require(dplyr)
require(knitr)
require(ggplot2)
require(plotly)

Read data

wb <- read.csv("PanelDataProject.csv")

Whole world temp plot

w <- wb %>% 
        filter(Year %in% 1990 : 2012)

ggplot( w ,aes(x=Year, y=meantemp, color=region)) + geom_point()

IN different Plots

ggplot( w ,aes(x=Year, y=meantemp, group = Country)) + 
  geom_line(size = 0.1) + 
  theme_minimal() + 
  facet_grid( .~ region )

Bangladesh data

bd <- w %>% 
      filter(Country == "Bangladesh")

Dotplot BD

ggplot( bd ,aes(x=Year, y=meantemp)) + 
  geom_point() + 
  geom_smooth(method = "lm", se = F)

## `geom_smooth()` using formula 'y ~ x'

Lineplot / timeseries BD

ggplot( bd ,aes(x=Year, y=meantemp)) + 
  geom_line(size=2) + 
  geom_smooth(method = "lm", se = F) + 
  theme_minimal()

## `geom_smooth()` using formula 'y ~ x'

Asian Countries

asia <- wb %>% 
        filter(region == "ASIA")


ggplot( asia ,aes(x=Year, y=meantemp, color=Country,)) + 
  geom_line(size=0.2) + 
  geom_smooth(method = "lm", se = F) + 
  theme_minimal()

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 35 rows containing non-finite values (stat_smooth).

## Warning: Removed 35 row(s) containing missing values (geom_path).

Asian Warm Countries

asiaselected <- wb %>% 
  filter(region == "ASIA", Country %in% c("Bangladesh",
                                          "India",
                                          "Indonesia", 
                                          "Singapore",
                                          "Sri Lanka",
                                          "Maynmar",
                                          "Philippines",
                                          "Thailand"
  ), Year %in% 1990 : 2012 )


ggplot( asiaselected ,aes(x=Year, y=meantemp, color=Country)) + 
  geom_line() +
  labs(title = "Asian Warm Countries Temparature over Years") + 
  geom_smooth(method = "lm", se = F) + 
  theme_minimal() + 
  theme(legend.position="bottom")

List of Countries

wb[wb$country == ""]

w <- wb %>% 
  select(country, region) %>%
  group_by(country, region) %>%
  summarise(n()) %>% 
  arrange(region, country) %>%
  select(country, region)

## `summarise()` has grouped output by 'country'. You can override using the `.groups` argument.

kable(w, col.names = c("Country", "Continent"))

Country	Continent
Algeria	AFRICA
Angola	AFRICA
Botswana	AFRICA
Burundi	AFRICA
Cameroon	AFRICA
Chad	AFRICA
Congo	AFRICA
Cote d’Ivoire	AFRICA
Egypt	AFRICA
Ethiopia	AFRICA
Gambia, The	AFRICA
Ghana	AFRICA
Guinea	AFRICA
Kenya	AFRICA
Liberia	AFRICA
Libya	AFRICA
Madagascar	AFRICA
Mali	AFRICA
Mauritius	AFRICA
Morocco	AFRICA
Mozambique	AFRICA
Namibia	AFRICA
Niger	AFRICA
Nigeria	AFRICA
Rwanda	AFRICA
Senegal	AFRICA
Sierra Leone	AFRICA
Somalia	AFRICA
South Africa	AFRICA
Sudan	AFRICA
Tanzania	AFRICA
Tunisia	AFRICA
Uganda	AFRICA
Zambia	AFRICA
Zimbabwe	AFRICA
Afghanistan	ASIA
Bahrain	ASIA
Bangladesh	ASIA
Bhutan	ASIA
Burma	ASIA
Cambodia	ASIA
China	ASIA
India	ASIA
Indonesia	ASIA
Iran	ASIA
Iraq	ASIA
Israel	ASIA
Japan	ASIA
Jordan	ASIA
Kuwait	ASIA
Lebanon	ASIA
Macau	ASIA
Malaysia	ASIA
Mongolia	ASIA
Nepal	ASIA
North Korea	ASIA
Oman	ASIA
Pakistan	ASIA
Philippines	ASIA
Qatar	ASIA
Saudi Arabia	ASIA
Singapore	ASIA
South Korea	ASIA
Sri Lanka	ASIA
Syria	ASIA
Thailand	ASIA
Turkey	ASIA
United Arab Emirates	ASIA
Vietnam	ASIA
Yemen	ASIA
Albania	EUROPE
Armenia	EUROPE
Austria	EUROPE
Azerbaijan	EUROPE
Belarus	EUROPE
Belgium	EUROPE
Bosnia & Herzegovina	EUROPE
Bulgaria	EUROPE
Croatia	EUROPE
Czech Republic	EUROPE
Denmark	EUROPE
Estonia	EUROPE
Faroe Islands	EUROPE
Finland	EUROPE
France	EUROPE
Georgia	EUROPE
Germany	EUROPE
Greece	EUROPE
Hungary	EUROPE
Iceland	EUROPE
Ireland	EUROPE
Italy	EUROPE
Kazakhstan	EUROPE
Kyrgyzstan	EUROPE
Latvia	EUROPE
Liechtenstein	EUROPE
Lithuania	EUROPE
Luxembourg	EUROPE
Macedonia	EUROPE
Malta	EUROPE
Moldova	EUROPE
Monaco	EUROPE
Netherlands	EUROPE
Norway	EUROPE
Poland	EUROPE
Portugal	EUROPE
Romania	EUROPE
Russia	EUROPE
Serbia	EUROPE
Slovakia	EUROPE
Slovenia	EUROPE
Spain	EUROPE
Sweden	EUROPE
Switzerland	EUROPE
Ukraine	EUROPE
United Kingdom	EUROPE
Uzbekistan	EUROPE
Canada	NORTH AMERICA
Greenland	NORTH AMERICA
United States	NORTH AMERICA
American Samoa	OCEANIA
Australia	OCEANIA
Fiji	OCEANIA
French Polynesia	OCEANIA
Guam	OCEANIA
New Zealand	OCEANIA
Palau	OCEANIA
Papua New Guinea	OCEANIA
Solomon Islands	OCEANIA
Tonga	OCEANIA
Argentina	SOUTH AMERICA
Bahamas	SOUTH AMERICA
Barbados	SOUTH AMERICA
Belize	SOUTH AMERICA
Bolivia	SOUTH AMERICA
Brazil	SOUTH AMERICA
Chile	SOUTH AMERICA
Colombia	SOUTH AMERICA
Costa Rica	SOUTH AMERICA
Cuba	SOUTH AMERICA
Dominican Republic	SOUTH AMERICA
Ecuador	SOUTH AMERICA
El Salvador	SOUTH AMERICA
Guatemala	SOUTH AMERICA
Haiti	SOUTH AMERICA
Honduras	SOUTH AMERICA
Jamaica	SOUTH AMERICA
Mexico	SOUTH AMERICA
Nicaragua	SOUTH AMERICA
Panama	SOUTH AMERICA
Paraguay	SOUTH AMERICA
Peru	SOUTH AMERICA
Puerto Rico	SOUTH AMERICA
Trinidad and Tobago	SOUTH AMERICA
Uruguay	SOUTH AMERICA
Venezuela	SOUTH AMERICA

Selected Variables

sv <- wb%>% 
      select(country, 
             Year, 
             region, 
             meantemp,
             
             Agricultural.land..sq..km. , 
             CO2.emissions..metric.tons.per.capita. , 
             CO2.emissions..kt. , 
              
             
             Forest.area..sq..km., 
             GDP..constant.2010.US.. , 
             
             Land.area..sq..km.  , 
             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., 
              
             Total.greenhouse.gas.emissions..kt.of.CO2.equivalent. , 
             
             Urban.population....of.total. ) %>% 
  filter(Year %in% 1990:2012 )
            
write.csv(sv, "Selectedvariables.csv")

Selected Countries

sc <- sv %>% 
       filter(country %in% c(
"Algeria",
# "Botswana",
"Cameroon",
"Congo",
"Egypt",
"Ghana",
"Kenya",
"Nigeria",
# "South Africa",
# "Zimbabwe",

"Bangladesh",
"Bhutan",
"Burma",
"China",
"India",
"Indonesia",
"Japan",
"Malaysia",
"Nepal",
"Phillipines",
# "Singapore",
"Sri Lanka",
"Thailand",
"Vietnam",

"Austria",
# "Azerbaijan",
# "Belgium",
# "Czech Republic",
"Denmark",
"France",
# "Germany",
"Italy",
# "Luxembourg",
"Netherlands",
"Spain",
"Sweden",
"Switzerland",
"United Kingdom",

"Canada",
# "Greenland",
"United States",

"Australia",
"New Zealand",
"Papua New Guinea",
"Fiji",

"Argentina",
"Bolivia",
"Brazil",
"Chile",
"Colombia",
"Costa Rica",
# "Haiti",
"Mexico",
"Paraguay",
# "Puerto Rico",
"Uruguay",
"Venezuela"

))


write.csv(sc, "Selectedvariables.csv")

IN different Plots

ggplot( sc ,aes(x=Year, y=meantemp, group = country)) + 
  geom_line(size = 0.1) + 
  theme_minimal() + 
  facet_grid( .~ region )

asia <- sc %>% 
        filter(region == "ASIA" , meantemp > 20)


africa <- sc %>% 
        filter(region == "AFRICA")

europe <- sc %>% 
        filter(region == "EUROPE", meantemp > -10)


ggplot( asia ,aes(x=Year, y=meantemp, color=country)) + 
  geom_line() +
  labs(title = "Asian Temparature over Years") + 
  geom_smooth(method = "lm", se = F) + 
  theme_minimal() + 
  theme(legend.position="bottom")

## `geom_smooth()` using formula 'y ~ x'

ggplot( africa ,aes(x=Year, y=meantemp, color=country)) + 
  geom_line() +
  labs(title = "African Countries Temparature over Years") + 
  geom_smooth(method = "lm", se = F) + 
  theme_minimal() + 
  theme(legend.position="bottom")

## `geom_smooth()` using formula 'y ~ x'

ggplot( europe ,aes(x=Year, y=meantemp, color=country)) + 
  geom_line() +
  labs(title = "Europian Countries Temparature over Years") + 
  geom_smooth(method = "lm", se = F) + 
  theme_minimal() + 
  theme(legend.position="bottom")

## `geom_smooth()` using formula 'y ~ x'

northamerica <- sc %>% 
        filter(region == "NORTH AMERICA")


ggplot( northamerica ,aes(x=Year, y=meantemp, color=country)) + 
  geom_line() +
  labs(title = "North American Temparature over Years") + 
  geom_smooth(method = "lm", se = F) + 
  theme_minimal() + 
  theme(legend.position="bottom")

## `geom_smooth()` using formula 'y ~ x'

southamerica <- sc %>% 
        filter(region == "SOUTH AMERICA")


ggplot( southamerica ,aes(x=Year, y=meantemp, color=country)) + 
  geom_line() +
  labs(title = "South American Temparature over Years") + 
  geom_smooth(method = "lm", se = F) + 
  theme_minimal() + 
  theme(legend.position="bottom")

## `geom_smooth()` using formula 'y ~ x'

Least Squares Dummy Variable Fixed Effect Model

dat <- read.csv("Selectedvariables.csv")

require(dplyr)
require()

asia <- dat %>% 
        filter(region == "ASIA")

names(asia)

##  [1] "X"                                                                                        
##  [2] "country"                                                                                  
##  [3] "Year"                                                                                     
##  [4] "region"                                                                                   
##  [5] "meantemp"                                                                                 
##  [6] "Agricultural.land..sq..km."                                                               
##  [7] "CO2.emissions..metric.tons.per.capita."                                                   
##  [8] "CO2.emissions..kt."                                                                       
##  [9] "Forest.area..sq..km."                                                                     
## [10] "GDP..constant.2010.US.."                                                                  
## [11] "Land.area..sq..km."                                                                       
## [12] "Methane.emissions..kt.of.CO2.equivalent."                                                 
## [13] "Nitrous.oxide.emissions..thousand.metric.tons.of.CO2.equivalent."                         
## [14] "Other.greenhouse.gas.emissions..HFC..PFC.and.SF6..thousand.metric.tons.of.CO2.equivalent."
## [15] "Total.greenhouse.gas.emissions..kt.of.CO2.equivalent."                                    
## [16] "Urban.population....of.total."

lsdv <- lm( meantemp ~ 
              Agricultural.land..sq..km. + 
              CO2.emissions..kt. + 
              Forest.area..sq..km. + 
              GDP..constant.2010.US.. + 
              #Land.area..sq..km. + 
              #Methane.emissions..kt.of.CO2.equivalent. + 
              #Nitrous.oxide.emissions..thousand.metric.tons.of.CO2.equivalent. + 
              # Removed due to multicliinearity
              Other.greenhouse.gas.emissions..HFC..PFC.and.SF6..thousand.metric.tons.of.CO2.equivalent. + 
              Total.greenhouse.gas.emissions..kt.of.CO2.equivalent. + 
              Urban.population....of.total.  + 
              country,
              data = asia )

require(broom)
require(knitr)

out <- tidy(lsdv)

out$term[6] <- "Other sg6, hfc, pfc"
out$term[7] <- "Total Greenhouse gas emmission"

Asia

kable(out)

term	estimate	std.error	statistic	p.value
(Intercept)	24.7143583	0.1483413	166.6047460	0.0000000
Agricultural.land..sq..km.	-0.0000011	0.0000010	-1.0350050	0.3016395
CO2.emissions..kt.	-0.0000010	0.0000004	-2.5054464	0.0128489
Forest.area..sq..km.	0.0000014	0.0000007	2.0761153	0.0388775
GDP..constant.2010.US..	0.0000000	0.0000000	-0.3724937	0.7098323
Other sg6, hfc, pfc	-0.0000011	0.0000004	-2.4960995	0.0131840
Total Greenhouse gas emmission	0.0000008	0.0000003	2.4514371	0.0148951
Urban.population….of.total.	0.0269897	0.0047553	5.6757533	0.0000000
countryBhutan	-13.0948741	0.1237151	-105.8470557	0.0000000
countryBurma	-1.5786176	0.2457833	-6.4228028	0.0000000
countryChina	-15.6664150	5.5649472	-2.8151956	0.0052523
countryIndia	-0.3564716	1.8797925	-0.1896335	0.8497460
countryIndonesia	-0.5674967	0.8652084	-0.6559076	0.5124703
countryJapan	-14.0219217	0.6297860	-22.2645810	0.0000000
countryMalaysia	-0.1202816	0.2532131	-0.4750214	0.6351752
countryNepal	-9.6091573	0.1046111	-91.8560225	0.0000000
countrySri Lanka	2.3895085	0.1091157	21.8988472	0.0000000
countryThailand	0.9220283	0.1875329	4.9166219	0.0000016
countryVietnam	-1.3221613	0.1100774	-12.0111922	0.0000000

plot(asia[, -c(1:4)])

Europe

europe <- dat %>% 
        filter(region == "EUROPE")

names(europe)

##  [1] "X"                                                                                        
##  [2] "country"                                                                                  
##  [3] "Year"                                                                                     
##  [4] "region"                                                                                   
##  [5] "meantemp"                                                                                 
##  [6] "Agricultural.land..sq..km."                                                               
##  [7] "CO2.emissions..metric.tons.per.capita."                                                   
##  [8] "CO2.emissions..kt."                                                                       
##  [9] "Forest.area..sq..km."                                                                     
## [10] "GDP..constant.2010.US.."                                                                  
## [11] "Land.area..sq..km."                                                                       
## [12] "Methane.emissions..kt.of.CO2.equivalent."                                                 
## [13] "Nitrous.oxide.emissions..thousand.metric.tons.of.CO2.equivalent."                         
## [14] "Other.greenhouse.gas.emissions..HFC..PFC.and.SF6..thousand.metric.tons.of.CO2.equivalent."
## [15] "Total.greenhouse.gas.emissions..kt.of.CO2.equivalent."                                    
## [16] "Urban.population....of.total."

lsdv <- lm( meantemp ~ 
              Agricultural.land..sq..km. + 
              CO2.emissions..kt. + 
              Forest.area..sq..km. + 
              GDP..constant.2010.US.. + 
              #Land.area..sq..km. + 
              #Methane.emissions..kt.of.CO2.equivalent. + 
              #Nitrous.oxide.emissions..thousand.metric.tons.of.CO2.equivalent. + 
              # Removed due to multicliinearity
              Other.greenhouse.gas.emissions..HFC..PFC.and.SF6..thousand.metric.tons.of.CO2.equivalent. + 
              Total.greenhouse.gas.emissions..kt.of.CO2.equivalent. + 
              Urban.population....of.total.  + 
              country,
              data = europe )

require(broom)
require(knitr)

out <- tidy(lsdv)

out$term[6] <- "Other sg6, hfc, pfc"
out$term[7] <- "Total Greenhouse gas emmission"

Europe

kable(out)

term	estimate	std.error	statistic	p.value
(Intercept)	7.9208343	1.8129826	4.3689522	0.0000205
Agricultural.land..sq..km.	0.0000006	0.0000159	0.0406336	0.9676304
CO2.emissions..kt.	-0.0000191	0.0000070	-2.7261404	0.0070042
Forest.area..sq..km.	0.0000060	0.0000183	0.3288206	0.7426516
GDP..constant.2010.US..	0.0000000	0.0000000	2.9377532	0.0037130
Other sg6, hfc, pfc	-0.0000690	0.0000228	-3.0214238	0.0028606
Total Greenhouse gas emmission	0.0000142	0.0000059	2.4109702	0.0168575
Urban.population….of.total.	-0.0204455	0.0240203	-0.8511760	0.3957375
countryDenmark	-23.6019416	0.8060315	-29.2816624	0.0000000
countryFrance	2.4438547	5.7128487	0.4277821	0.6692917
countryItaly	4.6264101	2.5736468	1.7976088	0.0738186
countryNetherlands	3.7735804	1.0899064	3.4622976	0.0006609
countrySpain	5.8462371	5.6955067	1.0264648	0.3059708
countrySweden	-5.0583021	4.4474605	-1.1373462	0.2568184
countrySwitzerland	0.8389772	0.6768291	1.2395703	0.2166553
countryUnited Kingdom	0.4132159	2.8639836	0.1442801	0.8854314

plot(europe[, -c(1:4)])

Africa

africa <- dat %>% 
        filter(region == "AFRICA")

names(europe)

##  [1] "X"                                                                                        
##  [2] "country"                                                                                  
##  [3] "Year"                                                                                     
##  [4] "region"                                                                                   
##  [5] "meantemp"                                                                                 
##  [6] "Agricultural.land..sq..km."                                                               
##  [7] "CO2.emissions..metric.tons.per.capita."                                                   
##  [8] "CO2.emissions..kt."                                                                       
##  [9] "Forest.area..sq..km."                                                                     
## [10] "GDP..constant.2010.US.."                                                                  
## [11] "Land.area..sq..km."                                                                       
## [12] "Methane.emissions..kt.of.CO2.equivalent."                                                 
## [13] "Nitrous.oxide.emissions..thousand.metric.tons.of.CO2.equivalent."                         
## [14] "Other.greenhouse.gas.emissions..HFC..PFC.and.SF6..thousand.metric.tons.of.CO2.equivalent."
## [15] "Total.greenhouse.gas.emissions..kt.of.CO2.equivalent."                                    
## [16] "Urban.population....of.total."

lsdv <- lm( meantemp ~ 
              Agricultural.land..sq..km. + 
              CO2.emissions..kt. + 
              Forest.area..sq..km. + 
              GDP..constant.2010.US.. + 
              #Land.area..sq..km. + 
              #Methane.emissions..kt.of.CO2.equivalent. + 
              #Nitrous.oxide.emissions..thousand.metric.tons.of.CO2.equivalent. + 
              # Removed due to multicliinearity
              Other.greenhouse.gas.emissions..HFC..PFC.and.SF6..thousand.metric.tons.of.CO2.equivalent. + 
              Total.greenhouse.gas.emissions..kt.of.CO2.equivalent. + 
              Urban.population....of.total.  + 
              country,
              data = africa )

require(broom)
require(knitr)

out <- tidy(lsdv)

out$term[6] <- "Other sg6, hfc, pfc"
out$term[7] <- "Total Greenhouse gas emmission"

Africa

kable(out)

term	estimate	std.error	statistic	p.value
(Intercept)	22.1701565	1.1054202	20.0558628	0.0000000
Agricultural.land..sq..km.	-0.0000044	0.0000030	-1.4805619	0.1408635
CO2.emissions..kt.	0.0000021	0.0000031	0.6721722	0.5025292
Forest.area..sq..km.	0.0000017	0.0000035	0.4764779	0.6344414
GDP..constant.2010.US..	0.0000000	0.0000000	0.3442886	0.7311213
Other sg6, hfc, pfc	-0.0000008	0.0000029	-0.2830001	0.7775749
Total Greenhouse gas emmission	0.0000027	0.0000029	0.9295207	0.3541432
Urban.population….of.total.	0.0483792	0.0090386	5.3525268	0.0000003
countryCameroon	0.3078050	1.0144552	0.3034190	0.7619995
countryCongo	-0.0370429	1.0164680	-0.0364428	0.9709788
countryEgypt	-1.5298607	1.1036468	-1.3861869	0.1677889
countryGhana	3.3027204	0.7222997	4.5725071	0.0000102
countryKenya	2.4788033	0.4005675	6.1882280	0.0000000
countryNigeria	5.3700959	1.1168383	4.8083020	0.0000037

plot(africa[, -c(1:4)])

South America

southamerica <- dat %>% 
        filter(region == "SOUTH AMERICA")

names(southamerica)

##  [1] "X"                                                                                        
##  [2] "country"                                                                                  
##  [3] "Year"                                                                                     
##  [4] "region"                                                                                   
##  [5] "meantemp"                                                                                 
##  [6] "Agricultural.land..sq..km."                                                               
##  [7] "CO2.emissions..metric.tons.per.capita."                                                   
##  [8] "CO2.emissions..kt."                                                                       
##  [9] "Forest.area..sq..km."                                                                     
## [10] "GDP..constant.2010.US.."                                                                  
## [11] "Land.area..sq..km."                                                                       
## [12] "Methane.emissions..kt.of.CO2.equivalent."                                                 
## [13] "Nitrous.oxide.emissions..thousand.metric.tons.of.CO2.equivalent."                         
## [14] "Other.greenhouse.gas.emissions..HFC..PFC.and.SF6..thousand.metric.tons.of.CO2.equivalent."
## [15] "Total.greenhouse.gas.emissions..kt.of.CO2.equivalent."                                    
## [16] "Urban.population....of.total."

lsdv <- lm( meantemp ~ 
              Agricultural.land..sq..km. + 
              CO2.emissions..kt. + 
              Forest.area..sq..km. + 
              GDP..constant.2010.US.. + 
              #Land.area..sq..km. + 
              #Methane.emissions..kt.of.CO2.equivalent. + 
              #Nitrous.oxide.emissions..thousand.metric.tons.of.CO2.equivalent. + 
              # Removed due to multicliinearity
              Other.greenhouse.gas.emissions..HFC..PFC.and.SF6..thousand.metric.tons.of.CO2.equivalent. + 
              Total.greenhouse.gas.emissions..kt.of.CO2.equivalent. + 
              Urban.population....of.total.  + 
              country,
              data = southamerica )

require(broom)
require(knitr)

out <- tidy(lsdv)

out$term[6] <- "Other sg6, hfc, pfc"
out$term[7] <- "Total Greenhouse gas emmission"

South America

kable(out)

term	estimate	std.error	statistic	p.value
(Intercept)	14.4856992	1.4035342	10.3208737	0.0000000
Agricultural.land..sq..km.	-0.0000003	0.0000008	-0.3960393	0.6924725
CO2.emissions..kt.	0.0000020	0.0000017	1.2029631	0.2303260
Forest.area..sq..km.	0.0000001	0.0000010	0.0527474	0.9579826
GDP..constant.2010.US..	0.0000000	0.0000000	-1.1466896	0.2527964
Other sg6, hfc, pfc	-0.0000013	0.0000014	-0.9686108	0.3338379
Total Greenhouse gas emmission	0.0000014	0.0000013	1.0784067	0.2820720
Urban.population….of.total.	0.0069490	0.0062878	1.1051508	0.2703410
countryBolivia	6.4813238	0.6220244	10.4197256	0.0000000
countryBrazil	9.9350924	6.0035690	1.6548644	0.0994247
countryChile	-5.2583489	1.0001588	-5.2575141	0.0000004
countryColombia	10.3160438	0.5761799	17.9042075	0.0000000
countryCosta Rica	11.4167526	1.2314841	9.2707266	0.0000000
countryMexico	5.3849921	0.4781983	11.2610026	0.0000000
countryParaguay	9.0782544	0.9910279	9.1604426	0.0000000
countryUruguay	2.8090079	1.1140701	2.5213925	0.0124212
countryVenezuela	10.2594934	0.7801806	13.1501522	0.0000000

plot(southamerica[, -c(1:4)])

EDA Global Warming

FRK

May 11, 2018

Read data

Whole world temp plot

IN different Plots

Bangladesh data

Dotplot BD

Lineplot / timeseries BD

Asian Countries

Asian Warm Countries

List of Countries

Selected Variables

Selected Countries

IN different Plots

Least Squares Dummy Variable Fixed Effect Model

Asia

Europe

Europe

Africa

Africa

South America

South America