Andrew Loh 2018-12-06
R Interface for Singapore's Weather, UV and Pollutants Data from National Environment Agency (NEA) APIs.
No API key is required for these APIs. The library can hence be installed without any authentication required.
As a caveat, this API client will be significantly slower than other APIs, as the NEA APIs allow only for queries of individual dates, one at a time. This interface helps to automate the querying of a list of dates, and the tidying of appropriate data into tidy data frames.
library(devtools)
install_github("andrew-loh/neaSG", force = TRUE)To list all functions supported by this package:
library(neaSG)
ls(package:neaSG)
#> [1] "get_airtemp" "get_humidity" "get_pollutants" "get_rainfall"
#> [5] "get_UV" "get_weatherstns" "get_weathersumm"Air temperature, humidity and rainfall are given by weather station IDs. To get data frame of weather stations in Singapore by name, geographic coordinates and ID:
get_weatherstns()
#> id device_id name location.latitude
#> 1: S77 S77 Alexandra Road 1.29370
#> 2: S109 S109 Ang Mo Kio Avenue 5 1.37640
#> 3: S117 S117 Banyan Road 1.25600
#> 4: S55 S55 Buangkok Green 1.38370
#> 5: S64 S64 Bukit Panjang Road 1.38240
#> 6: S90 S90 Bukit Timah Road 1.31910
#> 7: S61 S61 Chai Chee Street 1.32300
#> 8: S114 S114 Choa Chu Kang Avenue 4 1.38000
#> 9: S11 S11 Choa Chu Kang Road 1.37460
#> 10: S50 S50 Clementi Road 1.33370
#> 11: S101 S101 Corporation Road 1.35053
#> 12: S107 S107 East Coast Parkway 1.31350
#> 13: S215 S215 GEYLANG EAST CENTRAL 1.32785
#> 14: S118 S118 Handy Road 1.29940
#> 15: S120 S120 Holland Road 1.30874
#> 16: S33 S33 Jurong Pier Road 1.30810
#> 17: S71 S71 Kent Ridge Road 1.29230
#> 18: S43 S43 Kim Chuan Road 1.33990
#> 19: S66 S66 Kranji Way 1.43870
#> 20: S112 S112 Lim Chu Kang Road 1.43854
#> 21: S07 S07 Lornie Road 1.34150
#> 22: S40 S40 Mandai Lake Road 1.40440
#> 23: S108 S108 Marina Gardens Drive 1.27990
#> 24: S113 S113 Marine Parade Road 1.30648
#> 25: S44 S44 Nanyang Avenue 1.34583
#> 26: S119 S119 Nicoll Highway 1.30105
#> 27: S121 S121 Old Choa Chu Kang Road 1.37288
#> 28: S35 S35 Old Toh Tuck Road 1.33290
#> 29: S29 S29 Pasir Ris Drive 12 1.38700
#> 30: S94 S94 Pasir Ris Street 51 1.36620
#> 31: S78 S78 Poole Road 1.30703
#> 32: S106 S106 Pulau Ubin 1.41680
#> 33: S81 S81 Punggol Central 1.40290
#> 34: S111 S111 Scotts Road 1.31055
#> 35: S900 S900 Seletar Aerospace View 1.41284
#> 36: S122 S122 Sembawang Road 1.41731
#> 37: S60 S60 Sentosa 1.25000
#> 38: S84 S84 Simei Avenue 1.34370
#> 39: S79 S79 Somerset Road 1.30040
#> 40: S92 S92 South Buona Vista Road 1.28410
#> 41: S88 S88 Toa Payoh North 1.34270
#> 42: S123 S123 Towner Road 1.32140
#> 43: S89 S89 Tuas Road 1.31985
#> 44: S115 S115 Tuas South Avenue 3 1.29377
#> 45: S82 S82 Tuas West Road 1.32470
#> 46: S24 S24 Upper Changi Road North 1.36780
#> 47: S69 S69 Upper Peirce Reservoir Park 1.37000
#> 48: S08 S08 Upper Thomson Road 1.37010
#> 49: S116 S116 West Coast Highway 1.28100
#> 50: S100 S100 Woodlands Road 1.41720
#> 51: S91 S91 Yishun Avenue 5 1.43010
#> 52: S46 S46 Sime Road 1.34160
#> 53: S36 S36 Upper Serangoon Road 1.33820
#> id device_id name location.latitude
#> location.longitude
#> 1: 103.8125
#> 2: 103.8492
#> 3: 103.6790
#> 4: 103.8860
#> 5: 103.7603
#> 6: 103.8191
#> 7: 103.9217
#> 8: 103.7300
#> 9: 103.6938
#> 10: 103.7768
#> 11: 103.7134
#> 12: 103.9625
#> 13: 103.8890
#> 14: 103.8461
#> 15: 103.8180
#> 16: 103.7100
#> 17: 103.7815
#> 18: 103.8878
#> 19: 103.7363
#> 20: 103.7013
#> 21: 103.8334
#> 22: 103.7896
#> 23: 103.8703
#> 24: 103.9104
#> 25: 103.6817
#> 26: 103.8666
#> 27: 103.7224
#> 28: 103.7556
#> 29: 103.9350
#> 30: 103.9528
#> 31: 103.8907
#> 32: 103.9673
#> 33: 103.9092
#> 34: 103.8365
#> 35: 103.8692
#> 36: 103.8249
#> 37: 103.8279
#> 38: 103.9444
#> 39: 103.8372
#> 40: 103.7886
#> 41: 103.8482
#> 42: 103.8577
#> 43: 103.6616
#> 44: 103.6184
#> 45: 103.6351
#> 46: 103.9826
#> 47: 103.8050
#> 48: 103.8271
#> 49: 103.7540
#> 50: 103.7485
#> 51: 103.8308
#> 52: 103.8106
#> 53: 103.8657
#> location.longitudeTemperature is provided in degrees Celsius. Documentation is provided here. To get data frame of per-minute air temperature in Singapore for specific dates:
#Dates must be input as characters in a "YYYY-MM-DD" format
data <- get_airtemp(from = "2017-12-31", to = "2018-01-04")
#> [1] "2017-12-31"
#> [1] "2018-01-04"
head(data)
#> timestamp readings.station_id readings.value
#> 1: 2017-12-31T00:01:00+08:00 S109 24.8
#> 2: 2017-12-31T00:02:00+08:00 S109 24.8
#> 3: 2017-12-31T00:03:00+08:00 S109 24.8
#> 4: 2017-12-31T00:04:00+08:00 S109 24.8
#> 5: 2017-12-31T00:05:00+08:00 S109 24.8
#> 6: 2017-12-31T00:06:00+08:00 S109 24.8
#> readings.station_id.1 readings.value.1 readings.station_id.2
#> 1: S117 25.0 S107
#> 2: S117 24.9 S107
#> 3: S117 24.9 S50
#> 4: S117 25.0 S107
#> 5: S117 25.0 S50
#> 6: S117 25.0 S107
#> readings.value.2 readings.station_id.3 readings.value.3
#> 1: 26.8 S43 25.2
#> 2: 26.8 S43 25.2
#> 3: 24.5 S107 26.8
#> 4: 26.8 S43 25.3
#> 5: 24.5 S107 26.8
#> 6: 26.8 S43 25.3
#> readings.station_id.4 readings.value.4 readings.station_id.5
#> 1: S44 25.0 S121
#> 2: S44 25.0 S121
#> 3: S43 25.3 S44
#> 4: S44 24.9 S121
#> 5: S43 25.3 S44
#> 6: S44 24.9 S121
#> readings.value.5 readings.station_id.6 readings.value.6
#> 1: 25.5 S106 24.9
#> 2: 25.5 S106 24.9
#> 3: 24.9 S121 25.4
#> 4: 25.4 S106 24.8
#> 5: 24.9 S121 25.5
#> 6: 25.5 S106 24.8
#> readings.station_id.7 readings.value.7 readings.station_id.8
#> 1: S111 24.4 S122
#> 2: S111 24.4 S122
#> 3: S106 24.8 S111
#> 4: S111 24.4 S122
#> 5: S106 24.9 S111
#> 6: S111 24.4 S122
#> readings.value.8 readings.station_id.9 readings.value.9
#> 1: 25.0 S60 25.2
#> 2: 25.0 S60 25.2
#> 3: 24.4 S60 25.2
#> 4: 25.0 S60 25.2
#> 5: 24.4 S122 25.0
#> 6: 25.0 S60 25.1
#> readings.station_id.10 readings.value.10 readings.station_id.11
#> 1: S115 26.2 S24
#> 2: S115 26.2 S24
#> 3: S115 26.2 S24
#> 4: S115 26.2 S24
#> 5: S60 25.2 S115
#> 6: S115 26.1 S24
#> readings.value.11 readings.station_id.12 readings.value.12
#> 1: 25.7 S116 25.1
#> 2: 25.7 S116 25.2
#> 3: 25.7 S116 25.2
#> 4: 25.7 S116 25.2
#> 5: 26.2 S24 25.7
#> 6: 25.7 S116 25.2
#> readings.station_id.13 readings.value.13 readings.station_id.14
#> 1: S100 25.7 <NA>
#> 2: S100 25.7 <NA>
#> 3: S100 25.7 <NA>
#> 4: S100 25.7 <NA>
#> 5: S116 25.2 S100
#> 6: S100 25.7 <NA>
#> readings.value.14 readings.station_id.15 readings.value.15
#> 1: NA <NA> NA
#> 2: NA <NA> NA
#> 3: NA <NA> NA
#> 4: NA <NA> NA
#> 5: 25.7 <NA> NA
#> 6: NA <NA> NA
#> readings.station_id.16 readings.value.16
#> 1: <NA> NA
#> 2: <NA> NA
#> 3: <NA> NA
#> 4: <NA> NA
#> 5: <NA> NA
#> 6: <NA> NARelative humidity is given as percentage, as is convention. Documentation is provided here. To get data frame of per-minute relative humidity readings in Singapore for specific dates:
#Dates must be input as characters in a "YYYY-MM-DD" format
data <- get_humidity(from = "2017-12-31", to = "2018-01-04")
#> [1] "2017-12-31"
#> [1] "2018-01-04"
head(data)
#> timestamp readings.station_id readings.value
#> 1: 2017-12-31T00:01:00+08:00 S117 95.4
#> 2: 2017-12-31T00:02:00+08:00 S117 95.6
#> 3: 2017-12-31T00:03:00+08:00 S117 95.8
#> 4: 2017-12-31T00:04:00+08:00 S117 95.9
#> 5: 2017-12-31T00:05:00+08:00 S117 96.1
#> 6: 2017-12-31T00:06:00+08:00 S117 96.2
#> readings.station_id.1 readings.value.1 readings.station_id.2
#> 1: S107 89.1 S43
#> 2: S107 89.5 S43
#> 3: S50 99.2 S107
#> 4: S107 89.6 S43
#> 5: S50 99.2 S107
#> 6: S107 89.7 S43
#> readings.value.2 readings.station_id.3 readings.value.3
#> 1: 99.4 S121 69.5
#> 2: 99.8 S121 69.9
#> 3: 89.6 S43 100.3
#> 4: 100.5 S121 70.0
#> 5: 89.6 S43 100.8
#> 6: 100.9 S121 70.1
#> readings.station_id.4 readings.value.4 readings.station_id.5
#> 1: S111 97.4 S60
#> 2: S111 97.5 S60
#> 3: S121 70.0 S111
#> 4: S111 97.6 S60
#> 5: S121 69.9 S111
#> 6: S111 97.7 S60
#> readings.value.5 readings.station_id.6 readings.value.6
#> 1: 95.0 S115 85.1
#> 2: 95.1 S115 85.2
#> 3: 97.6 S60 95.1
#> 4: 95.1 S115 85.5
#> 5: 97.7 S60 95.2
#> 6: 95.3 S115 85.7
#> readings.station_id.7 readings.value.7 readings.station_id.8
#> 1: S24 93.3 S100
#> 2: S24 93.4 S100
#> 3: S115 85.3 S24
#> 4: S24 93.6 S100
#> 5: S115 85.7 S24
#> 6: S24 93.7 S100
#> readings.value.8 readings.station_id.9 readings.value.9
#> 1: 90.9 <NA> NA
#> 2: 91.0 <NA> NA
#> 3: 93.5 S100 91.1
#> 4: 91.2 <NA> NA
#> 5: 93.7 S100 91.2
#> 6: 91.3 <NA> NA
#> readings.station_id.10 readings.value.10 readings.station_id.11
#> 1: <NA> NA <NA>
#> 2: <NA> NA <NA>
#> 3: <NA> NA <NA>
#> 4: <NA> NA <NA>
#> 5: <NA> NA <NA>
#> 6: <NA> NA <NA>
#> readings.value.11 readings.station_id.12 readings.value.12
#> 1: NA <NA> NA
#> 2: NA <NA> NA
#> 3: NA <NA> NA
#> 4: NA <NA> NA
#> 5: NA <NA> NA
#> 6: NA <NA> NA
#> readings.station_id.13 readings.value.13 readings.station_id.14
#> 1: <NA> NA <NA>
#> 2: <NA> NA <NA>
#> 3: <NA> NA <NA>
#> 4: <NA> NA <NA>
#> 5: <NA> NA <NA>
#> 6: <NA> NA <NA>
#> readings.value.14 readings.station_id.15 readings.value.15
#> 1: NA <NA> NA
#> 2: NA <NA> NA
#> 3: NA <NA> NA
#> 4: NA <NA> NA
#> 5: NA <NA> NA
#> 6: NA <NA> NA
#> readings.station_id.16 readings.value.16
#> 1: <NA> NA
#> 2: <NA> NA
#> 3: <NA> NA
#> 4: <NA> NA
#> 5: <NA> NA
#> 6: <NA> NARainfall is given by mm/5-minutes. Documentation is provided here. To get data frame of 5-minute rainfall readings in Singapore for specific dates:
#Dates must be input as characters in a "YYYY-MM-DD" format
data <- get_rainfall(from = "2017-12-31", to = "2018-01-04")
#> [1] "2017-12-31"
#> [1] "2018-01-04"
head(data)
#> timestamp readings.station_id readings.value
#> 1: 2017-12-31T00:05:00+08:00 S77 5.2
#> 2: 2017-12-31T00:10:00+08:00 S77 4.4
#> 3: 2017-12-31T00:15:00+08:00 S77 1.0
#> 4: 2017-12-31T00:20:00+08:00 S77 0.4
#> 5: 2017-12-31T00:25:00+08:00 S77 0.2
#> 6: 2017-12-31T00:30:00+08:00 S77 0.0
#> readings.station_id.1 readings.value.1 readings.station_id.2
#> 1: S109 0 S117
#> 2: S109 0 S117
#> 3: S109 0 S117
#> 4: S109 0 S117
#> 5: S109 0 S117
#> 6: S109 0 S117
#> readings.value.2 readings.station_id.3 readings.value.3
#> 1: 0.4 S55 0
#> 2: 0.8 S55 0
#> 3: 1.2 S55 0
#> 4: 1.0 S55 0
#> 5: 0.4 S55 0
#> 6: 0.4 S55 0
#> readings.station_id.4 readings.value.4 readings.station_id.5
#> 1: S64 0.4 S90
#> 2: S64 0.2 S90
#> 3: S64 0.0 S90
#> 4: S64 0.0 S90
#> 5: S64 0.2 S90
#> 6: S64 0.2 S90
#> readings.value.5 readings.station_id.6 readings.value.6
#> 1: 0.4 S61 0
#> 2: 0.6 S61 0
#> 3: 0.2 S61 0
#> 4: 0.0 S61 0
#> 5: 0.0 S61 0
#> 6: 0.0 S61 0
#> readings.station_id.7 readings.value.7 readings.station_id.8
#> 1: S114 0.0 S11
#> 2: S114 0.2 S11
#> 3: S114 0.0 S11
#> 4: S114 0.0 S11
#> 5: S114 0.0 S11
#> 6: S114 0.0 S11
#> readings.value.8 readings.station_id.9 readings.value.9
#> 1: 0 S50 0.2
#> 2: 0 S50 0.2
#> 3: 0 S50 0.0
#> 4: 0 S50 0.0
#> 5: 0 S50 0.0
#> 6: 0 S50 0.2
#> readings.station_id.10 readings.value.10 readings.station_id.11
#> 1: S107 0 S118
#> 2: S107 0 S118
#> 3: S107 0 S118
#> 4: S107 0 S118
#> 5: S107 0 S118
#> 6: S107 0 S118
#> readings.value.11 readings.station_id.12 readings.value.12
#> 1: 4.4 S120 3.8
#> 2: 2.2 S120 0.6
#> 3: 2.6 S120 0.4
#> 4: 3.4 S120 0.0
#> 5: 0.8 S120 0.0
#> 6: 0.2 S120 0.0
#> readings.station_id.13 readings.value.13 readings.station_id.14
#> 1: S33 0.2 S71
#> 2: S33 0.0 S71
#> 3: S33 0.0 S71
#> 4: S33 0.2 S71
#> 5: S33 0.2 S71
#> 6: S33 0.4 S71
#> readings.value.14 readings.station_id.15 readings.value.15
#> 1: 0.0 S43 1.6
#> 2: 0.0 S43 0.2
#> 3: 0.2 S43 0.2
#> 4: 1.0 S43 0.0
#> 5: 1.4 S43 0.0
#> 6: 0.0 S43 0.0
#> readings.station_id.16 readings.value.16 readings.station_id.17
#> 1: S66 0.2 S112
#> 2: S66 0.0 S112
#> 3: S66 0.0 S112
#> 4: S66 0.0 S112
#> 5: S66 0.0 S112
#> 6: S66 0.2 S112
#> readings.value.17 readings.station_id.18 readings.value.18
#> 1: 0.0 S07 0.2
#> 2: 0.2 S07 0.0
#> 3: 0.0 S07 0.0
#> 4: 0.2 S07 0.0
#> 5: 0.2 S07 0.0
#> 6: 0.0 S07 0.0
#> readings.station_id.19 readings.value.19 readings.station_id.20
#> 1: S40 0.0 S113
#> 2: S40 0.0 S113
#> 3: S40 0.2 S113
#> 4: S40 0.0 S113
#> 5: S40 0.6 S113
#> 6: S40 0.0 S113
#> readings.value.20 readings.station_id.21 readings.value.21
#> 1: 0 S44 0
#> 2: 0 S44 0
#> 3: 0 S44 0
#> 4: 0 S44 0
#> 5: 0 S44 0
#> 6: 0 S44 0
#> readings.station_id.22 readings.value.22 readings.station_id.23
#> 1: S119 1.4 S121
#> 2: S119 3.2 S121
#> 3: S119 4.2 S121
#> 4: S119 1.0 S121
#> 5: S119 0.4 S121
#> 6: S119 0.0 S121
#> readings.value.23 readings.station_id.24 readings.value.24
#> 1: 0.0 S35 2.0
#> 2: 0.0 S35 1.8
#> 3: 0.0 S35 1.2
#> 4: 0.0 S35 0.2
#> 5: 0.0 S35 0.0
#> 6: 0.2 S35 0.2
#> readings.station_id.25 readings.value.25 readings.station_id.26
#> 1: S29 0 S94
#> 2: S29 0 S94
#> 3: S29 0 S94
#> 4: S29 0 S94
#> 5: S29 0 S94
#> 6: S29 0 S94
#> readings.value.26 readings.station_id.27 readings.value.27
#> 1: 0 S78 0.2
#> 2: 0 S78 0.2
#> 3: 0 S78 0.0
#> 4: 0 S78 0.0
#> 5: 0 S78 0.0
#> 6: 0 S78 0.0
#> readings.station_id.28 readings.value.28 readings.station_id.29
#> 1: S106 0 S81
#> 2: S106 0 S81
#> 3: S106 0 S81
#> 4: S106 0 S81
#> 5: S106 0 S81
#> 6: S106 0 S81
#> readings.value.29 readings.station_id.30 readings.value.30
#> 1: 0 S111 1.8
#> 2: 0 S111 0.4
#> 3: 0 S111 0.0
#> 4: 0 S111 0.6
#> 5: 0 S111 0.0
#> 6: 0 S111 0.0
#> readings.station_id.31 readings.value.31 readings.station_id.32
#> 1: S900 0 S122
#> 2: S900 0 S122
#> 3: S900 0 S122
#> 4: S900 0 S122
#> 5: S900 0 S122
#> 6: S900 0 S122
#> readings.value.32 readings.station_id.33 readings.value.33
#> 1: 0.0 S60 0.2
#> 2: 0.6 S60 0.2
#> 3: 0.4 S60 1.0
#> 4: 0.0 S60 2.2
#> 5: 0.0 S60 0.4
#> 6: 0.0 S60 0.8
#> readings.station_id.34 readings.value.34 readings.station_id.35
#> 1: S46 0.0 S84
#> 2: S46 0.2 S84
#> 3: S46 0.0 S84
#> 4: S46 0.0 S84
#> 5: S46 0.0 S84
#> 6: S46 0.0 S84
#> readings.value.35 readings.station_id.36 readings.value.36
#> 1: 0 S79 4.6
#> 2: 0 S79 1.2
#> 3: 0 S79 1.4
#> 4: 0 S79 1.8
#> 5: 0 S79 1.6
#> 6: 0 S79 0.0
#> readings.station_id.37 readings.value.37 readings.station_id.38
#> 1: S92 0.2 S88
#> 2: S92 3.0 S88
#> 3: S92 3.6 S88
#> 4: S92 4.0 S88
#> 5: S92 1.0 S88
#> 6: S92 0.2 S88
#> readings.value.38 readings.station_id.39 readings.value.39
#> 1: 0.2 S123 0.2
#> 2: 0.2 S123 0.6
#> 3: 0.0 S123 0.4
#> 4: 0.0 S123 0.4
#> 5: 0.0 S123 0.2
#> 6: 0.0 S123 0.2
#> readings.station_id.40 readings.value.40 readings.station_id.41
#> 1: S89 0.0 S115
#> 2: S89 0.0 S115
#> 3: S89 0.0 S115
#> 4: S89 0.0 S115
#> 5: S89 0.2 S115
#> 6: S89 0.2 S115
#> readings.value.41 readings.station_id.42 readings.value.42
#> 1: 0.0 S82 0.0
#> 2: 0.0 S82 0.0
#> 3: 0.0 S82 0.0
#> 4: 0.0 S82 0.2
#> 5: 0.0 S82 0.4
#> 6: 0.4 S82 0.0
#> readings.station_id.43 readings.value.43 readings.station_id.44
#> 1: S24 0 S69
#> 2: S24 0 S69
#> 3: S24 0 S69
#> 4: S24 0 S69
#> 5: S24 0 S69
#> 6: S24 0 S69
#> readings.value.44 readings.station_id.45 readings.value.45
#> 1: 0.0 S36 0.0
#> 2: 0.0 S36 0.4
#> 3: 0.4 S36 0.4
#> 4: 0.0 S36 0.2
#> 5: 0.0 S36 0.0
#> 6: 0.4 S36 0.0
#> readings.station_id.46 readings.value.46 readings.station_id.47
#> 1: S08 0 S116
#> 2: S08 0 S116
#> 3: S08 0 S116
#> 4: S08 0 S116
#> 5: S08 0 S116
#> 6: S08 0 S116
#> readings.value.47 readings.station_id.48 readings.value.48
#> 1: 0.2 S100 0.0
#> 2: 0.0 S100 0.0
#> 3: 0.2 S100 0.0
#> 4: 2.4 S100 0.2
#> 5: 1.8 S100 0.0
#> 6: 0.6 S100 0.0
#> readings.station_id.49 readings.value.49 readings.station_id.50
#> 1: S91 0.2 <NA>
#> 2: S91 0.2 <NA>
#> 3: S91 0.0 <NA>
#> 4: S91 0.0 <NA>
#> 5: S91 0.0 <NA>
#> 6: S91 0.0 <NA>
#> readings.value.50 readings.station_id.51 readings.value.51
#> 1: NA <NA> NA
#> 2: NA <NA> NA
#> 3: NA <NA> NA
#> 4: NA <NA> NA
#> 5: NA <NA> NA
#> 6: NA <NA> NAUV Index provides data for hours when the sun is up - from 0700 to 1900hrs. As Singapore is a tropical country, sunrise and sunset times do not vary significantly through the year. Documentation is provided here.
#Dates must be input as characters in a "YYYY-MM-DD" format
data <- get_UV(from = "2017-12-31", to = "2018-01-04")
#> [1] "2017-12-31"
#> [1] "2018-01-04"
head(data)
#> value timestamp
#> 1: 0 2017-12-31T19:00:00+08:00
#> 2: 0 2017-12-31T18:00:00+08:00
#> 3: 0 2017-12-31T17:00:00+08:00
#> 4: 0 2017-12-31T16:00:00+08:00
#> 5: 0 2017-12-31T15:00:00+08:00
#> 6: 1 2017-12-31T14:00:00+08:00Pollutants are given by hourly measures for respective indices. Data is provided for national and 5 subnational regions, in the form of 12 pollutant indices: O3 Sub-Index, PM10 24-hourly, PM10 Sub-Index, CO Sub-Index, PM2.5 24-Hourly, SO2 Sub-Index, CO 8-Hour-Max, NO2 1-Hour-Max, SO2 24-Hourly, PM2.5 Sub-Index, PSI 24-Hourly and O3 8-Hour Max. Documentation is provided here.
Additional argument natl_only is, by default, TRUE. If true, provides only national readings. If the user desires subnational readings, set natl_only = FALSE
#Dates must be input as characters in a "YYYY-MM-DD" format
data <- get_pollutants(from = "2017-12-31", to = "2018-01-04")
#> [1] "2017-12-31"
#> [1] "2018-01-04"
head(data)
#> timestamp update_timestamp
#> 1: 2017-12-31T01:00:00+08:00 2017-12-31T01:06:18+08:00
#> 2: 2017-12-31T02:00:00+08:00 2017-12-31T02:06:18+08:00
#> 3: 2017-12-31T03:00:00+08:00 2017-12-31T03:06:18+08:00
#> 4: 2017-12-31T04:00:00+08:00 2017-12-31T04:06:17+08:00
#> 5: 2017-12-31T05:00:00+08:00 2017-12-31T05:06:18+08:00
#> 6: 2017-12-31T06:00:00+08:00 2017-12-31T06:06:18+08:00
#> readings.o3_sub_index.national
#> 1: 11
#> 2: 11
#> 3: 11
#> 4: 12
#> 5: 12
#> 6: 12
#> readings.pm10_twenty_four_hourly.national
#> 1: 42
#> 2: 42
#> 3: 42
#> 4: 41
#> 5: 41
#> 6: 41
#> readings.pm10_sub_index.national readings.co_sub_index.national
#> 1: 42 5
#> 2: 42 5
#> 3: 42 5
#> 4: 41 5
#> 5: 41 5
#> 6: 41 5
#> readings.pm25_twenty_four_hourly.national
#> 1: 12
#> 2: 12
#> 3: 12
#> 4: 12
#> 5: 12
#> 6: 12
#> readings.so2_sub_index.national readings.co_eight_hour_max.national
#> 1: 3 0.53
#> 2: 3 0.54
#> 3: 3 0.53
#> 4: 3 0.52
#> 5: 3 0.51
#> 6: 3 0.50
#> readings.no2_one_hour_max.national
#> 1: 32
#> 2: 31
#> 3: 18
#> 4: 25
#> 5: 27
#> 6: 30
#> readings.so2_twenty_four_hourly.national
#> 1: 5
#> 2: 5
#> 3: 5
#> 4: 5
#> 5: 5
#> 6: 5
#> readings.pm25_sub_index.national
#> 1: 49
#> 2: 49
#> 3: 49
#> 4: 49
#> 5: 48
#> 6: 48
#> readings.psi_twenty_four_hourly.national
#> 1: 49
#> 2: 49
#> 3: 49
#> 4: 49
#> 5: 48
#> 6: 48
#> readings.o3_eight_hour_max.national
#> 1: 27
#> 2: 27
#> 3: 27
#> 4: 27
#> 5: 28
#> 6: 27The final function aims to provide an hourly weather summary for specified dates. Hourly aggregated weather data on air temperature, humidity, rainfall, UV and PSI 24-Hourly readings will be returned as a data frame. As each separate API returns data of varying granularity - geographical or temporal, this summary will aggregate across all of Singapore and over each hour. Documentation for each separate index can be acquired from each of the separate functions above.
data <- get_weathersumm(from = "2017-12-31", to = "2018-01-04")
#> [1] "2017-12-31"
#> [1] "2018-01-04"
head(data)
#> Hour Average Hourly Rainfall UV Index
#> 1 2017-12-31 00:00:00 2.901818 0
#> 2 2017-12-31 01:00:00 5.016000 0
#> 3 2017-12-31 02:00:00 13.878449 0
#> 4 2017-12-31 03:00:00 3.803020 0
#> 5 2017-12-31 04:00:00 0.896000 0
#> 6 2017-12-31 05:00:00 0.176000 0
#> Average Air Temperature PSI 24-Hourly Average Relative Humidity
#> 1 25.20341 NA 91.71302
#> 2 25.03904 49 93.62464
#> 3 24.85914 49 93.89126
#> 4 24.81281 49 93.99837
#> 5 24.94275 49 93.63647
#> 6 25.07144 48 93.64695