Instalar paquetes y llamar librerias

#install.packages("riem")
library(riem)
#install.packages("tidyverse")
library(tidyverse)
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## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
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#install.packages("lubridate")
library(lubridate)
#install.packages("ggplot2")
library(ggplot2)
#install.packages("plotly")
library(plotly)
## 
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Paso 2. Buscar la red (pais) Ejemplo: Mexico. Y copiar CODE

view(riem_networks())

Paso 3. Buscar la estacion (ciudad) Ejemplo: Monterrey y copiar el ID

view(riem_stations("MX__ASOS"))

Paso 4. Obtener informacion

monterrey <- riem_measures("MMMY")
str(monterrey)
## tibble [82,340 × 32] (S3: tbl_df/tbl/data.frame)
##  $ station          : chr [1:82340] "MMMY" "MMMY" "MMMY" "MMMY" ...
##  $ valid            : POSIXct[1:82340], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
##  $ lon              : num [1:82340] -100 -100 -100 -100 -100 ...
##  $ lat              : num [1:82340] 25.8 25.8 25.8 25.8 25.8 ...
##  $ tmpf             : num [1:82340] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
##  $ dwpf             : num [1:82340] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
##  $ relh             : num [1:82340] 93.5 93.5 93.5 100 100 ...
##  $ drct             : num [1:82340] 0 120 120 120 110 100 110 130 60 0 ...
##  $ sknt             : num [1:82340] 0 3 5 6 5 5 4 3 3 0 ...
##  $ p01i             : num [1:82340] 0 0 0 0 0 0 0 0 0 0 ...
##  $ alti             : num [1:82340] 30.3 30.3 30.3 30.3 30.3 ...
##  $ mslp             : num [1:82340] NA NA NA NA NA ...
##  $ vsby             : num [1:82340] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
##  $ gust             : num [1:82340] NA NA NA NA NA NA NA NA NA NA ...
##  $ skyc1            : chr [1:82340] "SCT" "SCT" "SCT" "VV " ...
##  $ skyc2            : chr [1:82340] "BKN" "BKN" "BKN" NA ...
##  $ skyc3            : chr [1:82340] "OVC" "OVC" "OVC" NA ...
##  $ skyc4            : chr [1:82340] NA NA NA NA ...
##  $ skyl1            : num [1:82340] 700 300 200 200 100 100 100 100 100 100 ...
##  $ skyl2            : num [1:82340] 1200 400 300 NA NA NA NA NA NA NA ...
##  $ skyl3            : num [1:82340] 4000 900 500 NA NA NA NA NA NA NA ...
##  $ skyl4            : num [1:82340] NA NA NA NA NA NA NA NA NA NA ...
##  $ wxcodes          : chr [1:82340] NA "BR" "BR" "FG" ...
##  $ ice_accretion_1hr: logi [1:82340] NA NA NA NA NA NA ...
##  $ ice_accretion_3hr: logi [1:82340] NA NA NA NA NA NA ...
##  $ ice_accretion_6hr: logi [1:82340] NA NA NA NA NA NA ...
##  $ peak_wind_gust   : logi [1:82340] NA NA NA NA NA NA ...
##  $ peak_wind_drct   : logi [1:82340] NA NA NA NA NA NA ...
##  $ peak_wind_time   : logi [1:82340] NA NA NA NA NA NA ...
##  $ feel             : num [1:82340] 48.2 47.2 45.6 42.9 43.5 ...
##  $ metar            : chr [1:82340] "MMMY 010016Z 00000KT 4SM SCT007 BKN012 OVC040 09/08 A3028 RMK 8/5// BR" "MMMY 010049Z 12003KT 3SM BR SCT003 BKN004 OVC009 09/08 A3028 RMK 8/5// -DZ OCNL" "MMMY 010116Z 12005KT 1SM BR SCT002 BKN003 OVC005 09/08 A3028 RMK 8/6// -DZ OCNL" "MMMY 010120Z 12006KT 1/4SM FG VV002 08/08 A3029 RMK 8//// BC FG MOV SE/NW" ...
##  $ snowdepth        : logi [1:82340] NA NA NA NA NA NA ...

Paso 5. Agregar temperatura en grados centigrados

monterrey$tmpc <- (monterrey$tmpf - 32)/1.8
str(monterrey)
## tibble [82,340 × 33] (S3: tbl_df/tbl/data.frame)
##  $ station          : chr [1:82340] "MMMY" "MMMY" "MMMY" "MMMY" ...
##  $ valid            : POSIXct[1:82340], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
##  $ lon              : num [1:82340] -100 -100 -100 -100 -100 ...
##  $ lat              : num [1:82340] 25.8 25.8 25.8 25.8 25.8 ...
##  $ tmpf             : num [1:82340] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
##  $ dwpf             : num [1:82340] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
##  $ relh             : num [1:82340] 93.5 93.5 93.5 100 100 ...
##  $ drct             : num [1:82340] 0 120 120 120 110 100 110 130 60 0 ...
##  $ sknt             : num [1:82340] 0 3 5 6 5 5 4 3 3 0 ...
##  $ p01i             : num [1:82340] 0 0 0 0 0 0 0 0 0 0 ...
##  $ alti             : num [1:82340] 30.3 30.3 30.3 30.3 30.3 ...
##  $ mslp             : num [1:82340] NA NA NA NA NA ...
##  $ vsby             : num [1:82340] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
##  $ gust             : num [1:82340] NA NA NA NA NA NA NA NA NA NA ...
##  $ skyc1            : chr [1:82340] "SCT" "SCT" "SCT" "VV " ...
##  $ skyc2            : chr [1:82340] "BKN" "BKN" "BKN" NA ...
##  $ skyc3            : chr [1:82340] "OVC" "OVC" "OVC" NA ...
##  $ skyc4            : chr [1:82340] NA NA NA NA ...
##  $ skyl1            : num [1:82340] 700 300 200 200 100 100 100 100 100 100 ...
##  $ skyl2            : num [1:82340] 1200 400 300 NA NA NA NA NA NA NA ...
##  $ skyl3            : num [1:82340] 4000 900 500 NA NA NA NA NA NA NA ...
##  $ skyl4            : num [1:82340] NA NA NA NA NA NA NA NA NA NA ...
##  $ wxcodes          : chr [1:82340] NA "BR" "BR" "FG" ...
##  $ ice_accretion_1hr: logi [1:82340] NA NA NA NA NA NA ...
##  $ ice_accretion_3hr: logi [1:82340] NA NA NA NA NA NA ...
##  $ ice_accretion_6hr: logi [1:82340] NA NA NA NA NA NA ...
##  $ peak_wind_gust   : logi [1:82340] NA NA NA NA NA NA ...
##  $ peak_wind_drct   : logi [1:82340] NA NA NA NA NA NA ...
##  $ peak_wind_time   : logi [1:82340] NA NA NA NA NA NA ...
##  $ feel             : num [1:82340] 48.2 47.2 45.6 42.9 43.5 ...
##  $ metar            : chr [1:82340] "MMMY 010016Z 00000KT 4SM SCT007 BKN012 OVC040 09/08 A3028 RMK 8/5// BR" "MMMY 010049Z 12003KT 3SM BR SCT003 BKN004 OVC009 09/08 A3028 RMK 8/5// -DZ OCNL" "MMMY 010116Z 12005KT 1SM BR SCT002 BKN003 OVC005 09/08 A3028 RMK 8/6// -DZ OCNL" "MMMY 010120Z 12006KT 1/4SM FG VV002 08/08 A3029 RMK 8//// BC FG MOV SE/NW" ...
##  $ snowdepth        : logi [1:82340] NA NA NA NA NA NA ...
##  $ tmpc             : num [1:82340] 9 9 9 8 8 8 8 8 8 8 ...

Paso 6. Filtrar informacion - Ejemplo a Marzo 2023

este_año <- subset(monterrey, valid >= as.POSIXct("2023-01-01 0:00") & valid <= as.POSIXct("2023-03-10 07:00"))

Paso 7. Graficar temperatura en 2023

plot(este_año$valid,este_año$tmpc)

Paso 8. Promediar informacion por dia

este_año <- este_año %>%
  mutate(date=ymd_hms(valid), date = as.Date(date)) %>%
  group_by(date) %>%
  summarize_if(is.numeric, ~mean(.,na.rm = TRUE))

Paso 9. Graficar temperatura en 2023

plot(este_año$date,este_año$tmpc, type = "l", main = "Temperatura Promedio en Monterrey", xlab = "Fecha", ylab = "Grados Centigrados")

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