This is the assignment report for week-2. In this assignment, I have worked on five problems assigned as week-2 homework. By working on this assignment I have learned about importing various types of files(.csv, .xlsx etc) into R. For this purpose three data sets have been used namely: 1. Reddit data 2. HUD data 3. Average daily temperatures for Cincinnati
To complete this assignment and run the codes I have used the following packages:
1.Download & import the csv file located at: https://bradleyboehmke.github.io/public/data/reddit.csv
## Parsed with column specification:
## cols(
## id = col_integer(),
## gender = col_integer(),
## age.range = col_character(),
## marital.status = col_character(),
## employment.status = col_character(),
## military.service = col_character(),
## children = col_character(),
## education = col_character(),
## country = col_character(),
## state = col_character(),
## income.range = col_character(),
## fav.reddit = col_character(),
## dog.cat = col_character(),
## cheese = col_character()
## )## # A tibble: 6 × 14
## id gender age.range marital.status
## <int> <int> <chr> <chr>
## 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
## # ... with 10 more variables: employment.status <chr>,
## # military.service <chr>, children <chr>, education <chr>,
## # country <chr>, state <chr>, income.range <chr>, fav.reddit <chr>,
## # dog.cat <chr>, cheese <chr>## Classes 'tbl_df', 'tbl' and '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 ...
## - attr(*, "spec")=List of 2
## ..$ cols :List of 14
## .. ..$ id : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ gender : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ age.range : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ marital.status : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ employment.status: list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ military.service : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ children : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ education : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ country : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ state : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ income.range : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ fav.reddit : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ dog.cat : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ cheese : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## ..$ default: list()
## .. ..- attr(*, "class")= chr "collector_guess" "collector"
## ..- attr(*, "class")= chr "col_spec"2.Import the above csv file directly from the url provided (without downloading to your local hard drive).
## Parsed with column specification:
## cols(
## id = col_integer(),
## gender = col_integer(),
## age.range = col_character(),
## marital.status = col_character(),
## employment.status = col_character(),
## military.service = col_character(),
## children = col_character(),
## education = col_character(),
## country = col_character(),
## state = col_character(),
## income.range = col_character(),
## fav.reddit = col_character(),
## dog.cat = col_character(),
## cheese = col_character()
## )## # A tibble: 6 × 14
## id gender age.range marital.status
## <int> <int> <chr> <chr>
## 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
## # ... with 10 more variables: employment.status <chr>,
## # military.service <chr>, children <chr>, education <chr>,
## # country <chr>, state <chr>, income.range <chr>, fav.reddit <chr>,
## # dog.cat <chr>, cheese <chr>## Classes 'tbl_df', 'tbl' and '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 ...
## - attr(*, "spec")=List of 2
## ..$ cols :List of 14
## .. ..$ id : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ gender : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ age.range : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ marital.status : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ employment.status: list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ military.service : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ children : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ education : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ country : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ state : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ income.range : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ fav.reddit : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ dog.cat : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ cheese : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## ..$ default: list()
## .. ..- attr(*, "class")= chr "collector_guess" "collector"
## ..- attr(*, "class")= chr "col_spec"3. Download & import the .xlsx file located at: http://www.huduser.gov/portal/datasets/fmr/fmr2017/FY2017_4050_FMR.xlsx
## # A tibble: 6 × 21
## fips2010 fips2000 fmr2 fmr0 fmr1 fmr3 fmr4 State
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2300512300 <NA> 1078 755 851 1454 1579 23
## 2 6099999999 <NA> 677 502 506 987 1038 60
## 3 6999999999 <NA> 666 411 498 961 1158 69
## 4 0100199999 0100199999 822 587 682 1054 1425 1
## 5 0100399999 0100399999 977 807 847 1422 1634 1
## 6 0100599999 0100599999 671 501 505 839 958 1
## # ... with 13 more variables: Metro_code <chr>, areaname <chr>,
## # county <dbl>, CouSub <chr>, countyname <chr>, county_town_name <chr>,
## # pop2010 <dbl>, acs_2016_2 <dbl>, state_alpha <chr>, fmr_type <dbl>,
## # metro <dbl>, FMR_PCT_Change <dbl>, FMR_Dollar_Change <dbl>## 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. Import the above .xlsx file directly from the url provided (without downloading to your local hard drive)
## 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## '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 "A\xf1asco Municipio",..: 462 42 1265 92 99 110 163 178 239 249 ...
## $ county_town_name : Factor w/ 3175 levels "A\xf1asco Municipio",..: 533 61 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
## V1 V2 V3 V4
## 1 1 1 1995 41.1
## 2 1 2 1995 22.2
## 3 1 3 1995 22.8
## 4 1 4 1995 14.9
## 5 1 5 1995 9.5
## 6 1 6 1995 23.8## '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 ...