As a regular user of Madrid public libraries I’m glad of having found data about borrowed stuff. So let’s take a look at it and answer a couple of questions.

Miguel de Cervantes Prize is one of the most richest literary prizes and the most prestigious of the prizes to writers in spanish.

So, what’s the impact in the popularity of a writer measured by the number of borrowing in public libraries?

suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(lubridate))
suppressPackageStartupMessages(library(dygraphs))
suppressPackageStartupMessages(library(XML))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(tidyr))
suppressPackageStartupMessages(library(htmlwidgets))
suppressPackageStartupMessages(library(htmltools))

master <- readLines("http://datos.madrid.es/egob/catalogo/212700-0-bibliotecas-prestamos-historico.dcat")
master <- master[grepl("accessURL",master)]
master <- gsub(pattern = " ",replacement = "",master)
master <- gsub('<.*?>','',master)
master <- master[grepl("csv",master)]

rawdata <- vector("list",length(master))
n <- 1
for (url in master){
  rawdata[[n]] <- read.csv(file = url,header = TRUE,sep = ";",quote = '"',fileEncoding = "latin1",colClasses = "character")
  #nos vamos a quedar con el tipo de objeto T; texto monografías (más o menos libros)
  if ("prcocs" %in% colnames(rawdata[[n]])){
    rawdata[[n]] <- rawdata[[n]] %>% dplyr::filter(prcocs=="T    ")
  }
  if ("phcocs" %in% colnames(rawdata[[n]])){
    rawdata[[n]] <- rawdata[[n]] %>% dplyr::filter(phcocs=="T    ") %>% rename(prcocs=phcocs)
  }
  if ("phadul" %in% colnames(rawdata[[n]])){
    rawdata[[n]] <- rawdata[[n]] %>% rename(pradul=phadul)
  }
  if ("prbarc" %in% colnames(rawdata[[n]])){
    rawdata[[n]] <- rawdata[[n]] %>% select(prbarc,prfpre,tiauto,pradul)
  } else {
    rawdata[[n]] <- rawdata[[n]] %>% select(phbarc,phfpre,tiauto,pradul)
  }
  colnames(rawdata[[n]]) <- c("codigo","fecha","autor","infantil")
  n <- n + 1
}

rawdata.df <- bind_rows(rawdata)
rawdata.df <- rawdata.df[grepl("../../..",rawdata.df$fecha),]
rawdata.df$fecha <- dmy(rawdata.df$fecha)
rawdata.df <- rawdata.df[complete.cases(rawdata.df),]

#vamos a hacer un gráfico con gráficos diarios que siempre es bonito
# prestamos.diarios <- rawdata.df %>% dplyr::group_by(fecha) %>% summarise(prestamos=n())
# xts.prestamos.diarios <- xts::xts(x = prestamos.diarios$prestamos,order.by = prestamos.diarios$fecha)
# dy <- dygraph(data=xts.prestamos.diarios) %>% dyRangeSelector()
#2014 Goytisolo, Juan
cerv2014 <- rawdata.df[grepl("Goytisolo, Juan",rawdata.df$autor),]
cerv2014 <- cerv2014 %>% group_by(fecha) %>% summarise(prestamos2014=n())

cerv2015 <- rawdata.df[grepl("Paso",rawdata.df$autor),]
cerv2015 <- cerv2015[grepl("Fernando",cerv2015$autor),]
cerv2015 <- cerv2015 %>% group_by(fecha) %>% summarise(prestamos2015=n())

cerv2016 <- rawdata.df[grepl("Mendoza, Eduardo",rawdata.df$autor),]
cerv2016 <- cerv2016 %>% group_by(fecha) %>% summarise(prestamos2016=n())

cerv2017 <- rawdata.df[grepl("Ramírez, Sergio",rawdata.df$autor),]
cerv2017 <- cerv2017 %>% group_by(fecha) %>% summarise(prestamos2017=n())

cerv <- full_join(cerv2014,cerv2015,by="fecha")
cerv <- full_join(cerv,cerv2016,by="fecha")
cerv <- full_join(cerv,cerv2017,by="fecha")
cerv.graf <- cerv %>% gather(key = year,value = prestamos,2:5)

xts.2014 <- xts::xts(x = cerv2014$prestamos2014,order.by = cerv2014$fecha)
colnames(xts.2014) <- "prestamos"
dy2014 <- dygraph(xts.2014,group = "cervantes",height = 400,main="Premio Cervantes 2014 - Juan Goytisolo") %>% dyEvent("2014-11-24","Fallo") %>% dyEvent("2015-04-23","Entrega") %>% dyEvent("2017-06-04","Muerte") %>% dyRangeSelector() %>% dySeries(name = "prestamos",label = "Juan Goytisolo") %>% dyOptions(stepPlot = TRUE)

xts.2015 <- xts::xts(x = cerv2015$prestamos2015,order.by = cerv2015$fecha)
colnames(xts.2015) <- "prestamos"
dy2015 <- dygraph(xts.2015,group = "cervantes",height = 400,main="Premio Cervantes 2015 - Fernando del Paso") %>% dyEvent("2015-11-12","Fallo") %>% dyEvent("2016-04-23","Entrega") %>% dyRangeSelector() %>% dySeries(name = "prestamos",label = "Fernado del Paso") %>% dyOptions(stepPlot = TRUE)

xts.2016 <- xts::xts(x = cerv2016$prestamos2016,order.by = cerv2016$fecha)
colnames(xts.2016) <- "prestamos"
dy2016 <- dygraph(xts.2016,group = "cervantes",height = 400,main="Premio Cervantes 2016 - Eduardo Mendoza") %>% dyEvent("2016-11-30","Fallo") %>% dyEvent("2017-04-23","Entrega") %>% dyRangeSelector() %>% dySeries(name = "prestamos",label = "Eduardo Mendoza") %>% dyOptions(stepPlot = TRUE)

bdy <- browsable(tagList(dy2014,dy2015,dy2016))
bdy

Finally another fact, who is the most borrowed author in Madrid public libraries? Dedicated to my 7 years old son.

top.autores <- rawdata.df %>% dplyr::filter(autor!="") %>% group_by(autor) %>% summarise(total=100*n()/nrow(rawdata.df)) %>% arrange(-total)
head(top.autores)
## # A tibble: 6 x 2
##               autor     total
##               <chr>     <dbl>
## 1 Stilton, Geronimo 1.5344535
## 2      Stilton, Tea 0.4415608
## 3      Kinney, Jeff 0.4290774
## 4    Denou, Violeta 0.4264427
## 5           Bat Pat 0.3638376
## 6   Ibáñez, F.1936- 0.3617047