Fix spelling errors

NEWS.md

@@ -11,7 +11,7 @@ Key is globally managed via an environment variable: see `aemet_api_key()`.
 
 -   Results are provided on `tibble/tidyverse` format.
 -   Results are parsed to the correct formats (numbers and dates when possible).
--   Spatial support: New option `return_sf` would return `sf` objects intead of tibbles. `sf (>= 0.9)` required, listed on 'Suggests' so it is not strictly required.
+-   Spatial support: New option `return_sf` would return `sf` objects instead of tibbles. `sf (>= 0.9)` required, listed on 'Suggests' so it is not strictly required.
 -   API functions gain new parameters, as `verbose`, to check results.
 
 ## Enhancements

R/aemet_api_key.R

@@ -13,7 +13,7 @@
 #'   * Run `Sys.setenv(AEMET_API_KEY = "Your_Key")`. You would need to run this
 #'   command on each session (Similar to `install = FALSE`).
 #'   * Write this line on your .Renviron file: `AEMET_API_KEY = "Your_Key"` (
-#'    same behaviour than `install = TRUE`). This would store your API key
+#'    same behavior than `install = TRUE`). This would store your API key
 #'    permanently.
 #'
 #' @return None

R/stripes.R

@@ -119,7 +119,7 @@ climatestripes_station <-
 #'   (default 11).
 #'
 #' @param col_pal Character string indicating the name of the
-#'   [hcl.pals()] colour palette to be used for plotting.
+#'   [hcl.pals()] color palette to be used for plotting.
 #'
 #' @param ... further arguments passed to [ggplot2::theme()].
 #'

R/windrose.R

@@ -208,7 +208,7 @@ windrose_period <-
 #' @param legend_title Character string to be used for the legend title.
 #' @param plot_title Character string to be used for the plot title.
 #' @param col_pal Character string indicating the name of the
-#'   [hcl.pals()] colour palette to be used for plotting.
+#'   [hcl.pals()] color palette to be used for plotting.
 #' @param n_col The number of columns of plots (default 1).
 #' @param ... further arguments (ignored).
 #'

codemeta.json

@@ -2,7 +2,7 @@
   "@context": "https://doi.org/10.5063/schema/codemeta-2.0",
   "@type": "SoftwareSourceCode",
   "identifier": "climaemet",
-  "description": "Tools to download the climatic data of the Spanish\n    Meteorological Agency (AEMET) directly from R using their API and\n    create scientific graphs (climate charts, trend analysis of climate\n    time series, temperature and precipitation anomalies maps, warming\n    stripes graphics, climatograms, etc.).",
+  "description": "Tools to download the climatic data of the Spanish Meteorological Agency (AEMET) directly from R using their API and create scientific graphs (climate charts, trend analysis of climate time series, temperature and precipitation anomalies maps, warming stripes graphics, climatograms, etc.).",
   "name": "climaemet: Climate AEMET Tools",
   "codeRepository": "https://github.com/rOpenSpain/climaemet",
   "relatedLink": "https://ropenspain.github.io/climaemet/",
@@ -278,7 +278,7 @@
   },
   "applicationCategory": "Meteorology",
   "isPartOf": "https://ropenspain.es/",
-  "fileSize": "432.174KB",
+  "fileSize": "436.219KB",
   "citation": [
     {
       "@type": "SoftwareSourceCode",

inst/WORDLIST

@@ -0,0 +1,148 @@
+AEMET
+AEROPUERTO
+ARENYS
+Avilés
+BLUP
+Bogdan
+CMD
+CRS
+Climatogram
+DMS
+DOI
+EDA
+EPSG
+ESDA
+ETRS
+FABRA
+Fernández
+Filomena
+Firts
+Geogrid
+Geospatial
+Geostatistical
+Gerhardus
+Guijarro
+IDW
+Jena
+Klimadiagramm
+Krige
+Lieth
+MANRESA
+Mattern
+ORCID
+OpenData
+Renviron
+Rosca
+Royé
+SABADELL
+ShowYourStripes
+Varias
+WGS
+WLS
+Weltatlas
+Windrose
+YYYY
+ZARAGOZA
+Zaragoza
+aemet
+altitud
+anioMax
+anioMedAlta
+anioMedBaja
+anioMedMax
+anioMedMin
+anioMin
+api
+auth
+centrodedescargas
+choropleth
+climatogram
+climatograms
+climatol
+climatological
+codigo
+config
+cran
+des
+diaMax
+diaMin
+dir
+dms
+english
+evap
+fecha
+geostatistical
+geostatistics
+ggplot
+github
+gra
+horaPresMax
+horaPresMin
+horaracha
+horatmax
+horatmin
+https
+indicativo
+indsinop
+inicio
+inso
+io
+jguijarrop
+json
+kms
+latitud
+llu
+longitud
+mapSpain
+md
+mesMax
+mesMedAlta
+mesMedBaja
+mesMedMax
+mesMedMin
+mesMin
+metacran
+nie
+nombre
+np
+nt
+nv
+nw
+obtencionAPIKey
+omnidirectional
+opendata
+prec
+presMax
+presMin
+priori
+provincia
+quartile
+quartiles
+rOS
+racha
+regionalized
+repo
+ropenspain
+semivariogram
+semivariograms
+spanish
+stochastics
+temMax
+temMedAlta
+temMedBaja
+temMedMax
+temMedMin
+temMin
+tibble
+tibbles
+tm
+tmax
+tmed
+tmin
+ubicacion
+unprojected
+vectorized
+velmedia
+windrose
+windroses
+º

vignettes/articles/Spatial-Interpolation-with-climaemet.Rmd

@@ -24,7 +24,6 @@ hlp_install <- function(pkg) {
     install.packages(pkg)
   }
 }
-
 ```
 
 **climaemet** can retrieve data from the stations included on [AEMET Open

vignettes/articles/dealing_spatial.Rmd

@@ -388,7 +388,7 @@ clim_data_clean_nodup <- st_as_sf(clim_data_clean_nodup_sp)
 
 ## Exploratory Spatial Data Analysis (ESDA)
 
-Exploratory Data Analysis (EDA) is the first important step of data modelling, so ESDA is also the first
+Exploratory Data Analysis (EDA) is the first important step of data modeling, so ESDA is also the first
 step in spatial statistics. **What do the data tell me about the relationship
 between `X` and `Y` coordinates and the variable `tmin` ?**
 
@@ -492,7 +492,7 @@ plot(vario.geor, pch = 20)
 semivariogram by eye. It is an intuitive function to play with the types and
 parameters of the semivariogram. It can help you to fit the empirical
 semivariogram to a theoretical one. Of course, there are other statistical
-methods to fit the semiariogram: Ordinary Least Squares (OLS), Weighted Least
+methods to fit the semivariogram: Ordinary Least Squares (OLS), Weighted Least
 Squares (WLS), Maximum Likelihood (ML), Restricted Maximum Likelihood (REML).
 
 Run it on your PC!
@@ -501,7 +501,7 @@ Run it on your PC!
 eyefit(vario.geor)
 ```
 
-With `geoR::eyefit()` we have observed that there **differents types of
+With `geoR::eyefit()` we have observed that there **different types of
 semivariograms** and each type contains **several parameters** that have to be
 fitted.
 
@@ -528,15 +528,15 @@ The main types of semivariograms are:
 
 - *Nugget*
 
-A graphiacal summary of the most common **spatial semivariogram models** can be found here:
+A graphical summary of the most common **spatial semivariogram models** can be found here:
 
 ```{r}
 show.vgms()
 ```
 
 Regarding the **parameters**, the main ones are:
 
--   *Sill*: is defined as the a priori variance of the random funcioion.
+-   *Sill*: is defined as the a priori variance of the random function.
 
 -   *Range*: is the distance at which the sill is reached, which defines the
     threshold of spatial dependence.

vignettes/articles/x01_aemet_stations.Rmd

@@ -15,7 +15,6 @@ hlp_install <- function(pkg) {
     install.packages(pkg)
   }
 }
-
 ```
 
 This annex shows an interactive and searchable version of the results given by