Analisis del Clima mediante API:ASOS
David Dominguez - A01570975
2022-09-19
#
Instalar programas requeridos
library(riem)
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library(plotly)##
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## layoutPaso 1. Buscar la red
Ejemplo: TEXAS, y copiar CODE
view(riem_networks())Paso 2. Buscar la estación (ciudad)
Ejemplo: Atlanta
view(riem_stations("TX_ASOS"))Paso 3. Escribir ID
atlanta <- riem_measures("ATA")
str(atlanta)## tibble [8,035 × 32] (S3: tbl_df/tbl/data.frame)
## $ station : chr [1:8035] "ATA" "ATA" "ATA" "ATA" ...
## $ valid : POSIXct[1:8035], format: "2022-05-19 10:55:00" "2022-05-19 12:35:00" ...
## $ lon : num [1:8035] -94.2 -94.2 -94.2 -94.2 -94.2 ...
## $ lat : num [1:8035] 33.1 33.1 33.1 33.1 33.1 ...
## $ tmpf : num [1:8035] 71.1 71.6 72.1 73.2 74.5 75.9 77.4 79.2 79.9 80.6 ...
## $ dwpf : num [1:8035] 69.6 70 70 70.3 70.7 71.2 71.4 71.4 70.3 70.5 ...
## $ relh : num [1:8035] 95 94.7 93.1 90.6 88 ...
## $ drct : num [1:8035] 180 180 160 170 180 200 220 220 210 190 ...
## $ sknt : num [1:8035] 5 5 5 5 10 5 9 10 9 11 ...
## $ p01i : num [1:8035] 0 0 0 0 0 0 0 0 0 0 ...
## $ alti : num [1:8035] 29.8 29.8 29.8 29.8 29.8 ...
## $ mslp : num [1:8035] 1000 1000 1000 1000 1000 ...
## $ vsby : num [1:8035] 10 10 10 10 10 10 10 10 10 10 ...
## $ gust : num [1:8035] NA NA NA NA NA NA NA 15 NA 17 ...
## $ skyc1 : chr [1:8035] "CLR" "CLR" "CLR" "CLR" ...
## $ skyc2 : chr [1:8035] NA NA NA NA ...
## $ skyc3 : chr [1:8035] NA NA NA NA ...
## $ skyc4 : chr [1:8035] NA NA NA NA ...
## $ skyl1 : num [1:8035] NA NA NA NA 1600 1500 1600 2000 2400 2500 ...
## $ skyl2 : num [1:8035] NA NA NA NA NA NA NA NA NA NA ...
## $ skyl3 : num [1:8035] NA NA NA NA NA NA NA NA NA NA ...
## $ skyl4 : num [1:8035] NA NA NA NA NA NA NA NA NA NA ...
## $ wxcodes : chr [1:8035] NA NA NA NA ...
## $ ice_accretion_1hr: logi [1:8035] NA NA NA NA NA NA ...
## $ ice_accretion_3hr: logi [1:8035] NA NA NA NA NA NA ...
## $ ice_accretion_6hr: logi [1:8035] NA NA NA NA NA NA ...
## $ peak_wind_gust : num [1:8035] NA NA NA NA NA NA NA NA NA NA ...
## $ peak_wind_drct : num [1:8035] NA NA NA NA NA NA NA NA NA NA ...
## $ peak_wind_time : chr [1:8035] NA NA NA NA ...
## $ feel : num [1:8035] 71.1 71.6 72.1 73.2 74.5 ...
## $ metar : chr [1:8035] "KATA 191055Z AUTO 18005KT 10SM CLR 22/21 A2981 RMK AO2 SLP995 T02170209 $" "KATA 191235Z AUTO 18005KT 10SM CLR 22/21 A2983 RMK AO2 SLP001 T02200211 $" "KATA 191255Z AUTO 16005KT 10SM CLR 22/21 A2982 RMK AO2 SLP998 T02230211 $" "KATA 191315Z AUTO 17005KT 10SM CLR 23/21 A2982 RMK AO2 SLP997 T02290213 $" ...
## $ snowdepth : logi [1:8035] NA NA NA NA NA NA ...Paso 4. Información
Extraer información de este mes
este_mes <- subset(atlanta, valid >= as.POSIXct("2022-09-01 00:00") & valid <= as.POSIXct("2022-09-18 23:59"))Paso 5. Graficar
plot(este_mes$valid, este_mes$relh)