Assign 3 Pre Pos
s3686502 Dan Enoka and s3350855 Grace Darlene
8 October 2018
Information is from the site ststed below
http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/6523.02015-16?OpenDocument
In particular I want to look at three files from the above site
Household Income and Income Distribution, Victoria
Household Income and Income Distribution, Tasmania
Household Income and Income Distribution, Western Australia
Viewable at ( Published to ) <<== dont forget to update B4 submission
What is this ??
a look at three states household incomes for the years 1995 - 2016
please note : NOT all years were recorded for each state, however the years shown were recorded for each state
# Load packages
library(readr)
library(tidyr) # Restructuring original datasets (from wide to long format)
library(dplyr) # Data manipulation (factor levels, labels etc.)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyverse) # the whole shebeng)## -- Attaching packages ---------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.0.0 v purrr 0.2.5
## v tibble 1.4.2 v stringr 1.3.1
## v ggplot2 3.0.0 v forcats 0.3.0## -- Conflicts ------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggplot2)
library(readxl)Comparisum of Income between Victoria, Tasmania and Western Australia
Please NOTE , I had to rename the data sets to :
TasHouse .xlsx and
VicHouse .xlsx
WestHouse .xlxs
Because they had the SAME name, and importing them caused probs
as one would simply overwrite the other
Import Section
#library(readxl)
# TasHouse <- read_excel("C:/Users/dan/Desktop/Pre Pro 3/TasHouse.xlsx")
# #View(TasHouse)
# VicHouse <- read_excel("C:/Users/dan/Desktop/Pre Pro 3/VicHouse.xlsx")
# #View(VivHouse)
# WestHouse <- read_excel("C:/Users/dan/Desktop/Pre Pro 3/WestHouse.xlsx")
# #View(WestHouse)
#library(readxl)
TasHouse <- read_excel("TasHouse.xlsx")
#View(TasHouse)
#library(readxl)
VicHouse <- read_excel("VicHouse.xlsx")
#View(VicHouse)
#library(readxl)
WestHouse <- read_excel("WestHouse.xlsx")
#View(WestHouse)extract certain row and / or columns
All three Sections below will prepare each of the imported data sets
in readiness for analysis
Section 1 ######## Preparing Data for Victoria
All section 1 for Victorian household income <<<==== added
Sort out what i wish to look at in the Victoria data frame
#need rows
vic94a <- VicHouse[,1:16]
vic94b <- VicHouse[ 7:13 , 1:16]
vic94c <- VicHouse[ 16:24 , 1:16]
vic <- rbind(as.data.frame(vic94a, vic94b, vic94c))
vic1 <- vic[-c(25:117),]
#View(vic1)
vic1 <- vic1[-c(1:3),]
#View(vic1)
vic1 <- vic1[-c(2),]
#View(vic1)
vic1 <- vic1[-c(10),]
#View(vic1)
rownames(vic1) <- NULL ## Renumber rows
#View(vic1)
#head(vic1)
##-------------I'm using the row names in this section to
##-------------Identify which rows are what later in the coding
vic1[1, 1] <- "Years" ## Change the name of the rows
vic1[1, 2] <- "Dollars"
rownames(vic1) <- NULL## Now remove row 2
vic2 <- vic1[-c(2),] ############## <<<<<=========== Change to vic2 here
rownames(vic2) <- NULL## Now remove row 2 ## because this makes it easier to
#View(vic2) ## know which works from what dosent
vic2 <- vic2[-c(9),]
rownames(vic2) <- NULL## Now remove row 2
#View(vic2)
## NOTE rows 1 - 8 are Wages , and Rows 9 - 17 are Percentiles <<===== For the Victorian data ##########
vic2 <- vic2[-c(2:6),]
rownames(vic2) <- NULL## Now remove row 2
#View(vic2)
vic2 <- vic2[-c(3:12),] ###
rownames(vic2) <- NULL## Now remove row
vic2[,2]<- NULL ## Remove column 2 <<<<<========== Reduce to 2 columns here
#str(vic2)
## Rename columns
names(vic2) <- c("Years", "1995", "1996", "1997", "1998",
"2000", "2001", "2003", "2004",
"2006", "2008", "2010", "2012",
"2014", "2016")
vic2 <- vic2[-1,] # c(3:12)
rownames(vic2) <- NULL## Now remove row 2
#str(vic2)
#View(vic2)
## Used gather here to Transform wide to long <<<=============
vic2 <- gather(vic2) ## used gather here <<==============
#str(vic2)
colnames(vic2)[1] <- 'Years'
colnames(vic2)[2] <- 'VicHouseholds'
## Now remove row 1, then change all to numeric
vic2 <- vic2[-1,]
## Now renumber rows
rownames(vic2) <- NULL## Now remove row 2
vic2 <- as.data.frame(vic2) #, dimnames = NULL
vic2$Years <- as.numeric(vic2$Years)
vic2$VicHouseholds <- as.numeric(vic2$VicHouseholds)
str(vic2) ## Doing a structure check here check here <<===============## 'data.frame': 14 obs. of 2 variables:
## $ Years : num 1995 1996 1997 1998 2000 ...
## $ VicHouseholds: num 1340 1297 1342 1400 1534 ...## Checks and Winsoring <<========== Also imputational if needed
##---------------------------------------------------- KEEP -------Check functions
## This looks for NaN, Inf, -Inf , and then tells me if they are
## present in this particular column
is.special <- function(vic2){
if (is.numeric(vic2)) !is.finite(vic2) else is.na(vic2)
}
sum (sapply(vic2, is.special)) ## <<=== check here ## [1] 0# Use Imputational method below ## Not needed , so no variable changed <<============
#surveys_combined$hindfoot_length <- impute(surveys_combined$hindfoot_length, fun = mean)
#View(vic2)Section 2 ######## Preparing Data for Tasmania
Redo all below for Tasmanian household income <<<==== added
———————————
Sort out what i wish to look at in the Victoria data frame
#need rows
t94a <- TasHouse[,1:16]
t94b <- TasHouse[ 7:13 , 1:16]
t94c <- TasHouse[ 16:24 , 1:16]
tas <- rbind(as.data.frame(t94a, t94b, t94c))
tas1 <- tas[-c(25:117),]
#View(vic1)
tas1 <- tas1[-c(1:3),]
#View(vic1)
tas1 <- tas1[-c(2),]
#View(vic1)
tas1 <- tas1[-c(10),]
#View(vic1)
rownames(vic1) <- NULL ## Renumber rows
#View(vic1)
#head(vic1)
##-------------I'm using the row names in this section to
##-------------Identify which rows are what later in the coding
tas1[1, 1] <- "Years" ## Change the name of the rows
tas1[1, 2] <- "Dollars"
rownames(tas1) <- NULL## Now remove row 2
## NOTE rows 1 - 8 are Wages , and Rows 9 - 17 are Percentiles <<===== For the Victorian data ##########
tas2 <- tas1[-c(2),] ############## <<<<<=========== Change to vic2 here
rownames(tas2) <- NULL## Now remove row 2 ## because this makes it easier to
#View(vic2) ## know which works from what dosent
tas2 <- tas2[-c(9),]
rownames(tas2) <- NULL## Now remove row 2
View(tas2)
tas2 <- tas2[-c(2:6),]
rownames(tas2) <- NULL## Now remove row 2
#View(vic2)
tas2 <- tas2[-c(3:12),] ###
rownames(tas2) <- NULL## Now remove row
tas2[,2]<- NULL ## Remove column 2 <<<<<========== Reduce to 2 columns here
#str(vic2)
## Rename columns
names(tas2) <- c("Years", "1995", "1996", "1997", "1998",
"2000", "2001", "2003", "2004",
"2006", "2008", "2010", "2012",
"2014", "2016")
tas2 <- tas2[-1,] # c(3:12)
rownames(tas2) <- NULL## Now remove row 2
#str(vic2)
#View(vic2)
## Used gather here to Transform wide to long <<<=============
tas2 <- gather(tas2)
#str(vic2)
colnames(tas2)[1] <- 'Years'
colnames(tas2)[2] <- 'TasHouseholds'
## Now remove row 1, then change all to numeric
tas2 <- tas2[-1,]
## Now renumber rows
rownames(tas) <- NULL## Now remove row 2
## Change column 2 of this data frame to numeric for analysis and NA check
tas2 <- as.data.frame(tas2) #, dimnames = NULL
tas2$Years <- as.numeric(tas2$Years)
tas2$TasHouseholds <- as.numeric(tas2$TasHouseholds)
str(tas2) ## Doing a structure check here check here <<===============## 'data.frame': 14 obs. of 2 variables:
## $ Years : num 1995 1996 1997 1998 2000 ...
## $ TasHouseholds: num 1146 1138 1122 1107 1134 ...## Checks and Winsoring <<========== Also imputational if needed
##---------------------------------------------------- KEEP -------Check functions
## This looks for NaN, Inf, -Inf , and then tells me if they are
## present in this particular column
is.special <- function(tas2){
if (is.numeric(tas2)) !is.finite(tas2) else is.na(tas2)
}
sum (sapply(tas2, is.special)) ## <<=== check here ## [1] 0# Use Imputational method below ## Not needed , so no variable changed <<============
#surveys_combined$hindfoot_length <- impute(surveys_combined$hindfoot_length, fun = mean)
#View(tas2)Section 3 ######## Preparing Data for Western Australia
Redo all below for Western Aus household income <<<==== added
———————————
Sort out what i wish to look at in the Victoria data frame
#need rows
w94a <- WestHouse[,1:16]
w94b <- WestHouse[ 7:13 , 1:16]
w94c <- WestHouse[ 16:24 , 1:16]
wes <- rbind(as.data.frame(w94a, w94b, w94c))
wes1 <- wes[-c(25:117),]
#View(vic1)
wes2 <- wes1[-c(1:3),]
#View(vic1)
wes2 <- wes2[-c(2),]
#View(vic1)
wes2 <- wes2[-c(10),]
#View(vic1)
rownames(wes2) <- NULL ## Renumber rows
#View(vic1)
#head(vic1)
##-------------I'm using the row names in this section to
##-------------Identify which rows are what later in the coding
wes2[1, 1] <- "Years" ## Change the name of the rows
wes2[1, 2] <- "Dollars"
rownames(wes2) <- NULL## Now remove row 2
wes2 <- wes2[-c(2),] ############## <<<<<=========== Change to vic2 here
rownames(wes2) <- NULL## Now remove row 2 ## because this makes it easier to
#View(vic2) ## know which works from what dosent
## NOTE rows 1 - 8 are Wages , and Rows 9 - 17 are Percentiles <<===== For the Victorian data ##########
wes2 <- wes2[-c(9),]
rownames(wes2) <- NULL## Now remove row 2
View(wes2)
wes2 <- wes2[-c(2:6),]
rownames(tas2) <- NULL## Now remove row 2
#View(vic2)
wes2 <- wes2[-c(3:12),] ###
rownames(wes2) <- NULL## Now remove row
wes2[,2]<- NULL ## Remove column 2 <<<<<========== Reduce to 2 columns here
#str(vic2)
## Rename columns
names(wes2) <- c("Years", "1995", "1996", "1997", "1998",
"2000", "2001", "2003", "2004",
"2006", "2008", "2010", "2012",
"2014", "2016")
wes2 <- wes2[-1,] # c(3:12)
rownames(wes2) <- NULL## Now remove row 2
#str(vic2)
#View(vic2)
## Used gather here to Transform wide to long <<<=============
wes2<- gather(wes2)
#str(vic2)
colnames(wes2)[1] <- 'Years'
colnames(wes2)[2] <- 'WesHouseholds'
## Now remove row 1, then change all to numeric
wes2 <- wes2[-1,]
## Now renumber rows
rownames(wes2) <- NULL## Now remove row 2
## Change column 2 of this data frame to numeric for analysis and NA check
wes2 <- as.data.frame(wes2) #, dimnames = NULL
wes2$Years <- as.numeric(wes2$Years)
wes2$WesHouseholds <- as.numeric(wes2$WesHouseholds)
str(wes2) ## Doing a structure check here check here <<===============## 'data.frame': 14 obs. of 2 variables:
## $ Years : num 1995 1996 1997 1998 2000 ...
## $ WesHouseholds: num 1339 1341 1396 1394 1449 ...## Checks and Winsoring <<========== Also imputational if needed
##---------------------------------------------------- KEEP -------Check functions
## This looks for NaN, Inf, -Inf , and then tells me if they are
## present in this particular column
is.special <- function(wes2){
if (is.numeric(wes2)) !is.finite(wes2) else is.na(wes2)
}
sum (sapply(vic2, is.special)) ## <<=== check here ## [1] 0#View(wes2)
# Use Imputational method below ## Not needed , so no variable changed <<============
#surveys_combined$hindfoot_length <- impute(surveys_combined$hindfoot_length, fun = mean)Merge all three cleaned data sets into one data set using left_join and inner_join
Combined <- left_join(vic2, tas2 ) ## This is the first join ## Joining, by = "Years"Combined <- inner_join(Combined, wes2) ## This is the second join ## Joining, by = "Years" ## All joined by years <<=========
#View(Combined)
#head(Combined)
#str(Combined)
#class(Combined)Use Mutate to add % dif columns in Household incomes between the three states
Combined <- mutate(Combined, VicTasDif = TasHouseholds / VicHouseholds )
Combined$VicTasDif = round(Combined$VicTasDif, 3)
Combined <- mutate(Combined, VicWesDif = WesHouseholds / VicHouseholds )
Combined$VicWesDif = round(Combined$VicWesDif, 3)
Combined <- mutate(Combined, TasWesDif = TasHouseholds / WesHouseholds)
Combined$TasWesDif = round(Combined$TasWesDif, 3)
#str(Combined)
#View(Combined)
#head(Combined)ggplots
ggplot(Combined, aes(Combined$Years)) +
geom_line(aes(y = Combined$VicHouseholds, colour = "red")) +
geom_line(aes(y = Combined$TasHouseholds, colour = "blue")) +
geom_line(aes(y = Combined$WesHouseholds, colour = "green")) +
xlab("Years") + ylab("Household Incomes per State ($)") +
scale_colour_discrete(name="States", labels=c("Tas","WA","Vic")) +
theme(panel.border = element_blank())
## Plot of WA & Tas showing % difference in household to Victoria
ggplot(Combined, aes(Combined$Years)) +
geom_line(aes(y = Combined$VicTasDif, colour = "red")) +
geom_line(aes(y = Combined$VicWesDif, colour = "blue")) +
#geom_line(aes(y = Combined$TasWesDif, colour = "green")) +
xlab("Years") + ylab("As % to Victoria Household Income") +
scale_colour_discrete(name="States", labels=c("WA","Tas","Vic")) +
theme(panel.border = element_blank())
write.csv(Combined, file = "Combined.csv") ## Create new file and save back to your folder