# Load the readr package
library(readr)## Warning: package 'readr' was built under R version 3.2.5# Import the csv file: pools
url_csv <- "http://s3.amazonaws.com/assets.datacamp.com/course/importing_data_into_r/swimming_pools.csv"
pools <- read_csv(url_csv)
# Import the txt file: potatoes
url_delim <- "http://s3.amazonaws.com/assets.datacamp.com/course/importing_data_into_r/potatoes.txt"
potatoes <- read_tsv(url_delim)
# Print pools and potatoes
pools## Name
## 1 Acacia Ridge Leisure Centre
## 2 Bellbowrie Pool
## 3 Carole Park
## 4 Centenary Pool (inner City)
## 5 Chermside Pool
## 6 Colmslie Pool (Morningside)
## 7 Spring Hill Baths (inner City)
## 8 Dunlop Park Pool (Corinda)
## 9 Fortitude Valley Pool
## 10 Hibiscus Sports Complex (upper MtGravatt)
## 11 Ithaca Pool ( Paddington)
## 12 Jindalee Pool
## 13 Manly Pool
## 14 Mt Gravatt East Aquatic Centre
## 15 Musgrave Park Pool (South Brisbane)
## 16 Newmarket Pool
## 17 Runcorn Pool
## 18 Sandgate Pool
## 19 Langlands Parks Pool (Stones Corner)
## 20 Yeronga Park Pool
## Address Latitude Longitude
## 1 1391 Beaudesert Road, Acacia Ridge -27.58616 153.0264
## 2 Sugarwood Street, Bellbowrie -27.56547 152.8911
## 3 Cnr Boundary Road and Waterford Road Wacol -27.60744 152.9315
## 4 400 Gregory Terrace, Spring Hill -27.45537 153.0251
## 5 375 Hamilton Road, Chermside -27.38583 153.0351
## 6 400 Lytton Road, Morningside -27.45516 153.0789
## 7 14 Torrington Street, Springhill -27.45960 153.0215
## 8 794 Oxley Road, Corinda -27.54652 152.9806
## 9 432 Wickham Street, Fortitude Valley -27.45390 153.0368
## 10 90 Klumpp Road, Upper Mount Gravatt -27.55183 153.0735
## 11 131 Caxton Street, Paddington -27.46226 153.0103
## 12 11 Yallambee Road, Jindalee -27.53236 152.9427
## 13 1 Fairlead Crescent, Manly -27.45228 153.1874
## 14 Cnr wecker Road and Newnham Road, Mansfield -27.53214 153.0943
## 15 100 Edmonstone Street, South Brisbane -27.47978 153.0168
## 16 71 Alderson Stret, Newmarket -27.42968 153.0062
## 17 37 Bonemill Road, Runcorn -27.59156 153.0764
## 18 231 Flinders Parade, Sandgate -27.31196 153.0691
## 19 5 Panitya Street, Stones Corner -27.49769 153.0487
## 20 81 School Road, Yeronga -27.52053 153.0185potatoes## area temp size storage method texture flavor moistness
## 1 1 1 1 1 1 2.9 3.2 3.0
## 2 1 1 1 1 2 2.3 2.5 2.6
## 3 1 1 1 1 3 2.5 2.8 2.8
## 4 1 1 1 1 4 2.1 2.9 2.4
## 5 1 1 1 1 5 1.9 2.8 2.2
## 6 1 1 1 2 1 1.8 3.0 1.7
## 7 1 1 1 2 2 2.6 3.1 2.4
## 8 1 1 1 2 3 3.0 3.0 2.9
## 9 1 1 1 2 4 2.2 3.2 2.5
## 10 1 1 1 2 5 2.0 2.8 1.9
## 11 1 1 1 3 1 1.8 2.6 1.5
## 12 1 1 1 3 2 2.0 2.8 1.9
## 13 1 1 1 3 3 2.6 2.6 2.6
## 14 1 1 1 3 4 2.1 3.2 2.1
## 15 1 1 1 3 5 2.5 3.0 2.1
## 16 1 1 1 4 1 2.6 3.1 2.4
## 17 1 1 1 4 2 2.7 2.9 2.4
## 18 1 1 1 4 3 2.2 3.1 2.3
## 19 1 1 1 4 4 3.1 3.4 2.7
## 20 1 1 1 4 5 3.0 2.6 2.7
## 21 1 1 2 1 1 3.1 3.0 2.8
## 22 1 1 2 1 2 2.7 2.8 2.7
## 23 1 1 2 1 3 2.4 3.0 2.9
## 24 1 1 2 1 4 2.2 2.9 2.3
## 25 1 1 2 1 5 1.9 2.9 2.0
## 26 1 1 2 2 1 1.8 2.6 1.8
## 27 1 1 2 2 2 2.2 2.9 2.1
## 28 1 1 2 2 3 2.8 3.2 2.8
## 29 1 1 2 2 4 2.3 3.2 2.4
## 30 1 1 2 2 5 2.0 3.0 2.0
## 31 1 1 2 3 1 1.9 3.0 1.8
## 32 1 1 2 3 2 1.8 2.7 1.8
## 33 1 1 2 3 3 3.3 3.2 3.2
## 34 1 1 2 3 4 2.5 3.1 2.2
## 35 1 1 2 3 5 2.5 3.4 2.3
## 36 1 1 2 4 1 1.5 2.6 1.3
## 37 1 1 2 4 2 1.4 2.6 1.3
## 38 1 1 2 4 3 2.1 2.5 2.0
## 39 1 1 2 4 4 1.8 3.1 1.7
## 40 1 1 2 4 5 1.7 2.7 1.7
## 41 1 2 1 1 1 2.8 2.6 3.0
## 42 1 2 1 1 2 2.5 2.4 2.8
## 43 1 2 1 1 3 3.2 2.7 3.2
## 44 1 2 1 1 4 2.4 2.4 2.6
## 45 1 2 1 1 5 2.0 2.5 2.2
## 46 1 2 1 2 1 2.3 2.9 1.9
## 47 1 2 1 2 2 2.8 2.7 2.5
## 48 1 2 1 2 3 3.7 3.3 3.1
## 49 1 2 1 2 4 2.8 2.7 2.5
## 50 1 2 1 2 5 2.6 2.6 2.3
## 51 1 2 1 3 1 2.4 2.7 2.0
## 52 1 2 1 3 2 2.7 2.5 2.1
## 53 1 2 1 3 3 2.7 2.9 2.7
## 54 1 2 1 3 4 2.6 2.8 2.2
## 55 1 2 1 3 5 2.6 2.8 1.8
## 56 1 2 1 4 1 3.0 3.2 2.4
## 57 1 2 1 4 2 3.1 3.1 2.8
## 58 1 2 1 4 3 3.6 3.0 3.3
## 59 1 2 1 4 4 3.4 3.4 2.9
## 60 1 2 1 4 5 2.7 3.0 2.6
## 61 1 2 2 1 1 2.2 2.6 2.6
## 62 1 2 2 1 2 2.3 2.3 2.4
## 63 1 2 2 1 3 2.7 2.6 2.7
## 64 1 2 2 1 4 2.0 2.5 2.0
## 65 1 2 2 1 5 1.4 2.4 1.7
## 66 1 2 2 2 1 2.5 2.6 1.9
## 67 1 2 2 2 2 3.2 2.9 2.9
## 68 1 2 2 2 3 3.0 3.1 1.9
## 69 1 2 2 2 4 2.6 2.8 2.7
## 70 1 2 2 2 5 2.6 3.1 2.2
## 71 1 2 2 3 1 2.4 3.0 2.2
## 72 1 2 2 3 2 2.8 2.8 2.5
## 73 1 2 2 3 3 3.3 3.1 3.1
## 74 1 2 2 3 4 2.8 3.1 2.6
## 75 1 2 2 3 5 2.9 2.9 2.4
## 76 1 2 2 4 1 1.4 2.9 1.4
## 77 1 2 2 4 2 2.1 2.5 1.6
## 78 1 2 2 4 3 2.3 2.6 1.8
## 79 1 2 2 4 4 1.8 2.9 1.6
## 80 1 2 2 4 5 1.5 2.4 1.5
## 81 2 1 1 1 1 2.5 2.7 2.6
## 82 2 1 1 1 2 2.8 2.9 2.7
## 83 2 1 1 1 3 2.2 3.0 3.0
## 84 2 1 1 1 4 2.5 3.1 2.4
## 85 2 1 1 1 5 2.7 3.0 2.3
## 86 2 1 1 2 1 2.7 2.8 2.4
## 87 2 1 1 2 2 2.5 2.9 2.6
## 88 2 1 1 2 3 1.6 3.1 1.8
## 89 2 1 1 2 4 2.5 3.2 2.3
## 90 2 1 1 2 5 2.5 2.9 2.5
## 91 2 1 1 3 1 2.2 2.8 2.3
## 92 2 1 1 3 2 2.4 2.9 2.1
## 93 2 1 1 3 3 2.2 3.1 2.3
## 94 2 1 1 3 4 3.1 3.1 2.6
## 95 2 1 1 3 5 2.9 3.3 2.8
## 96 2 1 1 4 1 2.4 3.4 2.4
## 97 2 1 1 4 2 3.1 3.1 2.7
## 98 2 1 1 4 3 2.3 3.2 2.5
## 99 2 1 1 4 4 3.2 3.5 3.1
## 100 2 1 1 4 5 2.9 2.7 2.7
## 101 2 1 2 1 1 2.6 3.3 2.6
## 102 2 1 2 1 2 2.7 3.0 2.7
## 103 2 1 2 1 3 2.5 2.9 2.7
## 104 2 1 2 1 4 2.4 3.0 2.5
## 105 2 1 2 1 5 2.0 2.9 2.1
## 106 2 1 2 2 1 2.0 3.0 1.9
## 107 2 1 2 2 2 2.3 3.1 2.3
## 108 2 1 2 2 3 1.7 3.1 2.4
## 109 2 1 2 2 4 2.6 3.1 2.5
## 110 2 1 2 2 5 2.2 2.9 2.1
## 111 2 1 2 3 1 1.7 3.2 1.5
## 112 2 1 2 3 2 2.2 3.2 2.0
## 113 2 1 2 3 3 1.7 3.1 2.0
## 114 2 1 2 3 4 2.8 3.2 2.7
## 115 2 1 2 3 5 2.6 3.3 2.6
## 116 2 1 2 4 1 2.0 3.5 2.2
## 117 2 1 2 4 2 1.8 3.0 2.0
## 118 2 1 2 4 3 1.6 3.4 2.1
## 119 2 1 2 4 4 2.8 3.3 2.6
## 120 2 1 2 4 5 2.7 2.3 2.6
## 121 2 2 1 1 1 2.8 2.6 2.5
## 122 2 2 1 1 2 2.9 2.0 2.7
## 123 2 2 1 1 3 3.0 2.7 2.9
## 124 2 2 1 1 4 2.6 3.0 2.5
## 125 2 2 1 1 5 2.8 2.2 2.6
## 126 2 2 1 2 1 3.4 3.2 2.8
## 127 2 2 1 2 2 3.5 2.9 3.0
## 128 2 2 1 2 3 2.6 2.8 2.5
## 129 2 2 1 2 4 3.3 3.0 3.1
## 130 2 2 1 2 5 2.0 2.8 2.5
## 131 2 2 1 3 1 2.8 2.8 2.6
## 132 2 2 1 3 2 3.5 2.8 3.0
## 133 2 2 1 3 3 2.5 3.2 2.3
## 134 2 2 1 3 4 3.3 3.0 2.7
## 135 2 2 1 3 5 3.5 2.9 2.9
## 136 2 2 1 4 1 3.2 3.4 2.5
## 137 2 2 1 4 2 3.3 2.8 2.8
## 138 2 2 1 4 3 3.0 3.0 2.8
## 139 2 2 1 4 4 3.5 3.2 3.1
## 140 2 2 1 4 5 3.4 3.0 2.8
## 141 2 2 2 1 1 2.7 2.5 2.5
## 142 2 2 2 1 2 2.5 2.7 2.3
## 143 2 2 2 1 3 3.2 2.7 3.0
## 144 2 2 2 1 4 2.4 2.7 2.5
## 145 2 2 2 1 5 2.7 2.1 2.3
## 146 2 2 2 2 1 2.2 2.7 2.3
## 147 2 2 2 2 2 3.1 2.9 2.6
## 148 2 2 2 2 3 2.2 2.8 3.1
## 149 2 2 2 2 4 2.9 3.0 2.7
## 150 2 2 2 2 5 2.8 2.7 2.6
## 151 2 2 2 3 1 2.5 3.2 2.3
## 152 2 2 2 3 2 2.9 3.3 2.7
## 153 2 2 2 3 3 2.5 3.1 2.5
## 154 2 2 2 3 4 3.0 2.9 2.5
## 155 2 2 2 3 5 2.9 3.1 3.1
## 156 2 2 2 4 1 2.7 3.3 2.6
## 157 2 2 2 4 2 2.6 2.8 2.3
## 158 2 2 2 4 3 2.5 3.1 2.6
## 159 2 2 2 4 4 3.4 3.3 3.0
## 160 2 2 2 4 5 2.5 2.8 2.3# https URL to the swimming_pools csv file.
url_csv <- "https://s3.amazonaws.com/assets.datacamp.com/course/importing_data_into_r/swimming_pools.csv"
# Import the file using read.csv(): pools1
pools1 <- read.csv(url_csv)
# Import the file using read_csv(): pools2
pools2 <- read_csv(url_csv)
# Print the structure of pools1 and pools2
str(pools1)## 'data.frame': 20 obs. of 4 variables:
## $ Name : Factor w/ 20 levels "Acacia Ridge Leisure Centre",..: 1 2 3 4 5 6 19 7 8 9 ...
## $ Address : Factor w/ 20 levels "1 Fairlead Crescent, Manly",..: 5 20 18 10 9 11 6 15 12 17 ...
## $ Latitude : num -27.6 -27.6 -27.6 -27.5 -27.4 ...
## $ Longitude: num 153 153 153 153 153 ...str(pools2)## Classes 'tbl_df', 'tbl' and 'data.frame': 20 obs. of 4 variables:
## $ Name : chr "Acacia Ridge Leisure Centre" "Bellbowrie Pool" "Carole Park" "Centenary Pool (inner City)" ...
## $ Address : chr "1391 Beaudesert Road, Acacia Ridge" "Sugarwood Street, Bellbowrie" "Cnr Boundary Road and Waterford Road Wacol" "400 Gregory Terrace, Spring Hill" ...
## $ Latitude : num -27.6 -27.6 -27.6 -27.5 -27.4 ...
## $ Longitude: num 153 153 153 153 153 ...# Load the readxl and gdata package
#library(readxl)
#library(gdata)
##Unable to locate valid perl interpreter
##gdata:
##gdata: read.xls() will be unable to read Excel XLS and XLSX files unless the 'perl=' argument is
##gdata: used to specify the location of a valid perl intrpreter.
# Specification of url: url_xls
##url_xls <- "http://s3.amazonaws.com/assets.datacamp.com/course/importing_data_into_r/latitude.xls"
# Import the .xls file with gdata: excel_gdata
##excel_gdata <- read.xls(url_xls)
# Download file behind URL, name it local_latitude.xls
##dest_path <- file.path("~", "local_latitude.xls")
##download.file(url_xls, dest_path)
# Import the local .xls file with readxl: excel_readxl
##excel_readxl <- read_excel("local_latitude.xls")# https URL to the wine RData file.
##url_rdata <- "https://s3.amazonaws.com/assets.datacamp.com/course/importing_data_into_r/wine.RData"
# Download the wine file to your working directory
##dest_path <- file.path("~", "wine_local.RData")
##download.file(url_rdata, dest_path)
# Load the wine data into your workspace using load()
##load("wine_local.RData")
# Print out the summary of the wine data
##summary(wine)# Load the httr package
library(httr)## Warning: package 'httr' was built under R version 3.2.5# Get the url, save response to resp
url <- "http://www.example.com/"
resp <- GET(url)
# Print resp
resp## Response [http://www.example.com/]
## Date: 2016-06-01 17:47
## Status: 200
## Content-Type: text/html
## Size: 1.27 kB## No encoding supplied: defaulting to UTF-8.## <!doctype html>
## <html>
## <head>
## <title>Example Domain</title>
##
## <meta charset="utf-8" />
## <meta http-equiv="Content-type" content="text/html; charset=utf-8" />
## <meta name="viewport" content="width=device-width, initial-scale=1" />
## <style type="text/css">
## body {
## ...# Get the raw content of resp
raw_content <- content(resp, as = "raw")
# Print the head of content
head(raw_content)## [1] 3c 21 64 6f 63 74# Get the url
url <- "https://www.omdbapi.com/?t=Annie+Hall&y=&plot=short&r=json"
resp <- GET(url)
# Print resp
resp## Response [https://www.omdbapi.com/?t=Annie+Hall&y=&plot=short&r=json]
## Date: 2016-06-01 17:47
## Status: 200
## Content-Type: application/json; charset=utf-8
## Size: 662 B# Print content of resp as text
content(resp, as = "text")## [1] "{\"Title\":\"Annie Hall\",\"Year\":\"1977\",\"Rated\":\"PG\",\"Released\":\"20 Apr 1977\",\"Runtime\":\"93 min\",\"Genre\":\"Comedy, Romance\",\"Director\":\"Woody Allen\",\"Writer\":\"Woody Allen, Marshall Brickman\",\"Actors\":\"Woody Allen, Diane Keaton, Tony Roberts, Carol Kane\",\"Plot\":\"Neurotic New York comedian Alvy Singer falls in love with the ditzy Annie Hall.\",\"Language\":\"English, German\",\"Country\":\"USA\",\"Awards\":\"Won 4 Oscars. Another 26 wins & 8 nominations.\",\"Poster\":\"http://ia.media-imdb.com/images/M/MV5BMTU1NDM2MjkwM15BMl5BanBnXkFtZTcwODU3OTYwNA@@._V1_SX300.jpg\",\"Metascore\":\"N/A\",\"imdbRating\":\"8.1\",\"imdbVotes\":\"188,690\",\"imdbID\":\"tt0075686\",\"Type\":\"movie\",\"Response\":\"True\"}"# Print content of resp. Use content() to get the content of resp, but this time do not specify a second argument. R figures out automatically that you're dealing with a JSON, and converts the JSON to a named R list.
content(resp)## $Title
## [1] "Annie Hall"
##
## $Year
## [1] "1977"
##
## $Rated
## [1] "PG"
##
## $Released
## [1] "20 Apr 1977"
##
## $Runtime
## [1] "93 min"
##
## $Genre
## [1] "Comedy, Romance"
##
## $Director
## [1] "Woody Allen"
##
## $Writer
## [1] "Woody Allen, Marshall Brickman"
##
## $Actors
## [1] "Woody Allen, Diane Keaton, Tony Roberts, Carol Kane"
##
## $Plot
## [1] "Neurotic New York comedian Alvy Singer falls in love with the ditzy Annie Hall."
##
## $Language
## [1] "English, German"
##
## $Country
## [1] "USA"
##
## $Awards
## [1] "Won 4 Oscars. Another 26 wins & 8 nominations."
##
## $Poster
## [1] "http://ia.media-imdb.com/images/M/MV5BMTU1NDM2MjkwM15BMl5BanBnXkFtZTcwODU3OTYwNA@@._V1_SX300.jpg"
##
## $Metascore
## [1] "N/A"
##
## $imdbRating
## [1] "8.1"
##
## $imdbVotes
## [1] "188,690"
##
## $imdbID
## [1] "tt0075686"
##
## $Type
## [1] "movie"
##
## $Response
## [1] "True"# Load the jsonlite package
library(jsonlite)## Warning: package 'jsonlite' was built under R version 3.2.2##
## Attaching package: 'jsonlite'
##
## The following object is masked from 'package:utils':
##
## View# Convert wine_json to a list: wine
wine_json <- '{"name":"Chateau Migraine", "year":1997, "alcohol_pct":12.4, "color":"red", "awarded":false}'
#Use fromJSON() to convert it to a list, named wine.
wine <- fromJSON(wine_json)
# Import Quandl data: quandl_data
quandl_url <- "http://www.quandl.com/api/v1/datasets/IWS/INTERNET_INDIA.json?auth_token=i83asDsiWUUyfoypkgMz"
quandl_data <- fromJSON(quandl_url)
# Print structure of wine and quandl_data
str(wine)## List of 5
## $ name : chr "Chateau Migraine"
## $ year : int 1997
## $ alcohol_pct: num 12.4
## $ color : chr "red"
## $ awarded : logi FALSEstr(quandl_data)## List of 18
## $ errors : Named list()
## $ id : int 2351831
## $ source_name : chr "Internet World Stats"
## $ source_code : chr "IWS"
## $ code : chr "INTERNET_INDIA"
## $ name : chr "India Internet Usage"
## $ urlize_name : chr "India-Internet-Usage"
## $ display_url : chr "http://www.internetworldstats.com/asia/in.htm"
## $ description : chr "Internet Usage and Population Statistics"
## $ updated_at : chr "2016-01-01T04:23:55.235Z"
## $ frequency : chr "annual"
## $ from_date : chr "1998-12-31"
## $ to_date : chr "2012-12-31"
## $ column_names: chr [1:4] "YEAR" "Users" "Population" "% Pen."
## $ private : logi FALSE
## $ type : NULL
## $ premium : logi FALSE
## $ data : chr [1:13, 1:4] "2012-12-31" "2010-12-31" "2009-12-31" "2007-12-31" ...# Experiment 1
json1 <- '[1, 2, 3,4, 5,6]'
fromJSON(json1)## [1] 1 2 3 4 5 6# Experiment 2
json2 <- '{"a": [1, 2, 3], "b":[4,5,6]}'
fromJSON(json2)## $a
## [1] 1 2 3
##
## $b
## [1] 4 5 6# Experiment 3
json3 <- '[[1, 2], [3, 4]]'
fromJSON(json3)## [,1] [,2]
## [1,] 1 2
## [2,] 3 4# Experiment 4
json4 <- '[{"a": 1, "b": 2}, {"a": 3, "b": 4}, {"a": 5, "b": 6}]'
fromJSON(json4)## a b
## 1 1 2
## 2 3 4
## 3 5 6# Definition of the URLs
url_sw4 <- "http://www.omdbapi.com/?i=tt0076759&r=json"
url_sw3 <- "http://www.omdbapi.com/?i=tt0121766&r=json"
# Import two URLs with fromJSON(): sw4 and sw3
sw4 <- fromJSON(url_sw4)
sw3 <- fromJSON(url_sw3)
# Print out the Title element of both lists
sw4$Title## [1] "Star Wars: Episode IV - A New Hope"sw3$Title## [1] "Star Wars: Episode III - Revenge of the Sith"# Is the release year of sw4 later than sw3?
sw4$Year > sw3$Year## [1] FALSE# URL pointing to the .csv file
url_csv <- "http://s3.amazonaws.com/assets.datacamp.com/course/importing_data_into_r/water.csv"
# Import the .csv file located at url_csv
water <- read.csv(url_csv, stringsAsFactors = FALSE)
# Convert the data file according to the requirements
water_json <- toJSON(water)
# Print out water_json
water_json## [{"water":"Algeria","X1992":0.064,"X2002":0.017},{"water":"American Samoa"},{"water":"Angola","X1992":0.0001,"X2002":0.0001},{"water":"Antigua and Barbuda","X1992":0.0033},{"water":"Argentina","X1992":0.0007,"X1997":0.0007,"X2002":0.0007},{"water":"Australia","X1992":0.0298,"X2002":0.0298},{"water":"Austria","X1992":0.0022,"X2002":0.0022},{"water":"Bahamas","X1992":0.0013,"X2002":0.0074},{"water":"Bahrain","X1992":0.0441,"X2002":0.0441,"X2007":0.1024},{"water":"Barbados","X2007":0.0146},{"water":"British Virgin Islands","X2007":0.0042},{"water":"Canada","X1992":0.0027,"X2002":0.0027},{"water":"Cape Verde","X1992":0.002,"X1997":0.0017},{"water":"Cayman Islands","X1992":0.0033},{"water":"Central African Rep."},{"water":"Chile","X1992":0.0048,"X2002":0.0048},{"water":"Colombia","X1992":0.0027,"X2002":0.0027},{"water":"Cuba","X1992":0.0069,"X1997":0.0069,"X2002":0.0069},{"water":"Cyprus","X1992":0.003,"X1997":0.003,"X2002":0.0335},{"water":"Czech Rep.","X1992":0.0002,"X2002":0.0002},{"water":"Denmark","X1992":0.015,"X2002":0.015},{"water":"Djibouti","X1992":0.0001,"X2002":0.0001},{"water":"Ecuador","X1992":0.0022,"X1997":0.0022,"X2002":0.0022},{"water":"Egypt","X1992":0.025,"X1997":0.025,"X2002":0.1},{"water":"El Salvador","X1992":0.0001,"X2002":0.0001},{"water":"Finland","X1992":0.0001,"X2002":0.0001},{"water":"France","X1992":0.0117,"X2002":0.0117},{"water":"Gibraltar","X1992":0.0077},{"water":"Greece","X1992":0.01,"X2002":0.01},{"water":"Honduras","X1992":0.0002,"X2002":0.0002},{"water":"Hungary","X1992":0.0002,"X2002":0.0002},{"water":"India","X1997":0.0005,"X2002":0.0005},{"water":"Indonesia","X1992":0.0187,"X2002":0.0187},{"water":"Iran","X1992":0.003,"X1997":0.003,"X2002":0.003,"X2007":0.2},{"water":"Iraq","X1997":0.0074,"X2002":0.0074},{"water":"Ireland","X1992":0.0002,"X2002":0.0002},{"water":"Israel","X1992":0.0256,"X2002":0.0256,"X2007":0.14},{"water":"Italy","X1992":0.0973,"X2002":0.0973},{"water":"Jamaica","X1992":0.0005,"X1997":0.0005,"X2002":0.0005},{"water":"Japan","X1997":0.04,"X2002":0.04},{"water":"Jordan","X1997":0.002,"X2007":0.0098},{"water":"Kazakhstan","X1997":1.328,"X2002":1.328},{"water":"Kuwait","X1992":0.507,"X1997":0.231,"X2002":0.4202},{"water":"Lebanon","X2007":0.0473},{"water":"Libya","X2002":0.018},{"water":"Malaysia","X1992":0.0043,"X2002":0.0043},{"water":"Maldives","X1992":0.0004},{"water":"Malta","X1992":0.024,"X1997":0.031,"X2002":0.031},{"water":"Marshall Islands","X1992":0.0007},{"water":"Mauritania","X1992":0.002,"X2002":0.002},{"water":"Mexico","X1992":0.0307,"X2002":0.0307},{"water":"Morocco","X1992":0.0034,"X1997":0.0034,"X2002":0.007},{"water":"Namibia","X1992":0.0003,"X2002":0.0003},{"water":"Netherlands Antilles","X1992":0.063},{"water":"Nicaragua","X1992":0.0002,"X2002":0.0002},{"water":"Nigeria","X1992":0.003,"X2002":0.003},{"water":"Norway","X1992":0.0001,"X2002":0.0001},{"water":"Oman","X1997":0.034,"X2002":0.034,"X2007":0.109},{"water":"Peru","X1992":0.0054,"X2002":0.0054},{"water":"Poland","X1992":0.007,"X2002":0.007},{"water":"Portugal","X1992":0.0016,"X2002":0.0016},{"water":"Qatar","X1992":0.065,"X1997":0.099,"X2002":0.099,"X2007":0.18},{"water":"Saudi Arabia","X1992":0.683,"X1997":0.727,"X2002":0.863,"X2007":1.033},{"water":"Senegal","X1992":0.0001,"X2002":0.0001},{"water":"Somalia","X1992":0.0001,"X2002":0.0001},{"water":"South Africa","X1992":0.018,"X2002":0.018},{"water":"Spain","X1992":0.1002,"X2002":0.1002},{"water":"Sudan","X1992":0.0004,"X1997":0.0004,"X2002":0.0004},{"water":"Sweden","X1992":0.0002,"X2002":0.0002},{"water":"Trinidad and Tobago","X2007":0.036},{"water":"Tunisia","X1992":0.008,"X2002":0.013},{"water":"Turkey","X1992":0.0005,"X2002":0.0005,"X2007":0.0005},{"water":"United Arab Emirates","X1992":0.163,"X1997":0.385,"X2007":0.95},{"water":"United Kingdom","X1992":0.0333,"X2002":0.0333},{"water":"United States","X1992":0.58,"X2002":0.58},{"water":"Venezuela","X1992":0.0052,"X2002":0.0052},{"water":"Yemen, Rep.","X1992":0.01,"X2002":0.01}]# Mini
## {"a":1,"b":2,"c":{"x":5,"y":6}}
# Pretty
## {
## "a": 1,
## "b": 2,
## "c": {
## "x": 5,
## "y": 6
## }
##}# Convert mtcars to a pretty JSON: pretty_json
pretty_json <- toJSON(mtcars, pretty = TRUE)
# Print pretty_json
pretty_json## [
## {
## "mpg": 21,
## "cyl": 6,
## "disp": 160,
## "hp": 110,
## "drat": 3.9,
## "wt": 2.62,
## "qsec": 16.46,
## "vs": 0,
## "am": 1,
## "gear": 4,
## "carb": 4,
## "_row": "Mazda RX4"
## },
## {
## "mpg": 21,
## "cyl": 6,
## "disp": 160,
## "hp": 110,
## "drat": 3.9,
## "wt": 2.875,
## "qsec": 17.02,
## "vs": 0,
## "am": 1,
## "gear": 4,
## "carb": 4,
## "_row": "Mazda RX4 Wag"
## },
## {
## "mpg": 22.8,
## "cyl": 4,
## "disp": 108,
## "hp": 93,
## "drat": 3.85,
## "wt": 2.32,
## "qsec": 18.61,
## "vs": 1,
## "am": 1,
## "gear": 4,
## "carb": 1,
## "_row": "Datsun 710"
## },
## {
## "mpg": 21.4,
## "cyl": 6,
## "disp": 258,
## "hp": 110,
## "drat": 3.08,
## "wt": 3.215,
## "qsec": 19.44,
## "vs": 1,
## "am": 0,
## "gear": 3,
## "carb": 1,
## "_row": "Hornet 4 Drive"
## },
## {
## "mpg": 18.7,
## "cyl": 8,
## "disp": 360,
## "hp": 175,
## "drat": 3.15,
## "wt": 3.44,
## "qsec": 17.02,
## "vs": 0,
## "am": 0,
## "gear": 3,
## "carb": 2,
## "_row": "Hornet Sportabout"
## },
## {
## "mpg": 18.1,
## "cyl": 6,
## "disp": 225,
## "hp": 105,
## "drat": 2.76,
## "wt": 3.46,
## "qsec": 20.22,
## "vs": 1,
## "am": 0,
## "gear": 3,
## "carb": 1,
## "_row": "Valiant"
## },
## {
## "mpg": 14.3,
## "cyl": 8,
## "disp": 360,
## "hp": 245,
## "drat": 3.21,
## "wt": 3.57,
## "qsec": 15.84,
## "vs": 0,
## "am": 0,
## "gear": 3,
## "carb": 4,
## "_row": "Duster 360"
## },
## {
## "mpg": 24.4,
## "cyl": 4,
## "disp": 146.7,
## "hp": 62,
## "drat": 3.69,
## "wt": 3.19,
## "qsec": 20,
## "vs": 1,
## "am": 0,
## "gear": 4,
## "carb": 2,
## "_row": "Merc 240D"
## },
## {
## "mpg": 22.8,
## "cyl": 4,
## "disp": 140.8,
## "hp": 95,
## "drat": 3.92,
## "wt": 3.15,
## "qsec": 22.9,
## "vs": 1,
## "am": 0,
## "gear": 4,
## "carb": 2,
## "_row": "Merc 230"
## },
## {
## "mpg": 19.2,
## "cyl": 6,
## "disp": 167.6,
## "hp": 123,
## "drat": 3.92,
## "wt": 3.44,
## "qsec": 18.3,
## "vs": 1,
## "am": 0,
## "gear": 4,
## "carb": 4,
## "_row": "Merc 280"
## },
## {
## "mpg": 17.8,
## "cyl": 6,
## "disp": 167.6,
## "hp": 123,
## "drat": 3.92,
## "wt": 3.44,
## "qsec": 18.9,
## "vs": 1,
## "am": 0,
## "gear": 4,
## "carb": 4,
## "_row": "Merc 280C"
## },
## {
## "mpg": 16.4,
## "cyl": 8,
## "disp": 275.8,
## "hp": 180,
## "drat": 3.07,
## "wt": 4.07,
## "qsec": 17.4,
## "vs": 0,
## "am": 0,
## "gear": 3,
## "carb": 3,
## "_row": "Merc 450SE"
## },
## {
## "mpg": 17.3,
## "cyl": 8,
## "disp": 275.8,
## "hp": 180,
## "drat": 3.07,
## "wt": 3.73,
## "qsec": 17.6,
## "vs": 0,
## "am": 0,
## "gear": 3,
## "carb": 3,
## "_row": "Merc 450SL"
## },
## {
## "mpg": 15.2,
## "cyl": 8,
## "disp": 275.8,
## "hp": 180,
## "drat": 3.07,
## "wt": 3.78,
## "qsec": 18,
## "vs": 0,
## "am": 0,
## "gear": 3,
## "carb": 3,
## "_row": "Merc 450SLC"
## },
## {
## "mpg": 10.4,
## "cyl": 8,
## "disp": 472,
## "hp": 205,
## "drat": 2.93,
## "wt": 5.25,
## "qsec": 17.98,
## "vs": 0,
## "am": 0,
## "gear": 3,
## "carb": 4,
## "_row": "Cadillac Fleetwood"
## },
## {
## "mpg": 10.4,
## "cyl": 8,
## "disp": 460,
## "hp": 215,
## "drat": 3,
## "wt": 5.424,
## "qsec": 17.82,
## "vs": 0,
## "am": 0,
## "gear": 3,
## "carb": 4,
## "_row": "Lincoln Continental"
## },
## {
## "mpg": 14.7,
## "cyl": 8,
## "disp": 440,
## "hp": 230,
## "drat": 3.23,
## "wt": 5.345,
## "qsec": 17.42,
## "vs": 0,
## "am": 0,
## "gear": 3,
## "carb": 4,
## "_row": "Chrysler Imperial"
## },
## {
## "mpg": 32.4,
## "cyl": 4,
## "disp": 78.7,
## "hp": 66,
## "drat": 4.08,
## "wt": 2.2,
## "qsec": 19.47,
## "vs": 1,
## "am": 1,
## "gear": 4,
## "carb": 1,
## "_row": "Fiat 128"
## },
## {
## "mpg": 30.4,
## "cyl": 4,
## "disp": 75.7,
## "hp": 52,
## "drat": 4.93,
## "wt": 1.615,
## "qsec": 18.52,
## "vs": 1,
## "am": 1,
## "gear": 4,
## "carb": 2,
## "_row": "Honda Civic"
## },
## {
## "mpg": 33.9,
## "cyl": 4,
## "disp": 71.1,
## "hp": 65,
## "drat": 4.22,
## "wt": 1.835,
## "qsec": 19.9,
## "vs": 1,
## "am": 1,
## "gear": 4,
## "carb": 1,
## "_row": "Toyota Corolla"
## },
## {
## "mpg": 21.5,
## "cyl": 4,
## "disp": 120.1,
## "hp": 97,
## "drat": 3.7,
## "wt": 2.465,
## "qsec": 20.01,
## "vs": 1,
## "am": 0,
## "gear": 3,
## "carb": 1,
## "_row": "Toyota Corona"
## },
## {
## "mpg": 15.5,
## "cyl": 8,
## "disp": 318,
## "hp": 150,
## "drat": 2.76,
## "wt": 3.52,
## "qsec": 16.87,
## "vs": 0,
## "am": 0,
## "gear": 3,
## "carb": 2,
## "_row": "Dodge Challenger"
## },
## {
## "mpg": 15.2,
## "cyl": 8,
## "disp": 304,
## "hp": 150,
## "drat": 3.15,
## "wt": 3.435,
## "qsec": 17.3,
## "vs": 0,
## "am": 0,
## "gear": 3,
## "carb": 2,
## "_row": "AMC Javelin"
## },
## {
## "mpg": 13.3,
## "cyl": 8,
## "disp": 350,
## "hp": 245,
## "drat": 3.73,
## "wt": 3.84,
## "qsec": 15.41,
## "vs": 0,
## "am": 0,
## "gear": 3,
## "carb": 4,
## "_row": "Camaro Z28"
## },
## {
## "mpg": 19.2,
## "cyl": 8,
## "disp": 400,
## "hp": 175,
## "drat": 3.08,
## "wt": 3.845,
## "qsec": 17.05,
## "vs": 0,
## "am": 0,
## "gear": 3,
## "carb": 2,
## "_row": "Pontiac Firebird"
## },
## {
## "mpg": 27.3,
## "cyl": 4,
## "disp": 79,
## "hp": 66,
## "drat": 4.08,
## "wt": 1.935,
## "qsec": 18.9,
## "vs": 1,
## "am": 1,
## "gear": 4,
## "carb": 1,
## "_row": "Fiat X1-9"
## },
## {
## "mpg": 26,
## "cyl": 4,
## "disp": 120.3,
## "hp": 91,
## "drat": 4.43,
## "wt": 2.14,
## "qsec": 16.7,
## "vs": 0,
## "am": 1,
## "gear": 5,
## "carb": 2,
## "_row": "Porsche 914-2"
## },
## {
## "mpg": 30.4,
## "cyl": 4,
## "disp": 95.1,
## "hp": 113,
## "drat": 3.77,
## "wt": 1.513,
## "qsec": 16.9,
## "vs": 1,
## "am": 1,
## "gear": 5,
## "carb": 2,
## "_row": "Lotus Europa"
## },
## {
## "mpg": 15.8,
## "cyl": 8,
## "disp": 351,
## "hp": 264,
## "drat": 4.22,
## "wt": 3.17,
## "qsec": 14.5,
## "vs": 0,
## "am": 1,
## "gear": 5,
## "carb": 4,
## "_row": "Ford Pantera L"
## },
## {
## "mpg": 19.7,
## "cyl": 6,
## "disp": 145,
## "hp": 175,
## "drat": 3.62,
## "wt": 2.77,
## "qsec": 15.5,
## "vs": 0,
## "am": 1,
## "gear": 5,
## "carb": 6,
## "_row": "Ferrari Dino"
## },
## {
## "mpg": 15,
## "cyl": 8,
## "disp": 301,
## "hp": 335,
## "drat": 3.54,
## "wt": 3.57,
## "qsec": 14.6,
## "vs": 0,
## "am": 1,
## "gear": 5,
## "carb": 8,
## "_row": "Maserati Bora"
## },
## {
## "mpg": 21.4,
## "cyl": 4,
## "disp": 121,
## "hp": 109,
## "drat": 4.11,
## "wt": 2.78,
## "qsec": 18.6,
## "vs": 1,
## "am": 1,
## "gear": 4,
## "carb": 2,
## "_row": "Volvo 142E"
## }
## ]# Minify pretty_json: mini_json
mini_json <- minify(pretty_json)
# Print mini_json
mini_json## [{"mpg":21,"cyl":6,"disp":160,"hp":110,"drat":3.9,"wt":2.62,"qsec":16.46,"vs":0,"am":1,"gear":4,"carb":4,"_row":"Mazda RX4"},{"mpg":21,"cyl":6,"disp":160,"hp":110,"drat":3.9,"wt":2.875,"qsec":17.02,"vs":0,"am":1,"gear":4,"carb":4,"_row":"Mazda RX4 Wag"},{"mpg":22.8,"cyl":4,"disp":108,"hp":93,"drat":3.85,"wt":2.32,"qsec":18.61,"vs":1,"am":1,"gear":4,"carb":1,"_row":"Datsun 710"},{"mpg":21.4,"cyl":6,"disp":258,"hp":110,"drat":3.08,"wt":3.215,"qsec":19.44,"vs":1,"am":0,"gear":3,"carb":1,"_row":"Hornet 4 Drive"},{"mpg":18.7,"cyl":8,"disp":360,"hp":175,"drat":3.15,"wt":3.44,"qsec":17.02,"vs":0,"am":0,"gear":3,"carb":2,"_row":"Hornet Sportabout"},{"mpg":18.1,"cyl":6,"disp":225,"hp":105,"drat":2.76,"wt":3.46,"qsec":20.22,"vs":1,"am":0,"gear":3,"carb":1,"_row":"Valiant"},{"mpg":14.3,"cyl":8,"disp":360,"hp":245,"drat":3.21,"wt":3.57,"qsec":15.84,"vs":0,"am":0,"gear":3,"carb":4,"_row":"Duster 360"},{"mpg":24.4,"cyl":4,"disp":146.7,"hp":62,"drat":3.69,"wt":3.19,"qsec":20,"vs":1,"am":0,"gear":4,"carb":2,"_row":"Merc 240D"},{"mpg":22.8,"cyl":4,"disp":140.8,"hp":95,"drat":3.92,"wt":3.15,"qsec":22.9,"vs":1,"am":0,"gear":4,"carb":2,"_row":"Merc 230"},{"mpg":19.2,"cyl":6,"disp":167.6,"hp":123,"drat":3.92,"wt":3.44,"qsec":18.3,"vs":1,"am":0,"gear":4,"carb":4,"_row":"Merc 280"},{"mpg":17.8,"cyl":6,"disp":167.6,"hp":123,"drat":3.92,"wt":3.44,"qsec":18.9,"vs":1,"am":0,"gear":4,"carb":4,"_row":"Merc 280C"},{"mpg":16.4,"cyl":8,"disp":275.8,"hp":180,"drat":3.07,"wt":4.07,"qsec":17.4,"vs":0,"am":0,"gear":3,"carb":3,"_row":"Merc 450SE"},{"mpg":17.3,"cyl":8,"disp":275.8,"hp":180,"drat":3.07,"wt":3.73,"qsec":17.6,"vs":0,"am":0,"gear":3,"carb":3,"_row":"Merc 450SL"},{"mpg":15.2,"cyl":8,"disp":275.8,"hp":180,"drat":3.07,"wt":3.78,"qsec":18,"vs":0,"am":0,"gear":3,"carb":3,"_row":"Merc 450SLC"},{"mpg":10.4,"cyl":8,"disp":472,"hp":205,"drat":2.93,"wt":5.25,"qsec":17.98,"vs":0,"am":0,"gear":3,"carb":4,"_row":"Cadillac Fleetwood"},{"mpg":10.4,"cyl":8,"disp":460,"hp":215,"drat":3,"wt":5.424,"qsec":17.82,"vs":0,"am":0,"gear":3,"carb":4,"_row":"Lincoln Continental"},{"mpg":14.7,"cyl":8,"disp":440,"hp":230,"drat":3.23,"wt":5.345,"qsec":17.42,"vs":0,"am":0,"gear":3,"carb":4,"_row":"Chrysler Imperial"},{"mpg":32.4,"cyl":4,"disp":78.7,"hp":66,"drat":4.08,"wt":2.2,"qsec":19.47,"vs":1,"am":1,"gear":4,"carb":1,"_row":"Fiat 128"},{"mpg":30.4,"cyl":4,"disp":75.7,"hp":52,"drat":4.93,"wt":1.615,"qsec":18.52,"vs":1,"am":1,"gear":4,"carb":2,"_row":"Honda Civic"},{"mpg":33.9,"cyl":4,"disp":71.1,"hp":65,"drat":4.22,"wt":1.835,"qsec":19.9,"vs":1,"am":1,"gear":4,"carb":1,"_row":"Toyota Corolla"},{"mpg":21.5,"cyl":4,"disp":120.1,"hp":97,"drat":3.7,"wt":2.465,"qsec":20.01,"vs":1,"am":0,"gear":3,"carb":1,"_row":"Toyota Corona"},{"mpg":15.5,"cyl":8,"disp":318,"hp":150,"drat":2.76,"wt":3.52,"qsec":16.87,"vs":0,"am":0,"gear":3,"carb":2,"_row":"Dodge Challenger"},{"mpg":15.2,"cyl":8,"disp":304,"hp":150,"drat":3.15,"wt":3.435,"qsec":17.3,"vs":0,"am":0,"gear":3,"carb":2,"_row":"AMC Javelin"},{"mpg":13.3,"cyl":8,"disp":350,"hp":245,"drat":3.73,"wt":3.84,"qsec":15.41,"vs":0,"am":0,"gear":3,"carb":4,"_row":"Camaro Z28"},{"mpg":19.2,"cyl":8,"disp":400,"hp":175,"drat":3.08,"wt":3.845,"qsec":17.05,"vs":0,"am":0,"gear":3,"carb":2,"_row":"Pontiac Firebird"},{"mpg":27.3,"cyl":4,"disp":79,"hp":66,"drat":4.08,"wt":1.935,"qsec":18.9,"vs":1,"am":1,"gear":4,"carb":1,"_row":"Fiat X1-9"},{"mpg":26,"cyl":4,"disp":120.3,"hp":91,"drat":4.43,"wt":2.14,"qsec":16.7,"vs":0,"am":1,"gear":5,"carb":2,"_row":"Porsche 914-2"},{"mpg":30.4,"cyl":4,"disp":95.1,"hp":113,"drat":3.77,"wt":1.513,"qsec":16.9,"vs":1,"am":1,"gear":5,"carb":2,"_row":"Lotus Europa"},{"mpg":15.8,"cyl":8,"disp":351,"hp":264,"drat":4.22,"wt":3.17,"qsec":14.5,"vs":0,"am":1,"gear":5,"carb":4,"_row":"Ford Pantera L"},{"mpg":19.7,"cyl":6,"disp":145,"hp":175,"drat":3.62,"wt":2.77,"qsec":15.5,"vs":0,"am":1,"gear":5,"carb":6,"_row":"Ferrari Dino"},{"mpg":15,"cyl":8,"disp":301,"hp":335,"drat":3.54,"wt":3.57,"qsec":14.6,"vs":0,"am":1,"gear":5,"carb":8,"_row":"Maserati Bora"},{"mpg":21.4,"cyl":4,"disp":121,"hp":109,"drat":4.11,"wt":2.78,"qsec":18.6,"vs":1,"am":1,"gear":4,"carb":2,"_row":"Volvo 142E"}]