Can't convert JSON to dataframe
I want to convert json file to dataframe in R. With the following code:
link <- 'https://www.dropbox.com/s/ckfn1fpkcix1ccu/bevingenbag.json'
document <- fromJSON(file = link, method = 'C')
bev <- do.call("cbind", document)
I get this:
type features
1 FeatureCollection list(type = "Feature", geometry = list(type = "Point", coordinates = c(6.54800000288927, 52.9920000044505)), properties = list(gid = "1496600", yymmdd = "19861226", lat = "52.992", lon = "6.548", mag = "2.8", depth = "1.0", knmilocatie = "Assen", baglocatie = "Assen", tijd = "74751"))
which is the first row of the matrix. All other lines have the same structure. I am interested in the part properties = list(gid = "1496600", yymmdd = "19861226", lat = "52.992", lon = "6.548", mag = "2.8", depth = "1.0", knmilocatie = "Assen", baglocatie = "Assen", tijd = "74751")
that needs to be converted to a dataframe with columns gid, yymmdd, lat, lon, mag, depth, knmilocatie, baglocatie, tijd
.
I've searched and tried several solutions but none of them worked. I used the rjson package for this. I also tried the RJSONIO and jsonlite package, but couldn't extract the information I needed.
Does anyone know how to fix this problem?
source to share
You can get the dataframe here:
library(rjson)
document <- fromJSON(file = "bevingenbag.json", method = 'C')
dat <- do.call(rbind, lapply(document$features,
function(x) data.frame(x$properties)))
Edit: how to replace empty values ββwith NA
:
dat$baglocatie[dat$baglocatie == ""] <- NA
Result:
head(dat)
gid yymmdd lat lon mag depth knmilocatie baglocatie tijd
1 1496600 19861226 52.992 6.548 2.8 1.0 Assen Assen 74751
2 1496601 19871214 52.928 6.552 2.5 1.5 Hooghalen Hooghalen 204951
3 1496602 19891201 52.529 4.971 2.7 1.2 Purmerend Kwadijk 200914
4 1496603 19910215 52.771 6.914 2.2 3.0 Emmen Emmen 21116
5 1496604 19910425 52.952 6.575 2.6 3.0 Geelbroek Ekehaar 102631
6 1496605 19910808 52.965 6.573 2.7 3.0 Eleveld Assen 40114
source to share
This is another very similar approach.
An approach@SvenHohenstein creates a dataframe at every step, an expensive process. It's much faster for creating vectors and re-entering the entire result at the end. Also, Sven's approach makes each column a factor, which may or may not be what you want. The approach below works about 200 times faster. This can be important if you intend to do this multiple times. Finally, you will need to convert the columns lon, lat, mag, and depth
to numeric.
library(microbenchmark)
library(rjson)
document <- fromJSON(file = "bevingenbag.json", method = 'C')
json2df.1 <- function(json){ # @SvenHohenstein approach
df <- do.call(rbind, lapply(json$features,
function(x) data.frame(x$properties, stringsAsFactors=F)))
return(df)
}
json2df.2 <- function(json){
df <- do.call(rbind,lapply(json[["features"]],function(x){c(x$properties)}))
df <- data.frame(apply(result,2,as.character), stringsAsFactors=F)
return(df)
}
microbenchmark(x<-json2df.1(document), y<-json2df.2(document), times=10)
# Unit: milliseconds
# expr min lq median uq max neval
# x <- json2df.1(document) 2304.34378 2654.95927 2822.73224 2977.75666 3227.30996 10
# y <- json2df.2(document) 13.44385 15.27091 16.78201 18.53474 19.70797 10
identical(x,y)
# [1] TRUE
source to share