README.md
In VermeirJellen/essentialjsonserializer_r: JSON (de)serialization of complex R-objects
EssentialJSONSerializer (R package)
Jellen Vermeir
March 20, 2019
The intention of the package fuctionality is to (de)serialize language specific complex objects towards generic JSON-datatype objects. The JSON conversion allows for flexible cross language communication when the package is implemented across different languages. This package provides an R implementation of the (de)serialization functionality.
the package can be installed from github by using the devtools utility:
# install.packages("devtools") # if devtools currently not installed
devtools::install_github("VermeirJellen/essentialjsonserializer_r")
JSON (de)serialization of complex R-objects.
Currently, 6 generic complex JSON-datatypes are provided. Corresponding R-mappings are defined as follows:
- JSON datetime - maps to R
POSIXct objects
- JSON timeseries - maps to R 1-dimensional
xts objects
- JSON timeseries_n_dim - maps to R n-dimensional
xts objects (n > 1)
- JSON series - maps to R 1-dimensional
dataframe objects
- JSON dataframe - maps to R n-dimensional
dataframe objects (n > 1)
- JSON matrix - maps to R
matrix objects
The package provides the user with the EssentialJSONSerializer reference class. Following functions are provided:
RToList: Convert complex R structures to a generic datatype list structure.RToJSON: Convert complex R structures to a generic datatype JSON structure.RFromList: Convert generic datatype list structure to complex R-specific objects.RFromJSON: Convert generic datatype JSON structure to complex R-specific objects.
Examples
EssentialJSONSerializer Object Construction
Sys.setenv(tz="UTC")
library(essentialjsonserializer)
essential.serializer <- EssentialJSONSerializer$new()
list
Serialize:
list.input <- list(c(1, 2, 3, 4, 5),
c(6, 7, 8), list(c(1, 2, 3)))
list.json <- essential.serializer$RToJSON(list.input)
cat(list.json)
## [[1,2,3,4,5],[6,7,8],[[1,2,3]]]
Deserialize:
list.json.revert <- essential.serializer$RFromJSON(list.json)
datetime
Serialize:
datetime.input <- as.POSIXct(Sys.time(), tz="UTC")
datetime.json <- essential.serializer$RToJSON(datetime.input)
cat(datetime.json)
## {"_type":"datetime","_data":"2019-03-20T15:33:28"}
Deserialize:
datetime.json.revert <- essential.serializer$RFromJSON(datetime.json)
datetime.json.revert
## [1] "2019-03-20 15:33:28 UTC"
datetime (nested)
Serialize:
datetime.nested.input <- list(input.1 = datetime.input,
input.2 = as.POSIXct(Sys.Date(), tz="UTC"))
datetime.nested.json <- essential.serializer$RToJSON(datetime.nested.input)
cat(datetime.nested.json)
## {"input.1":{"_type":"datetime","_data":"2019-03-20T15:33:28"},"input.2":{"_type":"datetime","_data":"2019-03-20T00:00:00"}}
Deserialize:
datetime.nested.json.revert <- essential.serializer$RFromJSON(datetime.nested.json)
datetime.nested.json.revert
## $input.1
## [1] "2019-03-20 15:33:28 UTC"
##
## $input.2
## [1] "2019-03-20 UTC"
timeseries
Serialize:
timeseries.input <- xts::xts(matrix(c(5, 7, 9, NA), ncol=1),
order.by = c(as.POSIXct("2017-01-01", tz="UTC"),
as.POSIXct("2017-01-02", tz="UTC"),
as.POSIXct("2017-01-03", tz="UTC"),
as.POSIXct("2017-01-04", tz="UTC")))
names(timeseries.input) <- "name_series"
timeseries.json <- essential.serializer$RToJSON(timeseries.input)
cat(timeseries.json)
## {"_type":"timeseries","_data":{"_timestamps":["2017-01-01T00:00:00","2017-01-02T00:00:00","2017-01-03T00:00:00","2017-01-04T00:00:00"],"name_series":[5,7,9,null]}}
Deserialize:
timeseries.json.revert <- essential.serializer$RFromJSON(timeseries.json)
timeseries.json.revert
## name_series
## 2017-01-01 5
## 2017-01-02 7
## 2017-01-03 9
## 2017-01-04 NA
timeseries_n_dim
Serialize:
timeseriesndim.input <- xts:::xts(matrix(c(5, 7, 9, 12, 15, 19), ncol=2),
order.by = c(as.POSIXct("2017-01-01", tz="UTC"),
as.POSIXct("2017-01-02", tz="UTC"),
as.POSIXct("2017-01-03", tz="UTC")))
names(timeseriesndim.input) <- c("name_series_1", "name_series_2")
timeseriesndim.json <- essential.serializer$RToJSON(timeseriesndim.input)
cat(timeseriesndim.json)
## {"_type":"timeseries_n_dim","_data":{"_timestamps":["2017-01-01T00:00:00","2017-01-02T00:00:00","2017-01-03T00:00:00"],"name_series_1":[5,7,9],"name_series_2":[12,15,19]}}
Deserialize:
timeseriesndim.json.revert <- essential.serializer$RFromJSON(timeseriesndim.json)
timeseriesndim.json.revert
## name_series_1 name_series_2
## 2017-01-01 5 12
## 2017-01-02 7 15
## 2017-01-03 9 19
timeseries_n_dim (nested)
Serialize:
timeseries.nested.input <- list(ts1 = timeseries.input,
ts2 = timeseriesndim.input)
timeseries.nested.json <- essential.serializer$RToJSON(timeseries.nested.input)
cat(timeseries.nested.json)
## {"ts1":{"_type":"timeseries","_data":{"_timestamps":["2017-01-01T00:00:00","2017-01-02T00:00:00","2017-01-03T00:00:00","2017-01-04T00:00:00"],"name_series":[5,7,9,null]}},"ts2":{"_type":"timeseries_n_dim","_data":{"_timestamps":["2017-01-01T00:00:00","2017-01-02T00:00:00","2017-01-03T00:00:00"],"name_series_1":[5,7,9],"name_series_2":[12,15,19]}}}
Deserialize:
timeseries.nested.json.revert <- essential.serializer$RFromJSON(timeseries.nested.json)
timeseries.nested.json.revert
## $ts1
## name_series
## 2017-01-01 5
## 2017-01-02 7
## 2017-01-03 9
## 2017-01-04 NA
##
## $ts2
## name_series_1 name_series_2
## 2017-01-01 5 12
## 2017-01-02 7 15
## 2017-01-03 9 19
series
Serialize:
series.input <- data.frame(c(1, 2, 3, 4),
row.names = c("one", "two", "three", "four"))
names(series.input) <- "only_column"
series.json <- essential.serializer$RToJSON(series.input)
cat(series.json)
## {"_type":"series","_data":{"_index":["one","two","three","four"],"only_column":[1,2,3,4]}}
Deserialize:
series.json.revert <- essential.serializer$RFromJSON(series.json)
series.json.revert
## only_column
## one 1
## two 2
## three 3
## four 4
dataframe
Serialize:
dataframe.input <- data.frame(c(1, 2, 3, 4), c(4, 5, 6, 7),
row.names = c("one", "two", "three", "four"))
names(dataframe.input) <- c("first_col", "second_col")
dataframe.json <- essential.serializer$RToJSON(dataframe.input)
cat(dataframe.json)
## {"_type":"dataframe","_data":{"_index":["one","two","three","four"],"first_col":[1,2,3,4],"second_col":[4,5,6,7]}}
Deserialize:
dataframe.json.revert <- essential.serializer$RFromJSON(dataframe.json)
dataframe.json.revert
## first_col second_col
## one 1 4
## two 2 5
## three 3 6
## four 4 7
matrix
Serialize:
matrix.input <- matrix(c(1, 2, 3, 4, 5, 6), ncol = 2, byrow = TRUE)
matrix.json <- essential.serializer$RToJSON(matrix.input)
cat(matrix.json)
## {"_type":"matrix","_data":[[1,2],[3,4],[5,6]]}
Deserialize:
matrix.json.revert <- essential.serializer$RFromJSON(matrix.json)
matrix.json.revert
## [,1] [,2]
## [1,] 1 2
## [2,] 3 4
## [3,] 5 6
nested structure
Serialize:
r.input <- list(list.input = list.input,
datetime.input = datetime.input,
datetime.nested = datetime.nested.input,
timeseries.input = timeseries.input,
timeseriesndim.input = timeseriesndim.input,
series.input = series.input,
dataframe.input = dataframe.input,
matrix.input = matrix.input,
timeseries.nested = timeseries.nested.input)
r.json <- essential.serializer$RToJSON(r.input)
cat(r.json)
## {"list.input":[[1,2,3,4,5],[6,7,8],[[1,2,3]]],"datetime.input":{"_type":"datetime","_data":"2019-03-20T15:33:28"},"datetime.nested":{"input.1":{"_type":"datetime","_data":"2019-03-20T15:33:28"},"input.2":{"_type":"datetime","_data":"2019-03-20T00:00:00"}},"timeseries.input":{"_type":"timeseries","_data":{"_timestamps":["2017-01-01T00:00:00","2017-01-02T00:00:00","2017-01-03T00:00:00","2017-01-04T00:00:00"],"name_series":[5,7,9,null]}},"timeseriesndim.input":{"_type":"timeseries_n_dim","_data":{"_timestamps":["2017-01-01T00:00:00","2017-01-02T00:00:00","2017-01-03T00:00:00"],"name_series_1":[5,7,9],"name_series_2":[12,15,19]}},"series.input":{"_type":"series","_data":{"_index":["one","two","three","four"],"only_column":[1,2,3,4]}},"dataframe.input":{"_type":"dataframe","_data":{"_index":["one","two","three","four"],"first_col":[1,2,3,4],"second_col":[4,5,6,7]}},"matrix.input":{"_type":"matrix","_data":[[1,2],[3,4],[5,6]]},"timeseries.nested":{"ts1":{"_type":"timeseries","_data":{"_timestamps":["2017-01-01T00:00:00","2017-01-02T00:00:00","2017-01-03T00:00:00","2017-01-04T00:00:00"],"name_series":[5,7,9,null]}},"ts2":{"_type":"timeseries_n_dim","_data":{"_timestamps":["2017-01-01T00:00:00","2017-01-02T00:00:00","2017-01-03T00:00:00"],"name_series_1":[5,7,9],"name_series_2":[12,15,19]}}}}
Deserialize:
r.json.revert <- essential.serializer$RFromJSON(r.json)
r.json.revert
## $list.input
## $list.input[[1]]
## [1] 1 2 3 4 5
##
## $list.input[[2]]
## [1] 6 7 8
##
## $list.input[[3]]
## $list.input[[3]][[1]]
## [1] 1 2 3
##
##
##
## $datetime.input
## [1] "2019-03-20 15:33:28 UTC"
##
## $datetime.nested
## $datetime.nested$input.1
## [1] "2019-03-20 15:33:28 UTC"
##
## $datetime.nested$input.2
## [1] "2019-03-20 UTC"
##
##
## $timeseries.input
## name_series
## 2017-01-01 5
## 2017-01-02 7
## 2017-01-03 9
## 2017-01-04 NA
##
## $timeseriesndim.input
## name_series_1 name_series_2
## 2017-01-01 5 12
## 2017-01-02 7 15
## 2017-01-03 9 19
##
## $series.input
## only_column
## one 1
## two 2
## three 3
## four 4
##
## $dataframe.input
## first_col second_col
## one 1 4
## two 2 5
## three 3 6
## four 4 7
##
## $matrix.input
## [,1] [,2]
## [1,] 1 2
## [2,] 3 4
## [3,] 5 6
##
## $timeseries.nested
## $timeseries.nested$ts1
## name_series
## 2017-01-01 5
## 2017-01-02 7
## 2017-01-03 9
## 2017-01-04 NA
##
## $timeseries.nested$ts2
## name_series_1 name_series_2
## 2017-01-01 5 12
## 2017-01-02 7 15
## 2017-01-03 9 19
Donations
If you find this software useful and/or you would like to see additional extensions, feel free to donate some btc:
- BTC: 3NmxUnuK8ZqAszzFzcpBerKsy4ajQpb8mi
Licensing
Copyright 2019 Essential Data Science Consulting ltd. (EssentialDataScience.com / jellenvermeir@essentialdatascience.com). This software is copyrighted under the MIT license: View added LICENSE file.
VermeirJellen/essentialjsonserializer_r documentation built on May 16, 2019, 4:24 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
EssentialJSONSerializer (R package)
Jellen Vermeir March 20, 2019
The intention of the package fuctionality is to (de)serialize language specific complex objects towards generic JSON-datatype objects. The JSON conversion allows for flexible cross language communication when the package is implemented across different languages. This package provides an R implementation of the (de)serialization functionality.
the package can be installed from github by using the devtools utility:
# install.packages("devtools") # if devtools currently not installed
devtools::install_github("VermeirJellen/essentialjsonserializer_r")
JSON (de)serialization of complex R-objects.
Currently, 6 generic complex JSON-datatypes are provided. Corresponding R-mappings are defined as follows:
- JSON datetime - maps to R
POSIXctobjects - JSON timeseries - maps to R 1-dimensional
xtsobjects - JSON timeseries_n_dim - maps to R n-dimensional
xtsobjects (n > 1) - JSON series - maps to R 1-dimensional
dataframeobjects - JSON dataframe - maps to R n-dimensional
dataframeobjects (n > 1) - JSON matrix - maps to R
matrixobjects
The package provides the user with the EssentialJSONSerializer reference class. Following functions are provided:
RToList: Convert complex R structures to a generic datatype list structure.RToJSON: Convert complex R structures to a generic datatype JSON structure.RFromList: Convert generic datatype list structure to complex R-specific objects.RFromJSON: Convert generic datatype JSON structure to complex R-specific objects.
Examples
EssentialJSONSerializer Object Construction
Sys.setenv(tz="UTC")
library(essentialjsonserializer)
essential.serializer <- EssentialJSONSerializer$new()
list
Serialize:
list.input <- list(c(1, 2, 3, 4, 5),
c(6, 7, 8), list(c(1, 2, 3)))
list.json <- essential.serializer$RToJSON(list.input)
cat(list.json)
## [[1,2,3,4,5],[6,7,8],[[1,2,3]]]
Deserialize:
list.json.revert <- essential.serializer$RFromJSON(list.json)
datetime
Serialize:
datetime.input <- as.POSIXct(Sys.time(), tz="UTC")
datetime.json <- essential.serializer$RToJSON(datetime.input)
cat(datetime.json)
## {"_type":"datetime","_data":"2019-03-20T15:33:28"}
Deserialize:
datetime.json.revert <- essential.serializer$RFromJSON(datetime.json)
datetime.json.revert
## [1] "2019-03-20 15:33:28 UTC"
datetime (nested)
Serialize:
datetime.nested.input <- list(input.1 = datetime.input,
input.2 = as.POSIXct(Sys.Date(), tz="UTC"))
datetime.nested.json <- essential.serializer$RToJSON(datetime.nested.input)
cat(datetime.nested.json)
## {"input.1":{"_type":"datetime","_data":"2019-03-20T15:33:28"},"input.2":{"_type":"datetime","_data":"2019-03-20T00:00:00"}}
Deserialize:
datetime.nested.json.revert <- essential.serializer$RFromJSON(datetime.nested.json)
datetime.nested.json.revert
## $input.1
## [1] "2019-03-20 15:33:28 UTC"
##
## $input.2
## [1] "2019-03-20 UTC"
timeseries
Serialize:
timeseries.input <- xts::xts(matrix(c(5, 7, 9, NA), ncol=1),
order.by = c(as.POSIXct("2017-01-01", tz="UTC"),
as.POSIXct("2017-01-02", tz="UTC"),
as.POSIXct("2017-01-03", tz="UTC"),
as.POSIXct("2017-01-04", tz="UTC")))
names(timeseries.input) <- "name_series"
timeseries.json <- essential.serializer$RToJSON(timeseries.input)
cat(timeseries.json)
## {"_type":"timeseries","_data":{"_timestamps":["2017-01-01T00:00:00","2017-01-02T00:00:00","2017-01-03T00:00:00","2017-01-04T00:00:00"],"name_series":[5,7,9,null]}}
Deserialize:
timeseries.json.revert <- essential.serializer$RFromJSON(timeseries.json)
timeseries.json.revert
## name_series
## 2017-01-01 5
## 2017-01-02 7
## 2017-01-03 9
## 2017-01-04 NA
timeseries_n_dim
Serialize:
timeseriesndim.input <- xts:::xts(matrix(c(5, 7, 9, 12, 15, 19), ncol=2),
order.by = c(as.POSIXct("2017-01-01", tz="UTC"),
as.POSIXct("2017-01-02", tz="UTC"),
as.POSIXct("2017-01-03", tz="UTC")))
names(timeseriesndim.input) <- c("name_series_1", "name_series_2")
timeseriesndim.json <- essential.serializer$RToJSON(timeseriesndim.input)
cat(timeseriesndim.json)
## {"_type":"timeseries_n_dim","_data":{"_timestamps":["2017-01-01T00:00:00","2017-01-02T00:00:00","2017-01-03T00:00:00"],"name_series_1":[5,7,9],"name_series_2":[12,15,19]}}
Deserialize:
timeseriesndim.json.revert <- essential.serializer$RFromJSON(timeseriesndim.json)
timeseriesndim.json.revert
## name_series_1 name_series_2
## 2017-01-01 5 12
## 2017-01-02 7 15
## 2017-01-03 9 19
timeseries_n_dim (nested)
Serialize:
timeseries.nested.input <- list(ts1 = timeseries.input,
ts2 = timeseriesndim.input)
timeseries.nested.json <- essential.serializer$RToJSON(timeseries.nested.input)
cat(timeseries.nested.json)
## {"ts1":{"_type":"timeseries","_data":{"_timestamps":["2017-01-01T00:00:00","2017-01-02T00:00:00","2017-01-03T00:00:00","2017-01-04T00:00:00"],"name_series":[5,7,9,null]}},"ts2":{"_type":"timeseries_n_dim","_data":{"_timestamps":["2017-01-01T00:00:00","2017-01-02T00:00:00","2017-01-03T00:00:00"],"name_series_1":[5,7,9],"name_series_2":[12,15,19]}}}
Deserialize:
timeseries.nested.json.revert <- essential.serializer$RFromJSON(timeseries.nested.json)
timeseries.nested.json.revert
## $ts1
## name_series
## 2017-01-01 5
## 2017-01-02 7
## 2017-01-03 9
## 2017-01-04 NA
##
## $ts2
## name_series_1 name_series_2
## 2017-01-01 5 12
## 2017-01-02 7 15
## 2017-01-03 9 19
series
Serialize:
series.input <- data.frame(c(1, 2, 3, 4),
row.names = c("one", "two", "three", "four"))
names(series.input) <- "only_column"
series.json <- essential.serializer$RToJSON(series.input)
cat(series.json)
## {"_type":"series","_data":{"_index":["one","two","three","four"],"only_column":[1,2,3,4]}}
Deserialize:
series.json.revert <- essential.serializer$RFromJSON(series.json)
series.json.revert
## only_column
## one 1
## two 2
## three 3
## four 4
dataframe
Serialize:
dataframe.input <- data.frame(c(1, 2, 3, 4), c(4, 5, 6, 7),
row.names = c("one", "two", "three", "four"))
names(dataframe.input) <- c("first_col", "second_col")
dataframe.json <- essential.serializer$RToJSON(dataframe.input)
cat(dataframe.json)
## {"_type":"dataframe","_data":{"_index":["one","two","three","four"],"first_col":[1,2,3,4],"second_col":[4,5,6,7]}}
Deserialize:
dataframe.json.revert <- essential.serializer$RFromJSON(dataframe.json)
dataframe.json.revert
## first_col second_col
## one 1 4
## two 2 5
## three 3 6
## four 4 7
matrix
Serialize:
matrix.input <- matrix(c(1, 2, 3, 4, 5, 6), ncol = 2, byrow = TRUE)
matrix.json <- essential.serializer$RToJSON(matrix.input)
cat(matrix.json)
## {"_type":"matrix","_data":[[1,2],[3,4],[5,6]]}
Deserialize:
matrix.json.revert <- essential.serializer$RFromJSON(matrix.json)
matrix.json.revert
## [,1] [,2]
## [1,] 1 2
## [2,] 3 4
## [3,] 5 6
nested structure
Serialize:
r.input <- list(list.input = list.input,
datetime.input = datetime.input,
datetime.nested = datetime.nested.input,
timeseries.input = timeseries.input,
timeseriesndim.input = timeseriesndim.input,
series.input = series.input,
dataframe.input = dataframe.input,
matrix.input = matrix.input,
timeseries.nested = timeseries.nested.input)
r.json <- essential.serializer$RToJSON(r.input)
cat(r.json)
## {"list.input":[[1,2,3,4,5],[6,7,8],[[1,2,3]]],"datetime.input":{"_type":"datetime","_data":"2019-03-20T15:33:28"},"datetime.nested":{"input.1":{"_type":"datetime","_data":"2019-03-20T15:33:28"},"input.2":{"_type":"datetime","_data":"2019-03-20T00:00:00"}},"timeseries.input":{"_type":"timeseries","_data":{"_timestamps":["2017-01-01T00:00:00","2017-01-02T00:00:00","2017-01-03T00:00:00","2017-01-04T00:00:00"],"name_series":[5,7,9,null]}},"timeseriesndim.input":{"_type":"timeseries_n_dim","_data":{"_timestamps":["2017-01-01T00:00:00","2017-01-02T00:00:00","2017-01-03T00:00:00"],"name_series_1":[5,7,9],"name_series_2":[12,15,19]}},"series.input":{"_type":"series","_data":{"_index":["one","two","three","four"],"only_column":[1,2,3,4]}},"dataframe.input":{"_type":"dataframe","_data":{"_index":["one","two","three","four"],"first_col":[1,2,3,4],"second_col":[4,5,6,7]}},"matrix.input":{"_type":"matrix","_data":[[1,2],[3,4],[5,6]]},"timeseries.nested":{"ts1":{"_type":"timeseries","_data":{"_timestamps":["2017-01-01T00:00:00","2017-01-02T00:00:00","2017-01-03T00:00:00","2017-01-04T00:00:00"],"name_series":[5,7,9,null]}},"ts2":{"_type":"timeseries_n_dim","_data":{"_timestamps":["2017-01-01T00:00:00","2017-01-02T00:00:00","2017-01-03T00:00:00"],"name_series_1":[5,7,9],"name_series_2":[12,15,19]}}}}
Deserialize:
r.json.revert <- essential.serializer$RFromJSON(r.json)
r.json.revert
## $list.input
## $list.input[[1]]
## [1] 1 2 3 4 5
##
## $list.input[[2]]
## [1] 6 7 8
##
## $list.input[[3]]
## $list.input[[3]][[1]]
## [1] 1 2 3
##
##
##
## $datetime.input
## [1] "2019-03-20 15:33:28 UTC"
##
## $datetime.nested
## $datetime.nested$input.1
## [1] "2019-03-20 15:33:28 UTC"
##
## $datetime.nested$input.2
## [1] "2019-03-20 UTC"
##
##
## $timeseries.input
## name_series
## 2017-01-01 5
## 2017-01-02 7
## 2017-01-03 9
## 2017-01-04 NA
##
## $timeseriesndim.input
## name_series_1 name_series_2
## 2017-01-01 5 12
## 2017-01-02 7 15
## 2017-01-03 9 19
##
## $series.input
## only_column
## one 1
## two 2
## three 3
## four 4
##
## $dataframe.input
## first_col second_col
## one 1 4
## two 2 5
## three 3 6
## four 4 7
##
## $matrix.input
## [,1] [,2]
## [1,] 1 2
## [2,] 3 4
## [3,] 5 6
##
## $timeseries.nested
## $timeseries.nested$ts1
## name_series
## 2017-01-01 5
## 2017-01-02 7
## 2017-01-03 9
## 2017-01-04 NA
##
## $timeseries.nested$ts2
## name_series_1 name_series_2
## 2017-01-01 5 12
## 2017-01-02 7 15
## 2017-01-03 9 19
Donations
If you find this software useful and/or you would like to see additional extensions, feel free to donate some btc:
- BTC: 3NmxUnuK8ZqAszzFzcpBerKsy4ajQpb8mi
Licensing
Copyright 2019 Essential Data Science Consulting ltd. (EssentialDataScience.com / jellenvermeir@essentialdatascience.com). This software is copyrighted under the MIT license: View added LICENSE file.
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