr)
library(readxl)
library(foreign)

# set working directory

# setwd("~/ownCloud/Innovation Network Analysis/Case studies/HF") # MacBook
setwd("d:/Andrew/ownCloud/Innovation Network Analysis/Case studies/HF") # Home PC

# read node attribute data

nodes <- read_excel("surveydata.xlsx", sheet = 1)
write.csv(nodes, file = "temp.csv",row.names = FALSE)
nodes <- read.csv("temp.csv")

# remove strings from numeric data

nodes$Age <- as.numeric(gsub("([0-9]*).*","\\1",nodes$Age)) # extract years only
nodes$Experience <- as.numeric(gsub("([0-9]*).*","\\1",nodes$Experience)) # extract years only
nodes$Tenure <- as.numeric(gsub("([0-9]*).*","\\1",nodes$Tenure)) # extract years only

# fix column names

colnames(nodes)[10] <- "Occupation"

# totalize scale items

## first we need to reverse scores where necessary

nodes$Openness2 <- 10 - nodes$Openness2 # openness scale item 2
nodes$Conscientiousness1 <- 10 - nodes$Conscientiousness1 # conscientious scale item 1
nodes$Agreeableness2 <- 10 - nodes$Agreeableness2 # agreeableness scale item 2

## next we need to aggregate 12 scales from 32 items

## TIPI personality

nodes$Openness <- round(rowMeans(subset(nodes, select = c(Openness1,Openness2)), na.rm = TRUE)/10, digits = 2) # openness
nodes$Conscietiousness <- round(rowMeans(subset(nodes, select = c(Conscientiousness1,Conscietiousness2)), na.rm = TRUE)/10, digits = 2) # consceintiousness
nodes$Agreeableness <- round(rowMeans(subset(nodes, select = c(Agreeableness1,Agreeableness2)), na.rm = TRUE)/10, digits = 2) # agreeableness

##

nodes$Competence <- round(rowMeans(subset(nodes, select = c(Competence1,Competence2,Competence3)), na.rm = TRUE)/10, digits = 2) # job competence

##

nodes$SelfDetermination <- round(rowMeans(subset(nodes, select = c(SelfDetermination1,SelfDetermination2,SelfDetermination3)), na.rm = TRUE)/10, digits = 2) # self determination

## creative self-efficacy

nodes$Creativity <- round(rowMeans(subset(nodes, select = c(Creativity1,Creativity2,Creativity3,Creativity4)), na.rm = TRUE)/10, digits = 2) # creativie self-efficacy

## work motivation

nodes$Amotivation <- round(rowMeans(subset(nodes, select = c(Amotivation1,Amotivation2,Amotivation3)), na.rm = TRUE)/10, digits = 2) # amotivation
nodes$ExtrinsicRegulationSocial <- round(rowMeans(subset(nodes, select = c(ExtrinsicRegulationSocial1,ExtrinsicRegulationSocial2,ExtrinsicRegulationSocial3)), na.rm = TRUE)/10, digits = 2) # extrinsic regulation - social
nodes$ExtrinsicRegulationMaterial <- round(rowMeans(subset(nodes, select = c(ExtrinsicRegulationMaterial1,ExtrinsicRegulationMaterial2,ExtrinsicRegulationMaterial3)), na.rm = TRUE)/10, digits = 2) # extrinsic regulation material
nodes$IntrojectedRegulation <- round(rowMeans(subset(nodes, select = c(IntrojectedRegulation1,IntrojectedRegulation2,IntrojectedRegulation3,IntrojectedRegulation4)), na.rm = TRUE)/10, digits = 2) # introjected regulation
nodes$IdentifiedRegulation <- round(rowMeans(subset(nodes, select = c(IdentifiedRegulation1,IdentifiedRegulation2,IdentifiedRegulation3)), na.rm = TRUE)/10, digits = 2) # identified regulation
nodes$IntrinsicMotivation <- round(rowMeans(subset(nodes, select = c(IntrinsicMotivation1,IntrinsicMotivation2,IntrinsicMotivation3)), na.rm = TRUE)/10, digits = 2) # intrinsic motivation

# remove unwanted columns now we have totalized scores

column_names <- as.data.frame(names(nodes)) # get numbered list of columns
column_names # lists column names with corresponding column numbers
node_summary <- subset(nodes, select=-c(2:4,13:51)) # drop unwanted columns using column numbers

# write out cleaned-up node data

## binary actor attribute file

gender <- subset(nodes, select = -c(2:4,6:63))
file <- "gender.txt"
writeLines("id,gender", file)
write.table(gender, file = file,
            sep = "\t", na = "", quote = FALSE,
            col.names = FALSE, row.names = FALSE,
            append = TRUE)