This file illustrates how to import different data files (csv, text, excel) using two differnt approaches - From local machine and from the web directly (without downloading data locally)
1. Importing a csv file stored in the working directory
reddit_imported <- read.csv("reddit.csv", stringsAsFactors = FALSE)
#head_data <- head(reddit_imported)
#library(knitr)
#knitr::kable(head_data, format = "html")
DT::datatable(reddit_imported,options= list(pageLength=5))#kable(head(reddit_imported,n=4))
str(reddit_imported)## '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 ...2. Importing a csv file from web
url <- "https://bradleyboehmke.github.io/public/data/reddit.csv"
reddit_web <- read.csv(url)
DT::datatable(reddit_web, options=list(pageLength=5))3. Importing Xlsx file stored in working directory
library(xlsx)
library(readxl)
xlsx_imported <- read_excel("FY2017_4050_FMR.xlsx" , sheet = "EXCEL_DATA")
DT::datatable(xlsx_imported,options = list(pageLength=5))str(xlsx_imported)## 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. Importing Xlsx file from web
library(gdata)
url_xlsx <- "http://www.huduser.gov/portal/datasets/fmr/fmr2017/FY2017_4050_FMR.xlsx"
xlsx_web <- read.xls(url_xlsx , sheet = "EXCEL_DATA")
DT::datatable(xlsx_web,options=list(pageLength=5))5. Importing file from a web link
library(XML)
library(stringr)
url_udayton <-"http://academic.udayton.edu/kissock/http/Weather/citylistUS.htm"
url_link_cincy <- url_udayton %>%
getHTMLLinks() %>%
.[str_detect(.,"CINCIN")] %>%
paste0(str_sub(url_udayton,1,regexpr("/citylistUS",url_udayton)),.)
cincy_data <- read.table(url_link_cincy, col.names=c("Month","Day","Year","Temp"))
DT::datatable(cincy_data,options=list(pageLength=5))str(cincy_data)## 'data.frame': 7963 obs. of 4 variables:
## $ Month: int 1 1 1 1 1 1 1 1 1 1 ...
## $ Day : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Year : int 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 ...
## $ Temp : num 41.1 22.2 22.8 14.9 9.5 23.8 31.1 26.9 31.3 31.5 ...