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

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#####################################################
#                                                   #
#                R script to generate               #
#         rose diagrams showing demographics        #
#               Version 2016-10-01                  #
#                                                   #
#####################################################

# Load requisite libraries.

library(ggplot2)
library(plyr)
library(reshape2)
library(ggthemes)
library(scales)

# Set working directory.

setwd("~/ownCloud/Innovation Network Analysis/Quantitative Data") # MacBook
setwd("d:/Andrew/ownCloud/Innovation Network Analysis/Quantitative Data") # Home PC
# setwd("c:/Users/ter053/ownCloud/Innovation Network Analysis/Quantitative Data") # work PC


# Load categorical data generated using export_to_MPNet.R.

hf_cat <- na.omit(read.table("Case 1/MPNet Data/categorical_data.txt", sep = "", header = T))
amr_cat <- na.omit(read.table("Case 2/MPNet Data/categorical_data.txt", sep = "", header = T))
gihh_cat <- na.omit(read.table("Case 3/MPNet Data/categorical_data.txt", sep = "", header = T))

# load continuous data generated using export_to_MPNet.R

hf_cont <- na.omit(read.table("Case 1/MPNet Data/continuous_data.txt", sep = "", header = T))
amr_cont <- na.omit(read.table("Case 2/MPNet Data/continuous_data.txt", sep = "", header = T))
gihh_cont <- na.omit(read.table("Case 3/MPNet Data/continuous_data.txt", sep = "", header = T))

# merge continuous data

hf_cont$case <- 1
amr_cont$case <-2
gihh_cont$case <-3

cont <- rbind(hf_cont,amr_cont,gihh_cont)
cont <- cont[,c(1:3,19)]

x <- ddply(cont,"case", summarise,
      N = length(age),
      mean.age = mean(age),
      mean.experience = mean(work.experience),
      mean.tenure = mean(current.job.tenure),
      sd.age = sd(age),
      sd.experience = sd(work.experience),
      sd.tenure = sd(current.job.tenure),
      range.age = max(age) - min(age),
      range.experience = max(work.experience) - min(work.experience),
      range.tenure = max(current.job.tenure) - min(current.job.tenure))

case_no <- ddply(hf_cont,"case", summarise,
           N = length(age),
           mean.age = mean(age),
           mean.experience = mean(work.experience),
           mean.tenure = mean(current.job.tenure),
           sd.age = sd(age),
           sd.experience = sd(work.experience),
           sd.tenure = sd(current.job.tenure),
           range.age = max(age) - min(age),
           range.experience = max(work.experience) - min(work.experience),
           range.tenure = max(current.job.tenure) - min(current.job.tenure))


# compute frequency - education

hf <- count(hf_cont,"education.level")
hf$case <- 1

amr <- count(amr_cont,"education.level")
amr$case <- 2

gihh <- count(gihh_cont,"education.level")
gihh$case <- 3

ed_rose <- rbind(hf,amr,gihh)

# compute frequency - field

hf_f <- count(hf_cat,"broad.education.field")
hf_f$case <- 1

amr_f <- count(amr_cat,"broad.education.field")
amr_f$case <- 2

gihh_f <- count(gihh_cat,"broad.education.field")
gihh_f$case <- 3

field_rose <- rbind(hf_f,amr_f,gihh_f)


# specify labels

case_id <- c("1" = "Case 1", "2" = "Case 2", "3" = "Case 3")

ed_level <- c("Secondary Education","Certificate Level","Diploma/Advanced Diploma",
              "Bachelors Degree","Graduate Certificate/Diploma",
              "Masters Degree","Doctoral Degree")

ed_field <- c("Natural & Physical Sciences", "Information Technology", "Engineering & Related Technologies",
              "Architecture & Building", "Agricultural, Environmental & Related Studies",
              "Health","Education", "Management & Commerce", "Society & Culture", "Creative Arts",
              "Food, Hospitality & Personal Services", "Mixed Field Programmes")


# Plots Case 1


ggplot(hf, aes(factor(education.level), freq, fill = factor(education.level))) +
  geom_bar(stat = "identity", position = "dodge", color = "white") +
  geom_text(aes(label=freq), nudge_y = 0.7, size = 3) +
  coord_polar() +
  scale_y_continuous(breaks = c(1,2,3,4,5,6)) +
  theme_fivethirtyeight()+
  theme(axis.text.x = element_blank(), axis.text.y = element_blank(),
        axis.ticks = element_blank(), axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        legend.title.align=0.5) +
  guides(fill=guide_legend(ncol=2,title.position = "top")) +
  scale_fill_discrete(name="EDUCATION LEVEL",
                      breaks=c(2:8),
                      labels= ed_level)

ggplot(hf_f, aes(factor(broad.education.field), freq, fill = factor(broad.education.field))) +
  geom_bar(stat = "identity", position = "dodge", color = "white") +
  geom_text(aes(label=freq), nudge_y = 0.7, size = 3) +
  coord_polar() +
  scale_y_continuous(breaks = c(1,2,3,4,5,6)) +
  theme_fivethirtyeight() +
  theme(axis.text.x = element_blank(), axis.text.y = element_blank(),
        axis.ticks = element_blank(), axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        legend.title.align=0.5) +
  guides(fill=guide_legend(ncol=2,title.position = "top")) +
  scale_fill_discrete(name="EDUCATION FIELD",
                      breaks=c(1:12),
                      labels= ed_field)

hf_cont$case <- as.factor(hf_cont$case)

dat.m <- melt(hf_cont,id.vars = 'case', measure.vars=c('age','work.experience','current.job.tenure'))

p <- ggplot(dat.m) +
  theme_fivethirtyeight() +
  theme(axis.title = element_text(size = 12)) +
  theme(axis.text.x = element_text(vjust = 0.5, size = 12)) +
  theme(axis.text.y = element_text(size = 10)) +
  ylab('Years') +
  geom_violin(aes(x=factor(variable), y = value, fill = variable), width = 0.25, outlier.colour = NA) +
  geom_jitter(alpha=0.4, aes(x=factor(variable), y = value), color= "black",position = position_jitter(width = .05))+
  scale_y_continuous(breaks = c(0,5,10,15,20,25,30,35,40,45,50,55,60,65,70)) +
  scale_x_discrete(name = "", labels = c("Age", "Work \nExperience", "Job \nTenure")) +
  theme(legend.position = "none")
p

dev.off()