DATA 607 PROJECT 2
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.
Wide Data Set # 1
Choose a wide data set from the Week 5/6 discussions to convert into long data set and generate analysis from the long data set. Screenshot of discussion data set below. I chose this dataset because it was a wide dataset.
CSV file has been loaded to github
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.
df_wide<- read.csv("C://Users//jkks9//Documents//DATA 607//Unemployment Rate.csv", header=TRUE, sep=",")
head(df_wide[1:20])## X Advanced.Economies Argentina Australia Austria Belgium Bulgaria
## 1 NA NA NA NA NA NA NA
## 2 1990 5.800582 NA 6.943297 5.373002 6.550260 NA
## 3 1991 6.728688 NA 9.614137 5.823096 6.439812 NA
## 4 1992 7.511064 NA 10.750080 5.941711 7.088092 13.23500
## 5 1993 7.936175 NA 10.866170 6.811381 8.619130 15.85583
## 6 1994 7.715897 NA 9.705695 6.545480 9.753554 14.06583
## Bahrain Belarus Brazil Canada Switzerland Chile China Colombia Cyprus
## 1 NA NA NA NA NA NA NA NA NA
## 2 NA NA NA 8.15000 0.501328 NA NA NA NA
## 3 NA NA NA 10.31667 1.090451 NA NA NA NA
## 4 NA NA NA 11.21667 2.563105 NA NA NA NA
## 5 NA NA NA 11.37500 4.516116 NA NA NA NA
## 6 NA NA NA 10.39167 4.718465 NA NA NA NA
## Czech.Republic Germany Denmark Dominican.Republic
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA 4.864885 NA NA
## 4 NA 5.764563 NA NA
## 5 4.333333 6.931370 NA NA
## 6 4.283333 7.340639 NA NAcolnames(df_wide[1:20])## [1] "X" "Advanced.Economies" "Argentina"
## [4] "Australia" "Austria" "Belgium"
## [7] "Bulgaria" "Bahrain" "Belarus"
## [10] "Brazil" "Canada" "Switzerland"
## [13] "Chile" "China" "Colombia"
## [16] "Cyprus" "Czech.Republic" "Germany"
## [19] "Denmark" "Dominican.Republic"There are over 80 variable country columns. 10 of the variable country name columns will be chosen to be renamed as a abbreviated country name along with the year. NA values will be removed. And then the abbreviated variable country names except the year column will be turned into rows. This will turn the wide data set to a long data set.
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)
df2_wide<-rename(df_wide,Year=X,ARG=Argentina,SWE=Sweden,US=United.States,KOR=Korea..Rep.,JPN=Japan,BRA=Brazil,CAN=Canada,IND=India,MEX=Mexico,IRE=Ireland)
df3_wide<-select(df2_wide,Year,ARG,SWE,US,KOR,JPN,BRA,CAN,IND,MEX,IRE)
df4_wide<-na.omit(df3_wide)
head(df4_wide)## Year ARG SWE US KOR JPN BRA CAN
## 25 2013 7.076472 8.004120 7.358333 3.108333 4.021680 7.191545 7.100000
## 26 2014 7.270868 7.931666 6.158333 3.500000 3.587407 6.784583 6.925000
## 27 2015 6.611389 7.378905 5.275000 3.600000 3.371420 8.304733 6.900000
## 28 2016 8.467953 6.940763 4.875000 3.675000 3.112862 11.281130 6.991667
## 29 2017 8.344703 6.674000 4.350000 3.683333 2.806622 12.764030 6.333333
## IND MEX IRE
## 25 8.2 4.903333 13.775000
## 26 9.3 4.824167 11.900000
## 27 8.5 4.347500 9.950000
## 28 8.0 3.882500 8.391667
## 29 8.8 3.420833 6.733333df_long<-gather(df4_wide,Country,UnemploymentRate,-Year)
head(df_long)## Year Country UnemploymentRate
## 1 2013 ARG 7.076472
## 2 2014 ARG 7.270868
## 3 2015 ARG 6.611389
## 4 2016 ARG 8.467953
## 5 2017 ARG 8.344703
## 6 2013 SWE 8.004120Analysis of data will be performed by creating visualization of unemployment rates of a single country for time period of 5 years. The country chosen is ARG which is Argentina. Based on the chart, Argentina has had a unemployment rate hovering between 6% and 8% between 2013 and 2017. It is trending downward as 2017 was lower than the previous year.
library(ggplot2)
df2_long<-subset(df_long,Country=="ARG")
ggplot(df2_long, aes(x=df2_long$Year, y=df2_long$UnemploymentRate, fill=df2_long$Country)) + geom_bar(stat='identity', position='dodge')