“ASOS”
Daniela Cárdenas Z // A01720535
2022-09-20
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 ──
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## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ 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'
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## date, intersect, setdiff, union#install.packages("ggplot2")
library(ggplot2)
#install.packages("plotly")
library(plotly)##
## Attaching package: 'plotly'
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## The following object is masked from 'package:ggplot2':
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## last_plot
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## filter
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## layoutBuscar País y Ciudad
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 y Entender la Información
Obtener la información
monterrey <- riem_measures("MMMY")Entender la información
str(monterrey)## tibble [78,354 × 32] (S3: tbl_df/tbl/data.frame)
## $ station : chr [1:78354] "MMMY" "MMMY" "MMMY" "MMMY" ...
## $ valid : POSIXct[1:78354], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
## $ lon : num [1:78354] -100 -100 -100 -100 -100 ...
## $ lat : num [1:78354] 25.8 25.8 25.8 25.8 25.8 ...
## $ tmpf : num [1:78354] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
## $ dwpf : num [1:78354] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
## $ relh : num [1:78354] 93.5 93.5 93.5 100 100 ...
## $ drct : num [1:78354] 0 120 120 120 110 100 110 130 60 0 ...
## $ sknt : num [1:78354] 0 3 5 6 5 5 4 3 3 0 ...
## $ p01i : num [1:78354] 0 0 0 0 0 0 0 0 0 0 ...
## $ alti : num [1:78354] 30.3 30.3 30.3 30.3 30.3 ...
## $ mslp : num [1:78354] NA NA NA NA NA ...
## $ vsby : num [1:78354] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
## $ gust : num [1:78354] NA NA NA NA NA NA NA NA NA NA ...
## $ skyc1 : chr [1:78354] "SCT" "SCT" "SCT" "VV " ...
## $ skyc2 : chr [1:78354] "BKN" "BKN" "BKN" NA ...
## $ skyc3 : chr [1:78354] "OVC" "OVC" "OVC" NA ...
## $ skyc4 : chr [1:78354] NA NA NA NA ...
## $ skyl1 : num [1:78354] 700 300 200 200 100 100 100 100 100 100 ...
## $ skyl2 : num [1:78354] 1200 400 300 NA NA NA NA NA NA NA ...
## $ skyl3 : num [1:78354] 4000 900 500 NA NA NA NA NA NA NA ...
## $ skyl4 : num [1:78354] NA NA NA NA NA NA NA NA NA NA ...
## $ wxcodes : chr [1:78354] NA "BR" "BR" "FG" ...
## $ ice_accretion_1hr: logi [1:78354] NA NA NA NA NA NA ...
## $ ice_accretion_3hr: logi [1:78354] NA NA NA NA NA NA ...
## $ ice_accretion_6hr: logi [1:78354] NA NA NA NA NA NA ...
## $ peak_wind_gust : logi [1:78354] NA NA NA NA NA NA ...
## $ peak_wind_drct : logi [1:78354] NA NA NA NA NA NA ...
## $ peak_wind_time : logi [1:78354] NA NA NA NA NA NA ...
## $ feel : num [1:78354] 48.2 47.2 45.6 42.9 43.5 ...
## $ metar : chr [1:78354] "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 [1:78354] NA NA NA NA NA NA ...summary(monterrey)## station valid lon
## Length:78354 Min. :2014-01-01 00:16:00.0 Min. :-100.1
## Class :character 1st Qu.:2016-03-15 01:57:00.0 1st Qu.:-100.1
## Mode :character Median :2018-05-14 19:13:30.0 Median :-100.1
## Mean :2018-05-19 12:01:04.2 Mean :-100.1
## 3rd Qu.:2020-07-15 19:25:15.0 3rd Qu.:-100.1
## Max. :2022-10-05 23:40:00.0 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 : 72.51 Mean :58.01 Mean : 65.08
## 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 : 5.813 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 :67355
## vsby gust skyc1 skyc2
## Min. : 0.000 Min. : 13.00 Length:78354 Length:78354
## 1st Qu.: 6.000 1st Qu.: 20.00 Class :character Class :character
## Median :10.000 Median : 24.00 Mode :character Mode :character
## Mean : 9.133 Mean : 24.64
## 3rd Qu.:12.000 3rd Qu.: 28.00
## Max. :40.000 Max. :210.00
## NA's :31 NA's :75897
## skyc3 skyc4 skyl1 skyl2
## Length:78354 Length:78354 Min. : 0 Min. : 0
## Class :character Class :character 1st Qu.: 1500 1st Qu.: 2000
## Mode :character Mode :character Median : 3000 Median : 7000
## Mean : 5383 Mean : 8019
## 3rd Qu.: 7000 3rd Qu.:10000
## Max. :37000 Max. :30000
## NA's :23208 NA's :51985
## skyl3 skyl4 wxcodes ice_accretion_1hr
## Min. : 400 Min. : 3000 Length:78354 Mode:logical
## 1st Qu.: 8000 1st Qu.:20000 Class :character NA's:78354
## Median :16000 Median :20000 Mode :character
## Mean :14780 Mean :20656
## 3rd Qu.:20000 3rd Qu.:25000
## Max. :30000 Max. :25000
## NA's :73605 NA's :78159
## ice_accretion_3hr ice_accretion_6hr peak_wind_gust peak_wind_drct
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:78354 NA's:78354 NA's:78354 NA's:78354
##
##
##
##
##
## peak_wind_time feel metar snowdepth
## Mode:logical Min. : 9.11 Length:78354 Mode:logical
## NA's:78354 1st Qu.: 64.40 Class :character NA's:78354
## Median : 73.40 Mode :character
## Mean : 73.18
## 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 informacion por dia
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: 3,167 × 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 3,157 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 [3,167 × 19] (S3: tbl_df/tbl/data.frame)
## $ date : Date[1:3167], format: "2014-01-01" "2014-01-02" ...
## $ lon : num [1:3167] -100 -100 -100 -100 -100 ...
## $ lat : num [1:3167] 25.8 25.8 25.8 25.8 25.8 ...
## $ tmpf : num [1:3167] 50.7 53.9 45.5 44.8 37.6 ...
## $ dwpf : num [1:3167] 47.5 47.4 34.4 36 36.3 ...
## $ relh : num [1:3167] 90.2 81.3 69 71.7 95.3 ...
## $ drct : num [1:3167] 90.3 238.3 97.2 78.9 82.6 ...
## $ sknt : num [1:3167] 2.42 8.13 4.16 2.22 2.96 ...
## $ p01i : num [1:3167] 0 0 0 0 0 0 0 0 0 0 ...
## $ alti : num [1:3167] 30.2 30.2 30.4 30.1 30.2 ...
## $ mslp : num [1:3167] 1023 1024 1030 1022 1026 ...
## $ vsby : num [1:3167] 2.29 8.48 15 15 1.78 ...
## $ gust : num [1:3167] NaN 27.1 NaN NaN NaN ...
## $ skyl1: num [1:3167] 1527 7150 12000 1700 348 ...
## $ skyl2: num [1:3167] 8400 10812 NaN NaN 580 ...
## $ skyl3: num [1:3167] 9080 20000 NaN NaN NaN ...
## $ skyl4: num [1:3167] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
## $ feel : num [1:3167] 49.8 53.6 44.1 43.4 34.9 ...
## $ tmpc : num [1:3167] 10.36 12.17 7.52 7.11 3.09 ...centigrados$feelc <- (centigrados$feel-32)/1.8
str(centigrados)## tibble [3,167 × 20] (S3: tbl_df/tbl/data.frame)
## $ date : Date[1:3167], format: "2014-01-01" "2014-01-02" ...
## $ lon : num [1:3167] -100 -100 -100 -100 -100 ...
## $ lat : num [1:3167] 25.8 25.8 25.8 25.8 25.8 ...
## $ tmpf : num [1:3167] 50.7 53.9 45.5 44.8 37.6 ...
## $ dwpf : num [1:3167] 47.5 47.4 34.4 36 36.3 ...
## $ relh : num [1:3167] 90.2 81.3 69 71.7 95.3 ...
## $ drct : num [1:3167] 90.3 238.3 97.2 78.9 82.6 ...
## $ sknt : num [1:3167] 2.42 8.13 4.16 2.22 2.96 ...
## $ p01i : num [1:3167] 0 0 0 0 0 0 0 0 0 0 ...
## $ alti : num [1:3167] 30.2 30.2 30.4 30.1 30.2 ...
## $ mslp : num [1:3167] 1023 1024 1030 1022 1026 ...
## $ vsby : num [1:3167] 2.29 8.48 15 15 1.78 ...
## $ gust : num [1:3167] NaN 27.1 NaN NaN NaN ...
## $ skyl1: num [1:3167] 1527 7150 12000 1700 348 ...
## $ skyl2: num [1:3167] 8400 10812 NaN NaN 580 ...
## $ skyl3: num [1:3167] 9080 20000 NaN NaN NaN ...
## $ skyl4: num [1:3167] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
## $ feel : num [1:3167] 49.8 53.6 44.1 43.4 34.9 ...
## $ tmpc : num [1:3167] 10.36 12.17 7.52 7.11 3.09 ...
## $ feelc: num [1:3167] 9.87 12.01 6.71 6.36 1.6 ...Filtrar informacion - Ejemplo:2022
este_año <- centigrados[centigrados$date >= "2022-01-02" & centigrados$date <= "2022-09-07",]Graficar - Ejemplo: Temperatura promedio Septiembre 2022
plot(este_año$date, este_año$tmpc, type = "l", main = "Temperatura Promedio Monterrey Durante el 2022", xlab = "Fecha", ylab = "C")
Conclusión y Aprendizaje
El paquete de ASOS nos sirve dentro de la predicción de datos en modelos como por ejemplo el de las bicis, ya que si se combinan se pueden hacer predicciones mucho más precisas que combinen las rentas con el clima, de igual manera en los supermercados lo podrían implementar para saber que días incrementar la producción de panadería suponiendo que se compra más en días fríos o lluviosos, es por eso que se necesita o podría ser provechoso para esos métodos de datos, de igual manera seguir viendo si para los supers esto es viable por medio de su software de stock.
Esto puede ser utilizado únicamente al implementar las librerías correpondientes en R Studio.
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