ASOS
Adrián Elizondo A00835160
2024-09-11
Introducción
Información obtenida del Automated Surface Observing System (ASOS) de los aeropuertos de todo el mundo.
Instalar paquetes y llamar librerías
# install.packages("riem")
library(riem)
# install.packages("tidyverse")
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# install.packages("ggplot2")
library(ggplot2)
# install.packages("lubridate")
library(lubridate)
# install.packages("plyr")
library(plyr)## ------------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## ------------------------------------------------------------------------------
##
## Attaching package: 'plyr'
##
## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following object is masked from 'package:purrr':
##
## compactObtener y graficar la información
# PASO 1. Buscar la red o (país) - Ejemplo: México, y copiar CODE
#view(riem_networks())
# PASO 2. Buscar la estación (cuidad) - Ejemplo: Monterrey, y copiar ID
#view(riem_stations("MX__ASOS"))
# PASO 3. Obtener informacion de la estación
monterrey <- riem_measures("MMMY")
# Analisis Descriptivo
summary(monterrey)## station valid lon
## Length:95917 Min. :2014-01-01 00:16:00.00 Min. :-100.1
## Class :character 1st Qu.:2016-09-04 00:51:00.00 1st Qu.:-100.1
## Mode :character Median :2019-05-03 13:42:00.00 Median :-100.1
## Mean :2019-05-12 11:49:36.21 Mean :-100.1
## 3rd Qu.:2022-01-21 00:40:00.00 3rd Qu.:-100.1
## Max. :2024-09-11 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.:51.80 1st Qu.: 48.32
## Median :25.78 Median : 73.40 Median :62.60 Median : 69.14
## Mean :25.78 Mean : 72.66 Mean :58.24 Mean : 65.41
## 3rd Qu.:25.78 3rd Qu.: 82.40 3rd Qu.:68.00 3rd Qu.: 83.44
## Max. :25.78 Max. :111.20 Max. :86.00 Max. :100.00
## NA's :137 NA's :1734 NA's :1779
## 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.:29.87 1st Qu.:1011.4
## Median :110.0 Median : 5.000 Median :0 Median :29.97 Median :1014.4
## Mean :131.1 Mean : 5.844 Mean :0 Mean :29.97 Mean :1015.2
## 3rd Qu.:160.0 3rd Qu.: 8.000 3rd Qu.:0 3rd Qu.:30.07 3rd Qu.:1018.1
## Max. :360.0 Max. :98.000 Max. :0 Max. :30.81 Max. :1103.4
## NA's :73 NA's :73 NA's :28 NA's :84061
## vsby gust skyc1 skyc2
## Min. : 0.000 Min. : 13.00 Length:95917 Length:95917
## 1st Qu.: 6.000 1st Qu.: 20.00 Class :character Class :character
## Median :10.000 Median : 24.00 Mode :character Mode :character
## Mean : 8.938 Mean : 24.31
## 3rd Qu.:12.000 3rd Qu.: 27.00
## Max. :40.000 Max. :210.00
## NA's :32 NA's :92817
## skyc3 skyc4 skyl1 skyl2
## Length:95917 Length:95917 Min. : 0 Min. : 0
## Class :character Class :character 1st Qu.: 1500 1st Qu.: 2000
## Mode :character Mode :character Median : 3000 Median : 7000
## Mean : 5348 Mean : 8034
## 3rd Qu.: 7000 3rd Qu.:10000
## Max. :37000 Max. :30000
## NA's :29109 NA's :64576
## skyl3 skyl4 wxcodes ice_accretion_1hr
## Min. : 400 Min. : 3000 Length:95917 Mode:logical
## 1st Qu.: 8000 1st Qu.:20000 Class :character NA's:95917
## Median :20000 Median :20000 Mode :character
## Mean :14721 Mean :20646
## 3rd Qu.:20000 3rd Qu.:25000
## Max. :30000 Max. :25000
## NA's :90537 NA's :95719
## ice_accretion_3hr ice_accretion_6hr peak_wind_gust peak_wind_drct
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:95917 NA's:95917 NA's:95917 NA's:95917
##
##
##
##
##
## peak_wind_time feel metar snowdepth
## Mode:logical Min. : 9.11 Length:95917 Mode:logical
## NA's:95917 1st Qu.: 64.40 Class :character NA's:95917
## Median : 73.79 Mode :character
## Mean : 73.37
## 3rd Qu.: 83.90
## Max. :131.06
## NA's :1782str(monterrey)## tibble [95,917 × 32] (S3: tbl_df/tbl/data.frame)
## $ station : chr [1:95917] "MMMY" "MMMY" "MMMY" "MMMY" ...
## $ valid : POSIXct[1:95917], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
## $ lon : num [1:95917] -100 -100 -100 -100 -100 ...
## $ lat : num [1:95917] 25.8 25.8 25.8 25.8 25.8 ...
## $ tmpf : num [1:95917] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
## $ dwpf : num [1:95917] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
## $ relh : num [1:95917] 93.5 93.5 93.5 100 100 ...
## $ drct : num [1:95917] 0 120 120 120 110 100 110 130 60 0 ...
## $ sknt : num [1:95917] 0 3 5 6 5 5 4 3 3 0 ...
## $ p01i : num [1:95917] 0 0 0 0 0 0 0 0 0 0 ...
## $ alti : num [1:95917] 30.3 30.3 30.3 30.3 30.3 ...
## $ mslp : num [1:95917] NA NA NA NA NA ...
## $ vsby : num [1:95917] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
## $ gust : num [1:95917] NA NA NA NA NA NA NA NA NA NA ...
## $ skyc1 : chr [1:95917] "SCT" "SCT" "SCT" "VV " ...
## $ skyc2 : chr [1:95917] "BKN" "BKN" "BKN" NA ...
## $ skyc3 : chr [1:95917] "OVC" "OVC" "OVC" NA ...
## $ skyc4 : chr [1:95917] NA NA NA NA ...
## $ skyl1 : num [1:95917] 700 300 200 200 100 100 100 100 100 100 ...
## $ skyl2 : num [1:95917] 1200 400 300 NA NA NA NA NA NA NA ...
## $ skyl3 : num [1:95917] 4000 900 500 NA NA NA NA NA NA NA ...
## $ skyl4 : num [1:95917] NA NA NA NA NA NA NA NA NA NA ...
## $ wxcodes : chr [1:95917] NA "BR" "BR" "FG" ...
## $ ice_accretion_1hr: logi [1:95917] NA NA NA NA NA NA ...
## $ ice_accretion_3hr: logi [1:95917] NA NA NA NA NA NA ...
## $ ice_accretion_6hr: logi [1:95917] NA NA NA NA NA NA ...
## $ peak_wind_gust : logi [1:95917] NA NA NA NA NA NA ...
## $ peak_wind_drct : logi [1:95917] NA NA NA NA NA NA ...
## $ peak_wind_time : logi [1:95917] NA NA NA NA NA NA ...
## $ feel : num [1:95917] 48.2 47.2 45.6 42.9 43.5 ...
## $ metar : chr [1:95917] "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 [1:95917] NA NA NA NA NA NA ...# Filtrar información del último mes
mty_ago_24 <- subset(monterrey, valid >= as.POSIXct("2024-08-01 00:00") & valid <= as.POSIXct("2024-08-31 23:59"))
# Ejercicio 1: Realizar una gráfica de barras de la temperatura promedio diario en agosto en Monterrey en C.
promedio <- mty_ago_24 %>%
mutate(fecha = as.Date(valid)) %>%
group_by(fecha) %>%
summarize(temp_promedio = mean(tmpf, na.rm = TRUE))
promedio$tempc <- (promedio$temp_promedio-32)/1.8
# ggplot(promedio, aes(x = fecha, y = tempc)) +
# geom_col(fill = "blue") +
# labs(title = "Temperatura Promedio en Monterrey durante Agosto 2024", x = "Fecha", y = "Temperatura (C)")
# geom_text(aes(label=round(tempc,1)), vjust = -0.5, size = 3)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