This HTML document is created from associated R Markdown file. It uses following three data sets to perfrom five importing activities. Output of each activity is displayed under respective sections.
Packages used in assignment to exceute R code are mentioned below:
library(readr) ##Used to read data from csv file
library(xlsx) ##Used to read data from xlsx file
library(gdata) ##Used to retreive data from url in excel format
library(XML) ##Provides the getHTMLLinks() function to identify the links that points to tabular data sets
library(stringr) ##Extract names for desired links and paste to urlurl = "https://bradleyboehmke.github.io/public/data/reddit.csv"
download.file(url,dest="reddit.csv",mode="wb")
reddit_data <- read_csv("reddit.csv") #Reading csv file## 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()
## )head(reddit_data)## # 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>str(reddit_data)## 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"url = "https://bradleyboehmke.github.io/public/data/reddit.csv"
reddit_data_url <- read_csv(url) #Reading csv file## 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()
## )head(reddit_data_url)## # 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>str(reddit_data_url)## 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"url1 <- "http://www.huduser.gov/portal/datasets/fmr/fmr2017/FY2017_4050_FMR.xlsx"
download.file(url1,dest="FY2017_4050_FMR.xlsx",mode="wb")
huduser_data <- read.xlsx("FY2017_4050_FMR.xlsx",sheetIndex = 1) #Reading xlsx file
head(huduser_data)## 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 0100199999 0100199999 822 587 682 1054 1425 1 METRO33860M33860
## 5 0100399999 0100399999 977 807 847 1422 1634 1 METRO19300M19300
## 6 0100599999 0100599999 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 35str(huduser_data)## 'data.frame': 4769 obs. of 21 variables:
## $ fips2010 : Factor w/ 4769 levels "0100199999","0100399999",..: 1431 4686 4688 1 2 3 4 5 6 7 ...
## $ fips2000 : Factor w/ 4763 levels "0100199999","0100399999",..: NA NA NA 1 2 3 4 5 6 7 ...
## $ 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 : 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 : num NA 999 999 1 3 5 7 9 11 13 ...
## $ CouSub : Factor w/ 1532 levels "00100","00170",..: 234 1532 1532 1532 1532 1532 1532 1532 1532 1532 ...
## $ countyname : Factor w/ 1961 levels "Añasco Municipio",..: 462 42 1265 92 99 110 163 178 239 249 ...
## $ county_town_name : Factor w/ 3175 levels "Añasco Municipio",..: 533 61 2024 136 149 165 254 277 386 401 ...
## $ pop2010 : num 341 55519 53883 54571 182265 ...
## $ acs_2016_2 : num 1109 653 642 788 873 ...
## $ state_alpha : Factor w/ 56 levels "AK","AL","AR",..: 24 4 28 2 2 2 2 2 2 2 ...
## $ 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 ...url1 <- "http://www.huduser.gov/portal/datasets/fmr/fmr2017/FY2017_4050_FMR.xlsx"
huduser_data_url <- read.xls(url1) #Reading xlsx file
head(huduser_data_url)## 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 35str(huduser_data_url)## '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.Importing the Cincinnati (OHCINCIN.txt) file from webpage http://academic.udayton.edu/kissock/http/Weather/citylistUS.htm
url3 <- "http://academic.udayton.edu/kissock/http/Weather/"
fullUSWebLink <- paste0(url3,"citylistUS.htm")
fullUSWebLink## [1] "http://academic.udayton.edu/kissock/http/Weather/citylistUS.htm"#Extracting all weblinks from webpage
allWebPagelinks <- getHTMLLinks(fullUSWebLink)
allWebPagelinks## [1] "gsod95-current/ALBIRMIN.txt"
## [2] "gsod95-current/ALHUNTSV.txt"
## [3] "gsod95-current/ALMOBILE.txt"
## [4] "gsod95-current/ALMONTGO.txt"
## [5] "gsod95-current/AKANCHOR.txt"
## [6] "gsod95-current/AKFAIRBA.txt"
## [7] "gsod95-current/AKJUNEAU.txt"
## [8] "gsod95-current/AZFLAGST.txt"
## [9] "gsod95-current/AZPHOENI.txt"
## [10] "gsod95-current/AZTUCSON.txt"
## [11] "gsod95-current/AZYUMA.txt"
## [12] "gsod95-current/ARFTSMIT.txt"
## [13] "gsod95-current/ARLIROCK.txt"
## [14] "gsod95-current/CAFRESNO.txt"
## [15] "gsod95-current/CALOSANG.txt"
## [16] "gsod95-current/CASACRAM.txt"
## [17] "gsod95-current/CASANDIE.txt"
## [18] "gsod95-current/CASANFRA.txt"
## [19] "gsod95-current/COCOSPGS.txt"
## [20] "gsod95-current/CODENVER.txt"
## [21] "gsod95-current/COGRNDJU.txt"
## [22] "gsod95-current/COPUEBLO.txt"
## [23] "gsod95-current/CTBRIDGE.txt"
## [24] "gsod95-current/CTHARTFO.txt"
## [25] "gsod95-current/DEWILMIN.txt"
## [26] "gsod95-current/MDWASHDC.txt"
## [27] "gsod95-current/FLDAYTNA.txt"
## [28] "gsod95-current/FLJACKSV.txt"
## [29] "gsod95-current/FLMIAMIB.txt"
## [30] "gsod95-current/FLORLAND.txt"
## [31] "http://www.engr.udayton.edu/faculty/jkissock/gsod/FLTALLAH.txt"
## [32] "gsod95-current/FLTAMPA.txt"
## [33] "gsod95-current/FLWPALMB.txt"
## [34] "gsod95-current/GAATLANT.txt"
## [35] "gsod95-current/GACOLMBS.txt"
## [36] "gsod95-current/GAMACON.txt"
## [37] "gsod95-current/GASAVANN.txt"
## [38] "gsod95-current/HIHONOLU.txt"
## [39] "gsod95-current/IDBOISE.txt"
## [40] "gsod95-current/IDPOCATE.txt"
## [41] "gsod95-current/ILCHICAG.txt"
## [42] "gsod95-current/ILPEORIA.txt"
## [43] "gsod95-current/ILROCKFO.txt"
## [44] "gsod95-current/ILSPRING.txt"
## [45] "gsod95-current/INEVANSV.txt"
## [46] "gsod95-current/INFTWAYN.txt"
## [47] "gsod95-current/ININDIAN.txt"
## [48] "gsod95-current/INSOBEND.txt"
## [49] "gsod95-current/IADESMOI.txt"
## [50] "gsod95-current/IASIOCTY.txt"
## [51] "gsod95-current/KSGOODLA.txt"
## [52] "gsod95-current/KSTOPEKA.txt"
## [53] "gsod95-current/KSWICHIT.txt"
## [54] "gsod95-current/KYLEXING.txt"
## [55] "gsod95-current/KYLOUISV.txt"
## [56] "gsod95-current/KYPADUCA.txt"
## [57] "gsod95-current/LABATONR.txt"
## [58] "gsod95-current/LALAKECH.txt"
## [59] "gsod95-current/LANEWORL.txt"
## [60] "gsod95-current/LASHREVE.txt"
## [61] "gsod95-current/MECARIBO.txt"
## [62] "gsod95-current/MEPORTLA.txt"
## [63] "gsod95-current/MDBALTIM.txt"
## [64] "gsod95-current/MDWASHDC.txt"
## [65] "gsod95-current/MABOSTON.txt"
## [66] "gsod95-current/MIDETROI.txt"
## [67] "gsod95-current/MIFLINT.txt"
## [68] "gsod95-current/MIGRNDRA.txt"
## [69] "gsod95-current/MILANSIN.txt"
## [70] "gsod95-current/MISTEMAR.txt"
## [71] "gsod95-current/MNDULUTH.txt"
## [72] "gsod95-current/MNMINPLS.txt"
## [73] "gsod95-current/MSJACKSO.txt"
## [74] "gsod95-current/MSTUPELO.txt"
## [75] "gsod95-current/MOKANCTY.txt"
## [76] "gsod95-current/MOSPRING.txt"
## [77] "gsod95-current/MOSTLOUI.txt"
## [78] "gsod95-current/MTBILLIN.txt"
## [79] "gsod95-current/MTGRFALL.txt"
## [80] "gsod95-current/MTHELENA.txt"
## [81] "gsod95-current/NELINCOL.txt"
## [82] "gsod95-current/NENPLATT.txt"
## [83] "gsod95-current/NEOMAHA.txt"
## [84] "gsod95-current/NVRENO.txt"
## [85] "gsod95-current/NVLASVEG.txt"
## [86] "gsod95-current/NHCONCOR.txt"
## [87] "gsod95-current/NJATLCTY.txt"
## [88] "gsod95-current/NJNEWARK.txt"
## [89] "gsod95-current/NMALBUQU.txt"
## [90] "gsod95-current/NYALBANY.txt"
## [91] "gsod95-current/NYBUFFAL.txt"
## [92] "gsod95-current/NYNEWYOR.txt"
## [93] "gsod95-current/NYROCHES.txt"
## [94] "gsod95-current/NYSYRACU.txt"
## [95] "gsod95-current/NCASHEVI.txt"
## [96] "gsod95-current/NCCHARLO.txt"
## [97] "gsod95-current/NCGRNSBO.txt"
## [98] "gsod95-current/NCRALEIG.txt"
## [99] "gsod95-current/NDBISMAR.txt"
## [100] "gsod95-current/NDFARGO.txt"
## [101] "gsod95-current/OHAKRON.txt"
## [102] "gsod95-current/OHCINCIN.txt"
## [103] "gsod95-current/OHCLEVEL.txt"
## [104] "gsod95-current/OHCOLMBS.txt"
## [105] "gsod95-current/OHDAYTON.txt"
## [106] "gsod95-current/OHTOLEDO.txt"
## [107] "gsod95-current/OHYOUNGS.txt"
## [108] "gsod95-current/OKOKLCTY.txt"
## [109] "gsod95-current/OKTULSA.txt"
## [110] "gsod95-current/OREUGENE.txt"
## [111] "gsod95-current/ORMEDFOR.txt"
## [112] "gsod95-current/ORPORTLA.txt"
## [113] "gsod95-current/ORSALEM.txt"
## [114] "gsod95-current/PAALLENT.txt"
## [115] "gsod95-current/PAERIE.txt"
## [116] "gsod95-current/PAHARRIS.txt"
## [117] "gsod95-current/PAPHILAD.txt"
## [118] "gsod95-current/PAPITTSB.txt"
## [119] "gsod95-current/PAWILKES.txt"
## [120] "gsod95-current/RIPROVID.txt"
## [121] "gsod95-current/SCCHARLE.txt"
## [122] "gsod95-current/SCCOLMBA.txt"
## [123] "gsod95-current/SDRAPCTY.txt"
## [124] "gsod95-current/SDRAPCTY.txt"
## [125] "gsod95-current/TNCHATTA.txt"
## [126] "gsod95-current/TNKNOXVI.txt"
## [127] "gsod95-current/TNMEMPHI.txt"
## [128] "gsod95-current/TNNASHVI.txt"
## [129] "gsod95-current/TXABILEN.txt"
## [130] "gsod95-current/TXAMARIL.txt"
## [131] "gsod95-current/TXAUSTIN.txt"
## [132] "gsod95-current/TXBROWNS.txt"
## [133] "gsod95-current/TXCORPUS.txt"
## [134] "gsod95-current/TXDALLAS.txt"
## [135] "gsod95-current/TXELPASO.txt"
## [136] "gsod95-current/TXHOUSTO.txt"
## [137] "gsod95-current/TXLUBBOC.txt"
## [138] "gsod95-current/TXMIDLAN.txt"
## [139] "gsod95-current/TXSANANG.txt"
## [140] "gsod95-current/TXSANANT.txt"
## [141] "gsod95-current/TXWACO.txt"
## [142] "gsod95-current/TXWICHFA.txt"
## [143] "gsod95-current/UTSALTLK.txt"
## [144] "gsod95-current/VTBURLIN.txt"
## [145] "gsod95-current/VANORFOL.txt"
## [146] "gsod95-current/VARICHMO.txt"
## [147] "gsod95-current/VAROANOK.txt"
## [148] "gsod95-current/WASEATTL.txt"
## [149] "gsod95-current/WASPOKAN.txt"
## [150] "gsod95-current/WAYAKIMA.txt"
## [151] "gsod95-current/WVCHARLE.txt"
## [152] "gsod95-current/WVELKINS.txt"
## [153] "gsod95-current/WIGREBAY.txt"
## [154] "gsod95-current/WIMADISO.txt"
## [155] "gsod95-current/WIMILWAU.txt"
## [156] "gsod95-current/WYCASPER.txt"
## [157] "gsod95-current/WYCHEYEN.txt"
## [158] "gsod95-current/PRSANJUA.txt"
## [159] "citylistUS.htm"
## [160] "default.htm"#Extracting desired weblink using matching expression
links_data <- allWebPagelinks[str_detect(allWebPagelinks,"OHCINCIN.txt")]
links_data## [1] "gsod95-current/OHCINCIN.txt"cincinnatiPageLink <- paste0(url3,links_data)
cincinnatiPageLink## [1] "http://academic.udayton.edu/kissock/http/Weather/gsod95-current/OHCINCIN.txt"#Saving data from Cincinnati's URL to data set
cincinnati_data <- read.table(cincinnatiPageLink,stringsAsFactors = F,header = T)
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 31.1str(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 ...