This R markdown file made by me summarizes the packages I have used to complete my homework assignment. RMD file is actually a very helpful tool that summarizes the whole task. Our code along with their results get documented neatly, as summarized below.
Quite a number of packages were installed and used for the following assignment:
library(readr) #Used to read tabular data into R
library(gdata) #Used to pull data from a url excel file
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
## The following object is masked from 'package:stats':
##
## nobs
## The following object is masked from 'package:utils':
##
## object.size
## The following object is masked from 'package:base':
##
## startsWith
library(xlsx) #Helps in reading data from excel
## Loading required package: rJava
## Loading required package: xlsxjars
library(stringr) #Used to detect specific patern in string
library(XML) #Uses getHTMLLinks() function to identify file linksPerformed the five data importing exercises listed in #1. For each problem I imported the data and saved it as a data frame. Used head() to display the first few rows of the data frame, used str() to display the structure of each data frame.
#1. R-script week-2.R
#download & import the csv file located at:url
reddit_data<-read.csv('reddit.csv', stringsAsFactors = FALSE)
head(reddit_data)
## id gender age.range marital.status
## 1 1 0 25-34 <NA>
## 2 2 0 25-34 <NA>
## 3 3 1 18-24 <NA>
## 4 4 0 25-34 <NA>
## 5 5 1 25-34 <NA>
## 6 6 0 25-34 Married/civil union/domestic partnership
## employment.status military.service children education
## 1 Employed full time <NA> No Bachelor's degree
## 2 Employed full time <NA> No Bachelor's degree
## 3 Freelance <NA> No Some college
## 4 Freelance <NA> No Bachelor's degree
## 5 Employed full time <NA> No Bachelor's degree
## 6 Employed full time No No Bachelor's degree
## country state income.range fav.reddit dog.cat
## 1 United States New York $150,000 or more getmotivated <NA>
## 2 United States New York $150,000 or more gaming <NA>
## 3 United States Virginia Under $20,000 snackexchange <NA>
## 4 United States New York $150,000 or more spacedicks <NA>
## 5 United States California $70,000 - $99,999 aww <NA>
## 6 United States New York $150,000 or more gaming I like dogs.
## cheese
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 Cheddar
str(reddit_data)
## 'data.frame': 32754 obs. of 14 variables:
## $ id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ gender : int 0 0 1 0 1 0 0 0 0 0 ...
## $ age.range : chr "25-34" "25-34" "18-24" "25-34" ...
## $ marital.status : chr NA NA NA NA ...
## $ employment.status: chr "Employed full time" "Employed full time" "Freelance" "Freelance" ...
## $ military.service : chr NA NA NA NA ...
## $ children : chr "No" "No" "No" "No" ...
## $ education : chr "Bachelor's degree" "Bachelor's degree" "Some college" "Bachelor's degree" ...
## $ country : chr "United States" "United States" "United States" "United States" ...
## $ state : chr "New York" "New York" "Virginia" "New York" ...
## $ income.range : chr "$150,000 or more" "$150,000 or more" "Under $20,000" "$150,000 or more" ...
## $ fav.reddit : chr "getmotivated" "gaming" "snackexchange" "spacedicks" ...
## $ dog.cat : chr NA NA NA NA ...
## $ cheese : chr NA NA NA NA ...
#now import the above csv file directly from the url provided
url <- "https://bradleyboehmke.github.io/public/data/reddit.csv"
download.file(url,dest="reddit.csv",mode="wb")
data_reddit <- read.csv(url, stringsAsFactors = FALSE)
head(data_reddit)
## id gender age.range marital.status
## 1 1 0 25-34 <NA>
## 2 2 0 25-34 <NA>
## 3 3 1 18-24 <NA>
## 4 4 0 25-34 <NA>
## 5 5 1 25-34 <NA>
## 6 6 0 25-34 Married/civil union/domestic partnership
## employment.status military.service children education
## 1 Employed full time <NA> No Bachelor's degree
## 2 Employed full time <NA> No Bachelor's degree
## 3 Freelance <NA> No Some college
## 4 Freelance <NA> No Bachelor's degree
## 5 Employed full time <NA> No Bachelor's degree
## 6 Employed full time No No Bachelor's degree
## country state income.range fav.reddit dog.cat
## 1 United States New York $150,000 or more getmotivated <NA>
## 2 United States New York $150,000 or more gaming <NA>
## 3 United States Virginia Under $20,000 snackexchange <NA>
## 4 United States New York $150,000 or more spacedicks <NA>
## 5 United States California $70,000 - $99,999 aww <NA>
## 6 United States New York $150,000 or more gaming I like dogs.
## cheese
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 Cheddar
str(data_reddit)
## 'data.frame': 32754 obs. of 14 variables:
## $ id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ gender : int 0 0 1 0 1 0 0 0 0 0 ...
## $ age.range : chr "25-34" "25-34" "18-24" "25-34" ...
## $ marital.status : chr NA NA NA NA ...
## $ employment.status: chr "Employed full time" "Employed full time" "Freelance" "Freelance" ...
## $ military.service : chr NA NA NA NA ...
## $ children : chr "No" "No" "No" "No" ...
## $ education : chr "Bachelor's degree" "Bachelor's degree" "Some college" "Bachelor's degree" ...
## $ country : chr "United States" "United States" "United States" "United States" ...
## $ state : chr "New York" "New York" "Virginia" "New York" ...
## $ income.range : chr "$150,000 or more" "$150,000 or more" "Under $20,000" "$150,000 or more" ...
## $ fav.reddit : chr "getmotivated" "gaming" "snackexchange" "spacedicks" ...
## $ dog.cat : chr NA NA NA NA ...
## $ cheese : chr NA NA NA NA ...
#download & import the .xlsx file located at:url_2
url2 <- "http://www.huduser.gov/portal/datasets/fmr/fmr2017/FY2017_4050_FMR.xlsx"
download.file(url2,dest="FY2017_4050_FMR.xlsx",mode="wb")
sheetCount('FY2017_4050_FMR.xlsx')
## [1] 1
FY2017<-read.xls('FY2017_4050_FMR.xlsx')
head(FY2017)
## fips2010 fips2000 fmr2 fmr0 fmr1 fmr3 fmr4 State Metro_code
## 1 2300512300 NA 1078 755 851 1454 1579 23 METRO38860MM6400
## 2 6099999999 NA 677 502 506 987 1038 60 NCNTY60999N60999
## 3 6999999999 NA 666 411 498 961 1158 69 NCNTY69999N69999
## 4 100199999 100199999 822 587 682 1054 1425 1 METRO33860M33860
## 5 100399999 100399999 977 807 847 1422 1634 1 METRO19300M19300
## 6 100599999 100599999 671 501 505 839 958 1 NCNTY01005N01005
## areaname county CouSub countyname
## 1 Portland, ME HUD Metro FMR Area NA 12300 Cumberland County
## 2 American Samoa 999 99999 American Samoa
## 3 Northern Mariana Islands 999 99999 Northern Mariana Islands
## 4 Montgomery, AL MSA 1 99999 Autauga County
## 5 Daphne-Fairhope-Foley, AL MSA 3 99999 Baldwin County
## 6 Barbour County, AL 5 99999 Barbour County
## county_town_name pop2010 acs_2016_2 state_alpha fmr_type metro
## 1 Chebeague Island town 341 1109 ME 40 1
## 2 American Samoa 55519 653 AS 40 0
## 3 Northern Mariana Islands 53883 642 MP 40 0
## 4 Autauga County 54571 788 AL 40 1
## 5 Baldwin County 182265 873 AL 40 1
## 6 Barbour County 27457 636 AL 40 0
## FMR_PCT_Change FMR_Dollar_Change
## 1 0.9720469 -31
## 2 1.0367534 24
## 3 1.0373832 24
## 4 1.0431472 34
## 5 1.1191294 104
## 6 1.0550314 35
str(FY2017)
## 'data.frame': 4769 obs. of 21 variables:
## $ fips2010 : num 2.3e+09 6.1e+09 7.0e+09 1.0e+08 1.0e+08 ...
## $ fips2000 : num NA NA NA 1e+08 1e+08 ...
## $ fmr2 : int 1078 677 666 822 977 671 866 866 621 621 ...
## $ fmr0 : int 755 502 411 587 807 501 665 665 491 464 ...
## $ fmr1 : int 851 506 498 682 847 505 751 751 494 467 ...
## $ fmr3 : int 1454 987 961 1054 1422 839 1163 1163 853 849 ...
## $ fmr4 : int 1579 1038 1158 1425 1634 958 1298 1298 856 1094 ...
## $ State : int 23 60 69 1 1 1 1 1 1 1 ...
## $ Metro_code : Factor w/ 2598 levels "METRO10180M10180",..: 451 2592 2594 384 160 625 55 55 626 627 ...
## $ areaname : Factor w/ 2598 levels " Santa Ana-Anaheim-Irvine, CA HUD Metro FMR Area",..: 1902 52 1723 1633 571 122 186 186 263 271 ...
## $ county : int NA 999 999 1 3 5 7 9 11 13 ...
## $ CouSub : int 12300 99999 99999 99999 99999 99999 99999 99999 99999 99999 ...
## $ countyname : Factor w/ 1961 levels "Abbeville County",..: 462 41 1265 91 99 110 163 178 239 249 ...
## $ county_town_name : Factor w/ 3175 levels "Abbeville County",..: 533 60 2024 135 149 165 254 277 386 401 ...
## $ pop2010 : int 341 55519 53883 54571 182265 27457 22915 57322 10914 20947 ...
## $ acs_2016_2 : int 1109 653 642 788 873 636 840 840 569 569 ...
## $ state_alpha : Factor w/ 56 levels "AK","AL","AR",..: 24 4 28 2 2 2 2 2 2 2 ...
## $ fmr_type : int 40 40 40 40 40 40 40 40 40 40 ...
## $ metro : int 1 0 0 1 1 0 1 1 0 0 ...
## $ FMR_PCT_Change : num 0.972 1.037 1.037 1.043 1.119 ...
## $ FMR_Dollar_Change: int -31 24 24 34 104 35 26 26 52 52 ...
#now import the above xlsx file directly from the url provided
data_FY<-read.xls(url2)
head(data_FY)
## fips2010 fips2000 fmr2 fmr0 fmr1 fmr3 fmr4 State Metro_code
## 1 2300512300 NA 1078 755 851 1454 1579 23 METRO38860MM6400
## 2 6099999999 NA 677 502 506 987 1038 60 NCNTY60999N60999
## 3 6999999999 NA 666 411 498 961 1158 69 NCNTY69999N69999
## 4 100199999 100199999 822 587 682 1054 1425 1 METRO33860M33860
## 5 100399999 100399999 977 807 847 1422 1634 1 METRO19300M19300
## 6 100599999 100599999 671 501 505 839 958 1 NCNTY01005N01005
## areaname county CouSub countyname
## 1 Portland, ME HUD Metro FMR Area NA 12300 Cumberland County
## 2 American Samoa 999 99999 American Samoa
## 3 Northern Mariana Islands 999 99999 Northern Mariana Islands
## 4 Montgomery, AL MSA 1 99999 Autauga County
## 5 Daphne-Fairhope-Foley, AL MSA 3 99999 Baldwin County
## 6 Barbour County, AL 5 99999 Barbour County
## county_town_name pop2010 acs_2016_2 state_alpha fmr_type metro
## 1 Chebeague Island town 341 1109 ME 40 1
## 2 American Samoa 55519 653 AS 40 0
## 3 Northern Mariana Islands 53883 642 MP 40 0
## 4 Autauga County 54571 788 AL 40 1
## 5 Baldwin County 182265 873 AL 40 1
## 6 Barbour County 27457 636 AL 40 0
## FMR_PCT_Change FMR_Dollar_Change
## 1 0.9720469 -31
## 2 1.0367534 24
## 3 1.0373832 24
## 4 1.0431472 34
## 5 1.1191294 104
## 6 1.0550314 35
str(data_FY)
## 'data.frame': 4769 obs. of 21 variables:
## $ fips2010 : num 2.3e+09 6.1e+09 7.0e+09 1.0e+08 1.0e+08 ...
## $ fips2000 : num NA NA NA 1e+08 1e+08 ...
## $ fmr2 : int 1078 677 666 822 977 671 866 866 621 621 ...
## $ fmr0 : int 755 502 411 587 807 501 665 665 491 464 ...
## $ fmr1 : int 851 506 498 682 847 505 751 751 494 467 ...
## $ fmr3 : int 1454 987 961 1054 1422 839 1163 1163 853 849 ...
## $ fmr4 : int 1579 1038 1158 1425 1634 958 1298 1298 856 1094 ...
## $ State : int 23 60 69 1 1 1 1 1 1 1 ...
## $ Metro_code : Factor w/ 2598 levels "METRO10180M10180",..: 451 2592 2594 384 160 625 55 55 626 627 ...
## $ areaname : Factor w/ 2598 levels " Santa Ana-Anaheim-Irvine, CA HUD Metro FMR Area",..: 1902 52 1723 1633 571 122 186 186 263 271 ...
## $ county : int NA 999 999 1 3 5 7 9 11 13 ...
## $ CouSub : int 12300 99999 99999 99999 99999 99999 99999 99999 99999 99999 ...
## $ countyname : Factor w/ 1961 levels "Abbeville County",..: 462 41 1265 91 99 110 163 178 239 249 ...
## $ county_town_name : Factor w/ 3175 levels "Abbeville County",..: 533 60 2024 135 149 165 254 277 386 401 ...
## $ pop2010 : int 341 55519 53883 54571 182265 27457 22915 57322 10914 20947 ...
## $ acs_2016_2 : int 1109 653 642 788 873 636 840 840 569 569 ...
## $ state_alpha : Factor w/ 56 levels "AK","AL","AR",..: 24 4 28 2 2 2 2 2 2 2 ...
## $ fmr_type : int 40 40 40 40 40 40 40 40 40 40 ...
## $ metro : int 1 0 0 1 1 0 1 1 0 0 ...
## $ FMR_PCT_Change : num 0.972 1.037 1.037 1.043 1.119 ...
## $ FMR_Dollar_Change: int -31 24 24 34 104 35 26 26 52 52 ...
#go to this University of Dayton webpage and import a txt file
url3 <- "http://academic.udayton.edu/kissock/http/Weather/"
complete_links <- paste0(url3,"citylistUS.htm")
web_links <- getHTMLLinks(complete_links)
data_links <- web_links[str_detect(web_links,"OHCINCIN.txt")]
cincinnati <- paste0(url3,data_links)
cincinnati_data <- read.table(cincinnati,stringsAsFactors = FALSE,header = TRUE)
head(cincinnati_data)
## X1 X1.1 X1995 X41.1
## 1 1 2 1995 22.2
## 2 1 3 1995 22.8
## 3 1 4 1995 14.9
## 4 1 5 1995 9.5
## 5 1 6 1995 23.8
## 6 1 7 1995 31.1
str(cincinnati_data)
## 'data.frame': 7962 obs. of 4 variables:
## $ X1 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ X1.1 : int 2 3 4 5 6 7 8 9 10 11 ...
## $ X1995: int 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 ...
## $ X41.1: num 22.2 22.8 14.9 9.5 23.8 31.1 26.9 31.3 31.5 44.4 ...