datagovsgRThe goal of this package is to create API wrappers for the APIs found on the GovTech. GovTech is the Singapore department focused on data and technology, which currently has a list of API tools for developers to leverage on, found on their website. These developer API tools are publicly accessible, containing tools such as realtime weather readings, carpark availability and locations of available taxis. The API wrappers are listed below.
This functions calls upon the carpark availability API from data.gov.sg and processes the returning page, returning a dataframe containing the carpark id, type, last update, total lots, and current lots. A date-time has to be specified, otherwise the default is set to return the current date-time.
carpark_availability(date = "2019-06-05T10:10:10") %>% head()
#> Closest timestamp: 2019-06-05T10:09:27+08:00
#> id type last_update total_lots availability_lots
#> 1 HE12 C 2019-06-05T10:08:42 83 0
#> 2 HLM C 2019-06-05T10:08:52 583 183
#> 3 RHM C 2019-06-05T10:08:42 322 128
#> 4 BM29 C 2019-06-05T10:08:49 97 49
#> 5 Q81 C 2019-06-05T10:08:48 96 21
#> 6 C20 C 2019-06-05T10:08:30 173 0This functions calls upon the PM2.5 API from data.gov.sg and returns
a data frame of the different measures of PM2.5 across 5 different areas
in Singapore. This data provided by the API is updated hourly from NEA.
There are 2 functions, the pm25 which returns the data for
a given date-time, and pm25_summary which returns the data
summary for a given date.
# pm25(date_time = "2018-01-04T09:16:17")# pm25_summary(date = "2018-01-04") %>% head()This functions calls upon the PSI API from data.gov.sg and returns a
data frame of the different measures of the PSI across 5 different areas
in Singapores and the overall measure for the given data-time. This data
provided by the API is updated hourly. There are 2 functions, the
psi which returns the data for a given date-time, and
psi_summary which returns the data summary for a given
date.
psi(date = "2019-11-08T17:30:00") %>% head()
#> Closest timestamp:
#> data frame with 0 columns and 0 rowspsi_summary(date = "2018-01-04") %>% head()
#> [1] "No data returned from API."This functions calls upon the taxi availability API from data.gov.sg and returns a data frame of the locations of all availible taxis in longitude and latitude values. A date-time has to be specified, otherwise the default is set to return the current date-time.
taxi_availability(date = "2019-08-07T09:30:00") %>% head()
#> Timestamp: 2019-08-07T09:29:55+08:00
#> Availible Taxis: 4586
#> long lat
#> 1 103.6142 1.25267
#> 2 103.6235 1.28648
#> 3 103.6236 1.30047
#> 4 103.6238 1.28678
#> 5 103.6275 1.31123
#> 6 103.6282 1.31332This functions calls upon the weather forecast API from data.gov.sg and returns a data frame containing different metrics of the forecast. 2-hour, 24-hour and 4-day forecasts are availible. It then returns a dataframe containing the forecast which is dependent on the date-time and parameter of forecast. This data provided by the API is updated half-hourly. A date-time has to be specified, otherwise the default is set to return the current date-time.
A 2-hour forecast returns the general forecast for each area in Singapore for the next 2-hours; a 24-hour forecast returns the general forecast for the next day along with the weather metrics; a 4-day forecast returns the general forecast for each of the next four days.
weather_forecast(date = "2019-11-08T17:30:00", forecast = "2-hour") %>% head()
#> Closest timestamp: 2019-11-08T17:30:00+08:00
#> Forecast valid to: 2019-11-08T19:30:00+08:00
#> area forecast
#> 1 Ang Mo Kio Partly Cloudy (Day)
#> 2 Bedok Partly Cloudy (Day)
#> 3 Bishan Partly Cloudy (Day)
#> 4 Boon Lay Partly Cloudy (Day)
#> 5 Bukit Batok Partly Cloudy (Day)
#> 6 Bukit Merah Partly Cloudy (Day)weather_forecast(date = "2018-01-04T09:16:17", forecast = "4-day")
#> Closest timestamp: 2018-01-04T05:16:00+08:00
#> date general_forecast
#> 1 2018-01-05 Late morning and early afternoon thundery showers.
#> 2 2018-01-06 Afternoon thundery showers.
#> 3 2018-01-07 Late morning and early afternoon thundery showers.
#> 4 2018-01-08 Afternoon thundery showers.
#> relative_humidity_low relative_humidity_high temperature_low temperature_high
#> 1 65 95 23 32
#> 2 60 95 24 33
#> 3 60 95 24 33
#> 4 60 95 24 33
#> wind_direction wind_speed_low wind_speed_high
#> 1 VARIABLE 5 15
#> 2 S 5 15
#> 3 SE 10 20
#> 4 ENE 10 20This functions calls upon the weather reading API from data.gov.sg and returns a list containing the air temperature, rainfall, relative humidity, wind direction and speed across Singapore. Data is updated every 5 minutes for the rainfall API, and every half minute for the other 4 API from NEA. A date-time has to be specified, otherwise the default is set to return the current date-time. Do note that this API takes slighlty longer than the other APIs in the package as 5 APIs are wrapped within this function.
The simplify parameter is FALSE on default,
returning a list of each of the 5 metrics. Otherwise,
simplify = TRUE would return a data frame where all 5
metrics are joined according to weather stations, but return several
NAs, as most weather stations collect rainfall data only.
weather_reading(date = "2019-11-08T17:30:00")
#> $air_temp
#> # A tibble: 14 × 2
#> station_id value
#> <chr> <dbl>
#> 1 S117 29.7
#> 2 S50 30.1
#> 3 S107 29.6
#> 4 S43 30.6
#> 5 S44 29.4
#> 6 S121 30.5
#> 7 S106 29.5
#> 8 S111 30.4
#> 9 S122 29.7
#> 10 S60 30.7
#> 11 S115 28.5
#> 12 S116 30.1
#> 13 S104 30.2
#> 14 S100 30.3
#>
#> $rainfall
#> # A tibble: 0 × 0
#>
#> $relative_humidity
#> # A tibble: 0 × 0
#>
#> $wind_direction
#> # A tibble: 15 × 2
#> station_id value
#> <chr> <int>
#> 1 S109 297
#> 2 S117 262
#> 3 S50 245
#> 4 S107 232
#> 5 S43 289
#> 6 S108 229
#> 7 S44 286
#> 8 S106 255
#> 9 S122 312
#> 10 S60 246
#> 11 S115 302
#> 12 S24 283
#> 13 S116 266
#> 14 S104 291
#> 15 S100 266
#>
#> $wind_speed
#> # A tibble: 15 × 2
#> station_id value
#> <chr> <dbl>
#> 1 S109 4.6
#> 2 S117 3.6
#> 3 S50 3.1
#> 4 S107 11.9
#> 5 S43 4.3
#> 6 S108 6.9
#> 7 S44 6.3
#> 8 S106 2.5
#> 9 S122 5.7
#> 10 S60 3.6
#> 11 S115 6.8
#> 12 S24 4.9
#> 13 S116 16.4
#> 14 S104 5.4
#> 15 S100 2.9