ASOS
Diego Salazar A00835628
2024-09-11
#
Introducción Información obtenida
del Automated Surface Observing System (ASOS) de los
aeropuertos de todo el mundo.
LibrerÃas
#install.packages("riem")
library(riem)## Warning: package 'riem' was built under R version 4.3.3library(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 errorslibrary(ggplot2)
#install.packages("lubridate")
library(lubridate)Obtener y graficar información
Paso 1: Buscar la red o paÃs
#view(riem_networks())Paso 2: Buscar la estación (ciudad) - Mty
#view(riem_stations("MX__ASOS")) #ID: MMMY
monterrey <- riem_measures("MMMY")
#Análisis descriptivo
summary(monterrey)## station valid lon
## Length:95893 Min. :2014-01-01 00:16:00.00 Min. :-100.1
## Class :character 1st Qu.:2016-09-03 19:50:00.00 1st Qu.:-100.1
## Mode :character Median :2019-05-03 01:41:00.00 Median :-100.1
## Mean :2019-05-12 00:07:10.50 Mean :-100.1
## 3rd Qu.:2022-01-20 05:40:00.00 3rd Qu.:-100.1
## Max. :2024-09-10 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 :84045
## vsby gust skyc1 skyc2
## Min. : 0.000 Min. : 13.00 Length:95893 Length:95893
## 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 :92793
## skyc3 skyc4 skyl1 skyl2
## Length:95893 Length:95893 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 :29104 NA's :64555
## skyl3 skyl4 wxcodes ice_accretion_1hr
## Min. : 400 Min. : 3000 Length:95893 Mode:logical
## 1st Qu.: 8000 1st Qu.:20000 Class :character NA's:95893
## Median :20000 Median :20000 Mode :character
## Mean :14721 Mean :20646
## 3rd Qu.:20000 3rd Qu.:25000
## Max. :30000 Max. :25000
## NA's :90513 NA's :95695
## ice_accretion_3hr ice_accretion_6hr peak_wind_gust peak_wind_drct
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:95893 NA's:95893 NA's:95893 NA's:95893
##
##
##
##
##
## peak_wind_time feel metar snowdepth
## Mode:logical Min. : 9.11 Length:95893 Mode:logical
## NA's:95893 1st Qu.: 64.40 Class :character NA's:95893
## Median : 73.79 Mode :character
## Mean : 73.36
## 3rd Qu.: 83.90
## Max. :131.06
## NA's :1782str(monterrey)## tibble [95,893 × 32] (S3: tbl_df/tbl/data.frame)
## $ station : chr [1:95893] "MMMY" "MMMY" "MMMY" "MMMY" ...
## $ valid : POSIXct[1:95893], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
## $ lon : num [1:95893] -100 -100 -100 -100 -100 ...
## $ lat : num [1:95893] 25.8 25.8 25.8 25.8 25.8 ...
## $ tmpf : num [1:95893] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
## $ dwpf : num [1:95893] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
## $ relh : num [1:95893] 93.5 93.5 93.5 100 100 ...
## $ drct : num [1:95893] 0 120 120 120 110 100 110 130 60 0 ...
## $ sknt : num [1:95893] 0 3 5 6 5 5 4 3 3 0 ...
## $ p01i : num [1:95893] 0 0 0 0 0 0 0 0 0 0 ...
## $ alti : num [1:95893] 30.3 30.3 30.3 30.3 30.3 ...
## $ mslp : num [1:95893] NA NA NA NA NA ...
## $ vsby : num [1:95893] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
## $ gust : num [1:95893] NA NA NA NA NA NA NA NA NA NA ...
## $ skyc1 : chr [1:95893] "SCT" "SCT" "SCT" "VV " ...
## $ skyc2 : chr [1:95893] "BKN" "BKN" "BKN" NA ...
## $ skyc3 : chr [1:95893] "OVC" "OVC" "OVC" NA ...
## $ skyc4 : chr [1:95893] NA NA NA NA ...
## $ skyl1 : num [1:95893] 700 300 200 200 100 100 100 100 100 100 ...
## $ skyl2 : num [1:95893] 1200 400 300 NA NA NA NA NA NA NA ...
## $ skyl3 : num [1:95893] 4000 900 500 NA NA NA NA NA NA NA ...
## $ skyl4 : num [1:95893] NA NA NA NA NA NA NA NA NA NA ...
## $ wxcodes : chr [1:95893] NA "BR" "BR" "FG" ...
## $ ice_accretion_1hr: logi [1:95893] NA NA NA NA NA NA ...
## $ ice_accretion_3hr: logi [1:95893] NA NA NA NA NA NA ...
## $ ice_accretion_6hr: logi [1:95893] NA NA NA NA NA NA ...
## $ peak_wind_gust : logi [1:95893] NA NA NA NA NA NA ...
## $ peak_wind_drct : logi [1:95893] NA NA NA NA NA NA ...
## $ peak_wind_time : logi [1:95893] NA NA NA NA NA NA ...
## $ feel : num [1:95893] 48.2 47.2 45.6 42.9 43.5 ...
## $ metar : chr [1:95893] "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 [1:95893] NA NA NA NA NA NA ...#Filtrar información
mty_agosto24 <- subset(monterrey, valid >= as.POSIXct("2024-08-01 00:00") & valid <= as.POSIXct("2024-08-31 23:59"))
#Ejercicio 1: Realiza una gráfica de barras de la temperatura promedio diaria en agosto en Monterrey en °C.Solución 1
#Transformar de °F a °C:
mty_agosto24$tmpc <- (mty_agosto24$tmpf - 32)*5/9
mty_agosto24_tmpc <- mty_agosto24$tmpc
mty_agosto24_tmpc <- as.integer(mty_agosto24_tmpc)
mty_agosto24_tmpc## [1] 25 25 25 24 24 24 24 24 25 25 26 26 28 29 30 32 33 33 33 32 31 28 26 26 26
## [26] 26 25 25 24 23 23 21 23 25 26 26 29 30 31 33 32 33 33 32 28 29 26 26 25 24
## [51] 24 23 23 23 21 23 26 26 29 32 32 32 33 34 34 35 34 34 32 28 29 28 26 26 25
## [76] 24 24 23 23 21 24 26 28 30 32 32 33 34 35 34 34 34 32 29 28 29 26 26 26 25
## [101] 25 24 23 21 23 25 26 28 30 31 32 32 34 34 34 33 32 30 30 30 28 26 26 25 24
## [126] 24 23 23 23 26 28 29 30 32 33 34 34 34 34 34 32 30 30 28 28 26 26 25 24 24
## [151] 24 23 24 26 29 31 33 34 35 36 36 35 37 36 33 31 30 30 30 26 26 25 24 24 23
## [176] 21 24 26 30 33 35 37 38 38 38 38 37 36 35 31 31 30 29 29 26 26 25 25 25 25
## [201] 26 26 26 28 29 31 31 32 30 31 32 31 30 26 26 26 26 25 25 24 24 24 23 23 23
## [226] 23 25 26 29 30 31 32 33 34 34 34 34 32 29 28 26 26 26 26 25 25 24 23 23 23
## [251] 26 28 28 30 31 32 34 34 33 33 32 31 30 28 26 26 25 25 24 24 23 23 21 21 25
## [276] 26 28 30 31 32 34 32 34 34 33 32 31 29 29 26 26 26 25 24 24 23 21 21 25 25
## [301] 28 29 31 32 34 35 36 36 35 33 31 30 29 28 26 26 26 25 25 25 24 24 25 26 28
## [326] 29 30 32 32 33 35 34 34 32 30 30 29 28 26 26 26 25 24 23 23 23 25 26 26 29
## [351] 29 31 33 34 35 34 34 33 30 30 29 28 26 26 26 25 24 24 25 24 26 26 28 29 32
## [376] 33 33 34 34 35 34 32 30 29 29 26 26 26 25 25 25 25 25 25 25 26 26 29 30 32
## [401] 34 34 35 35 34 34 32 30 29 28 28 26 26 26 25 25 24 24 24 26 26 29 30 32 32
## [426] 34 34 34 34 33 32 30 30 28 28 26 26 26 25 24 24 24 24 25 26 30 32 32 33 34
## [451] 33 33 32 32 31 31 29 29 28 26 26 25 25 24 24 23 23 26 28 30 32 34 35 36 36
## [476] 36 36 36 34 31 29 28 28 26 26 25 25 24 23 24 24 26 29 31 33 35 36 37 38 38
## [501] 37 37 35 32 31 29 28 29 26 26 25 24 24 23 23 26 29 31 33 35 35 36 37 37 37
## [526] 36 34 31 31 30 29 28 26 26 25 24 24 23 23 26 26 30 31 33 34 35 36 36 36 36
## [551] 34 30 28 29 28 26 26 26 25 23 23 21 23 26 26 28 30 32 33 35 36 36 35 35 33
## [576] 31 31 29 26 26 26 26 25 24 24 24 24 25 26 26 29 30 32 33 34 34 35 33 33 30
## [601] 28 26 26 26 26 25 24 24 23 21 21 25 26 29 31 32 33 34 35 34 35 35 33 30 30
## [626] 28 26 26 26 26 26 26 25 25 24 26 28 29 31 32 34 33 33 33 31 28 30 26 26 25
## [651] 24 24 23 23 23 23 23 24 24 26 26 29 30 31 32 33 33 33 34 30 29 25 24 23 23
## [676] 23 23 23 23 23 23 24 24 25 26 26 29 29 30 32 33 34 34 33 33 32 30 29 28 26
## [701] 26 26 25 24 24 23 23 23 26 29 30 32 35 36 35 36 36 35 34 31 31 29 26 26 26
## [726] 26 26 25 25 25 25 25 25 26 29 29 31 33 32 29 26 24 24 23 25 26 28 26 26 25
## [751] 25 25 25 24n <- length(mty_agosto24_tmpc) %/% 24
promedios <- sapply(1:n, function(i) {
mean(mty_agosto24_tmpc[((i-1)*24 + 1):(i*24)])
})
dia <- c(1:31)
promedios <- as.integer(promedios)
df = data.frame(dia, promedios)ggplot(df, aes(x=dia, y=promedios))+
geom_col(fill="blue")
Solución 2
promedio <- mty_agosto24 %>%
mutate(date=ymd_hms(valid), date = as.Date(date)) %>%
group_by(date) %>%
summarize_if(is.numeric, ~ mean(., na.rm=TRUE))promedio$cels <- (promedio$tmpf - 32 )*5/9
ggplot(promedio, aes(x = date, y = cels))+
geom_col(fill = "black")+
geom_path(color = "pink")+
labs(title="Promedio de temperatura diaria en Monterrey", x = "Fecha", y = "Temperatura")
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