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## all_naCorelation Between Women’s Participation in Economy and GDP:
EconomicIndicatorGDP1<-read.csv("https://raw.githubusercontent.com/maliat-hossain/UNICEF-Datasets/main/GDP.csv")
head(EconomicIndicatorGDP1)## X
## 1 TABLE 12. SOCIAL PROTECTION AND EQUITY
## 2
## 3 Countries and areas
## 4
## 5
## 6
## X.1
## 1
## 2
## 3 Mothers with newborns receiving cash benefit (%)\n2010?2018*
## 4
## 5
## 6
## X.2 X.3
## 1 NA
## 2 NA
## 3 Proportion of children covered by social protection\n2010?2018* NA
## 4 NA
## 5 NA
## 6 NA
## X.4 X.5 X.6
## 1
## 2
## 3 Distribution of Social Protection Benefits (%, 2010?2016*)
## 4
## 5 bottom 40% top 20% bottom 20%
## 6
## X.7 X.8 X.9 X.10
## 1
## 2
## 3 Share of household income\n(%, 2010?2018*)
## 4
## 5 bottom 40% top 20% bottom 20%
## 6
## X.11 X.12
## 1
## 2
## 3 Gini Coefficient\n2010?2018* Palma Index of income inequality\n2010?2018*
## 4
## 5
## 6
## X.13
## 1
## 2
## 3 GDP per capita (current US$)\n2010?2018*
## 4
## 5
## 6### selecting only Countris name and GDP from the EconomicIndicatorGDP1 dataset
RefEconomicIndicatorGDP1<-EconomicIndicatorGDP1 %>% dplyr::select(1, 14)### removing unnecessary rows
RefEconomicIndicatorGDP1<-anti_join(RefEconomicIndicatorGDP1,RefEconomicIndicatorGDP1[1:3,])## Joining, by = c("NameofCountries1", "GDP")Womenempowerment<-read.csv("https://raw.githubusercontent.com/maliat-hossain/UNICEF-Datasets/main/Table-16-Womens-EN-1.csv")
head(Womenempowerment)## X X.1
## 1 NA TABLE 16. WOMEN'S ECONOMIC EMPOWERMENT
## 2 NA
## 3 NA Countries and areas
## 4 NA
## 5 NA
## 6 NA
## X.2
## 1
## 2
## 3 Social Institutions and Gender Index (SIGI) 2019
## 4
## 5
## 6
## X.3
## 1
## 2
## 3 Legal frameworks on gender equality in employment 2018
## 4
## 5
## 6
## X.4 X.5
## 1
## 2
## 3 Maternity leave benefits\n2018 Paternity leave benefits\n2018
## 4
## 5
## 6
## X.6 X.7
## 1
## 2
## 3 Demand for family planning satisfied with modern methods (%) \n2013?2018*
## 4
## 5
## 6
## X.8 X.9 X.10 X.11
## 1
## 2
## 3 Educational attainment \n2010?2017*
## 4 upper secondary
## 5 male female
## 6
## X.12 X.13 X.14 X.15 X.16 X.17
## 1
## 2
## 3 Labour force participation rate\n2010?2018*
## 4 male
## 5 rural urban total
## 6
## X.18 X.19 X.20 X.21 X.22 X.23 X.24 X.25 X.26
## 1
## 2
## 3 Unemployment rate\n2010?2018*
## 4 female male
## 5 rural urban total rural urban
## 6
## X.27 X.28 X.29 X.30 X.31 X.32 X.33 X.34 X.35
## 1
## 2
## 3
## 4 female
## 5 total rural urban total
## 6
## X.36 X.37 X.38 X.39
## 1 NA NA
## 2 NA NA
## 3 Mobile phone ownership\n2014?2017* NA NA
## 4 NA NA
## 5 male NA female NA
## 6 NA NA
## X.40 X.41 X.42
## 1 NA
## 2 NA
## 3 Financial inclusion \n2014?2017* NA
## 4 NA
## 5 male female NA
## 6 NAnames(Womenempowerment1)[1] <- "NameofCountries1"
names(Womenempowerment1)[2] <- "Men_partcipation_rate_in_economy"
names(Womenempowerment1)[3] <- "Womenen_partcipation_rate_in_economy"## Joining, by = c("NameofCountries1", "Men_partcipation_rate_in_economy", "Womenen_partcipation_rate_in_economy")## Joining, by = c("NameofCountries1", "Men_partcipation_rate_in_economy", "Womenen_partcipation_rate_in_economy")Womenempowerment1 <- filter(Womenempowerment1,Men_partcipation_rate_in_economy!="?")
Womenempowerment1 <- filter(Womenempowerment1,Womenen_partcipation_rate_in_economy!="?")fullJoinGDP <- full_join(RefEconomicIndicatorGDP1,Womenempowerment1,by="NameofCountries1")
View(fullJoinGDP)## NameofCountries1 GDP Men_partcipation_rate_in_economy
## 1 Afghanistan 556.3 73
## 3 Algeria 4048.3 60
## 5 Angola 4095.8 80
## 8 Armenia 3914.5 71
## 9 Australia 54093.6 71
## 10 Austria 47380.8 67
## Womenen_partcipation_rate_in_economy
## 1 21
## 3 13
## 5 75
## 8 53
## 9 60
## 10 56correlate<-data.frame(GDP_and_Womenempowerment1$GDP,GDP_and_Womenempowerment1$Womenen_partcipation_rate_in_economy)
correlate2<-rcorr(as.matrix(correlate))
correlate2## GDP_and_Womenempowerment1.GDP
## GDP_and_Womenempowerment1.GDP 1.00
## GDP_and_Womenempowerment1.Womenen_partcipation_rate_in_economy 0.24
## GDP_and_Womenempowerment1.Womenen_partcipation_rate_in_economy
## GDP_and_Womenempowerment1.GDP 0.24
## GDP_and_Womenempowerment1.Womenen_partcipation_rate_in_economy 1.00
##
## n= 135
##
##
## P
## GDP_and_Womenempowerment1.GDP
## GDP_and_Womenempowerment1.GDP
## GDP_and_Womenempowerment1.Womenen_partcipation_rate_in_economy 0.0049
## GDP_and_Womenempowerment1.Womenen_partcipation_rate_in_economy
## GDP_and_Womenempowerment1.GDP 0.0049
## GDP_and_Womenempowerment1.Womenen_partcipation_rate_in_economyNutrition Analysis:
## Warning: Missing column names filled in: 'X1' [1], 'X3' [3], 'X4' [4], 'X5' [5],
## 'X6' [6], 'X7' [7], 'X8' [8], 'X9' [9], 'X10' [10], 'X11' [11], 'X12' [12],
## 'X13' [13], 'X14' [14], 'X15' [15], 'X16' [16], 'X17' [17], 'X18' [18],
## 'X19' [19], 'X20' [20], 'X21' [21], 'X22' [22], 'X23' [23], 'X24' [24],
## 'X25' [25], 'X26' [26], 'X27' [27], 'X28' [28], 'X29' [29], 'X30' [30],
## 'X31' [31], 'X32' [32], 'X33' [33], 'X34' [34], 'X35' [35]## Parsed with column specification:
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## X27 = col_logical(),
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## X32 = col_logical(),
## X33 = col_logical(),
## X34 = col_logical(),
## X35 = col_logical()
## )## See spec(...) for full column specifications.names(Children_Nutrition_UNICEF)[2] <- "NameoftheCountries"
names(Children_Nutrition_UNICEF)[3] <- "Low_Birthweight"
names(Children_Nutrition_UNICEF)[11] <- "Introduction_to_Solid_SemiSolid_soft_foods"
names(Children_Nutrition_UNICEF)[25] <- "Zero_Vegetable_or_Fruit_Consumption"Children_Nutrition_UNICEF1<-Children_Nutrition_UNICEF %>% dplyr::select(2,3, 11,25)
view(Children_Nutrition_UNICEF1)## Joining, by = c("NameoftheCountries", "Low_Birthweight", "Introduction_to_Solid_SemiSolid_soft_foods", "Zero_Vegetable_or_Fruit_Consumption")Children_Nutrition_UNICEF2$Low_Birthweight <- as.numeric(Children_Nutrition_UNICEF2$Low_Birthweight)## Warning: NAs introduced by coercion## # A tibble: 142 x 4
## NameoftheCountri~ Low_Birthweight Introduction_to_Solid~ Zero_Vegetable_or_F~
## <chr> <dbl> <chr> <chr>
## 1 Albania 5 "89" "26"
## 2 Algeria 7 "77" "\u0096"
## 3 Andorra 7 "\u0096" "\u0096"
## 4 Angola 15 "79" "36"
## 5 Antigua and Barb~ 9 "\u0096" "\u0096"
## 6 Argentina 7 "97" "\u0096"
## 7 Armenia 9 "90" "22"
## 8 Australia 7 "\u0096" "\u0096"
## 9 Austria 7 "\u0096" "\u0096"
## 10 Azerbaijan 7 "77" "38"
## # ... with 132 more rows## Warning: NAs introduced by coercion## # A tibble: 114 x 4
## NameoftheCountri~ Low_Birthweight Introduction_to_Solid~ Zero_Vegetable_or_F~
## <chr> <dbl> <dbl> <chr>
## 1 Afghanistan NA 61 "59"
## 2 Albania 5 89 "26"
## 3 Algeria 7 77 "\u0096"
## 4 Angola 15 79 "36"
## 5 Argentina 7 97 "\u0096"
## 6 Armenia 9 90 "22"
## 7 Azerbaijan 7 77 "38"
## 8 Bangladesh 28 65 "53"
## 9 Barbados NA 90 "\u0096"
## 10 Belarus 5 95 "\u0096"
## # ... with 104 more rows## Warning: NAs introduced by coercionChildren_Nutrition_UNICEF2[!is.na(Children_Nutrition_UNICEF2$Zero_Vegetable_or_Fruit_Consumption), ]## # A tibble: 79 x 4
## NameoftheCountries Low_Birthweight Introduction_to_Soli~ Zero_Vegetable_or_~
## <chr> <dbl> <dbl> <dbl>
## 1 Afghanistan NA 61 59
## 2 Albania 5 89 26
## 3 Angola 15 79 36
## 4 Armenia 9 90 22
## 5 Azerbaijan 7 77 38
## 6 Bangladesh 28 65 53
## 7 Belize 9 79 30
## 8 Benin 17 56 54
## 9 Bolivia (Plurinati~ 7 81 20
## 10 Burkina Faso 13 75 75
## # ... with 69 more rows## # A tibble: 6 x 4
## NameoftheCountri~ Low_Birthweight Introduction_to_Solid_~ Zero_Vegetable_or_F~
## <chr> <dbl> <dbl> <dbl>
## 1 Afghanistan NA 61 59
## 2 Albania 5 89 26
## 3 Algeria 7 77 NA
## 4 Andorra 7 NA NA
## 5 Angola 15 79 36
## 6 Anguilla NA NA NA## # A tibble: 6 x 4
## NameoftheCountri~ Low_Birthweight Introduction_to_Solid_~ Zero_Vegetable_or_F~
## <chr> <dbl> <dbl> <dbl>
## 1 Albania 5 89 26
## 2 Angola 15 79 36
## 3 Armenia 9 90 22
## 4 Azerbaijan 7 77 38
## 5 Bangladesh 28 65 53
## 6 Belize 9 79 30Countries_String<-"Belize|Cuba|Dominican Republic|El Salvador|Guatemala|Mexico|Paraguay|Peru"
Latin_America_Children_Nutrition<-Children_Nutrition_UNICEF2 %>%
filter(str_detect(NameoftheCountries,Countries_String))correlate1<-data.frame(Latin_America_Children_Nutrition$Low_Birthweight,Latin_America_Children_Nutrition$Zero_Vegetable_or_Fruit_Consumption)
correlate3<-rcorr(as.matrix(correlate1))
correlate3## Latin_America_Children_Nutrition.Low_Birthweight
## Latin_America_Children_Nutrition.Low_Birthweight 1.00
## Latin_America_Children_Nutrition.Zero_Vegetable_or_Fruit_Consumption 0.16
## Latin_America_Children_Nutrition.Zero_Vegetable_or_Fruit_Consumption
## Latin_America_Children_Nutrition.Low_Birthweight 0.16
## Latin_America_Children_Nutrition.Zero_Vegetable_or_Fruit_Consumption 1.00
##
## n= 8
##
##
## P
## Latin_America_Children_Nutrition.Low_Birthweight
## Latin_America_Children_Nutrition.Low_Birthweight
## Latin_America_Children_Nutrition.Zero_Vegetable_or_Fruit_Consumption 0.7086
## Latin_America_Children_Nutrition.Zero_Vegetable_or_Fruit_Consumption
## Latin_America_Children_Nutrition.Low_Birthweight 0.7086
## Latin_America_Children_Nutrition.Zero_Vegetable_or_Fruit_Consumptioncorrelate4<-data.frame(Latin_America_Children_Nutrition$Low_Birthweight,Latin_America_Children_Nutrition$Introduction_to_Solid_SemiSolid_soft_foods)
correlate5<-rcorr(as.matrix(correlate4))
correlate5## Latin_America_Children_Nutrition.Low_Birthweight
## Latin_America_Children_Nutrition.Low_Birthweight 1.00
## Latin_America_Children_Nutrition.Introduction_to_Solid_SemiSolid_soft_foods -0.43
## Latin_America_Children_Nutrition.Introduction_to_Solid_SemiSolid_soft_foods
## Latin_America_Children_Nutrition.Low_Birthweight -0.43
## Latin_America_Children_Nutrition.Introduction_to_Solid_SemiSolid_soft_foods 1.00
##
## n= 8
##
##
## P
## Latin_America_Children_Nutrition.Low_Birthweight
## Latin_America_Children_Nutrition.Low_Birthweight
## Latin_America_Children_Nutrition.Introduction_to_Solid_SemiSolid_soft_foods 0.2834
## Latin_America_Children_Nutrition.Introduction_to_Solid_SemiSolid_soft_foods
## Latin_America_Children_Nutrition.Low_Birthweight 0.2834
## Latin_America_Children_Nutrition.Introduction_to_Solid_SemiSolid_soft_foodsChild Mortality Rate and GDP:
Child_Mortality_Rate<-read.csv("https://raw.githubusercontent.com/maliat-hossain/UNICEF-Datasets/main/Table-2-Child-Mortality-EN.csv")
head(Child_Mortality_Rate)## TABLE.2..CHILD.MORTALITY
## 1
## 2 Countries and areas
## 3
## 4
## 5 Afghanistan
## 6 Albania
## X X.1 X.2 X.3 X.4 X.5
## 1 NA NA NA
## 2 Under-5 mortality rate (deaths per 1,000 live births) NA NA NA
## 3 1990 NA 2000 NA 2018 NA
## 4 NA NA NA
## 5 179 NA 129 NA 62 NA
## 6 41 NA 26 NA 9 NA
## X.6 X.7
## 1 NA
## 2 Annual rate of reduction in under-5 mortality rate (%) \n2000–2018\n NA
## 3 NA
## 4 NA
## 5 4.1 NA
## 6 6.0 NA
## X.8 X.9 X.10
## 1 NA
## 2 Under-5 mortality rate by sex (deaths per 1,000 live births) 2018 NA
## 3 male NA female
## 4 NA
## 5 66 NA 59
## 6 9 NA 8
## X.11 X.12 X.13 X.14 X.15
## 1 NA NA NA
## 2 NA Infant mortality rate (deaths per 1,000 live births) NA NA
## 3 NA 1990 NA 2018 NA
## 4 NA NA NA
## 5 NA 121 NA 48 NA
## 6 NA 35 NA 8 NA
## X.16 X.17 X.18 X.19 X.20
## 1 NA NA
## 2 Neonatal mortality rate (deaths per 1,000 live births) NA NA
## 3 1990 NA 2000 NA 2018
## 4 NA NA
## 5 75 NA 61 NA 37
## 6 13 NA 12 NA 7
## X.21
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## X.22
## 1
## 2 Probability of dying among children aged 5–14 (deaths per 1,000 children aged 5)
## 3 1990
## 4
## 5 16
## 6 7
## X.23 X.24 X.25 X.26 X.27
## 1 NA NA NA
## 2 NA NA Annual number of under-5 deaths (thousands)\n2018 NA
## 3 NA 2018 NA NA
## 4 NA NA NA
## 5 NA 5 NA 74 NA
## 6 NA 2 NA 0 NA
## X.28 X.29
## 1 NA
## 2 Annual number of neonatal deaths (thousands)\n2018 NA
## 3 NA
## 4 NA
## 5 45 NA
## 6 0 NA
## X.30 X.31
## 1 NA
## 2 Neonatal deaths as proportion of all under-5 deaths (%)\n2018 NA
## 3 NA
## 4 NA
## 5 60 NA
## 6 74 NA
## X.32 X.33
## 1 NA
## 2 Number of deaths among children aged 5–14 (thousands)\n2018 NA
## 3 NA
## 4 NA
## 5 5 NA
## 6 0 NAnames(Child_Mortality_Rate)[1] <- "NameofCountries1"
names(Child_Mortality_Rate)[6] <- "mortality_rate"
head(Child_Mortality_Rate)## NameofCountries1 X X.1
## 1 NA
## 2 Countries and areas Under-5 mortality rate (deaths per 1,000 live births) NA
## 3 1990 NA
## 4 NA
## 5 Afghanistan 179 NA
## 6 Albania 41 NA
## X.2 X.3 mortality_rate X.5
## 1 NA NA
## 2 NA NA
## 3 2000 NA 2018 NA
## 4 NA NA
## 5 129 NA 62 NA
## 6 26 NA 9 NA
## X.6 X.7
## 1 NA
## 2 Annual rate of reduction in under-5 mortality rate (%) \n2000–2018\n NA
## 3 NA
## 4 NA
## 5 4.1 NA
## 6 6.0 NA
## X.8 X.9 X.10
## 1 NA
## 2 Under-5 mortality rate by sex (deaths per 1,000 live births) 2018 NA
## 3 male NA female
## 4 NA
## 5 66 NA 59
## 6 9 NA 8
## X.11 X.12 X.13 X.14 X.15
## 1 NA NA NA
## 2 NA Infant mortality rate (deaths per 1,000 live births) NA NA
## 3 NA 1990 NA 2018 NA
## 4 NA NA NA
## 5 NA 121 NA 48 NA
## 6 NA 35 NA 8 NA
## X.16 X.17 X.18 X.19 X.20
## 1 NA NA
## 2 Neonatal mortality rate (deaths per 1,000 live births) NA NA
## 3 1990 NA 2000 NA 2018
## 4 NA NA
## 5 75 NA 61 NA 37
## 6 13 NA 12 NA 7
## X.21
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## X.22
## 1
## 2 Probability of dying among children aged 5–14 (deaths per 1,000 children aged 5)
## 3 1990
## 4
## 5 16
## 6 7
## X.23 X.24 X.25 X.26 X.27
## 1 NA NA NA
## 2 NA NA Annual number of under-5 deaths (thousands)\n2018 NA
## 3 NA 2018 NA NA
## 4 NA NA NA
## 5 NA 5 NA 74 NA
## 6 NA 2 NA 0 NA
## X.28 X.29
## 1 NA
## 2 Annual number of neonatal deaths (thousands)\n2018 NA
## 3 NA
## 4 NA
## 5 45 NA
## 6 0 NA
## X.30 X.31
## 1 NA
## 2 Neonatal deaths as proportion of all under-5 deaths (%)\n2018 NA
## 3 NA
## 4 NA
## 5 60 NA
## 6 74 NA
## X.32 X.33
## 1 NA
## 2 Number of deaths among children aged 5–14 (thousands)\n2018 NA
## 3 NA
## 4 NA
## 5 5 NA
## 6 0 NA## NameofCountries1
## 1
## 2 Countries and areas
## 3
## 4
## 5 Afghanistan
## 6 Albania
## 7 Algeria
## 8 Andorra
## 9 Angola
## 10 Anguilla
## 11 Antigua and Barbuda
## 12 Argentina
## 13 Armenia
## 14 Australia
## 15 Austria
## 16 Azerbaijan
## 17 Bahamas
## 18 Bahrain
## 19 Bangladesh
## 20 Barbados
## 21 Belarus
## 22 Belgium
## 23 Belize
## 24 Benin
## 25 Bhutan
## 26 Bolivia (Plurinational State of)
## 27 Bosnia and Herzegovina
## 28 Botswana
## 29 Brazil
## 30 British Virgin Islands
## 31 Brunei Darussalam
## 32 Bulgaria
## 33 Burkina Faso
## 34 Burundi
## 35 Cabo Verde
## 36 Cambodia
## 37 Cameroon
## 38 Canada
## 39 Central African Republic
## 40 Chad
## 41 Chile
## 42 China
## 43 Colombia
## 44 Comoros
## 45 Congo
## 46 Cook Islands
## 47 Costa Rica
## 48 Côte d'Ivoire
## 49 Croatia
## 50 Cuba
## 51 Cyprus
## 52 Czechia
## 53 Democratic People's Republic of Korea
## 54 Democratic Republic of the Congo
## 55 Denmark
## 56 Djibouti
## 57 Dominica
## 58 Dominican Republic
## 59 Ecuador
## 60 Egypt
## 61 El Salvador
## 62 Equatorial Guinea
## 63 Eritrea
## 64 Estonia
## 65 Eswatini
## 66 Ethiopia
## 67 Fiji
## 68 Finland
## 69 France
## 70 Gabon
## 71 Gambia
## 72 Georgia
## 73 Germany
## 74 Ghana
## 75 Greece
## 76 Grenada
## 77 Guatemala
## 78 Guinea
## 79 Guinea-Bissau
## 80 Guyana
## 81 Haiti
## 82 Holy See
## 83 Honduras
## 84 Hungary
## 85 Iceland
## 86 India
## 87 Indonesia
## 88 Iran (Islamic Republic of)
## 89 Iraq
## 90 Ireland
## 91 Israel
## 92 Italy
## 93 Jamaica
## 94 Japan
## 95 Jordan
## 96 Kazakhstan
## 97 Kenya
## 98 Kiribati
## 99 Kuwait
## 100 Kyrgyzstan
## 101 Lao People's Democratic Republic
## 102 Latvia
## 103 Lebanon
## 104 Lesotho
## 105 Liberia
## 106 Libya
## 107 Liechtenstein
## 108 Lithuania
## 109 Luxembourg
## 110 Madagascar
## 111 Malawi
## 112 Malaysia
## 113 Maldives
## 114 Mali
## 115 Malta
## 116 Marshall Islands
## 117 Mauritania
## 118 Mauritius
## 119 Mexico
## 120 Micronesia (Federated States of)
## 121 Monaco
## 122 Mongolia
## 123 Montenegro
## 124 Montserrat
## 125 Morocco
## 126 Mozambique
## 127 Myanmar
## 128 Namibia
## 129 Nauru
## 130 Nepal
## 131 Netherlands
## 132 New Zealand
## 133 Nicaragua
## 134 Niger
## 135 Nigeria
## 136 Niue
## 137 North Macedonia
## 138 Norway
## 139 Oman
## 140 Pakistan
## 141 Palau
## 142 Panama
## 143 Papua New Guinea
## 144 Paraguay
## 145 Peru
## 146 Philippines
## 147 Poland
## 148 Portugal
## 149 Qatar
## 150 Republic of Korea
## 151 Republic of Moldova
## 152 Romania
## 153 Russian Federation
## 154 Rwanda
## 155 Saint Kitts and Nevis
## 156 Saint Lucia
## 157 Saint Vincent and the Grenadines
## 158 Samoa
## 159 San Marino
## 160 Sao Tome and Principe
## 161 Saudi Arabia
## 162 Senegal
## 163 Serbia
## 164 Seychelles
## 165 Sierra Leone
## 166 Singapore
## 167 Slovakia
## 168 Slovenia
## 169 Solomon Islands
## 170 Somalia
## 171 South Africa
## 172 South Sudan
## 173 Spain
## 174 Sri Lanka
## 175 State of Palestine
## 176 Sudan
## 177 Suriname
## 178 Sweden
## 179 Switzerland
## 180 Syrian Arab Republic
## 181 Tajikistan
## 182 Thailand
## 183 Timor-Leste
## 184 Togo
## 185 Tokelau
## 186 Tonga
## 187 Trinidad and Tobago
## 188 Tunisia
## 189 Turkey
## 190 Turkmenistan
## 191 Turks and Caicos Islands
## 192 Tuvalu
## 193 Uganda
## 194 Ukraine
## 195 United Arab Emirates
## 196 United Kingdom
## 197 United Republic of Tanzania
## 198 United States
## 199 Uruguay
## 200 Uzbekistan
## 201 Vanuatu
## 202 Venezuela (Bolivarian Republic of)
## 203 Viet Nam
## 204 Yemen
## 205 Zambia
## 206 Zimbabwe
## 207
## 208 SUMMARY
## 209 East Asia and Pacific
## 210 Europe and Central Asia
## 211 Eastern Europe and Central Asia
## 212 Western Europe
## 213 Latin America and Caribbean
## 214 Middle East and North Africa
## 215 North America
## 216 South Asia
## 217 Sub-Saharan Africa
## 218 Eastern and Southern Africa
## 219 West and Central Africa
## 220 Least developed countries
## 221 World
## 222
## 223 For a complete list of countries and areas in the regions, subregions and country categories, see page 182 or visit <data.unicef.org/regionalclassifications>.
## 224 It is not advisable to compare data from consecutive editions of The State of the World’s Children.
## 225
## 226 NOTES
## 227 ? Data not available.
## 228
## 229 DEFINITIONS OF THE INDICATORS
## 230 Under-5 mortality rate – Probability of dying between birth and exactly 5 years of age, expressed per 1,000 live births.
## 231 Infant mortality rate – Probability of dying between birth and exactly 1 year of age, expressed per 1,000 live births.
## 232 Neonatal mortality rate – Probability of dying during the first 28 days of life, expressed per 1,000 live births.
## 233 Probability of dying among children aged 5–14 – Probability of dying at age 5–14 years expressed per 1,000 children aged 5.
## 234
## 235 MAIN DATA SOURCES
## 236 Under-5, infant, neonatal and age 5–14 mortality rates – United Nations Inter-agency Group for Child Mortality Estimation (UNICEF, World Health Organization, United Nations Population Division and the World Bank Group). Last update: September 2019.
## 237 Under-5 deaths, neonatal deaths and deaths aged 5–14 – United Nations Inter-agency Group for Child Mortality Estimation (UNICEF, World Health Organization, United Nations Population Division and the World Bank Group). Last update: September 2019.
## 238
## 239
## 240
## 241
## 242
## 243
## 244
## mortality_rate
## 1
## 2
## 3 2018
## 4
## 5 62
## 6 9
## 7 23
## 8 3
## 9 77
## 10 ?
## 11 6
## 12 10
## 13 12
## 14 4
## 15 4
## 16 22
## 17 10
## 18 7
## 19 30
## 20 12
## 21 3
## 22 4
## 23 13
## 24 93
## 25 30
## 26 27
## 27 6
## 28 36
## 29 14
## 30 ?
## 31 12
## 32 7
## 33 76
## 34 58
## 35 19
## 36 28
## 37 76
## 38 5
## 39 116
## 40 119
## 41 7
## 42 9
## 43 14
## 44 67
## 45 50
## 46 8
## 47 9
## 48 81
## 49 5
## 50 5
## 51 2
## 52 3
## 53 18
## 54 88
## 55 4
## 56 59
## 57 36
## 58 29
## 59 14
## 60 21
## 61 14
## 62 85
## 63 42
## 64 3
## 65 54
## 66 55
## 67 26
## 68 2
## 69 4
## 70 45
## 71 58
## 72 10
## 73 4
## 74 48
## 75 4
## 76 15
## 77 26
## 78 101
## 79 81
## 80 30
## 81 65
## 82 ?
## 83 18
## 84 4
## 85 2
## 86 37
## 87 25
## 88 14
## 89 27
## 90 4
## 91 4
## 92 3
## 93 14
## 94 2
## 95 16
## 96 10
## 97 41
## 98 53
## 99 8
## 100 19
## 101 47
## 102 4
## 103 7
## 104 81
## 105 71
## 106 12
## 107 ?
## 108 4
## 109 2
## 110 54
## 111 50
## 112 8
## 113 9
## 114 98
## 115 7
## 116 33
## 117 76
## 118 16
## 119 13
## 120 31
## 121 3
## 122 16
## 123 3
## 124 ?
## 125 22
## 126 73
## 127 46
## 128 40
## 129 32
## 130 32
## 131 4
## 132 6
## 133 18
## 134 84
## 135 120
## 136 24
## 137 10
## 138 3
## 139 11
## 140 69
## 141 18
## 142 15
## 143 48
## 144 20
## 145 14
## 146 28
## 147 4
## 148 4
## 149 7
## 150 3
## 151 16
## 152 7
## 153 7
## 154 35
## 155 12
## 156 17
## 157 16
## 158 16
## 159 2
## 160 31
## 161 7
## 162 44
## 163 6
## 164 14
## 165 105
## 166 3
## 167 6
## 168 2
## 169 20
## 170 122
## 171 34
## 172 99
## 173 3
## 174 7
## 175 20
## 176 60
## 177 19
## 178 3
## 179 4
## 180 17
## 181 35
## 182 9
## 183 46
## 184 70
## 185 ?
## 186 16
## 187 18
## 188 17
## 189 11
## 190 46
## 191 ?
## 192 24
## 193 46
## 194 9
## 195 8
## 196 4
## 197 53
## 198 7
## 199 8
## 200 21
## 201 26
## 202 25
## 203 21
## 204 55
## 205 58
## 206 46
## 207
## 208
## 209 15
## 210 9
## 211 13
## 212 4
## 213 16
## 214 22
## 215 6
## 216 42
## 217 78
## 218 57
## 219 97
## 220 64
## 221 39
## 222
## 223
## 224
## 225
## 226
## 227
## 228
## 229
## 230
## 231
## 232
## 233
## 234
## 235
## 236
## 237
## 238
## 239
## 240
## 241
## 242
## 243
## 244### removing unnecessary rows
RefChild_Mortality_Rate<-anti_join(RefChild_Mortality_Rate,RefChild_Mortality_Rate[1:3,])## Joining, by = c("NameofCountries1", "mortality_rate")## NameofCountries1 mortality_rate
## 1 Afghanistan 62
## 2 Albania 9
## 3 Algeria 23
## 4 Andorra 3
## 5 Angola 77
## 6 Anguilla ?## Warning: NAs introduced by coercion## NameofCountries1 mortality_rate
## 1 Afghanistan 62
## 2 Albania 9
## 3 Algeria 23
## 4 Andorra 3
## 5 Angola 77
## 7 Antigua and Barbuda 6
## 8 Argentina 10
## 9 Armenia 12
## 10 Australia 4
## 11 Austria 4
## 12 Azerbaijan 22
## 13 Bahamas 10
## 14 Bahrain 7
## 15 Bangladesh 30
## 16 Barbados 12
## 17 Belarus 3
## 18 Belgium 4
## 19 Belize 13
## 20 Benin 93
## 21 Bhutan 30
## 22 Bolivia (Plurinational State of) 27
## 23 Bosnia and Herzegovina 6
## 24 Botswana 36
## 25 Brazil 14
## 27 Brunei Darussalam 12
## 28 Bulgaria 7
## 29 Burkina Faso 76
## 30 Burundi 58
## 31 Cabo Verde 19
## 32 Cambodia 28
## 33 Cameroon 76
## 34 Canada 5
## 35 Central African Republic 116
## 36 Chad 119
## 37 Chile 7
## 38 China 9
## 39 Colombia 14
## 40 Comoros 67
## 41 Congo 50
## 42 Cook Islands 8
## 43 Costa Rica 9
## 44 Côte d'Ivoire 81
## 45 Croatia 5
## 46 Cuba 5
## 47 Cyprus 2
## 48 Czechia 3
## 49 Democratic People's Republic of Korea 18
## 50 Democratic Republic of the Congo 88
## 51 Denmark 4
## 52 Djibouti 59
## 53 Dominica 36
## 54 Dominican Republic 29
## 55 Ecuador 14
## 56 Egypt 21
## 57 El Salvador 14
## 58 Equatorial Guinea 85
## 59 Eritrea 42
## 60 Estonia 3
## 61 Eswatini 54
## 62 Ethiopia 55
## 63 Fiji 26
## 64 Finland 2
## 65 France 4
## 66 Gabon 45
## 67 Gambia 58
## 68 Georgia 10
## 69 Germany 4
## 70 Ghana 48
## 71 Greece 4
## 72 Grenada 15
## 73 Guatemala 26
## 74 Guinea 101
## 75 Guinea-Bissau 81
## 76 Guyana 30
## 77 Haiti 65
## 79 Honduras 18
## 80 Hungary 4
## 81 Iceland 2
## 82 India 37
## 83 Indonesia 25
## 84 Iran (Islamic Republic of) 14
## 85 Iraq 27
## 86 Ireland 4
## 87 Israel 4
## 88 Italy 3
## 89 Jamaica 14
## 90 Japan 2
## 91 Jordan 16
## 92 Kazakhstan 10
## 93 Kenya 41
## 94 Kiribati 53
## 95 Kuwait 8
## 96 Kyrgyzstan 19
## 97 Lao People's Democratic Republic 47
## 98 Latvia 4
## 99 Lebanon 7
## 100 Lesotho 81
## 101 Liberia 71
## 102 Libya 12
## 104 Lithuania 4
## 105 Luxembourg 2
## 106 Madagascar 54
## 107 Malawi 50
## 108 Malaysia 8
## 109 Maldives 9
## 110 Mali 98
## 111 Malta 7
## 112 Marshall Islands 33
## 113 Mauritania 76
## 114 Mauritius 16
## 115 Mexico 13
## 116 Micronesia (Federated States of) 31
## 117 Monaco 3
## 118 Mongolia 16
## 119 Montenegro 3
## 121 Morocco 22
## 122 Mozambique 73
## 123 Myanmar 46
## 124 Namibia 40
## 125 Nauru 32
## 126 Nepal 32
## 127 Netherlands 4
## 128 New Zealand 6
## 129 Nicaragua 18
## 130 Niger 84
## 131 Nigeria 120
## 132 Niue 24
## 133 North Macedonia 10
## 134 Norway 3
## 135 Oman 11
## 136 Pakistan 69
## 137 Palau 18
## 138 Panama 15
## 139 Papua New Guinea 48
## 140 Paraguay 20
## 141 Peru 14
## 142 Philippines 28
## 143 Poland 4
## 144 Portugal 4
## 145 Qatar 7
## 146 Republic of Korea 3
## 147 Republic of Moldova 16
## 148 Romania 7
## 149 Russian Federation 7
## 150 Rwanda 35
## 151 Saint Kitts and Nevis 12
## 152 Saint Lucia 17
## 153 Saint Vincent and the Grenadines 16
## 154 Samoa 16
## 155 San Marino 2
## 156 Sao Tome and Principe 31
## 157 Saudi Arabia 7
## 158 Senegal 44
## 159 Serbia 6
## 160 Seychelles 14
## 161 Sierra Leone 105
## 162 Singapore 3
## 163 Slovakia 6
## 164 Slovenia 2
## 165 Solomon Islands 20
## 166 Somalia 122
## 167 South Africa 34
## 168 South Sudan 99
## 169 Spain 3
## 170 Sri Lanka 7
## 171 State of Palestine 20
## 172 Sudan 60
## 173 Suriname 19
## 174 Sweden 3
## 175 Switzerland 4
## 176 Syrian Arab Republic 17
## 177 Tajikistan 35
## 178 Thailand 9
## 179 Timor-Leste 46
## 180 Togo 70
## 182 Tonga 16
## 183 Trinidad and Tobago 18
## 184 Tunisia 17
## 185 Turkey 11
## 186 Turkmenistan 46
## 188 Tuvalu 24
## 189 Uganda 46
## 190 Ukraine 9
## 191 United Arab Emirates 8
## 192 United Kingdom 4
## 193 United Republic of Tanzania 53
## 194 United States 7
## 195 Uruguay 8
## 196 Uzbekistan 21
## 197 Vanuatu 26
## 198 Venezuela (Bolivarian Republic of) 25
## 199 Viet Nam 21
## 200 Yemen 55
## 201 Zambia 58
## 202 Zimbabwe 46
## 204 East Asia and Pacific 15
## 205 Europe and Central Asia 9
## 206 Eastern Europe and Central Asia 13
## 207 Western Europe 4
## 208 Latin America and Caribbean 16
## 209 Middle East and North Africa 22
## 210 North America 6
## 211 South Asia 42
## 212 Sub-Saharan Africa 78
## 213 Eastern and Southern Africa 57
## 214 West and Central Africa 97
## 215 Least developed countries 64
## 216 World 39fullJoinGDPANDMortalityRate <- full_join(RefEconomicIndicatorGDP1,RefChild_Mortality_Rate,by="NameofCountries1")
View(fullJoinGDPANDMortalityRate)#fullJoinGDP<-na.omit(fullJoinGDP)
fullJoinGDPANDMortalityRate<-na.omit(fullJoinGDPANDMortalityRate)
AnalyzingGDPandMortalityRate<-fullJoinGDPANDMortalityRate[1:189,]correlate7<-data.frame(AnalyzingGDPandMortalityRate$GDP,AnalyzingGDPandMortalityRate$mortality_rate)
correlate8<-rcorr(as.matrix(correlate7))
correlate8## AnalyzingGDPandMortalityRate.GDP
## AnalyzingGDPandMortalityRate.GDP 1.00
## AnalyzingGDPandMortalityRate.mortality_rate -0.46
## AnalyzingGDPandMortalityRate.mortality_rate
## AnalyzingGDPandMortalityRate.GDP -0.46
## AnalyzingGDPandMortalityRate.mortality_rate 1.00
##
## n= 189
##
##
## P
## AnalyzingGDPandMortalityRate.GDP
## AnalyzingGDPandMortalityRate.GDP
## AnalyzingGDPandMortalityRate.mortality_rate 0
## AnalyzingGDPandMortalityRate.mortality_rate
## AnalyzingGDPandMortalityRate.GDP 0
## AnalyzingGDPandMortalityRate.mortality_rate