Reputation: 53
First time poster, long time lurker. Be gentle. Moderate R user. I am sure there is a better, functional way to do what I need, but felt like I have researched do death with no insight.
I am trying to merge a data set to a pre-existing JSON structure. Where one row of records per JSON structure for many serialized JSON requests.
I load the data set to data which is 13 variables and change the column headers to match how they appear in the JSON structure
library(jsonlite)
#### Map Column headers to their respective names in the JSON Structure
colnames(data) <- c("default.A",
"default.B",
"default.C",
"items.A",
"items.B.1",
"items.B.2",
"items.B.3",
"items.B.4",
)
Create the blank JSON Structure. This is the format for which the JSON requests need to be handled. Simple nested structure.
sample <- '{
"default": {
"A": "",
"B": "",
"C": "",
},
"items": [{
"A": "",
"B": {
"1": "",
"2": "",
"3": "",
"4": "",
}
}]
}'
jsonstructure <- fromJSON(sample)
set everything as a DF. merge them. Fill NAs with Blanks
x <- as.data.frame(data)
y <- as.data.frame(jsonstructure)
Z <- merge(x, y, all = TRUE)
Z[is.na(Z)] <- ""
Convert to JSON
jsonZ <- toJSON(unname(split(Z, 1:nrow(Z))), pretty=TRUE)
cat(jsonZ)
Current output which does not match
[
[
{
"default.A": "",
"default.B": "1234567890",
"default.C": "",
"items.A": "1234567890",
"items.B.1": "1234",
"items.B.2": "1234",
"items.B.3": "1234",
"items.B.4": "1234",
}
],
[
{
"default.A": "",
"default.B": "0987654321",
"default.C": "",
"items.A": "0987654321",
"items.B.1": "4321",
"items.B.2": "4321",
"items.B.3": "4321",
"items.B.4": "4321",
}
]
]
Upvotes: 5
Views: 1402
Reputation: 4474
Could not reproduce your results - but here is my guess of what you want to achieve. See comments for help with the code.
library(jsonlite)
#data.frame with data - you have probably more than 2 rows
data=data.frame(rbind(t(c(NA,1234567890,NA,1234567890,1234,1234,1234,1234)),
t(c(1,NA,2,3,1,1000,NA,1234))))
cn=c("default.A",
"default.B",
"default.C",
"items.A",
"items.B.1",
"items.B.2",
"items.B.3",
"items.B.4")
colnames(data)=cn
#assuming that "." represents structure
mapping=strsplit(cn,"\\.")
#template JSON
jsonstructure <- fromJSON('{"default": {"A": "","B": "","C": ""},
"items": [{"A": "",
"B": {"1": "","2": "","3": "","4": ""}}]}')
#now loop through all rows in your data.frame and store them in JSON format
#this will give you a list with JSON objects (i.e., a list of lists)
json_list=lapply(split(data,1:nrow(data)),function(data_row) {
for (i in seq_along(mapping)) jsonstructure[[mapping[[i]]]]<-data_row[,cn[i]]
jsonstructure
})
Result:
toJSON(json_list[[2]],pretty = TRUE, auto_unbox=TRUE)
#{
# "default": {
# "A": 1,
# "B": "NA",
# "C": 2
# },
# "items": [
# {
# "A": 3,
# "B": {
# "1": 1,
# "2": 1000,
# "4": 1234
# }
# }
# ]
#}
Just another comment. My approach makes use of recursive subsetting of lists as described in the help to the [
operator:
[[ can be applied recursively to lists, so that if the single index i is a vector of length p, alist[[i]] is equivalent to alist[[i1]]...[[ip]] providing all but the final indexing results in a list.
Upvotes: 1
Reputation: 318
If you aren't dead set on jsonlite package, you could try the rjson package
library(rjson)
value = c("", "1234690","")
names(value) = c("A","B","C")
value2 = c("","0987654321","","0987654321")
names(value2) = c("1","2","3","4")
test <- toJSON(list( "default" = value, "items" = list(c("A" = "", "B" = list(value2))) ))
cat(test)
writeLines(test, "test.json")
Upvotes: 1