This is the homework for week 2 of “Data Wrangling with R” class. I have imported 5 kinds of datasets into R, using following methods:
The following packages were used for this assignment:
library(readxl) #to import excel file, using read_excel() function
library(gdata) #to scrap excel files from the internet
library(knitr) #to format the output of the assignment questionsBelow are the R codes and required output:
download & import the csv file located at: https://bradleyboehmke.github.io/public/data/reddit.csv
library(knitr)
file1 <- tempfile()
download.file("https://bradleyboehmke.github.io/public/data/reddit.csv",file1)
reddit_a <- read.csv(file1, stringsAsFactors = FALSE)
knitr::kable(head(reddit_a))| id | gender | age.range | marital.status | employment.status | military.service | children | education | country | state | income.range | fav.reddit | dog.cat | cheese |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 25-34 | NA | Employed full time | NA | No | Bachelor’s degree | United States | New York | $150,000 or more | getmotivated | NA | NA |
| 2 | 0 | 25-34 | NA | Employed full time | NA | No | Bachelor’s degree | United States | New York | $150,000 or more | gaming | NA | NA |
| 3 | 1 | 18-24 | NA | Freelance | NA | No | Some college | United States | Virginia | Under $20,000 | snackexchange | NA | NA |
| 4 | 0 | 25-34 | NA | Freelance | NA | No | Bachelor’s degree | United States | New York | $150,000 or more | spacedicks | NA | NA |
| 5 | 1 | 25-34 | NA | Employed full time | NA | No | Bachelor’s degree | United States | California | $70,000 - $99,999 | aww | NA | NA |
| 6 | 0 | 25-34 | Married/civil union/domestic partnership | Employed full time | No | No | Bachelor’s degree | United States | New York | $150,000 or more | gaming | I like dogs. | Cheddar |
str(reddit_a)## '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 (without downloading to your local hard drive)
url <- "https://bradleyboehmke.github.io/public/data/reddit.csv"
reddit_b <- read.csv(url, stringsAsFactors = FALSE)
knitr::kable(head(reddit_b))| id | gender | age.range | marital.status | employment.status | military.service | children | education | country | state | income.range | fav.reddit | dog.cat | cheese |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 25-34 | NA | Employed full time | NA | No | Bachelor’s degree | United States | New York | $150,000 or more | getmotivated | NA | NA |
| 2 | 0 | 25-34 | NA | Employed full time | NA | No | Bachelor’s degree | United States | New York | $150,000 or more | gaming | NA | NA |
| 3 | 1 | 18-24 | NA | Freelance | NA | No | Some college | United States | Virginia | Under $20,000 | snackexchange | NA | NA |
| 4 | 0 | 25-34 | NA | Freelance | NA | No | Bachelor’s degree | United States | New York | $150,000 or more | spacedicks | NA | NA |
| 5 | 1 | 25-34 | NA | Employed full time | NA | No | Bachelor’s degree | United States | California | $70,000 - $99,999 | aww | NA | NA |
| 6 | 0 | 25-34 | Married/civil union/domestic partnership | Employed full time | No | No | Bachelor’s degree | United States | New York | $150,000 or more | gaming | I like dogs. | Cheddar |
str(reddit_b)## '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: http://www.huduser.gov/portal/datasets/fmr/fmr2017/FY2017_4050_FMR.xlsx
library(readxl)
fmr_a <- read_excel("E:/UC work/data wrangling with R/Data Wrangling with R (BANA 8090)/Week-2-HW/FY2017_4050_FMR.xlsx",sheet = "EXCEL_DATA")
knitr::kable(head(fmr_a))| fips2010 | fips2000 | fmr2 | fmr0 | fmr1 | fmr3 | fmr4 | State | Metro_code | areaname | county | CouSub | countyname | county_town_name | pop2010 | acs_2016_2 | state_alpha | fmr_type | metro | FMR_PCT_Change | FMR_Dollar_Change |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2300512300 | NA | 1078 | 755 | 851 | 1454 | 1579 | 23 | METRO38860MM6400 | Portland, ME HUD Metro FMR Area | NA | 12300 | Cumberland County | Chebeague Island town | 341 | 1109 | ME | 40 | 1 | 0.9720469 | -31 |
| 6099999999 | NA | 677 | 502 | 506 | 987 | 1038 | 60 | NCNTY60999N60999 | American Samoa | 999 | 99999 | American Samoa | American Samoa | 55519 | 653 | AS | 40 | 0 | 1.0367534 | 24 |
| 6999999999 | NA | 666 | 411 | 498 | 961 | 1158 | 69 | NCNTY69999N69999 | Northern Mariana Islands | 999 | 99999 | Northern Mariana Islands | Northern Mariana Islands | 53883 | 642 | MP | 40 | 0 | 1.0373832 | 24 |
| 0100199999 | 0100199999 | 822 | 587 | 682 | 1054 | 1425 | 1 | METRO33860M33860 | Montgomery, AL MSA | 1 | 99999 | Autauga County | Autauga County | 54571 | 788 | AL | 40 | 1 | 1.0431472 | 34 |
| 0100399999 | 0100399999 | 977 | 807 | 847 | 1422 | 1634 | 1 | METRO19300M19300 | Daphne-Fairhope-Foley, AL MSA | 3 | 99999 | Baldwin County | Baldwin County | 182265 | 873 | AL | 40 | 1 | 1.1191294 | 104 |
| 0100599999 | 0100599999 | 671 | 501 | 505 | 839 | 958 | 1 | NCNTY01005N01005 | Barbour County, AL | 5 | 99999 | Barbour County | Barbour County | 27457 | 636 | AL | 40 | 0 | 1.0550314 | 35 |
str(fmr_a)## Classes 'tbl_df', 'tbl' and 'data.frame': 4769 obs. of 21 variables:
## $ fips2010 : chr "2300512300" "6099999999" "6999999999" "0100199999" ...
## $ fips2000 : chr NA NA NA "0100199999" ...
## $ fmr2 : num 1078 677 666 822 977 ...
## $ fmr0 : num 755 502 411 587 807 501 665 665 491 464 ...
## $ fmr1 : num 851 506 498 682 847 505 751 751 494 467 ...
## $ fmr3 : num 1454 987 961 1054 1422 ...
## $ fmr4 : num 1579 1038 1158 1425 1634 ...
## $ State : num 23 60 69 1 1 1 1 1 1 1 ...
## $ Metro_code : chr "METRO38860MM6400" "NCNTY60999N60999" "NCNTY69999N69999" "METRO33860M33860" ...
## $ areaname : chr "Portland, ME HUD Metro FMR Area" "American Samoa" "Northern Mariana Islands" "Montgomery, AL MSA" ...
## $ county : num NA 999 999 1 3 5 7 9 11 13 ...
## $ CouSub : chr "12300" "99999" "99999" "99999" ...
## $ countyname : chr "Cumberland County" "American Samoa" "Northern Mariana Islands" "Autauga County" ...
## $ county_town_name : chr "Chebeague Island town" "American Samoa" "Northern Mariana Islands" "Autauga County" ...
## $ pop2010 : num 341 55519 53883 54571 182265 ...
## $ acs_2016_2 : num 1109 653 642 788 873 ...
## $ state_alpha : chr "ME" "AS" "MP" "AL" ...
## $ fmr_type : num 40 40 40 40 40 40 40 40 40 40 ...
## $ metro : num 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: num -31 24 24 34 104 35 26 26 52 52 ...now import the above .xlsx file directly from the url provided (without downloading to your local hard drive)
library(gdata)
url <- "http://www.huduser.gov/portal/datasets/fmr/fmr2017/FY2017_4050_FMR.xlsx"
fmr_b <- read.xls(url)
knitr::kable(head(fmr_b))| fips2010 | fips2000 | fmr2 | fmr0 | fmr1 | fmr3 | fmr4 | State | Metro_code | areaname | county | CouSub | countyname | county_town_name | pop2010 | acs_2016_2 | state_alpha | fmr_type | metro | FMR_PCT_Change | FMR_Dollar_Change |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2300512300 | NA | 1078 | 755 | 851 | 1454 | 1579 | 23 | METRO38860MM6400 | Portland, ME HUD Metro FMR Area | NA | 12300 | Cumberland County | Chebeague Island town | 341 | 1109 | ME | 40 | 1 | 0.9720469 | -31 |
| 6099999999 | NA | 677 | 502 | 506 | 987 | 1038 | 60 | NCNTY60999N60999 | American Samoa | 999 | 99999 | American Samoa | American Samoa | 55519 | 653 | AS | 40 | 0 | 1.0367534 | 24 |
| 6999999999 | NA | 666 | 411 | 498 | 961 | 1158 | 69 | NCNTY69999N69999 | Northern Mariana Islands | 999 | 99999 | Northern Mariana Islands | Northern Mariana Islands | 53883 | 642 | MP | 40 | 0 | 1.0373832 | 24 |
| 100199999 | 100199999 | 822 | 587 | 682 | 1054 | 1425 | 1 | METRO33860M33860 | Montgomery, AL MSA | 1 | 99999 | Autauga County | Autauga County | 54571 | 788 | AL | 40 | 1 | 1.0431472 | 34 |
| 100399999 | 100399999 | 977 | 807 | 847 | 1422 | 1634 | 1 | METRO19300M19300 | Daphne-Fairhope-Foley, AL MSA | 3 | 99999 | Baldwin County | Baldwin County | 182265 | 873 | AL | 40 | 1 | 1.1191294 | 104 |
| 100599999 | 100599999 | 671 | 501 | 505 | 839 | 958 | 1 | NCNTY01005N01005 | Barbour County, AL | 5 | 99999 | Barbour County | Barbour County | 27457 | 636 | AL | 40 | 0 | 1.0550314 | 35 |
str(fmr_b)## '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",..: 1903 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 92 99 110 163 178 239 249 ...
## $ county_town_name : Factor w/ 3175 levels "Abbeville County",..: 533 60 2024 136 149 165 254 277 386 401 ...
## $ 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 http://academic.udayton.edu/kissock/http/Weather/citylistUS.htm, scroll down to Ohio and import the Cincinnati (OHCINCIN.txt) file
ohcincin <- read.table("E:/UC work/data wrangling with R/Data Wrangling with R (BANA 8090)/Week-2-HW/OHCINCIN.txt")
knitr::kable(head(ohcincin))| V1 | V2 | V3 | V4 |
|---|---|---|---|
| 1 | 1 | 1995 | 41.1 |
| 1 | 2 | 1995 | 22.2 |
| 1 | 3 | 1995 | 22.8 |
| 1 | 4 | 1995 | 14.9 |
| 1 | 5 | 1995 | 9.5 |
| 1 | 6 | 1995 | 23.8 |
str(ohcincin)## 'data.frame': 7963 obs. of 4 variables:
## $ V1: int 1 1 1 1 1 1 1 1 1 1 ...
## $ V2: int 1 2 3 4 5 6 7 8 9 10 ...
## $ V3: int 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 ...
## $ V4: num 41.1 22.2 22.8 14.9 9.5 23.8 31.1 26.9 31.3 31.5 ...