Please resist the urge to spend a your time manually entering data into a spreadsheet. No matter the format, there is almost certainly a way to get your data into R. Here are a few useful techniques:

I. Downloading a .csv and importing it into R.

Download the .csv file (Example)
Upload it into R
Use read.csv()

II. Importing an .xls or .xlsx file

Save it as a .csv file
Upload it into R
Use read.csv()

III. Pasting data into excel and separating into columns

Copy the data (Example)
Paste it into excel (you may need to use “Paste Special”)
If necessary, use “Data/Text to Columns”
Save it as a .csv

Note: An Alternative in this case:

rayfair <- read.table('https://fairmodel.econ.yale.edu/vote2012/pres.txt', header=TRUE)
head(rayfair)

##   YEAR     VP  I DPER   DUR WAR       G     P  Z
## 1 1916 51.682  1    1  0.00   0   2.229 4.252  3
## 2 1920 36.148  1    0  1.00   1 -11.463 0.000  0
## 3 1924 41.737 -1   -1  0.00   0  -3.872 5.161 10
## 4 1928 41.244 -1    0 -1.00   0   4.623 0.183  7
## 5 1932 59.149 -1   -1 -1.25   0 -14.586 7.164  4
## 6 1936 62.226  1    1  0.00   0  11.836 2.475  9

IV. Scraping a Website

library(rvest); library(dplyr)

html_bbref <- read_html('http://www.basketball-reference.com/leagues/NBA_2016_per_game.html')

temp <- html_bbref  %>%
  html_nodes("table") %>% .[[1]] %>%
  html_table()

head(temp)

##   Rk        Player Pos Age  Tm  G GS   MP  FG  FGA  FG%  3P 3PA  3P%  2P
## 1  1    Quincy Acy  PF  25 SAC 59 29 14.8 2.0  3.6 .556 0.3 0.8 .388 1.7
## 2  2  Jordan Adams  SG  21 MEM  2  0  7.5 1.0  3.0 .333 0.0 0.5 .000 1.0
## 3  3  Steven Adams   C  22 OKC 80 80 25.2 3.3  5.3 .613 0.0 0.0      3.3
## 4  4 Arron Afflalo  SG  30 NYK 71 57 33.4 5.0 11.3 .443 1.3 3.4 .382 3.7
## 5  5 Alexis Ajinca   C  27 NOP 59 17 14.6 2.5  5.3 .476 0.0 0.0 .000 2.5
## 6  6  Cole Aldrich   C  27 LAC 60  5 13.3 2.2  3.8 .596 0.0 0.0      2.2
##   2PA  2P% eFG%  FT FTA  FT% ORB DRB TRB AST STL BLK TOV  PF  PTS
## 1 2.8 .606 .600 0.8 1.2 .735 1.1 2.1 3.2 0.5 0.5 0.4 0.5 1.7  5.2
## 2 2.5 .400 .333 1.5 2.5 .600 0.0 1.0 1.0 1.5 1.5 0.0 1.0 1.0  3.5
## 3 5.3 .613 .613 1.4 2.5 .582 2.7 3.9 6.7 0.8 0.5 1.1 1.1 2.8  8.0
## 4 7.9 .469 .500 1.5 1.8 .840 0.3 3.4 3.7 2.0 0.4 0.1 1.2 2.0 12.8
## 5 5.3 .478 .476 0.9 1.1 .839 1.3 3.3 4.6 0.5 0.3 0.6 0.9 2.3  6.0
## 6 3.8 .596 .596 1.0 1.4 .714 1.4 3.4 4.8 0.8 0.8 1.1 1.1 2.3  5.5

V. Scraping a Series of Websites

bbdata <- temp
for (year in 2000:2015){
  url <- paste0("http://www.basketball-reference.com/leagues/NBA_", year, "per_game.html")
  
  temp <- html_bbref  %>%
    html_nodes("table") %>% .[[1]] %>%
    html_table()
  
  bbdata <- rbind(bbdata, temp)
  
}
nrow(bbdata)

## [1] 10217

V. Using an API

library(jsonlite)
primary_markets <- fromJSON("http://table-cache1.predictwise.com/latest/group_23.json")

str(primary_markets)

## List of 3
##  $ id    : int 23
##  $ name  : chr "GOP Primaries"
##  $ tables:'data.frame':  19 obs. of  10 variables:
##   ..$ id                : chr [1:19] "1698" "1699" "1700" "1701" ...
##   ..$ name              : chr [1:19] "Connecticut Primary - DEM" "Delaware Primary - DEM" "Maryland Primary - DEM" "Pennsylvania Primary - DEM" ...
##   ..$ notes             : chr [1:19] "" "" "" "" ...
##   ..$ timestamp         : chr [1:19] "04-27-2016 11:56PM" "04-27-2016 11:56PM" "04-27-2016 11:56PM" "04-28-2016 3:12PM" ...
##   ..$ suppress_timestamp: chr [1:19] "0" "0" "0" "0" ...
##   ..$ header            :List of 19
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   .. ..$ : chr [1:7] "Outcome" "PredictWise" "Derived Betfair Price" "Betfair Back" ...
##   ..$ table             :List of 19
##   .. ..$ : chr [1:2, 1:7] "Hillary Clinton" "Bernie Sanders" "0 %" "0 %" ...
##   .. ..$ : chr [1:2, 1:7] "Hillary Clinton" "Bernie Sanders" "0 %" "0 %" ...
##   .. ..$ : chr [1:2, 1:7] "Hillary Clinton" "Bernie Sanders" "0 %" "0 %" ...
##   .. ..$ : chr [1:2, 1:7] "Hillary Clinton" "Bernie Sanders" "0 %" "0 %" ...
##   .. ..$ : chr [1:2, 1:7] "Hillary Clinton" "Bernie Sanders" "0 %" "0 %" ...
##   .. ..$ : chr [1:3, 1:7] "Donald Trump" "Ted Cruz" "John Kasich" "0 %" ...
##   .. ..$ : chr [1:3, 1:7] "Donald Trump" "Ted Cruz" "John Kasich" "0 %" ...
##   .. ..$ : chr [1:3, 1:7] "Donald Trump" "Ted Cruz" "John Kasich" "0 %" ...
##   .. ..$ : chr [1:3, 1:7] "Donald Trump" "Ted Cruz" "John Kasich" "0 %" ...
##   .. ..$ : chr [1:3, 1:7] "Donald Trump" "Ted Cruz" "John Kasich" "0 %" ...
##   .. ..$ : chr [1:3, 1:7] "Donald Trump" "Ted Cruz" "John Kasich" "74 %" ...
##   .. ..$ : chr [1:3, 1:7] "Donald Trump" "Ted Cruz" "John Kasich" "32 %" ...
##   .. ..$ : chr [1:3, 1:7] "Donald Trump" "Ted Cruz" "John Kasich" "96 %" ...
##   .. ..$ : chr [1:3, 1:7] "Donald Trump" "Ted Cruz" "John Kasich" "86 %" ...
##   .. ..$ : chr [1:3, 1:7] "Donald Trump" "Ted Cruz" "John Kasich" "74 %" ...
##   .. ..$ : chr [1:2, 1:7] "Hillary Clinton" "Bernie Sanders" "70 %" "30 %" ...
##   .. ..$ : chr [1:2, 1:7] "Hillary Clinton" "Bernie Sanders" "53 %" "47 %" ...
##   .. ..$ : chr [1:2, 1:7] "Hillary Clinton" "Bernie Sanders" "85 %" "15 %" ...
##   .. ..$ : chr [1:2, 1:7] "Hillary Clinton" "Bernie Sanders" "18 %" "82 %" ...
##   ..$ default_sort      : chr [1:19] "2" "2" "2" "2" ...
##   ..$ default_sort_dir  : chr [1:19] "desc" "desc" "desc" "desc" ...
##   ..$ shade_cols        :List of 19
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"
##   .. ..$ : chr "1"

primary_markets$tables$name[11]

## [1] "Indiana Primary - GOP"

primary_markets$tables$header[11]

## [[1]]
## [1] "Outcome"               "PredictWise"           "Derived Betfair Price"
## [4] "Betfair Back"          "Betfair Lay"           "Pollster"             
## [7] "Derived PredictIt"

primary_markets$tables$table[11]

## [[1]]
##      [,1]           [,2]   [,3] [,4] [,5] [,6] [,7]     
## [1,] "Donald Trump" "74 %" NA   NA   NA   NA   "$ 0.755"
## [2,] "Ted Cruz"     "26 %" NA   NA   NA   NA   "$ 0.265"
## [3,] "John Kasich"  "0 %"  NA   NA   NA   NA   "$ 0.005"

Getting Data into R

Jared Cross

April 28, 2016