R Markdown
This project aims to work with data from UNICEF’s state of the World’s Children 2019 Statistical Tables and analyze the relationships among women empowerment, child nutrition,child mortality rate and GDP.
Analysis 1:Bivariate Relationship Between Women’s Participation in Economy and GDP:
The analysis only requires column 1 and 14. The columns are assigned name.
names(EconomicIndicatorGDP1)[1] <- "NameofCountries1"
names(EconomicIndicatorGDP1)[14] <- "GDP"Column 1 and 14 is selected.
### 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")Unnecessary punctuation is removed.
RefEconomicIndicatorGDP1 <- filter(RefEconomicIndicatorGDP1,GDP!="?")
head(RefEconomicIndicatorGDP1)%>%
knitr::kable(caption = "Table 1:GDP by countries in 2019 ")%>%
kableExtra::kable_styling(bootstrap_options = "striped")| NameofCountries1 | GDP |
|---|---|
| Afghanistan | 556.3 |
| Albania | 4532.9 |
| Algeria | 4048.3 |
| Andorra | 39134.4 |
| Angola | 4095.8 |
| Antigua and Barbuda | 15824.7 |
The data frame named “WOMEN’S ECONOMIC EMPOWERMENT” is downloaded.
Womenempowerment<-read.csv("https://raw.githubusercontent.com/maliat-hossain/UNICEF-Datasets/main/Table-16-Womens-EN-1.csv")
head(Womenempowerment)%>%
kbl() %>%
kable_material(c("striped"))| X | X.1 | X.2 | X.3 | X.4 | X.5 | X.6 | X.7 | X.8 | X.9 | X.10 | X.11 | X.12 | X.13 | X.14 | X.15 | X.16 | X.17 | X.18 | X.19 | X.20 | X.21 | X.22 | X.23 | X.24 | X.25 | X.26 | X.27 | X.28 | X.29 | X.30 | X.31 | X.32 | X.33 | X.34 | X.35 | X.36 | X.37 | X.38 | X.39 | X.40 | X.41 | X.42 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NA | TABLE 16. WOMEN’S ECONOMIC EMPOWERMENT | NA | NA | NA | ||||||||||||||||||||||||||||||||||||||
| NA | NA | NA | NA | |||||||||||||||||||||||||||||||||||||||
| NA | Countries and areas | Social Institutions and Gender Index (SIGI) 2019 | Legal frameworks on gender equality in employment 2018 | Maternity leave benefits 2018 | Paternity leave benefits 2018 | Demand for family planning satisfied with modern methods (%) 2013?2018* | Educational attainment 2010?2017* | Labour force participation rate 2010?2018* | Unemployment rate 2010?2018* | Mobile phone ownership 2014?2017* | NA | NA | Financial inclusion 2014?2017* | NA | ||||||||||||||||||||||||||||
| NA | upper secondary | male | female | male | female | NA | NA | NA | ||||||||||||||||||||||||||||||||||
| NA | male | female | rural | urban | total | rural | urban | total | rural | urban | total | rural | urban | total | male | NA | female | NA | male | female | NA | |||||||||||||||||||||
| NA | NA | NA | NA |
The necessary column are selected.
Womenempowerment1<-Womenempowerment %>% dplyr::select(2, 17,23)Name of the columns are assigned.
names(Womenempowerment1)[1] <- "NameofCountries1"
names(Womenempowerment1)[2] <- "Men_partcipation_rate_in_economy"
names(Womenempowerment1)[3] <- "Womenen_partcipation_rate_in_economy"unnecessary rows and puntuation marks are removed.
Womenempowerment1<-anti_join(Womenempowerment1,Womenempowerment1[1:3,])## Joining, by = c("NameofCountries1", "Men_partcipation_rate_in_economy", "Womenen_partcipation_rate_in_economy")Womenempowerment1<-anti_join(Womenempowerment1,Womenempowerment1[1,])## 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!="?")
head(Womenempowerment1)%>%
knitr::kable(caption = "Table 2:Women's participation in Economy ")%>%
kableExtra::kable_styling(bootstrap_options = "striped")| NameofCountries1 | Men_partcipation_rate_in_economy | Womenen_partcipation_rate_in_economy |
|---|---|---|
| Afghanistan | 73 | 21 |
| Algeria | 60 | 13 |
| Angola | 80 | 75 |
| Armenia | 71 | 53 |
| Australia | 71 | 60 |
| Austria | 67 | 56 |
Two data frames regarding the GDP and women’s participation in the economy is joined for the convinience of analysis.
fullJoinGDP <- full_join(RefEconomicIndicatorGDP1,Womenempowerment1,by="NameofCountries1")
View(fullJoinGDP)%>%
kbl() %>%
kable_material(c("striped"))na values are removed from the dataframe.
fullJoinGDP<-na.omit(fullJoinGDP)Rows from 1 to 135 only contains data regarding United Nation’s member countries.Summary values on the basis of continents are excluded for the purpose of analysis.
GDP_and_Womenempowerment1 <- fullJoinGDP[1:135,]
view(GDP_and_Womenempowerment1)%>%
knitr::kable(caption = "Table 3:Women's participation in Economy and GDP ")%>%
kableExtra::kable_styling(bootstrap_options = "striped")| NameofCountries1 | GDP | Men_partcipation_rate_in_economy | Womenen_partcipation_rate_in_economy | |
|---|---|---|---|---|
| 1 | Afghanistan | 556.3 | 73 | 21 |
| 3 | Algeria | 4048.3 | 60 | 13 |
| 5 | Angola | 4095.8 | 80 | 75 |
| 8 | Armenia | 3914.5 | 71 | 53 |
| 9 | Australia | 54093.6 | 71 | 60 |
| 10 | Austria | 47380.8 | 67 | 56 |
| 14 | Bangladesh | 1564.0 | 81 | 36 |
| 17 | Belgium | 43507.2 | 59 | 50 |
| 18 | Belize | 4956.8 | 79 | 49 |
| 19 | Benin | 827.4 | 74 | 70 |
| 20 | Bhutan | 3390.7 | 71 | 56 |
| 21 | Bolivia (Plurinational State of) | 3351.1 | 80 | 64 |
| 22 | Bosnia and Herzegovina | 5394.6 | 53 | 31 |
| 23 | Botswana | 7893.7 | 70 | 56 |
| 24 | Brazil | 9880.9 | 78 | 56 |
| 25 | Brunei Darussalam | 28572.1 | 69 | 56 |
| 26 | Bulgaria | 8228.0 | 62 | 49 |
| 27 | Burkina Faso | 642.0 | 75 | 58 |
| 28 | Burundi | 293.0 | 78 | 80 |
| 29 | Cabo Verde | 3295.3 | 63 | 49 |
| 30 | Cambodia | 1385.3 | 88 | 76 |
| 31 | Cameroon | 1421.6 | 78 | 67 |
| 32 | Canada | 45069.9 | 70 | 61 |
| 35 | Chile | 15037.4 | 71 | 49 |
| 37 | Colombia | 6375.9 | 80 | 57 |
| 38 | Comoros | 1312.4 | 51 | 33 |
| 39 | Congo | 1702.6 | 69 | 67 |
| 40 | Costa Rica | 11752.5 | 74 | 46 |
| 41 | Côte d’Ivoire | 1557.2 | 66 | 46 |
| 42 | Croatia | 13383.7 | 57 | 46 |
| 44 | Cyprus | 25760.8 | 68 | 57 |
| 45 | Czechia | 20379.9 | 69 | 53 |
| 46 | Democratic Republic of the Congo | 467.1 | 70 | 63 |
| 47 | Denmark | 57218.9 | 67 | 58 |
| 50 | Dominican Republic | 7222.6 | 76 | 49 |
| 51 | Ecuador | 6213.5 | 79 | 55 |
| 52 | Egypt | 2440.5 | 67 | 22 |
| 53 | El Salvador | 3902.2 | 77 | 46 |
| 56 | Estonia | 20200.4 | 71 | 57 |
| 57 | Eswatini | 3941.9 | 56 | 47 |
| 58 | Ethiopia | 768.0 | 85 | 75 |
| 59 | Fiji | 6006.4 | 77 | 39 |
| 60 | Finland | 45804.7 | 63 | 56 |
| 61 | France | 38679.1 | 60 | 52 |
| 63 | Gambia | 672.8 | 69 | 50 |
| 64 | Georgia | 4045.4 | 75 | 58 |
| 65 | Germany | 44681.1 | 67 | 56 |
| 66 | Ghana | 2025.9 | 59 | 55 |
| 67 | Greece | 18883.5 | 60 | 44 |
| 69 | Guatemala | 4470.6 | 85 | 39 |
| 70 | Guinea | 821.7 | 66 | 63 |
| 72 | Guyana | 4586.1 | 69 | 44 |
| 73 | Haiti | 765.6 | 74 | 54 |
| 74 | Honduras | 2432.9 | 84 | 46 |
| 75 | Hungary | 14278.9 | 66 | 49 |
| 76 | Iceland | 71314.8 | 86 | 78 |
| 77 | India | 1981.5 | 79 | 23 |
| 78 | Indonesia | 3836.9 | 82 | 52 |
| 79 | Iran (Islamic Republic of) | 5627.7 | 64 | 15 |
| 80 | Iraq | 5143.7 | 72 | 11 |
| 81 | Ireland | 68941.8 | 69 | 56 |
| 82 | Israel | 40543.6 | 68 | 60 |
| 83 | Italy | 32155.2 | 59 | 41 |
| 84 | Jamaica | 5060.5 | 73 | 61 |
| 88 | Kenya | 1568.2 | 78 | 71 |
| 91 | Kyrgyzstan | 1242.8 | 75 | 46 |
| 92 | Lao People’s Democratic Republic | 2423.8 | 45 | 37 |
| 93 | Latvia | 15684.6 | 68 | 56 |
| 96 | Liberia | 698.7 | 82 | 72 |
| 99 | Lithuania | 16809.6 | 68 | 57 |
| 100 | Luxembourg | 104498.7 | 64 | 56 |
| 101 | Madagascar | 448.4 | 89 | 84 |
| 102 | Malawi | 356.7 | 60 | 50 |
| 103 | Malaysia | 10117.6 | 80 | 54 |
| 104 | Maldives | 9801.6 | 78 | 47 |
| 105 | Mali | 828.6 | 81 | 55 |
| 106 | Malta | 27241.1 | 70 | 49 |
| 108 | Mauritania | 1161.8 | 63 | 28 |
| 110 | Mexico | 9281.1 | 77 | 43 |
| 113 | Mongolia | 3671.9 | 66 | 54 |
| 115 | Morocco | 3036.2 | 74 | 25 |
| 116 | Mozambique | 441.6 | 80 | 78 |
| 117 | Myanmar | 1249.8 | 77 | 48 |
| 118 | Namibia | 5646.5 | 63 | 55 |
| 120 | Nepal | 900.6 | 54 | 26 |
| 121 | Netherlands | 48482.8 | 70 | 59 |
| 122 | New Zealand | 42260.1 | 76 | 65 |
| 123 | Nicaragua | 2168.2 | 83 | 48 |
| 124 | Niger | 375.9 | 86 | 63 |
| 125 | Nigeria | 1968.6 | 56 | 49 |
| 126 | North Macedonia | 5417.6 | 67 | 43 |
| 127 | Norway | 75704.2 | 66 | 62 |
| 128 | Oman | 15170.4 | 88 | 30 |
| 129 | Pakistan | 1466.8 | 80 | 22 |
| 131 | Panama | 15166.1 | 76 | 50 |
| 132 | Papua New Guinea | 2640.2 | 49 | 48 |
| 133 | Paraguay | 5680.6 | 84 | 59 |
| 134 | Peru | 6700.8 | 83 | 69 |
| 135 | Philippines | 2981.9 | 72 | 45 |
| 136 | Poland | 13861.1 | 65 | 48 |
| 137 | Portugal | 21291.4 | 64 | 54 |
| 138 | Qatar | 61264.4 | 96 | 59 |
| 139 | Republic of Korea | 29742.8 | 74 | 53 |
| 140 | Republic of Moldova | 2724.5 | 46 | 41 |
| 141 | Romania | 10793.0 | 65 | 46 |
| 142 | Russian Federation | 10750.6 | 71 | 56 |
| 143 | Rwanda | 762.5 | 61 | 44 |
| 145 | Samoa | 4307.8 | 55 | 31 |
| 149 | Senegal | 1367.2 | 58 | 35 |
| 150 | Serbia | 6284.2 | 63 | 47 |
| 152 | Sierra Leone | 499.4 | 58 | 56 |
| 154 | Slovakia | 17579.3 | 68 | 52 |
| 155 | Slovenia | 23449.6 | 64 | 54 |
| 158 | South Africa | 6120.5 | 62 | 48 |
| 160 | Spain | 28208.3 | 64 | 52 |
| 161 | Sri Lanka | 4104.6 | 75 | 36 |
| 163 | Sudan | 3015.0 | 70 | 28 |
| 165 | Sweden | 53253.5 | 76 | 71 |
| 166 | Switzerland | 80333.4 | 74 | 63 |
| 167 | Tajikistan | 806.0 | 56 | 29 |
| 168 | Thailand | 6578.2 | 77 | 60 |
| 169 | Timor-Leste | 2000.6 | 73 | 61 |
| 170 | Togo | 619.1 | 49 | 61 |
| 173 | Tunisia | 3494.3 | 70 | 26 |
| 178 | Uganda | 631.5 | 62 | 45 |
| 179 | Ukraine | 2640.7 | 69 | 57 |
| 181 | United Kingdom | 39932.1 | 68 | 58 |
| 182 | United Republic of Tanzania | 1004.8 | 88 | 80 |
| 183 | United States | 59927.9 | 69 | 57 |
| 184 | Uruguay | 16437.2 | 72 | 56 |
| 186 | Vanuatu | 2976.1 | 78 | 60 |
| 187 | Venezuela (Bolivarian Republic of) | 16054.5 | 69 | 45 |
| 188 | Viet Nam | 2365.6 | 81 | 72 |
| 189 | Yemen | 963.5 | 65 | 6 |
| 190 | Zambia | 1534.9 | 45 | 28 |
The correlation between the women’s representation in the economy and GDP of a country is calculated. There is a correlation between women’s involvement in the economy and a strong GDP.
correlate<-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_economyAnalysis 2:Bivariate analysis of nutritious food consumption and children’s mortality rate:
This analysis will quantify the relatinship between nutrituous food consumption and low birth rate.
This analysis includes datasets nutrition datasets from The State of the World’s Children 2019 Statistical Tables.
I have chosen the discussion dataset suggested by Maria Alejandra Ginorio, named Nutrition:Newborn,Infant,and Young children from UNICEF’s State of the World’s Children 2019 Statistical Tables.
Children_Nutrition_UNICEF<-read_csv("https://raw.githubusercontent.com/maliat-hossain/UNICEF-Nutrition-Data/main/Children%20Nutrition%20Facts.csv", locale = readr::locale(encoding = "latin1"))## 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]##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_character(),
## X1 = col_logical(),
## X27 = col_logical(),
## X28 = col_logical(),
## X29 = col_logical(),
## X30 = col_logical(),
## X31 = col_logical(),
## X32 = col_logical(),
## X33 = col_logical(),
## X34 = col_logical(),
## X35 = col_logical()
## )
## i Use `spec()` for the full column specifications.Helpful column names are assigned to required columns for the analysis and required columns are selected using dplyr.
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)%>%
kbl() %>%
kable_material(c("striped"))| NameoftheCountries | Low_Birthweight | Introduction_to_Solid_SemiSolid_soft_foods | Zero_Vegetable_or_Fruit_Consumption |
|---|---|---|---|
| NA | NA | NA | NA |
| Countries and areas | Weight at birth | NA | NA |
| NA | Low birthweight (%) 2015 | Introduction to solid, semi-solid or soft foods (6<U+0096>8 months) (%) | Zero vegetable or fruit consumption (6<U+0096>23 months) (%) |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| Afghanistan | <U+0096> | 61 | 59 |
| Albania | 5 | 89 | 26 |
| Algeria | 7 | 77 | <U+0096> |
| Andorra | 7 | <U+0096> | <U+0096> |
| Angola | 15 | 79 | 36 |
| Anguilla | <U+0096> | <U+0096> | <U+0096> |
| Antigua and Barbuda | 9 | <U+0096> | <U+0096> |
| Argentina | 7 | 97 | <U+0096> |
| Armenia | 9 | 90 | 22 |
| Australia | 7 | <U+0096> | <U+0096> |
| Austria | 7 | <U+0096> | <U+0096> |
| Azerbaijan | 7 | 77 | 38 |
| Bahamas | 13 | <U+0096> | <U+0096> |
| Bahrain | 12 | <U+0096> | <U+0096> |
| Bangladesh | 28 | 65 | 53 |
| Barbados | <U+0096> | 90 | <U+0096> |
| Belarus | 5 | 95 | <U+0096> |
| Belgium | 7 | <U+0096> | <U+0096> |
| Belize | 9 | 79 | 30 |
| Benin | 17 | 56 | 54 |
| Bhutan | 12 | 87 | <U+0096> |
| Bolivia (Plurinational State of) | 7 | 81 | 20 |
| Bosnia and Herzegovina | 3 | 76 | <U+0096> |
| Botswana | 16 | 73 | <U+0096> |
| Brazil | 8 | 94 | <U+0096> |
| British Virgin Islands | <U+0096> | <U+0096> | <U+0096> |
| Brunei Darussalam | 11 | <U+0096> | <U+0096> |
| Bulgaria | 10 | <U+0096> | <U+0096> |
| Burkina Faso | 13 | 75 | 75 |
| Burundi | 15 | 86 | 9 |
| Cabo Verde | <U+0096> | <U+0096> | <U+0096> |
| Cambodia | 12 | 82 | 35 |
| Cameroon | 12 | 83 | 42 |
| Canada | 6 | <U+0096> | <U+0096> |
| Central African Republic | 15 | 94 | 22 |
| Chad | <U+0096> | 59 | 70 |
| Chile | 6 | <U+0096> | <U+0096> |
| China | 5 | 83 | <U+0096> |
| Colombia | 10 | 78 | <U+0096> |
| Comoros | 24 | 80 | 52 |
| Congo | 12 | 84 | 51 |
| Cook Islands | 3 | <U+0096> | <U+0096> |
| Costa Rica | 7 | 90 | <U+0096> |
| Côte d’Ivoire | 15 | 65 | 44 |
| Croatia | 5 | <U+0096> | <U+0096> |
| Cuba | 5 | 91 | 27 |
| Cyprus | <U+0096> | <U+0096> | <U+0096> |
| Czechia | 8 | <U+0096> | <U+0096> |
| Democratic People’s Republic of Korea | <U+0096> | 78 | <U+0096> |
| Democratic Republic of the Congo | 11 | 79 | 29 |
| Denmark | 5 | <U+0096> | <U+0096> |
| Djibouti | <U+0096> | <U+0096> | <U+0096> |
| Dominica | <U+0096> | <U+0096> | <U+0096> |
| Dominican Republic | 11 | 81 | 35 |
| Ecuador | 11 | 74 | <U+0096> |
| Egypt | <U+0096> | 75 | 45 |
| El Salvador | 10 | 90 | 16 |
| Equatorial Guinea | <U+0096> | <U+0096> | <U+0096> |
| Eritrea | <U+0096> | 44 | <U+0096> |
| Estonia | 4 | <U+0096> | <U+0096> |
| Eswatini | 10 | 90 | <U+0096> |
| Ethiopia | <U+0096> | 60 | 69 |
| Fiji | <U+0096> | <U+0096> | <U+0096> |
| Finland | 4 | <U+0096> | <U+0096> |
| France | 7 | <U+0096> | <U+0096> |
| Gabon | 14 | 82 | 52 |
| Gambia | 17 | 55 | 76 |
| Georgia | 6 | 85 | <U+0096> |
| Germany | 7 | <U+0096> | <U+0096> |
| Ghana | 14 | 73 | 51 |
| Greece | 9 | <U+0096> | <U+0096> |
| Grenada | <U+0096> | <U+0096> | <U+0096> |
| Guatemala | 11 | 80 | 27 |
| Guinea | <U+0096> | 54 | 85 |
| Guinea-Bissau | 21 | 57 | 44 |
| Guyana | 16 | 81 | 33 |
| Haiti | <U+0096> | 91 | 55 |
| Holy See | <U+0096> | <U+0096> | <U+0096> |
| Honduras | 11 | 86 | 36 |
| Hungary | 9 | <U+0096> | <U+0096> |
| Iceland | 4 | <U+0096> | <U+0096> |
| India | <U+0096> | 46 | 55 |
| Indonesia | 10 | 86 | 18 |
| Iran (Islamic Republic of) | <U+0096> | 76 | <U+0096> |
| Iraq | <U+0096> | 85 | 25 |
| Ireland | 6 | <U+0096> | <U+0096> |
| Israel | 8 | <U+0096> | <U+0096> |
| Italy | 7 | <U+0096> | <U+0096> |
| Jamaica | 15 | 64 | <U+0096> |
| Japan | 9 | <U+0096> | <U+0096> |
| Jordan | 14 | 83 | 41 |
| Kazakhstan | 5 | 66 | 21 |
| Kenya | 11 | 80 | 29 |
| Kiribati | <U+0096> | 70 | <U+0096> |
| Kuwait | 10 | <U+0096> | <U+0096> |
| Kyrgyzstan | 6 | 91 | 14 |
| Lao People’s Democratic Republic | 17 | 87 | 36 |
| Latvia | 5 | <U+0096> | <U+0096> |
| Lebanon | 9 | <U+0096> | <U+0096> |
| Lesotho | 15 | 83 | 50 |
| Liberia | <U+0096> | 47 | 39 |
| Libya | <U+0096> | <U+0096> | <U+0096> |
| Liechtenstein | <U+0096> | <U+0096> | <U+0096> |
| Lithuania | 5 | <U+0096> | <U+0096> |
| Luxembourg | 7 | <U+0096> | <U+0096> |
| Madagascar | 17 | 90 | 33 |
| Malawi | 14 | 85 | 23 |
| Malaysia | 11 | <U+0096> | <U+0096> |
| Maldives | 12 | 97 | 15 |
| Mali | <U+0096> | 42 | 70 |
| Malta | 6 | <U+0096> | <U+0096> |
| Marshall Islands | <U+0096> | 64 | 46 |
| Mauritania | <U+0096> | 74 | 51 |
| Mauritius | 17 | <U+0096> | <U+0096> |
| Mexico | 8 | 82 | 18 |
| Micronesia (Federated States of) | <U+0096> | <U+0096> | <U+0096> |
| Monaco | 5 | <U+0096> | <U+0096> |
| Mongolia | 5 | 97 | 37 |
| Montenegro | 5 | 95 | 9 |
| Montserrat | <U+0096> | <U+0096> | <U+0096> |
| Morocco | 17 | 84 | <U+0096> |
| Mozambique | 14 | 95 | 36 |
| Myanmar | 12 | 75 | 56 |
| Namibia | 16 | 80 | 52 |
| Nauru | <U+0096> | <U+0096> | <U+0096> |
| Nepal | 22 | 84 | 38 |
| Netherlands | 6 | <U+0096> | <U+0096> |
| New Zealand | 6 | <U+0096> | <U+0096> |
| Nicaragua | 11 | 89 | <U+0096> |
| Niger | <U+0096> | 62 | 67 |
| Nigeria | <U+0096> | 66 | 32 |
| Niue | <U+0096> | <U+0096> | <U+0096> |
| North Macedonia | 9 | 87 | <U+0096> |
| Norway | 4 | <U+0096> | <U+0096> |
| Oman | 11 | 95 | <U+0096> |
| Pakistan | <U+0096> | 65 | 61 |
| Palau | <U+0096> | <U+0096> | <U+0096> |
| Panama | 10 | 78 | <U+0096> |
| Papua New Guinea | <U+0096> | <U+0096> | <U+0096> |
| Paraguay | 8 | 87 | 16 |
| Peru | 9 | 95 | 7 |
| Philippines | 20 | 89 | 22 |
| Poland | 6 | <U+0096> | <U+0096> |
| Portugal | 9 | <U+0096> | <U+0096> |
| Qatar | 7 | 74 | <U+0096> |
| Republic of Korea | 6 | <U+0096> | <U+0096> |
| Republic of Moldova | 5 | 75 | 10 |
| Romania | 8 | <U+0096> | <U+0096> |
| Russian Federation | 6 | <U+0096> | <U+0096> |
| Rwanda | 8 | 57 | 25 |
| Saint Kitts and Nevis | <U+0096> | <U+0096> | <U+0096> |
| Saint Lucia | <U+0096> | <U+0096> | <U+0096> |
| Saint Vincent and the Grenadines | <U+0096> | <U+0096> | <U+0096> |
| Samoa | <U+0096> | 74 | <U+0096> |
| San Marino | 3 | <U+0096> | <U+0096> |
| Sao Tome and Principe | 7 | 74 | 27 |
| Saudi Arabia | <U+0096> | <U+0096> | <U+0096> |
| Senegal | 18 | 67 | 52 |
| Serbia | 5 | 97 | 3 |
| Seychelles | 12 | <U+0096> | <U+0096> |
| Sierra Leone | 14 | 68 | 41 |
| Singapore | 10 | <U+0096> | <U+0096> |
| Slovakia | 8 | <U+0096> | <U+0096> |
| Slovenia | 6 | <U+0096> | <U+0096> |
| Solomon Islands | <U+0096> | <U+0096> | <U+0096> |
| Somalia | <U+0096> | 17 | <U+0096> |
| South Africa | 14 | 83 | 37 |
| South Sudan | <U+0096> | 42 | <U+0096> |
| Spain | 8 | <U+0096> | <U+0096> |
| Sri Lanka | 16 | 88 | <U+0096> |
| State of Palestine | 8 | 90 | 27 |
| Sudan | <U+0096> | 61 | 67 |
| Suriname | 15 | 79 | <U+0096> |
| Sweden | 2 | <U+0096> | <U+0096> |
| Switzerland | 6 | <U+0096> | <U+0096> |
| Syrian Arab Republic | <U+0096> | 44 | <U+0096> |
| Tajikistan | 6 | 63 | 58 |
| Thailand | 11 | 85 | 22 |
| Timor-Leste | <U+0096> | 63 | 35 |
| Togo | 16 | 67 | 45 |
| Tokelau | <U+0096> | <U+0096> | <U+0096> |
| Tonga | <U+0096> | <U+0096> | <U+0096> |
| Trinidad and Tobago | 12 | 56 | <U+0096> |
| Tunisia | 7 | 97 | 20 |
| Turkey | 11 | 75 | <U+0096> |
| Turkmenistan | 5 | 82 | 9 |
| Turks and Caicos Islands | <U+0096> | <U+0096> | <U+0096> |
| Tuvalu | <U+0096> | <U+0096> | <U+0096> |
| Uganda | <U+0096> | 81 | 44 |
| Ukraine | 6 | 75 | <U+0096> |
| United Arab Emirates | 13 | <U+0096> | <U+0096> |
| United Kingdom | 7 | <U+0096> | <U+0096> |
| United Republic of Tanzania | 10 | 92 | 29 |
| United States | 8 | <U+0096> | <U+0096> |
| Uruguay | 8 | <U+0096> | <U+0096> |
| Uzbekistan | 5 | 47 | <U+0096> |
| Vanuatu | 11 | 72 | <U+0096> |
| Venezuela (Bolivarian Republic of) | 9 | <U+0096> | <U+0096> |
| Viet Nam | 8 | 91 | 14 |
| Yemen | <U+0096> | 69 | 66 |
| Zambia | 12 | 82 | 35 |
| Zimbabwe | 13 | 91 | 32 |
| NA | NA | NA | NA |
| SUMMARY | NA | NA | NA |
| East Asia and Pacific | 8 | 84 | 23 |
| Europe and Central Asia | 7 | <U+0096> | <U+0096> |
| Eastern Europe and Central Asia | 7 | 75 | <U+0096> |
| Western Europe | 7 | <U+0096> | <U+0096> |
| Latin America and Caribbean | 9 | 84 | 21 |
| Middle East and North Africa | 11 | 78 | 42 |
| North America | 8 | <U+0096> | <U+0096> |
| South Asia | 27 | 52 | 55 |
| Sub-Saharan Africa | 14 | 72 | 42 |
| Eastern and Southern Africa | 14 | 77 | 44 |
| West and Central Africa | 14 | 68 | 40 |
| Least developed countries | 16 | 72 | 49 |
| World | 15 | 69 | 44 |
| NA | NA | NA | NA |
| For a complete list of countries and areas in the regions, subregions and country categories, see page 182 or visit <data.unicef.org/regionalclassifications>. | NA | NA | NA |
| It is not advisable to compare data from consecutive editions of The State of the World<U+0092>s Children. | NA | NA | NA |
| NA | NA | NA | NA |
| NOTES | NA | NA | NA |
| <U+0096> Data not available. | NA | NA | NA |
| x Data refer to years or periods other than those specified in the column heading. Such data are not included in the calculation of regional and global averages. Estimates from years prior to 2000 are not displayed. | NA | NA | NA |
| p Based on small denominators (typically 25-49 unweighted cases). No data based on fewer than 25 unweighted cases are displayed. | NA | NA | NA |
| q Regional estimates for East Asia and Pacific exclude China, Latin America and the Caribbean exclude Brazil, Eastern Europe and Central Asia exclude the Russian Federation. | NA | NA | NA |
| r Disaggregated data are from different sources than the data presented for all children for the same indicator. | NA | NA | NA |
| zThe estimate is based on partial data for the most recent survey, therefore modeled estimates are not shown for the individual country but have been used in regional and global estimates. | NA | NA | NA |
|
NA | NA | NA |
| NA | NA | NA | NA |
| DEFINITIONS OF THE INDICATORS | NA | NA | NA |
| Low birthweight <U+0096> Percentage of infants weighing less than 2,500 grams at birth. | NA | NA | NA |
| Unweighed at birth <U+0096> Percentage of births without a birthweight in the data source; Note that (i) estimates from household surveys include live births among women age 15<U+0096>49 years in the survey reference period (e.g. last 2 years) for which a birthweight was not available from an official document (e.g. health card) or could not be recalled by the respondent at the time of interview and may have been recalculated to count birthweights <250g and >5500g as missing and (ii) estimates from administrative sources (e.g. Health Management Information Systems) were calculated using numerator data from the country administrative source and denominator data were the number of annual births according to the United Nations Population Division World Population Prospects, 2017 edition. These estimates include unweighed births and weighed births not recorded in the system. | NA | NA | NA |
| Early initiation of breastfeeding <U+0096> Percentage of children born in the last 24 months who were put to the breast within one hour of birth. | NA | NA | NA |
| Exclusive breastfeeding (<6 months) <U+0096> Percentage of infants 0-5 months of age who were fed exclusively with breastmilk during the previous day. | NA | NA | NA |
| Continued breastfeeding (12-23 months) <U+0096> Percentage of children 12-23 months of age who were fed with breastmilk during the previous day. | NA | NA | NA |
| Introduction of solid, semi-solid or soft foods (6<U+0096>8 months) <U+0096> Percentage of infants 6-8 months of age who were fed with solid, semi-solid or soft food during the previous day. | NA | NA | NA |
| Minimum Diet Diversity (6<U+0096>23 months) <U+0096> Percentage of children 6-23 months of age who received foods from at least 5 out of 8 defined food groups during the previous day. | NA | NA | NA |
| Minimum Meal Frequency (6<U+0096>23 months) <U+0096> Percentage of children 6-23 months of age who received solid, semi-solid, or soft foods (but also including milk feeds for non-breastfed children) the minimum number of times or more during the previous day. | NA | NA | NA |
| Minimum Acceptable Diet (6<U+0096>23 months) <U+0096> Percentage of children 6-23 months of age who received a minimum acceptable diet during the previous day. | NA | NA | NA |
| Zero vegetable or fruit consumption (6<U+0096>23 months) <U+0096> Percentage of children 6-23 months of age who did not consume any vegetables or fruits during the previous day. | NA | NA | NA |
| NA | NA | NA | NA |
| MAIN DATA SOURCES | NA | NA | NA |
| Low birthweight <U+0096> Modelled estimates from UNICEF and WHO. Last update: May 2019. | NA | NA | NA |
| Unweighed at birth <U+0096> Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), other national household surveys, data from routine reporting systems. Last update: June 2019. | NA | NA | NA |
| Infant and young child feeding (0-23 months) <U+0096> DHS, MICS and other national household surveys. Last update: June 2019. | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
Unnecessary rows are removed.
Children_Nutrition_UNICEF1<-anti_join(Children_Nutrition_UNICEF1,Children_Nutrition_UNICEF1[1:5,])## Joining, by = c("NameoftheCountries", "Low_Birthweight", "Introduction_to_Solid_SemiSolid_soft_foods", "Zero_Vegetable_or_Fruit_Consumption")Necessary rows containg countries name are selected.
Children_Nutrition_UNICEF2<-Children_Nutrition_UNICEF1[1:196,]head(Children_Nutrition_UNICEF2)%>%
knitr::kable(caption = "Table 4:Understanding of Nutritious Diet for Children and Low Birthweight ")%>%
kableExtra::kable_styling(bootstrap_options = "striped")| NameoftheCountries | Low_Birthweight | Introduction_to_Solid_SemiSolid_soft_foods | Zero_Vegetable_or_Fruit_Consumption |
|---|---|---|---|
| Afghanistan | <U+0096> | 61 | 59 |
| Albania | 5 | 89 | 26 |
| Algeria | 7 | 77 | <U+0096> |
| Andorra | 7 | <U+0096> | <U+0096> |
| Angola | 15 | 79 | 36 |
| Anguilla | <U+0096> | <U+0096> | <U+0096> |
Unnecessary punctuation is removed first transforming the values as numeric. After the transformation the punctuation is replaced as na. na values are omitted afterwords.
Children_Nutrition_UNICEF2$Low_Birthweight <- as.numeric(Children_Nutrition_UNICEF2$Low_Birthweight)## Warning: NAs introduced by coercionChildren_Nutrition_UNICEF2[!is.na(Children_Nutrition_UNICEF2$Low_Birthweight), ]## # A tibble: 142 x 4
## NameoftheCountri~ Low_Birthweight Introduction_to_Solid~ Zero_Vegetable_or_F~
## <chr> <dbl> <chr> <chr>
## 1 Albania 5 "89" "26"
## 2 Algeria 7 "77" "\u0096"
## 3 Andorra 7 "\u0096" "\u0096"
## 4 Angola 15 "79" "36"
## 5 Antigua and Barb~ 9 "\u0096" "\u0096"
## 6 Argentina 7 "97" "\u0096"
## 7 Armenia 9 "90" "22"
## 8 Australia 7 "\u0096" "\u0096"
## 9 Austria 7 "\u0096" "\u0096"
## 10 Azerbaijan 7 "77" "38"
## # ... with 132 more rowsChildren_Nutrition_UNICEF2$Introduction_to_Solid_SemiSolid_soft_foods<- as.numeric(Children_Nutrition_UNICEF2$Introduction_to_Solid_SemiSolid_soft_foods)## Warning: NAs introduced by coercionChildren_Nutrition_UNICEF2[!is.na(Children_Nutrition_UNICEF2$Introduction_to_Solid_SemiSolid_soft_foods), ]## # 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 rowsChildren_Nutrition_UNICEF2$Zero_Vegetable_or_Fruit_Consumption<- as.numeric(Children_Nutrition_UNICEF2$Zero_Vegetable_or_Fruit_Consumption)## 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 rowshead(Children_Nutrition_UNICEF2)## # 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 NAChildren_Nutrition_UNICEF2<-na.omit(Children_Nutrition_UNICEF2)
head(Children_Nutrition_UNICEF2)## # 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 30Only Latin Americans countries are selected for this analysis.After removing countries containg na values remaing Latin American countries are selected through filteration.
Countries_String<-"Belize|Cuba|Dominican Republic|El Salvador|Guatemala|Mexico|Paraguay|Peru"
Latin_America_Children_Nutrition<-Children_Nutrition_UNICEF2 %>%
filter(str_detect(NameoftheCountries,Countries_String))view(Latin_America_Children_Nutrition)%>%
knitr::kable(caption = "Table 5:Analysis of Latin American Children's Low Birthweight and Nutritious Diet ")%>%
kableExtra::kable_styling(bootstrap_options = "striped")| NameoftheCountries | Low_Birthweight | Introduction_to_Solid_SemiSolid_soft_foods | Zero_Vegetable_or_Fruit_Consumption |
|---|---|---|---|
| Belize | 9 | 79 | 30 |
| Cuba | 5 | 91 | 27 |
| Dominican Republic | 11 | 81 | 35 |
| El Salvador | 10 | 90 | 16 |
| Guatemala | 11 | 80 | 27 |
| Mexico | 8 | 82 | 18 |
| Paraguay | 8 | 87 | 16 |
| Peru | 9 | 95 | 7 |
The pearson’s correlation between the nutrious food and low birthweight is calculated to measure the bivariate relatiship between low birthweight and nutritious food consumption in the Latin American countries.However, the correlation is not strong enough.
correlate1<-data.frame(Latin_America_Children_Nutrition$Low_Birthweight,Latin_America_Children_Nutrition$Zero_Vegetable_or_Fruit_Consumption)
correlate3<-rcorr(as.matrix(correlate1))
correlate3## Latin_America_Children_Nutrition.Low_Birthweight
## Latin_America_Children_Nutrition.Low_Birthweight 1.00
## Latin_America_Children_Nutrition.Zero_Vegetable_or_Fruit_Consumption 0.16
## Latin_America_Children_Nutrition.Zero_Vegetable_or_Fruit_Consumption
## Latin_America_Children_Nutrition.Low_Birthweight 0.16
## Latin_America_Children_Nutrition.Zero_Vegetable_or_Fruit_Consumption 1.00
##
## n= 8
##
##
## P
## Latin_America_Children_Nutrition.Low_Birthweight
## Latin_America_Children_Nutrition.Low_Birthweight
## Latin_America_Children_Nutrition.Zero_Vegetable_or_Fruit_Consumption 0.7086
## Latin_America_Children_Nutrition.Zero_Vegetable_or_Fruit_Consumption
## Latin_America_Children_Nutrition.Low_Birthweight 0.7086
## Latin_America_Children_Nutrition.Zero_Vegetable_or_Fruit_Consumptionggplot(data =Latin_America_Children_Nutrition,aes(y=Low_Birthweight,x=Zero_Vegetable_or_Fruit_Consumption) )+
geom_point()
correlate4<-data.frame(Latin_America_Children_Nutrition$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_foodsggplot(data =Latin_America_Children_Nutrition,aes(y=Low_Birthweight,x=Introduction_to_Solid_SemiSolid_soft_foods) )+
geom_point()
Analysis3:Bivariate relationship between Child Mortality Rate and GDP:
For this analysis Child mortality rate from the UNICEF’s The State of the World’s Children 2019 Statistical Tables is selected.
Child_Mortality_Rate<-read.csv("https://raw.githubusercontent.com/maliat-hossain/UNICEF-Datasets/main/Table-2-Child-Mortality-EN.csv")
head(Child_Mortality_Rate)%>%
kbl() %>%
kable_material(c("striped"))| TABLE.2..CHILD.MORTALITY | X | X.1 | X.2 | X.3 | X.4 | X.5 | X.6 | X.7 | X.8 | X.9 | X.10 | X.11 | X.12 | X.13 | X.14 | X.15 | X.16 | X.17 | X.18 | X.19 | X.20 | X.21 | X.22 | X.23 | X.24 | X.25 | X.26 | X.27 | X.28 | X.29 | X.30 | X.31 | X.32 | X.33 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | ||||||||||||||||||
| Countries and areas | Under-5 mortality rate (deaths per 1,000 live births) | NA | NA | NA | Annual rate of reduction in under-5 mortality rate (%) 2000–2018 | NA | Under-5 mortality rate by sex (deaths per 1,000 live births) 2018 | NA | NA | Infant mortality rate (deaths per 1,000 live births) | NA | NA | Neonatal mortality rate (deaths per 1,000 live births) | NA | NA | NA | Probability of dying among children aged 5–14 (deaths per 1,000 children aged 5) | NA | NA | Annual number of under-5 deaths (thousands) 2018 | NA | Annual number of neonatal deaths (thousands) 2018 | NA | Neonatal deaths as proportion of all under-5 deaths (%) 2018 | NA | Number of deaths among children aged 5–14 (thousands) 2018 | NA | |||||||
| 1990 | NA | 2000 | NA | 2018 | NA | NA | male | NA | female | NA | 1990 | NA | 2018 | NA | 1990 | NA | 2000 | NA | 2018 | NA | 1990 | NA | 2018 | NA | NA | NA | NA | NA | ||||||
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | ||||||||||||||||||
| Afghanistan | 179 | NA | 129 | NA | 62 | NA | 4.1 | NA | 66 | NA | 59 | NA | 121 | NA | 48 | NA | 75 | NA | 61 | NA | 37 | NA | 16 | NA | 5 | NA | 74 | NA | 45 | NA | 60 | NA | 5 | NA |
| Albania | 41 | NA | 26 | NA | 9 | NA | 6.0 | NA | 9 | NA | 8 | NA | 35 | NA | 8 | NA | 13 | NA | 12 | NA | 7 | NA | 7 | NA | 2 | NA | 0 | NA | 0 | NA | 74 | NA | 0 | NA |
Required columns are selected and named. Children’s mortality rate from the year of 2018 is selected.
names(Child_Mortality_Rate)[1] <- "NameofCountries1"
names(Child_Mortality_Rate)[6] <- "mortality_rate"
head(Child_Mortality_Rate)%>%
knitr::kable(caption = "Table 6:Children's Mortality Rate ")%>%
kableExtra::kable_styling(bootstrap_options = "striped")| NameofCountries1 | X | X.1 | X.2 | X.3 | mortality_rate | X.5 | X.6 | X.7 | X.8 | X.9 | X.10 | X.11 | X.12 | X.13 | X.14 | X.15 | X.16 | X.17 | X.18 | X.19 | X.20 | X.21 | X.22 | X.23 | X.24 | X.25 | X.26 | X.27 | X.28 | X.29 | X.30 | X.31 | X.32 | X.33 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | ||||||||||||||||||
| Countries and areas | Under-5 mortality rate (deaths per 1,000 live births) | NA | NA | NA | Annual rate of reduction in under-5 mortality rate (%) 2000–2018 | NA | Under-5 mortality rate by sex (deaths per 1,000 live births) 2018 | NA | NA | Infant mortality rate (deaths per 1,000 live births) | NA | NA | Neonatal mortality rate (deaths per 1,000 live births) | NA | NA | NA | Probability of dying among children aged 5–14 (deaths per 1,000 children aged 5) | NA | NA | Annual number of under-5 deaths (thousands) 2018 | NA | Annual number of neonatal deaths (thousands) 2018 | NA | Neonatal deaths as proportion of all under-5 deaths (%) 2018 | NA | Number of deaths among children aged 5–14 (thousands) 2018 | NA | |||||||
| 1990 | NA | 2000 | NA | 2018 | NA | NA | male | NA | female | NA | 1990 | NA | 2018 | NA | 1990 | NA | 2000 | NA | 2018 | NA | 1990 | NA | 2018 | NA | NA | NA | NA | NA | ||||||
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | ||||||||||||||||||
| Afghanistan | 179 | NA | 129 | NA | 62 | NA | 4.1 | NA | 66 | NA | 59 | NA | 121 | NA | 48 | NA | 75 | NA | 61 | NA | 37 | NA | 16 | NA | 5 | NA | 74 | NA | 45 | NA | 60 | NA | 5 | NA |
| Albania | 41 | NA | 26 | NA | 9 | NA | 6.0 | NA | 9 | NA | 8 | NA | 35 | NA | 8 | NA | 13 | NA | 12 | NA | 7 | NA | 7 | NA | 2 | NA | 0 | NA | 0 | NA | 74 | NA | 0 | NA |
RefChild_Mortality_Rate<-Child_Mortality_Rate %>% dplyr::select(1, 6)
view(RefChild_Mortality_Rate)%>%
kbl() %>%
kable_material(c("striped"))| NameofCountries1 | mortality_rate |
|---|---|
| Countries and areas | |
| 2018 | |
| Afghanistan | 62 |
| Albania | 9 |
| Algeria | 23 |
| Andorra | 3 |
| Angola | 77 |
| Anguilla | ? |
| Antigua and Barbuda | 6 |
| Argentina | 10 |
| Armenia | 12 |
| Australia | 4 |
| Austria | 4 |
| Azerbaijan | 22 |
| Bahamas | 10 |
| Bahrain | 7 |
| Bangladesh | 30 |
| Barbados | 12 |
| Belarus | 3 |
| Belgium | 4 |
| Belize | 13 |
| Benin | 93 |
| Bhutan | 30 |
| Bolivia (Plurinational State of) | 27 |
| Bosnia and Herzegovina | 6 |
| Botswana | 36 |
| Brazil | 14 |
| British Virgin Islands | ? |
| Brunei Darussalam | 12 |
| Bulgaria | 7 |
| Burkina Faso | 76 |
| Burundi | 58 |
| Cabo Verde | 19 |
| Cambodia | 28 |
| Cameroon | 76 |
| Canada | 5 |
| Central African Republic | 116 |
| Chad | 119 |
| Chile | 7 |
| China | 9 |
| Colombia | 14 |
| Comoros | 67 |
| Congo | 50 |
| Cook Islands | 8 |
| Costa Rica | 9 |
| Côte d’Ivoire | 81 |
| Croatia | 5 |
| Cuba | 5 |
| Cyprus | 2 |
| Czechia | 3 |
| Democratic People’s Republic of Korea | 18 |
| Democratic Republic of the Congo | 88 |
| Denmark | 4 |
| Djibouti | 59 |
| Dominica | 36 |
| Dominican Republic | 29 |
| Ecuador | 14 |
| Egypt | 21 |
| El Salvador | 14 |
| Equatorial Guinea | 85 |
| Eritrea | 42 |
| Estonia | 3 |
| Eswatini | 54 |
| Ethiopia | 55 |
| Fiji | 26 |
| Finland | 2 |
| France | 4 |
| Gabon | 45 |
| Gambia | 58 |
| Georgia | 10 |
| Germany | 4 |
| Ghana | 48 |
| Greece | 4 |
| Grenada | 15 |
| Guatemala | 26 |
| Guinea | 101 |
| Guinea-Bissau | 81 |
| Guyana | 30 |
| Haiti | 65 |
| Holy See | ? |
| Honduras | 18 |
| Hungary | 4 |
| Iceland | 2 |
| India | 37 |
| Indonesia | 25 |
| Iran (Islamic Republic of) | 14 |
| Iraq | 27 |
| Ireland | 4 |
| Israel | 4 |
| Italy | 3 |
| Jamaica | 14 |
| Japan | 2 |
| Jordan | 16 |
| Kazakhstan | 10 |
| Kenya | 41 |
| Kiribati | 53 |
| Kuwait | 8 |
| Kyrgyzstan | 19 |
| Lao People’s Democratic Republic | 47 |
| Latvia | 4 |
| Lebanon | 7 |
| Lesotho | 81 |
| Liberia | 71 |
| Libya | 12 |
| Liechtenstein | ? |
| Lithuania | 4 |
| Luxembourg | 2 |
| Madagascar | 54 |
| Malawi | 50 |
| Malaysia | 8 |
| Maldives | 9 |
| Mali | 98 |
| Malta | 7 |
| Marshall Islands | 33 |
| Mauritania | 76 |
| Mauritius | 16 |
| Mexico | 13 |
| Micronesia (Federated States of) | 31 |
| Monaco | 3 |
| Mongolia | 16 |
| Montenegro | 3 |
| Montserrat | ? |
| Morocco | 22 |
| Mozambique | 73 |
| Myanmar | 46 |
| Namibia | 40 |
| Nauru | 32 |
| Nepal | 32 |
| Netherlands | 4 |
| New Zealand | 6 |
| Nicaragua | 18 |
| Niger | 84 |
| Nigeria | 120 |
| Niue | 24 |
| North Macedonia | 10 |
| Norway | 3 |
| Oman | 11 |
| Pakistan | 69 |
| Palau | 18 |
| Panama | 15 |
| Papua New Guinea | 48 |
| Paraguay | 20 |
| Peru | 14 |
| Philippines | 28 |
| Poland | 4 |
| Portugal | 4 |
| Qatar | 7 |
| Republic of Korea | 3 |
| Republic of Moldova | 16 |
| Romania | 7 |
| Russian Federation | 7 |
| Rwanda | 35 |
| Saint Kitts and Nevis | 12 |
| Saint Lucia | 17 |
| Saint Vincent and the Grenadines | 16 |
| Samoa | 16 |
| San Marino | 2 |
| Sao Tome and Principe | 31 |
| Saudi Arabia | 7 |
| Senegal | 44 |
| Serbia | 6 |
| Seychelles | 14 |
| Sierra Leone | 105 |
| Singapore | 3 |
| Slovakia | 6 |
| Slovenia | 2 |
| Solomon Islands | 20 |
| Somalia | 122 |
| South Africa | 34 |
| South Sudan | 99 |
| Spain | 3 |
| Sri Lanka | 7 |
| State of Palestine | 20 |
| Sudan | 60 |
| Suriname | 19 |
| Sweden | 3 |
| Switzerland | 4 |
| Syrian Arab Republic | 17 |
| Tajikistan | 35 |
| Thailand | 9 |
| Timor-Leste | 46 |
| Togo | 70 |
| Tokelau | ? |
| Tonga | 16 |
| Trinidad and Tobago | 18 |
| Tunisia | 17 |
| Turkey | 11 |
| Turkmenistan | 46 |
| Turks and Caicos Islands | ? |
| Tuvalu | 24 |
| Uganda | 46 |
| Ukraine | 9 |
| United Arab Emirates | 8 |
| United Kingdom | 4 |
| United Republic of Tanzania | 53 |
| United States | 7 |
| Uruguay | 8 |
| Uzbekistan | 21 |
| Vanuatu | 26 |
| Venezuela (Bolivarian Republic of) | 25 |
| Viet Nam | 21 |
| Yemen | 55 |
| Zambia | 58 |
| Zimbabwe | 46 |
| SUMMARY | |
| East Asia and Pacific | 15 |
| Europe and Central Asia | 9 |
| Eastern Europe and Central Asia | 13 |
| Western Europe | 4 |
| Latin America and Caribbean | 16 |
| Middle East and North Africa | 22 |
| North America | 6 |
| South Asia | 42 |
| Sub-Saharan Africa | 78 |
| Eastern and Southern Africa | 57 |
| West and Central Africa | 97 |
| Least developed countries | 64 |
| World | 39 |
| For a complete list of countries and areas in the regions, subregions and country categories, see page 182 or visit <data.unicef.org/regionalclassifications>. | |
| It is not advisable to compare data from consecutive editions of The State of the World’s Children. | |
| NOTES | |
| ? Data not available. | |
| DEFINITIONS OF THE INDICATORS | |
| Under-5 mortality rate – Probability of dying between birth and exactly 5 years of age, expressed per 1,000 live births. | |
| Infant mortality rate – Probability of dying between birth and exactly 1 year of age, expressed per 1,000 live births. | |
| Neonatal mortality rate – Probability of dying during the first 28 days of life, expressed per 1,000 live births. | |
| Probability of dying among children aged 5–14 – Probability of dying at age 5–14 years expressed per 1,000 children aged 5. | |
| MAIN DATA SOURCES | |
| 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. | |
| 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. | |
##RefChild_Mortality_Rate%>%
## kbl() %>%
##kable_material(c("striped"))### removing unnecessary rows
RefChild_Mortality_Rate<-anti_join(RefChild_Mortality_Rate,RefChild_Mortality_Rate[1:3,])## Joining, by = c("NameofCountries1", "mortality_rate")head(RefChild_Mortality_Rate)%>%
kbl() %>%
kable_material(c("striped"))| NameofCountries1 | mortality_rate |
|---|---|
| Afghanistan | 62 |
| Albania | 9 |
| Algeria | 23 |
| Andorra | 3 |
| Angola | 77 |
| Anguilla | ? |
puncuations from the datasets are removed.
RefChild_Mortality_Rate$mortality_rate<-as.numeric(RefChild_Mortality_Rate$mortality_rate)## Warning: NAs introduced by coercionRefChild_Mortality_Rate[!is.na(RefChild_Mortality_Rate$mortality_rate), ]## 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 39view(RefChild_Mortality_Rate)%>%
knitr::kable(caption = "Table 6:Children's Mortality rate around the world in 2018 ")%>%
kableExtra::kable_styling(bootstrap_options = "striped")| NameofCountries1 | mortality_rate |
|---|---|
| Afghanistan | 62 |
| Albania | 9 |
| Algeria | 23 |
| Andorra | 3 |
| Angola | 77 |
| Anguilla | NA |
| Antigua and Barbuda | 6 |
| Argentina | 10 |
| Armenia | 12 |
| Australia | 4 |
| Austria | 4 |
| Azerbaijan | 22 |
| Bahamas | 10 |
| Bahrain | 7 |
| Bangladesh | 30 |
| Barbados | 12 |
| Belarus | 3 |
| Belgium | 4 |
| Belize | 13 |
| Benin | 93 |
| Bhutan | 30 |
| Bolivia (Plurinational State of) | 27 |
| Bosnia and Herzegovina | 6 |
| Botswana | 36 |
| Brazil | 14 |
| British Virgin Islands | NA |
| Brunei Darussalam | 12 |
| Bulgaria | 7 |
| Burkina Faso | 76 |
| Burundi | 58 |
| Cabo Verde | 19 |
| Cambodia | 28 |
| Cameroon | 76 |
| Canada | 5 |
| Central African Republic | 116 |
| Chad | 119 |
| Chile | 7 |
| China | 9 |
| Colombia | 14 |
| Comoros | 67 |
| Congo | 50 |
| Cook Islands | 8 |
| Costa Rica | 9 |
| Côte d’Ivoire | 81 |
| Croatia | 5 |
| Cuba | 5 |
| Cyprus | 2 |
| Czechia | 3 |
| Democratic People’s Republic of Korea | 18 |
| Democratic Republic of the Congo | 88 |
| Denmark | 4 |
| Djibouti | 59 |
| Dominica | 36 |
| Dominican Republic | 29 |
| Ecuador | 14 |
| Egypt | 21 |
| El Salvador | 14 |
| Equatorial Guinea | 85 |
| Eritrea | 42 |
| Estonia | 3 |
| Eswatini | 54 |
| Ethiopia | 55 |
| Fiji | 26 |
| Finland | 2 |
| France | 4 |
| Gabon | 45 |
| Gambia | 58 |
| Georgia | 10 |
| Germany | 4 |
| Ghana | 48 |
| Greece | 4 |
| Grenada | 15 |
| Guatemala | 26 |
| Guinea | 101 |
| Guinea-Bissau | 81 |
| Guyana | 30 |
| Haiti | 65 |
| Holy See | NA |
| Honduras | 18 |
| Hungary | 4 |
| Iceland | 2 |
| India | 37 |
| Indonesia | 25 |
| Iran (Islamic Republic of) | 14 |
| Iraq | 27 |
| Ireland | 4 |
| Israel | 4 |
| Italy | 3 |
| Jamaica | 14 |
| Japan | 2 |
| Jordan | 16 |
| Kazakhstan | 10 |
| Kenya | 41 |
| Kiribati | 53 |
| Kuwait | 8 |
| Kyrgyzstan | 19 |
| Lao People’s Democratic Republic | 47 |
| Latvia | 4 |
| Lebanon | 7 |
| Lesotho | 81 |
| Liberia | 71 |
| Libya | 12 |
| Liechtenstein | NA |
| Lithuania | 4 |
| Luxembourg | 2 |
| Madagascar | 54 |
| Malawi | 50 |
| Malaysia | 8 |
| Maldives | 9 |
| Mali | 98 |
| Malta | 7 |
| Marshall Islands | 33 |
| Mauritania | 76 |
| Mauritius | 16 |
| Mexico | 13 |
| Micronesia (Federated States of) | 31 |
| Monaco | 3 |
| Mongolia | 16 |
| Montenegro | 3 |
| Montserrat | NA |
| Morocco | 22 |
| Mozambique | 73 |
| Myanmar | 46 |
| Namibia | 40 |
| Nauru | 32 |
| Nepal | 32 |
| Netherlands | 4 |
| New Zealand | 6 |
| Nicaragua | 18 |
| Niger | 84 |
| Nigeria | 120 |
| Niue | 24 |
| North Macedonia | 10 |
| Norway | 3 |
| Oman | 11 |
| Pakistan | 69 |
| Palau | 18 |
| Panama | 15 |
| Papua New Guinea | 48 |
| Paraguay | 20 |
| Peru | 14 |
| Philippines | 28 |
| Poland | 4 |
| Portugal | 4 |
| Qatar | 7 |
| Republic of Korea | 3 |
| Republic of Moldova | 16 |
| Romania | 7 |
| Russian Federation | 7 |
| Rwanda | 35 |
| Saint Kitts and Nevis | 12 |
| Saint Lucia | 17 |
| Saint Vincent and the Grenadines | 16 |
| Samoa | 16 |
| San Marino | 2 |
| Sao Tome and Principe | 31 |
| Saudi Arabia | 7 |
| Senegal | 44 |
| Serbia | 6 |
| Seychelles | 14 |
| Sierra Leone | 105 |
| Singapore | 3 |
| Slovakia | 6 |
| Slovenia | 2 |
| Solomon Islands | 20 |
| Somalia | 122 |
| South Africa | 34 |
| South Sudan | 99 |
| Spain | 3 |
| Sri Lanka | 7 |
| State of Palestine | 20 |
| Sudan | 60 |
| Suriname | 19 |
| Sweden | 3 |
| Switzerland | 4 |
| Syrian Arab Republic | 17 |
| Tajikistan | 35 |
| Thailand | 9 |
| Timor-Leste | 46 |
| Togo | 70 |
| Tokelau | NA |
| Tonga | 16 |
| Trinidad and Tobago | 18 |
| Tunisia | 17 |
| Turkey | 11 |
| Turkmenistan | 46 |
| Turks and Caicos Islands | NA |
| Tuvalu | 24 |
| Uganda | 46 |
| Ukraine | 9 |
| United Arab Emirates | 8 |
| United Kingdom | 4 |
| United Republic of Tanzania | 53 |
| United States | 7 |
| Uruguay | 8 |
| Uzbekistan | 21 |
| Vanuatu | 26 |
| Venezuela (Bolivarian Republic of) | 25 |
| Viet Nam | 21 |
| Yemen | 55 |
| Zambia | 58 |
| Zimbabwe | 46 |
| SUMMARY | NA |
| East Asia and Pacific | 15 |
| Europe and Central Asia | 9 |
| Eastern Europe and Central Asia | 13 |
| Western Europe | 4 |
| Latin America and Caribbean | 16 |
| Middle East and North Africa | 22 |
| North America | 6 |
| South Asia | 42 |
| Sub-Saharan Africa | 78 |
| Eastern and Southern Africa | 57 |
| West and Central Africa | 97 |
| Least developed countries | 64 |
| World | 39 |
| For a complete list of countries and areas in the regions, subregions and country categories, see page 182 or visit <data.unicef.org/regionalclassifications>. | NA |
| It is not advisable to compare data from consecutive editions of The State of the World’s Children. | NA |
| NOTES | NA |
| ? Data not available. | NA |
| DEFINITIONS OF THE INDICATORS | NA |
| Under-5 mortality rate – Probability of dying between birth and exactly 5 years of age, expressed per 1,000 live births. | NA |
| Infant mortality rate – Probability of dying between birth and exactly 1 year of age, expressed per 1,000 live births. | NA |
| Neonatal mortality rate – Probability of dying during the first 28 days of life, expressed per 1,000 live births. | NA |
| Probability of dying among children aged 5–14 – Probability of dying at age 5–14 years expressed per 1,000 children aged 5. | NA |
| MAIN DATA SOURCES | NA |
| 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. | NA |
| 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. | NA |
Two dataframes regarding children’s mortality rate and GDP information are joined.
fullJoinGDPANDMortalityRate <- full_join(RefEconomicIndicatorGDP1,RefChild_Mortality_Rate,by="NameofCountries1")
View(fullJoinGDPANDMortalityRate)na values are omitted. Required values from UNICEF member countries are sected. Summary values are excluded.
#fullJoinGDP<-na.omit(fullJoinGDP)
fullJoinGDPANDMortalityRate<-na.omit(fullJoinGDPANDMortalityRate)
AnalyzingGDPandMortalityRate<-fullJoinGDPANDMortalityRate[1:189,]
view(AnalyzingGDPandMortalityRate)%>%
knitr::kable(caption = "Table 7: Analyzing GDP of countriesChildren's Mortality rate around the world in 2018 ")%>%
kableExtra::kable_styling(bootstrap_options = "striped")| NameofCountries1 | GDP | mortality_rate | |
|---|---|---|---|
| 1 | Afghanistan | 556.3 | 62 |
| 2 | Albania | 4532.9 | 9 |
| 3 | Algeria | 4048.3 | 23 |
| 4 | Andorra | 39134.4 | 3 |
| 5 | Angola | 4095.8 | 77 |
| 6 | Antigua and Barbuda | 15824.7 | 6 |
| 7 | Argentina | 14591.9 | 10 |
| 8 | Armenia | 3914.5 | 12 |
| 9 | Australia | 54093.6 | 4 |
| 10 | Austria | 47380.8 | 4 |
| 11 | Azerbaijan | 4147.1 | 22 |
| 12 | Bahamas | 31857.9 | 10 |
| 13 | Bahrain | 23715.5 | 7 |
| 14 | Bangladesh | 1564.0 | 30 |
| 15 | Barbados | 16327.6 | 12 |
| 16 | Belarus | 5761.7 | 3 |
| 17 | Belgium | 43507.2 | 4 |
| 18 | Belize | 4956.8 | 13 |
| 19 | Benin | 827.4 | 93 |
| 20 | Bhutan | 3390.7 | 30 |
| 21 | Bolivia (Plurinational State of) | 3351.1 | 27 |
| 22 | Bosnia and Herzegovina | 5394.6 | 6 |
| 23 | Botswana | 7893.7 | 36 |
| 24 | Brazil | 9880.9 | 14 |
| 25 | Brunei Darussalam | 28572.1 | 12 |
| 26 | Bulgaria | 8228.0 | 7 |
| 27 | Burkina Faso | 642.0 | 76 |
| 28 | Burundi | 293.0 | 58 |
| 29 | Cabo Verde | 3295.3 | 19 |
| 30 | Cambodia | 1385.3 | 28 |
| 31 | Cameroon | 1421.6 | 76 |
| 32 | Canada | 45069.9 | 5 |
| 33 | Central African Republic | 471.6 | 116 |
| 34 | Chad | 664.3 | 119 |
| 35 | Chile | 15037.4 | 7 |
| 36 | China | 8759.0 | 9 |
| 37 | Colombia | 6375.9 | 14 |
| 38 | Comoros | 1312.4 | 67 |
| 39 | Congo | 1702.6 | 50 |
| 40 | Costa Rica | 11752.5 | 9 |
| 41 | Côte d’Ivoire | 1557.2 | 81 |
| 42 | Croatia | 13383.7 | 5 |
| 43 | Cuba | 8541.2 | 5 |
| 44 | Cyprus | 25760.8 | 2 |
| 45 | Czechia | 20379.9 | 3 |
| 46 | Democratic Republic of the Congo | 467.1 | 88 |
| 47 | Denmark | 57218.9 | 4 |
| 48 | Djibouti | 1953.9 | 59 |
| 49 | Dominica | 6951.3 | 36 |
| 50 | Dominican Republic | 7222.6 | 29 |
| 51 | Ecuador | 6213.5 | 14 |
| 52 | Egypt | 2440.5 | 21 |
| 53 | El Salvador | 3902.2 | 14 |
| 54 | Equatorial Guinea | 9738.4 | 85 |
| 55 | Eritrea | 811.4 | 42 |
| 56 | Estonia | 20200.4 | 3 |
| 57 | Eswatini | 3941.9 | 54 |
| 58 | Ethiopia | 768.0 | 55 |
| 59 | Fiji | 6006.4 | 26 |
| 60 | Finland | 45804.7 | 2 |
| 61 | France | 38679.1 | 4 |
| 62 | Gabon | 7212.5 | 45 |
| 63 | Gambia | 672.8 | 58 |
| 64 | Georgia | 4045.4 | 10 |
| 65 | Germany | 44681.1 | 4 |
| 66 | Ghana | 2025.9 | 48 |
| 67 | Greece | 18883.5 | 4 |
| 68 | Grenada | 10163.6 | 15 |
| 69 | Guatemala | 4470.6 | 26 |
| 70 | Guinea | 821.7 | 101 |
| 71 | Guinea-Bissau | 736.7 | 81 |
| 72 | Guyana | 4586.1 | 30 |
| 73 | Haiti | 765.6 | 65 |
| 74 | Honduras | 2432.9 | 18 |
| 75 | Hungary | 14278.9 | 4 |
| 76 | Iceland | 71314.8 | 2 |
| 77 | India | 1981.5 | 37 |
| 78 | Indonesia | 3836.9 | 25 |
| 79 | Iran (Islamic Republic of) | 5627.7 | 14 |
| 80 | Iraq | 5143.7 | 27 |
| 81 | Ireland | 68941.8 | 4 |
| 82 | Israel | 40543.6 | 4 |
| 83 | Italy | 32155.2 | 3 |
| 84 | Jamaica | 5060.5 | 14 |
| 85 | Japan | 38332.0 | 2 |
| 86 | Jordan | 4168.6 | 16 |
| 87 | Kazakhstan | 9030.3 | 10 |
| 88 | Kenya | 1568.2 | 41 |
| 89 | Kiribati | 1625.6 | 53 |
| 90 | Kuwait | 29474.5 | 8 |
| 91 | Kyrgyzstan | 1242.8 | 19 |
| 92 | Lao People’s Democratic Republic | 2423.8 | 47 |
| 93 | Latvia | 15684.6 | 4 |
| 94 | Lebanon | 7838.3 | 7 |
| 95 | Lesotho | 1232.8 | 81 |
| 96 | Liberia | 698.7 | 71 |
| 97 | Libya | 5792.1 | 12 |
| 99 | Lithuania | 16809.6 | 4 |
| 100 | Luxembourg | 104498.7 | 2 |
| 101 | Madagascar | 448.4 | 54 |
| 102 | Malawi | 356.7 | 50 |
| 103 | Malaysia | 10117.6 | 8 |
| 104 | Maldives | 9801.6 | 9 |
| 105 | Mali | 828.6 | 98 |
| 106 | Malta | 27241.1 | 7 |
| 107 | Marshall Islands | 3516.7 | 33 |
| 108 | Mauritania | 1161.8 | 76 |
| 109 | Mauritius | 10484.9 | 16 |
| 110 | Mexico | 9281.1 | 13 |
| 111 | Micronesia (Federated States of) | 3018.4 | 31 |
| 112 | Monaco | 166726.1 | 3 |
| 113 | Mongolia | 3671.9 | 16 |
| 114 | Montenegro | 7784.1 | 3 |
| 115 | Morocco | 3036.2 | 22 |
| 116 | Mozambique | 441.6 | 73 |
| 117 | Myanmar | 1249.8 | 46 |
| 118 | Namibia | 5646.5 | 40 |
| 119 | Nauru | 8844.4 | 32 |
| 120 | Nepal | 900.6 | 32 |
| 121 | Netherlands | 48482.8 | 4 |
| 122 | New Zealand | 42260.1 | 6 |
| 123 | Nicaragua | 2168.2 | 18 |
| 124 | Niger | 375.9 | 84 |
| 125 | Nigeria | 1968.6 | 120 |
| 126 | North Macedonia | 5417.6 | 10 |
| 127 | Norway | 75704.2 | 3 |
| 128 | Oman | 15170.4 | 11 |
| 129 | Pakistan | 1466.8 | 69 |
| 130 | Palau | 16274.9 | 18 |
| 131 | Panama | 15166.1 | 15 |
| 132 | Papua New Guinea | 2640.2 | 48 |
| 133 | Paraguay | 5680.6 | 20 |
| 134 | Peru | 6700.8 | 14 |
| 135 | Philippines | 2981.9 | 28 |
| 136 | Poland | 13861.1 | 4 |
| 137 | Portugal | 21291.4 | 4 |
| 138 | Qatar | 61264.4 | 7 |
| 139 | Republic of Korea | 29742.8 | 3 |
| 140 | Republic of Moldova | 2724.5 | 16 |
| 141 | Romania | 10793.0 | 7 |
| 142 | Russian Federation | 10750.6 | 7 |
| 143 | Rwanda | 762.5 | 35 |
| 144 | Saint Vincent and the Grenadines | 7149.6 | 16 |
| 145 | Samoa | 4307.8 | 16 |
| 146 | San Marino | 48494.6 | 2 |
| 147 | Sao Tome and Principe | 1811.0 | 31 |
| 148 | Saudi Arabia | 20803.7 | 7 |
| 149 | Senegal | 1367.2 | 44 |
| 150 | Serbia | 6284.2 | 6 |
| 151 | Seychelles | 15683.7 | 14 |
| 152 | Sierra Leone | 499.4 | 105 |
| 153 | Singapore | 60297.8 | 3 |
| 154 | Slovakia | 17579.3 | 6 |
| 155 | Slovenia | 23449.6 | 2 |
| 156 | Solomon Islands | 2077.1 | 20 |
| 157 | Somalia | 488.6 | 122 |
| 158 | South Africa | 6120.5 | 34 |
| 159 | South Sudan | 283.5 | 99 |
| 160 | Spain | 28208.3 | 3 |
| 161 | Sri Lanka | 4104.6 | 7 |
| 162 | State of Palestine | 3254.5 | 20 |
| 163 | Sudan | 3015.0 | 60 |
| 164 | Suriname | 5379.1 | 19 |
| 165 | Sweden | 53253.5 | 3 |
| 166 | Switzerland | 80333.4 | 4 |
| 167 | Tajikistan | 806.0 | 35 |
| 168 | Thailand | 6578.2 | 9 |
| 169 | Timor-Leste | 2000.6 | 46 |
| 170 | Togo | 619.1 | 70 |
| 171 | Tonga | 4217.5 | 16 |
| 172 | Trinidad and Tobago | 16076.1 | 18 |
| 173 | Tunisia | 3494.3 | 17 |
| 174 | Turkey | 10499.7 | 11 |
| 175 | Turkmenistan | 6587.1 | 46 |
| 177 | Tuvalu | 3572.6 | 24 |
| 178 | Uganda | 631.5 | 46 |
| 179 | Ukraine | 2640.7 | 9 |
| 180 | United Arab Emirates | 40325.4 | 8 |
| 181 | United Kingdom | 39932.1 | 4 |
| 182 | United Republic of Tanzania | 1004.8 | 53 |
| 183 | United States | 59927.9 | 7 |
| 184 | Uruguay | 16437.2 | 8 |
| 185 | Uzbekistan | 1826.6 | 21 |
| 186 | Vanuatu | 2976.1 | 26 |
| 187 | Venezuela (Bolivarian Republic of) | 16054.5 | 25 |
| 188 | Viet Nam | 2365.6 | 21 |
| 189 | Yemen | 963.5 | 55 |
| 190 | Zambia | 1534.9 | 58 |
| 191 | Zimbabwe | 1602.4 | 46 |
The bivariate relationship between GDP and children’s mortality rate is measured. However the relationship is not strong.However,It is really unfortunate to see the high amount of children mortality rate in many countries. I really hope one day it will be eradicated.
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_ratelibrary(ggplot2)
ggplot(data =AnalyzingGDPandMortalityRate,aes(y=AnalyzingGDPandMortalityRate$mortality_rate,x=AnalyzingGDPandMortalityRate$GDP) )+
geom_point()