DATA607_Project2
IvanTikhonov
2022-10-08
The goal of this assignment is to give you practice in preparing different datasets for downstream analysis work. Your task is to: (1) Choose any three of the “wide” datasets identified in the Week 6 Discussion items. (You may use your own dataset; please don’t use my Sample Post dataset, since that was used in your Week 6 assignment!) For each of the three chosen datasets: Create a .CSV file (or optionally, a MySQL database!) that includes all of the information included in the dataset. You’re encouraged to use a “wide” structure similar to how the information appears in the discussion item, so that you can practice tidying and transformations as described below. Read the information from your .CSV file into R, and use tidyr and dplyr as needed to tidy and transform your data. [Most of your grade will be based on this step!] Perform the analysis requested in the discussion item. Your code should be in an R Markdown file, posted to rpubs.com, and should include narrative descriptions of your data cleanup work, analysis, and conclusions. (2) Please include in your homework submission, for each of the three chosen datasets: The URL to the .Rmd file in your GitHub repository, and The URL for your rpubs.com web page.
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 )
getURL <- "https://raw.githubusercontent.com/IvanGrozny88/DATA607_Project2/main/Age5-14_deaths_2018.csv"
Children_death_estimates_df <- read.csv(getURL, header = TRUE, sep = ",", skip = 12)
head(Children_death_estimates_df)## Region.Name Uncertainty.bounds. X1990.5 X2000.5 X2010.5 X2015.5
## 1 Sub-Saharan Africa Lower 575,856 582,903 519,030 480,283
## 2 Sub-Saharan Africa Median 604,397 599,931 543,210 516,208
## 3 Sub-Saharan Africa Upper 651,966 623,935 579,074 575,013
## 4 West and Central Africa Lower 262,531 291,554 291,672 283,610
## 5 West and Central Africa Median 285,507 305,245 314,220 317,710
## 6 West and Central Africa Upper 323,545 322,440 344,649 370,029
## X2017.5 X X.1 X.2 X.3 X.4 X.5 X.6 X.7 X.8 X.9 X.10 X.11 X.12 X.13 X.14 X.15
## 1 469,876 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 508,174 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 578,156 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 279,679 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 317,039 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 378,544 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## X.16 X.17 X.18 X.19 X.20 X.21 X.22 X.23 X.24 X.25 X.26 X.27 X.28 X.29 X.30
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## X.31 X.32 X.33 X.34 X.35 X.36 X.37
## 1 NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NANow we are melting the data set, that means all the columns which are data are being converted to separate columns.
Children_death_estimates_df_molten <- Children_death_estimates_df %>% gather("year", "Number.of.deaths", 3:43)
head(Children_death_estimates_df_molten)## Region.Name Uncertainty.bounds. X.36 X.37 year
## 1 Sub-Saharan Africa Lower NA NA X1990.5
## 2 Sub-Saharan Africa Median NA NA X1990.5
## 3 Sub-Saharan Africa Upper NA NA X1990.5
## 4 West and Central Africa Lower NA NA X1990.5
## 5 West and Central Africa Median NA NA X1990.5
## 6 West and Central Africa Upper NA NA X1990.5
## Number.of.deaths
## 1 575,856
## 2 604,397
## 3 651,966
## 4 262,531
## 5 285,507
## 6 323,545Children_death_estimates_df_molten$year <- substr(Children_death_estimates_df_molten$year, 2, 5)
Children_death_estimates_df_molten$Number.of.deaths <- gsub(",", "", Children_death_estimates_df_molten$Number.of.deaths)
head(Children_death_estimates_df_molten)## Region.Name Uncertainty.bounds. X.36 X.37 year Number.of.deaths
## 1 Sub-Saharan Africa Lower NA NA 1990 575856
## 2 Sub-Saharan Africa Median NA NA 1990 604397
## 3 Sub-Saharan Africa Upper NA NA 1990 651966
## 4 West and Central Africa Lower NA NA 1990 262531
## 5 West and Central Africa Median NA NA 1990 285507
## 6 West and Central Africa Upper NA NA 1990 323545Children_death_estimates_df_molten_median <- Children_death_estimates_df_molten %>% filter(Children_death_estimates_df_molten$Uncertainty.bounds. == "Median", Children_death_estimates_df_molten$Region.Name != "World")
head(Children_death_estimates_df_molten_median)## Region.Name Uncertainty.bounds. X.36 X.37 year
## 1 Sub-Saharan Africa Median NA NA 1990
## 2 West and Central Africa Median NA NA 1990
## 3 Eastern and Southern Africa Median NA NA 1990
## 4 Middle East and North Africa Median NA NA 1990
## 5 South Asia Median NA NA 1990
## 6 East Asia and Pacific Median NA NA 1990
## Number.of.deaths
## 1 604397
## 2 285507
## 3 318890
## 4 66409
## 5 584906
## 6 319190Children_death_estimates_df_molten_median <- Children_death_estimates_df_molten_median %>% arrange(Region.Name, year)DATASET-2 The secod data set is for the country wise data for Balance of payment as a percent of that country’s GDP Step-1 - To read the data set from Github location into R https://raw.githubusercontent.com/IvanGrozny88/DATA607_Project2/main/UN_MigrantStockByOriginAndDestination_2019.csv Original file was excel file which was saved as a csv file. The data from the csv file will be transformed from a wide set to a long set along with other cleanup where neccessary in order to do further analysis on the data set. Reference to website that linked the dataset and explained methods to tidy the dataset. The methods contained in the website was mostly used to understand the data, the only one directly used was selecting the columns with the select function. https://www.un.org/en/development/desa/population/migration/data/estimates2/data/UN_MigrantStockByOriginAndDestination_2019.xlsx
df_wide<- read.csv("C:/Users/Ivan/OneDrive/Desktop/UN_MigrantStockByOriginAndDestination_2019.csv", header=FALSE, sep=",")
df2_wide<-df_wide[-c(1:15),]
head(df2_wide[1:10])## V1 V2 V3 V4 V5 V6 V7 V8
## 16 Total Other South
## 17 1990 1990001 WORLD 900 153,011,473 6,548,526
## 18 1990 1990002 UN development groups .. ..
## 19 1990 1990003 More developed regions b 901 82,767,216 3,385,103
## 20 1990 1990004 Less developed regions c 902 70,244,257 3,163,423
## 21 1990 1990005 Least developed countries d 941 11,060,221 482,753
## V9 V10
## 16 Other North Afghanistan
## 17 2,366,800 6,823,350
## 18 .. ..
## 19 1,077,179 119,386
## 20 1,289,621 6,703,964
## 21 239,756 0There are over 200 variable country columns. Destination countries will be picked to turn into rows. Subset will be applied on Origin Country, with the origin country being United States of America.
library(tidyr)
df3_wide<-select(df2_wide,-V2,-V4,-V5,-V7)
df4_wide<-rename(df3_wide,Year=V1,Origin=V3,group=V6,Afghanistan=V10,Albania=V11,Algeria=V12,Angola=V15)
df5_wide<- subset(df4_wide,Origin=="United States of America")
df6_wide<-select(df5_wide,Year,Origin,Afghanistan,Albania,Algeria,Angola)
df2_long<-gather(df6_wide,Destination,Persons,-Origin,-Year)
df2_long## Year Origin Destination Persons
## 1 1990 United States of America Afghanistan 28,444
## 2 1995 United States of America Afghanistan 37,312
## 3 2000 United States of America Afghanistan 46,342
## 4 2005 United States of America Afghanistan 51,965
## 5 2010 United States of America Afghanistan 59,330
## 6 2015 United States of America Afghanistan 74,106
## 7 2019 United States of America Afghanistan 80,026
## 8 1990 United States of America Albania 5,627
## 9 1995 United States of America Albania 22,440
## 10 2000 United States of America Albania 39,644
## 11 2005 United States of America Albania 54,825
## 12 2010 United States of America Albania 72,085
## 13 2015 United States of America Albania 91,865
## 14 2019 United States of America Albania 99,205
## 15 1990 United States of America Algeria 4,629
## 16 1995 United States of America Algeria 7,859
## 17 2000 United States of America Algeria 11,158
## 18 2005 United States of America Algeria 12,680
## 19 2010 United States of America Algeria 14,633
## 20 2015 United States of America Algeria 19,633
## 21 2019 United States of America Algeria 21,201
## 22 1990 United States of America Angola 2,252
## 23 1995 United States of America Angola 3,352
## 24 2000 United States of America Angola 4,475
## 25 2005 United States of America Angola 7,294
## 26 2010 United States of America Angola 10,411
## 27 2015 United States of America Angola 14,068
## 28 2019 United States of America Angola 15,192Chart created to show relationship between United Stated migration to Afghanistan. Chart is showing number of person migrating to a specific country by year. In this case, it is showing the number of persons migrating to Afghanistan from the US has been increasing every 5 yrs from about 28,000 in 1990 to 80,000 in 2019.
library(ggplot2)
df3_long<-subset(df2_long,Destination=="Afghanistan")
ggplot(df3_long, aes(x=df3_long$Year, y=df3_long$Persons, fill=df3_long$Destination)) + geom_bar(stat='identity', position='dodge')## Warning: Use of `df3_long$Year` is discouraged. Use `Year` instead.## Warning: Use of `df3_long$Persons` is discouraged. Use `Persons` instead.## Warning: Use of `df3_long$Destination` is discouraged. Use `Destination`
## instead.