Last updated: 2023-05-19
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 50c92d7 | Dave Tang | 2023-05-19 | JSON and YAML formats |
JSON and YAML are popular serialisation formats.
In computing, serialization (or serialisation) is the process of translating a data structure or object state into a format that can be stored (e.g. files in secondary storage devices, data buffers in primary storage devices) or transmitted (e.g. data streams over computer networks) and reconstructed later (possibly in a different computer environment).
Install the jsonlite and yaml packages, so that we can generate JSON and YAML.
install.packages(c("jsonlite", "yaml"))Installing packages into '/packages'
(as 'lib' is unspecified)Load libraries.
library(jsonlite)
library(yaml)As a first example, we will convert the women data set,
which is a small data set with 15 observations for 2 variables.
women height weight
1 58 115
2 59 117
3 60 120
4 61 123
5 62 126
6 63 129
7 64 132
8 65 135
9 66 139
10 67 142
11 68 146
12 69 150
13 70 154
14 71 159
15 72 164Convert women to JSON.
women_json <- toJSON(women, pretty = TRUE)
women_json[
{
"height": 58,
"weight": 115
},
{
"height": 59,
"weight": 117
},
{
"height": 60,
"weight": 120
},
{
"height": 61,
"weight": 123
},
{
"height": 62,
"weight": 126
},
{
"height": 63,
"weight": 129
},
{
"height": 64,
"weight": 132
},
{
"height": 65,
"weight": 135
},
{
"height": 66,
"weight": 139
},
{
"height": 67,
"weight": 142
},
{
"height": 68,
"weight": 146
},
{
"height": 69,
"weight": 150
},
{
"height": 70,
"weight": 154
},
{
"height": 71,
"weight": 159
},
{
"height": 72,
"weight": 164
}
] Convert women to YAML.
women_yaml <- as.yaml(women, indent = 3)
writeLines(women_yaml)height:
- 58.0
- 59.0
- 60.0
- 61.0
- 62.0
- 63.0
- 64.0
- 65.0
- 66.0
- 67.0
- 68.0
- 69.0
- 70.0
- 71.0
- 72.0
weight:
- 115.0
- 117.0
- 120.0
- 123.0
- 126.0
- 129.0
- 132.0
- 135.0
- 139.0
- 142.0
- 146.0
- 150.0
- 154.0
- 159.0
- 164.0JSON to data frame.
fromJSON(women_json) height weight
1 58 115
2 59 117
3 60 120
4 61 123
5 62 126
6 63 129
7 64 132
8 65 135
9 66 139
10 67 142
11 68 146
12 69 150
13 70 154
14 71 159
15 72 164YAML to data frame. This does not work for more complex data structures (see below).
yaml.load(women_yaml, handlers = list(map = function(x) as.data.frame(x) )) height weight
1 58 115
2 59 117
3 60 120
4 61 123
5 62 126
6 63 129
7 64 132
8 65 135
9 66 139
10 67 142
11 68 146
12 69 150
13 70 154
14 71 159
15 72 164A data frame containing lists.
my_df <- data.frame(
id = 1:3,
title = letters[1:3]
)
my_df$keywords = list(
c('aa', 'aaa', 'aaaa'),
c('bb', 'bbb'),
c('cc', 'ccc', 'cccc', 'ccccc')
)
my_df id title keywords
1 1 a aa, aaa, aaaa
2 2 b bb, bbb
3 3 c cc, ccc, cccc, cccccConvert my_df to JSON.
my_df_json <- toJSON(my_df, pretty = TRUE)
my_df_json[
{
"id": 1,
"title": "a",
"keywords": ["aa", "aaa", "aaaa"]
},
{
"id": 2,
"title": "b",
"keywords": ["bb", "bbb"]
},
{
"id": 3,
"title": "c",
"keywords": ["cc", "ccc", "cccc", "ccccc"]
}
] Convert my_df to YAML.
my_df_yaml <- as.yaml(my_df, indent = 3)
writeLines(my_df_yaml)id:
- 1
- 2
- 3
title:
- a
- b
- c
keywords:
- - aa
- aaa
- aaaa
- - bb
- bbb
- - cc
- ccc
- cccc
- cccccConverting from JSON to YAML is easy.
identical(writeLines(as.yaml(fromJSON(my_df_json))), writeLines(my_df_yaml))id:
- 1
- 2
- 3
title:
- a
- b
- c
keywords:
- - aa
- aaa
- aaaa
- - bb
- bbb
- - cc
- ccc
- cccc
- ccccc
id:
- 1
- 2
- 3
title:
- a
- b
- c
keywords:
- - aa
- aaa
- aaaa
- - bb
- bbb
- - cc
- ccc
- cccc
- ccccc[1] TRUEConverting from YAML to JSON for my_df is not as
straight-forward because of the different number of keywords.
my_df_list <- yaml.load(my_df_yaml)
my_df_list$id
[1] 1 2 3
$title
[1] "a" "b" "c"
$keywords
$keywords[[1]]
[1] "aa" "aaa" "aaaa"
$keywords[[2]]
[1] "bb" "bbb"
$keywords[[3]]
[1] "cc" "ccc" "cccc" "ccccc"This conversion is different from the original data frame to JSON conversion because this creates a single object, where as the original conversion creates an array with three objects.
toJSON(my_df_list, pretty = TRUE){
"id": [1, 2, 3],
"title": ["a", "b", "c"],
"keywords": [
["aa", "aaa", "aaaa"],
["bb", "bbb"],
["cc", "ccc", "cccc", "ccccc"]
]
} my_df_json[
{
"id": 1,
"title": "a",
"keywords": ["aa", "aaa", "aaaa"]
},
{
"id": 2,
"title": "b",
"keywords": ["bb", "bbb"]
},
{
"id": 3,
"title": "c",
"keywords": ["cc", "ccc", "cccc", "ccccc"]
}
] I could probably write a hacky function to make the conversion but I won’t.
sessionInfo()R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] yaml_2.3.7 jsonlite_1.8.4 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] vctrs_0.6.2 httr_1.4.5 cli_3.6.1 knitr_1.42
[5] rlang_1.1.0 xfun_0.39 stringi_1.7.12 processx_3.8.1
[9] promises_1.2.0.1 glue_1.6.2 rprojroot_2.0.3 git2r_0.32.0
[13] htmltools_0.5.5 httpuv_1.6.9 ps_1.7.5 sass_0.4.5
[17] fansi_1.0.4 rmarkdown_2.21 jquerylib_0.1.4 tibble_3.2.1
[21] evaluate_0.20 fastmap_1.1.1 lifecycle_1.0.3 whisker_0.4.1
[25] stringr_1.5.0 compiler_4.3.0 fs_1.6.2 pkgconfig_2.0.3
[29] Rcpp_1.0.10 rstudioapi_0.14 later_1.3.0 digest_0.6.31
[33] R6_2.5.1 utf8_1.2.3 pillar_1.9.0 callr_3.7.3
[37] magrittr_2.0.3 bslib_0.4.2 tools_4.3.0 cachem_1.0.7
[41] getPass_0.2-2