Processing JSON data (Optional)
Last updated on 2024-11-19 | Edit this page
Estimated time: 45 minutes
- This is an optional lessons intended to introduce learners to JSON data, as well as how to read JSON data into R and how to convert the data into a data frame or array.
- Note that his lesson was community-contributed and remains a work in progress. As such, it could benefit from feedback from instructors and/or workshop participants.
Overview
Questions
- What is JSON format?
- How can I convert JSON to an R dataframe?
- How can I convert an array of JSON record into a table?
Objectives
- Describe the JSON data format
- Understand where JSON is typically used
- Appreciate some advantages of using JSON over tabular data
- Appreciate some disadvantages of processing JSON documents
- Use the jsonLite package to read a JSON file
- Display formatted JSON as dataframe
- Select and display nested dataframe fields from a JSON document
- Write tabular data from selected elements from a JSON document to a csv file
The JSON data format
The JSON data format was designed as a way of allowing different machines or processes within machines to communicate with each other by sending messages constructed in a well defined format. JSON is now the preferred data format used by APIs (Application Programming Interfaces).
The JSON format although somewhat verbose is not only Human readable but it can also be mapped very easily to an R dataframe.
We are going to read a file of data formatted as JSON, convert it into a dataframe in R then selectively create a csv file from the extracted data.
The JSON file we are going to use is the SAFI.json file. This is the output file from an electronic survey system called ODK. The JSON represents the answers to a series of survey questions. The questions themselves have been replaced with unique Keys, the values are the answers.
Because detailed surveys are by nature nested structures making it possible to record different levels of detail or selectively ask a set of specific questions based on the answer given to a previous question, the structure of the answers for the survey can not only be complex and convoluted, it could easily be different from one survey respondent’s set of answers to another.
Advantages of JSON
- Very popular data format for APIs (e.g. results from an Internet search)
- Human readable
- Each record (or document as they are called) is self contained. The equivalent of the column name and column values are in every record.
- Documents do not all have to have the same structure within the same file
- Document structures can be complex and nested
Use the JSON package to read a JSON file
R
library(jsonlite)
As with reading in a CSV, you have a couple of options for how to access the JSON file.
You can read the JSON file directly into R with
read_json() or the comparable fromJSON()
function, though this does not download the file.
R
json_data <- read_json(
"https://raw.githubusercontent.com/datacarpentry/r-socialsci/main/episodes/data/SAFI.json"
)
To download the file you can copy and paste the contents of the file
on GitHub,
creating a SAFI.json file in your data
directory, or you can download the file with R.
R
download.file(
"https://raw.githubusercontent.com/datacarpentry/r-socialsci/main/episodes/data/SAFI.json",
"data/SAFI.json", mode = "wb")
Once you have the data downloaded, you can read it into R with
read_json():
R
json_data <- read_json("data/SAFI.json")
We can see that a new object called json_data has appeared in our
Environment. It is described as a Large list (131 elements). In this
current form, our data is messy. You can have a glimpse of it with the
head() or view() functions. It will look not
much more structured than if you were to open the JSON file with a text
editor.
This is because, by default, the read_json() function’s
parameter simplifyVector, which specifies whether or not to
simplify vectors is set to FALSE. This means that the default setting
does not simplify nested lists into vectors and data frames. However, we
can set this to TRUE, and our data will be read directly as a
dataframe:
R
json_data <- read_json("data/SAFI.json", simplifyVector = TRUE)
Now we can see we have this json data in a dataframe format. For
consistency with the rest of the lesson, let’s coerce it to be a tibble
and use glimpse to take a peek inside (these functions were
loaded by library(tidyverse)):
R
json_data <- json_data %>% as_tibble()
glimpse(json_data)
OUTPUT
Rows: 131
Columns: 74
$ C06_rooms <int> 1, 1, 1, 1, 1, 1, 1, 3, 1, 5, 1, 3, 1, …
$ B19_grand_liv <chr> "no", "yes", "no", "no", "yes", "no", "…
$ A08_ward <chr> "ward2", "ward2", "ward2", "ward2", "wa…
$ E01_water_use <chr> "no", "yes", "no", "no", "no", "no", "y…
$ B18_sp_parents_liv <chr> "yes", "yes", "no", "no", "no", "no", "…
$ B16_years_liv <int> 4, 9, 15, 6, 40, 3, 38, 70, 6, 23, 20, …
$ E_yes_group_count <chr> NA, "3", NA, NA, NA, NA, "4", "2", "3",…
$ F_liv <list> [<data.frame[1 x 2]>], [<data.frame[3 …
$ `_note2` <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ instanceID <chr> "uuid:ec241f2c-0609-46ed-b5e8-fe575f6ce…
$ B20_sp_grand_liv <chr> "yes", "yes", "no", "no", "no", "no", "…
$ F10_liv_owned_other <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ `_note1` <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ F12_poultry <chr> "yes", "yes", "yes", "yes", "yes", "no"…
$ D_plots_count <chr> "2", "3", "1", "3", "2", "1", "4", "2",…
$ C02_respondent_wall_type_other <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ C02_respondent_wall_type <chr> "muddaub", "muddaub", "burntbricks", "b…
$ C05_buildings_in_compound <int> 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, …
$ `_remitters` <list> [<data.frame[0 x 0]>], [<data.frame[0 …
$ E18_months_no_water <list> <NULL>, <"Aug", "Sept">, <NULL>, <NULL…
$ F07_use_income <chr> NA, "Alimentação e pagamento de educa…
$ G01_no_meals <int> 2, 2, 2, 2, 2, 2, 3, 2, 3, 3, 2, 3, 2, …
$ E17_no_enough_water <chr> NA, "yes", NA, NA, NA, NA, "yes", "yes"…
$ F04_need_money <chr> NA, "no", NA, NA, NA, NA, "no", "no", "…
$ A05_end <chr> "2017-04-02T17:29:08.000Z", "2017-04-02…
$ C04_window_type <chr> "no", "no", "yes", "no", "no", "no", "n…
$ E21_other_meth <chr> NA, "no", NA, NA, NA, NA, "no", "no", "…
$ D_no_plots <int> 2, 3, 1, 3, 2, 1, 4, 2, 3, 2, 2, 2, 4, …
$ F05_money_source <list> <NULL>, <NULL>, <NULL>, <NULL>, <NULL>…
$ A07_district <chr> "district1", "district1", "district1", …
$ C03_respondent_floor_type <chr> "earth", "earth", "cement", "earth", "e…
$ E_yes_group <list> [<data.frame[0 x 0]>], [<data.frame[3 …
$ A01_interview_date <chr> "2016-11-17", "2016-11-17", "2016-11-17…
$ B11_remittance_money <chr> "no", "no", "no", "no", "no", "no", "no…
$ A04_start <chr> "2017-03-23T09:49:57.000Z", "2017-04-02…
$ D_plots <list> [<data.frame[2 x 8]>], [<data.frame[3 …
$ F_items <list> [<data.frame[3 x 3]>], [<data.frame[2 …
$ F_liv_count <chr> "1", "3", "1", "2", "4", "1", "1", "2",…
$ F10_liv_owned <list> "poultry", <"oxen", "cows", "goats">, …
$ B_no_membrs <int> 3, 7, 10, 7, 7, 3, 6, 12, 8, 12, 6, 7, …
$ F13_du_look_aftr_cows <chr> "no", "no", "no", "no", "no", "no", "no…
$ E26_affect_conflicts <chr> NA, "once", NA, NA, NA, NA, "never", "n…
$ F14_items_owned <list> <"bicycle", "television", "solar_panel…
$ F06_crops_contr <chr> NA, "more_half", NA, NA, NA, NA, "more_…
$ B17_parents_liv <chr> "no", "yes", "no", "no", "yes", "no", "…
$ G02_months_lack_food <list> "Jan", <"Jan", "Sept", "Oct", "Nov", "…
$ A11_years_farm <dbl> 11, 2, 40, 6, 18, 3, 20, 16, 16, 22, 6,…
$ F09_du_labour <chr> "no", "no", "yes", "yes", "no", "yes", …
$ E_no_group_count <chr> "2", NA, "1", "3", "2", "1", NA, NA, NA…
$ E22_res_change <list> <NULL>, <NULL>, <NULL>, <NULL>, <NULL>…
$ E24_resp_assoc <chr> NA, "no", NA, NA, NA, NA, NA, "yes", NA…
$ A03_quest_no <chr> "01", "01", "03", "04", "05", "6", "7",…
$ `_members` <list> [<data.frame[3 x 12]>], [<data.frame[7…
$ A06_province <chr> "province1", "province1", "province1", …
$ `gps:Accuracy` <dbl> 14, 19, 13, 5, 10, 12, 11, 9, 11, 14, 1…
$ E20_exper_other <chr> NA, "yes", NA, NA, NA, NA, "yes", "yes"…
$ A09_village <chr> "village2", "village2", "village2", "vi…
$ C01_respondent_roof_type <chr> "grass", "grass", "mabatisloping", "mab…
$ `gps:Altitude` <dbl> 698, 690, 674, 679, 689, 692, 709, 700,…
$ `gps:Longitude` <dbl> 33.48346, 33.48342, 33.48345, 33.48342,…
$ E23_memb_assoc <chr> NA, "yes", NA, NA, NA, NA, "no", "yes",…
$ E19_period_use <dbl> NA, 2, NA, NA, NA, NA, 10, 10, 6, 22, N…
$ E25_fees_water <chr> NA, "no", NA, NA, NA, NA, "no", "no", "…
$ C07_other_buildings <chr> "no", "no", "no", "no", "no", "no", "ye…
$ observation <chr> "None", "Estes primeiros inquéritos na…
$ `_note` <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ A12_agr_assoc <chr> "no", "yes", "no", "no", "no", "no", "n…
$ G03_no_food_mitigation <list> <"na", "rely_less_food", "reduce_meals…
$ F05_money_source_other <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ `gps:Latitude` <dbl> -19.11226, -19.11248, -19.11211, -19.11…
$ E_no_group <list> [<data.frame[2 x 6]>], [<data.frame[0 …
$ F14_items_owned_other <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ F08_emply_lab <chr> "no", "yes", "no", "no", "no", "no", "n…
$ `_members_count` <chr> "3", "7", "10", "7", "7", "3", "6", "12…Looking good, but you might notice that actually we have a variable, F_liv that is a list of dataframes! It is very important to know what you are expecting from your data to be able to look for things like this. For example, if you are getting your JSON from an API, have a look at the API documentation, so you know what to look for.
Often when we have a very large number of columns, it can become
difficult to determine all the variables which may require some special
attention, like lists. Fortunately, we can use special verbs like
where to quickly select all the list columns.
R
json_data %>%
select(where(is.list)) %>%
glimpse()
OUTPUT
Rows: 131
Columns: 14
$ F_liv <list> [<data.frame[1 x 2]>], [<data.frame[3 x 2]>], …
$ `_remitters` <list> [<data.frame[0 x 0]>], [<data.frame[0 x 0]>], …
$ E18_months_no_water <list> <NULL>, <"Aug", "Sept">, <NULL>, <NULL>, <NULL…
$ F05_money_source <list> <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>…
$ E_yes_group <list> [<data.frame[0 x 0]>], [<data.frame[3 x 14]>],…
$ D_plots <list> [<data.frame[2 x 8]>], [<data.frame[3 x 8]>], …
$ F_items <list> [<data.frame[3 x 3]>], [<data.frame[2 x 3]>], …
$ F10_liv_owned <list> "poultry", <"oxen", "cows", "goats">, "none", …
$ F14_items_owned <list> <"bicycle", "television", "solar_panel", "tabl…
$ G02_months_lack_food <list> "Jan", <"Jan", "Sept", "Oct", "Nov", "Dec">, <…
$ E22_res_change <list> <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>…
$ `_members` <list> [<data.frame[3 x 12]>], [<data.frame[7 x 12]>]…
$ G03_no_food_mitigation <list> <"na", "rely_less_food", "reduce_meals", "day_…
$ E_no_group <list> [<data.frame[2 x 6]>], [<data.frame[0 x 0]>], …So what can we do about F_liv, the column of dataframes?
Well first things first, we can access each one. For example to access
the dataframe in the first row, we can use the bracket ([)
subsetting. Here we use single bracket, but you could also use double
bracket ([[). The [[ form allows only a single
element to be selected using integer or character indices, whereas
[ allows indexing by vectors.
R
json_data$F_liv[1]
OUTPUT
[[1]]
F11_no_owned F_curr_liv
1 1 poultryWe can also choose to view the nested dataframes at all the rows of our main dataframe where a particular condition is met (for example where the value for the variable C06_rooms is equal to 4):
R
json_data$F_liv[which(json_data$C06_rooms == 4)]
OUTPUT
[[1]]
F11_no_owned F_curr_liv
1 3 oxen
2 2 cows
3 5 goats
[[2]]
F11_no_owned F_curr_liv
1 4 oxen
2 5 cows
3 3 goats
[[3]]
data frame with 0 columns and 0 rows
[[4]]
F11_no_owned F_curr_liv
1 4 oxen
2 4 cows
3 4 goats
4 1 sheep
[[5]]
F11_no_owned F_curr_liv
1 2 cowsWrite the JSON file to csv
If we try to write our json_data dataframe to a csv as we would
usually in a regular dataframe, we won’t get the desired result. Using
the write_csv function from the readr
package won’t give you an error for list columns, but you’ll only see
missing (i.e. NA) values in these columns. Let’s try it out
to confirm:
R
write_csv(json_data, "json_data_with_list_columns.csv")
read_csv("json_data_with_list_columns.csv")
To write out as a csv while maintaining the data within the list
columns, we will need to “flatten” these columns. One way to do this is
to convert these list columns into character types. (However, we don’t
want to change the data types for any of the other columns). Here’s one
way to do this using tidyverse. This command only applies the
as.character command to those columns ‘where’
is.list is TRUE.
R
flattened_json_data <- json_data %>%
mutate(across(where(is.list), as.character))
flattened_json_data
OUTPUT
# A tibble: 131 × 74
C06_rooms B19_grand_liv A08_ward E01_water_use B18_sp_parents_liv
<int> <chr> <chr> <chr> <chr>
1 1 no ward2 no yes
2 1 yes ward2 yes yes
3 1 no ward2 no no
4 1 no ward2 no no
5 1 yes ward2 no no
6 1 no ward2 no no
7 1 yes ward2 yes no
8 3 yes ward1 yes yes
9 1 yes ward2 yes no
10 5 no ward2 yes no
# ℹ 121 more rows
# ℹ 69 more variables: B16_years_liv <int>, E_yes_group_count <chr>,
# F_liv <chr>, `_note2` <lgl>, instanceID <chr>, B20_sp_grand_liv <chr>,
# F10_liv_owned_other <lgl>, `_note1` <lgl>, F12_poultry <chr>,
# D_plots_count <chr>, C02_respondent_wall_type_other <lgl>,
# C02_respondent_wall_type <chr>, C05_buildings_in_compound <int>,
# `_remitters` <chr>, E18_months_no_water <chr>, F07_use_income <chr>, …Now you can write this to a csv file:
R
write_csv(flattened_json_data, "data_output/json_data_with_flattened_list_columns.csv")
Note: this means that when you read this csv back into R, the column of the nested dataframes will now be read in as a character vector. Converting it back to list to extract elements might be complicated, so it is probably better to keep storing these data in a JSON format if you will have to do this.
You can also write out the individual nested dataframes to a csv. For example:
R
write_csv(json_data$F_liv[[1]], "data_output/F_liv_row1.csv")
Key Points
- JSON is a popular data format for transferring data used by a great many Web based APIs
- The complex structure of a JSON document means that it cannot easily be ‘flattened’ into tabular data
- We can use R code to extract values of interest and place them in a csv file