1 Información sistema

# Información Sesion

Información Sesion

sessionInfo()

## R version 4.0.2 (2020-06-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
## 
## locale:
##  [1] LC_CTYPE=es_ES.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=es_ES.UTF-8        LC_COLLATE=es_ES.UTF-8    
##  [5] LC_MONETARY=es_ES.UTF-8    LC_MESSAGES=es_ES.UTF-8   
##  [7] LC_PAPER=es_ES.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## loaded via a namespace (and not attached):
##  [1] compiler_4.0.2  magrittr_1.5    tools_4.0.2     htmltools_0.5.0
##  [5] yaml_2.2.1      stringi_1.5.3   rmarkdown_2.4   knitr_1.30     
##  [9] stringr_1.4.0   xfun_0.18       digest_0.6.25   rlang_0.4.7    
## [13] evaluate_0.14

1.1 Preparación

1.1.1 Limpieza Global Enviroment

1.1.2 Limpieza Consola

cat("\014")

# Generamos función
clc <- 0
class(clc) <- 'limpiar'
print.limpiar <- function(rObject) cat("\014")

1.1.3 Directorio de trabajo

## [1] "/home/oscar/Documentos/Medium/crosstalk_R"

1.2 Instalar modulos

#devtools::install_github("rstudio/crosstalk")
#devtools::install_github("jcheng5/d3scatter")
#devtools::install_github("rstudio/leaflet")


packages <- c("dplyr", "DT", "crosstalk","d3scatter","leaflet")
newpack  = packages[!(packages %in% installed.packages()[,"Package"])]

if(length(newpack)) install.packages(newpack)
a=lapply(packages, library, character.only=TRUE)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

2 Cervezas

2.1 Carga dataset

beer <- read.csv("./beer_dataset_clean.csv")
head(beer)

##               Style Size.L.      OG     FG  ABV   IBU Color BoilSize BoilTime
## 1      American IPA   10.41  1.0620 1.0090 6.95 19.91  5.46    12.30       60
## 2 American Pale Ale    5.00 14.0118 2.3984 6.24 42.07  8.12    10.00       60
## 3 American Pale Ale   22.00  1.0540 1.0130 5.39 21.63  9.64    26.00       60
## 4 American Pale Ale   10.00  1.0520 1.0100 5.62 67.70 11.72    17.50       60
## 5            Saison   20.82  1.0540 1.0070 6.08 12.90  3.14    24.61       90
## 6      American IPA   17.00  1.0510 1.0120 5.09 39.74  7.07    21.00       60
##   BoilGravity Efficiency       SugarScale BrewMethod
## 1       1.014         35 Specific Gravity    extract
## 2       7.200         71            Plato       BIAB
## 3       1.046         75 Specific Gravity  All Grain
## 4       1.030         70 Specific Gravity       BIAB
## 5       1.039         85 Specific Gravity       BIAB
## 6       1.041         75 Specific Gravity  All Grain

2.2 Gráficas interactivas

2.2.1 Primera

shared_beer <- SharedData$new(beer)
bscols(widths = c(3,NA,NA),
  list(
    filter_checkbox("Style", "Estilo", shared_beer, ~Style, inline = TRUE),
    filter_slider("IBU", "Amargor", shared_beer, ~IBU, width = "100%"),
    filter_select("ABV", "Alcohol", shared_beer, ~ABV)
  ),
  d3scatter(shared_beer, ~ABV, ~IBU, ~factor(Style), width="100%", height=450),
  d3scatter(shared_beer, ~Color, ~ABV, ~factor(Style), width="100%", height=450)
)

2.2.2 Segunda

bscols(
  d3scatter(shared_beer, ~Color, ~ABV, ~Style, width="100%", height=450),
  d3scatter(shared_beer, ~Color, ~IBU, ~Style, width="100%", height=450)
)

2.2.3 Tercero

row.names(beer) <- NULL
sd_beer_all <- SharedData$new(beer, group = "beer_subset")
sd_beer_auto <- SharedData$new(beer[beer$SugarScale == "Specific Gravity",], 
                               group = "beer_subset")
sd_beer_manual <- SharedData$new(beer[beer$SugarScale == "Plato",], 
                                 group = "beer_subset")

bscols(widths = c(8, 4),
  d3scatter(sd_beer_all, ~IBU, ~Color, ~factor(Style),
    x_lim = ~range(IBU), y_lim = ~range(Color),
    width = "100%", height = 500),
  list(
    d3scatter(sd_beer_auto, ~IBU, ~Color, ~factor(Style),
      x_lim = range(beer$IBU), y_lim = range(beer$Color),
      width = "100%", height = 250),
    d3scatter(sd_beer_manual, ~IBU, ~Color, ~factor(Style),
      x_lim = range(beer$IBU), y_lim = range(beer$Color),
      width = "100%", height = 250)
  )
)

3 Parques eolicos

###Carga de dataset

data <- read.csv("./datos_limpios_eolicos.csv")

head(data)

##                              Name  Country                  City On_service
## 1                    Dithmarschen Alemania          Dithmarschen    Unknown
## 2                     Wesselburen Alemania           Wesselburen    Unknown
## 3 CÃ´te de lâ\u0080\u0099Epinette  Francia La Chaussee sur Marne    2002/11
## 4                      Le Quarnon  Francia                 Pogny    2005/01
## 5                        DonzÃ¨re  Francia              DonzÃ¨re    1999/09
## 6                       Tarcienne  Belgica              Walcourt    2005/12
##   Energy_power On_service.1 Latitude Longitude Tower_length           Province
## 1         2250           si 54.32000  9.189972      Unknown Schleswig-Holstein
## 2         1500           si 54.21997  8.929972      Unknown Schleswig-Holstein
## 3         1500           si 48.85528  4.528750      Unknown                51 
## 4         4000           si 48.86225  4.520944      Unknown                51 
## 5         3000           si 44.45000  4.750000      Unknown                26 
## 6         9000           si 50.30281  4.504472      Unknown             Namur 
##                    CCAA Power_KW Diameter_mts   Brand   Model Towers
## 1    Schleswig-Holstein        0            0         Unknown      0
## 2    Schleswig-Holstein        0            0 Unknown Unknown      0
## 3             Grand Est        0            0 Unknown Unknown      0
## 4             Grand Est     2000           82 Repower    MM82      2
## 5 Auvergne-RhÃ´ne-Alpes      600           43  Nordex N43/600      5
## 6              Wallonie     1500           77 Repower    MD77      6

3.0.1 Selección variables

df <- data %>%
  select(-On_service,-City,-On_service.1,-Latitude,-Longitude,-Province,-CCAA)
colnames(df) <- c("Name", "Country", "Energy", "Tower L", "Power", "Diameter", "Brand", "Model", "Num.Tow")

3.0.2 Dimensión

dim(df)

## [1] 14330     9

3.1 Dataset con un filtro

library(DT)
datatable(
  df, options = list(search = list(regex = TRUE, caseInsensitive = FALSE, search = ' ', escape=FALSE, 
      options = list(sDom  = '<"top">lrt<"bottom">ip')), 
                     filter = list(position = 'top', selection='multiple', clear = FALSE),
    pageLength = 10,
  lengthMenu = c(10, 10, 10, 10, 10, 10, 10, 10, 10)
  )
)

## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html

## Dataset con filtros por atributo

# individual column filters
datatable(df, options = list(searchHighlight = TRUE), filter = 'top')

## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html

3.2 Dataset con 1 filtro

options(DT.options = list(pageLength = 5))
# global search
datatable(df, options = list(searchHighlight = TRUE, 
                             search = list(search = 'da')))

## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html

3.3 ETL Torres Eolicas

data1 <- data%>%
    dplyr::select(Latitude, Longitude, Energy_power, Power_KW, Diameter_mts)%>%
    rename(
        lat = Latitude,
        long = Longitude,
        depth = Energy_power,
        mag = Power_KW,
        stations = Diameter_mts
        )

3.4 Mapa completo

Power KW

## Assuming "long" and "lat" are longitude and latitude, respectively

3.5 Mapa con torres eolicas

sd <- SharedData$new(data1[sample(nrow(data1), 1000),]) # Wrap data frame in SharedData
filter_slider("mag", "Power KW", sd, column=~mag, step=0.1, width=450)   # Create a filter input

Power KW

# Use SharedData like a dataframe with Crosstalk-enabled widgets
bscols(
    leaflet(sd) %>% 
    addTiles() %>% 
    addMarkers(),
    datatable(sd, 
              extensions="Scroller", style="bootstrap", 
                           class="compact", width="100%",
                           options=list(deferRender=TRUE, scrollY=300, 
                                        scroller=TRUE))
)

## Assuming "long" and "lat" are longitude and latitude, respectively

Ejemplo Clasificación Cervezas y Parques Eolicas

Cervezas y Torres eolicas

Oscar Rojo

18/9/2020

1 Información sistema

1.1 Preparación

1.1.1 Limpieza Global Enviroment

1.1.2 Limpieza Consola

1.1.3 Directorio de trabajo

1.2 Instalar modulos

2 Cervezas

2.1 Carga dataset

2.2 Gráficas interactivas

2.2.1 Primera

2.2.2 Segunda

2.2.3 Tercero

3 Parques eolicos

3.0.1 Selección variables

3.0.2 Dimensión

3.1 Dataset con un filtro

3.2 Dataset con 1 filtro

3.3 ETL Torres Eolicas

3.4 Mapa completo

3.5 Mapa con torres eolicas

3.6 Link Mapa Interactivo

3.6.0.1 WIND FARM 2020