ASOS

# 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(
  station = "MMMY",
  date_start = "2024-08-01",
  date_end   = "2024-08-31"
)

# Análisis Descriptivo
summary(monterrey)

##    station              valid                          tmpf       
##  Length:725         Min.   :2024-08-01 00:40:00   Min.   : 71.60  
##  Class :character   1st Qu.:2024-08-08 11:40:00   1st Qu.: 77.00  
##  Mode  :character   Median :2024-08-15 23:44:00   Median : 82.40  
##                     Mean   :2024-08-15 23:33:43   Mean   : 83.75  
##                     3rd Qu.:2024-08-23 11:40:00   3rd Qu.: 89.60  
##                     Max.   :2024-08-30 23:40:00   Max.   :100.40  
##                                                                   
##       dwpf            relh             drct             sknt             p01i  
##  Min.   :53.60   Min.   : 24.89   Min.   :  0.00   Min.   : 0.000   Min.   :0  
##  1st Qu.:68.00   1st Qu.: 49.36   1st Qu.: 70.00   1st Qu.: 4.000   1st Qu.:0  
##  Median :69.80   Median : 65.95   Median :100.00   Median : 6.000   Median :0  
##  Mean   :69.66   Mean   : 65.99   Mean   : 94.58   Mean   : 5.811   Mean   :0  
##  3rd Qu.:71.60   3rd Qu.: 83.44   3rd Qu.:120.00   3rd Qu.: 8.000   3rd Qu.:0  
##  Max.   :75.20   Max.   :100.00   Max.   :340.00   Max.   :16.000   Max.   :0  
##                                                                                
##       alti            mslp             vsby             gust      
##  Min.   :29.80   Min.   : 987.8   Min.   : 3.000   Min.   :15.00  
##  1st Qu.:29.99   1st Qu.:1013.6   1st Qu.: 8.000   1st Qu.:17.00  
##  Median :30.03   Median :1014.9   Median :10.000   Median :18.00  
##  Mean   :30.02   Mean   :1014.6   Mean   : 9.611   Mean   :18.86  
##  3rd Qu.:30.07   3rd Qu.:1016.2   3rd Qu.:10.000   3rd Qu.:19.50  
##  Max.   :30.18   Max.   :1020.1   Max.   :15.000   Max.   :26.00  
##                  NA's   :485                       NA's   :718    
##     skyc1              skyc2              skyc3            skyc4        
##  Length:725         Length:725         Length:725         Mode:logical  
##  Class :character   Class :character   Class :character   NA's:725      
##  Mode  :character   Mode  :character   Mode  :character                 
##                                                                         
##                                                                         
##                                                                         
##                                                                         
##      skyl1           skyl2           skyl3        skyl4        
##  Min.   :  500   Min.   :  800   Min.   : 8000   Mode:logical  
##  1st Qu.: 2000   1st Qu.: 7000   1st Qu.:20000   NA's:725      
##  Median : 3000   Median :10000   Median :20000                 
##  Mean   : 5513   Mean   :12568   Mean   :19600                 
##  3rd Qu.: 4500   3rd Qu.:20000   3rd Qu.:20000                 
##  Max.   :20000   Max.   :20000   Max.   :20000                 
##  NA's   :254     NA's   :548     NA's   :695                   
##    wxcodes          ice_accretion_1hr ice_accretion_3hr ice_accretion_6hr
##  Length:725         Mode:logical      Mode:logical      Mode:logical     
##  Class :character   NA's:725          NA's:725          NA's:725         
##  Mode  :character                                                        
##                                                                          
##                                                                          
##                                                                          
##                                                                          
##  peak_wind_gust peak_wind_drct peak_wind_time      feel       
##  Mode:logical   Mode:logical   Mode:logical   Min.   : 71.60  
##  NA's:725       NA's:725       NA's:725       1st Qu.: 77.00  
##                                               Median : 87.18  
##                                               Mean   : 86.33  
##                                               3rd Qu.: 94.85  
##                                               Max.   :102.50  
##                                                               
##     metar           snowdepth     
##  Length:725         Mode:logical  
##  Class :character   NA's:725      
##  Mode  :character                 
##                                   
##                                   
##                                   
## 

str(monterrey)

## tibble [725 × 30] (S3: tbl_df/tbl/data.frame)
##  $ station          : chr [1:725] "MMMY" "MMMY" "MMMY" "MMMY" ...
##  $ valid            : POSIXct[1:725], format: "2024-08-01 00:40:00" "2024-08-01 01:40:00" ...
##  $ tmpf             : num [1:725] 87.8 86 82.4 80.6 78.8 77 77 77 77 75.2 ...
##  $ dwpf             : num [1:725] 66.2 64.4 68 73.4 73.4 73.4 73.4 73.4 73.4 73.4 ...
##  $ relh             : num [1:725] 48.8 48.6 61.8 78.8 83.5 ...
##  $ drct             : num [1:725] 150 130 130 90 100 90 90 90 100 80 ...
##  $ sknt             : num [1:725] 15 13 9 12 11 8 7 7 7 3 ...
##  $ p01i             : num [1:725] 0 0 0 0 0 0 0 0 0 0 ...
##  $ alti             : num [1:725] 30 30 30 30 30 ...
##  $ mslp             : num [1:725] NA NA 1014 NA NA ...
##  $ vsby             : num [1:725] 6 7 7 6 6 6 6 6 6 6 ...
##  $ gust             : num [1:725] NA NA NA NA NA NA NA NA NA NA ...
##  $ skyc1            : chr [1:725] "CLR" "CLR" "CLR" "CLR" ...
##  $ skyc2            : chr [1:725] NA NA NA NA ...
##  $ skyc3            : chr [1:725] NA NA NA NA ...
##  $ skyc4            : logi [1:725] NA NA NA NA NA NA ...
##  $ skyl1            : num [1:725] NA NA NA NA NA NA 2000 1500 2000 2000 ...
##  $ skyl2            : num [1:725] NA NA NA NA NA NA NA NA NA NA ...
##  $ skyl3            : num [1:725] NA NA NA NA NA NA NA NA NA NA ...
##  $ skyl4            : logi [1:725] NA NA NA NA NA NA ...
##  $ wxcodes          : chr [1:725] NA NA NA NA ...
##  $ ice_accretion_1hr: logi [1:725] NA NA NA NA NA NA ...
##  $ ice_accretion_3hr: logi [1:725] NA NA NA NA NA NA ...
##  $ ice_accretion_6hr: logi [1:725] NA NA NA NA NA NA ...
##  $ peak_wind_gust   : logi [1:725] NA NA NA NA NA NA ...
##  $ peak_wind_drct   : logi [1:725] NA NA NA NA NA NA ...
##  $ peak_wind_time   : logi [1:725] NA NA NA NA NA NA ...
##  $ feel             : num [1:725] 90.3 87.5 85.4 85.3 78.8 ...
##  $ metar            : chr [1:725] "MMMY 010040Z 15015KT 6SM SKC 31/19 A2996 RMK HZY" "MMMY 010140Z 13013KT 7SM SKC 30/18 A2997 RMK HZY" "MMMY 010240Z 13009KT 7SM SKC 28/20 A3000 RMK SLP139 52013 914 HZY" "MMMY 010340Z 09012KT 6SM SKC 27/23 A3002 RMK HZY" ...
##  $ snowdepth        : logi [1:725] NA NA NA NA NA NA ...

# Filtrar información del último mes
mty_ago_24 <- subset(monterrey, valid >= as.POSIXct("2024-08-01 00:00") &
valid <= as.POSIXct("2024-08-31 23:59"))
# Ejercicio 1: Realizar una gráfica de barras de la temperatura promedio diario en agosto en Monterrey en °C.
mty_ago_24 <- mty_ago_24 %>%
  mutate(date = as.Date(valid),
         temp_c = (tmpf - 32) * 5/9)

temp_diaria <- mty_ago_24 %>%
  group_by(date) %>%
  summarise(temp_prom_c = mean(temp_c, na.rm = TRUE), .groups = "drop")

ggplot(temp_diaria, aes(x = date, y = temp_prom_c)) +
  geom_col(fill = "#FF1493") +
  labs(
    title = "Temperatura promedio diaria en Monterrey (Agosto 2024)",
    x = "Fecha",
    y = "Temperatura promedio (°C)"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))