Asos
Karla López A00839790
2026-02-26
Introducción
Información obtenida del Automated Surface Observing System (ASOS) de los aeropuertos de todo el mundo.
Instalar paquetes y llamar librerías
#install.packages("riem")
library(riem)
# install.packages("tidyverse")
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.0 ✔ readr 2.1.6
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.2 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.1
## ── 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 (país) - 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 = "2025-12-01")
# Análisis Descriptivo
summary(monterrey)## station valid tmpf
## Length:2145 Min. :2025-12-01 00:40:00 Min. :28.40
## Class :character 1st Qu.:2025-12-22 16:41:00 1st Qu.:55.40
## Mode :character Median :2026-01-13 09:40:00 Median :60.80
## Mean :2026-01-13 08:53:30 Mean :62.45
## 3rd Qu.:2026-02-03 18:40:00 3rd Qu.:69.80
## Max. :2026-02-25 23:40:00 Max. :98.60
##
## dwpf relh drct sknt p01i
## Min. :12.20 Min. : 9.76 Min. : 0.0 Min. : 0.000 Min. :0
## 1st Qu.:37.40 1st Qu.: 40.83 1st Qu.: 70.0 1st Qu.: 3.000 1st Qu.:0
## Median :48.20 Median : 61.99 Median :130.0 Median : 5.000 Median :0
## Mean :46.32 Mean : 61.01 Mean :160.4 Mean : 5.234 Mean :0
## 3rd Qu.:57.20 3rd Qu.: 81.47 3rd Qu.:290.0 3rd Qu.: 7.000 3rd Qu.:0
## Max. :66.20 Max. :100.00 Max. :360.0 Max. :25.000 Max. :0
## NA's :3
## alti mslp vsby gust
## Min. :29.62 Min. :1002 Min. : 0.120 Min. :12.00
## 1st Qu.:29.99 1st Qu.:1015 1st Qu.: 6.000 1st Qu.:18.00
## Median :30.13 Median :1020 Median : 8.000 Median :22.50
## Mean :30.14 Mean :1021 Mean : 8.371 Mean :22.74
## 3rd Qu.:30.26 3rd Qu.:1025 3rd Qu.:10.000 3rd Qu.:25.75
## Max. :30.69 Max. :1101 Max. :15.000 Max. :48.00
## NA's :1449 NA's :2015
## skyc1 skyc2 skyc3 skyc4
## Length:2145 Length:2145 Length:2145 Mode:logical
## Class :character Class :character Class :character NA's:2145
## Mode :character Mode :character Mode :character
##
##
##
##
## skyl1 skyl2 skyl3 skyl4
## Min. : 100 Min. : 500 Min. : 1600 Mode:logical
## 1st Qu.: 1500 1st Qu.: 1500 1st Qu.:20000 NA's:2145
## Median : 3000 Median : 7000 Median :20000
## Mean : 6529 Mean : 7619 Mean :17584
## 3rd Qu.: 8000 3rd Qu.: 8000 3rd Qu.:20000
## Max. :20000 Max. :20000 Max. :20000
## NA's :896 NA's :1627 NA's :2120
## wxcodes ice_accretion_1hr ice_accretion_3hr ice_accretion_6hr
## Length:2145 Mode:logical Mode:logical Mode:logical
## Class :character NA's:2145 NA's:2145 NA's:2145
## Mode :character
##
##
##
##
## peak_wind_gust peak_wind_drct peak_wind_time feel
## Mode:logical Mode:logical Mode:logical Min. :27.99
## NA's:2145 NA's:2145 NA's:2145 1st Qu.:55.40
## Median :60.80
## Mean :61.92
## 3rd Qu.:69.80
## Max. :94.14
##
## metar snowdepth
## Length:2145 Mode:logical
## Class :character NA's:2145
## Mode :character
##
##
##
## str(monterrey)## tibble [2,145 × 30] (S3: tbl_df/tbl/data.frame)
## $ station : chr [1:2145] "MMMY" "MMMY" "MMMY" "MMMY" ...
## $ valid : POSIXct[1:2145], format: "2025-12-01 00:40:00" "2025-12-01 01:40:00" ...
## $ tmpf : num [1:2145] 53.6 53.6 53.6 53.6 51.8 51.8 50 50 48.2 48.2 ...
## $ dwpf : num [1:2145] 51.8 51.8 51.8 51.8 50 50 50 50 48.2 46.4 ...
## $ relh : num [1:2145] 93.6 93.6 93.6 93.6 93.5 ...
## $ drct : num [1:2145] 340 280 240 240 280 270 270 260 290 290 ...
## $ sknt : num [1:2145] 3 5 5 6 8 6 6 4 6 6 ...
## $ p01i : num [1:2145] 0 0 0 0 0 0 0 0 0 0 ...
## $ alti : num [1:2145] 30.2 30.2 30.2 30.2 30.2 ...
## $ mslp : num [1:2145] NA NA 1024 NA NA ...
## $ vsby : num [1:2145] 2 3 3 3 3 3 3 3 3 2 ...
## $ gust : num [1:2145] NA NA NA NA NA NA NA NA NA NA ...
## $ skyc1 : chr [1:2145] "BKN" "BKN" "OVC" "BKN" ...
## $ skyc2 : chr [1:2145] "OVC" "OVC" NA "OVC" ...
## $ skyc3 : chr [1:2145] NA NA NA NA ...
## $ skyc4 : logi [1:2145] NA NA NA NA NA NA ...
## $ skyl1 : num [1:2145] 800 800 1000 500 500 500 500 500 500 500 ...
## $ skyl2 : num [1:2145] 1200 1200 NA 800 800 800 800 800 800 800 ...
## $ skyl3 : num [1:2145] NA NA NA NA NA NA NA NA NA NA ...
## $ skyl4 : logi [1:2145] NA NA NA NA NA NA ...
## $ wxcodes : chr [1:2145] "DZ BR" "-DZ BR" "-DZ BR" "-DZ BR" ...
## $ ice_accretion_1hr: logi [1:2145] NA NA NA NA NA NA ...
## $ ice_accretion_3hr: logi [1:2145] NA NA NA NA NA NA ...
## $ ice_accretion_6hr: logi [1:2145] NA NA NA NA NA NA ...
## $ peak_wind_gust : logi [1:2145] NA NA NA NA NA NA ...
## $ peak_wind_drct : logi [1:2145] NA NA NA NA NA NA ...
## $ peak_wind_time : logi [1:2145] NA NA NA NA NA NA ...
## $ feel : num [1:2145] 53.6 53.6 53.6 53.6 51.8 ...
## $ metar : chr [1:2145] "MMMY 010040Z 34003KT 2SM DZ BR BKN008 OVC012 12/11 A3017 RMK 60025 8/7//" "MMMY 010140Z 28005KT 3SM -DZ BR BKN008 OVC012 12/11 A3020 RMK 60015 8/7//" "MMMY 010240Z 24005KT 3SM -DZ BR OVC010 12/11 A3022 RMK SLP235 52019 942 60005 8/7// DZ INTMT" "MMMY 010342Z 24006KT 3SM -DZ BR BKN005 OVC008 12/11 A3022 RMK 60005 8/6// -DZ INTMT" ...
## $ snowdepth : logi [1:2145] NA NA NA NA NA NA ...# Filtrar información del último mes
mty_ene_26 <- 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 enero en Monterrey en °C.
promedio <- mty_ene_26 %>%
mutate(
fecha = as.Date(valid),
celcius = (tmpf - 32) * 5/9
) %>%
group_by(fecha) %>%
summarize(temp_mean = mean(celcius, na.rm = TRUE))ggplot(promedio, aes(x = fecha, y = temp_mean)) +
geom_col(fill = "steelblue") +
labs(
title = "Temperatura promedio diaria en enero en Monterrey (°C)",
x = "Fecha",
y = "Temperatura prom en celcius") +
theme_minimal()