ASOS
Natalia Sofia Martinez Dominguez
2026-02-26
Introducción
Información obtenida del Automated Surface Observing System (ASOS) de los aeropuertos de todo el mundo. # Instalar paquetes y llamar librerÃas
# install.packages("riem")
library(riem)
# install.packages("tidyverse")
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# install.packages("ggplot2")
library(ggplot2)
# install.packages("lubridate")
library(lubridate)Obtener y graficar la información
# PASO 1. Buscar la red (paÃs) - Ejemplo: México, y copiar CODE
#view(riem_networks())
# PASO 2. Buscar la estación (ciudad) - Ejemplo: Monterrey, y copiar ID
#view(riem_stations("MX__ASOS"))
# PASO 3. Obtener información de la estación
monterrey <- riem_measures("MMMY", date_start = "2026-01-01", date_end = "2026-01-31")
# Análisis Descriptivo
summary(monterrey)## station valid tmpf
## Length:746 Min. :2026-01-01 00:40:00.00 Min. :28.40
## Class :character 1st Qu.:2026-01-08 16:55:00.00 1st Qu.:50.00
## Mode :character Median :2026-01-16 09:10:00.00 Median :59.00
## Mean :2026-01-16 04:34:14.72 Mean :58.78
## 3rd Qu.:2026-01-23 17:27:15.00 3rd Qu.:66.20
## Max. :2026-01-30 23:40:00.00 Max. :89.60
##
## dwpf relh drct sknt p01i
## Min. :15.8 Min. : 14.60 Min. : 0 Min. : 0.000 Min. :0
## 1st Qu.:33.8 1st Qu.: 40.53 1st Qu.: 70 1st Qu.: 3.000 1st Qu.:0
## Median :41.0 Median : 60.01 Median :140 Median : 5.000 Median :0
## Mean :42.8 Mean : 60.23 Mean :169 Mean : 5.166 Mean :0
## 3rd Qu.:55.4 3rd Qu.: 77.77 3rd Qu.:290 3rd Qu.: 7.000 3rd Qu.:0
## Max. :64.4 Max. :100.00 Max. :360 Max. :20.000 Max. :0
## NA's :1
## alti mslp vsby gust
## Min. :29.72 Min. :1006 Min. : 0.120 Min. :15.00
## 1st Qu.:29.99 1st Qu.:1015 1st Qu.: 6.000 1st Qu.:20.00
## Median :30.15 Median :1021 Median :10.000 Median :23.50
## Mean :30.16 Mean :1022 Mean : 8.708 Mean :23.55
## 3rd Qu.:30.32 3rd Qu.:1028 3rd Qu.:10.000 3rd Qu.:26.00
## Max. :30.69 Max. :1040 Max. :15.000 Max. :37.00
## NA's :506 NA's :706
## skyc1 skyc2 skyc3 skyc4
## Length:746 Length:746 Length:746 Mode:logical
## Class :character Class :character Class :character NA's:746
## Mode :character Mode :character Mode :character
##
##
##
##
## skyl1 skyl2 skyl3 skyl4
## Min. : 200 Min. : 500 Min. : 4000 Mode:logical
## 1st Qu.: 2000 1st Qu.: 2000 1st Qu.:20000 NA's:746
## Median : 7000 Median : 7000 Median :20000
## Mean : 7905 Mean : 9363 Mean :16444
## 3rd Qu.:10000 3rd Qu.:20000 3rd Qu.:20000
## Max. :20000 Max. :20000 Max. :20000
## NA's :269 NA's :560 NA's :737
## wxcodes ice_accretion_1hr ice_accretion_3hr ice_accretion_6hr
## Length:746 Mode:logical Mode:logical Mode:logical
## Class :character NA's:746 NA's:746 NA's:746
## Mode :character
##
##
##
##
## peak_wind_gust peak_wind_drct peak_wind_time feel
## Mode:logical Mode:logical Mode:logical Min. :27.99
## NA's:746 NA's:746 NA's:746 1st Qu.:50.00
## Median :59.00
## Mean :57.87
## 3rd Qu.:66.20
## Max. :86.24
##
## metar snowdepth
## Length:746 Mode:logical
## Class :character NA's:746
## Mode :character
##
##
##
## str(monterrey)## tibble [746 × 30] (S3: tbl_df/tbl/data.frame)
## $ station : chr [1:746] "MMMY" "MMMY" "MMMY" "MMMY" ...
## $ valid : POSIXct[1:746], format: "2026-01-01 00:40:00" "2026-01-01 01:40:00" ...
## $ tmpf : num [1:746] 57.2 55.4 53.6 51.8 51.8 50 46.4 42.8 44.6 44.6 ...
## $ dwpf : num [1:746] 37.4 35.6 35.6 35.6 33.8 33.8 33.8 33.8 33.8 33.8 ...
## $ relh : num [1:746] 47.4 47.1 50.4 53.8 50.1 ...
## $ drct : num [1:746] 80 0 120 60 70 70 0 0 340 300 ...
## $ sknt : num [1:746] 4 0 3 4 3 5 0 0 3 6 ...
## $ p01i : num [1:746] 0 0 0 0 0 0 0 0 0 0 ...
## $ alti : num [1:746] 30.2 30.2 30.2 30.2 30.2 ...
## $ mslp : num [1:746] NA NA 1024 NA NA ...
## $ vsby : num [1:746] 10 10 10 10 10 10 10 10 10 10 ...
## $ gust : num [1:746] NA NA NA NA NA NA NA NA NA NA ...
## $ skyc1 : chr [1:746] "CLR" "CLR" "CLR" "CLR" ...
## $ skyc2 : chr [1:746] NA NA NA NA ...
## $ skyc3 : chr [1:746] NA NA NA NA ...
## $ skyc4 : logi [1:746] NA NA NA NA NA NA ...
## $ skyl1 : num [1:746] NA NA NA NA NA NA NA NA NA NA ...
## $ skyl2 : num [1:746] NA NA NA NA NA NA NA NA NA NA ...
## $ skyl3 : num [1:746] NA NA NA NA NA NA NA NA NA NA ...
## $ skyl4 : logi [1:746] NA NA NA NA NA NA ...
## $ wxcodes : chr [1:746] NA NA NA NA ...
## $ ice_accretion_1hr: logi [1:746] NA NA NA NA NA NA ...
## $ ice_accretion_3hr: logi [1:746] NA NA NA NA NA NA ...
## $ ice_accretion_6hr: logi [1:746] NA NA NA NA NA NA ...
## $ peak_wind_gust : logi [1:746] NA NA NA NA NA NA ...
## $ peak_wind_drct : logi [1:746] NA NA NA NA NA NA ...
## $ peak_wind_time : logi [1:746] NA NA NA NA NA NA ...
## $ feel : num [1:746] 57.2 55.4 53.6 51.8 51.8 ...
## $ metar : chr [1:746] "MMMY 010040Z 08004KT 10SM SKC 14/03 A3022 RMK ISOL CI" "MMMY 010140Z 00000KT 10SM SKC 13/02 A3022" "MMMY 010240Z 12003KT 10SM SKC 12/02 A3022 RMK SLP245 52004 993" "MMMY 010340Z 06004KT 10SM SKC 11/02 A3022" ...
## $ snowdepth : logi [1:746] NA NA NA NA NA NA ...# Filtrar información del último mes
mty_ago_24 <- subset(monterrey, valid >= as.POSIXct("2026-01-01 00:00") &
valid <= as.POSIXct("2026-01-31 23:59"))
# Ejercicio 1: Realizar una gráfica de barras de la temperatura promedio diario en agosto en Monterrey en °C.
# Convertir a °C
monterrey$temp_c <- (monterrey$tmpf - 32) * 5/9
# Promedio diario
prom <- aggregate(temp_c ~ as.Date(valid), data = monterrey, mean)
# Gráfica
barplot(prom$temp_c,
names.arg = prom$`as.Date(valid)`,
las = 2,
main = "Temp promedio diaria (°C)")