class: center, middle, inverse, title-slide .title[ # Querying APIs ] .author[ ### Justin Post ] --- layout: true <div class="my-footer"><img src="data:image/png;base64,#img/logo.png" style="height: 60px;"/></div> --- # Reading Data Data comes in many formats such as - 'Delimited' data: Character (such as [','](https://www4.stat.ncsu.edu/~online/datasets/scoresFull.csv) , ['\>'](https://www4.stat.ncsu.edu/~online/datasets/umps2012.txt), or \[' '\]) separated data - [Fixed field](https://www4.stat.ncsu.edu/~online/datasets/cigarettes.txt) data - [Excel](https://www4.stat.ncsu.edu/~online/datasets/Dry_Bean_Dataset.xlsx) data - From other statistical software, Ex: [SPSS formatted](https://www4.stat.ncsu.edu/~online/datasets/bodyFat.sav) data or [SAS data sets](https://www4.stat.ncsu.edu/~online/datasets/house.sas7bdat) - From a database - From an Application Programming Interface (API) --- # APIs Application Programming Interfaces (APIs) - a defined method for asking for information from a computer - Basically a protocol for computers to talk to one another - Useful for getting data - Useful for allowing others to access something you make (say a model) --- # APIs - Most major sites with data now have an API. A key is usually required + Documentation can be spotty + Some have written functions for us :) - Consider the[Census API](https://api.census.gov/data.html) + A `tidycensus` package exists! ```r library(tidycensus) #install first! ``` --- # Census APIs - Consider the American Community Survey + Accessed via `get_acs()` function + [Variable list available](https://api.census.gov/data/2021/acs/acs5/profile/variables.html) --- # Census APIs - Consider the American Community Survey + Accessed via `get_acs()` function + [Variable list available](https://api.census.gov/data/2021/acs/acs5/profile/variables.html) ```r rent <- "DP04_0142PE" #PE means percentage rent_data <- get_acs(variables = rent, geography = "county", geometry = TRUE,#returns the polygon data and allows for maps easily survey = "acs5", show_call = TRUE, key = "e267f117801b2ef741e54620602b0903c5f4d3c8" ) #can add state and other things ``` ``` ## Downloading: 1 B Downloading: 1 B Downloading: 1.4 kB Downloading: 1.4 kB 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show_call = FALSE) { base <- paste("https://api.census.gov/data", as.character(year), "acs", survey, sep = "/") if (grepl("^DP", formatted_variables)) { message("Using the ACS Data Profile") base <- paste0(base, "/profile") } ... ``` --- # API Access in R - Awesome! When someone has done the work it is great :) - Some resources on API packages: + [Someone's Github List](https://gist.github.com/zhiiiyang/fc19995f7e350f3c7fb940757f6213cf) + [Another one!](https://github.com/RomanTsegelskyi/r-api-wrappers) - [List of APIs](https://apilist.fun/) --- # API Example: Building it Ourselves - Let's investigate the National Hockey League's (NHL) API - Google shows a number of packages... but they get out of date or aren't maintained. Let's do it ourselves! - Unfortunately, the NHL API is very poorly documented... + [Thanks Zmalski](https://github.com/Zmalski/NHL-API-Reference), this helps! --- # API Example: Building it Ourselves Process: - Build the appropriate URL - Use `httr:GET()` to contact the web site - Data is usually JSON (or possibly XML). Parse it! - Try to put into a data frame --- # Aside: JSON Data - Most APIs return data in JSON format + **JSON** - JavaScript Object Notation + Can represent usual 2D data or heirarchical data --- # Aside: JSON Data - Uses key-value pairs ```r { { "name": "Barry Sanders" "games" : 153 "position": "RB" }, { "name": "Joe Montana" "games": 192 "position": "QB" } } ``` --- # JSON Packages in R Four major R packages 1. `rjson` 2. `RJSONIO` 3. `jsonlite` + many nice features + a little slower implementation 4. `tidyjson` --- # `jsonlite` Package [`jsonlite`](https://www.rdocumentation.org/packages/jsonlite/) basic functions: Function | Description ----------- | -------------------------------------------------- `fromJSON` | Reads JSON data from file path or character string. Converts and simplfies to R object `toJSON` | Writes R object to JSON object `stream_in` | Accepts a *file connection* - can read streaming JSON data --- # Build the URL - First we want to build the URL to contact a particular end point of the API - Suppose we first want team information. Documentation says <img src="data:image/png;base64,#img/team_info.png" width="500px" style="display: block; margin: auto;" /> --- # Build the URL We create a string for the URL: ```r URL_ids <- "https://api.nhle.com/stats/rest/en/team" ``` - Now use `GET` from `httr` package ```r id_info <- httr::GET(URL_ids) str(id_info, max.level = 1) ``` ``` ## List of 10 ## $ url : chr "https://api.nhle.com/stats/rest/en/team" ## $ status_code: int 200 ## $ headers :List of 15 ## ..- attr(*, "class")= chr [1:2] "insensitive" "list" ## $ all_headers:List of 1 ## $ cookies :'data.frame': 0 obs. of 7 variables: ## $ content : raw [1:6664] 7b 22 64 61 ... ## $ date : POSIXct[1:1], format: "2025-05-07 18:55:30" ## $ times : Named num [1:6] 0 0.161 0.177 0.456 0.515 ... ## ..- attr(*, "names")= chr [1:6] "redirect" "namelookup" "connect" "pretransfer" ... ## $ request :List of 7 ## ..- attr(*, "class")= chr "request" ## $ handle :Class 'curl_handle' <externalptr> ## - attr(*, "class")= chr "response" ``` --- # Build the URL - Must parse this a bit... Usually data is in `content` or `results` element + Often use `rawToChar()` with `jsonlite::fromJSON()` ```r library(jsonlite) parsed <- fromJSON(rawToChar(id_info$content)) team_info <- as_tibble(parsed$data) team_info ``` ``` ## # A tibble: 62 x 6 ## id franchiseId fullName leagueId rawTricode triCode ## <int> <int> <chr> <int> <chr> <chr> ## 1 32 27 Quebec Nordiques 133 QUE QUE ## 2 8 1 Montréal Canadiens 133 MTL MTL ## 3 58 5 Toronto St. Patricks 133 TSP TSP ## 4 7 19 Buffalo Sabres 133 BUF BUF ## 5 46 13 Oakland Seals 133 OAK OAK ## # i 57 more rows ``` --- # Build the URL - Now we can get some team stats through the same process! <img src="data:image/png;base64,#img/team_stats.png" width="500px" style="display: block; margin: auto;" /> --- # Build the URL - A few things can be modified but it isn't clear here what the values could be. ```r URL_team_stats <- "https://api.nhle.com/stats/rest/en/team/summary?sort=wins&cayenneExp=seasonId=20232024%20and%20gameTypeId=2" ``` - `GET()` it and parse it with the same process ```r team_stats_return <- httr::GET(URL_team_stats) parsed_team_stats <- fromJSON(rawToChar(team_stats_return$content)) team_stats <- as_tibble(parsed_team_stats$data) ``` --- # Check it Out ```r team_stats |> select(teamId, teamFullName, everything()) ``` ``` ## # A tibble: 32 x 24 ## teamId teamFullName faceoffWinPct gamesPlayed goalsAgainst goalsAgainstPerGame ## <int> <chr> <dbl> <int> <int> <dbl> ## 1 28 San Jose Sh~ 0.490 82 326 3.98 ## 2 16 Chicago Bla~ 0.463 82 289 3.52 ## 3 24 Anaheim Duc~ 0.466 82 293 3.57 ## 4 29 Columbus Bl~ 0.472 82 298 3.63 ## 5 8 Montréal C~ 0.515 82 281 3.43 ## # i 27 more rows ## # i 18 more variables: goalsFor <int>, goalsForPerGame <dbl>, losses <int>, ## # otLosses <int>, penaltyKillNetPct <dbl>, penaltyKillPct <dbl>, ## # pointPct <dbl>, points <int>, powerPlayNetPct <dbl>, powerPlayPct <dbl>, ## # regulationAndOtWins <int>, seasonId <int>, shotsAgainstPerGame <dbl>, ## # shotsForPerGame <dbl>, ties <lgl>, wins <int>, winsInRegulation <int>, ## # winsInShootout <int> ``` --- # Implementing a Model in Production Later: Need a way to make your model available to others - Can write an API that accesses your model - Hosted on a server or locally - Not traditionally done in R but can be! --- # Recap - APIs are a common tool used for communicating about data + Can be used for other things as well - Accessing data through an API involves building appropriate communication message (URL usually) - Some API packages already exist - Others, we need to parse the data ourselves!