This R markedown file contains the code and output for 5 questions of Assignment 2 of Data WQQrangling with R. In this assignment we were required load packages and to import csv xlsx and txt files in two ways. Import from file on computer and import directly from url. The output will show head() and str() of the imported files
Import and Install Packages
install.packages("readr")
library(readr)Improve speed of functions over base r package, consistency to importing functions, produce data frames in data.table format for larger data sets, defualt setting removes need to use stringsASFactors,and provide column specification #flexibility, additional arguments to adjusting read in data, and functions to import text files
install.packages("readxl")
library(readxl)Newest package to access Excel data and works with old and new versions of Excel
install.packages("gdata")
library(gdata)This packages allows Excel data to be pulled from url’s
install.packages("tidyverse")install.packages("RSocrata")Install packages for class
library(knitr)
redditdata <- read.csv("reddit.csv")
head(redditdata)## id gender age.range marital.status
## 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
## employment.status military.service children education
## 1 Employed full time <NA> No Bachelor's degree
## 2 Employed full time <NA> No Bachelor's degree
## 3 Freelance <NA> No Some college
## 4 Freelance <NA> No Bachelor's degree
## 5 Employed full time <NA> No Bachelor's degree
## 6 Employed full time No No Bachelor's degree
## country state income.range fav.reddit dog.cat
## 1 United States New York $150,000 or more getmotivated <NA>
## 2 United States New York $150,000 or more gaming <NA>
## 3 United States Virginia Under $20,000 snackexchange <NA>
## 4 United States New York $150,000 or more spacedicks <NA>
## 5 United States California $70,000 - $99,999 aww <NA>
## 6 United States New York $150,000 or more gaming I like dogs.
## cheese
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 Cheddarstr(redditdata)## '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 ...url<-"https://bradleyboehmke.github.io/public/data/reddit.csv"
redditurl<-read.csv(url )
head(redditurl)## id gender age.range marital.status
## 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
## employment.status military.service children education
## 1 Employed full time <NA> No Bachelor's degree
## 2 Employed full time <NA> No Bachelor's degree
## 3 Freelance <NA> No Some college
## 4 Freelance <NA> No Bachelor's degree
## 5 Employed full time <NA> No Bachelor's degree
## 6 Employed full time No No Bachelor's degree
## country state income.range fav.reddit dog.cat
## 1 United States New York $150,000 or more getmotivated <NA>
## 2 United States New York $150,000 or more gaming <NA>
## 3 United States Virginia Under $20,000 snackexchange <NA>
## 4 United States New York $150,000 or more spacedicks <NA>
## 5 United States California $70,000 - $99,999 aww <NA>
## 6 United States New York $150,000 or more gaming I like dogs.
## cheese
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 Cheddarstr(redditurl)## '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 ...library(readxl)
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':
##
## nobs## The following object is masked from 'package:utils':
##
## object.size## The following object is masked from 'package:base':
##
## startsWithexceldata<-read.xls("FY2017_4050_FMR.xlsx")
head(exceldata)## 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(exceldata)## '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 ...library(gdata)
excelurl<-read.xls("http://www.huduser.gov/portal/datasets/fmr/fmr2017/FY2017_4050_FMR.xlsx",perl="C:\\Perl64\\bin\\perl.exe")
head(excelurl)## 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(excelurl)## '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 ...CINCIN<-read.table("http://academic.udayton.edu/kissock/http/Weather/gsod95-current/OHCINCIN.txt")
head(CINCIN)## 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 23.8str (CINCIN)## '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 ...