##Final Statistics Assignment on Effectiveness of Female Leadership##
##Importing data and installing packages for graphs
library(readxl)
Gender_Participation <- read_excel("C:/Users/beryl/Documents/stats project/Adri's Work/Gender Participation.xlsx")
GDP_per_capita <- read_excel("C:/Users/beryl/Documents/stats project/Adri's Work/GDP per capita.xlsx")
CPI<- read_excel("C:/Users/beryl/Documents/stats project/Adri's Work/CPI.xlsx")
Covid<-read_excel("C:/Users/beryl/Documents/stats project/Adri's Work/Covid19.xlsx")
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.0.5
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## v tibble 3.1.1 v dplyr 1.0.6
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1
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## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(ggthemes)
## Warning: package 'ggthemes' was built under R version 4.0.5
##5 Number Summaries for Gender Participation in each country from 2008-2019
aus<-summary(as.numeric(Gender_Participation[2, 3:14]))
aut<-summary(as.numeric(Gender_Participation[3, 3:14]))
bel<-summary(as.numeric(Gender_Participation[4, 3:14]))
can<-summary(as.numeric(Gender_Participation[5, 3:14]))
chil<-summary(as.numeric(Gender_Participation[6, 3:14]))
col<-summary(as.numeric(Gender_Participation[7, 3:14]))
cze<-summary(as.numeric(Gender_Participation[8, 3:14]))
dnk<-summary(as.numeric(Gender_Participation[9, 3:14]))
est<-summary(as.numeric(Gender_Participation[10, 3:14]))
fin<-summary(as.numeric(Gender_Participation[11, 3:14]))
fra<-summary(as.numeric(Gender_Participation[12, 3:14]))
deu<-summary(as.numeric(Gender_Participation[13, 3:14]))
grc<-summary(as.numeric(Gender_Participation[14, 3:14]))
hun<-summary(as.numeric(Gender_Participation[15, 3:14]))
isl<-summary(as.numeric(Gender_Participation[16, 3:14]))
irl<-summary(as.numeric(Gender_Participation[17, 3:14]))
isr<-summary(as.numeric(Gender_Participation[18, 3:14]))
ita<-summary(as.numeric(Gender_Participation[19, 3:14]))
jpn<-summary(as.numeric(Gender_Participation[20, 3:14]))
kor<-summary(as.numeric(Gender_Participation[21, 3:14]))
lva<-summary(as.numeric(Gender_Participation[22, 3:14]))
ltu<-summary(as.numeric(Gender_Participation[23, 3:14]))
lux<-summary(as.numeric(Gender_Participation[24, 3:14]))
mex<-summary(as.numeric(Gender_Participation[25, 3:14]))
nld<-summary(as.numeric(Gender_Participation[26, 3:14]))
nzk<-summary(as.numeric(Gender_Participation[27, 3:14]))
nor<-summary(as.numeric(Gender_Participation[28, 3:14]))
pol<-summary(as.numeric(Gender_Participation[29, 3:14]))
prt<-summary(as.numeric(Gender_Participation[30, 3:14]))
svk<-summary(as.numeric(Gender_Participation[31, 3:14]))
svn<-summary(as.numeric(Gender_Participation[32, 3:14]))
esp<-summary(as.numeric(Gender_Participation[33, 3:14]))
swe<-summary(as.numeric(Gender_Participation[34, 3:14]))
che<-summary(as.numeric(Gender_Participation[35, 3:14]))
tur<-summary(as.numeric(Gender_Participation[36, 3:14]))
grb<-summary(as.numeric(Gender_Participation[37, 3:14]))
usa<-summary(as.numeric(Gender_Participation[38, 3:14]))
## Standard deviation of Gender Participation in each country from 2008-2019
aus2<-sd(as.numeric(Gender_Participation[2, 3:14]))
aut2<-sd(as.numeric(Gender_Participation[3, 3:14]))
bel2<-sd(as.numeric(Gender_Participation[4, 3:14]))
can2<-sd(as.numeric(Gender_Participation[5, 3:14]))
chil2<-sd(as.numeric(Gender_Participation[6, 3:14]))
col2<-sd(as.numeric(Gender_Participation[7, 3:14]))
cze2<-sd(as.numeric(Gender_Participation[8, 3:14]))
dnk2<-sd(as.numeric(Gender_Participation[9, 3:14]))
est2<-sd(as.numeric(Gender_Participation[10, 3:14]))
fin2<-sd(as.numeric(Gender_Participation[11, 3:14]))
fra2<-sd(as.numeric(Gender_Participation[12, 3:14]))
deu2<-sd(as.numeric(Gender_Participation[13, 3:14]))
grc2<-sd(as.numeric(Gender_Participation[14, 3:14]))
hun2<-sd(as.numeric(Gender_Participation[15, 3:14]))
isl2<-sd(as.numeric(Gender_Participation[16, 3:14]))
irl2<-sd(as.numeric(Gender_Participation[17, 3:14]))
isr2<-sd(as.numeric(Gender_Participation[18, 3:14]))
ita2<-sd(as.numeric(Gender_Participation[19, 3:14]))
jpn2<-sd(as.numeric(Gender_Participation[20, 3:14]))
kor2<-sd(as.numeric(Gender_Participation[21, 3:14]))
lva2<-sd(as.numeric(Gender_Participation[22, 3:14]))
ltu2<-sd(as.numeric(Gender_Participation[23, 3:14]))
lux2<-sd(as.numeric(Gender_Participation[24, 3:14]))
mex2<-sd(as.numeric(Gender_Participation[25, 3:14]))
nld2<-sd(as.numeric(Gender_Participation[26, 3:14]))
nzk2<-sd(as.numeric(Gender_Participation[27, 3:14]))
nor2<-sd(as.numeric(Gender_Participation[28, 3:14]))
pol2<-sd(as.numeric(Gender_Participation[29, 3:14]))
prt2<-sd(as.numeric(Gender_Participation[30, 3:14]))
svk2<-sd(as.numeric(Gender_Participation[31, 3:14]))
svn2<-sd(as.numeric(Gender_Participation[32, 3:14]))
esp2<-sd(as.numeric(Gender_Participation[33, 3:14]))
swe2<-sd(as.numeric(Gender_Participation[34, 3:14]))
che2<-sd(as.numeric(Gender_Participation[35, 3:14]))
tur2<-sd(as.numeric(Gender_Participation[36, 3:14]))
grb2<-sd(as.numeric(Gender_Participation[37, 3:14]))
usa2<-sd(as.numeric(Gender_Participation[38, 3:14]))
##Histograms of main measures (min, max, median, mean, stan.dev.)
min<-c(aus[1], aut[1], bel[1], can[1], chil[1], col[1], cze[1], dnk[1],
est[1], fin[1], fra[1], deu[1], grc[1], hun[1],isl[1], irl[1],
isr[1], ita[1], jpn[1], kor[1], lva[1], ltu[1], lux[1], mex[1],
nld[1], nzk[1], nor[1], pol[1], prt[1], svk[1], svn[1], esp[1],
swe[1], che[1], tur[1], grb[1], usa[1])
dfmin<-data.frame(min)
ggplot(data=dfmin, aes(x=min)) +
geom_histogram(bins = 10, color="white", fill="cadetblue3", alpha=0.9) +
labs(title = "Lowest Level of Gender Participation",
subtitle = "Across OECD countries from 2008-2019",
x = "Female Representation in Government (%)",
y = "Frequency") +
theme_gdocs()
max<-c(aus[2], aut[2], bel[2], can[2], chil[2], col[2], cze[2], dnk[2],
est[2], fin[2], fra[2], deu[2], grc[2], hun[2],isl[2], irl[2],
isr[2], ita[2], jpn[2], kor[2], lva[2], ltu[2], lux[2], mex[2],
nld[2], nzk[2], nor[2], pol[2], prt[2], svk[2], svn[2], esp[2],
swe[2], che[2], tur[2], grb[2], usa[2])
dfmax<-data.frame(max)
ggplot(data=dfmax, aes(x=max)) +
geom_histogram(bins = 10, color="white", fill="cadetblue3", alpha=0.9) +
labs(title = "Highest Level of Gender Participation",
subtitle = "Across OECD countries from 2008-2019",
x = "Female Representation in Government (%)",
y = "Frequency") +
theme_gdocs()
med<-c(aus[3], aut[3], bel[3], can[3], chil[3], col[3], cze[3], dnk[3],
est[3], fin[3], fra[3], deu[3], grc[3], hun[3],isl[3], irl[3],
isr[3], ita[3], jpn[3], kor[3], lva[3], ltu[3], lux[3], mex[3],
nld[3], nzk[3], nor[3], pol[3], prt[3], svk[3], svn[3], esp[3],
swe[3], che[3], tur[3], grb[3], usa[3])
dfmed<-data.frame(med)
ggplot(data=dfmed, aes(x=med)) +
geom_histogram(bins = 5, color="white", fill="chartreuse3", alpha=0.7) +
labs(title = "Median Gender Participation",
subtitle = "Across OECD countries from 2008-2019",
x = "Female Representation in Government (%)",
y = "Frequency") +
theme_gdocs()
mean<-c(aus[4], aut[4], bel[4], can[4], chil[4], col[4], cze[4], dnk[4],
est[4], fin[4], fra[4], deu[4], grc[4], hun[4],isl[4], irl[4],
isr[4], ita[4], jpn[4], kor[4], lva[4], ltu[4], lux[4], mex[4],
nld[4], nzk[4], nor[4], pol[4], prt[4], svk[4], svn[4], esp[4],
swe[4], che[4], tur[4], grb[4], usa[4])
dfmean<-data.frame(mean)
ggplot(data=dfmean, aes(x=mean)) +
geom_histogram(bins = 10, color="white", fill="chartreuse3", alpha=0.7) +
labs(title = "Mean Gender Participation",
subtitle = "Across OECD countries from 2008-2019",
x = "Female Representation in Government (%)",
y = "Frequency") +
theme_gdocs()
sd<-c(aus2, aut2, bel2, can2, chil2, col2, cze2, dnk2,
est2, fin2, fra2, deu2, grc2, hun2,isl2, irl2,
isr2, ita2, jpn2, kor2, lva2, ltu2, lux2, mex2,
nld2, nzk2, nor2, pol2, prt2, svk2, svn2, esp2,
swe2, che2, tur2, grb2, usa2)
dfsd<-data.frame(sd)
ggplot(data=dfsd, aes(x=sd)) +
geom_histogram(bins = 5, color="white", fill="coral1", alpha=0.7) +
labs(title = "Standard Deviation in Gender Participation",
subtitle = "Across OECD countries from 2008-2019",
x = "Female Representation in Government (%)",
y = "Frequency") +
theme_gdocs()
##Scatter Plots and Correlation test for each year between Gender Participation and GDP per Capital##
##The method adopted for correlation will Spearman Ranked test (Spearman because it is a better indicator for non-normal data, since some of the variables are non-normal.)
##A confidence level of 95% is used, (or level of significance = 5%)
##For both correlation coefficients, the parameters below are used
# 1 = perfect correlation,
# between +/-0.50 and 1, strong correlation
# between +/-0.30 and +/-0.49, moderate correlation
# between +/-0.29 and 0, weak correlation
# 0 = no significant correlation
##The hypothesis that will be used to draw conclusions is as follows;
#H0: the rho value is not significantly different from 0
#Ha: the rho value IS significantly different from 0
Country<-Gender_Participation$`Country Name`
gp2008<-c(Gender_Participation$`2008`)
gdp2008<-c(GDP_per_capita$`2008`)
df2008<-data.frame(Country, gp2008, gdp2008)
graph2008<-ggplot(data = df2008, aes(x = gp2008, y = gdp2008, color = Country)) + geom_point()
graph2008 +
labs(title = "2008",
subtitle = "Correlation between Gender Participation & Economic Growth",
x = "Gender Participation in Government (%)",
y = "Growth in GDP per Capita (%)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2008`, GDP_per_capita$`2008`, method = "spearman")
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2008` and GDP_per_capita$`2008`
## S = 10244, p-value = 0.202
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.2143196
#P>0.05, fail to reject null hypothesis, no significant correlation
gp2009<-c(Gender_Participation$`2009`)
gdp2009<-c(GDP_per_capita$`2009`)
df2009<-data.frame(Country, gp2009, gdp2009)
graph2009<-ggplot(data = df2009, aes(x = gp2009, y = gdp2009, color = Country)) + geom_point()
graph2009 +
labs(title = "2009",
subtitle = "Correlation between Gender Participation & Economic Growth",
x = "Gender Participation in Government (%)",
y = "Growth in GDP per Capita (%)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2009`, GDP_per_capita$`2009`, method = "spearman", exact = FALSE)
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2009` and GDP_per_capita$`2009`
## S = 8596, p-value = 0.9113
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.01896746
#P>0.05, fail to reject null hypothesis, no significant correlation
gp2010<-c(Gender_Participation$`2010`)
gdp2010<-c(GDP_per_capita$`2010`)
df2010<-data.frame(Country, gp2010, gdp2010)
graph2010<-ggplot(data = df2010, aes(x = gp2010, y = gdp2010, color = Country)) + geom_point()
graph2010 +
labs(title = "2010",
subtitle = "Correlation between Gender Participation & Economic Growth",
x = "Gender Participation in Government (%)",
y = "Growth in GDP per Capita (%)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2010`, GDP_per_capita$`2010`, method = 'spearman')
## Warning in cor.test.default(Gender_Participation$`2010`,
## GDP_per_capita$`2010`, : Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2010` and GDP_per_capita$`2010`
## S = 11005, p-value = 0.06689
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.3044999
#P>0.05, fail to reject null hypothesis, no significant correlation
gp2011<-c(Gender_Participation$`2011`)
gdp2011<-c(GDP_per_capita$`2011`)
df2011<-data.frame(Country, gp2011, gdp2011)
graph2011<-ggplot(data = df2011, aes(x = gp2011, y = gdp2011, color = Country)) + geom_point()
graph2011 +
labs(title = "2011",
subtitle = "Correlation between Gender Participation & Economic Growth",
x = "Gender Participation in Government (%)",
y = "Growth in GDP per Capita (%)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2011`, GDP_per_capita$`2011`, method = "spearman")
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2011` and GDP_per_capita$`2011`
## S = 10722, p-value = 0.1048
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.2709815
#P>0.05, fail to reject null hypothesis, no significant correlation
gp2012<-c(Gender_Participation$`2012`)
gdp2012<-c(GDP_per_capita$`2012`)
df2012<-data.frame(Country, gp2012, gdp2012)
graph2012<-ggplot(data = df2012, aes(x = gp2012, y = gdp2012, color = Country)) + geom_point()
graph2012 +
labs(title = "2012",
subtitle = "Correlation between Gender Participation & Economic Growth",
x = "Gender Participation in Government (%)",
y = "Growth in GDP per Capita (%)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2012`, GDP_per_capita$`2012`, method = "spearman")
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2012` and GDP_per_capita$`2012`
## S = 11184, p-value = 0.04965
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.3257468
#p<0.05, reject Null Hypothesis; -0.33, moderate negative correlation
gp2013<-c(Gender_Participation$`2013`)
gdp2013<-c(GDP_per_capita$`2013`)
df2013<-data.frame(Country, gp2013, gdp2013)
graph2013<-ggplot(data = df2013, aes(x = gp2013, y = gdp2013, color = Country)) + geom_point()
graph2013 +
labs(title = "2013",
subtitle = "Correlation between Gender Participation & Economic Growth",
x = "Gender Participation in Government (%)",
y = "Growth in GDP per Capita (%)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2013`, GDP_per_capita$`2013`, method = "spearman")
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2013` and GDP_per_capita$`2013`
## S = 13066, p-value = 0.000535
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.5488383
#p<0.05, reject Null Hypothesis; -0.55, strong negative correlation
gp2014<-c(Gender_Participation$`2014`)
gdp2014<-c(GDP_per_capita$`2014`)
df2014<-data.frame(Country, gp2014, gdp2014)
graph2014<-ggplot(data = df2014, aes(x = gp2014, y = gdp2014, color = Country)) + geom_point()
graph2014 +
labs(title = "2014",
subtitle = "Correlation between Gender Participation & Economic Growth",
x = "Gender Participation in Government (%)",
y = "Growth in GDP per Capita (%)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2014`, GDP_per_capita$`2014`, method = "spearman")
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2014` and GDP_per_capita$`2014`
## S = 12714, p-value = 0.001563
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.5071124
#p<0.05, reject Null Hypothesis, -0.51, strong negative correlation
gp2015<-c(Gender_Participation$`2015`)
gdp2015<-c(GDP_per_capita$`2015`)
df2015<-data.frame(Country, gp2015, gdp2015)
graph2015<-ggplot(data = df2015, aes(x = gp2015, y = gdp2015, color = Country)) + geom_point()
graph2015 +
labs(title = "2015",
subtitle = "Correlation between Gender Participation & Economic Growth",
x = "Gender Participation in Government (%)",
y = "Growth in GDP per Capita (%)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2015`, GDP_per_capita$`2015`, method = "spearman", exact = FALSE)
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2015` and GDP_per_capita$`2015`
## S = 10514, p-value = 0.1416
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.2463399
#P>0.05, fail to reject null hypothesis, no significant correlation
gp2016<-c(Gender_Participation$`2016`)
gdp2016<-c(GDP_per_capita$`2016`)
df2016<-data.frame(Country, gp2016, gdp2016)
graph2016<-ggplot(data = df2016, aes(x = gp2016, y = gdp2016, color = Country)) + geom_point()
graph2016 +
labs(title = "2016",
subtitle = "Correlation between Gender Participation & Economic Growth",
x = "Gender Participation in Government (%)",
y = "Growth in GDP per Capita (%)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2016`, GDP_per_capita$`2016`, method = "spearman", exact = FALSE)
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2016` and GDP_per_capita$`2016`
## S = 8035, p-value = 0.7799
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.04753719
#P>0.05, fail to reject null hypothesis, no significant correlation
gp2017<-c(Gender_Participation$`2017`)
gdp2017<-c(GDP_per_capita$`2017`)
df2017<-data.frame(Country, gp2017, gdp2017)
graph2017<-ggplot(data = df2017, aes(x = gp2017, y = gdp2017, color = Country)) + geom_point()
graph2017 +
labs(title = "2017",
subtitle = "Correlation between Gender Participation & Economic Growth",
x = "Gender Participation in Government (%)",
y = "Growth in GDP per Capita (%)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2017`, GDP_per_capita$`2017`, exact = FALSE)
##
## Pearson's product-moment correlation
##
## data: Gender_Participation$`2017` and GDP_per_capita$`2017`
## t = -1.7473, df = 35, p-value = 0.08935
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.55622236 0.04488544
## sample estimates:
## cor
## -0.2832531
#P>0.05, fail to reject null hypothesis, no significant correlation
gp2018<-c(Gender_Participation$`2018`)
gdp2018<-c(GDP_per_capita$`2018`)
df2018<-data.frame(Country, gp2018, gdp2018)
graph2018<-ggplot(data = df2018, aes(x = gp2018, y = gdp2018, color = Country)) + geom_point()
graph2018 +
labs(title = "2018",
subtitle = "Correlation between Gender Participation & Economic Growth",
x = "Gender Participation in Government (%)",
y = "Growth in GDP per Capita (%)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2018`, GDP_per_capita$`2018`, method = "spearman", exact = FALSE)
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2018` and GDP_per_capita$`2018`
## S = 11255, p-value = 0.04323
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.3341829
#p<0.05, reject Null Hypothesis,; -0.33, moderate negative correlation
gp2019<-c(Gender_Participation$`2019`)
gdp2019<-c(GDP_per_capita$`2019`)
df2019<-data.frame(Country, gp2019, gdp2019)
graph2019<-ggplot(data = df2019, aes(x = gp2019, y = gdp2019, color = Country)) + geom_point()
graph2019 +
labs(title = "2019",
subtitle = "Correlation between Gender Participation & Economic Growth",
x = "Gender Participation in Government (%)",
y = "Growth in GDP per Capita (%)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2019`, GDP_per_capita$`2019`, method = "spearman")
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2019` and GDP_per_capita$`2019`
## S = 11654, p-value = 0.02045
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.3814604
#significant at p<0.05, reject Null Hypothesis; -0.38, moderate negative correlation
##Scatter Plots and Correlation test for each year between Gender Participation and CPI##
##Same prior norms for correlation tests apply
cpi2008<-c(CPI$`2008`)
dfc2008<-data.frame(Country, gp2008, cpi2008)
graphc2008<-ggplot(data = dfc2008, aes(x = gp2008, y = cpi2008, color = Country)) + geom_point()
graphc2008 +
labs(title = "2008",
subtitle = "Correlation between Gender Participation & Corruption Peceptions Index",
x = "Gender Participation in Government (%)",
y = "CPI Score (out of 10)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2008`, CPI$`2008`, method = "spearman", exact = FALSE)
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2008` and CPI$`2008`
## S = 2986.9, p-value = 1.576e-05
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.6459388
#p<0.05, reject null hypothesis; 0.51, strong positive correlation
cpi2009<-c(CPI$`2009`)
dfc2009<-data.frame(Country, gp2009, cpi2009)
graphc2009<-ggplot(data = dfc2009, aes(x = gp2009, y = cpi2009, color = Country)) + geom_point()
graphc2009 +
labs(title = "2009",
subtitle = "Correlation between Gender Participation & Corruption Peceptions Index",
x = "Gender Participation in Government (%)",
y = "CPI Score (out of 10)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2009`, CPI$`2009`, method = "spearman", exact = TRUE)
## Warning in cor.test.default(Gender_Participation$`2009`, CPI$`2009`, method =
## "spearman", : Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2009` and CPI$`2009`
## S = 3227.3, p-value = 4.684e-05
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.6174378
#p<0.05, reject null hypothesis; 0.61, strong positive correlation
cpi2010<-c(CPI$`2010`)
dfc2010<-data.frame(Country, gp2010, cpi2010)
graphc2010<-ggplot(data = dfc2010, aes(x = gp2010, y = cpi2010, color = Country)) + geom_point()
graphc2010 +
labs(title = "2010",
subtitle = "Correlation between Gender Participation & Corruption Peceptions Index",
x = "Gender Participation in Government (%)",
y = "CPI Score (out of 10)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2010`, CPI$`2010`, method = "spearman", exact = TRUE)
## Warning in cor.test.default(Gender_Participation$`2010`, CPI$`2010`, method =
## "spearman", : Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2010` and CPI$`2010`
## S = 3591, p-value = 0.0002015
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.5743267
#p<0.05, reject null hypothesis; 0.57, strong positive correlation
cpi2011<-c(CPI$`2011`)
dfc2011<-data.frame(Country, gp2011, cpi2011)
graphc2011<-ggplot(data = dfc2011, aes(x = gp2011, y = cpi2011, color = Country)) + geom_point()
graphc2011 +
labs(title = "2011",
subtitle = "Correlation between Gender Participation & Corruption Peceptions Index",
x = "Gender Participation in Government (%)",
y = "CPI Score (out of 10)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2011`, CPI$`2011`, method = "spearman", exact = TRUE)
## Warning in cor.test.default(Gender_Participation$`2011`, CPI$`2011`, method =
## "spearman", : Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2011` and CPI$`2011`
## S = 3228.8, p-value = 4.715e-05
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.6172547
#p<0.05, reject null hypothesis; 0.61, strong positive correlation
cpi2012<-c(CPI$`2012`)
dfc2012<-data.frame(Country, gp2012, cpi2012)
graphc2012<-ggplot(data = dfc2012, aes(x = gp2012, y = cpi2012, color = Country)) + geom_point()
graphc2012 +
labs(title = "2012",
subtitle = "Correlation between Gender Participation & Corruption Peceptions Index",
x = "Gender Participation in Government (%)",
y = "CPI Score (out of 10)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2012`, CPI$`2012`, method = "spearman", exact = TRUE)
## Warning in cor.test.default(Gender_Participation$`2012`, CPI$`2012`, method =
## "spearman", : Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2012` and CPI$`2012`
## S = 3703.4, p-value = 0.0003039
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.5610062
#p<0.05, reject null hypothesis; 0.56, strong positive correlation
cpi2013<-c(CPI$`2013`)
dfc2013<-data.frame(Country, gp2013, cpi2013)
graphc2013<-ggplot(data = dfc2013, aes(x = gp2013, y = cpi2013, color = Country)) + geom_point()
graphc2013 +
labs(title = "2013",
subtitle = "Correlation between Gender Participation & Corruption Peceptions Index",
x = "Gender Participation in Government (%)",
y = "CPI Score (out of 10)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2013`, CPI$`2013`, method = "spearman", exact = TRUE)
## Warning in cor.test.default(Gender_Participation$`2013`, CPI$`2013`, method =
## "spearman", : Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2013` and CPI$`2013`
## S = 4320.8, p-value = 0.002194
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.4878136
#p<0.05, reject null hypothesis; 0.48, moderate positive correlation
cpi2014<-c(CPI$`2014`)
dfc2014<-data.frame(Country, gp2014, cpi2014)
graphc2014<-ggplot(data = dfc2014, aes(x = gp2014, y = cpi2014, color = Country)) + geom_point()
graphc2014 +
labs(title = "2014",
subtitle = "Correlation between Gender Participation & Corruption Peceptions Index",
x = "Gender Participation in Government (%)",
y = "CPI Score (out of 10)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2014`, CPI$`2014`, method = "spearman", exact = TRUE)
## Warning in cor.test.default(Gender_Participation$`2014`, CPI$`2014`, method =
## "spearman", : Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2014` and CPI$`2014`
## S = 4436.4, p-value = 0.003033
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.474112
#p<0.05, reject null hypothesis; 0.47, moderate positive correlation
cpi2015<-c(CPI$`2015`)
dfc2015<-data.frame(Country, gp2015, cpi2015)
graphc2015<-ggplot(data = dfc2015, aes(x = gp2015, y = cpi2015, color = Country)) + geom_point()
graphc2015 +
labs(title = "2015",
subtitle = "Correlation between Gender Participation & Corruption Peceptions Index",
x = "Gender Participation in Government (%)",
y = "CPI Score (out of 10)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2015`, CPI$`2015`, method = "spearman", exact = TRUE)
## Warning in cor.test.default(Gender_Participation$`2015`, CPI$`2015`, method =
## "spearman", : Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2015` and CPI$`2015`
## S = 4394.6, p-value = 0.002702
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.479063
#p<0.05, reject null hypothesis; 0.48, moderate positive correlation
cpi2016<-c(CPI$`2016`)
dfc2016<-data.frame(Country, gp2016, cpi2016)
graphc2016<-ggplot(data = dfc2016, aes(x = gp2016, y = cpi2016, color = Country)) + geom_point()
graphc2016 +
labs(title = "2016",
subtitle = "Correlation between Gender Participation & Corruption Peceptions Index",
x = "Gender Participation in Government (%)",
y = "CPI Score (out of 10)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2016`, CPI$`2016`, method = "spearman", exact = TRUE)
## Warning in cor.test.default(Gender_Participation$`2016`, CPI$`2016`, method =
## "spearman", : Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2016` and CPI$`2016`
## S = 4098.2, p-value = 0.001131
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.514201
#p<0.05, reject null hypothesis; 0.51, strong positive correlation
cpi2017<-c(CPI$`2017`)
dfc2017<-data.frame(Country, gp2017, cpi2017)
graphc2017<-ggplot(data = dfc2017, aes(x = gp2017, y = cpi2017, color = Country)) + geom_point()
graphc2017 +
labs(title = "2017",
subtitle = "Correlation between Gender Participation & Corruption Peceptions Index",
x = "Gender Participation in Government (%)",
y = "CPI Score (out of 10)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2017`, CPI$`2017`, method = "spearman", exact = TRUE)
## Warning in cor.test.default(Gender_Participation$`2017`, CPI$`2017`, method =
## "spearman", : Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2017` and CPI$`2017`
## S = 4113.6, p-value = 0.001187
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.5123736
#p<0.05, reject null hypothesis; 0.51, strong positive correlation
cpi2018<-c(CPI$`2018`)
dfc2018<-data.frame(Country, gp2018, cpi2018)
graphc2018<-ggplot(data = dfc2018, aes(x = gp2018, y = cpi2018, color = Country)) + geom_point()
graphc2018 +
labs(title = "2018",
subtitle = "Correlation between Gender Participation & Corruption Peceptions Index",
x = "Gender Participation in Government (%)",
y = "CPI Score (out of 10)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2018`, CPI$`2018`, method = "spearman", exact = TRUE)
## Warning in cor.test.default(Gender_Participation$`2018`, CPI$`2018`, method =
## "spearman", : Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2018` and CPI$`2018`
## S = 4365.9, p-value = 0.002493
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.4824703
#p<0.05, reject null hypothesis; 0.48, moderate positive correlation
cpi2019<-c(CPI$`2019`)
dfc2019<-data.frame(Country, gp2019, cpi2019)
graphc2019<-ggplot(data = dfc2019, aes(x = gp2019, y = cpi2019, color = Country)) + geom_point()
graphc2019 +
labs(title = "2019",
subtitle = "Correlation between Gender Participation & Corruption Peceptions Index",
x = "Gender Participation in Government (%)",
y = "CPI Score (out of 10)",
color = "Country")+
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 0.25,
nudge_y = -0.25,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2019`, CPI$`2019`, method = "spearman", exact = TRUE)
## Warning in cor.test.default(Gender_Participation$`2019`, CPI$`2019`, method =
## "spearman", : Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2019` and CPI$`2019`
## S = 3759.2, p-value = 0.0003703
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.5543889
#p<0.05, reject null hypothesis; 0.51, strong positive correlation
## Checking the correlation between gender participation and covid 19 response
gp2020<-c(Gender_Participation$`2020`)
cases<-c(Covid$`Total cases/1mill of population`)
dfcases<-data.frame(Country, gp2020, cases)
graphcases<-ggplot(data = dfcases, aes(x = gp2020, y = cases, color = Country)) + geom_point()
graphcases +
labs(title = "Total Case Ratio",
subtitle = "Total Cases of Covid19 per 1million of the Population",
x = "Gender Participation in Government (%)",
y = "Cases per 1mill",
color = "Country") +
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 2.3,
nudge_y = -1600,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2020`, Covid$`Total cases/1mill of population`, method = "spearman")
## Warning in cor.test.default(Gender_Participation$`2020`, Covid$`Total cases/
## 1mill of population`, : Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2020` and Covid$`Total cases/1mill of population`
## S = 8374.5, p-value = 0.9658
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.007291481
#P>0.05, fail to reject null hypothesis, no significant correlation
deaths<-c(Covid$`Total deaths/1mill of the population`)
dfdeaths<-data.frame(Country, gp2020, deaths)
graphdeaths<-ggplot(data = dfdeaths, aes(x = gp2020, y = deaths, color = Country)) + geom_point()
graphdeaths +
labs(title = "Total Fatality Ratio",
subtitle = "Total Covid19 Deaths per 1million of the Population",
x = "Gender Participation in Government (%)",
y = "Deaths per 1mill",
color = "Country") +
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 2,
nudge_y = -75,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2020`, Covid$`Total deaths/1mill of the population`, method = "spearman")
## Warning in cor.test.default(Gender_Participation$`2020`, Covid$`Total deaths/
## 1mill of the population`, : Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2020` and Covid$`Total deaths/1mill of the population`
## S = 9103.6, p-value = 0.6415
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.07913925
#P>0.05, fail to reject null hypothesis, no significant correlation
tests<-c(Covid$`Total tests/ 1 mill of the population`)
dftests<-data.frame(Country, gp2020, tests)
graphtests<-ggplot(data = dftests, aes(x = gp2020, y = tests, color = Country)) + geom_point()
graphtests +
labs(title = "Total Testing Ratio",
subtitle = "Total Covid19 tests done per 1million of the Population",
x = "Gender Participation in Government (%)",
y = "Tests per 1mill",
color = "Country") +
theme_gdocs() +
theme(legend.position = "none") +
geom_smooth(method = "lm",
se = FALSE,
color = 'cornflowerblue',
linetype = 'dotted') +
geom_text(label = Country,
size = 2.5,
nudge_x = 2.5,
nudge_y = -100000,
check_overlap = T)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).
cor.test(Gender_Participation$`2020`, Covid$`Total tests/ 1 mill of the population`, method = "spearman")
## Warning in cor.test.default(Gender_Participation$`2020`, Covid$`Total tests/ 1
## mill of the population`, : Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: Gender_Participation$`2020` and Covid$`Total tests/ 1 mill of the population`
## S = 6215.1, p-value = 0.1154
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.263264
#P>0.05, fail to reject null hypothesis, no significant correlation