Scrape ISU salary data by year from the web and turn it into a tidy dataframe
See CyChecks
sal_df(limit = 1000, offset = 0, fiscal_year = 2007, token = NULL)
| limit | The number of data entries (rows in the dataframe) you'd like to receive (default = 1000) |
|---|---|
| offset | Where you'd like the data entries to start pulling from (default = 0). If using the default of 0, then the dataset will start pulling from the beginning of alphabet. This argument is useful to change when using the function multiple times. |
| fiscal_year | The fiscal year the data are taken from. Limited to 2007-2018. Can only enter one fiscal year at a time. |
| token | An API token. Only necessary for large amounts of datascraping. Generated from this website |
A dataframe with salary information, position, and gender for Iowa State University employees in a given fiscal year.
An API (or APP) token isn't necessary for scraping data, but it will help speed up the data grabbing process and will allow users to get nearly unlimited data.
sal_df()#> # A tibble: 1,000 x 8 #> base_salary_date fiscal_year gender name place_of_reside~ position #> <dttm> <dbl> <chr> <chr> <chr> <chr> #> 1 2007-07-01 00:00:00 2007 M ABAS~ STORY POSTDOC~ #> 2 2007-07-01 00:00:00 2007 F ABAT~ STORY CASUAL ~ #> 3 2007-07-01 00:00:00 2007 M ABBA~ COOK SYS SUP~ #> 4 2007-07-01 00:00:00 2007 M ABBA~ OAKLAND CASUAL ~ #> 5 2007-07-01 00:00:00 2007 M ABBE~ STORY GRAD AS~ #> 6 2007-07-01 00:00:00 2007 M ABBE~ STORY CASUAL ~ #> 7 2007-07-01 00:00:00 2007 F ABBO~ STORY COMM SP~ #> 8 2007-07-01 00:00:00 2007 F ABBO~ BOONE ASST MG~ #> 9 2007-07-01 00:00:00 2007 M ABBO~ STORY PROF #> 10 2007-07-01 00:00:00 2007 F ABBO~ STORY CASUAL ~ #> # ... with 990 more rows, and 2 more variables: total_salary_paid <dbl>, #> # travel_subsistence <dbl>x <- sal_df(limit = 10, fiscal_year = 2015)