Global Population Growth and CO2 Emissions
Assignment 3 - MATH2349 Semester 1, 2019
Shuyu Huang s3743291
Required packages
library(readr)
library(dplyr)
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(tidyr)
library(stringr)
library(ggplot2)
library(knitr)Executive Summary
With the intention of studying correlation between global population growth and CO2 emissions, I found three datasets on the World Bank website. First, I examined all the variables, and found a common key to join the datasets together. Then, I looked at the data structure and attributes in detail, here I made the decision to convert some of the variables to the correct type (e.g. char to num). Next, I transformed the data from wide format to long format, this step took two attempts due to the different number of records for each country in two of the datasets as I discovered, which meant in the second go I had to split the data, apply transformation and rejoin the data. Furthermore, because I am interested in total emissions, I added up all the different types of emissions to create a new variable called “Total Emissions”. Also I scanned the data for any missing values, special values and outliers, and dealt with them by making missing values zero or deleting outliers upon closer inspection. Lastly, due to the massive difference in scale between Pop Growth and Total Emissions, I normalised Total Emissions by changing the scale for better understanding.
Data
1. Description
Weather reports on record-breaking temperatures and devastating natural disasters constantly remind us that global warming is real and climate change is unequivocal. Hence I decided to look at global population growth and CO2 emissions by country in the last half century or so with data from the World Bank. The population growth dataset also came with a sub-dataset, which categorises the countries in different income groups. Links: pop growth and emissions
Pop Growth Variables
Country Name: country name (qualitative variable)
Country Code: 3-letter code (qualitative variable)
Indicator Name: population growth (annual %) (constant qualitative variable)
Indicator Code: code for population growth (constant qualitative variable)
1960 to 2018 (58 columns): annual growth rate by country for respective year (quantitative variable)Metadata Country Variables
Country Code: 3-letter code (qualitative variable)
Region: geographic region (qualitative variable)
IncomeGroup: High income; Upper middle income; Lower middle income; Low income (qualitative variable)
SpecialNote: sources of population estimates for each country (qualitative variable)
TableName: country name (qualitative variable)Emissions Variables
Country Name: country name (qualitative variable)
Country Code: 3-letter code (qualitative variable)
Series Name: different types of emissions - all in kt of CO2 equivalent (qualitative variable)
Series Code: code for different types of emissions (qualitative variable)
1960 [YR1960] to 2018 [YR2018] (58 columns): annual emissions in thousand metric tonnes (quantitative variable)2. Read/Import Data
- readr package is used to read in the three csv files.
- head() is used to show the first six observations of the data.
pop <- read_csv("pop growth.csv",skip = 4)Missing column names filled in: 'X64' [64]Parsed with column specification:
cols(
.default = col_double(),
`Country Name` = col_character(),
`Country Code` = col_character(),
`Indicator Name` = col_character(),
`Indicator Code` = col_character(),
`2018` = col_character(),
X64 = col_character()
)
See spec(...) for full column specifications.head(pop)country <- read_csv("Metadata_Country.csv")Missing column names filled in: 'X6' [6]Parsed with column specification:
cols(
`Country Code` = col_character(),
Region = col_character(),
IncomeGroup = col_character(),
SpecialNotes = col_character(),
TableName = col_character(),
X6 = col_character()
)head(country)emissions <- read_csv("emissions.csv")Parsed with column specification:
cols(
.default = col_character()
)
See spec(...) for full column specifications.head(emissions)3. Merge Data
- I decided to use the pop growth dataset as the base, and join the other two datasets to it, assuming all countries have a growth rate value and if a country has a growth rate value then it should belong to an income group and also have an emissions value.
- Merged dataset saved as report_new. I can see that data from pop growth are repeated after joining because there is more than one line for each country in the emissions dataset. I will be tidying up the dataset to address this issue later.
report <- left_join(pop, country, by = "Country Code")
head(report)report_new <- left_join(report, emissions, by = "Country Code")
head(report_new)Understand
- Column names are mostly clean, a few columns are redundant for this exercise and will be filtered out later, and all the Year columns will also be tidied up at a later stage. Therefore, I am leaving the names as is for now.
dim(report_new)[1] 1848 131names(report_new) [1] "Country Name.x" "Country Code" "Indicator Name" "Indicator Code" "1960"
[6] "1961" "1962" "1963" "1964" "1965"
[11] "1966" "1967" "1968" "1969" "1970"
[16] "1971" "1972" "1973" "1974" "1975"
[21] "1976" "1977" "1978" "1979" "1980"
[26] "1981" "1982" "1983" "1984" "1985"
[31] "1986" "1987" "1988" "1989" "1990"
[36] "1991" "1992" "1993" "1994" "1995"
[41] "1996" "1997" "1998" "1999" "2000"
[46] "2001" "2002" "2003" "2004" "2005"
[51] "2006" "2007" "2008" "2009" "2010"
[56] "2011" "2012" "2013" "2014" "2015"
[61] "2016" "2017" "2018" "X64" "Region"
[66] "IncomeGroup" "SpecialNotes" "TableName" "X6" "Country Name.y"
[71] "Series Name" "Series Code" "1960 [YR1960]" "1961 [YR1961]" "1962 [YR1962]"
[76] "1963 [YR1963]" "1964 [YR1964]" "1965 [YR1965]" "1966 [YR1966]" "1967 [YR1967]"
[81] "1968 [YR1968]" "1969 [YR1969]" "1970 [YR1970]" "1971 [YR1971]" "1972 [YR1972]"
[86] "1973 [YR1973]" "1974 [YR1974]" "1975 [YR1975]" "1976 [YR1976]" "1977 [YR1977]"
[91] "1978 [YR1978]" "1979 [YR1979]" "1980 [YR1980]" "1981 [YR1981]" "1982 [YR1982]"
[96] "1983 [YR1983]" "1984 [YR1984]" "1985 [YR1985]" "1986 [YR1986]" "1987 [YR1987]"
[101] "1988 [YR1988]" "1989 [YR1989]" "1990 [YR1990]" "1991 [YR1991]" "1992 [YR1992]"
[106] "1993 [YR1993]" "1994 [YR1994]" "1995 [YR1995]" "1996 [YR1996]" "1997 [YR1997]"
[111] "1998 [YR1998]" "1999 [YR1999]" "2000 [YR2000]" "2001 [YR2001]" "2002 [YR2002]"
[116] "2003 [YR2003]" "2004 [YR2004]" "2005 [YR2005]" "2006 [YR2006]" "2007 [YR2007]"
[121] "2008 [YR2008]" "2009 [YR2009]" "2010 [YR2010]" "2011 [YR2011]" "2012 [YR2012]"
[126] "2013 [YR2013]" "2014 [YR2014]" "2015 [YR2015]" "2016 [YR2016]" "2017 [YR2017]"
[131] "2018 [YR2018]" str(report_new)Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 1848 obs. of 131 variables:
$ Country Name.x: chr "Aruba" "Aruba" "Aruba" "Aruba" ...
$ Country Code : chr "ABW" "ABW" "ABW" "ABW" ...
$ Indicator Name: chr "Population growth (annual %)" "Population growth (annual %)" "Population growth (annual %)" "Population growth (annual %)" ...
$ Indicator Code: chr "SP.POP.GROW" "SP.POP.GROW" "SP.POP.GROW" "SP.POP.GROW" ...
$ 1960 : num 3.15 3.15 3.15 3.15 3.15 ...
$ 1961 : num 2.24 2.24 2.24 2.24 2.24 ...
$ 1962 : num 1.41 1.41 1.41 1.41 1.41 ...
$ 1963 : num 0.832 0.832 0.832 0.832 0.832 ...
$ 1964 : num 0.593 0.593 0.593 0.593 0.593 ...
$ 1965 : num 0.573 0.573 0.573 0.573 0.573 ...
$ 1966 : num 0.617 0.617 0.617 0.617 0.617 ...
$ 1967 : num 0.587 0.587 0.587 0.587 0.587 ...
$ 1968 : num 0.569 0.569 0.569 0.569 0.569 ...
$ 1969 : num 0.581 0.581 0.581 0.581 0.581 ...
$ 1970 : num 0.572 0.572 0.572 0.572 0.572 ...
$ 1971 : num 0.636 0.636 0.636 0.636 0.636 ...
$ 1972 : num 0.671 0.671 0.671 0.671 0.671 ...
$ 1973 : num 0.671 0.671 0.671 0.671 0.671 ...
$ 1974 : num 0.472 0.472 0.472 0.472 0.472 ...
$ 1975 : num 0.213 0.213 0.213 0.213 0.213 ...
$ 1976 : num -0.117 -0.117 -0.117 -0.117 -0.117 ...
$ 1977 : num -0.364 -0.364 -0.364 -0.364 -0.364 ...
$ 1978 : num -0.437 -0.437 -0.437 -0.437 -0.437 ...
$ 1979 : num -0.205 -0.205 -0.205 -0.205 -0.205 ...
$ 1980 : num 0.193 0.193 0.193 0.193 0.193 ...
$ 1981 : num 0.781 0.781 0.781 0.781 0.781 ...
$ 1982 : num 1.28 1.28 1.28 1.28 1.28 ...
$ 1983 : num 1.39 1.39 1.39 1.39 1.39 ...
$ 1984 : num 1.02 1.02 1.02 1.02 1.02 ...
$ 1985 : num 0.302 0.302 0.302 0.302 0.302 ...
$ 1986 : num -0.608 -0.608 -0.608 -0.608 -0.608 ...
$ 1987 : num -1.3 -1.3 -1.3 -1.3 -1.3 ...
$ 1988 : num -1.23 -1.23 -1.23 -1.23 -1.23 ...
$ 1989 : num -0.077 -0.077 -0.077 -0.077 -0.077 ...
$ 1990 : num 1.81 1.81 1.81 1.81 1.81 ...
$ 1991 : num 3.9 3.9 3.9 3.9 3.9 ...
$ 1992 : num 5.44 5.44 5.44 5.44 5.44 ...
$ 1993 : num 6.07 6.07 6.07 6.07 6.07 ...
$ 1994 : num 5.63 5.63 5.63 5.63 5.63 ...
$ 1995 : num 4.62 4.62 4.62 4.62 4.62 ...
$ 1996 : num 3.52 3.52 3.52 3.52 3.52 ...
$ 1997 : num 2.67 2.67 2.67 2.67 2.67 ...
$ 1998 : num 2.11 2.11 2.11 2.11 2.11 ...
$ 1999 : num 1.96 1.96 1.96 1.96 1.96 ...
$ 2000 : num 2.06 2.06 2.06 2.06 2.06 ...
$ 2001 : num 2.23 2.23 2.23 2.23 2.23 ...
$ 2002 : num 2.23 2.23 2.23 2.23 2.23 ...
$ 2003 : num 2.11 2.11 2.11 2.11 2.11 ...
$ 2004 : num 1.76 1.76 1.76 1.76 1.76 ...
$ 2005 : num 1.3 1.3 1.3 1.3 1.3 ...
$ 2006 : num 0.798 0.798 0.798 0.798 0.798 ...
$ 2007 : num 0.384 0.384 0.384 0.384 0.384 ...
$ 2008 : num 0.131 0.131 0.131 0.131 0.131 ...
$ 2009 : num 0.0986 0.0986 0.0986 0.0986 0.0986 ...
$ 2010 : num 0.213 0.213 0.213 0.213 0.213 ...
$ 2011 : num 0.377 0.377 0.377 0.377 0.377 ...
$ 2012 : num 0.512 0.512 0.512 0.512 0.512 ...
$ 2013 : num 0.593 0.593 0.593 0.593 0.593 ...
$ 2014 : num 0.587 0.587 0.587 0.587 0.587 ...
$ 2015 : num 0.525 0.525 0.525 0.525 0.525 ...
$ 2016 : num 0.46 0.46 0.46 0.46 0.46 ...
$ 2017 : num 0.421 0.421 0.421 0.421 0.421 ...
$ 2018 : chr NA NA NA NA ...
$ X64 : chr NA NA NA NA ...
$ Region : chr "Latin America & Caribbean" "Latin America & Caribbean" "Latin America & Caribbean" "Latin America & Caribbean" ...
$ IncomeGroup : chr "High income" "High income" "High income" "High income" ...
$ SpecialNotes : chr "Central Bureau of Statistics and Central Bank of Aruba ; Source of population estimates: UN Population Division"| __truncated__ "Central Bureau of Statistics and Central Bank of Aruba ; Source of population estimates: UN Population Division"| __truncated__ "Central Bureau of Statistics and Central Bank of Aruba ; Source of population estimates: UN Population Division"| __truncated__ "Central Bureau of Statistics and Central Bank of Aruba ; Source of population estimates: UN Population Division"| __truncated__ ...
$ TableName : chr "Aruba" "Aruba" "Aruba" "Aruba" ...
$ X6 : chr NA NA NA NA ...
$ Country Name.y: chr "Aruba" "Aruba" "Aruba" "Aruba" ...
$ Series Name : chr "HFC gas emissions (thousand metric tons of CO2 equivalent)" "Methane emissions (kt of CO2 equivalent)" "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)" "Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent)" ...
$ Series Code : chr "EN.ATM.HFCG.KT.CE" "EN.ATM.METH.KT.CE" "EN.ATM.NOXE.KT.CE" "EN.ATM.GHGO.KT.CE" ...
$ 1960 [YR1960] : chr ".." ".." ".." ".." ...
$ 1961 [YR1961] : chr ".." ".." ".." ".." ...
$ 1962 [YR1962] : chr ".." ".." ".." ".." ...
$ 1963 [YR1963] : chr ".." ".." ".." ".." ...
$ 1964 [YR1964] : chr ".." ".." ".." ".." ...
$ 1965 [YR1965] : chr ".." ".." ".." ".." ...
$ 1966 [YR1966] : chr ".." ".." ".." ".." ...
$ 1967 [YR1967] : chr ".." ".." ".." ".." ...
$ 1968 [YR1968] : chr ".." ".." ".." ".." ...
$ 1969 [YR1969] : chr ".." ".." ".." ".." ...
$ 1970 [YR1970] : chr ".." "10.2469" "1.8261976" "4.44089209850063E-16" ...
$ 1971 [YR1971] : chr ".." "10.4531" "1.8269478" "-6.66133814775094E-16" ...
$ 1972 [YR1972] : chr ".." "10.657" "1.8448131" "-3.99680288865056E-15" ...
$ 1973 [YR1973] : chr ".." "10.8551" "1.8220901" "4.44089209850063E-16" ...
$ 1974 [YR1974] : chr ".." "11.0415" "1.821157" "3.10862446895044E-15" ...
$ 1975 [YR1975] : chr ".." "11.2194" "1.8296076" "-6.66133814775094E-16" ...
$ 1976 [YR1976] : chr ".." "11.5069" "2.6258457" "-1.33226762955019E-14" ...
$ 1977 [YR1977] : chr ".." "11.6718" "2.7775194" "7.99360577730113E-15" ...
$ 1978 [YR1978] : chr ".." "12.2338" "5.303201" "3.19744231092045E-14" ...
$ 1979 [YR1979] : chr ".." "12.4857" "6.067785" "2.39808173319034E-14" ...
$ 1980 [YR1980] : chr ".." "12.6755" "6.170271" "1.4210854715202E-14" ...
$ 1981 [YR1981] : chr ".." "12.937" "7.047013" "-5.15143483426073E-14" ...
$ 1982 [YR1982] : chr ".." "13.182" "7.624574" "6.83897383169096E-14" ...
$ 1983 [YR1983] : chr ".." "13.3622" "7.290487" "-4.17443857259059E-14" ...
$ 1984 [YR1984] : chr ".." "13.6488" "7.828492" "-7.90478793533111E-14" ...
$ 1985 [YR1985] : chr ".." "13.9676" "8.650364" "3.01980662698043E-14" ...
$ 1986 [YR1986] : chr ".." "13.8472" "6.47466" "1.4210854715202E-14" ...
[list output truncated]- After checking the structure of the data types, I can see that 2018 column from pop growth and all of the Year columns from emissions need to be converted to numerics, Region and IncomeGroup need to be factorised, the latter of which should be ordered as well.
report_new$`2018` <- as.numeric(report_new$`2018`)
report_new$`1960 [YR1960]` <- as.numeric(report_new$`1960 [YR1960]`)NAs introduced by coercionreport_new$`1961 [YR1961]` <- as.numeric(report_new$`1961 [YR1961]`)NAs introduced by coercionreport_new$`1962 [YR1962]` <- as.numeric(report_new$`1962 [YR1962]`)NAs introduced by coercionreport_new$`1963 [YR1963]` <- as.numeric(report_new$`1963 [YR1963]`)NAs introduced by coercionreport_new$`1964 [YR1964]` <- as.numeric(report_new$`1964 [YR1964]`)NAs introduced by coercionreport_new$`1965 [YR1965]` <- as.numeric(report_new$`1965 [YR1965]`)NAs introduced by coercionreport_new$`1966 [YR1966]` <- as.numeric(report_new$`1966 [YR1966]`)NAs introduced by coercionreport_new$`1967 [YR1967]` <- as.numeric(report_new$`1967 [YR1967]`)NAs introduced by coercionreport_new$`1968 [YR1968]` <- as.numeric(report_new$`1968 [YR1968]`)NAs introduced by coercionreport_new$`1969 [YR1969]` <- as.numeric(report_new$`1969 [YR1969]`)NAs introduced by coercionreport_new$`1970 [YR1970]` <- as.numeric(report_new$`1970 [YR1970]`)NAs introduced by coercionreport_new$`1971 [YR1971]` <- as.numeric(report_new$`1971 [YR1971]`)NAs introduced by coercionreport_new$`1972 [YR1972]` <- as.numeric(report_new$`1972 [YR1972]`)NAs introduced by coercionreport_new$`1973 [YR1973]` <- as.numeric(report_new$`1973 [YR1973]`)NAs introduced by coercionreport_new$`1974 [YR1974]` <- as.numeric(report_new$`1974 [YR1974]`)NAs introduced by coercionreport_new$`1975 [YR1975]` <- as.numeric(report_new$`1975 [YR1975]`)NAs introduced by coercionreport_new$`1976 [YR1976]` <- as.numeric(report_new$`1976 [YR1976]`)NAs introduced by coercionreport_new$`1977 [YR1977]` <- as.numeric(report_new$`1977 [YR1977]`)NAs introduced by coercionreport_new$`1978 [YR1978]` <- as.numeric(report_new$`1978 [YR1978]`)NAs introduced by coercionreport_new$`1979 [YR1979]` <- as.numeric(report_new$`1979 [YR1979]`)NAs introduced by coercionreport_new$`1980 [YR1980]` <- as.numeric(report_new$`1980 [YR1980]`)NAs introduced by coercionreport_new$`1981 [YR1981]` <- as.numeric(report_new$`1981 [YR1981]`)NAs introduced by coercionreport_new$`1982 [YR1982]` <- as.numeric(report_new$`1982 [YR1982]`)NAs introduced by coercionreport_new$`1983 [YR1983]` <- as.numeric(report_new$`1983 [YR1983]`)NAs introduced by coercionreport_new$`1984 [YR1984]` <- as.numeric(report_new$`1984 [YR1984]`)NAs introduced by coercionreport_new$`1985 [YR1985]` <- as.numeric(report_new$`1985 [YR1985]`)NAs introduced by coercionreport_new$`1986 [YR1986]` <- as.numeric(report_new$`1986 [YR1986]`)NAs introduced by coercionreport_new$`1987 [YR1987]` <- as.numeric(report_new$`1987 [YR1987]`)NAs introduced by coercionreport_new$`1988 [YR1988]` <- as.numeric(report_new$`1988 [YR1988]`)NAs introduced by coercionreport_new$`1989 [YR1989]` <- as.numeric(report_new$`1989 [YR1989]`)NAs introduced by coercionreport_new$`1990 [YR1990]` <- as.numeric(report_new$`1990 [YR1990]`)NAs introduced by coercionreport_new$`1991 [YR1991]` <- as.numeric(report_new$`1991 [YR1991]`)NAs introduced by coercionreport_new$`1992 [YR1992]` <- as.numeric(report_new$`1992 [YR1992]`)NAs introduced by coercionreport_new$`1993 [YR1993]` <- as.numeric(report_new$`1993 [YR1993]`)NAs introduced by coercionreport_new$`1994 [YR1994]` <- as.numeric(report_new$`1994 [YR1994]`)NAs introduced by coercionreport_new$`1995 [YR1995]` <- as.numeric(report_new$`1995 [YR1995]`)NAs introduced by coercionreport_new$`1996 [YR1996]` <- as.numeric(report_new$`1996 [YR1996]`)NAs introduced by coercionreport_new$`1997 [YR1997]` <- as.numeric(report_new$`1997 [YR1997]`)NAs introduced by coercionreport_new$`1998 [YR1998]` <- as.numeric(report_new$`1998 [YR1998]`)NAs introduced by coercionreport_new$`1999 [YR1999]` <- as.numeric(report_new$`1999 [YR1999]`)NAs introduced by coercionreport_new$`2000 [YR2000]` <- as.numeric(report_new$`2000 [YR2000]`)NAs introduced by coercionreport_new$`2001 [YR2001]` <- as.numeric(report_new$`2001 [YR2001]`)NAs introduced by coercionreport_new$`2002 [YR2002]` <- as.numeric(report_new$`2002 [YR2002]`)NAs introduced by coercionreport_new$`2003 [YR2003]` <- as.numeric(report_new$`2003 [YR2003]`)NAs introduced by coercionreport_new$`2004 [YR2004]` <- as.numeric(report_new$`2004 [YR2004]`)NAs introduced by coercionreport_new$`2005 [YR2005]` <- as.numeric(report_new$`2005 [YR2005]`)NAs introduced by coercionreport_new$`2006 [YR2006]` <- as.numeric(report_new$`2006 [YR2006]`)NAs introduced by coercionreport_new$`2007 [YR2007]` <- as.numeric(report_new$`2007 [YR2007]`)NAs introduced by coercionreport_new$`2008 [YR2008]` <- as.numeric(report_new$`2008 [YR2008]`)NAs introduced by coercionreport_new$`2009 [YR2009]` <- as.numeric(report_new$`2009 [YR2009]`)NAs introduced by coercionreport_new$`2010 [YR2010]` <- as.numeric(report_new$`2010 [YR2010]`)NAs introduced by coercionreport_new$`2011 [YR2011]` <- as.numeric(report_new$`2011 [YR2011]`)NAs introduced by coercionreport_new$`2012 [YR2012]` <- as.numeric(report_new$`2012 [YR2012]`)NAs introduced by coercionreport_new$`2013 [YR2013]` <- as.numeric(report_new$`2013 [YR2013]`)NAs introduced by coercionreport_new$`2014 [YR2014]` <- as.numeric(report_new$`2014 [YR2014]`)NAs introduced by coercionreport_new$`2015 [YR2015]` <- as.numeric(report_new$`2015 [YR2015]`)NAs introduced by coercionreport_new$`2016 [YR2016]` <- as.numeric(report_new$`2016 [YR2016]`)NAs introduced by coercionreport_new$`2017 [YR2017]` <- as.numeric(report_new$`2017 [YR2017]`)NAs introduced by coercionreport_new$`2018 [YR2018]` <- as.numeric(report_new$`2018 [YR2018]`)NAs introduced by coercionreport_new$Region <- factor(report_new$Region,
levels = c("East Asia & Pacific", "Europe & Central Asia", "Latin America & Caribbean", "Middle East & North Africa", "North America", "South Asia", "Sub-Saharan Africa"))
levels(report_new$Region)[1] "East Asia & Pacific" "Europe & Central Asia" "Latin America & Caribbean"
[4] "Middle East & North Africa" "North America" "South Asia"
[7] "Sub-Saharan Africa" report_new$IncomeGroup <- factor(report_new$IncomeGroup,
levels = c("High income", "Upper middle income", "Lower middle income", "Low income"),
ordered = TRUE)
levels(report_new$IncomeGroup)[1] "High income" "Upper middle income" "Lower middle income" "Low income" str(report_new)Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 1848 obs. of 131 variables:
$ Country Name.x: chr "Aruba" "Aruba" "Aruba" "Aruba" ...
$ Country Code : chr "ABW" "ABW" "ABW" "ABW" ...
$ Indicator Name: chr "Population growth (annual %)" "Population growth (annual %)" "Population growth (annual %)" "Population growth (annual %)" ...
$ Indicator Code: chr "SP.POP.GROW" "SP.POP.GROW" "SP.POP.GROW" "SP.POP.GROW" ...
$ 1960 : num 3.15 3.15 3.15 3.15 3.15 ...
$ 1961 : num 2.24 2.24 2.24 2.24 2.24 ...
$ 1962 : num 1.41 1.41 1.41 1.41 1.41 ...
$ 1963 : num 0.832 0.832 0.832 0.832 0.832 ...
$ 1964 : num 0.593 0.593 0.593 0.593 0.593 ...
$ 1965 : num 0.573 0.573 0.573 0.573 0.573 ...
$ 1966 : num 0.617 0.617 0.617 0.617 0.617 ...
$ 1967 : num 0.587 0.587 0.587 0.587 0.587 ...
$ 1968 : num 0.569 0.569 0.569 0.569 0.569 ...
$ 1969 : num 0.581 0.581 0.581 0.581 0.581 ...
$ 1970 : num 0.572 0.572 0.572 0.572 0.572 ...
$ 1971 : num 0.636 0.636 0.636 0.636 0.636 ...
$ 1972 : num 0.671 0.671 0.671 0.671 0.671 ...
$ 1973 : num 0.671 0.671 0.671 0.671 0.671 ...
$ 1974 : num 0.472 0.472 0.472 0.472 0.472 ...
$ 1975 : num 0.213 0.213 0.213 0.213 0.213 ...
$ 1976 : num -0.117 -0.117 -0.117 -0.117 -0.117 ...
$ 1977 : num -0.364 -0.364 -0.364 -0.364 -0.364 ...
$ 1978 : num -0.437 -0.437 -0.437 -0.437 -0.437 ...
$ 1979 : num -0.205 -0.205 -0.205 -0.205 -0.205 ...
$ 1980 : num 0.193 0.193 0.193 0.193 0.193 ...
$ 1981 : num 0.781 0.781 0.781 0.781 0.781 ...
$ 1982 : num 1.28 1.28 1.28 1.28 1.28 ...
$ 1983 : num 1.39 1.39 1.39 1.39 1.39 ...
$ 1984 : num 1.02 1.02 1.02 1.02 1.02 ...
$ 1985 : num 0.302 0.302 0.302 0.302 0.302 ...
$ 1986 : num -0.608 -0.608 -0.608 -0.608 -0.608 ...
$ 1987 : num -1.3 -1.3 -1.3 -1.3 -1.3 ...
$ 1988 : num -1.23 -1.23 -1.23 -1.23 -1.23 ...
$ 1989 : num -0.077 -0.077 -0.077 -0.077 -0.077 ...
$ 1990 : num 1.81 1.81 1.81 1.81 1.81 ...
$ 1991 : num 3.9 3.9 3.9 3.9 3.9 ...
$ 1992 : num 5.44 5.44 5.44 5.44 5.44 ...
$ 1993 : num 6.07 6.07 6.07 6.07 6.07 ...
$ 1994 : num 5.63 5.63 5.63 5.63 5.63 ...
$ 1995 : num 4.62 4.62 4.62 4.62 4.62 ...
$ 1996 : num 3.52 3.52 3.52 3.52 3.52 ...
$ 1997 : num 2.67 2.67 2.67 2.67 2.67 ...
$ 1998 : num 2.11 2.11 2.11 2.11 2.11 ...
$ 1999 : num 1.96 1.96 1.96 1.96 1.96 ...
$ 2000 : num 2.06 2.06 2.06 2.06 2.06 ...
$ 2001 : num 2.23 2.23 2.23 2.23 2.23 ...
$ 2002 : num 2.23 2.23 2.23 2.23 2.23 ...
$ 2003 : num 2.11 2.11 2.11 2.11 2.11 ...
$ 2004 : num 1.76 1.76 1.76 1.76 1.76 ...
$ 2005 : num 1.3 1.3 1.3 1.3 1.3 ...
$ 2006 : num 0.798 0.798 0.798 0.798 0.798 ...
$ 2007 : num 0.384 0.384 0.384 0.384 0.384 ...
$ 2008 : num 0.131 0.131 0.131 0.131 0.131 ...
$ 2009 : num 0.0986 0.0986 0.0986 0.0986 0.0986 ...
$ 2010 : num 0.213 0.213 0.213 0.213 0.213 ...
$ 2011 : num 0.377 0.377 0.377 0.377 0.377 ...
$ 2012 : num 0.512 0.512 0.512 0.512 0.512 ...
$ 2013 : num 0.593 0.593 0.593 0.593 0.593 ...
$ 2014 : num 0.587 0.587 0.587 0.587 0.587 ...
$ 2015 : num 0.525 0.525 0.525 0.525 0.525 ...
$ 2016 : num 0.46 0.46 0.46 0.46 0.46 ...
$ 2017 : num 0.421 0.421 0.421 0.421 0.421 ...
$ 2018 : num NA NA NA NA NA NA NA NA NA NA ...
$ X64 : chr NA NA NA NA ...
$ Region : Factor w/ 7 levels "East Asia & Pacific",..: 3 3 3 3 3 3 3 6 6 6 ...
$ IncomeGroup : Ord.factor w/ 4 levels "High income"<..: 1 1 1 1 1 1 1 4 4 4 ...
$ SpecialNotes : chr "Central Bureau of Statistics and Central Bank of Aruba ; Source of population estimates: UN Population Division"| __truncated__ "Central Bureau of Statistics and Central Bank of Aruba ; Source of population estimates: UN Population Division"| __truncated__ "Central Bureau of Statistics and Central Bank of Aruba ; Source of population estimates: UN Population Division"| __truncated__ "Central Bureau of Statistics and Central Bank of Aruba ; Source of population estimates: UN Population Division"| __truncated__ ...
$ TableName : chr "Aruba" "Aruba" "Aruba" "Aruba" ...
$ X6 : chr NA NA NA NA ...
$ Country Name.y: chr "Aruba" "Aruba" "Aruba" "Aruba" ...
$ Series Name : chr "HFC gas emissions (thousand metric tons of CO2 equivalent)" "Methane emissions (kt of CO2 equivalent)" "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)" "Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent)" ...
$ Series Code : chr "EN.ATM.HFCG.KT.CE" "EN.ATM.METH.KT.CE" "EN.ATM.NOXE.KT.CE" "EN.ATM.GHGO.KT.CE" ...
$ 1960 [YR1960] : num NA NA NA NA NA NA NA NA NA NA ...
$ 1961 [YR1961] : num NA NA NA NA NA NA NA NA NA NA ...
$ 1962 [YR1962] : num NA NA NA NA NA NA NA NA NA NA ...
$ 1963 [YR1963] : num NA NA NA NA NA NA NA NA NA NA ...
$ 1964 [YR1964] : num NA NA NA NA NA NA NA NA NA NA ...
$ 1965 [YR1965] : num NA NA NA NA NA NA NA NA NA NA ...
$ 1966 [YR1966] : num NA NA NA NA NA NA NA NA NA NA ...
$ 1967 [YR1967] : num NA NA NA NA NA NA NA NA NA NA ...
$ 1968 [YR1968] : num NA NA NA NA NA NA NA NA NA NA ...
$ 1969 [YR1969] : num NA NA NA NA NA NA NA NA NA NA ...
$ 1970 [YR1970] : num NA 1.02e+01 1.83 4.44e-16 NA ...
$ 1971 [YR1971] : num NA 1.05e+01 1.83 -6.66e-16 NA ...
$ 1972 [YR1972] : num NA 1.07e+01 1.84 -4.00e-15 NA ...
$ 1973 [YR1973] : num NA 1.09e+01 1.82 4.44e-16 NA ...
$ 1974 [YR1974] : num NA 1.10e+01 1.82 3.11e-15 NA ...
$ 1975 [YR1975] : num NA 1.12e+01 1.83 -6.66e-16 NA ...
$ 1976 [YR1976] : num NA 1.15e+01 2.63 -1.33e-14 NA ...
$ 1977 [YR1977] : num NA 1.17e+01 2.78 7.99e-15 NA ...
$ 1978 [YR1978] : num NA 1.22e+01 5.30 3.20e-14 NA ...
$ 1979 [YR1979] : num NA 1.25e+01 6.07 2.40e-14 NA ...
$ 1980 [YR1980] : num NA 1.27e+01 6.17 1.42e-14 NA ...
$ 1981 [YR1981] : num NA 1.29e+01 7.05 -5.15e-14 NA ...
$ 1982 [YR1982] : num NA 1.32e+01 7.62 6.84e-14 NA ...
$ 1983 [YR1983] : num NA 1.34e+01 7.29 -4.17e-14 NA ...
$ 1984 [YR1984] : num NA 1.36e+01 7.83 -7.90e-14 NA ...
$ 1985 [YR1985] : num NA 1.40e+01 8.65 3.02e-14 NA ...
$ 1986 [YR1986] : num NA 1.38e+01 6.47 1.42e-14 NA ...
[list output truncated]Tidy & Manipulate Data I
Attempt 1
- Firstly I excluded some irrelevant columns to make my dataset slightly smaller and saved this as report_tidy.
- In the dataset column names are values (e.g. 1960, 2018 [YR2018]) instead of variables, so I used gather() to transform data from wide to long format. Types of emissions (which is a variable) are stored in rows, resulting in repeated lines, so I used spread() to generate new columns from rows. EYear has an undesirable suffix (e.g. [YR2018]) so I used separate() to split into two columns. Saved as report_tidy1.
- I was hoping that column Year (from pop growth) would match EmYear (from emissions), but they do not due to the different number of records for each country in the two files. So I need a different approach.
report_tidy <- report_new %>% select(-(`Indicator Name`:`Indicator Code`), -X64, -(SpecialNotes:`Country Name.y`), -`Series Code`)
head(report_tidy)report_tidy1 <- report_tidy %>% gather(`1960`:`2018`,key = Year, value = "Pop Growth") %>% gather(`1960 [YR1960]`:`2018 [YR2018]`, key = EYear, value = Emissions) %>% spread(key = `Series Name`, value = Emissions) %>% separate(EYear, into = c("EmYear","YR"), sep = " ")
head(report_tidy1)identical(report_tidy1$Year,report_tidy1$EmYear)[1] FALSEAttempt 2
- I need to split the dataset into two groups (pop growth and emissions), tidy up each part first and then join them back together, so that each line is for the same country and same year.
- I took the pop growth dataset before it was joined with emissions so that there is no repeated lines, and then transformed using gather() as per Attempt 1. I also combined Country Name and Year, which will be used as the key for joining. Saved as report_new_pop.
- I chose to use the joined version for emissions because that version has the correct data type conversions. Then I applied gather(), spread(), and separate() as per Attempt 1. I also combined Country Name.y and Year, which will be used as the key for joining. Saved as report_new_em.
- report_new_pop and report_new_em are joined once again and saved as report_tidy2. Looks much better!
- Still a lot to tidy up, factorise Region and IncomeGroup again, convert Pop Growth to numeric, split Country Name and Year into two columns, and get rid of YR and Country Code columns. Saved as report_tidy3.
report_new_pop <- report %>% select(`Country Name`, `Country Code`, `1960`:`2018`, Region, IncomeGroup) %>% gather(`1960`:`2018`,key = Year, value = "Pop Growth") %>% unite(Key, `Country Name`, Year, sep = "-")
head(report_new_pop)report_new_em <- report_new %>% select(`Country Name.y`, `Series Name`, `1960 [YR1960]`:`2018 [YR2018]`) %>% gather(`1960 [YR1960]`:`2018 [YR2018]`, key = EYear, value = Emissions) %>% spread(key = `Series Name`, value = Emissions) %>% separate(EYear, into = c("Year","YR"), sep = " ") %>% unite(Key, `Country Name.y`, Year, sep = "-")
head(report_new_em)report_tidy2 <- report_new_pop %>% left_join(report_new_em, by = "Key")
head(report_tidy2)report_tidy2$Region <- factor(report_tidy2$Region,
levels = c("East Asia & Pacific", "Europe & Central Asia", "Latin America & Caribbean", "Middle East & North Africa", "North America", "South Asia", "Sub-Saharan Africa"))
levels(report_tidy2$Region)[1] "East Asia & Pacific" "Europe & Central Asia" "Latin America & Caribbean"
[4] "Middle East & North Africa" "North America" "South Asia"
[7] "Sub-Saharan Africa" report_tidy2$IncomeGroup <- factor(report_tidy2$IncomeGroup,
levels = c("High income", "Upper middle income", "Lower middle income", "Low income"),
ordered = TRUE)
levels(report_tidy2$IncomeGroup)[1] "High income" "Upper middle income" "Lower middle income" "Low income" report_tidy2$`Pop Growth` <- as.numeric(report_tidy2$`Pop Growth`)
report_tidy3 <- report_tidy2 %>% separate(Key, into = c("Country Name", "Year"), sep = "-") %>% select(-YR,-`Country Code`)Expected 2 pieces. Additional pieces discarded in 531 rows [61, 86, 141, 190, 197, 214, 216, 236, 240, 325, 350, 405, 454, 461, 478, 480, 500, 504, 589, 614, ...].head(report_tidy3, 30)Tidy & Manipulate Data II
- I want to see the total emissions by each country, to get this figure I summed up all the different types of emissions using mutate().
- The NAs in each column are producing NAs as the sum result. Missing values will be dealt with in the next section, and total emissions will be recalculated.
report_tidy4 <- report_tidy3 %>% mutate(`Total Emissions` = `CO2 emissions (kt)` + `HFC gas emissions (thousand metric tons of CO2 equivalent)` + `Methane emissions (kt of CO2 equivalent)` + `Nitrous oxide emissions (thousand metric tons of CO2 equivalent)` + `Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent)` + `PFC gas emissions (thousand metric tons of CO2 equivalent)` + `SF6 gas emissions (thousand metric tons of CO2 equivalent)`)
head(report_tidy4)Scan I
- First step was to find out the number of missing values in each column using colSums().
- Reason for missing values in Region and IncomeGroup is there are regions and sub-regions listed under Country Name. I will filter them out, as they will most likely skew the data.
- There are lots of missing values in the quantitative variables, which could be due to lack of public records/data sources. I decided to recode missing values with zeros, because each country’s circumstance is so different and it might be safer to assume 0% growth rate or 0 emisisons than to recode with mean/median/mode that is significantly wrong.
colSums(is.na(report_tidy4)) Country Name
0
Year
0
Region
2773
IncomeGroup
2773
Pop Growth
480
CO2 emissions (kt)
3321
HFC gas emissions (thousand metric tons of CO2 equivalent)
14873
Methane emissions (kt of CO2 equivalent)
4862
Nitrous oxide emissions (thousand metric tons of CO2 equivalent)
4819
Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent)
5627
PFC gas emissions (thousand metric tons of CO2 equivalent)
14872
SF6 gas emissions (thousand metric tons of CO2 equivalent)
14867
Total Emissions
14926 - Check for missing values again after recoding and recalculating Total Emissions.
report_tidy4$`Pop Growth`[is.na(report_tidy4$`Pop Growth`)] <- 0
report_tidy4$`CO2 emissions (kt)`[is.na(report_tidy4$`CO2 emissions (kt)`)] <- 0
report_tidy4$`HFC gas emissions (thousand metric tons of CO2 equivalent)`[is.na(report_tidy4$`HFC gas emissions (thousand metric tons of CO2 equivalent)`)] <- 0
report_tidy4$`Methane emissions (kt of CO2 equivalent)`[is.na(report_tidy4$`Methane emissions (kt of CO2 equivalent)`)] <- 0
report_tidy4$`Nitrous oxide emissions (thousand metric tons of CO2 equivalent)`[is.na(report_tidy4$`Nitrous oxide emissions (thousand metric tons of CO2 equivalent)`)] <- 0
report_tidy4$`Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent)`[is.na(report_tidy4$`Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent)`)] <- 0
report_tidy4$`PFC gas emissions (thousand metric tons of CO2 equivalent)`[is.na(report_tidy4$`PFC gas emissions (thousand metric tons of CO2 equivalent)`)] <- 0
report_tidy4$`SF6 gas emissions (thousand metric tons of CO2 equivalent)`[is.na(report_tidy4$`SF6 gas emissions (thousand metric tons of CO2 equivalent)`)] <- 0
report_tidy4 <- report_tidy4 %>% filter(!is.na(Region))
report_tidy5 <- report_tidy4 %>% mutate(`Total Emissions` = `CO2 emissions (kt)` + `HFC gas emissions (thousand metric tons of CO2 equivalent)` + `Methane emissions (kt of CO2 equivalent)` + `Nitrous oxide emissions (thousand metric tons of CO2 equivalent)` + `Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent)` + `PFC gas emissions (thousand metric tons of CO2 equivalent)` + `SF6 gas emissions (thousand metric tons of CO2 equivalent)`)
colSums(is.na(report_tidy5)) Country Name
0
Year
0
Region
0
IncomeGroup
0
Pop Growth
0
CO2 emissions (kt)
0
HFC gas emissions (thousand metric tons of CO2 equivalent)
0
Methane emissions (kt of CO2 equivalent)
0
Nitrous oxide emissions (thousand metric tons of CO2 equivalent)
0
Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent)
0
PFC gas emissions (thousand metric tons of CO2 equivalent)
0
SF6 gas emissions (thousand metric tons of CO2 equivalent)
0
Total Emissions
0 - Also checking the quantitative variables for any other special values (Inf or NaN) - there is none.
is.special <- function(x) {if(is.numeric(x))(is.infinite(x)|is.nan(x))}
sum(is.special(report_tidy5))[1] 0Scan II
- Since I only want to see the relationship between population growth and total emissions, I created a subset of the data by selecting those two variables only, and filtered out rows that have zero growth rate and zero emissions.
- Dataset is now bivariate, I could use a scatter plot to detect any outliers. There seems to be some high values in Total Emissions and a couple very negative rates in Pop Growth, so I looked at the top 20 emissions and the bottom 5 growth rates. I did this by sorting the data and saving them in separate subsets.
- Subsets show China takes the top 10 spots for Total Emissions, and one country experienced sizeable population decline in one particular year. Considering the size of the dataset, I decided to exclude emissions above 80 mil and growth rates below -7%, which would only affect 11 observations. Saved as report_tidy6.
report_num <- report_tidy5 %>% select(-(6:12)) %>% filter(`Total Emissions` != 0 & `Pop Growth` != 0)
report_num %>% plot(`Pop Growth`~`Total Emissions`, data = ., ylab = "Pop Growth", xlab = "Total Emissions")
report_sub_em <- report_num %>% arrange(desc(`Total Emissions`))
head(report_sub_em, 20)report_sub_pop <- report_num %>% arrange(`Pop Growth`)
head(report_sub_pop, 5)report_tidy6 <- report_tidy5 %>% filter(`Total Emissions` < 8000000) %>% filter(`Pop Growth` > -7)
report_tidy6 %>% plot(`Pop Growth`~`Total Emissions`, data = ., ylab = "Pop Growth", xlab = "Total Emissions", main = "After Removing Outliers")
Transform
- The two variables - Pop Growth and Total Emissions - have such different scales. Pop Growth ranges from -10 to 20, and Total Emissions from 0 to more than 70 mil. As the scale could have a huge impact on prediction accuracy should we do any further statistical analysis, I decided to normalise the Total Emission variable such that the two variables fall under the common range for better understanding. I used z-score standardisation.
report_scale <- report_tidy6 %>% select(`Total Emissions`)
report_scaled <- scale(report_scale, center = TRUE, scale = TRUE)
head(report_scaled) Total Emissions
[1,] -0.2466611
[2,] -0.2458115
[3,] -0.2455334
[4,] -0.2425110
[5,] -0.2466611
[6,] -0.2466386
---
title: "Global Population Growth and CO2 Emissions"
author: "Shuyu Huang s3743291"
subtitle: Assignment 3 - MATH2349 Semester 1, 2019
output:
  html_notebook: default
---

## Required packages 
```{r}
library(readr)
library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
library(knitr)
```

## Executive Summary
With the intention of studying correlation between global population growth and CO2 emissions, I found three datasets on the World Bank website. First, I examined all the variables, and found a common key to join the datasets together. Then, I looked at the data structure and attributes in detail, here I made the decision to convert some of the variables to the correct type (e.g. char to num). Next, I transformed the data from wide format to long format, this step took two attempts due to the different number of records for each country in two of the datasets as I discovered, which meant in the second go I had to split the data, apply transformation and rejoin the data. Furthermore, because I am interested in total emissions, I added up all the different types of emissions to create a new variable called "Total Emissions". Also I scanned the data for any missing values, special values and outliers, and dealt with them by making missing values zero or deleting outliers upon closer inspection. Lastly, due to the massive difference in scale between Pop Growth and Total Emissions, I normalised Total Emissions by changing the scale for better understanding.

## Data 
**1. Description**

Weather reports on record-breaking temperatures and devastating natural disasters constantly remind us that global warming is real and climate change is unequivocal. Hence I decided to look at global population growth and CO2 emissions by country in the last half century or so with data from the World Bank. The population growth dataset also came with a sub-dataset, which categorises the countries in different income groups. Links: [pop growth](https://data.worldbank.org/indicator/SP.POP.GROW) and [emissions]( https://data.worldbank.org/indicator/en.atm.co2e.pc)

**Pop Growth Variables**

    Country Name: country name (qualitative variable)

    Country Code: 3-letter code (qualitative variable)

    Indicator Name: population growth (annual %) (constant qualitative variable)

    Indicator Code: code for population growth (constant qualitative variable)

    1960 to 2018 (58 columns): annual growth rate by country for respective year (quantitative variable)

**Metadata Country Variables**

    Country Code: 3-letter code (qualitative variable)

    Region: geographic region (qualitative variable)

    IncomeGroup: High income; Upper middle income; Lower middle income; Low income (qualitative variable)

    SpecialNote: sources of population estimates for each country (qualitative variable)

    TableName: country name (qualitative variable)

**Emissions Variables**

    Country Name: country name (qualitative variable)

    Country Code: 3-letter code (qualitative variable)

    Series Name: different types of emissions - all in kt of CO2 equivalent (qualitative variable)

    Series Code: code for different types of emissions (qualitative variable)

    1960 [YR1960] to 2018 [YR2018] (58 columns): annual emissions in thousand metric tonnes (quantitative variable)


**2. Read/Import Data**

* readr package is used to read in the three csv files.
* head() is used to show the first six observations of the data.
```{r}
pop <- read_csv("pop growth.csv",skip = 4)
head(pop)
country <- read_csv("Metadata_Country.csv")
head(country)
emissions <- read_csv("emissions.csv")
head(emissions)
```
**3. Merge Data**

* I decided to use the pop growth dataset as the base, and join the other two datasets to it, assuming all countries have a growth rate value and if a country has a growth rate value then it should belong to an income group and also have an emissions value.
* Merged dataset saved as report_new. I can see that data from pop growth are repeated after joining because there is more than one line for each country in the emissions dataset. I will be tidying up the dataset to address this issue later.
```{r}
report <- left_join(pop, country, by = "Country Code")
head(report)
report_new <- left_join(report, emissions, by = "Country Code")
head(report_new)
```

## Understand 
* Column names are mostly clean, a few columns are redundant for this exercise and will be filtered out later, and all the Year columns will also be tidied up at a later stage. Therefore, I am leaving the names as is for now.
```{r}
dim(report_new)
names(report_new)
str(report_new)
```
* After checking the structure of the data types, I can see that 2018 column from pop growth and all of the Year columns from emissions need to be converted to numerics, Region and IncomeGroup need to be factorised, the latter of which should be ordered as well.
```{r}
report_new$`2018` <- as.numeric(report_new$`2018`)
report_new$`1960 [YR1960]` <- as.numeric(report_new$`1960 [YR1960]`)
report_new$`1961 [YR1961]` <- as.numeric(report_new$`1961 [YR1961]`)
report_new$`1962 [YR1962]` <- as.numeric(report_new$`1962 [YR1962]`)
report_new$`1963 [YR1963]` <- as.numeric(report_new$`1963 [YR1963]`)
report_new$`1964 [YR1964]` <- as.numeric(report_new$`1964 [YR1964]`)
report_new$`1965 [YR1965]` <- as.numeric(report_new$`1965 [YR1965]`)
report_new$`1966 [YR1966]` <- as.numeric(report_new$`1966 [YR1966]`)
report_new$`1967 [YR1967]` <- as.numeric(report_new$`1967 [YR1967]`)
report_new$`1968 [YR1968]` <- as.numeric(report_new$`1968 [YR1968]`)
report_new$`1969 [YR1969]` <- as.numeric(report_new$`1969 [YR1969]`)
report_new$`1970 [YR1970]` <- as.numeric(report_new$`1970 [YR1970]`)
report_new$`1971 [YR1971]` <- as.numeric(report_new$`1971 [YR1971]`)
report_new$`1972 [YR1972]` <- as.numeric(report_new$`1972 [YR1972]`)
report_new$`1973 [YR1973]` <- as.numeric(report_new$`1973 [YR1973]`)
report_new$`1974 [YR1974]` <- as.numeric(report_new$`1974 [YR1974]`)
report_new$`1975 [YR1975]` <- as.numeric(report_new$`1975 [YR1975]`)
report_new$`1976 [YR1976]` <- as.numeric(report_new$`1976 [YR1976]`)
report_new$`1977 [YR1977]` <- as.numeric(report_new$`1977 [YR1977]`)
report_new$`1978 [YR1978]` <- as.numeric(report_new$`1978 [YR1978]`)
report_new$`1979 [YR1979]` <- as.numeric(report_new$`1979 [YR1979]`)
report_new$`1980 [YR1980]` <- as.numeric(report_new$`1980 [YR1980]`)
report_new$`1981 [YR1981]` <- as.numeric(report_new$`1981 [YR1981]`)
report_new$`1982 [YR1982]` <- as.numeric(report_new$`1982 [YR1982]`)
report_new$`1983 [YR1983]` <- as.numeric(report_new$`1983 [YR1983]`)
report_new$`1984 [YR1984]` <- as.numeric(report_new$`1984 [YR1984]`)
report_new$`1985 [YR1985]` <- as.numeric(report_new$`1985 [YR1985]`)
report_new$`1986 [YR1986]` <- as.numeric(report_new$`1986 [YR1986]`)
report_new$`1987 [YR1987]` <- as.numeric(report_new$`1987 [YR1987]`)
report_new$`1988 [YR1988]` <- as.numeric(report_new$`1988 [YR1988]`)
report_new$`1989 [YR1989]` <- as.numeric(report_new$`1989 [YR1989]`)
report_new$`1990 [YR1990]` <- as.numeric(report_new$`1990 [YR1990]`)
report_new$`1991 [YR1991]` <- as.numeric(report_new$`1991 [YR1991]`)
report_new$`1992 [YR1992]` <- as.numeric(report_new$`1992 [YR1992]`)
report_new$`1993 [YR1993]` <- as.numeric(report_new$`1993 [YR1993]`)
report_new$`1994 [YR1994]` <- as.numeric(report_new$`1994 [YR1994]`)
report_new$`1995 [YR1995]` <- as.numeric(report_new$`1995 [YR1995]`)
report_new$`1996 [YR1996]` <- as.numeric(report_new$`1996 [YR1996]`)
report_new$`1997 [YR1997]` <- as.numeric(report_new$`1997 [YR1997]`)
report_new$`1998 [YR1998]` <- as.numeric(report_new$`1998 [YR1998]`)
report_new$`1999 [YR1999]` <- as.numeric(report_new$`1999 [YR1999]`)
report_new$`2000 [YR2000]` <- as.numeric(report_new$`2000 [YR2000]`)
report_new$`2001 [YR2001]` <- as.numeric(report_new$`2001 [YR2001]`)
report_new$`2002 [YR2002]` <- as.numeric(report_new$`2002 [YR2002]`)
report_new$`2003 [YR2003]` <- as.numeric(report_new$`2003 [YR2003]`)
report_new$`2004 [YR2004]` <- as.numeric(report_new$`2004 [YR2004]`)
report_new$`2005 [YR2005]` <- as.numeric(report_new$`2005 [YR2005]`)
report_new$`2006 [YR2006]` <- as.numeric(report_new$`2006 [YR2006]`)
report_new$`2007 [YR2007]` <- as.numeric(report_new$`2007 [YR2007]`)
report_new$`2008 [YR2008]` <- as.numeric(report_new$`2008 [YR2008]`)
report_new$`2009 [YR2009]` <- as.numeric(report_new$`2009 [YR2009]`)
report_new$`2010 [YR2010]` <- as.numeric(report_new$`2010 [YR2010]`)
report_new$`2011 [YR2011]` <- as.numeric(report_new$`2011 [YR2011]`)
report_new$`2012 [YR2012]` <- as.numeric(report_new$`2012 [YR2012]`)
report_new$`2013 [YR2013]` <- as.numeric(report_new$`2013 [YR2013]`)
report_new$`2014 [YR2014]` <- as.numeric(report_new$`2014 [YR2014]`)
report_new$`2015 [YR2015]` <- as.numeric(report_new$`2015 [YR2015]`)
report_new$`2016 [YR2016]` <- as.numeric(report_new$`2016 [YR2016]`)
report_new$`2017 [YR2017]` <- as.numeric(report_new$`2017 [YR2017]`)
report_new$`2018 [YR2018]` <- as.numeric(report_new$`2018 [YR2018]`)
report_new$Region <- factor(report_new$Region,
                            levels = c("East Asia & Pacific", "Europe & Central Asia", "Latin America & Caribbean", "Middle East & North Africa", "North America", "South Asia", "Sub-Saharan Africa"))
levels(report_new$Region)
report_new$IncomeGroup <- factor(report_new$IncomeGroup,
                                 levels = c("High income", "Upper middle income", "Lower middle income", "Low income"),
                                 ordered = TRUE)
levels(report_new$IncomeGroup)
str(report_new)
```

##	Tidy & Manipulate Data I 
**Attempt 1**

* Firstly I excluded some irrelevant columns to make my dataset slightly smaller and saved this as report_tidy.
* In the dataset column names are values (e.g. 1960, 2018 [YR2018]) instead of variables, so I used gather() to transform data from wide to long format. Types of emissions (which is a variable) are stored in rows, resulting in repeated lines, so I used spread() to generate new columns from rows. EYear has an undesirable suffix (e.g. [YR2018]) so I used separate() to split into two columns. Saved as report_tidy1.
* I was hoping that column Year (from pop growth) would match EmYear (from emissions), but they do not due to the different number of records for each country in the two files. So I need a different approach.
```{r}
report_tidy <- report_new %>% select(-(`Indicator Name`:`Indicator Code`), -X64, -(SpecialNotes:`Country Name.y`), -`Series Code`)
head(report_tidy)
report_tidy1 <- report_tidy %>% gather(`1960`:`2018`,key = Year, value = "Pop Growth") %>% gather(`1960 [YR1960]`:`2018 [YR2018]`, key = EYear, value = Emissions) %>% spread(key = `Series Name`, value = Emissions) %>% separate(EYear, into = c("EmYear","YR"), sep = " ")
head(report_tidy1)
identical(report_tidy1$Year,report_tidy1$EmYear)
```
**Attempt 2**

* I need to split the dataset into two groups (pop growth and emissions), tidy up each part first and then join them back together, so that each line is for the same country and same year.
* I took the pop growth dataset before it was joined with emissions so that there is no repeated lines, and then transformed using gather() as per Attempt 1. I also combined Country Name and Year, which will be used as the key for joining. Saved as report_new_pop.
* I chose to use the joined version for emissions because that version has the correct data type conversions. Then I applied gather(), spread(), and separate() as per Attempt 1. I also combined Country Name.y and Year, which will be used as the key for joining. Saved as report_new_em.
* report_new_pop and report_new_em are joined once again and saved as report_tidy2. Looks much better!
* Still a lot to tidy up, factorise Region and IncomeGroup again, convert Pop Growth to numeric, split Country Name and Year into two columns, and get rid of YR and Country Code columns. Saved as report_tidy3.
```{r}
report_new_pop <- report %>% select(`Country Name`, `Country Code`, `1960`:`2018`, Region, IncomeGroup) %>% gather(`1960`:`2018`,key = Year, value = "Pop Growth") %>% unite(Key, `Country Name`, Year, sep = "-")
head(report_new_pop)
report_new_em <- report_new %>% select(`Country Name.y`, `Series Name`, `1960 [YR1960]`:`2018 [YR2018]`) %>% gather(`1960 [YR1960]`:`2018 [YR2018]`, key = EYear, value = Emissions) %>% spread(key = `Series Name`, value = Emissions) %>% separate(EYear, into = c("Year","YR"), sep = " ") %>% unite(Key, `Country Name.y`, Year, sep = "-")
head(report_new_em)
report_tidy2 <- report_new_pop %>% left_join(report_new_em, by = "Key")
head(report_tidy2)
report_tidy2$Region <- factor(report_tidy2$Region,
                            levels = c("East Asia & Pacific", "Europe & Central Asia", "Latin America & Caribbean", "Middle East & North Africa", "North America", "South Asia", "Sub-Saharan Africa"))
levels(report_tidy2$Region)
report_tidy2$IncomeGroup <- factor(report_tidy2$IncomeGroup,
                                 levels = c("High income", "Upper middle income", "Lower middle income", "Low income"),
                                 ordered = TRUE)
levels(report_tidy2$IncomeGroup)
report_tidy2$`Pop Growth` <- as.numeric(report_tidy2$`Pop Growth`)
report_tidy3 <- report_tidy2 %>% separate(Key, into = c("Country Name", "Year"), sep = "-") %>% select(-YR,-`Country Code`)
head(report_tidy3, 30)
```

##	Tidy & Manipulate Data II 
* I want to see the total emissions by each country, to get this figure I summed up all the different types of emissions using mutate().
* The NAs in each column are producing NAs as the sum result. Missing values will be dealt with in the next section, and total emissions will be recalculated.
```{r}
report_tidy4 <- report_tidy3 %>% mutate(`Total Emissions` = `CO2 emissions (kt)` + `HFC gas emissions (thousand metric tons of CO2 equivalent)` + `Methane emissions (kt of CO2 equivalent)` + `Nitrous oxide emissions (thousand metric tons of CO2 equivalent)` + `Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent)` + `PFC gas emissions (thousand metric tons of CO2 equivalent)` + `SF6 gas emissions (thousand metric tons of CO2 equivalent)`)
head(report_tidy4)
```

##	Scan I 
* First step was to find out the number of missing values in each column using colSums().
* Reason for missing values in Region and IncomeGroup is there are regions and sub-regions listed under Country Name. I will filter them out, as they will most likely skew the data.
* There are lots of missing values in the quantitative variables, which could be due to lack of public records/data sources. I decided to recode missing values with zeros, because each country's circumstance is so different and it might be safer to assume 0% growth rate or 0 emisisons than to recode with mean/median/mode that is significantly wrong.
```{r}
colSums(is.na(report_tidy4))
```
* Check for missing values again after recoding and recalculating Total Emissions.
```{r}
report_tidy4$`Pop Growth`[is.na(report_tidy4$`Pop Growth`)] <- 0
report_tidy4$`CO2 emissions (kt)`[is.na(report_tidy4$`CO2 emissions (kt)`)] <- 0
report_tidy4$`HFC gas emissions (thousand metric tons of CO2 equivalent)`[is.na(report_tidy4$`HFC gas emissions (thousand metric tons of CO2 equivalent)`)] <- 0
report_tidy4$`Methane emissions (kt of CO2 equivalent)`[is.na(report_tidy4$`Methane emissions (kt of CO2 equivalent)`)] <- 0
report_tidy4$`Nitrous oxide emissions (thousand metric tons of CO2 equivalent)`[is.na(report_tidy4$`Nitrous oxide emissions (thousand metric tons of CO2 equivalent)`)] <- 0
report_tidy4$`Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent)`[is.na(report_tidy4$`Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent)`)] <- 0
report_tidy4$`PFC gas emissions (thousand metric tons of CO2 equivalent)`[is.na(report_tidy4$`PFC gas emissions (thousand metric tons of CO2 equivalent)`)] <- 0
report_tidy4$`SF6 gas emissions (thousand metric tons of CO2 equivalent)`[is.na(report_tidy4$`SF6 gas emissions (thousand metric tons of CO2 equivalent)`)] <- 0
report_tidy4 <- report_tidy4 %>% filter(!is.na(Region))
report_tidy5 <- report_tidy4 %>% mutate(`Total Emissions` = `CO2 emissions (kt)` + `HFC gas emissions (thousand metric tons of CO2 equivalent)` + `Methane emissions (kt of CO2 equivalent)` + `Nitrous oxide emissions (thousand metric tons of CO2 equivalent)` + `Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent)` + `PFC gas emissions (thousand metric tons of CO2 equivalent)` + `SF6 gas emissions (thousand metric tons of CO2 equivalent)`)
colSums(is.na(report_tidy5))
```
* Also checking the quantitative variables for any other special values (Inf or NaN) - there is none.
```{r}
is.special <- function(x) {if(is.numeric(x))(is.infinite(x)|is.nan(x))}
sum(is.special(report_tidy5))
```

##	Scan II
* Since I only want to see the relationship between population growth and total emissions, I created a subset of the data by selecting those two variables only, and filtered out rows that have zero growth rate and zero emissions.
* Dataset is now bivariate, I could use a scatter plot to detect any outliers. There seems to be some high values in Total Emissions and a couple very negative rates in Pop Growth, so I looked at the top 20 emissions and the bottom 5 growth rates. I did this by sorting the data and saving them in separate subsets.
* Subsets show China takes the top 10 spots for Total Emissions, and one country experienced sizeable population decline in one particular year. Considering the size of the dataset, I decided to exclude emissions above 80 mil and growth rates below -7%, which would only affect 11 observations. Saved as report_tidy6.
```{r}
report_num <- report_tidy5 %>% select(-(6:12)) %>% filter(`Total Emissions` != 0 & `Pop Growth` != 0)
report_num %>% plot(`Pop Growth`~`Total Emissions`, data = ., ylab = "Pop Growth", xlab = "Total Emissions")
report_sub_em <- report_num %>% arrange(desc(`Total Emissions`))
head(report_sub_em, 20)
report_sub_pop <- report_num %>% arrange(`Pop Growth`)
head(report_sub_pop, 5)
report_tidy6 <- report_tidy5 %>% filter(`Total Emissions` < 8000000) %>% filter(`Pop Growth` > -7)
report_tidy6 %>% plot(`Pop Growth`~`Total Emissions`, data = ., ylab = "Pop Growth", xlab = "Total Emissions", main = "After Removing Outliers")
```

##	Transform 
* The two variables - Pop Growth and Total Emissions - have such different scales. Pop Growth ranges from -10 to 20, and Total Emissions from 0 to more than 70 mil. As the scale could have a huge impact on prediction accuracy should we do any further statistical analysis, I decided to normalise the Total Emission variable such that the two variables fall under the common range for better understanding. I used z-score standardisation.
```{r}
report_scale <- report_tidy6 %>% select(`Total Emissions`)
report_scaled <- scale(report_scale, center = TRUE, scale = TRUE)
head(report_scaled)
```
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