###CLIMAS### informacion sobre climas

options(repos = list(CRAN="http://cran.rstudio.com/"))
install.packages('riem')
## Warning in download.file(url, destfile, method, mode = "wb", ...): URL
## 'http://cran.rstudio.com/bin/windows/contrib/4.2/riem_0.3.0.zip': status was
## 'Failure when receiving data from the peer'
## Error in download.file(url, destfile, method, mode = "wb", ...) : 
##   no fue posible abrir la URL 'http://cran.rstudio.com/bin/windows/contrib/4.2/riem_0.3.0.zip'
## Warning in download.packages(pkgs, destdir = tmpd, available = available, :
## download of package 'riem' failed
library(riem)
install.packages('tidyverse')
## package 'tidyverse' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\Invitad2\AppData\Local\Temp\RtmpyIx366\downloaded_packages
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()
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install.packages('lubridate')
## Warning: package 'lubridate' is in use and will not be installed
library(lubridate)
install.packages('ggplot2')
## Warning: package 'ggplot2' is in use and will not be installed
library(ggplot2)
install.packages('plotly')
## Warning in download.file(url, destfile, method, mode = "wb", ...): URL
## 'http://cran.rstudio.com/bin/windows/contrib/4.2/plotly_4.10.1.zip': status was
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## Error in download.file(url, destfile, method, mode = "wb", ...) : 
##   no fue posible abrir la URL 'http://cran.rstudio.com/bin/windows/contrib/4.2/plotly_4.10.1.zip'
## Warning in download.packages(pkgs, destdir = tmpd, available = available, :
## download of package 'plotly' failed
library(plotly)
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## Attaching package: 'plotly'
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## 
##     last_plot
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##     filter
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##     layout
# Paso 2 Buscar la red (país).
view(riem_networks())

# Paso 3 Buscar la estación (ciudad) y copiar ID
view(riem_stations('MX__ASOS'))

#Paso 4 Obtener informacion
monterrey<- riem_measures("MMMY")

# Paso 5 Agregar Temperatura en grados centigrados
monterrey$tmpc<- (monterrey$tmpf -32)/1.8
str(monterrey)
## tibble [82,414 × 33] (S3: tbl_df/tbl/data.frame)
##  $ station          : chr [1:82414] "MMMY" "MMMY" "MMMY" "MMMY" ...
##  $ valid            : POSIXct[1:82414], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
##  $ lon              : num [1:82414] -100 -100 -100 -100 -100 ...
##  $ lat              : num [1:82414] 25.8 25.8 25.8 25.8 25.8 ...
##  $ tmpf             : num [1:82414] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
##  $ dwpf             : num [1:82414] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
##  $ relh             : num [1:82414] 93.5 93.5 93.5 100 100 ...
##  $ drct             : num [1:82414] 0 120 120 120 110 100 110 130 60 0 ...
##  $ sknt             : num [1:82414] 0 3 5 6 5 5 4 3 3 0 ...
##  $ p01i             : num [1:82414] 0 0 0 0 0 0 0 0 0 0 ...
##  $ alti             : num [1:82414] 30.3 30.3 30.3 30.3 30.3 ...
##  $ mslp             : num [1:82414] NA NA NA NA NA ...
##  $ vsby             : num [1:82414] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
##  $ gust             : num [1:82414] NA NA NA NA NA NA NA NA NA NA ...
##  $ skyc1            : chr [1:82414] "SCT" "SCT" "SCT" "VV " ...
##  $ skyc2            : chr [1:82414] "BKN" "BKN" "BKN" NA ...
##  $ skyc3            : chr [1:82414] "OVC" "OVC" "OVC" NA ...
##  $ skyc4            : chr [1:82414] NA NA NA NA ...
##  $ skyl1            : num [1:82414] 700 300 200 200 100 100 100 100 100 100 ...
##  $ skyl2            : num [1:82414] 1200 400 300 NA NA NA NA NA NA NA ...
##  $ skyl3            : num [1:82414] 4000 900 500 NA NA NA NA NA NA NA ...
##  $ skyl4            : num [1:82414] NA NA NA NA NA NA NA NA NA NA ...
##  $ wxcodes          : chr [1:82414] NA "BR" "BR" "FG" ...
##  $ ice_accretion_1hr: logi [1:82414] NA NA NA NA NA NA ...
##  $ ice_accretion_3hr: logi [1:82414] NA NA NA NA NA NA ...
##  $ ice_accretion_6hr: logi [1:82414] NA NA NA NA NA NA ...
##  $ peak_wind_gust   : logi [1:82414] NA NA NA NA NA NA ...
##  $ peak_wind_drct   : logi [1:82414] NA NA NA NA NA NA ...
##  $ peak_wind_time   : logi [1:82414] NA NA NA NA NA NA ...
##  $ feel             : num [1:82414] 48.2 47.2 45.6 42.9 43.5 ...
##  $ metar            : chr [1:82414] "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 [1:82414] NA NA NA NA NA NA ...
##  $ tmpc             : num [1:82414] 9 9 9 8 8 8 8 8 8 8 ...
summary(monterrey)
##    station              valid                             lon        
##  Length:82414       Min.   :2014-01-01 00:16:00.00   Min.   :-100.1  
##  Class :character   1st Qu.:2016-04-24 14:03:00.00   1st Qu.:-100.1  
##  Mode  :character   Median :2018-08-06 20:13:00.00   Median :-100.1  
##                     Mean   :2018-08-10 08:48:45.13   Mean   :-100.1  
##                     3rd Qu.:2020-11-18 06:25:00.00   3rd Qu.:-100.1  
##                     Max.   :2023-03-19 23:40:00.00   Max.   :-100.1  
##                                                                      
##       lat             tmpf             dwpf           relh       
##  Min.   :25.78   Min.   : 23.00   Min.   :-5.8   Min.   :  2.32  
##  1st Qu.:25.78   1st Qu.: 64.40   1st Qu.:50.0   1st Qu.: 47.79  
##  Median :25.78   Median : 73.40   Median :60.8   Median : 68.94  
##  Mean   :25.78   Mean   : 72.15   Mean   :57.6   Mean   : 65.05  
##  3rd Qu.:25.78   3rd Qu.: 80.60   3rd Qu.:68.0   3rd Qu.: 83.32  
##  Max.   :25.78   Max.   :111.20   Max.   :86.0   Max.   :100.00  
##                  NA's   :101      NA's   :1698   NA's   :1743    
##       drct            sknt           p01i        alti            mslp       
##  Min.   :  0.0   Min.   : 0.0   Min.   :0   Min.   : 0.04   Min.   : 913.2  
##  1st Qu.: 70.0   1st Qu.: 4.0   1st Qu.:0   1st Qu.:29.88   1st Qu.:1011.4  
##  Median :110.0   Median : 5.0   Median :0   Median :29.97   Median :1014.5  
##  Mean   :131.8   Mean   : 5.8   Mean   :0   Mean   :29.98   Mean   :1015.3  
##  3rd Qu.:160.0   3rd Qu.: 8.0   3rd Qu.:0   3rd Qu.:30.08   3rd Qu.:1018.4  
##  Max.   :360.0   Max.   :98.0   Max.   :0   Max.   :30.81   Max.   :1103.4  
##  NA's   :72      NA's   :72                 NA's   :26      NA's   :71414   
##       vsby             gust           skyc1              skyc2          
##  Min.   : 0.000   Min.   : 13.00   Length:82414       Length:82414      
##  1st Qu.: 6.000   1st Qu.: 20.00   Class :character   Class :character  
##  Median :10.000   Median : 24.00   Mode  :character   Mode  :character  
##  Mean   : 9.112   Mean   : 24.64                                        
##  3rd Qu.:12.000   3rd Qu.: 28.00                                        
##  Max.   :40.000   Max.   :210.00                                        
##  NA's   :31       NA's   :79822                                         
##     skyc3              skyc4               skyl1           skyl2      
##  Length:82414       Length:82414       Min.   :    0   Min.   :    0  
##  Class :character   Class :character   1st Qu.: 1500   1st Qu.: 2000  
##  Mode  :character   Mode  :character   Median : 3000   Median : 6000  
##                                        Mean   : 5369   Mean   : 7972  
##                                        3rd Qu.: 7000   3rd Qu.:10000  
##                                        Max.   :37000   Max.   :30000  
##                                        NA's   :24872   NA's   :55309  
##      skyl3           skyl4         wxcodes          ice_accretion_1hr
##  Min.   :  400   Min.   : 3000   Length:82414       Mode:logical     
##  1st Qu.: 8000   1st Qu.:20000   Class :character   NA's:82414       
##  Median :15500   Median :20000   Mode  :character                    
##  Mean   :14743   Mean   :20656                                       
##  3rd Qu.:20000   3rd Qu.:25000                                       
##  Max.   :30000   Max.   :25000                                       
##  NA's   :77646   NA's   :82219                                       
##  ice_accretion_3hr ice_accretion_6hr peak_wind_gust peak_wind_drct
##  Mode:logical      Mode:logical      Mode:logical   Mode:logical  
##  NA's:82414        NA's:82414        NA's:82414     NA's:82414    
##                                                                   
##                                                                   
##                                                                   
##                                                                   
##                                                                   
##  peak_wind_time      feel           metar           snowdepth     
##  Mode:logical   Min.   :  9.11   Length:82414       Mode:logical  
##  NA's:82414     1st Qu.: 64.40   Class :character   NA's:82414    
##                 Median : 73.40   Mode  :character                 
##                 Mean   : 72.76                                    
##                 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 informacion - 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 promedio en monterrey", xlab="Fecha", ylab="´Centigrados")

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