RK_607_Project02-01

Child Mortality

My Inspiration: In my part time, I work for a volunteer organization which works tirelessly toward upliftment of Women and Children in under-developed countries. I have been involved in many projects from building schools to building modern medical facilities which provide equal access for children and women for all economic backgrounds. Sadly even in 21st century, Child Mortality remains high due to lack of basic care for children under age of 5. In this analysis I would like to review the data on child mortality.

Childmortality.org is an organization that publishes Child Mortality estimates for all the countries around the world.

They provide all available data and the latest child mortality estimates for each country based on the research of the UN Inter-agency Group for Child Mortality Estimation.

While doing the research for this discussion topic, I was looking at their Estimates for under-five, infant and neonatal mortality and noticed that data is in wide format and contains six variables with values of interest.

---

My Hypothesis: 1. I believe that due to advent of modern medicine, the infant mortality rates should be decreasing in the years 2. I also believe that the nations with worst rates would belong to the developing world 3. I think larger nations should have greater total deaths

INDEX (Step by Step)

STEP 1. Load Libraries

STEP 2. Load the file

STEP 3. Analysis by Data Munging: from Wide to Long Format

STEP 4. Conclusion

STEP 5. Plot the data

STEP 1 : Load your libraries

# Load the libraries
library(tidyverse)  #For Tidyverse

## -- Attaching packages ------------------------------------------------------ tidyverse 1.2.1 --

## v ggplot2 2.2.1     v purrr   0.2.4
## v tibble  1.4.1     v dplyr   0.7.4
## v tidyr   0.8.0     v stringr 1.2.0
## v readr   1.1.1     v forcats 0.3.0

## -- Conflicts --------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(RCurl)      #For File Operations

## Loading required package: bitops

## 
## Attaching package: 'RCurl'

## The following object is masked from 'package:tidyr':
## 
##     complete

library(dplyr)      #For Manipulating the data frames
library(DT)         #For Data table package
library(ggplot2)    #For Visualizations
library(rworldmap)

## Loading required package: sp

## ### Welcome to rworldmap ###

## For a short introduction type :   vignette('rworldmap')

STEP 2 : Load the File

# Good Practise: Basic house keeping: cleanup the env before you start new work
rm(list=ls())

# Garbage collector to free the memory
gc()

##           used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 1020181 54.5    1770749 94.6  1442291 77.1
## Vcells 1297236  9.9    2552219 19.5  1779896 13.6

# Good Practise: Set the graphics drivers 
dev.off()

## null device 
##           1

# Good Practise: Set up the Working Directory when working with a file system
setwd("C:\\CUNY\\607Data\\Assignments\\project02")

# Read the File
untidy_data <- read.csv("Data02_01_UNIGME_Rates_Deaths_Under5.csv")


# check the dimenstions
dim(untidy_data)

## [1] 585 405

# Structure of the data frame
#str(untidy_data)

# Names of the variables
names(untidy_data) 

##   [1] "ISO.Code"               "CountryName"           
##   [3] "Uncertainty.bounds"     "U5MR.1950"             
##   [5] "U5MR.1951"              "U5MR.1952"             
##   [7] "U5MR.1953"              "U5MR.1954"             
##   [9] "U5MR.1955"              "U5MR.1956"             
##  [11] "U5MR.1957"              "U5MR.1958"             
##  [13] "U5MR.1959"              "U5MR.1960"             
##  [15] "U5MR.1961"              "U5MR.1962"             
##  [17] "U5MR.1963"              "U5MR.1964"             
##  [19] "U5MR.1965"              "U5MR.1966"             
##  [21] "U5MR.1967"              "U5MR.1968"             
##  [23] "U5MR.1969"              "U5MR.1970"             
##  [25] "U5MR.1971"              "U5MR.1972"             
##  [27] "U5MR.1973"              "U5MR.1974"             
##  [29] "U5MR.1975"              "U5MR.1976"             
##  [31] "U5MR.1977"              "U5MR.1978"             
##  [33] "U5MR.1979"              "U5MR.1980"             
##  [35] "U5MR.1981"              "U5MR.1982"             
##  [37] "U5MR.1983"              "U5MR.1984"             
##  [39] "U5MR.1985"              "U5MR.1986"             
##  [41] "U5MR.1987"              "U5MR.1988"             
##  [43] "U5MR.1989"              "U5MR.1990"             
##  [45] "U5MR.1991"              "U5MR.1992"             
##  [47] "U5MR.1993"              "U5MR.1994"             
##  [49] "U5MR.1995"              "U5MR.1996"             
##  [51] "U5MR.1997"              "U5MR.1998"             
##  [53] "U5MR.1999"              "U5MR.2000"             
##  [55] "U5MR.2001"              "U5MR.2002"             
##  [57] "U5MR.2003"              "U5MR.2004"             
##  [59] "U5MR.2005"              "U5MR.2006"             
##  [61] "U5MR.2007"              "U5MR.2008"             
##  [63] "U5MR.2009"              "U5MR.2010"             
##  [65] "U5MR.2011"              "U5MR.2012"             
##  [67] "U5MR.2013"              "U5MR.2014"             
##  [69] "U5MR.2015"              "U5MR.2016"             
##  [71] "IMR.1950"               "IMR.1951"              
##  [73] "IMR.1952"               "IMR.1953"              
##  [75] "IMR.1954"               "IMR.1955"              
##  [77] "IMR.1956"               "IMR.1957"              
##  [79] "IMR.1958"               "IMR.1959"              
##  [81] "IMR.1960"               "IMR.1961"              
##  [83] "IMR.1962"               "IMR.1963"              
##  [85] "IMR.1964"               "IMR.1965"              
##  [87] "IMR.1966"               "IMR.1967"              
##  [89] "IMR.1968"               "IMR.1969"              
##  [91] "IMR.1970"               "IMR.1971"              
##  [93] "IMR.1972"               "IMR.1973"              
##  [95] "IMR.1974"               "IMR.1975"              
##  [97] "IMR.1976"               "IMR.1977"              
##  [99] "IMR.1978"               "IMR.1979"              
## [101] "IMR.1980"               "IMR.1981"              
## [103] "IMR.1982"               "IMR.1983"              
## [105] "IMR.1984"               "IMR.1985"              
## [107] "IMR.1986"               "IMR.1987"              
## [109] "IMR.1988"               "IMR.1989"              
## [111] "IMR.1990"               "IMR.1991"              
## [113] "IMR.1992"               "IMR.1993"              
## [115] "IMR.1994"               "IMR.1995"              
## [117] "IMR.1996"               "IMR.1997"              
## [119] "IMR.1998"               "IMR.1999"              
## [121] "IMR.2000"               "IMR.2001"              
## [123] "IMR.2002"               "IMR.2003"              
## [125] "IMR.2004"               "IMR.2005"              
## [127] "IMR.2006"               "IMR.2007"              
## [129] "IMR.2008"               "IMR.2009"              
## [131] "IMR.2010"               "IMR.2011"              
## [133] "IMR.2012"               "IMR.2013"              
## [135] "IMR.2014"               "IMR.2015"              
## [137] "IMR.2016"               "NMR.1950"              
## [139] "NMR.1951"               "NMR.1952"              
## [141] "NMR.1953"               "NMR.1954"              
## [143] "NMR.1955"               "NMR.1956"              
## [145] "NMR.1957"               "NMR.1958"              
## [147] "NMR.1959"               "NMR.1960"              
## [149] "NMR.1961"               "NMR.1962"              
## [151] "NMR.1963"               "NMR.1964"              
## [153] "NMR.1965"               "NMR.1966"              
## [155] "NMR.1967"               "NMR.1968"              
## [157] "NMR.1969"               "NMR.1970"              
## [159] "NMR.1971"               "NMR.1972"              
## [161] "NMR.1973"               "NMR.1974"              
## [163] "NMR.1975"               "NMR.1976"              
## [165] "NMR.1977"               "NMR.1978"              
## [167] "NMR.1979"               "NMR.1980"              
## [169] "NMR.1981"               "NMR.1982"              
## [171] "NMR.1983"               "NMR.1984"              
## [173] "NMR.1985"               "NMR.1986"              
## [175] "NMR.1987"               "NMR.1988"              
## [177] "NMR.1989"               "NMR.1990"              
## [179] "NMR.1991"               "NMR.1992"              
## [181] "NMR.1993"               "NMR.1994"              
## [183] "NMR.1995"               "NMR.1996"              
## [185] "NMR.1997"               "NMR.1998"              
## [187] "NMR.1999"               "NMR.2000"              
## [189] "NMR.2001"               "NMR.2002"              
## [191] "NMR.2003"               "NMR.2004"              
## [193] "NMR.2005"               "NMR.2006"              
## [195] "NMR.2007"               "NMR.2008"              
## [197] "NMR.2009"               "NMR.2010"              
## [199] "NMR.2011"               "NMR.2012"              
## [201] "NMR.2013"               "NMR.2014"              
## [203] "NMR.2015"               "NMR.2016"              
## [205] "Under.five.Deaths.1950" "Under.five.Deaths.1951"
## [207] "Under.five.Deaths.1952" "Under.five.Deaths.1953"
## [209] "Under.five.Deaths.1954" "Under.five.Deaths.1955"
## [211] "Under.five.Deaths.1956" "Under.five.Deaths.1957"
## [213] "Under.five.Deaths.1958" "Under.five.Deaths.1959"
## [215] "Under.five.Deaths.1960" "Under.five.Deaths.1961"
## [217] "Under.five.Deaths.1962" "Under.five.Deaths.1963"
## [219] "Under.five.Deaths.1964" "Under.five.Deaths.1965"
## [221] "Under.five.Deaths.1966" "Under.five.Deaths.1967"
## [223] "Under.five.Deaths.1968" "Under.five.Deaths.1969"
## [225] "Under.five.Deaths.1970" "Under.five.Deaths.1971"
## [227] "Under.five.Deaths.1972" "Under.five.Deaths.1973"
## [229] "Under.five.Deaths.1974" "Under.five.Deaths.1975"
## [231] "Under.five.Deaths.1976" "Under.five.Deaths.1977"
## [233] "Under.five.Deaths.1978" "Under.five.Deaths.1979"
## [235] "Under.five.Deaths.1980" "Under.five.Deaths.1981"
## [237] "Under.five.Deaths.1982" "Under.five.Deaths.1983"
## [239] "Under.five.Deaths.1984" "Under.five.Deaths.1985"
## [241] "Under.five.Deaths.1986" "Under.five.Deaths.1987"
## [243] "Under.five.Deaths.1988" "Under.five.Deaths.1989"
## [245] "Under.five.Deaths.1990" "Under.five.Deaths.1991"
## [247] "Under.five.Deaths.1992" "Under.five.Deaths.1993"
## [249] "Under.five.Deaths.1994" "Under.five.Deaths.1995"
## [251] "Under.five.Deaths.1996" "Under.five.Deaths.1997"
## [253] "Under.five.Deaths.1998" "Under.five.Deaths.1999"
## [255] "Under.five.Deaths.2000" "Under.five.Deaths.2001"
## [257] "Under.five.Deaths.2002" "Under.five.Deaths.2003"
## [259] "Under.five.Deaths.2004" "Under.five.Deaths.2005"
## [261] "Under.five.Deaths.2006" "Under.five.Deaths.2007"
## [263] "Under.five.Deaths.2008" "Under.five.Deaths.2009"
## [265] "Under.five.Deaths.2010" "Under.five.Deaths.2011"
## [267] "Under.five.Deaths.2012" "Under.five.Deaths.2013"
## [269] "Under.five.Deaths.2014" "Under.five.Deaths.2015"
## [271] "Under.five.Deaths.2016" "Infant.Deaths.1950"    
## [273] "Infant.Deaths.1951"     "Infant.Deaths.1952"    
## [275] "Infant.Deaths.1953"     "Infant.Deaths.1954"    
## [277] "Infant.Deaths.1955"     "Infant.Deaths.1956"    
## [279] "Infant.Deaths.1957"     "Infant.Deaths.1958"    
## [281] "Infant.Deaths.1959"     "Infant.Deaths.1960"    
## [283] "Infant.Deaths.1961"     "Infant.Deaths.1962"    
## [285] "Infant.Deaths.1963"     "Infant.Deaths.1964"    
## [287] "Infant.Deaths.1965"     "Infant.Deaths.1966"    
## [289] "Infant.Deaths.1967"     "Infant.Deaths.1968"    
## [291] "Infant.Deaths.1969"     "Infant.Deaths.1970"    
## [293] "Infant.Deaths.1971"     "Infant.Deaths.1972"    
## [295] "Infant.Deaths.1973"     "Infant.Deaths.1974"    
## [297] "Infant.Deaths.1975"     "Infant.Deaths.1976"    
## [299] "Infant.Deaths.1977"     "Infant.Deaths.1978"    
## [301] "Infant.Deaths.1979"     "Infant.Deaths.1980"    
## [303] "Infant.Deaths.1981"     "Infant.Deaths.1982"    
## [305] "Infant.Deaths.1983"     "Infant.Deaths.1984"    
## [307] "Infant.Deaths.1985"     "Infant.Deaths.1986"    
## [309] "Infant.Deaths.1987"     "Infant.Deaths.1988"    
## [311] "Infant.Deaths.1989"     "Infant.Deaths.1990"    
## [313] "Infant.Deaths.1991"     "Infant.Deaths.1992"    
## [315] "Infant.Deaths.1993"     "Infant.Deaths.1994"    
## [317] "Infant.Deaths.1995"     "Infant.Deaths.1996"    
## [319] "Infant.Deaths.1997"     "Infant.Deaths.1998"    
## [321] "Infant.Deaths.1999"     "Infant.Deaths.2000"    
## [323] "Infant.Deaths.2001"     "Infant.Deaths.2002"    
## [325] "Infant.Deaths.2003"     "Infant.Deaths.2004"    
## [327] "Infant.Deaths.2005"     "Infant.Deaths.2006"    
## [329] "Infant.Deaths.2007"     "Infant.Deaths.2008"    
## [331] "Infant.Deaths.2009"     "Infant.Deaths.2010"    
## [333] "Infant.Deaths.2011"     "Infant.Deaths.2012"    
## [335] "Infant.Deaths.2013"     "Infant.Deaths.2014"    
## [337] "Infant.Deaths.2015"     "Infant.Deaths.2016"    
## [339] "Neonatal.Deaths.1950"   "Neonatal.Deaths.1951"  
## [341] "Neonatal.Deaths.1952"   "Neonatal.Deaths.1953"  
## [343] "Neonatal.Deaths.1954"   "Neonatal.Deaths.1955"  
## [345] "Neonatal.Deaths.1956"   "Neonatal.Deaths.1957"  
## [347] "Neonatal.Deaths.1958"   "Neonatal.Deaths.1959"  
## [349] "Neonatal.Deaths.1960"   "Neonatal.Deaths.1961"  
## [351] "Neonatal.Deaths.1962"   "Neonatal.Deaths.1963"  
## [353] "Neonatal.Deaths.1964"   "Neonatal.Deaths.1965"  
## [355] "Neonatal.Deaths.1966"   "Neonatal.Deaths.1967"  
## [357] "Neonatal.Deaths.1968"   "Neonatal.Deaths.1969"  
## [359] "Neonatal.Deaths.1970"   "Neonatal.Deaths.1971"  
## [361] "Neonatal.Deaths.1972"   "Neonatal.Deaths.1973"  
## [363] "Neonatal.Deaths.1974"   "Neonatal.Deaths.1975"  
## [365] "Neonatal.Deaths.1976"   "Neonatal.Deaths.1977"  
## [367] "Neonatal.Deaths.1978"   "Neonatal.Deaths.1979"  
## [369] "Neonatal.Deaths.1980"   "Neonatal.Deaths.1981"  
## [371] "Neonatal.Deaths.1982"   "Neonatal.Deaths.1983"  
## [373] "Neonatal.Deaths.1984"   "Neonatal.Deaths.1985"  
## [375] "Neonatal.Deaths.1986"   "Neonatal.Deaths.1987"  
## [377] "Neonatal.Deaths.1988"   "Neonatal.Deaths.1989"  
## [379] "Neonatal.Deaths.1990"   "Neonatal.Deaths.1991"  
## [381] "Neonatal.Deaths.1992"   "Neonatal.Deaths.1993"  
## [383] "Neonatal.Deaths.1994"   "Neonatal.Deaths.1995"  
## [385] "Neonatal.Deaths.1996"   "Neonatal.Deaths.1997"  
## [387] "Neonatal.Deaths.1998"   "Neonatal.Deaths.1999"  
## [389] "Neonatal.Deaths.2000"   "Neonatal.Deaths.2001"  
## [391] "Neonatal.Deaths.2002"   "Neonatal.Deaths.2003"  
## [393] "Neonatal.Deaths.2004"   "Neonatal.Deaths.2005"  
## [395] "Neonatal.Deaths.2006"   "Neonatal.Deaths.2007"  
## [397] "Neonatal.Deaths.2008"   "Neonatal.Deaths.2009"  
## [399] "Neonatal.Deaths.2010"   "Neonatal.Deaths.2011"  
## [401] "Neonatal.Deaths.2012"   "Neonatal.Deaths.2013"  
## [403] "Neonatal.Deaths.2014"   "Neonatal.Deaths.2015"  
## [405] "Neonatal.Deaths.2016"

Analysis 1: As we can see that the data has 405 variables and only 585 observations, it is in a wide format.

---

U5MR: Under 5 mortality Rate has country wide data from year 1950 to 2016
IMR: Infant Mortality Rate has country wide data from year 1950 to 2016
NMR: Neo Natal Mortality Rate has country wide data from year 1950 to 2016
Under.five.Deaths: Total Under 5 Mortality Rate by country from year 1950 to 2016
Infant.Deaths: Total Infant Deaths by country from year 1950 to 2016
Neonatal.Deaths: Neo Natal Deaths by country from year 1950 to 2016

DPLYR: Using DPlyr to convert data from wide to long format

We see above that the year field has year values in format U5MR.1951. We need to fix it to get the year correctly.

# Create dataframe with rates
all_rates_long <- untidy_data %>% 
    select(1:204) %>% 
    gather(category, rate, 4:204) %>% 
    mutate(year = as.numeric(str_extract(category, "\\d{4}")), 
           category = str_sub(category,1,-6)) %>% 
    rename(country = CountryName, bounds=Uncertainty.bounds)
    

# Create dataframe with values
all_values_long <- untidy_data %>% select(1:3,205:405) %>% 
    gather(category, value, 4:204) %>% 
    mutate(year = as.numeric(str_extract(category, "\\d{4}")), 
           category = str_sub(category,1,-6)) %>% 
    rename(country = CountryName, bounds=Uncertainty.bounds)


# Spread the rate data based on the category field
all_rates_tidy <- all_rates_long %>% 
    filter(!is.na(rate)) %>% 
    spread(category, rate) 

all_values_tidy <- all_values_long %>% 
    filter(!is.na(value)) %>% 
    spread(category, value) 

Create Data tables for these tidy dataframes

# Rates Dataframe
datatable(all_rates_tidy)

## Warning in instance$preRenderHook(instance): It seems your data is too
## big for client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html

# Values Dataframes
datatable(all_values_tidy)

## Warning in instance$preRenderHook(instance): It seems your data is too
## big for client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html

Analysis 1: For year 2016, Which 100 countries had the highest Mortality Rates

# Worst countries for U5MR
country2016df <- all_rates_tidy %>% 
    filter(year==2016 & 
               bounds=="Median") %>% 
    arrange(desc(U5MR)) %>% 
    slice(1:100)    

barplotCountryData(country2016df,'U5MR','country',scaleSameInPanels = TRUE, color=country,numPanels = 5,cex = .9,main='Under 5 Mortality Rates by Country for Year 2016')

# Worst countries for IMR
country2016df <- all_rates_tidy %>% 
    filter(year==2016 & 
               bounds=="Median") %>% 
    arrange(desc(IMR)) %>% 
    slice(1:100)    
barplotCountryData(country2016df,'IMR','country',scaleSameInPanels = TRUE, color=country,numPanels = 5,cex = .9,main='Infant Mortality Rates by Country for Year 2016')

# Worst countries for NMR
country2016df <- all_rates_tidy %>% 
    filter(year==2016 & 
               bounds=="Median") %>% 
    arrange(desc(NMR)) %>% 
    slice(1:100)    

barplotCountryData(country2016df,'NMR','country',scaleSameInPanels = TRUE, color=country,numPanels = 5,cex = .9,main='Under 5 Mortality Rates by Country for Year 2016')

Based on above analysis

Somalia was the worst Under 5 Mortality Rate
Centra African Republic was the worst Under Infant Mortality Rate
Pakistan was the worst Neonatal Mortality Rate

#####This Proves my Hypothesis#2 that mostly countries of developing world have high child mortality rates

Analysis 2: How have the worst 25 countries done in term of Mortality Rates by the year

#find 25 top countries with highest mortality
worstcountrytop25 <- all_rates_tidy %>% 
    arrange(desc(U5MR)) %>% 
    select(country, bounds, U5MR, year) %>% 
    filter(year==2016 & 
               bounds=="Median") %>% 
    slice(1:25) %>% 
    select(country)

head(worstcountrytop25)

## # A tibble: 6 x 1
##   country                 
##   <fctr>                  
## 1 Somalia                 
## 2 Chad                    
## 3 Central African Republic
## 4 Sierra Leone            
## 5 Mali                    
## 6 Nigeria

# Now find the rates of these worst countries 
U5MR_rates_tidy_selected <- all_rates_tidy %>% 
    filter(bounds=="Median"&
               country %in% worstcountrytop25$country)
head(U5MR_rates_tidy_selected)

##   ISO.Code     country bounds year   IMR NMR  U5MR
## 1      AFG Afghanistan Median 1960 245.7  NA 363.7
## 2      AFG Afghanistan Median 1961 241.2  NA 357.5
## 3      AFG Afghanistan Median 1962 236.9  NA 351.5
## 4      AFG Afghanistan Median 1963 232.7  NA 345.5
## 5      AFG Afghanistan Median 1964 228.5  NA 339.7
## 6      AFG Afghanistan Median 1965 224.4  NA 333.9

U5MR_rates_tidy_selected$U5MR <- as.numeric(as.character(U5MR_rates_tidy_selected$U5MR))


ggplot(U5MR_rates_tidy_selected, aes(x = year, y = U5MR, color = country)) +
#  geom_line() +
    geom_point() +
  scale_y_continuous() +
  scale_x_continuous(breaks = seq(1950, 2016, by = 5)) + 
#  scale_x_continuous(limits = c(1950, 2016)) +
  theme_linedraw() +
  ggtitle("Under 5 Mortality Rate By Country and Year") +
  xlab("Year") +
  ylab("Mortality Rate") +
  theme(plot.title = element_text(lineheight = .8, face = "bold"))

This GGPlot proves my hypothesis #1 that mortality rates have come down with time

use the rmapworld library to create a world map with rates data

U5MR_rates_tidy_selected_2016  <-   all_rates_tidy %>% 
    filter(year==2016 & bounds=="Median") %>% 
    select(ISO.Code, U5MR)
dim(U5MR_rates_tidy_selected_2016)

## [1] 195   2

countryjoin <- joinCountryData2Map( U5MR_rates_tidy_selected_2016,joinCode = "ISO3", nameJoinColumn = "ISO.Code")

## 195 codes from your data successfully matched countries in the map
## 0 codes from your data failed to match with a country code in the map
## 48 codes from the map weren't represented in your data

library(RColorBrewer)
par(mai=c(0,0,0.4,0),xaxs="i",yaxs="i")
palette <-brewer.pal(7,'OrRd') 
mapParams <- mapCountryData(countryjoin,
                            nameColumnToPlot='U5MR',
                             colourPalette=palette,
                            missingCountryCol = 'gray', 
                            addLegend ='FALSE',
                            oceanCol = 'lightblue',
                            mapTitle = "Infant Mortality Rate Per 1000 \nfor Year: 2016")

do.call( addMapLegend, c( mapParams, 
                          legendLabels = "all", 
                        legendShrink=.45,
                        labelFontSize=.8, 
                          legendWidth = .5 ))

Analysis 3: Total Death of Infants by the country and year

# Use Group By and summarise the data by countries
country_total_death <- all_values_tidy %>% 
    group_by(country, year) %>% 
    summarise(total_deaths = sum(Infant.Deaths+Neonatal.Deaths+Under.five.Deaths, na.rm=TRUE)) %>% 
    summarise(total_deaths = sum(total_deaths)) %>% arrange(desc(total_deaths)) 

Hythesis# 3: This tables in interesting as it shows how India has the highest total number of death, which was bit expected as India is the second highest population in the world. But the rest of the countries are not in the top 5 populous countries, which shows how these countries had very high rates of child deaths. China shows up at #6 in this list as expected.

datatable(country_total_death)

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.