diff --git a/docs/articles/climatic-indices.html b/docs/articles/climatic-indices.html index b70f7f1..21df9be 100644 --- a/docs/articles/climatic-indices.html +++ b/docs/articles/climatic-indices.html @@ -82,7 +82,7 @@ -
+

Calculating basic climatic indices with data from easyclimate

@@ -140,7 +140,7 @@

summarise(Tmin.site = mean(Tmin), Tmean.site = mean(Tmean), Tmax.site = mean(Tmax)) -## # A tibble: 1 x 4 +## # A tibble: 1 × 4 ## ID_coords Tmin.site Tmean.site Tmax.site ## <dbl> <dbl> <dbl> <dbl> ## 1 1 11.7 18.4 25.0 @@ -521,7 +521,7 @@

summarise(max_heatwave = max(length), # longest heatwave per year (in days) mean_heatwave = mean(length), # average length of heatwaves per year n_heatwave = n()) %>% # No. of events per year - complete(ID_coords, year, + complete(ID_coords, year, fill = list(max_heatwave = NA, mean_heatwave = NA, nevents = 0)) %>% kable(digits = 1)

@@ -585,7 +585,7 @@

filter(event == 1) %>% group_by(ID_coords, year, n_frost) %>% summarise(Tmin_frost_avg = mean(Tmin)/100) %>% # Mean minimum temperature of frost days - complete(ID_coords, year, fill = list(n_frost = 0, Tmin_frost_avg = NA)) %>% + complete(ID_coords, year, fill = list(n_frost = 0, Tmin_frost_avg = NA)) %>% kable()

@@ -644,7 +644,7 @@

summarise(max_days_norain = max(length), # Maximum length of periods of consecutive days without rain per year mean_days_norain = mean(length), nevents = sum(length > threshold)) %>% # No. of events per year - complete(ID_coords, year, + complete(ID_coords, year, fill = list(max_days_norain = NA, mean_days_norain = NA, nevents = 0)) %>% kable(digits = 1)

diff --git a/docs/articles/points-df-mat-sf.html b/docs/articles/points-df-mat-sf.html index 0888765..4da49fc 100644 --- a/docs/articles/points-df-mat-sf.html +++ b/docs/articles/points-df-mat-sf.html @@ -82,7 +82,7 @@ -
+

Analysing the climate at spatial points for a given period

@@ -95,7 +95,6 @@

Analysing the climate at spatial points for a given period

## Warning: package 'knitr' was built under R version 4.0.5

With easyclimate you can easily download daily climate data for a given set of points or polygons within Europe. To download and install the latest version of easyclimate from github follow the instructions in https://github.com/VeruGHub/easyclimate


In this tutorial we will work through the basics of using easyclimate with coordinate points. You can enter coordinates as a data.frame, matrix, or sf object. At the end we will have a data frame with climate variables for each point.

@@ -104,7 +103,7 @@

Analysing the climate at spatial points for a given period

Example 1: Introducing coordinates as a data frame

First, specify longitude and latitude coordinates in a data frame with the column names lon and lat. Here we are simulating coordinates for three random sites in southern Spain

-
+
 library(easyclimate)
 library(ggplot2)
 library(dplyr)
@@ -124,10 +123,10 @@ 

 

 


 
Now, download the climatic data for the selected locations. It is very simple, all you have to do is use the function get_daily_climate, specify the period (e.g. 2008-05-25 for a single day or 2008:2010 for several years), and the variables to be downloaded (precipitation Prcp, minimum temperature Tmin or maximum temperature Tmax).

-
+
 
 Sys.time() # to know how much it takes to download
-## [1] "2021-07-09 23:33:45 EDT"
+## [1] "2021-10-14 11:55:41 CEST"
 
 df_prcp <- get_daily_climate(
   coords = coords, 
@@ -148,18 +147,18 @@ 

 )
 
 Sys.time()
-## [1] "2021-07-09 23:39:36 EDT"
+## [1] "2021-10-14 11:57:28 CEST"
 
 head(df_prcp[, c("lon", "lat", "date", "Prcp")])
 ##         lon      lat       date Prcp
-## 1 -5.836705 37.42518 2008-01-01  730
-## 2 -5.836705 37.42518 2008-01-02 2349
-## 3 -5.836705 37.42518 2008-01-03  309
-## 4 -5.836705 37.42518 2008-01-04    0
-## 5 -5.836705 37.42518 2008-01-05    0
-## 6 -5.836705 37.42518 2008-01-06    0

+## 1 -5.356912 37.69926 2008-01-01  518
+## 2 -5.356912 37.69926 2008-01-02 2720
+## 3 -5.356912 37.69926 2008-01-03  560
+## 4 -5.356912 37.69926 2008-01-04    0
+## 5 -5.356912 37.69926 2008-01-05    0
+## 6 -5.356912 37.69926 2008-01-06    0

 
Temperature and precipitation units are ºC*100 and mm*100, respectively, to avoid floating values. You can change the units using the mutate function and join all the variables in a data frame.

-
+
 
 prcp_date <- df_prcp %>%
   mutate(prcp = Prcp / 100) %>% 
@@ -200,75 +199,75 @@
- - + + - - - + + + - - + + - - - + + + - - + + - - - + + + - - + + - - + + - - + + - - + + - - + + - - + +
1-5.83670537.42518-5.35691237.69926 2008-01-01 08 enero7.304.5214.395.183.0613.92
1-5.83670537.42518-5.35691237.69926 2008-01-02 08 enero23.499.8515.9127.207.7915.99
1-5.83670537.42518-5.35691237.69926 2008-01-03 08 enero3.098.5414.605.606.9714.82
1-5.83670537.42518-5.35691237.69926 2008-01-04 08 enero 0.006.5514.215.6814.50
1-5.83670537.42518-5.35691237.69926 2008-01-05 08 enero 0.006.2114.645.2314.73
1-5.83670537.42518-5.35691237.69926 2008-01-06 08 enero 0.0012.0615.5210.6215.99


Finally, you can visualize the daily climate results. For example, let’s plot the precipitation for one of the sites:

-
+
 clim_site1 <- clim_join %>% 
   filter(ID_coords == 1)
 
@@ -279,12 +278,12 @@ 

 

 


 
Or calculate the daily mean temperature and plot it against tmin and tmax:

-
+
 library(tidyr)
 
 clim_df <- clim_join %>%
   mutate(tmean = (tmin + tmax) / 2) %>%
-  pivot_longer(
+  pivot_longer(
     cols = c("tmin", "tmax", "tmean"),
     names_to = "temp_vars",
     values_to = "temp_values")
@@ -295,11 +294,11 @@ 

   ylab("Temperature (ºC)") + xlab("") +
   theme_bw()

 

-
+
 
 ggplot(clim_df, aes(x = date, y = temp_values, color = temp_vars)) + 
   geom_point(alpha = .1) +
-  scale_color_discrete(name = "Variables", 
+  scale_color_discrete(name = "Variables", 
                        guide = guide_legend(override.aes = list(alpha = 1))) +
   ylab("Temperature (ºC)") + xlab("Date") +
   theme_bw()

@@ -311,12 +310,12 @@ 

 Example 2: Introducing coordinates as a matrix

 
easyclimate handles different input data, try now with matrices!

 
Here we are retrieving daily precipitation data for a single year (2008).

-
+
 
 coords_mat <- as.matrix(coords)
 
 Sys.time()
-## [1] "2021-07-09 23:39:42 EDT"
+## [1] "2021-10-14 11:57:29 CEST"
 
 mat_prcp <- get_daily_climate( 
   coords = coords_mat, 
@@ -325,16 +324,16 @@ 

 )
 
 Sys.time()
-## [1] "2021-07-09 23:40:01 EDT"
+## [1] "2021-10-14 11:57:36 CEST"
 
 head(mat_prcp)
 ##   ID_coords         x        y       date Prcp
-## 1         1 -5.836705 37.42518 2008-01-01  730
-## 2         1 -5.836705 37.42518 2008-01-02 2349
-## 3         1 -5.836705 37.42518 2008-01-03  309
-## 4         1 -5.836705 37.42518 2008-01-04    0
-## 5         1 -5.836705 37.42518 2008-01-05    0
-## 6         1 -5.836705 37.42518 2008-01-06    0
+## 1         1 -5.356912 37.69926 2008-01-01  518
+## 2         1 -5.356912 37.69926 2008-01-02 2720
+## 3         1 -5.356912 37.69926 2008-01-03  560
+## 4         1 -5.356912 37.69926 2008-01-04    0
+## 5         1 -5.356912 37.69926 2008-01-05    0
+## 6         1 -5.356912 37.69926 2008-01-06    0
 
 clim_mat <- mat_prcp %>%
   mutate(
@@ -353,7 +352,7 @@ 

   ylab("Daily precipitation (mm)") + xlab("Date") +
   theme_bw()

 

-
+
 
 month_name <- format(ISOdate(2021, 1:12, 1), "%B")
 
@@ -372,7 +371,7 @@ 

 

 Example 3: Introducing coordinates as simple feature objects

 
Here we introduce coordinates as a sf object, and retrieve minimum temperature for a single day (1 January 2001).

-
+
 library(sf)
 
 coords_sf <- st_as_sf(
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diff --git a/docs/articles/polygons-raster.html b/docs/articles/polygons-raster.html
index 734c6e3..6cb0790 100644
--- a/docs/articles/polygons-raster.html
+++ b/docs/articles/polygons-raster.html
@@ -82,7 +82,7 @@
 
       
 
-      

+      

   

     

       
Analysing the climate of an area for a given period

@@ -106,7 +106,7 @@ 
Analysing the climate of an area for a given period

 coords_t <- vect("POLYGON ((-4.5 41, -4.5 40.5, -5 40.5, -5 41))")
 
 Sys.time() # to know how much it takes to download
-## [1] "2021-07-12 15:48:33 EDT"
+## [1] "2021-10-14 11:57:54 CEST"
 
 df_tmax <- get_daily_climate(
   coords_t,
@@ -116,7 +116,7 @@ 
Analysing the climate of an area for a given period

   )
 
 Sys.time()
-## [1] "2021-07-12 15:48:57 EDT"
+## [1] "2021-10-14 11:58:07 CEST"
 
 head(df_tmax)
 ##   ID_coords         x        y       date Tmax
@@ -161,7 +161,7 @@ 
Analysing the climate of an area for a given period

 
 
 Sys.time()
-## [1] "2021-07-12 15:49:18 EDT"
+## [1] "2021-10-14 11:58:09 CEST"
 
 ras_tmax <- get_daily_climate(
   coords_t,
@@ -171,14 +171,14 @@ 
Analysing the climate of an area for a given period

   )
 
 Sys.time()
-## [1] "2021-07-12 15:49:42 EDT"
+## [1] "2021-10-14 11:58:23 CEST"
 
 ras_tmax
 ## class       : SpatRaster 
 ## dimensions  : 60, 60, 2  (nrow, ncol, nlyr)
 ## resolution  : 0.008333333, 0.008333333  (x, y)
 ## extent      : -5, -4.5, 40.5, 41  (xmin, xmax, ymin, ymax)
-## coord. ref. : +proj=longlat +datum=WGS84 +no_defs 
+## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
 ## source      : memory 
 ## names       : 2012-01-01, 2012-08-01 
 ## min values  :        981,       2605 
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diff --git a/docs/index.html b/docs/index.html
index 6954bef..4ae51b0 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -88,9 +88,9 @@
       

   

 
-

+

 

-easyclimate
+easyclimate 
 

 

 

@@ -179,9 +179,9 @@ 

 

 CITATION

 
If you use easyclimate, please cite both the data source and the package as:

+
Moreno A, Hasenauer H (2016). “Spatial downscaling of European climate data.” International Journal of Climatology, 1444–1458. <URL: https://doi.org/10.1002/joc.4436>.

 
Rammer W, Pucher C, Neumann M (2018). Description, Evaluation and Validation of Downscaled Daily Climate Data Version 2. <URL: ftp://palantir.boku.ac.at/Public/ClimateData/>.

-
Moreno A, Hasenauer H (2016). “Spatial downscaling of European climate data.” International Journal of Climatology, 1444–1458.

-
Cruz-Alonso V, Rodríguez-Sánchez F, Pucher C, Ratcliffe S, Astigarraga J, Neumann M, Ruiz-Benito P (2021). easyclimate: Easy access to high-resolution daily climate data for Europe. <URL: https://github.com/VeruGHub/easyclimate>.

+
Cruz-Alonso V, Rodríguez-Sánchez F, Pucher C, Ruiz-Benito P, Astigarraga J, Neumann M, Ratcliffe S (2021). easyclimate: Easy access to high-resolution daily climate data for Europe. <URL: https://github.com/VeruGHub/easyclimate>.

 

 

 
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 7a1d13a..70312bb 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -1,9 +1,9 @@
-pandoc: 2.11.2
+pandoc: 2.11.4
 pkgdown: 1.6.1
 pkgdown_sha: ~
 articles:
   climatic-indices: climatic-indices.html
   points-df-mat-sf: points-df-mat-sf.html
   polygons-raster: polygons-raster.html
-last_built: 2021-07-19T15:10Z
+last_built: 2021-10-14T09:52Z
 
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