This is course package meant to help ease the burden of downloading and accessing the course data. Functions will likely be added to the package as the course goes along.
You can install edld652 with:
remotes::install_github("datalorax/edld652")If you are a student in this class, a shared access signature (SAS) will
be shared with you. This is what authenticates you to obtain access to
the data. To start, you will need to pass the SAS to set_key() as a
string (e.g., set_key("abc-key-but-really-long-and-complicated")). You
will only need to do this once, and the package will prompt you to do
this upon loading it if it hasn’t been done previously. The SAS will be
stored in your .REnviron (you could use something like
usethis::edit_r_environ() if you want to see it). After running
set_key() you will be prompted to restart R for the changes to take
effect.
Once you have stored your SAS authentication token with set_key(), you
can import any dataset available with the get_data() function.
However, you might be asking yourself “how do I know what file I want?”.
We can get a list of all available datasets with the list_datasets()
function. For example:
library(edld652)
list_datasets()
#> [1] "EDFacts_acgr_lea_2011_2019"
#> [2] "EDFacts_acgr_sch_2011_2019"
#> [3] "EDFacts_math_achievement_lea_2010_2019"
#> [4] "EDFacts_math_achievement_sch_2010_2019"
#> [5] "EDFacts_math_participation_lea_2013_2019"
#> [6] "EDFacts_math_participation_sch_2013_2019"
#> [7] "EDFacts_rla_achievement_lea_2010_2019"
#> [8] "EDFacts_rla_achievement_sch_2010_2019"
#> [9] "EDFacts_rla_participation_lea_2013_2019"
#> [10] "EDFacts_rla_participation_sch_2013_2019"
#> [11] "NCES_CCD_fiscal_district_2010"
#> [12] "NCES_CCD_fiscal_district_2011"
#> [13] "NCES_CCD_fiscal_district_2012"
#> [14] "NCES_CCD_fiscal_district_2013"
#> [15] "NCES_CCD_fiscal_district_2014"
#> [16] "NCES_CCD_fiscal_district_2015"
#> [17] "NCES_CCD_fiscal_district_2016"
#> [18] "NCES_CCD_fiscal_district_2017"
#> [19] "NCES_CCD_fiscal_district_2018"
#> [20] "NCES_CCD_nonfiscal_district_2017_2021_directory"
#> [21] "NCES_CCD_nonfiscal_district_2017_2021_disabilities"
#> [22] "NCES_CCD_nonfiscal_district_2017_2021_english_learners"
#> [23] "NCES_CCD_nonfiscal_district_2017_2021_membership"
#> [24] "NCES_CCD_nonfiscal_district_2017_2021_staff"
#> [25] "NCES_CCD_nonfiscal_school_2017_2020_lunch_program"
#> [26] "NCES_CCD_nonfiscal_school_2017_2020_school_characteristics"
#> [27] "NCES_CCD_nonfiscal_school_2017_2020_staff"
#> [28] "NCES_CCD_nonfiscal_school_2017_2021_directory"
#> [29] "NCES_CCD_nonfiscal_school_2017_membership"
#> [30] "NCES_CCD_nonfiscal_school_2018_membership"
#> [31] "NCES_CCD_nonfiscal_school_2019_membership"
#> [32] "NCES_CCD_nonfiscal_school_2020_membership"
#> [33] "NCES_CCD_nonfiscal_state_2017_2020_directory"
#> [34] "NCES_CCD_nonfiscal_state_2017_2020_staff"
#> [35] "NCES_CCD_nonfiscal_state_2017_2021_membership"From here, we can just copy the string of the dataset we want to import
and pass that to get_data(). For example, if we wanted to read in
"EDFacts_acgr_lea_2011_2019", we would use the following code
district_grad_rates <- get_data("EDFacts_acgr_lea_2011_2019")
#> Rows: 11326 Columns: 29
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (17): ALL_RATE, CWD_RATE, DATE_CUR, ECD_RATE, FIPST, FILEURL, LEAID, LE...
#> dbl (11): ALL_COHORT, CWD_COHORT, ECD_COHORT, LEP_COHORT, MAM_COHORT, MAS_C...
#> dttm (1): DL_INGESTION_DATETIME
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
district_grad_rates
#> # A tibble: 11,326 × 29
#> ALL_COHORT ALL_RATE CWD_COHORT CWD_RATE DATE_CUR ECD_COHORT ECD_RATE FIPST
#> <dbl> <chr> <dbl> <chr> <chr> <dbl> <chr> <chr>
#> 1 252 80 3 PS 03OCT15 121 65-69 01
#> 2 398 75 47 70-79 03OCT15 233 65-69 01
#> 3 1020 89 51 40-49 03OCT15 175 75-79 01
#> 4 750 91 35 60-69 03OCT15 102 80-84 01
#> 5 128 55-59 15 LT50 03OCT15 68 40-44 01
#> 6 166 90-94 9 GE50 03OCT15 53 70-79 01
#> 7 336 90 30 60-79 03OCT15 35 60-69 01
#> 8 273 77 11 LT50 03OCT15 93 70-74 01
#> 9 134 70-74 4 PS 03OCT15 60 50-59 01
#> 10 266 58 33 50-59 03OCT15 195 55-59 01
#> # … with 11,316 more rows, and 21 more variables: FILEURL <chr>, LEAID <chr>,
#> # LEANM <chr>, LEP_COHORT <dbl>, LEP_RATE <chr>, MAM_COHORT <dbl>,
#> # MAM_RATE <chr>, MAS_COHORT <dbl>, MAS_RATE <chr>, MBL_COHORT <dbl>,
#> # MBL_RATE <chr>, MHI_COHORT <dbl>, MHI_RATE <chr>, MTR_COHORT <dbl>,
#> # MTR_RATE <chr>, MWH_COHORT <dbl>, MWH_RATE <chr>, STNAM <chr>, YEAR <dbl>,
#> # PIPELINE <chr>, DL_INGESTION_DATETIME <dttm>Documenation for any of these datasets is available via the
get_documentation() function, passing the name of the dataset you’d
like documentation on. Note that these will return either Microsoft Word
or Excel files, which should open automatically after downloading. For
example running
get_documentation("EDFacts_acgr_lea_2011_2019")will create a data-documentation directory in your current working
directory (if it does not already currently exist), download the Word or
Excel file to that directory, and open the corresponding file. Running
get_documentation() for additional datasets will add the documentation
to the data-documenatation folder (i.e., it will not overwrite any
previous documentation downloaded). Similarly, if the documentation has
previously been downloaded, it will open that documentation rather than
downloading the file again. In other words, running
get_documentation("NCES_CCD_nonfiscal_school_2018_membership")would add (and open) a new documentation file to the
data-documentation directory, because it is different documentation
than requested previously, but running that command again would only
open the file (not download it again).
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