The following is my report for the week 2 homework assignment. The focus of this assignment is to learn how to import data into R for use and analysis. This homework consists of five different problems as detailed below and involves three different data sets: artificial Reddit user data, 2017 Fair Market Rent Data from HUD, and average daily temperatures for Cincinnati dating back to 1995.
To complete the exercises in this assignment, the following packages were used:
library(readxl) ## This package was used for problem 3.## Warning: package 'readxl' was built under R version 3.2.4library(gdata) ## This package was used for problem 4.## 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.sizeFor each of the below problems, I imported the data and saved it. I then used head() function to display a small portion of the data and the str() function to display the structure of the data.
#############################################
# Problem 1: Download and Import a csv file #
#############################################
reddit_data <- read.csv("/Users/MatthewMurphy/Dropbox (Personal)/School/5th Year/Data Wrangling with R/bootcamp/data/reddit.csv", stringsAsFactors = FALSE)
head(reddit_data, n = 5)## id gender age.range marital.status employment.status military.service
## 1 1 0 25-34 <NA> Employed full time <NA>
## 2 2 0 25-34 <NA> Employed full time <NA>
## 3 3 1 18-24 <NA> Freelance <NA>
## 4 4 0 25-34 <NA> Freelance <NA>
## 5 5 1 25-34 <NA> Employed full time <NA>
## children education country state income.range
## 1 No Bachelor's degree United States New York $150,000 or more
## 2 No Bachelor's degree United States New York $150,000 or more
## 3 No Some college United States Virginia Under $20,000
## 4 No Bachelor's degree United States New York $150,000 or more
## 5 No Bachelor's degree United States California $70,000 - $99,999
## fav.reddit dog.cat cheese
## 1 getmotivated <NA> <NA>
## 2 gaming <NA> <NA>
## 3 snackexchange <NA> <NA>
## 4 spacedicks <NA> <NA>
## 5 aww <NA> <NA>str(reddit_data)## '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 ...#############################################
# Problem 2: Import a csv file from the web #
#############################################
url <- "https://bradleyboehmke.github.io/public/data/reddit.csv"
reddit_data_web <- read.csv(url)
head(reddit_data_web, n = 5)## id gender age.range marital.status employment.status military.service
## 1 1 0 25-34 <NA> Employed full time <NA>
## 2 2 0 25-34 <NA> Employed full time <NA>
## 3 3 1 18-24 <NA> Freelance <NA>
## 4 4 0 25-34 <NA> Freelance <NA>
## 5 5 1 25-34 <NA> Employed full time <NA>
## children education country state income.range
## 1 No Bachelor's degree United States New York $150,000 or more
## 2 No Bachelor's degree United States New York $150,000 or more
## 3 No Some college United States Virginia Under $20,000
## 4 No Bachelor's degree United States New York $150,000 or more
## 5 No Bachelor's degree United States California $70,000 - $99,999
## fav.reddit dog.cat cheese
## 1 getmotivated <NA> <NA>
## 2 gaming <NA> <NA>
## 3 snackexchange <NA> <NA>
## 4 spacedicks <NA> <NA>
## 5 aww <NA> <NA>str(reddit_data_web)## '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 "","___","-","?",..: 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 ...#############################################
# Problem 3: Import a downloaded xlsx file #
#############################################
library(readxl)
mydata <- read_excel("/Users/MatthewMurphy/Dropbox (Personal)/School/5th Year/Data Wrangling with R/bootcamp/data/FY2017_4050_FMR.xlsx"
, sheet = "EXCEL_DATA")
head(mydata, n = 5)## 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
## 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
## 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
## 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 104str(mydata)## 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 ...##################################################
# Problem 4: Import a an xlsx file from the web #
##################################################
library(gdata)
url3 <- "http://www.huduser.gov/portal/datasets/fmr/fmr2017/FY2017_4050_FMR.xlsx"
mydata_web <- read.xls(url3)
head(mydata_web, n = 5)## 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
## 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
## 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
## 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 104str(mydata_web)## '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 ...###############################################
# Problem 5: Import a text file from the web #
###############################################
url3 <- "http://academic.udayton.edu/kissock/http/Weather/gsod95-current/OHCINCIN.txt"
mydata_temps <- read.table(url3,header = FALSE)
head(mydata_temps, n = 5)## 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 9.5str(mydata_temps)## '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 ...