Data 101 Final Project
Ivana Loffa
5/5/2022
Child Health and Well-Being Indicators Dataset
About this dataset..
WHO, UNICEF, and CAP 2030 developed this dataset intending to allow policymakers, governments, and organizations to easily monitor and analyze a variety of indicators by region, country, age group, domain, and income in order to improve adolescent and child health and well-being. It was created in support of the United Nations Convention on the Rights of the Child. There are 5516 rows and 23 variables in this dataset. It was last updated on April 2022. (This dataset was already cleaned, therefore I didn’t perform any major cleaning).
Source: https://data.unicef.org/resources/child-health-and-well-being-dashboard/
Why this dataset?
I really liked this dataset because of how dense it is. I enjoyed being able to explore many indicators within the same dataset. I also like that it was all very recent information. Additionally, these are important things to know and raise awareness of because children’s and adolescent’s well-being can help determine the health of the next generations and can help predict future public health challenges for families, communities, and the health care system.
Variables in this dataset
There are 20 categorical variables and 3 numeric variables in this dataset.
| Columns | Description |
|---|---|
| iso3 | iso3 code for each country |
| country | Official UN short country name in English |
| indicator | Full indicator name |
| indicator_id | Unique number for each indicator |
| age | Age categories |
| domain | Survival, Protection, Development Participation |
| disaggregator | Dimension of disaggregation |
| value | Estimate |
| total | Value for male-female combined, when available |
| units | Unit for the estimate |
| year | Year of the estimate |
| source | Source of the estimate |
| definition | Full indicator definition |
| indicator_cat | Progress category as a note for the popup |
| target | Global target, if available |
| progress | Descriptive progress category (“Good progress,” “Moderate progress,” |
| “Needs urgent attention”, for first three mortality indicators, | |
| “Target met,” “On-track,” “Acceleration needed”) | |
| region_sdg_name | SDG region |
| region_who_code | WHO region abbreviation |
| region_who_name | WHO region |
| region_unicef_code | UNICEF region abbreviation |
| region_unicef_name | UNICEF region |
| incomecat | WB FY22 Income Classification abbreviation |
| income | WB FY22 Income Classification |
What I explored:
- Neonatal mortality rate
- Child Labour
- Food insecurity
- Youth not in education or employment
- Mortality due to air pollution
Importing the dataset and Loading the libraries
library(readr)
Unicefdata <- read_csv("Unicefdata.csv")## Rows: 5516 Columns: 23
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (20): iso3, country, indicator, age, domain, disaggregator, total, units...
## dbl (3): indicator_id, value, year
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.#View(Unicefdata)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v dplyr 1.0.8
## v tibble 3.1.6 v stringr 1.4.0
## v tidyr 1.2.0 v forcats 0.5.1
## v purrr 0.3.4
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(dplyr)Exploration
There are 37 indicators of Child Health and Well Being, however, I want to focus only on certain indicators.
I want to focus on:
- Neonatal mortality rate
- Child Labour
- Food insecurity
- Youth not in education or employment
- Mortality due to air pollution
# Table for all the indicators in this dataset
table(Unicefdata$indicator)##
## % of government budget spent on health care
## 188
## Careseeking for fever
## 60
## Child labour
## 92
## Child mortality (1-4y)
## 195
## Clean fuels and technology
## 190
## Country has dedicated child rights/welfare act or law
## 142
## CRVS: Birth registration
## 214
## Developmentally on-track
## 98
## DTP3 vaccination
## 195
## Early initiation of breastfeeding
## 84
## Exclusive breastfeeding
## 83
## Food insecurity
## 121
## Heidelberg Conflict Barometer
## 132
## HPV vaccination
## 96
## International code for breastmilk substitutes
## 194
## Intimate partner violence
## 156
## Lower secondary school completion
## 242
## Maternity protection convention 183
## 165
## Mortality due to air pollution
## 183
## Mortality rate for 10-14y
## 195
## Mortality rate for 15-19y
## 195
## Mortality rate for 5-9y
## 195
## Neonatal mortality rate
## 195
## Out of pocket expenditure on health care
## 188
## Per capita territorial emissions at a country level
## 193
## Population using safe sanitation
## 120
## Population using safe water
## 116
## Positive discipline (1-4y)
## 50
## Positive discipline (5-9y)
## 49
## Postnatal care for newborn
## 60
## Postneonatal mortality rate
## 195
## Poverty
## 100
## Primary school completion
## 240
## Primary school net attendance rate
## 118
## Proficiency in reading/math (end of primary)
## 146
## Proficiency in reading/math (grade 2/3)
## 101
## Youth not in education or employment
## 230#structure
str(Unicefdata)## spec_tbl_df [5,516 x 23] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ iso3 : chr [1:5516] "AFG" "ALB" "DZA" "AND" ...
## $ country : chr [1:5516] "Afghanistan" "Albania" "Algeria" "Andorra" ...
## $ indicator : chr [1:5516] "Neonatal mortality rate" "Neonatal mortality rate" "Neonatal mortality rate" "Neonatal mortality rate" ...
## $ indicator_id : num [1:5516] 1 1 1 1 1 1 1 1 1 1 ...
## $ age : chr [1:5516] "0-27 days" "0-27 days" "0-27 days" "0-27 days" ...
## $ domain : chr [1:5516] "Survival" "Survival" "Survival" "Survival" ...
## $ disaggregator : chr [1:5516] "Total" "Total" "Total" "Total" ...
## $ value : num [1:5516] 35.19 7.78 16.29 1.3 27.28 ...
## $ total : chr [1:5516] "Not applicable" "Not applicable" "Not applicable" "Not applicable" ...
## $ units : chr [1:5516] "Deaths per 1,000 live births" "Deaths per 1,000 live births" "Deaths per 1,000 live births" "Deaths per 1,000 live births" ...
## $ year : num [1:5516] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 ...
## $ source : chr [1:5516] "UN_IGME" "UN_IGME" "UN_IGME" "UN_IGME" ...
## $ definition : chr [1:5516] "Neonatal (0-27 days) mortality rate (deaths per 1,000 live births)" "Neonatal (0-27 days) mortality rate (deaths per 1,000 live births)" "Neonatal (0-27 days) mortality rate (deaths per 1,000 live births)" "Neonatal (0-27 days) mortality rate (deaths per 1,000 live births)" ...
## $ indicator_cat : chr [1:5516] "Met target (green/blue): country has achieved the SDG target of a NMR of at least as low as 12 per 1000 live bi"| __truncated__ "Met target (green/blue): country has achieved the SDG target of a NMR of at least as low as 12 per 1000 live bi"| __truncated__ "Met target (green/blue): country has achieved the SDG target of a NMR of at least as low as 12 per 1000 live bi"| __truncated__ "Met target (green/blue): country has achieved the SDG target of a NMR of at least as low as 12 per 1000 live bi"| __truncated__ ...
## $ target : chr [1:5516] "SDG Target 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all\ncountr"| __truncated__ "SDG Target 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all\ncountr"| __truncated__ "SDG Target 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all\ncountr"| __truncated__ "SDG Target 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all\ncountr"| __truncated__ ...
## $ progress : chr [1:5516] "Acceleration needed" "Target met" "Acceleration needed" "Target met" ...
## $ region_sdg_name : chr [1:5516] "Central and Southern Asia" "Europe and Northern America" "Northern Africa and Western Asia" "Europe and Northern America" ...
## $ region_who_code : chr [1:5516] "EMR" "EUR" "AFR" "EUR" ...
## $ region_who_name : chr [1:5516] "Eastern Mediterranean Region" "European Region" "African Region" "European Region" ...
## $ region_unicef_code: chr [1:5516] "SA" "ECA" "MENA" "ECA" ...
## $ region_unicef_name: chr [1:5516] "South Asia" "Europe and Central Asia" "Middle East and North Africa" "Europe and Central Asia" ...
## $ incomecat : chr [1:5516] "LIC" "UMIC" "LMIC" "HIC" ...
## $ income : chr [1:5516] "Low income" "Upper middle income" "Lower middle income" "High income" ...
## - attr(*, "spec")=
## .. cols(
## .. iso3 = col_character(),
## .. country = col_character(),
## .. indicator = col_character(),
## .. indicator_id = col_double(),
## .. age = col_character(),
## .. domain = col_character(),
## .. disaggregator = col_character(),
## .. value = col_double(),
## .. total = col_character(),
## .. units = col_character(),
## .. year = col_double(),
## .. source = col_character(),
## .. definition = col_character(),
## .. indicator_cat = col_character(),
## .. target = col_character(),
## .. progress = col_character(),
## .. region_sdg_name = col_character(),
## .. region_who_code = col_character(),
## .. region_who_name = col_character(),
## .. region_unicef_code = col_character(),
## .. region_unicef_name = col_character(),
## .. incomecat = col_character(),
## .. income = col_character()
## .. )
## - attr(*, "problems")=<externalptr>#summary
summary(Unicefdata)## iso3 country indicator indicator_id
## Length:5516 Length:5516 Length:5516 Min. : 1.00
## Class :character Class :character Class :character 1st Qu.:11.75
## Mode :character Mode :character Mode :character Median :21.00
## Mean :19.84
## 3rd Qu.:29.00
## Max. :37.00
## age domain disaggregator value
## Length:5516 Length:5516 Length:5516 Min. : 0.000
## Class :character Class :character Class :character 1st Qu.: 2.254
## Mode :character Mode :character Mode :character Median : 15.100
## Mean : 33.688
## 3rd Qu.: 66.500
## Max. :147.000
## total units year source
## Length:5516 Length:5516 Min. :2016 Length:5516
## Class :character Class :character 1st Qu.:2019 Class :character
## Mode :character Mode :character Median :2020 Mode :character
## Mean :2019
## 3rd Qu.:2020
## Max. :2021
## definition indicator_cat target progress
## Length:5516 Length:5516 Length:5516 Length:5516
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## region_sdg_name region_who_code region_who_name region_unicef_code
## Length:5516 Length:5516 Length:5516 Length:5516
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## region_unicef_name incomecat income
## Length:5516 Length:5516 Length:5516
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
## Neonatal mortality rate Boxplot
Background information:
Sustainable Development Goal (SDG) regions are countries and areas are grouped into eight Sustainable Development Goal (SDG) regions as defined by the United Nations Statistics Division and used for The Sustainable Development Goals Report.
Source: https://population.un.org/wpp/DefinitionOfRegions/#:~:text=%20Sustainable%20Development%20Goal%20%28SDG%29%20regions%3A%20countries%20and,The%20Sustainable%20Development%20Goals%20Report%20%28%20https%3A%2F%2Funstats.un.org%2Fsdgs%2Findicators%2Fregional-groups%2F%20%29.
# Assigning and filtering the indicator I want to work with
neonatal <- Unicefdata %>% filter(Unicefdata$indicator == 'Neonatal mortality rate')# Creating a boxplot for this indicator
ggplot(Unicefdata, aes(value,region_sdg_name, fill = region_sdg_name))+
geom_boxplot()+
labs(title = "Neonatal mortality rate",
subtitle = "Neonatal (0-27 days) mortality rate (deaths per 1,000 live births)",
x = "Value (deaths per 1,000 live births)",
y = "SDG regions",
fill = 'SDG regions')+
theme_light()
From this boxplot we can see that there are outliers only for the Sub-Saharan African SDG region. Also, the Sub-Sharan SGD region had the highest neonatl mortality rate, approximately 125 neontal death per 1,000 live births. Northern Africa- Western Asia, Latin America - the Carribeans and Eastern- South Eastern Asian had similar medians at approximately 12 death per 1,000 live births.
Tableau Dashboard
I created a Tableau Dashboard with four indicators that I was interested in. I was inspired by the Tableau dashboard that Unicef created with this dataset, however, I wanted my data to be displayed differently which is why I decided to focus only on four indicators. These indicators can help improve adolescent and child health and well-being. For this dashboard, I would have liked to find a way where visualizations can open individually in the same dashboard’s tab instead of opening a new tab for each.
Dashboard Link:https://public.tableau.com/views/ChildHealthandWell-BeingIndicators/Dashboard1?:language=en-US&:display_count=n&:origin=viz_share_link
Tableau Dashboard findings:
- Child Labour
- We can see that Togo had the highest % for children (total females and males combined) engaged in economic activity by 65.80 %. This is a pretty alarming number. But why is this happening? According to the Bureau of International Labor Affairs, In 2020, Togo made moderate advancement in its efforts to eliminate the worst forms of child labor. The government adopted a National Action Plan for the Elimination of the Worst Forms of Child Labor and passed a ministerial decree, which defined and prohibited hazardous work for children under 18 years old. In addition, the government intercepted 250 children at risk of human trafficking at the border and provided them social services. However, children in Togo are subjected to the worst forms of child labor, including in commercial sexual exploitation, sometimes as a result of human trafficking. Children also engage in child labor in domestic work. The government has not devoted sufficient resources to combat child labor, and labor inspectors are not authorized to assess penalties for child labor violations. In addition, the government does not publish data related to its efforts to criminal enforcement of child labor laws.
- Source: https://www.dol.gov/agencies/ilab/resources/reports/child-labor/togo?msclkid=932a25cbd07f11ec8d69735f9506e991
- Food insecurity
- Switzerland had the lowest food insecurity percent (2.0%).
- Congo had the highest food insecurity percentage (88.30%).
- Mortality due to air pollution
- Nigeria, Chad and Niger had the highest amount of deaths caused by air pollution, 140-125 death per 100,000.The contributing factors to pollution are a reliance on using solid fuels for cooking, burning waste and traffic pollution from very old cars. Source:https://www.cnn.com/2016/05/31/africa/nigeria-cities-pollution/index.html?msclkid=376625d8d08911ec8d23c7a1d4211ac3
- Youth not in education or employment
- I was surprised to see a pattern of increasing and decreasing every year for the percentage of youth not in education or employment. In 2016 the percentage of youth not in education or employment was 421.9 % (total combined for all the countries available in this dataset), however, in 2017, it increased to 979.7%, until 2018 when it dropped again to 545.3%. Similarly, in 2019 it increased drastically to 1,348.3 %, and then we can see again another decrease in 2020 by 551.1%.
Bibliography
| Source | Link |
|---|---|
| Dataset | https://data.unicef.org/resources/child-health-and-well-being-dashboard/ |
| SDG Regions | https://population.un.org/wpp/DefinitionOfRegions/#:~:text=%20Sustainable%20Development%20Goal%20%28SDG%29%20regions%3A%20countries%2 |
| Tableau Dashboard | https://public.tableau.com/views/ChildHealthandWell-BeingIndicators/Dashboard1?:language=en-US&:display_count=n&:origin=viz_share_link |
| Child Labour in Togo | https://www.dol.gov/agencies/ilab/resources/reports/child-labor/togo?msclkid=932a25cbd07f11ec8d69735f9506e991 |
| Air pollution source | https://www.cnn.com/2016/05/31/africa/nigeria-cities-pollution/index.html?msclkid=376625d8d08911ec8d23c7a1d4211ac3 |