The downloaded data can be plotted using packages like plotly or leaflet.
Example 1. Interactive time series
library(ineapir)
library(plotly)
#> Loading required package: ggplot2
#>
#> 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':
#>
#> layout
# Filter cpi overall index
filter <- list(values = c("variación anual" , "índice general"))
# Request data of cpi
# Table url: https://www.ine.es/jaxiT3/Tabla.htm?t=50902&L=0
general <- get_data_table(idTable = 50902, filter = filter, unnest = TRUE,
tip = "A", validate = FALSE)
# Filter core cpi
filter <- list(values = c("variación anual" ,
"general sin alimentos no elaborados ni productos energéticos"))
# Request data of core cpi
# Table url: https://www.ine.es/jaxiT3/Tabla.htm?t=50907&L=0
subyacente <- get_data_table(idTable = 50907, filter = filter, unnest = TRUE,
tip = "A", validate = FALSE)
# Format Fecha column as date
general$Fecha <- as.Date(general$Fecha)
subyacente$Fecha <- as.Date(subyacente$Fecha)
# Plot cpi overall index
fig <- plot_ly(general, x = ~Fecha, y = ~Valor, name = 'General',
type = 'scatter', mode = 'lines')
## Plot core cpi
fig <- fig %>% add_trace(y = ~subyacente$Valor, name = 'Subyacente',
mode = 'lines') %>%
layout(yaxis = list(title="Variación anual (%)"),
legend = list(title=list(text='<b> IPC </b>'),
x = 0.25,
y = -0.25,
orientation = 'h'),
hovermode = 'x') %>%
config(displayModeBar = FALSE)
figExample 2. Population pyramid
library(forcats)
# Filter for total population of men and women
filter <- list(nacionalidad = "total", sexo = c("hombres", "mujeres"))
# Table: Resident population by date, sex, age group and nationality
# Table url: https://www.ine.es/jaxiT3/Tabla.htm?t=56936&L=0
poblacion <- get_data_table(idTable = 56936, filter = filter, validate = FALSE,
tip = "AM", unnest = TRUE, metanames = TRUE)
# Filter and calculate percentages
pob <- poblacion %>%
filter(Grupo.quinquenal.de.edad != "Todas las edades" & T3_Periodo == "1 de enero de") %>%
group_by(Anyo) %>%
mutate(Total = sum(Valor),
Porcentaje = Valor/Total*100,
Grupo.quinquenal.de.edad = fct_inorder(trimws(Grupo.quinquenal.de.edad))) %>%
ungroup() %>%
mutate(Porcentaje = ifelse(Sexo == "Hombres",
-Porcentaje, Porcentaje))
# Pyramid using plotly
fig <- plot_ly(pob, x = ~Porcentaje , y = ~Grupo.quinquenal.de.edad, color = ~Sexo,
colors = c("blue", "red"), type = 'bar', frame = ~Anyo) %>%
layout(bargap = 0.1, barmode = 'overlay',
xaxis = list(hoverformat = '.2f'),
yaxis = list(title="Edad"),
title = 'Población residente por sexo y grupo de edad') %>%
animation_slider(currentvalue = list(prefix = "Año: ")) %>%
config(displayModeBar = FALSE)
figExample 3. Interactive bar chart.
# Filter for life expectancy
filter = list(ccaa = "", sexo = "total", values = c("0 años", "esperanza de vida"))
# Table: Mortality tables by year, autonomous communities and cities, sex, age and functions.
# Table url: https://www.ine.es/jaxiT3/Tabla.htm?t=27154&L=0
esp <- get_data_table(idTable = 27154, filter = filter, nlast = 1, unnest = TRUE,
metanames = TRUE, tip = "AM", validate = FALSE)
# Bar chart using plotly
fig <- plot_ly(esp,
x = ~esp$Comunidades.y.Ciudades.Autónomas,
y = ~Valor, type = "bar") %>%
config(displayModeBar = FALSE) %>%
layout(xaxis = list(title="", tickangle = -45),
yaxis = list(title = "Años", hoverformat = '.2f'),
title = sprintf("Esperanza de vida al nacimiento por Comunidad Autónoma (%s)",
esp$Anyo[1])
)
figExample 4. Interactive map.
library(htmltools)
# Get the boundaries of the autonomous communities
ccaa <- read_sf("https://www.ine.es/wstempus/geojs/ES/CONTORNOS/70")
# Filter for life expectancy
filter = list(ccaa = "", sexo = "total", values = c("0 años", "esperanza de vida"))
# Table: Mortality tables by year, autonomous communities and cities, sex, age and functions.
# Table url: https://www.ine.es/jaxiT3/Tabla.htm?t=27154&L=0
esp <- get_data_table(idTable = 27154, filter = filter, nlast = 1, unnest = TRUE,
metacodes = TRUE, tip = "AM", validate = FALSE)
# Select a set on columns from data
esp <- subset(esp, select = c("Comunidades.y.Ciudades.Autónomas.Id", "T3_Periodo",
"Anyo","Valor"))
# Join boundaries information with data information
ccaa <- merge(ccaa, esp, by.x = "id_region",
by.y = "Comunidades.y.Ciudades.Autónomas.Id" )
# Create the palette of the legend
pal <- colorBin("plasma", domain = NULL, bins = c(quantile(ccaa$Valor)))
# Labels of the map
labels <- sprintf(
"<strong>%s</strong><br/> Esperanza de vida al nacimiento: %.2f años ",
ccaa$nom_region, ccaa$Valor
) %>% lapply(htmltools::HTML)
# Create the map
m <- leaflet(ccaa) %>%
addTiles() %>%
setView(-4, 40, zoom = 5) %>%
addPolygons(fillOpacity = 0.8,
fillColor = ~pal(Valor),
weight = 1,
color = "white",
label = labels,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto"
),
highlightOptions = highlightOptions(fillOpacity = 1, bringToFront = TRUE,
weight = 2, color = "white")
) %>%
addLegend(pal = pal, values = ~Valor, opacity = 1.0, position = "bottomright",
labFormat = labelFormat(suffix = " años", digits = 2),
title = sprintf("Esperanza de vida al nacimiento (%s)",
esp$Anyo[1]))
m