Paso 1. Instalar paquetes y llamar librerías

# Instalar paquetes y llamar librerías
#install.packages('riem')
library(riem)
#install.packages('tidyverse')
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.0     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.1     ✔ tibble    3.1.8
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
#install.packages('lubridate')
library(lubridate)
#install.packages('ggplot2')
library(ggplot2)
#install.packages('plotly')
library(plotly)
## 
## Attaching package: 'plotly'
## 
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following object is masked from 'package:graphics':
## 
##     layout

Paso2. Buscar la red (país). Ejemplo: México y copiar CODE

#view(riem_networks())

Paso 3.Buscar la estación (ciudad) y copiar ID

#view(riem_stations('MX__ASOS'))

Paso 4.Obtener información

monterrey <- riem_measures('MMMY')
str(monterrey)
## tibble [82,310 × 32] (S3: tbl_df/tbl/data.frame)
##  $ station          : chr [1:82310] "MMMY" "MMMY" "MMMY" "MMMY" ...
##  $ valid            : POSIXct[1:82310], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
##  $ lon              : num [1:82310] -100 -100 -100 -100 -100 ...
##  $ lat              : num [1:82310] 25.8 25.8 25.8 25.8 25.8 ...
##  $ tmpf             : num [1:82310] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
##  $ dwpf             : num [1:82310] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
##  $ relh             : num [1:82310] 93.5 93.5 93.5 100 100 ...
##  $ drct             : num [1:82310] 0 120 120 120 110 100 110 130 60 0 ...
##  $ sknt             : num [1:82310] 0 3 5 6 5 5 4 3 3 0 ...
##  $ p01i             : num [1:82310] 0 0 0 0 0 0 0 0 0 0 ...
##  $ alti             : num [1:82310] 30.3 30.3 30.3 30.3 30.3 ...
##  $ mslp             : num [1:82310] NA NA NA NA NA ...
##  $ vsby             : num [1:82310] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
##  $ gust             : num [1:82310] NA NA NA NA NA NA NA NA NA NA ...
##  $ skyc1            : chr [1:82310] "SCT" "SCT" "SCT" "VV " ...
##  $ skyc2            : chr [1:82310] "BKN" "BKN" "BKN" NA ...
##  $ skyc3            : chr [1:82310] "OVC" "OVC" "OVC" NA ...
##  $ skyc4            : chr [1:82310] NA NA NA NA ...
##  $ skyl1            : num [1:82310] 700 300 200 200 100 100 100 100 100 100 ...
##  $ skyl2            : num [1:82310] 1200 400 300 NA NA NA NA NA NA NA ...
##  $ skyl3            : num [1:82310] 4000 900 500 NA NA NA NA NA NA NA ...
##  $ skyl4            : num [1:82310] NA NA NA NA NA NA NA NA NA NA ...
##  $ wxcodes          : chr [1:82310] NA "BR" "BR" "FG" ...
##  $ ice_accretion_1hr: logi [1:82310] NA NA NA NA NA NA ...
##  $ ice_accretion_3hr: logi [1:82310] NA NA NA NA NA NA ...
##  $ ice_accretion_6hr: logi [1:82310] NA NA NA NA NA NA ...
##  $ peak_wind_gust   : logi [1:82310] NA NA NA NA NA NA ...
##  $ peak_wind_drct   : logi [1:82310] NA NA NA NA NA NA ...
##  $ peak_wind_time   : logi [1:82310] NA NA NA NA NA NA ...
##  $ feel             : num [1:82310] 48.2 47.2 45.6 42.9 43.5 ...
##  $ metar            : chr [1:82310] "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 [1:82310] NA NA NA NA NA NA ...

Paso 5.Agregar temperatura en grados centígrados

monterrey$tmpc <- (monterrey$tmpf - 32)/1.8
str(monterrey)
## tibble [82,310 × 33] (S3: tbl_df/tbl/data.frame)
##  $ station          : chr [1:82310] "MMMY" "MMMY" "MMMY" "MMMY" ...
##  $ valid            : POSIXct[1:82310], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
##  $ lon              : num [1:82310] -100 -100 -100 -100 -100 ...
##  $ lat              : num [1:82310] 25.8 25.8 25.8 25.8 25.8 ...
##  $ tmpf             : num [1:82310] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
##  $ dwpf             : num [1:82310] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
##  $ relh             : num [1:82310] 93.5 93.5 93.5 100 100 ...
##  $ drct             : num [1:82310] 0 120 120 120 110 100 110 130 60 0 ...
##  $ sknt             : num [1:82310] 0 3 5 6 5 5 4 3 3 0 ...
##  $ p01i             : num [1:82310] 0 0 0 0 0 0 0 0 0 0 ...
##  $ alti             : num [1:82310] 30.3 30.3 30.3 30.3 30.3 ...
##  $ mslp             : num [1:82310] NA NA NA NA NA ...
##  $ vsby             : num [1:82310] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
##  $ gust             : num [1:82310] NA NA NA NA NA NA NA NA NA NA ...
##  $ skyc1            : chr [1:82310] "SCT" "SCT" "SCT" "VV " ...
##  $ skyc2            : chr [1:82310] "BKN" "BKN" "BKN" NA ...
##  $ skyc3            : chr [1:82310] "OVC" "OVC" "OVC" NA ...
##  $ skyc4            : chr [1:82310] NA NA NA NA ...
##  $ skyl1            : num [1:82310] 700 300 200 200 100 100 100 100 100 100 ...
##  $ skyl2            : num [1:82310] 1200 400 300 NA NA NA NA NA NA NA ...
##  $ skyl3            : num [1:82310] 4000 900 500 NA NA NA NA NA NA NA ...
##  $ skyl4            : num [1:82310] NA NA NA NA NA NA NA NA NA NA ...
##  $ wxcodes          : chr [1:82310] NA "BR" "BR" "FG" ...
##  $ ice_accretion_1hr: logi [1:82310] NA NA NA NA NA NA ...
##  $ ice_accretion_3hr: logi [1:82310] NA NA NA NA NA NA ...
##  $ ice_accretion_6hr: logi [1:82310] NA NA NA NA NA NA ...
##  $ peak_wind_gust   : logi [1:82310] NA NA NA NA NA NA ...
##  $ peak_wind_drct   : logi [1:82310] NA NA NA NA NA NA ...
##  $ peak_wind_time   : logi [1:82310] NA NA NA NA NA NA ...
##  $ feel             : num [1:82310] 48.2 47.2 45.6 42.9 43.5 ...
##  $ metar            : chr [1:82310] "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 [1:82310] NA NA NA NA NA NA ...
##  $ tmpc             : num [1:82310] 9 9 9 8 8 8 8 8 8 8 ...
summary(monterrey)
##    station              valid                             lon        
##  Length:82310       Min.   :2014-01-01 00:16:00.00   Min.   :-100.1  
##  Class :character   1st Qu.:2016-04-23 16:02:15.00   1st Qu.:-100.1  
##  Mode  :character   Median :2018-08-04 17:10:00.00   Median :-100.1  
##                     Mean   :2018-08-08 05:51:01.19   Mean   :-100.1  
##                     3rd Qu.:2020-11-15 01:26:00.00   3rd Qu.:-100.1  
##                     Max.   :2023-03-15 23:40:00.00   Max.   :-100.1  
##                                                                      
##       lat             tmpf             dwpf            relh       
##  Min.   :25.78   Min.   : 23.00   Min.   :-5.80   Min.   :  2.32  
##  1st Qu.:25.78   1st Qu.: 64.40   1st Qu.:50.00   1st Qu.: 47.79  
##  Median :25.78   Median : 73.40   Median :60.80   Median : 68.94  
##  Mean   :25.78   Mean   : 72.17   Mean   :57.61   Mean   : 65.05  
##  3rd Qu.:25.78   3rd Qu.: 80.60   3rd Qu.:68.00   3rd Qu.: 83.32  
##  Max.   :25.78   Max.   :111.20   Max.   :86.00   Max.   :100.00  
##                  NA's   :101      NA's   :1698    NA's   :1743    
##       drct            sknt             p01i        alti            mslp       
##  Min.   :  0.0   Min.   : 0.000   Min.   :0   Min.   : 0.04   Min.   : 913.2  
##  1st Qu.: 70.0   1st Qu.: 4.000   1st Qu.:0   1st Qu.:29.88   1st Qu.:1011.4  
##  Median :110.0   Median : 5.000   Median :0   Median :29.97   Median :1014.5  
##  Mean   :131.7   Mean   : 5.798   Mean   :0   Mean   :29.98   Mean   :1015.3  
##  3rd Qu.:160.0   3rd Qu.: 8.000   3rd Qu.:0   3rd Qu.:30.08   3rd Qu.:1018.4  
##  Max.   :360.0   Max.   :98.000   Max.   :0   Max.   :30.81   Max.   :1103.4  
##  NA's   :72      NA's   :72                   NA's   :26      NA's   :71310   
##       vsby             gust           skyc1              skyc2          
##  Min.   : 0.000   Min.   : 13.00   Length:82310       Length:82310      
##  1st Qu.: 6.000   1st Qu.: 20.00   Class :character   Class :character  
##  Median :10.000   Median : 24.00   Mode  :character   Mode  :character  
##  Mean   : 9.114   Mean   : 24.62                                        
##  3rd Qu.:12.000   3rd Qu.: 28.00                                        
##  Max.   :40.000   Max.   :210.00                                        
##  NA's   :31       NA's   :79729                                         
##     skyc3              skyc4               skyl1           skyl2      
##  Length:82310       Length:82310       Min.   :    0   Min.   :    0  
##  Class :character   Class :character   1st Qu.: 1500   1st Qu.: 2000  
##  Mode  :character   Mode  :character   Median : 3000   Median : 6000  
##                                        Mean   : 5372   Mean   : 7973  
##                                        3rd Qu.: 7000   3rd Qu.:10000  
##                                        Max.   :37000   Max.   :30000  
##                                        NA's   :24856   NA's   :55232  
##      skyl3           skyl4         wxcodes          ice_accretion_1hr
##  Min.   :  400   Min.   : 3000   Length:82310       Mode:logical     
##  1st Qu.: 8000   1st Qu.:20000   Class :character   NA's:82310       
##  Median :15500   Median :20000   Mode  :character                    
##  Mean   :14743   Mean   :20656                                       
##  3rd Qu.:20000   3rd Qu.:25000                                       
##  Max.   :30000   Max.   :25000                                       
##  NA's   :77542   NA's   :82115                                       
##  ice_accretion_3hr ice_accretion_6hr peak_wind_gust peak_wind_drct
##  Mode:logical      Mode:logical      Mode:logical   Mode:logical  
##  NA's:82310        NA's:82310        NA's:82310     NA's:82310    
##                                                                   
##                                                                   
##                                                                   
##                                                                   
##                                                                   
##  peak_wind_time      feel           metar           snowdepth     
##  Mode:logical   Min.   :  9.11   Length:82310       Mode:logical  
##  NA's:82310     1st Qu.: 64.40   Class :character   NA's:82310    
##                 Median : 73.40   Mode  :character                 
##                 Mean   : 72.77                                    
##                 3rd Qu.: 82.90                                    
##                 Max.   :131.06                                    
##                 NA's   :1746                                      
##       tmpc      
##  Min.   :-5.00  
##  1st Qu.:18.00  
##  Median :23.00  
##  Mean   :22.31  
##  3rd Qu.:27.00  
##  Max.   :44.00  
##  NA's   :101

Paso 6.Filtrar información - Ejemplo: Enero a marzo 2023

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

Paso 7.Graficar temperatura en 2023

plot(este_año$valid,este_año$tmpc)

Paso 8.Promediar información por día

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 la temperatura en 2023

plot(este_año$date,este_año$tmpc, type='l', main ='Temperatura pormedio en Monterrey', xlab ='Fecha', ylab='Grados centígrados')

Paso 10. Promediar información por día.

#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 11. Graficar la temperatura en 2023

plot(este_año$date,este_año$tmpc, type='l', main ='Temperatura pormedio en Monterrey', xlab ='Fecha', ylab='Grados centígrados')

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