ASOS - Clima
Información del Automated Surface Observing System (ASOS)
Instalar paquete y llamar librerias
#install.packages("riem")
library(riem)
#install.packages("tidyverse")
#library(tidyverse)
#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)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionBuscar 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 la información
monterrey <- riem_measures("MMMY")Entender Información
str(monterrey)## tibble [77,961 × 32] (S3: tbl_df/tbl/data.frame)
## $ station : chr [1:77961] "MMMY" "MMMY" "MMMY" "MMMY" ...
## $ valid : POSIXct[1:77961], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
## $ lon : num [1:77961] -100 -100 -100 -100 -100 ...
## $ lat : num [1:77961] 25.8 25.8 25.8 25.8 25.8 ...
## $ tmpf : num [1:77961] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
## $ dwpf : num [1:77961] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
## $ relh : num [1:77961] 93.5 93.5 93.5 100 100 ...
## $ drct : num [1:77961] 0 120 120 120 110 100 110 130 60 0 ...
## $ sknt : num [1:77961] 0 3 5 6 5 5 4 3 3 0 ...
## $ p01i : num [1:77961] 0 0 0 0 0 0 0 0 0 0 ...
## $ alti : num [1:77961] 30.3 30.3 30.3 30.3 30.3 ...
## $ mslp : num [1:77961] NA NA NA NA NA ...
## $ vsby : num [1:77961] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
## $ gust : num [1:77961] NA NA NA NA NA NA NA NA NA NA ...
## $ skyc1 : chr [1:77961] "SCT" "SCT" "SCT" "VV " ...
## $ skyc2 : chr [1:77961] "BKN" "BKN" "BKN" NA ...
## $ skyc3 : chr [1:77961] "OVC" "OVC" "OVC" NA ...
## $ skyc4 : chr [1:77961] NA NA NA NA ...
## $ skyl1 : num [1:77961] 700 300 200 200 100 100 100 100 100 100 ...
## $ skyl2 : num [1:77961] 1200 400 300 NA NA NA NA NA NA NA ...
## $ skyl3 : num [1:77961] 4000 900 500 NA NA NA NA NA NA NA ...
## $ skyl4 : num [1:77961] NA NA NA NA NA NA NA NA NA NA ...
## $ wxcodes : chr [1:77961] NA "BR" "BR" "FG" ...
## $ ice_accretion_1hr: logi [1:77961] NA NA NA NA NA NA ...
## $ ice_accretion_3hr: logi [1:77961] NA NA NA NA NA NA ...
## $ ice_accretion_6hr: logi [1:77961] NA NA NA NA NA NA ...
## $ peak_wind_gust : logi [1:77961] NA NA NA NA NA NA ...
## $ peak_wind_drct : logi [1:77961] NA NA NA NA NA NA ...
## $ peak_wind_time : logi [1:77961] NA NA NA NA NA NA ...
## $ feel : num [1:77961] 48.2 47.2 45.6 42.9 43.5 ...
## $ metar : chr [1:77961] "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 [1:77961] NA NA NA NA NA NA ...summary(monterrey)## station valid lon
## Length:77961 Min. :2014-01-01 00:16:00 Min. :-100.1
## Class :character 1st Qu.:2016-03-11 07:40:00 1st Qu.:-100.1
## Mode :character Median :2018-05-06 17:40:00 Median :-100.1
## Mean :2018-05-11 11:21:04 Mean :-100.1
## 3rd Qu.:2020-07-03 16:41:00 3rd Qu.:-100.1
## Max. :2022-09-19 23:40:00 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.:51.80 1st Qu.: 48.05
## Median :25.78 Median : 73.40 Median :62.60 Median : 69.14
## Mean :25.78 Mean : 72.49 Mean :57.98 Mean : 65.06
## 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. :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 :66962
## vsby gust skyc1 skyc2
## Min. : 0.000 Min. : 13.00 Length:77961 Length:77961
## 1st Qu.: 6.000 1st Qu.: 20.00 Class :character Class :character
## Median :10.000 Median : 24.00 Mode :character Mode :character
## Mean : 9.124 Mean : 24.65
## 3rd Qu.:12.000 3rd Qu.: 28.00
## Max. :40.000 Max. :210.00
## NA's :31 NA's :75505
## skyc3 skyc4 skyl1 skyl2
## Length:77961 Length:77961 Min. : 0 Min. : 0
## Class :character Class :character 1st Qu.: 1500 1st Qu.: 2000
## Mode :character Mode :character Median : 3000 Median : 6000
## Mean : 5388 Mean : 8015
## 3rd Qu.: 7000 3rd Qu.:10000
## Max. :37000 Max. :30000
## NA's :23051 NA's :51698
## skyl3 skyl4 wxcodes ice_accretion_1hr
## Min. : 400 Min. : 3000 Length:77961 Mode:logical
## 1st Qu.: 8000 1st Qu.:20000 Class :character NA's:77961
## Median :16000 Median :20000 Mode :character
## Mean :14779 Mean :20656
## 3rd Qu.:20000 3rd Qu.:25000
## Max. :30000 Max. :25000
## NA's :73228 NA's :77766
## ice_accretion_3hr ice_accretion_6hr peak_wind_gust peak_wind_drct
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:77961 NA's:77961 NA's:77961 NA's:77961
##
##
##
##
##
## peak_wind_time feel metar snowdepth
## Mode:logical Min. : 9.11 Length:77961 Mode:logical
## NA's:77961 1st Qu.: 64.40 Class :character NA's:77961
## Median : 73.40 Mode :character
## Mean : 73.16
## 3rd Qu.: 83.29
## Max. :131.06
## NA's :1744Filtrar 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: 3,151 × 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 3,141 more rows, and 6 more variables: gust <dbl>, skyl1 <dbl>,
## # skyl2 <dbl>, skyl3 <dbl>, skyl4 <dbl>, feel <dbl>Agregar columnas de grados centigrados
centigrados <- promedio
centigrados$tmpc <- (centigrados$tmpf-32)/1.8
tibble(centigrados)## # A tibble: 3,151 × 19
## 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 3,141 more rows, and 7 more variables: gust <dbl>, skyl1 <dbl>,
## # skyl2 <dbl>, skyl3 <dbl>, skyl4 <dbl>, feel <dbl>, tmpc <dbl>centigrados$feelc <-(centigrados$feel-32)/1.8
tibble(centigrados)## # A tibble: 3,151 × 20
## 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 3,141 more rows, and 8 more variables: gust <dbl>, skyl1 <dbl>,
## # skyl2 <dbl>, skyl3 <dbl>, skyl4 <dbl>, feel <dbl>, tmpc <dbl>, feelc <dbl>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 de Monterrey durante 2022", xlab = "Fecha", ylab = "Cº")
Conclusiones
Obtuvimos la información por medio de Automated Surface Observing System, para poder obtener la información y graficarla, llamamos e instalamos varías paqueterías. Este ejercicio se hizo con el fin de obtener información relevante para agregarla a nuestra base de datos. Esto ayuda mucho en el modelo predictivo, ya que para las ventas es un factor importante el clima, debido a que gracias a esto será que aumentan o disminuyen. Continuando con el proceso, se realizo la búsqueda seleccionando el país, buscando la estación (Ciudad) y filtrando la información para obtener la predicción, por ejemplo Septiembre 2022. También promediamos la información por día, así como cambiamos los factores a centígrados, procedimos a filtrar nuevamente la información a la fecha que queremos y graficamos para tener un mejor panorama de visión sobre los resultados. De esta manera podemos tener un mejor control sobre las ventas, obteniendo información relevante incluso saber en qué fechas, dependiendo el clima, lanzar alguna oferta para obtener mejores resultados.
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