Reorganizando datos y corrigiendo script dia 2

Author: lidefi87

martes/Data/67632e6317d74225c2716f0f14779bb7.nc

@@ -143,7 +143,7 @@ temp_fria <- temp_sup_ecu$data %>%
   #Escogemos los meses de la epoca fria
   filter(lubridate::month(time) <= 4 | lubridate::month(time) == 12) %>% 
   #Agrupamos por latitud y longitud
-  group_by(lat, lon) %>% 
+  group_by(latitude, longitude) %>% 
   #Calculamos el promedio
   summarise(temp_prom = mean(sst, na.rm = T))
 ```
@@ -156,7 +156,7 @@ tierra <- rnaturalearth::ne_countries(returnclass = "sf")
 
 #Grafiquemos
 temp_fria %>% 
-  ggplot(aes(x = lon, y = lat))+
+  ggplot(aes(x = longitude, y = latitude))+
   geom_contour_filled(aes(z = temp_prom), binwidth = 2.5)+
   scale_fill_brewer(palette = "YlOrRd")+
   geom_sf(data = tierra, inherit.aes = F)+

martes/Dia2_Datos_espaciales_R/Data/718dcfde61b559a0aeb9011a8181c9f0.nc

@@ -16,11 +16,6 @@ Denisse Fierro Arcos
 
 # Analisis de datos espaciales en `R`
 
-Aqui describimos como acceder a datos en formato raster disponibles de
-manera libre de la NOAA a traves del servidor
-[ERDDAP](https://coastwatch.pfeg.noaa.gov/erddap/index.html) utilizando
-el paquete `rerddap`.
-
 Tambien cubriremos como crear mapas utilizando datos tipo raster y
 poligonos utilizando las librerias `sf` y `ggplot2`.
 
@@ -30,24 +25,19 @@ poligonos utilizando las librerias `sf` y `ggplot2`.
 library(tidyverse) #Incluye a ggplot2
 ```
 
-    ## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
-    ## ✔ ggplot2 3.4.0     ✔ purrr   1.0.1
-    ## ✔ tibble  3.1.8     ✔ dplyr   1.1.0
-    ## ✔ tidyr   1.3.0     ✔ stringr 1.5.0
-    ## ✔ readr   2.1.2     ✔ forcats 0.5.2
+    ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
+    ## ✔ dplyr     1.1.0     ✔ readr     2.1.4
+    ## ✔ forcats   1.0.0     ✔ stringr   1.5.0
+    ## ✔ ggplot2   3.4.1     ✔ tibble    3.1.8
+    ## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
+    ## ✔ purrr     1.0.1     
     ## ── 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 errors
 
 ``` r
 library(rerddap)
-```
-
-    ## Registered S3 method overwritten by 'hoardr':
-    ##   method           from
-    ##   print.cache_info httr
-
-``` r
 library(raster)
 ```
 
@@ -201,16 +191,58 @@ Revisemos los datos en mas detalle.
 info(temp_sup_res$info$dataset_id[15])
 ```
 
-    ## <ERDDAP info> erdMH1sstdmday 
+    ## <ERDDAP info> esrlIcoads2ge_LonPM180 
     ##  Base URL: https://upwell.pfeg.noaa.gov/erddap 
     ##  Dataset Type: griddap 
     ##  Dimensions (range):  
-    ##      time: (2003-01-16T00:00:00Z, 2019-10-16T00:00:00Z) 
-    ##      latitude: (-89.97918, 89.97916) 
-    ##      longitude: (-179.9792, 179.9792) 
+    ##      time: (1800-01-01T00:00:00Z, 2023-01-01T00:00:00Z) 
+    ##      latitude: (-89.0, 89.0) 
+    ##      longitude: (-179.0, 179.0) 
     ##  Variables:  
+    ##      air: 
+    ##          Units: degC 
+    ##      cldc: 
+    ##          Units: okta 
+    ##      lflx: 
+    ##          Units: grams/kg m/s 
+    ##      qsminq: 
+    ##          Units: grams/kg 
+    ##      rhum: 
+    ##          Units: % 
+    ##      sflx: 
+    ##          Units: degC m/s 
+    ##      shum: 
+    ##          Units: grams/kg 
+    ##      slp: 
+    ##          Units: millibars 
+    ##      smina: 
+    ##          Units: degC 
     ##      sst: 
-    ##          Units: degree_C
+    ##          Units: degC 
+    ##      uairt: 
+    ##          Units: degC m/s 
+    ##      ulflx: 
+    ##          Units: grams/kg m/s 
+    ##      upstr: 
+    ##          Units: m^2/s^2 
+    ##      uspeh: 
+    ##          Units: grams/kg m/s 
+    ##      uwnd: 
+    ##          Units: m/s 
+    ##      vairt: 
+    ##          Units: degC m/s 
+    ##      vlflx: 
+    ##          Units: grams/kg m/s 
+    ##      vpstr: 
+    ##          Units: m^2/s^2 
+    ##      vspeh: 
+    ##          Units: grams/kg m/s 
+    ##      vwnd: 
+    ##          Units: m/s 
+    ##      wspd: 
+    ##          Units: m/s 
+    ##      wspd3: 
+    ##          Units: m**3/s**3
 
 Encontramos los datos que necesitamos. Ahora los bajamos a nuestro
 disco.
@@ -231,28 +263,31 @@ temp_sup_ecu <- griddap(temp_sup_res$info$dataset_id[15],
 temp_sup_ecu
 ```
 
-    ## <ERDDAP griddap> erdMH1sstdmday
-    ##    Path: [Data//67632e6317d74225c2716f0f14779bb7.nc]
-    ##    Last updated: [2023-02-17 16:02:40]
-    ##    File size:    [3.45 mb]
-    ##    Dimensions (dims/vars):   [3 X 1]
+    ## <ERDDAP griddap> esrlIcoads2ge_LonPM180
+    ##    Path: [Data//718dcfde61b559a0aeb9011a8181c9f0.nc]
+    ##    Last updated: [2023-03-01 21:02:43]
+    ##    File size:    [0.07 mb]
+    ##    Dimensions (dims/vars):   [3 X 22]
     ##    Dim names: time, latitude, longitude
-    ##    Variable names: Masked Daytime Sea Surface Temperature
-    ##    data.frame (rows/columns):   [860256 X 4]
-    ## # A tibble: 860,256 × 4
-    ##    time                   lat   lon   sst
-    ##    <chr>                <dbl> <dbl> <dbl>
-    ##  1 2010-01-16T00:00:00Z  2.02 -92.0  26.5
-    ##  2 2010-01-16T00:00:00Z  2.02 -92.0  26.4
-    ##  3 2010-01-16T00:00:00Z  2.02 -91.9  26.6
-    ##  4 2010-01-16T00:00:00Z  2.02 -91.9  26.6
-    ##  5 2010-01-16T00:00:00Z  2.02 -91.9  26.7
-    ##  6 2010-01-16T00:00:00Z  2.02 -91.8  26.7
-    ##  7 2010-01-16T00:00:00Z  2.02 -91.8  26.8
-    ##  8 2010-01-16T00:00:00Z  2.02 -91.7  26.5
-    ##  9 2010-01-16T00:00:00Z  2.02 -91.7  26.1
-    ## 10 2010-01-16T00:00:00Z  2.02 -91.6  26.4
-    ## # … with 860,246 more rows
+    ##    Variable names: Air Temperature Monthly Mean at Surface, Cloudiness Monthly Mean at Surface, Latent Heat Parameter Monthly Mean at Surface, Sat Sp Hum at Sst Minus Sp Hum Monthly Mean at Surface, Relative Humidity Monthly Mean at Surface, Sensible Heat Parameter Monthly Mean at Surface, Specific Humidity Monthly Mean at Surface, Sea Level Pressure Monthly Mean at Surface, Sea-air Temperature Difference Monthly Mean at Surface, Sea Surface Temperature Monthly Mean at Surface, Sensible Heat Trans Eastward Param Monthly Mean at Surface, Zonal Latent Heat Parameter Monthly Mean at Surface, u-wind Stress Monthly Mean at Surface, Latent Heat Trans Eastward Param Monthly Mean at Surface, u-wind Monthly Mean at Surface, Sensible Heat Trans Northward Param Monthly Mean at Surface, Meridional Latent Heat Parameter Monthly Mean at Surface, v-wind Stress Monthly Mean at Surface, Latent Heat Trans Northward Param Monthly Mean at Surface, v-wind Monthly Mean at Surface, Scalar Wind Monthly Mean at Surface, Scalar Wind Cubed Monthly Mean at Surface
+    ##    data.frame (rows/columns):   [585 X 25]
+    ## # A tibble: 585 × 25
+    ##    longi…¹ latit…² time    air  cldc  lflx qsminq  rhum  sflx  shum   slp  smina
+    ##    <dbl[1> <dbl[1> <chr> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
+    ##  1     -91       3 2010…  26.9  5.70 33.5    4.91  89    3     18.7 1011.  1.22 
+    ##  2     -89       3 2010…  26.6  5.30 40      5.79  89.4  1.30  18.6 1010.  2.25 
+    ##  3     -87       3 2010…  24.7  5.70  9.60   3.69  94.2  5.20  18.3 1012   2    
+    ##  4     -85       3 2010…  26.6 NA    NA     NA     NA   NA     NA   1012. NA    
+    ##  5     -83       3 2010…  26.9  8    13.7    5.05  80    3.60  18.4 1010.  0.820
+    ##  6     -81       3 2010…  27.0  6.10 17.9    2.20  84.9  1.40  19.2 1010. -0.200
+    ##  7     -79       3 2010…  23.9  8    23.5    5.67  83.8 10.1   16.8 1010.  2.40 
+    ##  8     -77       3 2010…  26.0  7.5  11      4.22  77.3  2.20  19.0 1010.  0.450
+    ##  9     -75       3 2010…  NA   NA    NA     NA     NA   NA     NA     NA  NA    
+    ## 10     -91       1 2010…  NA   NA    NA     NA     NA   NA     NA     NA  NA    
+    ## # … with 575 more rows, 13 more variables: sst <dbl>, uairt <dbl>, ulflx <dbl>,
+    ## #   upstr <dbl>, uspeh <dbl>, uwnd <dbl>, vairt <dbl>, vlflx <dbl>,
+    ## #   vpstr <dbl>, vspeh <dbl>, vwnd <dbl>, wspd <dbl>, wspd3 <dbl>, and
+    ## #   abbreviated variable names ¹​longitude, ²​latitude
 
 Revisemos los archivos que acabamos de guardar en el disco.
 
@@ -261,27 +296,33 @@ archivo <- list.files("Data/", pattern = ".nc", full.names = T)
 archivo
 ```
 
-    ## [1] "Data/67632e6317d74225c2716f0f14779bb7.nc"
+    ## [1] "Data//718dcfde61b559a0aeb9011a8181c9f0.nc"
 
 ## Graficando datos
 
 Tenemos primero que subir los datos a nuestra sesion.
 
 ``` r
 temp_sup_ec <- raster(archivo)
+```
+
+    ## Warning in .varName(nc, varname, warn = warn): varname used is: air
+    ## If that is not correct, you can set it to one of: air, cldc, lflx, qsminq, rhum, sflx, shum, slp, smina, sst, uairt, ulflx, upstr, uspeh, uwnd, vairt, vlflx, vpstr, vspeh, vwnd, wspd, wspd3
+
+``` r
 temp_sup_ec
 ```
 
     ## class      : RasterLayer 
-    ## band       : 1  (of  12  bands)
-    ## dimensions : 174, 412, 71688  (nrow, ncol, ncell)
-    ## resolution : 0.04166667, 0.04166668  (x, y)
-    ## extent     : -92.04166, -74.875, -5.208338, 2.041664  (xmin, xmax, ymin, ymax)
+    ## band       : 1  (of  13  bands)
+    ## dimensions : 5, 9, 45  (nrow, ncol, ncell)
+    ## resolution : 2, 2  (x, y)
+    ## extent     : -92, -74, -6, 4  (xmin, xmax, ymin, ymax)
     ## crs        : +proj=longlat +datum=WGS84 +no_defs 
-    ## source     : 67632e6317d74225c2716f0f14779bb7.nc 
-    ## names      : Masked.Daytime.Sea.Surface.Temperature 
-    ## z-value    : 2010-01-16 
-    ## zvar       : sst
+    ## source     : 718dcfde61b559a0aeb9011a8181c9f0.nc 
+    ## names      : Air.Temperature.Monthly.Mean.at.Surface 
+    ## z-value    : 2010-01-01 
+    ## zvar       : air
 
 Este archivo tiene 12 bandas, una por cada mes en el anio. Revisemos el
 primer mes.
@@ -311,31 +352,37 @@ temp_fria <- temp_sup_ecu$data %>%
   #Escogemos los meses de la epoca fria
   filter(lubridate::month(time) <= 4 | lubridate::month(time) == 12) %>% 
   #Agrupamos por latitud y longitud
-  group_by(lat, lon) %>% 
+  group_by(latitude, longitude) %>% 
   #Calculamos el promedio
   summarise(temp_prom = mean(sst, na.rm = T))
 ```
 
-    ## `summarise()` has grouped output by 'lat'. You can override using the `.groups`
-    ## argument.
+    ## `summarise()` has grouped output by 'latitude'. You can override using the
+    ## `.groups` argument.
 
 Ahora podemos graficar este promedio.
 
 ``` r
 #Capa de continentes
 tierra <- rnaturalearth::ne_countries(returnclass = "sf")
+```
+
+    ## Registered S3 method overwritten by 'httr':
+    ##   method           from  
+    ##   print.cache_info hoardr
 
+``` r
 #Grafiquemos
 temp_fria %>% 
-  ggplot(aes(x = lon, y = lat))+
+  ggplot(aes(x = longitude, y = latitude))+
   geom_contour_filled(aes(z = temp_prom), binwidth = 2.5)+
   scale_fill_brewer(palette = "YlOrRd")+
   geom_sf(data = tierra, inherit.aes = F)+
   lims(x = c(-92, -74.9), y = c(-5.2, 2))+
   theme_bw()
 ```
 
-    ## Warning: Removed 23522 rows containing non-finite values
+    ## Warning: Removed 19 rows containing non-finite values
     ## (`stat_contour_filled()`).
 
 ![](Dia2_Datos_espaciales_R_files/figure-gfm/unnamed-chunk-13-1.png)<!-- -->