# R offers several packages for JSON encoding and decoding.
# We'll use the 'jsonlite' package in this example.

# Install and load the jsonlite package
if (!requireNamespace("jsonlite", quietly = TRUE)) {
  install.packages("jsonlite")
}
library(jsonlite)

# We'll use these two lists to demonstrate encoding and
# decoding of custom types below.
response1 <- list(
  Page = 1,
  Fruits = c("apple", "peach", "pear")
)

response2 <- list(
  page = 1,
  fruits = c("apple", "peach", "pear")
)

# First we'll look at encoding basic data types to
# JSON strings. Here are some examples for atomic
# values.
cat(toJSON(TRUE), "\n")
cat(toJSON(1), "\n")
cat(toJSON(2.34), "\n")
cat(toJSON("gopher"), "\n")

# And here are some for vectors and lists, which encode
# to JSON arrays and objects as you'd expect.
slcD <- c("apple", "peach", "pear")
cat(toJSON(slcD), "\n")

mapD <- list(apple = 5, lettuce = 7)
cat(toJSON(mapD), "\n")

# The jsonlite package can automatically encode your
# custom data types.
cat(toJSON(response1, auto_unbox = TRUE), "\n")
cat(toJSON(response2, auto_unbox = TRUE), "\n")

# Now let's look at decoding JSON data into R
# values. Here's an example for a generic data
# structure.
byt <- '{"num":6.13,"strs":["a","b"]}'

# Here's the actual decoding, and a check for
# associated errors.
dat <- tryCatch(
  fromJSON(byt),
  error = function(e) {
    stop(paste("Error decoding JSON:", e$message))
  }
)
print(dat)

# In order to use the values in the decoded list,
# we can access them directly.
num <- dat$num
print(num)

# Accessing nested data is straightforward in R.
str1 <- dat$strs[1]
print(str1)

# We can also decode JSON into custom data types.
str <- '{"page": 1, "fruits": ["apple", "peach"]}'
res <- fromJSON(str)
print(res)
print(res$fruits[1])

# In R, we can write JSON directly to a file or
# connection using the write_json function.
d <- list(apple = 5, lettuce = 7)
write_json(d, "")