Clima
Avril Lobato - A00833113
2023-03-17
Análisis del clima
ASOS: Informacion del Automated Surface Observing System Instalar paquetes y llamar librerías
library(riem)
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, unionlibrary(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ readr 2.1.4
## ✔ ggplot2 3.4.1 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.1.8
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errorslibrary(lubridate)
library(ggplot2)
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':
##
## layoutPaso2. Buscar la red (pais). Ejemplo: Mexico; y copiar CODE
#view(riem_networks())Paso 3. Buscar la estacióon (ciudad). Ejemplo: Monterrey; y copiar ID
#view(riem_stations("MX__ASOS"))Paso 4. Obtener información
monterrey<-riem_measures("MMMY")
str(monterrey)## tibble [82,340 × 32] (S3: tbl_df/tbl/data.frame)
## $ station : chr [1:82340] "MMMY" "MMMY" "MMMY" "MMMY" ...
## $ valid : POSIXct[1:82340], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
## $ lon : num [1:82340] -100 -100 -100 -100 -100 ...
## $ lat : num [1:82340] 25.8 25.8 25.8 25.8 25.8 ...
## $ tmpf : num [1:82340] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
## $ dwpf : num [1:82340] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
## $ relh : num [1:82340] 93.5 93.5 93.5 100 100 ...
## $ drct : num [1:82340] 0 120 120 120 110 100 110 130 60 0 ...
## $ sknt : num [1:82340] 0 3 5 6 5 5 4 3 3 0 ...
## $ p01i : num [1:82340] 0 0 0 0 0 0 0 0 0 0 ...
## $ alti : num [1:82340] 30.3 30.3 30.3 30.3 30.3 ...
## $ mslp : num [1:82340] NA NA NA NA NA ...
## $ vsby : num [1:82340] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
## $ gust : num [1:82340] NA NA NA NA NA NA NA NA NA NA ...
## $ skyc1 : chr [1:82340] "SCT" "SCT" "SCT" "VV " ...
## $ skyc2 : chr [1:82340] "BKN" "BKN" "BKN" NA ...
## $ skyc3 : chr [1:82340] "OVC" "OVC" "OVC" NA ...
## $ skyc4 : chr [1:82340] NA NA NA NA ...
## $ skyl1 : num [1:82340] 700 300 200 200 100 100 100 100 100 100 ...
## $ skyl2 : num [1:82340] 1200 400 300 NA NA NA NA NA NA NA ...
## $ skyl3 : num [1:82340] 4000 900 500 NA NA NA NA NA NA NA ...
## $ skyl4 : num [1:82340] NA NA NA NA NA NA NA NA NA NA ...
## $ wxcodes : chr [1:82340] NA "BR" "BR" "FG" ...
## $ ice_accretion_1hr: logi [1:82340] NA NA NA NA NA NA ...
## $ ice_accretion_3hr: logi [1:82340] NA NA NA NA NA NA ...
## $ ice_accretion_6hr: logi [1:82340] NA NA NA NA NA NA ...
## $ peak_wind_gust : logi [1:82340] NA NA NA NA NA NA ...
## $ peak_wind_drct : logi [1:82340] NA NA NA NA NA NA ...
## $ peak_wind_time : logi [1:82340] NA NA NA NA NA NA ...
## $ feel : num [1:82340] 48.2 47.2 45.6 42.9 43.5 ...
## $ metar : chr [1:82340] "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 [1:82340] NA NA NA NA NA NA ...Paso 5. Agregar temperatura en grados Celsius
monterrey$tmpc <- (monterrey$tmpf - 32)/1.8
str(monterrey)## tibble [82,340 × 33] (S3: tbl_df/tbl/data.frame)
## $ station : chr [1:82340] "MMMY" "MMMY" "MMMY" "MMMY" ...
## $ valid : POSIXct[1:82340], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
## $ lon : num [1:82340] -100 -100 -100 -100 -100 ...
## $ lat : num [1:82340] 25.8 25.8 25.8 25.8 25.8 ...
## $ tmpf : num [1:82340] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
## $ dwpf : num [1:82340] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
## $ relh : num [1:82340] 93.5 93.5 93.5 100 100 ...
## $ drct : num [1:82340] 0 120 120 120 110 100 110 130 60 0 ...
## $ sknt : num [1:82340] 0 3 5 6 5 5 4 3 3 0 ...
## $ p01i : num [1:82340] 0 0 0 0 0 0 0 0 0 0 ...
## $ alti : num [1:82340] 30.3 30.3 30.3 30.3 30.3 ...
## $ mslp : num [1:82340] NA NA NA NA NA ...
## $ vsby : num [1:82340] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
## $ gust : num [1:82340] NA NA NA NA NA NA NA NA NA NA ...
## $ skyc1 : chr [1:82340] "SCT" "SCT" "SCT" "VV " ...
## $ skyc2 : chr [1:82340] "BKN" "BKN" "BKN" NA ...
## $ skyc3 : chr [1:82340] "OVC" "OVC" "OVC" NA ...
## $ skyc4 : chr [1:82340] NA NA NA NA ...
## $ skyl1 : num [1:82340] 700 300 200 200 100 100 100 100 100 100 ...
## $ skyl2 : num [1:82340] 1200 400 300 NA NA NA NA NA NA NA ...
## $ skyl3 : num [1:82340] 4000 900 500 NA NA NA NA NA NA NA ...
## $ skyl4 : num [1:82340] NA NA NA NA NA NA NA NA NA NA ...
## $ wxcodes : chr [1:82340] NA "BR" "BR" "FG" ...
## $ ice_accretion_1hr: logi [1:82340] NA NA NA NA NA NA ...
## $ ice_accretion_3hr: logi [1:82340] NA NA NA NA NA NA ...
## $ ice_accretion_6hr: logi [1:82340] NA NA NA NA NA NA ...
## $ peak_wind_gust : logi [1:82340] NA NA NA NA NA NA ...
## $ peak_wind_drct : logi [1:82340] NA NA NA NA NA NA ...
## $ peak_wind_time : logi [1:82340] NA NA NA NA NA NA ...
## $ feel : num [1:82340] 48.2 47.2 45.6 42.9 43.5 ...
## $ metar : chr [1:82340] "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 [1:82340] NA NA NA NA NA NA ...
## $ tmpc : num [1:82340] 9 9 9 8 8 8 8 8 8 8 ...summary(monterrey)## station valid lon
## Length:82340 Min. :2014-01-01 00:16:00.00 Min. :-100.1
## Class :character 1st Qu.:2016-04-23 23:29:45.00 1st Qu.:-100.1
## Mode :character Median :2018-08-05 07:13:30.00 Median :-100.1
## Mean :2018-08-08 20:33:04.17 Mean :-100.1
## 3rd Qu.:2020-11-15 23:55:00.00 3rd Qu.:-100.1
## Max. :2023-03-16 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.:50.00 1st Qu.: 47.79
## Median :25.78 Median : 73.40 Median :60.80 Median : 68.94
## Mean :25.78 Mean : 72.16 Mean :57.61 Mean : 65.05
## 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. :100.00
## NA's :101 NA's :1698 NA's :1743
## 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 :131.7 Mean : 5.798 Mean :0 Mean :29.98 Mean :1015.3
## 3rd Qu.:160.0 3rd Qu.: 8.000 3rd Qu.:0 3rd Qu.:30.08 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 :71340
## vsby gust skyc1 skyc2
## Min. : 0.000 Min. : 13.00 Length:82340 Length:82340
## 1st Qu.: 6.000 1st Qu.: 20.00 Class :character Class :character
## Median :10.000 Median : 24.00 Mode :character Mode :character
## Mean : 9.112 Mean : 24.62
## 3rd Qu.:12.000 3rd Qu.: 28.00
## Max. :40.000 Max. :210.00
## NA's :31 NA's :79759
## skyc3 skyc4 skyl1 skyl2
## Length:82340 Length:82340 Min. : 0 Min. : 0
## Class :character Class :character 1st Qu.: 1500 1st Qu.: 2000
## Mode :character Mode :character Median : 3000 Median : 6000
## Mean : 5371 Mean : 7973
## 3rd Qu.: 7000 3rd Qu.:10000
## Max. :37000 Max. :30000
## NA's :24863 NA's :55262
## skyl3 skyl4 wxcodes ice_accretion_1hr
## Min. : 400 Min. : 3000 Length:82340 Mode:logical
## 1st Qu.: 8000 1st Qu.:20000 Class :character NA's:82340
## Median :15500 Median :20000 Mode :character
## Mean :14743 Mean :20656
## 3rd Qu.:20000 3rd Qu.:25000
## Max. :30000 Max. :25000
## NA's :77572 NA's :82145
## ice_accretion_3hr ice_accretion_6hr peak_wind_gust peak_wind_drct
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:82340 NA's:82340 NA's:82340 NA's:82340
##
##
##
##
##
## peak_wind_time feel metar snowdepth
## Mode:logical Min. : 9.11 Length:82340 Mode:logical
## NA's:82340 1st Qu.: 64.40 Class :character NA's:82340
## Median : 73.40 Mode :character
## Mean : 72.77
## 3rd Qu.: 82.90
## Max. :131.06
## NA's :1746
## tmpc
## Min. :-5.00
## 1st Qu.:18.00
## Median :23.00
## Mean :22.31
## 3rd Qu.:27.00
## Max. :44.00
## NA's :101Paso 6. Filtrar informacion - Ejemplo de Enero a Marzo 2023
este_año <- subset(monterrey, valid >= as.POSIXct("2023-01-01 00:00") & valid <= as.POSIXct("2023-03-10 07:00"))Paso 7. Graficar temperatura 2023
plot(este_año$valid,este_año$tmpc)
Paso 8. Promediar informaicón por día
este_año <- este_año %>%
mutate(date=ymd_hms(valid), date=as.Date(date)) %>%
group_by(date) %>%
summarize_if(is.numeric,~mean(.,na.rm=TRUE))Paso 9. Graficar temperatura en 2023
plot(este_año$date,este_año$tmpc, type="l", main = "Temperatura Promedio en Monterrey", xlab="Fecha", ylab="Grados Centígrados")
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