R markdown is a file format for making dynamic documents. The files can be displayed in pdf, html or as slides used for presentation. The purpose of this R markdown is to an introduction of the packages installed for this homework. Also, the code chunks for this homework is included in this markdown file. Csv file located at https://bradleyboehmke.github.io/public/data/reddit.csv, Excel file located at http://www.huduser.gov/portal/datasets/fmr/fmr2017/FY2017_4050_FMR.xlsx and Text file located at http://academic.udayton.edu/kissock/http/Weather/citylistUS.htm are used as sources of data. These files are downloaded and imported.
Packages that are installed are -
readxl package gdata package knitr install.packages(“knitr”)
??readxl- shows the support pages related to the readxl. We use library(readxl) to read the xlsx file in problem 3.
library(gdata)- this is used for scraping the file located at the url given in problem 4 library(knitr)- this library is loaded to present the output in a tabular structure.
Each of the data set is downloaded and then imported. The code used to perform this function is included in seperate r code chunks as follows.
1.download & import the csv file
#download & import the csv file located at:https://bradleyboehmke.github.io/public/data/reddit.csv
library(knitr)
xfile <- read.csv("C:/Users/Prarthana/Desktop/R/reddit.csv",head= TRUE)
kable(head(xfile))| 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(xfile)'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 : Factor w/ 7 levels "18-24","25-34",..: 2 2 1 2 2 2 2 1 3 2 ...
$ marital.status : Factor w/ 6 levels "Engaged","Forever Alone",..: NA NA NA NA NA 4 3 4 4 3 ...
$ employment.status: Factor w/ 6 levels "Employed full time",..: 1 1 2 2 1 1 1 4 1 2 ...
$ military.service : Factor w/ 2 levels "No","Yes": NA NA NA NA NA 1 1 1 1 1 ...
$ children : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
$ education : Factor w/ 7 levels "Associate degree",..: 2 2 5 2 2 2 5 2 2 5 ...
$ country : Factor w/ 439 levels " Canada"," Canada eh",..: 394 394 394 394 394 394 125 394 394 125 ...
$ state : Factor w/ 53 levels "","Alabama","Alaska",..: 33 33 48 33 6 33 1 6 33 1 ...
$ income.range : Factor w/ 8 levels "$100,000 - $149,999",..: 2 2 8 2 7 2 NA 7 2 7 ...
$ fav.reddit : Factor w/ 1834 levels "","'home' page (or front page if you prefer)",..: 720 691 1511 1528 188 691 1318 571 1629 1 ...
$ dog.cat : Factor w/ 3 levels "I like cats.",..: NA NA NA NA NA 2 2 2 1 1 ...
$ cheese : Factor w/ 11 levels "American","Brie",..: NA NA NA NA NA 3 3 1 10 7 ...2.now import the above csv file directly from the url provided (without downloading to your local hard drive)
#import the above csv file directly from the url provided (without downloading to your local hard drive)
library(knitr)
X <- read.csv(url("https://bradleyboehmke.github.io/public/data/reddit.csv"))
kable(head(X))| 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(X)'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 : Factor w/ 7 levels "18-24","25-34",..: 2 2 1 2 2 2 2 1 3 2 ...
$ marital.status : Factor w/ 6 levels "Engaged","Forever Alone",..: NA NA NA NA NA 4 3 4 4 3 ...
$ employment.status: Factor w/ 6 levels "Employed full time",..: 1 1 2 2 1 1 1 4 1 2 ...
$ military.service : Factor w/ 2 levels "No","Yes": NA NA NA NA NA 1 1 1 1 1 ...
$ children : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
$ education : Factor w/ 7 levels "Associate degree",..: 2 2 5 2 2 2 5 2 2 5 ...
$ country : Factor w/ 439 levels " Canada"," Canada eh",..: 394 394 394 394 394 394 125 394 394 125 ...
$ state : Factor w/ 53 levels "","Alabama","Alaska",..: 33 33 48 33 6 33 1 6 33 1 ...
$ income.range : Factor w/ 8 levels "$100,000 - $149,999",..: 2 2 8 2 7 2 NA 7 2 7 ...
$ fav.reddit : Factor w/ 1834 levels "","'home' page (or front page if you prefer)",..: 720 691 1511 1528 188 691 1318 571 1629 1 ...
$ dog.cat : Factor w/ 3 levels "I like cats.",..: NA NA NA NA NA 2 2 2 1 1 ...
$ cheese : Factor w/ 11 levels "American","Brie",..: NA NA NA NA NA 3 3 1 10 7 ...3.download & import the .xlsx file
library(knitr)
library(readxl)
dat <- read_excel("C:/Users/Prarthana/Desktop/R/FY2017_4050_FMR.xlsx")
kable(head(dat))| 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(dat)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 ...library(gdata)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':
nobsThe following object is masked from 'package:utils':
object.sizeThe following object is masked from 'package:base':
startsWithurl<- "http://www.huduser.gov/portal/datasets/fmr/fmr2017/FY2017_4050_FMR.xlsx"
URLData<- read.xls(url)
kable(head(URLData))| 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(URLData)'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,scroll down to Ohio and import the Cincinnati (OHCINCIN.txt) file
#dwnloading and importing a txt file
Ohiodata <-read.table(url("http://academic.udayton.edu/kissock/http/Weather/gsod95-current/OHDAYTON.txt"))
kable(head(Ohiodata))| V1 | V2 | V3 | V4 |
|---|---|---|---|
| 1 | 1 | 1995 | 39.0 |
| 1 | 2 | 1995 | 19.6 |
| 1 | 3 | 1995 | 20.6 |
| 1 | 4 | 1995 | 11.3 |
| 1 | 5 | 1995 | 6.8 |
| 1 | 6 | 1995 | 23.0 |
str(Ohiodata)'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 39 19.6 20.6 11.3 6.8 23 28.2 24.7 25 23.6 ...