COVID-19 Diet Data Analysis
Irish Ramos
2025-01-05
The aim of this analysis is to analyze the common Dietary Consumption trends among countries with better COVID 19 health outcomes by utilizing global data including Population, COVID 19 statistics (Confirmed, Deaths, Recovered, Active), and Annual Caloric consumption per Food Group in each country. The COVID 19 data covered the period of April 22, 2020 until February 06 2021.
This analysis uses a public data set from Kaggle by Maria Ren. click here link
The original sources of the data used include the Food and Agriculture Organization link, Johns Hopkins Center for Systems Science and Engineering link, and the Population Reference Beaureu link
Setting Up The Environment
Note: installing and loading the the necessary packages
install.packages(c("dplyr", "tidyr",
"readr", "ggplot2", "summarytools", "lubridate", "stringr",
"readxl"))## Installing packages into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)
library(readr)
library(ggplot2)
library(summarytools)## Warning in fun(libname, pkgname): couldn't connect to display ":0"## system might not have X11 capabilities; in case of errors when using dfSummary(), set st_options(use.x11 = FALSE)library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(stringr)
library(readxl)Data Preparation
Note: Uploading the raw data I’ll be using and exploring its contents
covid_diet_data <- read.csv("Food_Supply_kcal_Data.csv")
head(covid_diet_data)## Country Alcoholic.Beverages Animal.Products Animal.fats
## 1 Afghanistan 0.0000 4.7774 0.8504
## 2 Albania 0.9120 16.0930 1.0591
## 3 Algeria 0.0896 6.0326 0.1941
## 4 Angola 1.9388 4.6927 0.2644
## 5 Antigua and Barbuda 2.3041 15.3672 1.5429
## 6 Argentina 1.4354 14.9869 1.0650
## Aquatic.Products..Other Cereals...Excluding.Beer Eggs Fish..Seafood
## 1 0 37.1186 0.1501 0.0000
## 2 0 16.2107 0.8091 0.1471
## 3 0 25.0112 0.4181 0.1195
## 4 0 18.3521 0.0441 0.8372
## 5 0 13.7215 0.2057 1.7280
## 6 0 16.7927 0.8643 0.2006
## Fruits...Excluding.Wine Meat Milk...Excluding.Butter Miscellaneous Offals
## 1 1.4757 1.2006 2.4512 0.0250 0.1251
## 2 3.8982 3.8688 9.9441 0.0588 0.2648
## 3 3.1805 1.2543 3.9869 0.1045 0.0597
## 4 2.3133 2.9302 0.5067 0.0661 0.1102
## 5 3.6824 7.0356 4.6904 0.3086 0.1646
## 6 1.4663 9.4459 3.1641 0.0000 0.2624
## Oilcrops Pulses Spices Starchy.Roots Stimulants Sugar.Crops
## 1 0.1751 0.5003 0.1001 0.3252 0.0750 0
## 2 1.0886 0.8091 0.0000 1.2651 0.2501 0
## 3 0.2688 1.0900 0.1195 1.9262 0.1493 0
## 4 1.0795 1.4981 0.0000 12.6239 0.0441 0
## 5 0.5966 0.4526 0.3497 0.8434 0.4937 0
## 6 0.0309 0.1235 0.0309 1.4045 0.2315 0
## Sugar...Sweeteners Treenuts Vegetal.Products Vegetable.Oils Vegetables
## 1 2.2261 0.1251 45.2476 2.3012 0.7504
## 2 3.4422 0.3972 33.9070 2.8244 2.7508
## 3 3.9869 0.2240 43.9749 5.7638 2.0457
## 4 2.7539 0.0000 45.3184 4.2741 0.3525
## 5 5.8218 0.0823 34.6225 4.6904 1.2960
## 6 7.0536 0.0463 34.9900 5.5410 0.8643
## Obesity Undernourished Confirmed Deaths Recovered Active
## 1 4.5 29.8 0.14213420 0.006185779 0.1233739 0.012574497
## 2 22.3 6.2 2.96730092 0.050951374 1.7926357 1.123713883
## 3 26.6 3.9 0.24489708 0.006558153 0.1675722 0.070766734
## 4 6.8 25 0.06168747 0.001460550 0.0568077 0.003419224
## 5 19.1 <NA> 0.29387755 0.007142857 0.1908163 0.095918367
## 6 28.5 4.6 4.35614739 0.108226635 3.9051921 0.342728695
## Population Unit..all.except.Population.
## 1 38928000 %
## 2 2838000 %
## 3 44357000 %
## 4 32522000 %
## 5 98000 %
## 6 45377000 %str(covid_diet_data)## 'data.frame': 170 obs. of 32 variables:
## $ Country : chr "Afghanistan" "Albania" "Algeria" "Angola" ...
## $ Alcoholic.Beverages : num 0 0.912 0.0896 1.9388 2.3041 ...
## $ Animal.Products : num 4.78 16.09 6.03 4.69 15.37 ...
## $ Animal.fats : num 0.85 1.059 0.194 0.264 1.543 ...
## $ Aquatic.Products..Other : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Cereals...Excluding.Beer : num 37.1 16.2 25 18.4 13.7 ...
## $ Eggs : num 0.1501 0.8091 0.4181 0.0441 0.2057 ...
## $ Fish..Seafood : num 0 0.147 0.119 0.837 1.728 ...
## $ Fruits...Excluding.Wine : num 1.48 3.9 3.18 2.31 3.68 ...
## $ Meat : num 1.2 3.87 1.25 2.93 7.04 ...
## $ Milk...Excluding.Butter : num 2.451 9.944 3.987 0.507 4.69 ...
## $ Miscellaneous : num 0.025 0.0588 0.1045 0.0661 0.3086 ...
## $ Offals : num 0.1251 0.2648 0.0597 0.1102 0.1646 ...
## $ Oilcrops : num 0.175 1.089 0.269 1.079 0.597 ...
## $ Pulses : num 0.5 0.809 1.09 1.498 0.453 ...
## $ Spices : num 0.1 0 0.119 0 0.35 ...
## $ Starchy.Roots : num 0.325 1.265 1.926 12.624 0.843 ...
## $ Stimulants : num 0.075 0.2501 0.1493 0.0441 0.4937 ...
## $ Sugar.Crops : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Sugar...Sweeteners : num 2.23 3.44 3.99 2.75 5.82 ...
## $ Treenuts : num 0.1251 0.3972 0.224 0 0.0823 ...
## $ Vegetal.Products : num 45.2 33.9 44 45.3 34.6 ...
## $ Vegetable.Oils : num 2.3 2.82 5.76 4.27 4.69 ...
## $ Vegetables : num 0.75 2.751 2.046 0.352 1.296 ...
## $ Obesity : num 4.5 22.3 26.6 6.8 19.1 28.5 20.9 30.4 21.9 19.9 ...
## $ Undernourished : chr "29.8" "6.2" "3.9" "25" ...
## $ Confirmed : num 0.1421 2.9673 0.2449 0.0617 0.2939 ...
## $ Deaths : num 0.00619 0.05095 0.00656 0.00146 0.00714 ...
## $ Recovered : num 0.1234 1.7926 0.1676 0.0568 0.1908 ...
## $ Active : num 0.01257 1.12371 0.07077 0.00342 0.09592 ...
## $ Population : num 38928000 2838000 44357000 32522000 98000 ...
## $ Unit..all.except.Population.: chr "%" "%" "%" "%" ...colnames(covid_diet_data)## [1] "Country" "Alcoholic.Beverages"
## [3] "Animal.Products" "Animal.fats"
## [5] "Aquatic.Products..Other" "Cereals...Excluding.Beer"
## [7] "Eggs" "Fish..Seafood"
## [9] "Fruits...Excluding.Wine" "Meat"
## [11] "Milk...Excluding.Butter" "Miscellaneous"
## [13] "Offals" "Oilcrops"
## [15] "Pulses" "Spices"
## [17] "Starchy.Roots" "Stimulants"
## [19] "Sugar.Crops" "Sugar...Sweeteners"
## [21] "Treenuts" "Vegetal.Products"
## [23] "Vegetable.Oils" "Vegetables"
## [25] "Obesity" "Undernourished"
## [27] "Confirmed" "Deaths"
## [29] "Recovered" "Active"
## [31] "Population" "Unit..all.except.Population."summary(covid_diet_data)## Country Alcoholic.Beverages Animal.Products Animal.fats
## Length:170 Min. :0.0000 Min. : 1.624 Min. :0.0000
## Class :character 1st Qu.:0.3613 1st Qu.: 5.083 1st Qu.:0.3428
## Mode :character Median :1.2446 Median : 9.034 Median :0.8775
## Mean :1.3252 Mean : 9.295 Mean :1.2674
## 3rd Qu.:2.0280 3rd Qu.:13.175 3rd Qu.:1.7632
## Max. :5.1574 Max. :22.291 Max. :7.8007
##
## Aquatic.Products..Other Cereals...Excluding.Beer Eggs
## Min. :0.000000 Min. : 8.957 Min. :0.0188
## 1st Qu.:0.000000 1st Qu.:15.306 1st Qu.:0.1410
## Median :0.000000 Median :19.620 Median :0.4037
## Mean :0.002786 Mean :20.365 Mean :0.4285
## 3rd Qu.:0.000000 3rd Qu.:24.841 3rd Qu.:0.6330
## Max. :0.400700 Max. :37.526 Max. :1.4461
##
## Fish..Seafood Fruits...Excluding.Wine Meat
## Min. :0.0000 Min. :0.1471 Min. : 0.298
## 1st Qu.:0.2402 1st Qu.:1.2245 1st Qu.: 2.081
## Median :0.4783 Median :1.6948 Median : 3.687
## Mean :0.6315 Mean :2.0120 Mean : 3.896
## 3rd Qu.:0.8697 3rd Qu.:2.3707 3rd Qu.: 5.278
## Max. :4.4183 Max. :8.8540 Max. :10.567
##
## Milk...Excluding.Butter Miscellaneous Offals Oilcrops
## Min. :0.1169 Min. :0.00000 Min. :0.00000 Min. : 0.0179
## 1st Qu.:1.1078 1st Qu.:0.02470 1st Qu.:0.07825 1st Qu.: 0.2993
## Median :2.7198 Median :0.08805 Median :0.11825 Median : 0.6363
## Mean :2.9245 Mean :0.15933 Mean :0.14122 Mean : 1.1035
## 3rd Qu.:4.3196 3rd Qu.:0.19173 3rd Qu.:0.17663 3rd Qu.: 1.1902
## Max. :9.9441 Max. :1.18220 Max. :0.80150 Max. :10.4822
##
## Pulses Spices Starchy.Roots Stimulants
## Min. :0.0000 Min. :0.00000 Min. : 0.2938 Min. :0.00000
## 1st Qu.:0.2967 1st Qu.:0.03635 1st Qu.: 1.1123 1st Qu.:0.07765
## Median :0.7084 Median :0.08590 Median : 1.5449 Median :0.20675
## Mean :1.1089 Mean :0.18320 Mean : 3.0839 Mean :0.30537
## 3rd Qu.:1.5472 3rd Qu.:0.22798 3rd Qu.: 2.9245 3rd Qu.:0.42080
## Max. :7.5638 Max. :1.22020 Max. :19.6759 Max. :2.00900
##
## Sugar.Crops Sugar...Sweeteners Treenuts Vegetal.Products
## Min. :0.00000 Min. :0.6786 Min. :0.00000 Min. :27.71
## 1st Qu.:0.00000 1st Qu.:3.4222 1st Qu.:0.04662 1st Qu.:36.83
## Median :0.00000 Median :4.6784 Median :0.17400 Median :40.97
## Mean :0.01788 Mean :4.8212 Mean :0.26162 Mean :40.71
## 3rd Qu.:0.00000 3rd Qu.:6.3458 3rd Qu.:0.38958 3rd Qu.:44.94
## Max. :0.59300 Max. :9.5492 Max. :1.42100 Max. :48.39
##
## Vegetable.Oils Vegetables Obesity Undernourished
## Min. : 0.9325 Min. :0.0957 Min. : 2.10 Length:170
## 1st Qu.: 3.1263 1st Qu.:0.6026 1st Qu.: 8.50 Class :character
## Median : 4.6607 Median :1.0031 Median :21.20 Mode :character
## Mean : 4.8724 Mean :1.0863 Mean :18.71
## 3rd Qu.: 6.4279 3rd Qu.:1.3670 3rd Qu.:25.70
## Max. :10.3839 Max. :3.3524 Max. :45.60
## NA's :3
## Confirmed Deaths Recovered Active
## Min. : 0.000312 Min. :0.000000 Min. :0.00000 Min. :0.00000
## 1st Qu.: 0.140976 1st Qu.:0.002013 1st Qu.:0.09911 1st Qu.:0.01203
## Median : 1.011570 Median :0.011998 Median :0.47540 Median :0.08104
## Mean : 2.021972 Mean :0.039370 Mean :1.45236 Mean :0.53516
## 3rd Qu.: 3.487069 3rd Qu.:0.069503 3rd Qu.:2.62287 3rd Qu.:0.35818
## Max. :10.408199 Max. :0.185428 Max. :9.03987 Max. :8.01982
## NA's :6 NA's :6 NA's :6 NA's :8
## Population Unit..all.except.Population.
## Min. :5.400e+04 Length:170
## 1st Qu.:2.816e+06 Class :character
## Median :1.018e+07 Mode :character
## Mean :4.452e+07
## 3rd Qu.:3.272e+07
## Max. :1.402e+09
## Data Cleaning and Processing
Note: Involved removing blanks, NA and duplicated values, unifying data formats and other necessary filtering and organizing
sum(is.na(covid_diet_data)) #identified the NA values## [1] 36options(scipen = 999) #removed the scientific notation function for some valuescovid_diet_data$Confirmed <- round(covid_diet_data$Confirmed, 4) #limited the number of maximum
# decimal places for the Confirmed column to 4note: converted the data formats for the columns Confirmed, Deaths, Recovered and Active into whole number from percentage format to match the Population data which I will use together with data for these columns later for further calculations.
covid_diet_data$Confirmed <- round(covid_diet_data$Confirmed * 100)
covid_diet_data$Deaths <- round(covid_diet_data$Deaths * 100)
covid_diet_data$Recovered <- round(covid_diet_data$Recovered * 100)
covid_diet_data$Active <- round(covid_diet_data$Active * 100)covid_diet_data_clean <- na.omit(covid_diet_data) #removed non numeric row values.covid_diet_data_unique <- covid_diet_data_clean[!duplicated(covid_diet_data_clean$Country) & !duplicated(covid_diet_data_clean$Country, fromLast = TRUE), ] #removed duplicates form the "Country" columnNote: In this section I focused on removing and merging food category columns that had negligible nutritive values and those that essentially fell under the same food category.
covid_diet_data_unique <- subset(covid_diet_data_unique, select = -Alcoholic.Beverages)covid_diet_data_unique <- subset(covid_diet_data_unique, select = -Stimulants)columns_to_sum <-c("Animal.fats", "Oilcrops", "Vegetable.Oils")covid_diet_data_unique$Fats.and.Oils <- rowSums(covid_diet_data_unique[columns_to_sum])columns_to_sum <-c("Sugar.Crops", "Sugar...Sweeteners")covid_diet_data_unique$SugarCrops.and.Sweeteners <- rowSums(covid_diet_data_unique[columns_to_sum])print(as_tibble(head(covid_diet_data_unique, 20)))## # A tibble: 20 × 32
## Country Animal.Products Animal.fats Aquatic.Products..Other
## <chr> <dbl> <dbl> <dbl>
## 1 Afghanistan 4.78 0.850 0
## 2 Albania 16.1 1.06 0
## 3 Algeria 6.03 0.194 0
## 4 Angola 4.69 0.264 0
## 5 Argentina 15.0 1.06 0
## 6 Armenia 12.8 1.77 0
## 7 Australia 15.6 1.90 0
## 8 Austria 15.6 5.25 0
## 9 Azerbaijan 8.17 1.30 0
## 10 Bangladesh 2.37 0.154 0
## 11 Barbados 12.1 1.16 0
## 12 Belarus 12.3 2.67 0
## 13 Belgium 14.2 4.78 0
## 14 Belize 9.18 2.61 0
## 15 Benin 2.49 0.0907 0
## 16 Bolivia 10.0 0.913 0
## 17 Bosnia and Herzegovina 9.06 0.551 0
## 18 Botswana 8.12 1.05 0
## 19 Brazil 13.2 1.52 0
## 20 Bulgaria 12.3 2.46 0
## # ℹ 28 more variables: Cereals...Excluding.Beer <dbl>, Eggs <dbl>,
## # Fish..Seafood <dbl>, Fruits...Excluding.Wine <dbl>, Meat <dbl>,
## # Milk...Excluding.Butter <dbl>, Miscellaneous <dbl>, Offals <dbl>,
## # Oilcrops <dbl>, Pulses <dbl>, Spices <dbl>, Starchy.Roots <dbl>,
## # Sugar.Crops <dbl>, Sugar...Sweeteners <dbl>, Treenuts <dbl>,
## # Vegetal.Products <dbl>, Vegetable.Oils <dbl>, Vegetables <dbl>,
## # Obesity <dbl>, Undernourished <chr>, Confirmed <dbl>, Deaths <dbl>, …columns_to_sum <-c("Meat", "Offals")covid_diet_data_unique$Meat.and.ByProducts <- rowSums(covid_diet_data_unique[columns_to_sum])columns_to_sum <-c("Vegetables", "Vegetal.Products")covid_diet_data_unique$Vegetables.and.products <- rowSums(covid_diet_data_unique[columns_to_sum])columns_to_sum <-c("Aquatic.Products..Other",
"Fish..Seafood")
covid_diet_data_unique$Fish.Seafood.and.Products <- rowSums(covid_diet_data_unique[columns_to_sum])columns_to_sum <-c("Animal.Products", "Meat.and.ByProducts")
covid_diet_data_unique$Meat.and.Products <- rowSums(covid_diet_data_unique[columns_to_sum])columns_to_sum <-c("Cereals...Excluding.Beer", "Starchy.Roots")
covid_diet_data_unique$Grains <- rowSums(covid_diet_data_unique[columns_to_sum])columns_to_sum <-c("Pulses", "Treenuts")
covid_diet_data_unique$Legumes.and.Nuts <- rowSums(covid_diet_data_unique[columns_to_sum])note: Here I changed the column name ““Unit..all.except.Population” into “Unit.for.FoodCategories.NutritionStatus.HealthRates” to specify that only the Food Category columns are in Percentage format, while the rest are in whole number format.
colnames(covid_diet_data_unique)[colnames(covid_diet_data_unique) == "Unit..all.except.Population."] <- "Unit.for.FoodCategories.NutritionStatus.HealthRates"
#The categories of Dietary Diversity Score, Population, Deaths, Confirmed, Active, Recovered are not in whole number format to aid the calculations for analysis.covid_diet_data_unique <- subset(covid_diet_data_unique, select = -c(Sugar.Crops, Sugar...Sweeteners)) covid_diet_data_unique <- subset(covid_diet_data_unique, select = -c(Pulses, Treenuts, Fish..Seafood, Aquatic.Products..Other, Cereals...Excluding.Beer, Starchy.Roots, Animal.Products, Miscellaneous, Meat.and.ByProducts)) Data Analysis
#Calculations
#Demographic Calculations
Note: This area focuses on making calculations using the Non Food Category Columns to be used for my analysis later.
covid_diet_data_unique$MortalityRate <- (covid_diet_data_unique$Deaths /
covid_diet_data_unique$
Confirmed) * 100 covid_diet_data_unique$MorbidityRate <- (covid_diet_data_unique$Confirmed / covid_diet_data_unique$
Population) * 100covid_diet_data_unique$RecoveryRate <- (covid_diet_data_unique$Recovered / covid_diet_data_unique$
Confirmed) * 100covid_diet_data_unique <- covid_diet_data_unique[apply(covid_diet_data_unique[, c("MortalityRate",
"MorbidityRate", "RecoveryRate")], 1,
function(x) all(!is.na(as.numeric(x)))), ]
#ensuring all blanks and NA values were removed in data frame
# This is the cleaned data frame
print(as_tibble(head(covid_diet_data_unique, 20)))## # A tibble: 20 × 30
## Country Animal.fats Eggs Fruits...Excluding.Wine Meat
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Afghanistan 0.850 0.150 1.48 1.20
## 2 Albania 1.06 0.809 3.90 3.87
## 3 Algeria 0.194 0.418 3.18 1.25
## 4 Angola 0.264 0.0441 2.31 2.93
## 5 Argentina 1.06 0.864 1.47 9.45
## 6 Armenia 1.77 0.731 2.53 4.22
## 7 Australia 1.90 0.468 1.66 7.49
## 8 Austria 5.25 0.785 1.60 5.01
## 9 Azerbaijan 1.30 0.548 1.61 2.93
## 10 Bangladesh 0.154 0.231 0.655 0.347
## 11 Barbados 1.16 0.587 1.93 5.66
## 12 Belarus 2.67 0.851 1.40 5.71
## 13 Belgium 4.78 0.69 1.38 3.28
## 14 Belize 2.61 0.297 2.52 3.95
## 15 Benin 0.0907 0.0544 0.762 1.20
## 16 Bolivia 0.913 0.552 2.42 6.75
## 17 Bosnia and Herzegovina 0.551 0.291 1.90 3.03
## 18 Botswana 1.05 0.107 1.03 1.88
## 19 Brazil 1.52 0.446 1.72 7.16
## 20 Bulgaria 2.46 0.584 1.77 4.31
## # ℹ 25 more variables: Milk...Excluding.Butter <dbl>, Offals <dbl>,
## # Oilcrops <dbl>, Spices <dbl>, Vegetal.Products <dbl>, Vegetable.Oils <dbl>,
## # Vegetables <dbl>, Obesity <dbl>, Undernourished <chr>, Confirmed <dbl>,
## # Deaths <dbl>, Recovered <dbl>, Active <dbl>, Population <dbl>,
## # Unit.for.FoodCategories.NutritionStatus.HealthRates <chr>,
## # Fats.and.Oils <dbl>, SugarCrops.and.Sweeteners <dbl>,
## # Vegetables.and.products <dbl>, Fish.Seafood.and.Products <dbl>, …#Dietary Diversity Score Calculation
columns_to_count <- c("Eggs", "Fruits...Excluding.Wine", "Milk...Excluding.Butter", "SugarCrops.and.Sweeteners", "Fats.and.Oils", "Vegetables.and.products", "Meat.and.Products", "Grains", "Legumes.and.Nuts", "Fish.Seafood.and.Products") colnames (covid_diet_data_unique)## [1] "Country"
## [2] "Animal.fats"
## [3] "Eggs"
## [4] "Fruits...Excluding.Wine"
## [5] "Meat"
## [6] "Milk...Excluding.Butter"
## [7] "Offals"
## [8] "Oilcrops"
## [9] "Spices"
## [10] "Vegetal.Products"
## [11] "Vegetable.Oils"
## [12] "Vegetables"
## [13] "Obesity"
## [14] "Undernourished"
## [15] "Confirmed"
## [16] "Deaths"
## [17] "Recovered"
## [18] "Active"
## [19] "Population"
## [20] "Unit.for.FoodCategories.NutritionStatus.HealthRates"
## [21] "Fats.and.Oils"
## [22] "SugarCrops.and.Sweeteners"
## [23] "Vegetables.and.products"
## [24] "Fish.Seafood.and.Products"
## [25] "Meat.and.Products"
## [26] "Grains"
## [27] "Legumes.and.Nuts"
## [28] "MortalityRate"
## [29] "MorbidityRate"
## [30] "RecoveryRate"covid_diet_data_unique$Dietary.Diversity.Score <- apply(covid_diet_data_unique[, columns_to_count],
1,
function(x) sum(x >= 0.05))
#created a function to count only the values not less than 0.05 or 5 % as values below this would not have a significant contribution to a population's Dietary Intake. # Display the updated data frame
print(as_tibble(head(covid_diet_data_unique, 20)))## # A tibble: 20 × 31
## Country Animal.fats Eggs Fruits...Excluding.Wine Meat
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Afghanistan 0.850 0.150 1.48 1.20
## 2 Albania 1.06 0.809 3.90 3.87
## 3 Algeria 0.194 0.418 3.18 1.25
## 4 Angola 0.264 0.0441 2.31 2.93
## 5 Argentina 1.06 0.864 1.47 9.45
## 6 Armenia 1.77 0.731 2.53 4.22
## 7 Australia 1.90 0.468 1.66 7.49
## 8 Austria 5.25 0.785 1.60 5.01
## 9 Azerbaijan 1.30 0.548 1.61 2.93
## 10 Bangladesh 0.154 0.231 0.655 0.347
## 11 Barbados 1.16 0.587 1.93 5.66
## 12 Belarus 2.67 0.851 1.40 5.71
## 13 Belgium 4.78 0.69 1.38 3.28
## 14 Belize 2.61 0.297 2.52 3.95
## 15 Benin 0.0907 0.0544 0.762 1.20
## 16 Bolivia 0.913 0.552 2.42 6.75
## 17 Bosnia and Herzegovina 0.551 0.291 1.90 3.03
## 18 Botswana 1.05 0.107 1.03 1.88
## 19 Brazil 1.52 0.446 1.72 7.16
## 20 Bulgaria 2.46 0.584 1.77 4.31
## # ℹ 26 more variables: Milk...Excluding.Butter <dbl>, Offals <dbl>,
## # Oilcrops <dbl>, Spices <dbl>, Vegetal.Products <dbl>, Vegetable.Oils <dbl>,
## # Vegetables <dbl>, Obesity <dbl>, Undernourished <chr>, Confirmed <dbl>,
## # Deaths <dbl>, Recovered <dbl>, Active <dbl>, Population <dbl>,
## # Unit.for.FoodCategories.NutritionStatus.HealthRates <chr>,
## # Fats.and.Oils <dbl>, SugarCrops.and.Sweeteners <dbl>,
## # Vegetables.and.products <dbl>, Fish.Seafood.and.Products <dbl>, …desired_column_order <- c( "Country", "Eggs", "Fats.and.Oils", "Fish.Seafood.and.Products",
"Fruits...Excluding.Wine", "Grains", "Legumes.and.Nuts", "Meat.and.Products",
"Milk...Excluding.Butter", "SugarCrops.and.Sweeteners", "Vegetables.and.products",
"Dietary.Diversity.Score", "Obesity", "Undernourished",
"Population", "Confirmed",
"Deaths", "Recovered", "Active", "MortalityRate", "MorbidityRate", "RecoveryRate", "Unit.for.FoodCategories.NutritionStatus.HealthRates")
covid_diet_data_unique <- covid_diet_data_unique[, desired_column_order]
head(covid_diet_data_unique)## Country Eggs Fats.and.Oils Fish.Seafood.and.Products
## 1 Afghanistan 0.1501 3.3267 0.0000
## 2 Albania 0.8091 4.9721 0.1471
## 3 Algeria 0.4181 6.2267 0.1195
## 4 Angola 0.0441 5.6180 0.8372
## 6 Argentina 0.8643 6.6369 0.2006
## 7 Armenia 0.7310 5.5717 0.1787
## Fruits...Excluding.Wine Grains Legumes.and.Nuts Meat.and.Products
## 1 1.4757 37.4438 0.6254 6.1031
## 2 3.8982 17.4758 1.2063 20.2266
## 3 3.1805 26.9374 1.3140 7.3466
## 4 2.3133 30.9760 1.4981 7.7331
## 6 1.4663 18.1972 0.1698 24.6952
## 7 2.5341 20.5166 0.7472 17.3489
## Milk...Excluding.Butter SugarCrops.and.Sweeteners Vegetables.and.products
## 1 2.4512 2.2261 45.9980
## 2 9.9441 3.4422 36.6578
## 3 3.9869 3.9869 46.0206
## 4 0.5067 2.7539 45.6709
## 6 3.1641 7.0536 35.8543
## 7 5.6368 5.2956 40.3834
## Dietary.Diversity.Score Obesity Undernourished Population Confirmed Deaths
## 1 9 4.5 29.8 38928000 14 1
## 2 10 22.3 6.2 2838000 297 5
## 3 10 26.6 3.9 44357000 24 1
## 4 9 6.8 25 32522000 6 0
## 6 10 28.5 4.6 45377000 436 11
## 7 10 20.9 4.3 2956000 568 11
## Recovered Active MortalityRate MorbidityRate RecoveryRate
## 1 12 1 7.142857 0.00003596383 85.71429
## 2 179 112 1.683502 0.01046511628 60.26936
## 3 17 7 4.166667 0.00005410645 70.83333
## 4 6 0 0.000000 0.00001844905 100.00000
## 6 391 34 2.522936 0.00096083919 89.67890
## 7 540 18 1.936620 0.01921515562 95.07042
## Unit.for.FoodCategories.NutritionStatus.HealthRates
## 1 %
## 2 %
## 3 %
## 4 %
## 6 %
## 7 %Data Visualization
Note: Setting the data frames for demographic plots. I chose to focus on the countries with the Lowest Mortality and Morbidity rate, and countries with the Highest Recovery rates to examine the food categories potentially influencing these Health Rate types.
##COVID 19 Demographics
mortality_data <- covid_diet_data_unique %>%
arrange(MortalityRate) %>%
head(5) %>%
mutate(RateType = "MortalityRate")
print(mortality_data)## Country Eggs Fats.and.Oils Fish.Seafood.and.Products
## 1 Angola 0.0441 5.6180 0.8372
## 2 Australia 0.4681 10.9484 0.6040
## 3 Bangladesh 0.2312 3.7179 0.8284
## 4 Benin 0.0544 6.6061 0.5263
## 5 Burkina Faso 0.1471 8.0161 0.2574
## Fruits...Excluding.Wine Grains Legumes.and.Nuts Meat.and.Products
## 1 2.3133 30.9760 1.4981 7.7331
## 2 1.6611 12.9568 0.9816 23.4521
## 3 0.6550 39.4529 1.3485 2.7548
## 4 0.7623 32.9401 2.1597 3.7568
## 5 0.1471 32.0831 2.5004 4.0448
## Milk...Excluding.Butter SugarCrops.and.Sweeteners Vegetables.and.products
## 1 0.5067 2.7539 45.6709
## 2 4.8022 5.7989 35.5180
## 3 0.7706 1.4448 48.2566
## 4 0.5263 1.4338 48.3848
## 5 1.0112 1.7650 47.3984
## Dietary.Diversity.Score Obesity Undernourished Population Confirmed Deaths
## 1 9 6.8 25 32522000 6 0
## 2 10 30.4 <2.5 25754000 11 0
## 3 10 3.4 14.7 169809000 32 0
## 4 10 8.2 10.1 12209000 3 0
## 5 10 4.5 20 20903000 5 0
## Recovered Active MortalityRate MorbidityRate RecoveryRate
## 1 6 0 0 0.00001844905 100.00000
## 2 10 1 0 0.00004271181 90.90909
## 3 28 3 0 0.00001884470 87.50000
## 4 3 0 0 0.00002457204 100.00000
## 5 5 1 0 0.00002392001 100.00000
## Unit.for.FoodCategories.NutritionStatus.HealthRates RateType
## 1 % MortalityRate
## 2 % MortalityRate
## 3 % MortalityRate
## 4 % MortalityRate
## 5 % MortalityRatemorbidity_data <- covid_diet_data_unique %>%
arrange(MorbidityRate) %>%
head(5) %>%
mutate(RateType = "MorbidityRate")
print(morbidity_data)## Country Eggs Fats.and.Oils Fish.Seafood.and.Products
## 1 China 1.2349 5.8082 1.3081
## 2 Yemen 0.1455 4.4606 0.1697
## 3 Nigeria 0.1624 6.7181 0.3450
## 4 India 0.2384 6.8137 0.2582
## 5 Thailand 0.9020 5.7304 1.1496
## Fruits...Excluding.Wine Grains Legumes.and.Nuts Meat.and.Products
## 1 1.2924 18.7850 0.9524 26.7438
## 2 1.1879 32.3879 1.2363 5.6000
## 3 1.4410 33.8543 2.1514 2.3544
## 4 1.4303 28.7644 3.3373 5.8801
## 5 1.8394 24.2483 0.5306 9.5507
## Milk...Excluding.Butter SugarCrops.and.Sweeteners Vegetables.and.products
## 1 1.8890 3.3488 36.2880
## 2 0.6545 7.0303 46.9091
## 3 0.2233 1.6034 49.4824
## 4 3.2578 3.5757 45.5701
## 5 0.8490 7.7290 44.1812
## Dietary.Diversity.Score Obesity Undernourished Population Confirmed Deaths
## 1 10 6.6 8.5 1402385000 1 0
## 2 10 14.1 38.9 29826000 1 0
## 3 10 7.8 13.4 206140000 7 0
## 4 10 3.8 14.5 1400100000 77 1
## 5 10 10.8 7.8 66534000 4 0
## Recovered Active MortalityRate MorbidityRate RecoveryRate
## 1 1 0 0.000000 0.00000007130709 100.00000
## 2 0 0 0.000000 0.00000335277945 0.00000
## 3 5 1 0.000000 0.00000339575046 71.42857
## 4 75 1 1.298701 0.00000549960717 97.40260
## 5 3 1 0.000000 0.00000601196381 75.00000
## Unit.for.FoodCategories.NutritionStatus.HealthRates RateType
## 1 % MorbidityRate
## 2 % MorbidityRate
## 3 % MorbidityRate
## 4 % MorbidityRate
## 5 % MorbidityRaterecovery_data <- covid_diet_data_unique %>%
arrange(desc(RecoveryRate)) %>%
head(5) %>%
mutate(RateType = "RecoveryRate")
print(recovery_data)## Country Eggs Fats.and.Oils Fish.Seafood.and.Products
## 1 Angola 0.0441 5.6180 0.8372
## 2 Benin 0.0544 6.6061 0.5263
## 3 Burkina Faso 0.1471 8.0161 0.2574
## 4 Central African Republic 0.0569 7.8476 0.4265
## 5 Chad 0.0239 8.1379 0.4787
## Fruits...Excluding.Wine Grains Legumes.and.Nuts Meat.and.Products
## 1 2.3133 30.9760 1.4981 7.7331
## 2 0.7623 32.9401 2.1597 3.7568
## 3 0.1471 32.0831 2.5004 4.0448
## 4 1.9050 28.6324 2.2747 12.7097
## 5 0.3112 30.9957 2.0345 10.5553
## Milk...Excluding.Butter SugarCrops.and.Sweeteners Vegetables.and.products
## 1 0.5067 2.7539 45.6709
## 2 0.5263 1.4338 48.3848
## 3 1.0112 1.7650 47.3984
## 4 0.8814 1.7060 42.9059
## 5 2.0345 2.0823 43.1785
## Dietary.Diversity.Score Obesity Undernourished Population Confirmed Deaths
## 1 9 6.8 25 32522000 6 0
## 2 10 8.2 10.1 12209000 3 0
## 3 10 4.5 20 20903000 5 0
## 4 10 6.3 59.6 4830000 10 0
## 5 9 4.8 37.5 16877000 2 0
## Recovered Active MortalityRate MorbidityRate RecoveryRate
## 1 6 0 0 0.00001844905 100
## 2 3 0 0 0.00002457204 100
## 3 5 1 0 0.00002392001 100
## 4 10 0 0 0.00020703934 100
## 5 2 0 0 0.00001185045 100
## Unit.for.FoodCategories.NutritionStatus.HealthRates RateType
## 1 % RecoveryRate
## 2 % RecoveryRate
## 3 % RecoveryRate
## 4 % RecoveryRate
## 5 % RecoveryRatemortality_plot <- ggplot(mortality_data, aes(x = reorder(Country, -MortalityRate), y = MortalityRate,
fill = "MortalityRate")) +
geom_bar(stat = "identity", show.legend = FALSE) +
scale_fill_manual(values = c("MortalityRate" =
"#ADD8E6")) +
labs(title = "Lowest Mortality Rates by Country", x = "Country", y = "Mortality Rate") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust
= 1))
#rounded up the Top 5 Countries with the lowest Mortality Rate # Morbidity Rate Plot
morbidity_plot <- ggplot(morbidity_data, aes(x = reorder(Country, -MorbidityRate), y = MorbidityRate,
fill = "MorbidityRate")) +
geom_bar(stat = "identity", show.legend = FALSE) +
scale_fill_manual(values = c("MorbidityRate" =
"pink")) +
labs(title = "Lowest Morbidity Rates by Country",
x = "Country", y = "Morbidity Rate") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust
= 1))
#rounded up the Top 5 Countries with the lowest Morbdidity Rate # Recovery Rate Plot
recovery_plot <- ggplot(recovery_data, aes(x = reorder(Country, -RecoveryRate), y = RecoveryRate,
fill = "RecoveryRate")) +
geom_bar(stat = "identity", show.legend = FALSE) +
scale_fill_manual(values = c("RecoveryRate" =
"purple")) +
labs(title = "Highest Recovery Rates by Country",
x = "Country", y = "Recovery Rate") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45,
hjust = 1))
#rounded up the Top 5 Countries with the highest Recovery Rate # Save Mortality Rate Plot
ggsave("images/mortality_rate_plot.png", plot = mortality_plot, width = 8, height = 6)
Plot 1
Analysis: The top 5 Low Mortality Rate Countries with all recorded 0% Mortality Rate. It can be observed that most of the countries originated from Africa with the exception of Australia and Bangladesh.
# Save Morbidity Rate Plot
ggsave("images/morbidity_rate_plot.png", plot = morbidity_plot, width = 8, height = 6)
Plot 2
Analysis: the Top 5 Countries with the lowest Morbidity Rate recorded values between 0.0000 - 0.0006 %. It can be observed that this group consists primarily of Asian countries with the exception of Yemen and Nigeria.
# Save Recovery Rate Plot
ggsave("images/recovery_rate_plot.png", plot = recovery_plot, width = 8, height = 6)
Plot 3
Analysis: The Top 5 Countries with the Highest Recovery Rate consists of African countries. Recording Recovery Rate values of 100%.
##Food Consumption Data
Note: the focus of this area was to ranking the top 5 most consumed Food Categories for the Lowest Mortality, Lowest Morbidity Rate and Highest Recovery Rate countries
food_columns <- c("Eggs", "Fats.and.Oils", "Fish.Seafood.and.Products",
"Fruits...Excluding.Wine", "Grains", "Legumes.and.Nuts",
"Meat.and.Products", "Milk...Excluding.Butter",
"SugarCrops.and.Sweeteners", "Vegetables.and.products")
# Reshape the data for mortality_data to long format for plotting
mortality_long <- mortality_data %>%
select(Country, food_columns) %>% # Select the relevant columns
gather(key = "FoodCategory", value = "Consumption", -Country) %>% # Convert from wide to long format
arrange(desc(Consumption)) %>% # Sort by Consumption in descending order
group_by(Country) %>% # Group by Country
slice_head(n = 5) %>% # Get the top 5 highest food categories for each country
ungroup() # Ungroup after slicing## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(food_columns)
##
## # Now:
## data %>% select(all_of(food_columns))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.print(mortality_long)## # A tibble: 25 × 3
## Country FoodCategory Consumption
## <chr> <chr> <dbl>
## 1 Angola Vegetables.and.products 45.7
## 2 Angola Grains 31.0
## 3 Angola Meat.and.Products 7.73
## 4 Angola Fats.and.Oils 5.62
## 5 Angola SugarCrops.and.Sweeteners 2.75
## 6 Australia Vegetables.and.products 35.5
## 7 Australia Meat.and.Products 23.5
## 8 Australia Grains 13.0
## 9 Australia Fats.and.Oils 10.9
## 10 Australia SugarCrops.and.Sweeteners 5.80
## # ℹ 15 more rows# Create the facet-wrapped bar plot
food_mortality_plot <- ggplot(mortality_long, aes(x = reorder(FoodCategory, Consumption), y =
Consumption, fill = FoodCategory)) +
geom_bar(stat = "identity", width = 0.7) + # Bar plot for the consumption values
facet_wrap(~ Country, scales = "free_y") + # Facet by Country with free y-axis scales
labs(title = "Top Food Category Consumption of Low Mortality Countries",
x = "Food Category",
y = "Consumption") +
theme_minimal() +
theme(
legend.position = "none", # Hide the legend
strip.text.x = element_text(size = 8.5), # Adjust facet label size
axis.text.x = element_text(angle = 45, hjust = 1) # Tilt x-axis labels for readability
)
ggsave("images/food_mortality_plot.png", plot = food_mortality_plot, width = 10, height = 6, dpi = 300,
device = "png")
Plot 4
Analysis:
Among the Lowest Mortality Rate Countries, the primary Food Category that comprises the populations diet were Vegetables/Vegetable Products and Grains comprising between 38.5-45.5% and 12.9-39.4% respectively. Meat and Meat products came in fourth comprising 2.7% -23.4%, Fats and Oils comprising between 3.7-10.9%.
food_columns <- c("Eggs", "Fats.and.Oils", "Fish.Seafood.and.Products",
"Fruits...Excluding.Wine", "Grains", "Legumes.and.Nuts",
"Meat.and.Products", "Milk...Excluding.Butter",
"SugarCrops.and.Sweeteners", "Vegetables.and.products")
# Reshape the data for mortality_data to long format for plotting
morbidity_long <- morbidity_data %>%
select(Country, food_columns) %>% # Select the relevant columns
gather(key = "FoodCategory", value = "Consumption", -Country) %>% # Convert from wide to long format
arrange(desc(Consumption)) %>% # Sort by Consumption in descending order
group_by(Country) %>% # Group by Country
slice_head(n = 5) %>% # Get the top 5 highest food categories for each country
ungroup() # Ungroup after slicing
print(morbidity_long)## # A tibble: 25 × 3
## Country FoodCategory Consumption
## <chr> <chr> <dbl>
## 1 China Vegetables.and.products 36.3
## 2 China Meat.and.Products 26.7
## 3 China Grains 18.8
## 4 China Fats.and.Oils 5.81
## 5 China SugarCrops.and.Sweeteners 3.35
## 6 India Vegetables.and.products 45.6
## 7 India Grains 28.8
## 8 India Fats.and.Oils 6.81
## 9 India Meat.and.Products 5.88
## 10 India SugarCrops.and.Sweeteners 3.58
## # ℹ 15 more rows# Create the facet-wrapped bar plot
food_morbidity_plot <- ggplot(morbidity_long, aes(x = reorder(FoodCategory, Consumption), y =
Consumption, fill = FoodCategory)) +
geom_bar(stat = "identity", width = 0.7) + # Bar plot for the consumption values
facet_wrap(~ Country, scales = "free_y") + # Facet by Country with free y-axis scales
labs(title = "Top Food Category Consumption of Low Morbidity Countries",
x = "Food Category",
y = "Consumption") +
theme_minimal() +
theme(
legend.position = "none", # Hide the legend
strip.text.x = element_text(size = 8.5), # Adjust facet label size
axis.text.x = element_text(angle = 45, hjust = 1) # Tilt x-axis labels for readability
)
ggsave("images/food_morbidity_plot.png", plot = food_morbidity_plot, width = 10, height = 6, dpi = 300,
device = "png")
Plot 5
Analysis: Among the Lowest Morbidity Rate Countries, the primary Food Category that comprises the populations diet were Vegetables/Vegetable Products and Grains comprising between 36.2-49.5% and 18.7-33.8% respectively. Meat and Meat products comprising 2.4 - 26.7%. This was followed by Fats and oil ranging between 4.4 -6.8%.
# Define the food category columns
food_columns <- c("Eggs", "Fats.and.Oils", "Fish.Seafood.and.Products",
"Fruits...Excluding.Wine", "Grains", "Legumes.and.Nuts",
"Meat.and.Products", "Milk...Excluding.Butter",
"SugarCrops.and.Sweeteners", "Vegetables.and.products")
# Reshape the data for mortality_data to long format for plotting
recovery_long <- recovery_data %>%
select(Country, food_columns) %>% # Select the relevant columns
gather(key = "FoodCategory", value = "Consumption", -Country) %>% # Convert from wide to long format
arrange(desc(Consumption)) %>% # Sort by Consumption in descending order
group_by(Country) %>% # Group by Country
slice_head(n = 5) %>% # Get the top 5 highest food categories for each country
ungroup() # Ungroup after slicing
print(recovery_long)## # A tibble: 25 × 3
## Country FoodCategory Consumption
## <chr> <chr> <dbl>
## 1 Angola Vegetables.and.products 45.7
## 2 Angola Grains 31.0
## 3 Angola Meat.and.Products 7.73
## 4 Angola Fats.and.Oils 5.62
## 5 Angola SugarCrops.and.Sweeteners 2.75
## 6 Benin Vegetables.and.products 48.4
## 7 Benin Grains 32.9
## 8 Benin Fats.and.Oils 6.61
## 9 Benin Meat.and.Products 3.76
## 10 Benin Legumes.and.Nuts 2.16
## # ℹ 15 more rows# Create the facet-wrapped bar plot
food_recovery_plot <- ggplot(recovery_long, aes(x = reorder(FoodCategory, Consumption), y =
Consumption, fill = FoodCategory)) +
geom_bar(stat = "identity", width = 0.7) + # Bar plot for the consumption values
facet_wrap(~ Country, scales = "free_y") + # Facet by Country with free y-axis scales
labs(title = "Top Food Category Consumption of High Recovery
Rate Countries",
x = "Food Category",
y = "Consumption") +
theme_minimal() +
theme(
legend.position = "none", # Hide the legend
strip.text.x = element_text(size = 8.5), # Adjust facet label size
axis.text.x = element_text(angle = 45, hjust = 1) # Tilt x-axis labels for readability
)
ggsave("images/food_recovery_plot.png", plot = food_recovery_plot, width = 10, height = 6, dpi = 300,
device = "png")
Plot 6
Analysis: Among the High Recovery Rate Countries, the primary Food Category that comprises the populations diet were Vegetables/Vegetable Products and Grains comprising between 48.4-43.9% and 28.6-32.9% respectively. Meat and Meat products comprising 3.7 - 12.7%. This was followed by Fats and oil ranging between 5.6 -8.1%.
##Dietary Diversity Score
note: this area explored the correlation between the Dietary Diversity Score and COVID 19 health outcomes in Low Mortality Rate Countries, Low Morbidity Rate Countries and High Recovery Rate Countries.
mortality_data <- covid_diet_data_unique %>%
arrange(MortalityRate) %>%
head(5) %>%
mutate(RateType = "LowMortalityRate")
morbidity_data <- covid_diet_data_unique %>%
arrange(MorbidityRate) %>%
head(5) %>%
mutate(RateType = "LowMorbidityRate")
recovery_data <- covid_diet_data_unique %>%
arrange(desc(RecoveryRate)) %>%
head(5) %>%
mutate(RateType = "HighRecoveryRate")
# Combine the datasets into one and arrange by country in alphabetical order
combined_data <- bind_rows(
mortality_data,
morbidity_data,
recovery_data
) %>%
arrange(Country) # Sort by Country alphabetically
print(combined_data)## Country Eggs Fats.and.Oils Fish.Seafood.and.Products
## 1 Angola 0.0441 5.6180 0.8372
## 2 Angola 0.0441 5.6180 0.8372
## 3 Australia 0.4681 10.9484 0.6040
## 4 Bangladesh 0.2312 3.7179 0.8284
## 5 Benin 0.0544 6.6061 0.5263
## 6 Benin 0.0544 6.6061 0.5263
## 7 Burkina Faso 0.1471 8.0161 0.2574
## 8 Burkina Faso 0.1471 8.0161 0.2574
## 9 Central African Republic 0.0569 7.8476 0.4265
## 10 Chad 0.0239 8.1379 0.4787
## 11 China 1.2349 5.8082 1.3081
## 12 India 0.2384 6.8137 0.2582
## 13 Nigeria 0.1624 6.7181 0.3450
## 14 Thailand 0.9020 5.7304 1.1496
## 15 Yemen 0.1455 4.4606 0.1697
## Fruits...Excluding.Wine Grains Legumes.and.Nuts Meat.and.Products
## 1 2.3133 30.9760 1.4981 7.7331
## 2 2.3133 30.9760 1.4981 7.7331
## 3 1.6611 12.9568 0.9816 23.4521
## 4 0.6550 39.4529 1.3485 2.7548
## 5 0.7623 32.9401 2.1597 3.7568
## 6 0.7623 32.9401 2.1597 3.7568
## 7 0.1471 32.0831 2.5004 4.0448
## 8 0.1471 32.0831 2.5004 4.0448
## 9 1.9050 28.6324 2.2747 12.7097
## 10 0.3112 30.9957 2.0345 10.5553
## 11 1.2924 18.7850 0.9524 26.7438
## 12 1.4303 28.7644 3.3373 5.8801
## 13 1.4410 33.8543 2.1514 2.3544
## 14 1.8394 24.2483 0.5306 9.5507
## 15 1.1879 32.3879 1.2363 5.6000
## Milk...Excluding.Butter SugarCrops.and.Sweeteners Vegetables.and.products
## 1 0.5067 2.7539 45.6709
## 2 0.5067 2.7539 45.6709
## 3 4.8022 5.7989 35.5180
## 4 0.7706 1.4448 48.2566
## 5 0.5263 1.4338 48.3848
## 6 0.5263 1.4338 48.3848
## 7 1.0112 1.7650 47.3984
## 8 1.0112 1.7650 47.3984
## 9 0.8814 1.7060 42.9059
## 10 2.0345 2.0823 43.1785
## 11 1.8890 3.3488 36.2880
## 12 3.2578 3.5757 45.5701
## 13 0.2233 1.6034 49.4824
## 14 0.8490 7.7290 44.1812
## 15 0.6545 7.0303 46.9091
## Dietary.Diversity.Score Obesity Undernourished Population Confirmed Deaths
## 1 9 6.8 25 32522000 6 0
## 2 9 6.8 25 32522000 6 0
## 3 10 30.4 <2.5 25754000 11 0
## 4 10 3.4 14.7 169809000 32 0
## 5 10 8.2 10.1 12209000 3 0
## 6 10 8.2 10.1 12209000 3 0
## 7 10 4.5 20 20903000 5 0
## 8 10 4.5 20 20903000 5 0
## 9 10 6.3 59.6 4830000 10 0
## 10 9 4.8 37.5 16877000 2 0
## 11 10 6.6 8.5 1402385000 1 0
## 12 10 3.8 14.5 1400100000 77 1
## 13 10 7.8 13.4 206140000 7 0
## 14 10 10.8 7.8 66534000 4 0
## 15 10 14.1 38.9 29826000 1 0
## Recovered Active MortalityRate MorbidityRate RecoveryRate
## 1 6 0 0.000000 0.00001844904987 100.00000
## 2 6 0 0.000000 0.00001844904987 100.00000
## 3 10 1 0.000000 0.00004271181176 90.90909
## 4 28 3 0.000000 0.00001884470199 87.50000
## 5 3 0 0.000000 0.00002457203702 100.00000
## 6 3 0 0.000000 0.00002457203702 100.00000
## 7 5 1 0.000000 0.00002392001148 100.00000
## 8 5 1 0.000000 0.00002392001148 100.00000
## 9 10 0 0.000000 0.00020703933747 100.00000
## 10 2 0 0.000000 0.00001185044735 100.00000
## 11 1 0 0.000000 0.00000007130709 100.00000
## 12 75 1 1.298701 0.00000549960717 97.40260
## 13 5 1 0.000000 0.00000339575046 71.42857
## 14 3 1 0.000000 0.00000601196381 75.00000
## 15 0 0 0.000000 0.00000335277945 0.00000
## Unit.for.FoodCategories.NutritionStatus.HealthRates RateType
## 1 % LowMortalityRate
## 2 % HighRecoveryRate
## 3 % LowMortalityRate
## 4 % LowMortalityRate
## 5 % LowMortalityRate
## 6 % HighRecoveryRate
## 7 % LowMortalityRate
## 8 % HighRecoveryRate
## 9 % HighRecoveryRate
## 10 % HighRecoveryRate
## 11 % LowMorbidityRate
## 12 % LowMorbidityRate
## 13 % LowMorbidityRate
## 14 % LowMorbidityRate
## 15 % LowMorbidityRate#Distribution of DDS values
Dietary_Diversity_Score_Plot <- ggplot(combined_data, aes(x = Country, y =
Dietary.Diversity.Score, color = RateType)) +
geom_point(size = 3) + # Plot individual points with size 3
labs(title = "Dietary Diversity Score by Country",
x = "Country",
y = "Dietary Diversity Score") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 90, hjust = 1), # Rotate x-axis labels for readability
legend.position = "top" # Place the legend at the top for better visibility
) +
scale_y_continuous(breaks = c(9, 10)) # Set y-axis to display only 9 and 10ggsave("images/Dietary_Diversity_Score_Plot.png", plot = Dietary_Diversity_Score_Plot, width = 10, height = 6, dpi = 300, device = "png")
Plot 7
Analysis: By observing the Dot Plot, It can be deduced that a Dietary Diversity Score not lower than 9, is positively associated with better COVID 19 health outcomes. It shows that a Dietary Diversity Score of 9 could aid in the Recovery process. Meanwhile, a Dietary Diversity Score of 10 could prevent infection with the virus, improve recovery and potentially increase survival.
This could suggest that consuming a wider variety of food groups contributes to improved Nutritional Status which might increase protection, boost recovery and potentially increases the chances of survival from the virus.
Analysis Summary
Demographic Distribution
It can be observed that countries belonging to the categories of Lowest Mortality Rate, Low Morbidity Rate and High Recovery Rates are are predominantly from Asia and Africa. This reveals that geographic and demographic patterns that shape health outcomes. With African countries leading in Recovery Rates and Asian countries primarily constituting a great portion of Lowest Mortality Rates. This could suggest that Population density plays a role in these trends but varies widely across the regions.
It is important to note that factors such as the accuracy of COVID-19 case reporting and a country’s capacity for large-scale testing may influence the data and affect the reliability of the statistics.
Food Consumption
The vegetable/vegetable product and grain proportions in the diets of the countries belonging to the respective categories Lowest Mortality Rate, Lowest Morbidity Rates and Highest Recovery Rate countries are in line with WHO recommendations for a balanced and healthy diet. Their relatively low meat consumption aligns with WHO’s guidance to limit red and processed meats. Meanwhile their Fats and oils consumption also fall fit under general recommendations,but the types of fats consumed must be considered for a more comprehensive comparison. Overall, these dietary trends contribute to lower morbidity rates and reflect a generally healthy, plant-based approach to nutrition.
Dietary Diversity Score
A score of 9 appears to support the recovery process, while a score of 10 may help prevent infection, enhance recovery, and potentially improve survival rates.
This suggests that consuming a broader range of food groups contributes to improved Nutritional Status which might increase protection, boost recovery and potentially increases the chances of survival from the virus.