Synopsis

This R markdown file showcases the five data importing exercises I performed for my week 2 Assignment. It consists of importing the following 3 data sets: 1.Artificial Reddit user data 2.HUD data regarding 2017 fair market rent 3.Average daily temperatures for Cincinnati dating back to 1995

Packages Required

Following packages were used to perform the exercise:

library("readr")   # Read functions to import csv and xls file
library("xlsx")    # Helps read data from Excel
library("readxl")  # Read Excel files
library("gdata")   # Pull Data from url excel file
library("XML")     # Identifies file links using getHTMLLinks function
library("stringr") # Detect patterns in String
library("DT")      # To format data tables

Importing Exercise

For each exercise

  1. I imported the data.
  2. Saved it as a data frame.
  3. Displayed the first few rows of the data frame using head()
  4. Then displayed the structure its using str()

The respective R code and the outputs for each problem has been included below:

1. Download & import the csv file located at: https://bradleyboehmke.github.io/public/data/reddit.csv

MyReddit <- read.csv("reddit.csv")
datatable(head(MyReddit))
str(MyReddit);
## '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)

RedditURL <- "https://bradleyboehmke.github.io/public/data/reddit.csv"
MyRedditURL <- read.csv(RedditURL, stringsAsFactors = FALSE)
datatable(head(MyRedditURL))
str(MyReddit);
## '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. Import the .xlsx file located at: http://www.huduser.gov/portal/datasets/fmr/fmr2017/FY2017_4050_FMR.xlsx

FMR <- read_excel("FY2017_4050_FMR.xlsx")
datatable(head(FMR))
str(FMR);
## 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)

FmrURL <- "http://www.huduser.gov/portal/datasets/fmr/fmr2017/FY2017_4050_FMR.xlsx"
MyFmrURL<-read.xls(FmrURL,perl="C:\\Perl64\\bin\\perl.exe")
datatable(head(MyFmrURL))
str(MyFmrURL);
## '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

UodURL <- "http://academic.udayton.edu/kissock/http/Weather/citylistUS.htm"
UodLinks <- getHTMLLinks(UodURL)
LinksCincinnati <- UodLinks[str_detect(UodLinks, "OHCINCIN")]
OHCinciURL<-paste0(str_sub(UodURL,start=1,end=regexpr("/citylist",UodURL)),LinksCincinnati)
MyOHCinciURL <- read.table(OHCinciURL, header=F,sep="")  
datatable(head(MyOHCinciURL))
str(MyOHCinciURL)
## '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 ...

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.