enigma is an R package to search and retrieve data from Enigma.io
Enigma holds government data and provides a really nice set of APIs for data, metadata, and stats on each of the datasets. That is, you can request a dataset itself, metadata on the dataset, and summary statistics on the columns of each dataset.
Stable version from CRAN
install.packages("enigma")
Or, install development version from GitHub
install.packages("devtools")
devtools::install_github("ropengov/enigma")
library('enigma')
Enigma API calls require an API key. Go to your account at https://app.enigma.io/settings/account and your key should be on the left hand side near the bottom. You can either pass in your API key each function call with the key parameter, or save your key as either an R environment variable (use the name ENIGMA_KEY), or an R option (use the name enigmaKey).
out <- enigma_data(dataset='us.gov.whitehouse.visitor-list', select=c('namelast','visitee_namelast','last_updatedby'))
Some metadata on the results
out$info
#> $rows_limit
#> [1] 500
#>
#> $total_results
#> [1] 4462510
#>
#> $total_pages
#> [1] 8926
#>
#> $current_page
#> [1] 1
#>
#> $calls_remaining
#> [1] 49912
#>
#> $seconds_remaining
#> [1] 2084569
Look at the data, first 6 rows for readme brevity
head(out$result)
#> namelast visitee_namelast last_updatedby
#> 1 MATTHEWS POTUS LC
#> 2 DOYLE POTUS B3
#> 3 MATTHEWS POTUS LC
#> 4 MALONE POTUS DC
#> 5 WILSON POTUS DC
#> 6 ABEBE POTUS GB
out <- enigma_stats(dataset='us.gov.whitehouse.visitor-list', select='total_people')
Some summary stats
out$result[c('sum','avg','stddev','variance','min','max')]
#> $sum
#> [1] "1245086302"
#>
#> $avg
#> [1] "280.4864992845274096"
#>
#> $stddev
#> [1] "533.430430655024"
#>
#> $variance
#> [1] "284548.024348804258"
#>
#> $min
#> [1] "0"
#>
#> $max
#> [1] "5730"
Frequency details
head(out$result$frequency)
#> total_people count
#> 1 1 205885
#> 2 6 126360
#> 3 2 124847
#> 4 4 101829
#> 5 3 100926
#> 6 275 89107
out <- enigma_metadata(dataset='us.gov.whitehouse')
Paths
out$info$paths
#> [[1]]
#> [[1]]$level
#> [1] "us"
#>
#> [[1]]$label
#> [1] "United States"
#>
#> [[1]]$description
#> [1] "United States of America"
#>
#> [[1]]$description_lead
#> [1] "United States of America"
#>
#>
#> [[2]]
#> [[2]]$level
#> [1] "gov"
#>
#> [[2]]$label
#> [1] "U.S. Federal Government"
#>
#> [[2]]$description
#> [1] "Government from the Legislative, Executive, and Judicial branches of the United States of America."
#>
#> [[2]]$description_lead
#> [1] "Government comprising the Legislative, Executive, and Judicial branches of the United States of America."
#>
#>
#> [[3]]
#> [[3]]$level
#> [1] "whitehouse"
#>
#> [[3]]$label
#> [1] "The White House"
#>
#> [[3]]$description
#> [1] "Located at 1600 Pennsylvania Avenue in Washington D.C., the White House has served as the home and office for every U.S. president since John Adams."
#>
#> [[3]]$description_lead
#> [1] "Located at 1600 Pennsylvania Avenue in Washington D.C., the White House has served as the home and office for every U.S. president since John Adams."
Immediate nodes
out$info$immediate_nodes
#> [[1]]
#> [[1]]$datapath
#> [1] "us.gov.whitehouse.salaries"
#>
#> [[1]]$label
#> [1] "White House Salaries"
#>
#> [[1]]$description
#> [1] "The White House has been required to deliver a report to Congress listing the title and salary of every White House Office employee since 1995. Consistent with President Obama's commitment to transparency, this report is being publicly disclosed on our website as it is transmitted to Congress. In addition, this report also contains the title and salary details of administration officials who work at the Office of Policy Development, including the Domestic Policy Council and the National Economic Council -- along with White House Office employees."
Children tables
out$info$children_tables[[1]]
#> $datapath
#> [1] "us.gov.whitehouse.visitor-list"
#>
#> $label
#> [1] "Visitor Records"
#>
#> $description
#> [1] "Records of visitors to the White House from September 2009 to present."
#>
#> $db_boundary_datapath
#> [1] "us.gov.whitehouse"
#>
#> $db_boundary_label
#> [1] ""
First, get columns for the air carrier dataset
dset <- 'us.gov.dot.rita.trans-stats.air-carrier-statistics.t100d-market-all-carrier'
head(enigma_metadata(dset)$columns$table[,c(1:4)])
#> id label type index
#> 1 passengers Passengers type_numeric 0
#> 2 freight Freight (Lbs.) type_numeric 1
#> 3 mail Mail (Lbs.) type_numeric 2
#> 4 distance Distance (Mi.) type_numeric 3
#> 5 unique_carrier Unique Carrier type_varchar 4
#> 6 airline_id Airline ID type_varchar 5
Looks like there's a column called distance that we can search on. We by default for varchar type columns only frequency bake for the column.
out <- enigma_stats(dset, select='distance')
head(out$result$frequency)
#> distance count
#> 1 0.00 16456
#> 2 296.00 13595
#> 3 59.00 13504
#> 4 16.00 13101
#> 5 95.00 12669
#> 6 94.00 12354
Then we can do a bit of tidying and make a plot
library("ggplot2")
df <- out$result$frequency
df <- data.frame(distance=as.numeric(df$distance), count=as.numeric(df$count))
ggplot(df, aes(distance, count)) +
geom_bar(stat="identity") +
geom_point() +
theme_grey(base_size = 18) +
labs(y="flights", x="distance (miles)")
Enigma provides an endpoint .../export/<datasetid> to download a zipped csv file of the entire dataset.
enigma_fetch() gives you an easy way to download these to a specific place on your machine. And a message tells you that a file has been written to disk.
enigma_fetch(dataset='com.crunchbase.info.companies.acquisition')