Fruits & Vegetables Prices and Calorie Guidelines
How much do fruits and vegetables cost? Economic Research Service estimated average prices for over 150 commonly consumed fresh and processed fruits and vegetables. Reported estimates include each product’s average retail price per pound and per edible cup equivalent (the unit of measurement for Federal recommendations for fruit and vegetable consumption). For many fruits and vegetables, a 1-cup equivalent equals the weight of enough edible food to fill a measuring cup. ERS calculated average prices at retail stores using 2013 and 2016 retail scanner data from Information Resources, Inc. (IRI). A selection of retail establishments-grocery stores, supermarkets, supercenters, convenience stores, drug stores, and liquor stores-across the U.S. provides IRI with weekly retail sales data (revenue and quantity).
ERS reports average prices per edible cup equivalent to inform policymakers and nutritionists about how much money it costs Americans to eat a sufficient quantity and variety of fruits and vegetables. Every five years the Departments of Agriculture and Health and Human Services release a new version of the Dietary Guidelines for Americans with information about how individuals can achieve a healthy diet. However, the average American falls short in meeting these recommendations. Many people consume too many calories from refined grains, solid fats, and added sugars, and do not eat enough whole grains, fruits, and vegetables.
Are food prices a barrier to eating a healthy diet? ERS research using this data set shows that, in 2013, it was possible for a person on a 2,000-calorie diet to eat a sufficient quantity and variety of fruits and vegetables for about $2.10 to $2.60 per day. The report also illustrates the variety of fruits and vegetables affordable to a family on a limited budget
Analysis of the Impact on Food Caloric Intake and Costs of substituting certain groups of Snacks with certain categories of Fruits & Vegetables
Installing and Loading necessary packages
#install.packages("dplyr")
#install.packages("tidyr")
#install.packages("readxl")
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, union
library(tidyr)
library(stringr)
library(readxl)
library(ggplot2)
1st Data set - Calorie Intake Impact on Snack Substitution
Load data set (CSV) into R
Download target file locally from github “caloricimpacts (untidy dataset).xls”
setwd("C:/DATA/HHP/Personal/Degrees/Ms. Data Science (CUNY)/R Working Dir")
calorieimpact_raw <- data.frame(read_excel("caloricimpacts (untidy dataset).xls", skip = 1, col_names = TRUE))
dim(calorieimpact_raw)
## [1] 26 23
Data Wrangling
Pivoting columns to a key column and Renaming columns
calorieimpact <- gather(calorieimpact_raw, "FruitVeg", "DiffCalories", 3:22)
names(calorieimpact) <- c("Snack", "SnackCalories", "FruitVeg", "DiffCalories")
head(calorieimpact,22)
## Snack SnackCalories
## 1 Chocolate candy (milk chocolate candies) 262
## 2 Cookies (chocolate chip, soft) 123
## 3 Corn chips 140
## 4 Crackers (wheat) 114
## 5 Cupcakes (chocolate, with low-fat frosting) 174
## 6 Danish (with fruit) 271
## 7 Donuts (yeast-leavened, glazed) 235
## 8 Fruit rolls 82
## 9 Graham crackers 102
## 10 Granola bars (oats, fruit, and nut) 119
## 11 Ice cream (vanilla, light) 196
## 12 Muffins (blueberry) 369
## 13 Pizza, from frozen (cheese, regular crust) 252
## 14 Popsicles and bars (fruit/juice bars) 80
## 15 Potato chips (plain, salted) 169
## 16 Pretzels (hard, plain, salted) 168
## 17 Pudding, ready-to-eat (vanilla) 152
## 18 Sandwich crackers (rye with cheese filling) 183
## 19 Toaster pastries (fruit frosted) 299
## 20 Tortilla chips (white) 161
## 21 Chocolate candy (milk chocolate candies) 262
## 22 Cookies (chocolate chip, soft) 123
## FruitVeg DiffCalories
## 1 Apples 185
## 2 Apples 46
## 3 Apples 63
## 4 Apples 37
## 5 Apples 97
## 6 Apples 194
## 7 Apples 158
## 8 Apples 5
## 9 Apples 25
## 10 Apples 42
## 11 Apples 119
## 12 Apples 292
## 13 Apples 175
## 14 Apples 3
## 15 Apples 92
## 16 Apples 91
## 17 Apples 75
## 18 Apples 106
## 19 Apples 222
## 20 Apples 84
## 21 Applesauce..sweetened. 162
## 22 Applesauce..sweetened. 23
Casting data types, creating calculated-columns, arranging column layout
calorieimpact$SnackCalories <- as.numeric(calorieimpact$SnackCalories)
calorieimpact$DiffCalories <- as.numeric(calorieimpact$DiffCalories)
calorieimpact <- mutate(calorieimpact, FruitVegCalories = SnackCalories-DiffCalories)
avgCalChgbyFruitVeg <- summarise(group_by(calorieimpact, FruitVeg), AvgCalChgbyFruitVeg = round(mean(DiffCalories)))
calorieimpact <- calorieimpact %>% inner_join(avgCalChgbyFruitVeg, by = c("FruitVeg"))
calorieimpactfinal <- select(calorieimpact, Snack, SnackCalories, FruitVeg, FruitVegCalories, AvgCalChgbyFruitVeg, DiffCalories)
head(calorieimpactfinal,22)
## Snack SnackCalories
## 1 Chocolate candy (milk chocolate candies) 262
## 2 Cookies (chocolate chip, soft) 123
## 3 Corn chips 140
## 4 Crackers (wheat) 114
## 5 Cupcakes (chocolate, with low-fat frosting) 174
## 6 Danish (with fruit) 271
## 7 Donuts (yeast-leavened, glazed) 235
## 8 Fruit rolls 82
## 9 Graham crackers 102
## 10 Granola bars (oats, fruit, and nut) 119
## 11 Ice cream (vanilla, light) 196
## 12 Muffins (blueberry) 369
## 13 Pizza, from frozen (cheese, regular crust) 252
## 14 Popsicles and bars (fruit/juice bars) 80
## 15 Potato chips (plain, salted) 169
## 16 Pretzels (hard, plain, salted) 168
## 17 Pudding, ready-to-eat (vanilla) 152
## 18 Sandwich crackers (rye with cheese filling) 183
## 19 Toaster pastries (fruit frosted) 299
## 20 Tortilla chips (white) 161
## 21 Chocolate candy (milk chocolate candies) 262
## 22 Cookies (chocolate chip, soft) 123
## FruitVeg FruitVegCalories AvgCalChgbyFruitVeg
## 1 Apples 77 106
## 2 Apples 77 106
## 3 Apples 77 106
## 4 Apples 77 106
## 5 Apples 77 106
## 6 Apples 77 106
## 7 Apples 77 106
## 8 Apples 77 106
## 9 Apples 77 106
## 10 Apples 77 106
## 11 Apples 77 106
## 12 Apples 77 106
## 13 Apples 77 106
## 14 Apples 77 106
## 15 Apples 77 106
## 16 Apples 77 106
## 17 Apples 77 106
## 18 Apples 77 106
## 19 Apples 77 106
## 20 Apples 77 106
## 21 Applesauce..sweetened. 100 83
## 22 Applesauce..sweetened. 100 83
## DiffCalories
## 1 185
## 2 46
## 3 63
## 4 37
## 5 97
## 6 194
## 7 158
## 8 5
## 9 25
## 10 42
## 11 119
## 12 292
## 13 175
## 14 3
## 15 92
## 16 91
## 17 75
## 18 106
## 19 222
## 20 84
## 21 162
## 22 23
Analysis of the calorie intake impact of substituting Snacks with Fruits/Vegetables
ggplot(calorieimpactfinal, aes(x=DiffCalories, y=Snack, colour = FruitVeg)) + geom_point() + facet_wrap(~FruitVeg) + labs(y = "Snack", x = "Differential of Calories Snack vs Fruit/Vegetable", title = "Analysis of the calorie intake impact of substituting Snacks with Fruits/Vegetables") + theme_bw()
(Values < 0 mean calorie intake increase due to substitution)
Conclusion: With the exception of very few cases like: Sweet Potatoes, Raisins & Apple Sauce, the calorie intake is substantially decreased when substituting Snacks with Fruits/Vegetables
2nd Data set - Cost Impact on Snack Substitution
Load data set (CSV) into R
Download target file locally from github “costimpacts (untidy dataset).xls”
setwd("C:/DATA/HHP/Personal/Degrees/Ms. Data Science (CUNY)/R Working Dir")
costimpact_raw <- data.frame(read_excel("costimpacts (untidy dataset).xls", skip = 1, col_names = TRUE))
dim(costimpact_raw)
## [1] 25 23
Data Wrangling
Pivoting columns to a key column and Renaming columns
costimpact <- gather(costimpact_raw, "FruitVeg", "DiffCost", 3:22)
names(costimpact) <- c("Snack", "SnackCost", "FruitVeg", "DiffCost")
head(costimpact,22)
## Snack SnackCost FruitVeg DiffCost
## 1 Chocolate candy 0.23999999999999999 Apples -0.12
## 2 Cookies 0.16 Apples -0.20
## 3 Corn chips 0.20999999999999999 Apples -0.15
## 4 Crackers 0.16 Apples -0.20
## 5 Cupcakes 0.34000000000000002 Apples -0.02
## 6 Danish 0.46999999999999997 Apples 0.11
## 7 Donuts 0.35999999999999999 Apples 0.00
## 8 Fruit rolls 0.28000000000000003 Apples -0.08
## 9 Graham crackers 0.14000000000000001 Apples -0.22
## 10 Granola bars 0.29999999999999999 Apples -0.06
## 11 Ice cream 0.39000000000000001 Apples 0.03
## 12 Muffins 0.82999999999999996 Apples 0.47
## 13 Pizza, from frozen 0.63 Apples 0.27
## 14 Popsicles and bars 0.34000000000000002 Apples -0.02
## 15 Potato chips 0.27000000000000002 Apples -0.09
## 16 Pretzels 0.25 Apples -0.11
## 17 Pudding, ready-to-eat 0.38 Apples 0.02
## 18 Sandwich crackers 0.20000000000000001 Apples -0.16
## 19 Toaster pastries 0.34999999999999998 Apples -0.01
## 20 Tortilla chips 0.20999999999999999 Apples -0.15
## 21 Chocolate candy 0.23999999999999999 Applesauce..jarred 0.02
## 22 Cookies 0.16 Applesauce..jarred -0.06
Casting data types, creating calculated-columns, arranging column layout
costimpact$SnackCost <- as.numeric(format(costimpact$SnackCost, nsmall = 2))
costimpact$DiffCost <- as.numeric(format(costimpact$DiffCost, nsmall = 2))
costimpact <- mutate(costimpact, FruitVegCost = SnackCost-DiffCost)
totCostchgbyFruitVeg <- summarise(group_by(costimpact, FruitVeg), TotCostChgbyFruitVeg = sum(DiffCost))
costimpact <- costimpact %>% inner_join(totCostchgbyFruitVeg, by = c("FruitVeg"))
costimpactfinal <- select(costimpact, Snack, SnackCost, FruitVeg, FruitVegCost, TotCostChgbyFruitVeg, DiffCost)
head(costimpactfinal,22)
## Snack SnackCost FruitVeg FruitVegCost
## 1 Chocolate candy 0.24 Apples 0.36
## 2 Cookies 0.16 Apples 0.36
## 3 Corn chips 0.21 Apples 0.36
## 4 Crackers 0.16 Apples 0.36
## 5 Cupcakes 0.34 Apples 0.36
## 6 Danish 0.47 Apples 0.36
## 7 Donuts 0.36 Apples 0.36
## 8 Fruit rolls 0.28 Apples 0.36
## 9 Graham crackers 0.14 Apples 0.36
## 10 Granola bars 0.30 Apples 0.36
## 11 Ice cream 0.39 Apples 0.36
## 12 Muffins 0.83 Apples 0.36
## 13 Pizza, from frozen 0.63 Apples 0.36
## 14 Popsicles and bars 0.34 Apples 0.36
## 15 Potato chips 0.27 Apples 0.36
## 16 Pretzels 0.25 Apples 0.36
## 17 Pudding, ready-to-eat 0.38 Apples 0.36
## 18 Sandwich crackers 0.20 Apples 0.36
## 19 Toaster pastries 0.35 Apples 0.36
## 20 Tortilla chips 0.21 Apples 0.36
## 21 Chocolate candy 0.24 Applesauce..jarred 0.22
## 22 Cookies 0.16 Applesauce..jarred 0.22
## TotCostChgbyFruitVeg DiffCost
## 1 -0.69 -0.12
## 2 -0.69 -0.20
## 3 -0.69 -0.15
## 4 -0.69 -0.20
## 5 -0.69 -0.02
## 6 -0.69 0.11
## 7 -0.69 0.00
## 8 -0.69 -0.08
## 9 -0.69 -0.22
## 10 -0.69 -0.06
## 11 -0.69 0.03
## 12 -0.69 0.47
## 13 -0.69 0.27
## 14 -0.69 -0.02
## 15 -0.69 -0.09
## 16 -0.69 -0.11
## 17 -0.69 0.02
## 18 -0.69 -0.16
## 19 -0.69 -0.01
## 20 -0.69 -0.15
## 21 2.11 0.02
## 22 2.11 -0.06
Analysis of the cost impact of substituting Snacks with Fruits/Vegetables
ggplot(costimpactfinal, aes(x=DiffCost, y=Snack, colour = FruitVeg)) + geom_point() + facet_wrap(~FruitVeg)+ labs(y = "Snack", x = "Differential of Cost Snack vs Fruit/Vegetable ",
title = "Analysis of the cost impact of substituting Snacks with Fruits/Vegetables") + theme_bw()
(Values < 0 mean cost increase due to subsitution)
ggplot(costimpactfinal, aes(y = FruitVeg, x = TotCostChgbyFruitVeg)) + geom_point(aes(col=FruitVeg, size=TotCostChgbyFruitVeg)) + labs(y = "Fruit/Vegetable", x = "Total Cost Replacing all Snacks with specific Fruit/Veg",
title = "Total Cost of Replacing all Snacks with a specific Fruit/Vegetable") + theme_bw()
(Values < 0 mean cost increase due to subsitution)
Conclusion: With the exception of very few cases like: Red Peppers, Tangerines & Tomatoes, the cost is substantially decreased when substituting Snacks with Fruits/Vegetables. Some of the best savings contributors are: Celery, Carrots, Broccoli & Bananas
3rd Data set - United Nations Migration Data
Load data set (CSV) into R and eliminate headers
Download target file locally from github “UN_MigrantStockByOriginAndDestination_2017.xlsx”
setwd("C:/DATA/HHP/Personal/Degrees/Ms. Data Science (CUNY)/R Working Dir")
migration_raw <- data.frame(read_excel("UN_MigrantStockByOriginAndDestination_2017.xlsx", sheet = "Table 1", skip = 15, col_names = TRUE))
dim(migration_raw)
## [1] 1917 241
head(migration_raw,1)
## X__1 X__2 X__3 X__4 X__5 X__6 Total Other.North Other.South
## 1 1990 1990001 WORLD <NA> 900 <NA> 152542373 2144536 6342531
## Afghanistan Albania Algeria American.Samoa Andorra Angola Anguilla
## 1 6724681 179490 906030 2041 3792 809942 2047
## Antigua.and.Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan
## 1 21747 430322 899683 10597 303406 505818 1634120
## Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin
## 1 25172 12767 5451546 84917 1769029 365414 36114 233642
## Bermuda Bhutan Bolivia..Plurinational.State.of. Bosnia.and.Herzegovina
## 1 71703 28366 224687 863399
## Botswana Brazil British.Virgin.Islands Brunei.Darussalam Bulgaria
## 1 26037 500149 3094 26323 617155
## Burkina.Faso Burundi Cabo.Verde Cambodia Cameroon Canada
## 1 1018868 337199 91681 354608 115311 997144
## Caribbean.Netherlands Cayman.Islands Central.African.Republic Chad
## 1 4325 373 46240 336916
## Channel.Islands Chile China China..Hong.Kong.SAR China..Macao.SAR
## 1 18727 489955 4229860 551080 95648
## Colombia Comoros Congo Cook.Islands Costa.Rica Côte.d.Ivoire Croatia
## 1 1009148 40076 91302 17488 69724 370866 426201
## Cuba Curaçao Cyprus Czechia Dem..People.s.Republic.of.Korea
## 1 835546 41878 174364 277514 38574
## Democratic.Republic.of.the.Congo Denmark Djibouti Dominica
## 1 401962 202194 5281 42366
## Dominican.Republic Ecuador Egypt El.Salvador Equatorial.Guinea Eritrea
## 1 465022 213731 1321128 1241877 34972 170617
## Estonia Ethiopia Faeroe.Islands Falkland.Islands..Malvinas. Fiji
## 1 113928 1687517 7524 260 90156
## Finland France French.Guiana French.Polynesia Gabon Gambia Georgia
## 1 250690 1197097 2848 3149 15208 36149 920441
## Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam
## 1 3277677 373303 11994 1041455 9510 43250 5828 1376
## Guatemala Guinea Guinea.Bissau Guyana Haiti Holy.See Honduras Hungary
## 1 343623 352590 55320 233660 527307 32 156553 386934
## Iceland India Indonesia Iran..Islamic.Republic.of. Iraq Ireland
## 1 17635 6718862 1636326 629834 1506679 917639
## Isle.of.Man Israel Italy Jamaica Japan Jordan Kazakhstan Kenya
## 1 10735 278956 3416421 588820 609032 313737 2972433 242119
## Kiribati Kuwait Kyrgyzstan Lao.People.s.Democratic.Republic Latvia
## 1 4053 81482 522615 482094 215165
## Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg
## 1 506600 191993 516719 76071 3430 341083 27828
## Madagascar Malawi Malaysia Maldives Mali Malta Marshall.Islands
## 1 58633 121365 562617 2192 647744 110735 1428
## Martinique Mauritania Mauritius Mayotte Mexico
## 1 11041 134129 108385 1835 4394684
## Micronesia..Fed..States.of. Monaco Mongolia Montenegro Montserrat
## 1 7714 4407 24155 78678 7188
## Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands
## 1 1588218 2218009 685288 16057 1412 748046 728810
## New.Caledonia New.Zealand Nicaragua Niger Nigeria Niue
## 1 4151 388089 442037 132726 448460 5860
## Northern.Mariana.Islands Norway Oman Pakistan Palau Panama
## 1 2525 138537 12534 3341574 2958 134703
## Papua.New.Guinea Paraguay Peru Philippines Poland Portugal
## 1 3015 297925 313595 2029190 1533306 1880727
## Puerto.Rico Qatar Republic.of.Korea Republic.of.Moldova Réunion Romania
## 1 1200835 12204 1624797 625810 3087 813087
## Russian.Federation Rwanda Saint.Helena Saint.Kitts.and.Nevis Saint.Lucia
## 1 12664537 550719 884 20714 22006
## Saint.Pierre.and.Miquelon Saint.Vincent.and.the.Grenadines Samoa
## 1 485 37043 74861
## San.Marino Sao.Tome.and.Principe Saudi.Arabia Senegal Serbia Seychelles
## 1 1415 13941 107037 369263 708804 29376
## Sierra.Leone Singapore Sint.Maarten..Dutch.part. Slovakia Slovenia
## 1 60952 156201 14368 133461 91652
## Solomon.Islands Somalia South.Africa South.Sudan Spain Sri.Lanka
## 1 2212 846967 327661 514943 1449316 885836
## State.of.Palestine Sudan Suriname Swaziland Sweden Switzerland
## 1 1813068 587063 179657 35184 207067 323233
## Syrian.Arab.Republic Tajikistan TFYR.Macedonia Thailand Timor.Leste
## 1 620868 536252 429555 309088 11261
## Togo Tokelau Tonga Trinidad.and.Tobago Tunisia Turkey Turkmenistan
## 1 193369 1684 32666 197522 453933 2530619 259991
## Turks.and.Caicos.Islands Tuvalu Uganda Ukraine United.Arab.Emirates
## 1 2311 2350 311490 5549477 79540
## United.Kingdom United.Republic.of.Tanzania United.States.of.America
## 1 3795662 204272 1736288
## United.States.Virgin.Islands Uruguay Uzbekistan Vanuatu
## 1 2362 237258 1429956 5060
## Venezuela..Bolivarian.Republic.of. Viet.Nam Wallis.and.Futuna.Islands
## 1 185888 1235348 6484
## Western.Sahara Yemen Zambia Zimbabwe
## 1 168239 455442 83210 176697
Data Wrangling
Elminating irrelevant columns, including totals
migration_raw <- migration_raw[,-2]
migration_raw <- migration_raw[,-(3:6)]
Pivoting columns to a key column and Renaming columns
migration <- gather(migration_raw, "CountryOrig", "MigrantStock", 3:236)
names(migration) <- c("Year", "CountryDest", "CountryOrig", "MigrantStock")
head(migration,5)
## Year CountryDest
## 1 1990 WORLD
## 2 1990 More developed regions
## 3 1990 Less developed regions
## 4 1990 Least developed countries
## 5 1990 Less developed regions, excluding least developed countries
## CountryOrig MigrantStock
## 1 Other.North 2144536
## 2 Other.North 1094823
## 3 Other.North 1049713
## 4 Other.North 250575
## 5 Other.North 799138
Casting data types and filtering NAs and certain summary categories like: World, Developed, High Income Regions, Continents & Subcontinents, etc.
str(migration)
## 'data.frame': 448578 obs. of 4 variables:
## $ Year : num 1990 1990 1990 1990 1990 1990 1990 1990 1990 1990 ...
## $ CountryDest : chr "WORLD" "More developed regions" "Less developed regions" "Least developed countries" ...
## $ CountryOrig : chr "Other.North" "Other.North" "Other.North" "Other.North" ...
## $ MigrantStock: chr "2144536" "1094823" "1049713" "250575" ...
migration$MigrantStock <- as.numeric(migration$MigrantStock)
## Warning: NAs introduced by coercion
migration_final <- migration %>% filter(!is.na(MigrantStock))
migration_final <- migration_final %>% filter(!str_detect(CountryDest,"regions|countries|WORLD"))
migration_final <- migration_final %>% filter(!str_detect(CountryDest,"ASIA|AFRICA|EUROPE|LATIN|CARIBBEAN|OCEANIA|Central|Eastern|Western|Southern|Northern|NORTHERN|Other|Sub"))
migration_final <- migration_final %>% filter(!str_detect(CountryOrig,"Other"))
head(migration_final,10)
## Year CountryDest CountryOrig MigrantStock
## 1 1990 Egypt Afghanistan 237
## 2 1990 Libya Afghanistan 677
## 3 1990 Namibia Afghanistan 64
## 4 1990 South Africa Afghanistan 59
## 5 1990 Tajikistan Afghanistan 8485
## 6 1990 India Afghanistan 14159
## 7 1990 Iran (Islamic Republic of) Afghanistan 3123968
## 8 1990 Pakistan Afghanistan 3276673
## 9 1990 Malaysia Afghanistan 32
## 10 1990 Philippines Afghanistan 25
Preparing datasets for Ranking Top CountryDest and CountryOrigin by MigrationStock
TopCountryDest <- summarise(group_by(migration_final, CountryDest), MigrantStock=sum(MigrantStock)) %>% arrange(desc(MigrantStock)) %>% head(., 20)
TopCountryOrig <- summarise(group_by(migration_final, CountryOrig), MigrantStock=sum(MigrantStock)) %>% arrange(desc(MigrantStock)) %>% head(., 20)
Preparing dataset for Migration by CountryDest over time
TopCountryDestOT <- summarise(group_by(migration_final, CountryDest, Year), MigrantStock=sum(MigrantStock)) %>% arrange(desc(MigrantStock)) %>% filter(str_detect(CountryDest,"United States of America|Russia|Germany|Saudi|France|United Kingdom|Canada|Australia|India|New Zealand|Ukraine|United Arab|Pakistan|Italy|Spain|Iran|China"))
Analysis of the Migration Patterns
Most Attractive Countries for Immigrants
ggplot(TopCountryDest, aes(x=reorder(CountryDest, MigrantStock), y=MigrantStock, fill=CountryDest)) + geom_col(position = "dodge") + coord_flip() + labs(y = "Migration Stock", x = "Destination Country", title = "Most Attractive Countries for Immigrants") + theme(axis.text.y=element_blank())
Top Countries by # of Migrants
ggplot(TopCountryOrig, aes(x=reorder(CountryOrig, MigrantStock), y=MigrantStock, fill=CountryOrig)) + geom_col(position = "dodge") + coord_flip() + labs(y = "Migration Stock", x = "Origin Country", title = "Top Countries by # of Migrants") + theme(axis.text.y=element_blank())
MigrationStock over the Years for Top Destination Countries
ggplot(TopCountryDestOT, aes(x=Year, y=MigrantStock, group=CountryDest)) + geom_line(aes(color=CountryDest)) + geom_point(aes(color=CountryDest)) + labs(y = "Migration Stock", x = "Year", title = "MigrationStock over the Years for Top Destination Countries") + theme_bw()
Faceted View
ggplot(TopCountryDestOT, aes(x=Year, y=MigrantStock, group=CountryDest)) + geom_line(aes(color=CountryDest)) + geom_point(aes(color=CountryDest)) + facet_wrap(~CountryDest) + labs(y = "Migration Stock", x = "Year", title = "MigrationStock over the Years for Top Destination Countries") + theme_bw()
Conclusion: The United States of America stays as the top destination country for immigrants WW with a linear growth over the last four decades. Ukraine, Pakistan, India, China and Iran experiencing a decline or a flat trend. Developed countries and economies like: UK, UAE, Canada, Australia, France and Germany, showing a steady growth trend over the last decades. Saudi Arabia showing up with an almost linear growth rate during the last two decades