This R Markdown file is created as part of my Week 2 Assignment requirement for Data Wrangling in R course taught at UC. This includes exercises to import tabular data in csv, excel and tab delimitted fromats from my system as well as the web.
The following data sets have been used
I have used the following packages for importing data
library(printr)
library(readxl) # for importing .xlsx file from my system
library(gdata) # for importing .xlsx file from web1. Download & import the csv file located at: https://bradleyboehmke.github.io/public/data/reddit.csv
reddit_1<-read.csv("reddit.csv", stringsAsFactors =FALSE)
head(reddit_1)| 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_1)## '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 ...2. Now import the above csv file directly from the url provided (without downloading to your local hard drive)
csv_url<-"https://bradleyboehmke.github.io/public/data/reddit.csv"
reddit_2<-read.csv(csv_url,stringsAsFactors = FALSE)
head(reddit_2)| 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_2)## '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 ...3. Download & import the .xlsx file located at: http://www.huduser.gov/portal/datasets/fmr/fmr2017/FY2017_4050_FMR.xlsx
FYdata_1<-read_excel("FY2017_4050_FMR.xlsx", sheet =1)
head(FYdata_1)| 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(FYdata_1)## 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 ...4. Now import the above .xlsx file directly from the url provided (without downloading to your local hard drive)
xlsx_url<-"http://www.huduser.gov/portal/datasets/fmr/fmr2017/FY2017_4050_FMR.xlsx"
FYdata_2<-read.xls(xlsx_url)
head(FYdata_2)| 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(FYdata_2)## '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 ...5. 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
dlm_url<-"http://academic.udayton.edu/kissock/http/Weather/gsod95-current/OHCINCIN.txt"
cincy_data<-read.table(dlm_url, sep ="", header =FALSE, col.names=)
head(cincy_data)| 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(cincy_data)## '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 ...