“Clima”

Información del Automated Surface Observing System (ASOS)

# Instalar paquetes y llamar librerías
# install.packages("riem")
library(riem)
# install.packages("tidyverse")
library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.8     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.1
## ✔ readr   2.1.2     ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

# install.packages("lubridate")
library(lubridate)

## 
## Attaching package: 'lubridate'
## 
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

# 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':
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##     layout

# install.packages("dplyr")
library(dplyr)

Buscar la red (país) - Ejemplo México, y copiar CODE

view(riem_networks())

Buscar la estación (ciudad) Ejemplo: Monterrey, y copiar ID

view(riem_stations("MX__ASOS"))

Obtener información

monterrey <- riem_measures("MMMY")

Entender información

str(monterrey)

## tibble [78,008 × 32] (S3: tbl_df/tbl/data.frame)
##  $ station          : chr [1:78008] "MMMY" "MMMY" "MMMY" "MMMY" ...
##  $ valid            : POSIXct[1:78008], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
##  $ lon              : num [1:78008] -100 -100 -100 -100 -100 ...
##  $ lat              : num [1:78008] 25.8 25.8 25.8 25.8 25.8 ...
##  $ tmpf             : num [1:78008] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
##  $ dwpf             : num [1:78008] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
##  $ relh             : num [1:78008] 93.5 93.5 93.5 100 100 ...
##  $ drct             : num [1:78008] 0 120 120 120 110 100 110 130 60 0 ...
##  $ sknt             : num [1:78008] 0 3 5 6 5 5 4 3 3 0 ...
##  $ p01i             : num [1:78008] 0 0 0 0 0 0 0 0 0 0 ...
##  $ alti             : num [1:78008] 30.3 30.3 30.3 30.3 30.3 ...
##  $ mslp             : num [1:78008] NA NA NA NA NA ...
##  $ vsby             : num [1:78008] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
##  $ gust             : num [1:78008] NA NA NA NA NA NA NA NA NA NA ...
##  $ skyc1            : chr [1:78008] "SCT" "SCT" "SCT" "VV " ...
##  $ skyc2            : chr [1:78008] "BKN" "BKN" "BKN" NA ...
##  $ skyc3            : chr [1:78008] "OVC" "OVC" "OVC" NA ...
##  $ skyc4            : chr [1:78008] NA NA NA NA ...
##  $ skyl1            : num [1:78008] 700 300 200 200 100 100 100 100 100 100 ...
##  $ skyl2            : num [1:78008] 1200 400 300 NA NA NA NA NA NA NA ...
##  $ skyl3            : num [1:78008] 4000 900 500 NA NA NA NA NA NA NA ...
##  $ skyl4            : num [1:78008] NA NA NA NA NA NA NA NA NA NA ...
##  $ wxcodes          : chr [1:78008] NA "BR" "BR" "FG" ...
##  $ ice_accretion_1hr: logi [1:78008] NA NA NA NA NA NA ...
##  $ ice_accretion_3hr: logi [1:78008] NA NA NA NA NA NA ...
##  $ ice_accretion_6hr: logi [1:78008] NA NA NA NA NA NA ...
##  $ peak_wind_gust   : logi [1:78008] NA NA NA NA NA NA ...
##  $ peak_wind_drct   : logi [1:78008] NA NA NA NA NA NA ...
##  $ peak_wind_time   : logi [1:78008] NA NA NA NA NA NA ...
##  $ feel             : num [1:78008] 48.2 47.2 45.6 42.9 43.5 ...
##  $ metar            : chr [1:78008] "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 [1:78008] NA NA NA NA NA NA ...

summary(monterrey)

##    station              valid                             lon        
##  Length:78008       Min.   :2014-01-01 00:16:00.00   Min.   :-100.1  
##  Class :character   1st Qu.:2016-03-11 19:25:30.00   1st Qu.:-100.1  
##  Mode  :character   Median :2018-05-07 17:10:00.00   Median :-100.1  
##                     Mean   :2018-05-12 10:23:37.11   Mean   :-100.1  
##                     3rd Qu.:2020-07-05 03:55:00.00   3rd Qu.:-100.1  
##                     Max.   :2022-09-21 23:40:00.00   Max.   :-100.1  
##                                                                      
##       lat             tmpf            dwpf            relh       
##  Min.   :25.78   Min.   : 23.0   Min.   :-5.80   Min.   :  2.32  
##  1st Qu.:25.78   1st Qu.: 64.4   1st Qu.:51.80   1st Qu.: 48.05  
##  Median :25.78   Median : 73.4   Median :62.60   Median : 69.14  
##  Mean   :25.78   Mean   : 72.5   Mean   :57.98   Mean   : 65.07  
##  3rd Qu.:25.78   3rd Qu.: 80.6   3rd Qu.:68.00   3rd Qu.: 83.32  
##  Max.   :25.78   Max.   :111.2   Max.   :86.00   Max.   :163.20  
##                  NA's   :89      NA's   :1686    NA's   :1741    
##       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   :130.7   Mean   : 5.819   Mean   :0   Mean   :29.98   Mean   :1015.3  
##  3rd Qu.:160.0   3rd Qu.: 8.000   3rd Qu.:0   3rd Qu.:30.07   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   :67009   
##       vsby             gust           skyc1              skyc2          
##  Min.   : 0.000   Min.   : 13.00   Length:78008       Length:78008      
##  1st Qu.: 6.000   1st Qu.: 20.00   Class :character   Class :character  
##  Median :10.000   Median : 24.00   Mode  :character   Mode  :character  
##  Mean   : 9.125   Mean   : 24.65                                        
##  3rd Qu.:12.000   3rd Qu.: 28.00                                        
##  Max.   :40.000   Max.   :210.00                                        
##  NA's   :31       NA's   :75552                                         
##     skyc3              skyc4               skyl1           skyl2      
##  Length:78008       Length:78008       Min.   :    0   Min.   :    0  
##  Class :character   Class :character   1st Qu.: 1500   1st Qu.: 2000  
##  Mode  :character   Mode  :character   Median : 3000   Median : 6000  
##                                        Mean   : 5387   Mean   : 8015  
##                                        3rd Qu.: 7000   3rd Qu.:10000  
##                                        Max.   :37000   Max.   :30000  
##                                        NA's   :23065   NA's   :51737  
##      skyl3           skyl4         wxcodes          ice_accretion_1hr
##  Min.   :  400   Min.   : 3000   Length:78008       Mode:logical     
##  1st Qu.: 8000   1st Qu.:20000   Class :character   NA's:78008       
##  Median :16000   Median :20000   Mode  :character                    
##  Mean   :14779   Mean   :20656                                       
##  3rd Qu.:20000   3rd Qu.:25000                                       
##  Max.   :30000   Max.   :25000                                       
##  NA's   :73275   NA's   :77813                                       
##  ice_accretion_3hr ice_accretion_6hr peak_wind_gust peak_wind_drct
##  Mode:logical      Mode:logical      Mode:logical   Mode:logical  
##  NA's:78008        NA's:78008        NA's:78008     NA's:78008    
##                                                                   
##                                                                   
##                                                                   
##                                                                   
##                                                                   
##  peak_wind_time      feel           metar           snowdepth     
##  Mode:logical   Min.   :  9.11   Length:78008       Mode:logical  
##  NA's:78008     1st Qu.: 64.40   Class :character   NA's:78008    
##                 Median : 73.40   Mode  :character                 
##                 Mean   : 73.17                                    
##                 3rd Qu.: 83.29                                    
##                 Max.   :131.06                                    
##                 NA's   :1744

Filtrar información - Ejemplo: Septiembre 2022

este_mes <- subset(monterrey, valid >= as.POSIXct('2022-09-01 00:00') & valid <= as.POSIXct("2022-09-07 23:59"))

Graficar - Ejemplo_ Humedad Relativa durante septiembre 2022

plot(este_mes$valid, este_mes$relh)

Promediar información por día

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

tibble(promedio)

## # A tibble: 3,153 × 18
##    date         lon   lat  tmpf  dwpf  relh  drct  sknt  p01i  alti  mslp  vsby
##    <date>     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1 2014-01-01 -100.  25.8  50.7  47.5  90.2  90.3  2.42     0  30.2 1023.  2.29
##  2 2014-01-02 -100.  25.8  53.9  47.4  81.3 238.   8.13     0  30.2 1024.  8.48
##  3 2014-01-03 -100.  25.8  45.5  34.4  69.0  97.2  4.16     0  30.4 1030. 15   
##  4 2014-01-04 -100.  25.8  44.8  36    71.7  78.9  2.22     0  30.1 1022. 15   
##  5 2014-02-07 -100.  25.8  37.6  36.3  95.3  82.6  2.96     0  30.2 1026.  1.78
##  6 2014-02-08 -100.  25.8  44.8  41.5  90.0 100    2.74     0  30.2 1023.  1.81
##  7 2014-02-09 -100.  25.8  53.8  44.7  77.1 212.   5.38     0  30.1 1019.  6.46
##  8 2014-02-10 -100.  25.8  62.5  54.0  77.8 158.   7.68     0  29.9 1014.  7.27
##  9 2014-02-11 -100.  25.8  50.5  45.6  83.9 228.   7.58     0  30.0 1019.  6.81
## 10 2014-02-12 -100.  25.8  45.5  35.1  69.0 229.   8.85     0  30.2 1023.  9.56
## # … with 3,143 more rows, and 6 more variables: gust <dbl>, skyl1 <dbl>,
## #   skyl2 <dbl>, skyl3 <dbl>, skyl4 <dbl>, feel <dbl>

Agregar columnas de grados centígrados

centigrados <- promedio
centigrados$tmpc <-(centigrados$tmpf-32)/1.8
str(centigrados)

## tibble [3,153 × 19] (S3: tbl_df/tbl/data.frame)
##  $ date : Date[1:3153], format: "2014-01-01" "2014-01-02" ...
##  $ lon  : num [1:3153] -100 -100 -100 -100 -100 ...
##  $ lat  : num [1:3153] 25.8 25.8 25.8 25.8 25.8 ...
##  $ tmpf : num [1:3153] 50.7 53.9 45.5 44.8 37.6 ...
##  $ dwpf : num [1:3153] 47.5 47.4 34.4 36 36.3 ...
##  $ relh : num [1:3153] 90.2 81.3 69 71.7 95.3 ...
##  $ drct : num [1:3153] 90.3 238.3 97.2 78.9 82.6 ...
##  $ sknt : num [1:3153] 2.42 8.13 4.16 2.22 2.96 ...
##  $ p01i : num [1:3153] 0 0 0 0 0 0 0 0 0 0 ...
##  $ alti : num [1:3153] 30.2 30.2 30.4 30.1 30.2 ...
##  $ mslp : num [1:3153] 1023 1024 1030 1022 1026 ...
##  $ vsby : num [1:3153] 2.29 8.48 15 15 1.78 ...
##  $ gust : num [1:3153] NaN 27.1 NaN NaN NaN ...
##  $ skyl1: num [1:3153] 1527 7150 12000 1700 348 ...
##  $ skyl2: num [1:3153] 8400 10812 NaN NaN 580 ...
##  $ skyl3: num [1:3153] 9080 20000 NaN NaN NaN ...
##  $ skyl4: num [1:3153] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
##  $ feel : num [1:3153] 49.8 53.6 44.1 43.4 34.9 ...
##  $ tmpc : num [1:3153] 10.36 12.17 7.52 7.11 3.09 ...

centigrados$feelc <-(centigrados$feel-32)/1.8
str(centigrados)

## tibble [3,153 × 20] (S3: tbl_df/tbl/data.frame)
##  $ date : Date[1:3153], format: "2014-01-01" "2014-01-02" ...
##  $ lon  : num [1:3153] -100 -100 -100 -100 -100 ...
##  $ lat  : num [1:3153] 25.8 25.8 25.8 25.8 25.8 ...
##  $ tmpf : num [1:3153] 50.7 53.9 45.5 44.8 37.6 ...
##  $ dwpf : num [1:3153] 47.5 47.4 34.4 36 36.3 ...
##  $ relh : num [1:3153] 90.2 81.3 69 71.7 95.3 ...
##  $ drct : num [1:3153] 90.3 238.3 97.2 78.9 82.6 ...
##  $ sknt : num [1:3153] 2.42 8.13 4.16 2.22 2.96 ...
##  $ p01i : num [1:3153] 0 0 0 0 0 0 0 0 0 0 ...
##  $ alti : num [1:3153] 30.2 30.2 30.4 30.1 30.2 ...
##  $ mslp : num [1:3153] 1023 1024 1030 1022 1026 ...
##  $ vsby : num [1:3153] 2.29 8.48 15 15 1.78 ...
##  $ gust : num [1:3153] NaN 27.1 NaN NaN NaN ...
##  $ skyl1: num [1:3153] 1527 7150 12000 1700 348 ...
##  $ skyl2: num [1:3153] 8400 10812 NaN NaN 580 ...
##  $ skyl3: num [1:3153] 9080 20000 NaN NaN NaN ...
##  $ skyl4: num [1:3153] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
##  $ feel : num [1:3153] 49.8 53.6 44.1 43.4 34.9 ...
##  $ tmpc : num [1:3153] 10.36 12.17 7.52 7.11 3.09 ...
##  $ feelc: num [1:3153] 9.87 12.01 6.71 6.36 1.6 ...

Filtrar información - Ejemplo: 2022

este_año <- centigrados[centigrados$date >= "2022-01-01" & centigrados$date <= "2022-09-07", ]

Graficar - Ejemplo: Temperatura promedio Septiembre 2022

plot(este_año$date, este_año$tmpc, type = "l", main = "Temperatura Promedio en Monterrey durante 2022", xlab = "Fecha", ylab = "ºC" )

Conclusiones

R es un programa versátil que permite poder obtener la temperatura ya que es una variable importante en las bases de datos porque puede ser un gran factor al momento de crear modelos predictivos que nos ayuden a conocer las razones.

En este caso vemos como muestra la temperatura que ha habido en este año, que vemos que no ha habido gran lluvido y el promedio de como ha sido septiembre donde encontramos una temperatura diferente.