README.md
In jjchern/scorecard: College Scorecard Data, 1996-2017
About scorecard
The scorecard package includes processed datasets from the College
Scorecard, 1996-2017.
The Scorecard datasets are imbalance panels at the
colleges-by-school-year level. The data was last updated in 2019. See
the changelog for
more details.
The following datasets are available:
scorecard::mf1996_97scorecard::mf1997_98scorecard::mf1998_99scorecard::mf1999_00scorecard::mf2000_01scorecard::mf2001_02scorecard::mf2002_03scorecard::mf2003_04scorecard::mf2004_05scorecard::mf2005_06scorecard::mf2006_07scorecard::mf2007_08scorecard::mf2008_09scorecard::mf2009_10scorecard::mf2010_11scorecard::mf2011_12scorecard::mf2012_13scorecard::mf2013_14scorecard::mf2014_15scorecard::mf2015_16scorecard::mf2016_17scorecard::mf2017_18scorecard::codebookscorecard::cohort_map
The following functions are implemented:
scorecard::attach_var_label(): Assign variable labels.scorecard::attach_val_label(): Assign value labels for certain
variables
Related R Package
Benjamin Skinner has created a wonderful
R client rscorecard for the
College Scorecard GET
API. If you’re
interested in getting some specific variables quickly, I suggest using
the rscorecard package.
Installation
You can also download the datasets as an R package. It might take a
while to install and load into memory. To download the most recent
stable release, use
# install.packages("devtools")
devtools::install_github("jjchern/scorecard@v0.4.0")
# To uninstall the package, use:
# remove.packages("scorecard")
Examples
Loading the merged file for school year 2014-15
All datasets are tibbles:
library(tidyverse)
scorecard::mf2014_15
#> # A tibble: 7,703 x 1,660
#> mf_year unitid opeid opeid6 instnm city stabbr zip sch_deg main
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl+l>
#> 1 2014-15 100654 0010… 001002 Alaba… Norm… AL 35762 3 1 [Mai…
#> 2 2014-15 100663 0010… 001052 Unive… Birm… AL 3529… 3 1 [Mai…
#> 3 2014-15 100690 0250… 025034 Amrid… Mont… AL 3611… 3 1 [Mai…
#> 4 2014-15 100706 0010… 001055 Unive… Hunt… AL 35899 3 1 [Mai…
#> 5 2014-15 100724 0010… 001005 Alaba… Mont… AL 3610… 3 1 [Mai…
#> 6 2014-15 100751 0010… 001051 The U… Tusc… AL 3548… 3 1 [Mai…
#> 7 2014-15 100760 0010… 001007 Centr… Alex… AL 35010 2 1 [Mai…
#> 8 2014-15 100812 0010… 001008 Athen… Athe… AL 35611 3 1 [Mai…
#> 9 2014-15 100830 0083… 008310 Aubur… Mont… AL 3611… 3 1 [Mai…
#> 10 2014-15 100858 0010… 001009 Aubur… Aubu… AL 36849 3 1 [Mai…
#> # … with 7,693 more rows, and 1,650 more variables: numbranch <dbl>,
#> # preddeg <dbl+lbl>, highdeg <dbl+lbl>, control <dbl+lbl>,
#> # st_fips <dbl+lbl>, region <dbl+lbl>, adm_rate <dbl>,
#> # adm_rate_all <dbl>, satvr25 <dbl>, satvr75 <dbl>, satmt25 <dbl>,
#> # satmt75 <dbl>, satwr25 <dbl>, satwr75 <dbl>, satvrmid <dbl>,
#> # satmtmid <dbl>, satwrmid <dbl>, actcm25 <dbl>, actcm75 <dbl>,
#> # acten25 <dbl>, acten75 <dbl>, actmt25 <dbl>, actmt75 <dbl>,
#> # actwr25 <dbl>, actwr75 <dbl>, actcmmid <dbl>, actenmid <dbl>,
#> # actmtmid <dbl>, actwrmid <dbl>, sat_avg <dbl>, sat_avg_all <dbl>,
#> # pcip01 <dbl>, pcip03 <dbl>, pcip04 <dbl>, pcip05 <dbl>, pcip09 <dbl>,
#> # pcip10 <dbl>, pcip11 <dbl>, pcip12 <dbl>, pcip13 <dbl>, pcip14 <dbl>,
#> # pcip15 <dbl>, pcip16 <dbl>, pcip19 <dbl>, pcip22 <dbl>, pcip23 <dbl>,
#> # pcip24 <dbl>, pcip25 <dbl>, pcip26 <dbl>, pcip27 <dbl>, pcip29 <dbl>,
#> # pcip30 <dbl>, pcip31 <dbl>, pcip38 <dbl>, pcip39 <dbl>, pcip40 <dbl>,
#> # pcip41 <dbl>, pcip42 <dbl>, pcip43 <dbl>, pcip44 <dbl>, pcip45 <dbl>,
#> # pcip46 <dbl>, pcip47 <dbl>, pcip48 <dbl>, pcip49 <dbl>, pcip50 <dbl>,
#> # pcip51 <dbl>, pcip52 <dbl>, pcip54 <dbl>, cip01cert1 <dbl+lbl>,
#> # cip01cert2 <dbl+lbl>, cip01assoc <dbl+lbl>, cip01cert4 <dbl>,
#> # cip01bachl <dbl>, cip03cert1 <dbl>, cip03cert2 <dbl>,
#> # cip03assoc <dbl>, cip03cert4 <dbl>, cip03bachl <dbl>,
#> # cip04cert1 <dbl>, cip04cert2 <dbl>, cip04assoc <dbl>,
#> # cip04cert4 <dbl>, cip04bachl <dbl>, cip05cert1 <dbl>,
#> # cip05cert2 <dbl>, cip05assoc <dbl>, cip05cert4 <dbl>,
#> # cip05bachl <dbl>, cip09cert1 <dbl>, cip09cert2 <dbl>,
#> # cip09assoc <dbl>, cip09cert4 <dbl>, cip09bachl <dbl>,
#> # cip10cert1 <dbl>, cip10cert2 <dbl>, cip10assoc <dbl>,
#> # cip10cert4 <dbl>, cip10bachl <dbl>, cip11cert1 <dbl>, …
Working with variable and value labels
All the datasets have variable labels attached, which can be viewed in
RStudio’s Data Viewer:
View(scorecard::mf2014_15)
You can also use the labelled package:
scorecard::mf2014_15 %>%
select(1:8) %>%
labelled::var_label()
#> $mf_year
#> NULL
#>
#> $unitid
#> [1] "Unit ID for institution"
#>
#> $opeid
#> [1] "8-digit OPE ID for institution"
#>
#> $opeid6
#> [1] "6-digit OPE ID for institution"
#>
#> $instnm
#> [1] "Institution name"
#>
#> $city
#> [1] "City"
#>
#> $stabbr
#> [1] "State postcode"
#>
#> $zip
#> [1] "ZIP code"
Or work with the codebook directly:
## Show variable labels
scorecard::codebook %>%
select(var_name, var_label)
#> # A tibble: 2,231 x 2
#> var_name var_label
#> <chr> <chr>
#> 1 unitid Unit ID for institution
#> 2 opeid 8-digit OPE ID for institution
#> 3 opeid6 6-digit OPE ID for institution
#> 4 instnm Institution name
#> 5 city City
#> 6 stabbr State postcode
#> 7 zip ZIP code
#> 8 accredagency Accreditor for institution
#> 9 insturl URL for institution's homepage
#> 10 npcurl URL for institution's net price calculator
#> # … with 2,221 more rows
## Build a small function to shown value labels
show_val_label = . %>% {
filter(scorecard::codebook, var_name == .) %>%
mutate(val_label = glue::glue("{val_label} = {value}")) %>%
distinct(val_label)}
## Show value labels:
show_val_label("curroper")
#> # A tibble: 2 x 1
#> val_label
#> <glue>
#> 1 Not currently certified as an operating institution = 0
#> 2 Currently certified as operating = 1
show_val_label("distanceonly")
#> # A tibble: 2 x 1
#> val_label
#> <glue>
#> 1 Not distance-education only = 0
#> 2 Distance-education only = 1
Exploring codebook and plot distributions of in-state tuition
vars = c("mf_year", "iclevel", "control", "tuitionfee_in")
scorecard::codebook %>%
select(var_name, var_label, value, val_label) %>%
filter(var_name %in% vars) %>%
knitr::kable()
| var_name | var_label | value | val_label |
| :------------- | :------------------------ | ----: | :----------------- |
| control | Control of institution | 1 | Public |
| control | Control of institution | 2 | Private nonprofit |
| control | Control of institution | 3 | Private for-profit |
| tuitionfee_in | In-state tuition and fees | NA | NA |
| iclevel | Level of institution | 1 | 4-year |
| iclevel | Level of institution | 2 | 2-year |
| iclevel | Level of institution | 3 | Less-than-2-year |
dplyr_seq = . %>%
select(one_of(vars)) %>%
haven::as_factor() %>%
filter(iclevel %in% c("4-year", "2-year")) %>%
mutate(year = mf_year %>% parse_number() %>% as.factor()) %>%
group_by(iclevel, control) %>%
mutate_at(c("tuitionfee_in"),
~statar::winsorise(., probs = c(0.02, 0.98), verbose = FALSE)) %>%
ungroup()
## Test the functional sequence
scorecard::mf2014_15 %>% dplyr_seq()
#> # A tibble: 5,530 x 5
#> mf_year iclevel control tuitionfee_in year
#> <chr> <fct> <fct> <dbl> <fct>
#> 1 2014-15 4-year Public 9096 2014
#> 2 2014-15 4-year Public 7510 2014
#> 3 2014-15 4-year Private nonprofit 6900 2014
#> 4 2014-15 4-year Public 9158 2014
#> 5 2014-15 4-year Public 8720 2014
#> 6 2014-15 4-year Public 9826 2014
#> 7 2014-15 2-year Public 3491 2014
#> 8 2014-15 4-year Public NA 2014
#> 9 2014-15 4-year Public 9080 2014
#> 10 2014-15 4-year Public 10200 2014
#> # … with 5,520 more rows
bind_rows(
scorecard::mf2017_18 %>% dplyr_seq(),
scorecard::mf2016_17 %>% dplyr_seq(),
scorecard::mf2015_16 %>% dplyr_seq(),
scorecard::mf2014_15 %>% dplyr_seq(),
scorecard::mf2013_14 %>% dplyr_seq(),
scorecard::mf2012_13 %>% dplyr_seq(),
scorecard::mf2011_12 %>% dplyr_seq(),
scorecard::mf2010_11 %>% dplyr_seq(),
scorecard::mf2009_10 %>% dplyr_seq(),
scorecard::mf2008_09 %>% dplyr_seq(),
scorecard::mf2007_08 %>% dplyr_seq(),
scorecard::mf2006_07 %>% dplyr_seq(),
scorecard::mf2005_06 %>% dplyr_seq(),
scorecard::mf2004_05 %>% dplyr_seq(),
scorecard::mf2003_04 %>% dplyr_seq(),
scorecard::mf2002_03 %>% dplyr_seq(),
scorecard::mf2001_02 %>% dplyr_seq(),
scorecard::mf2000_01 %>% dplyr_seq()
) -> df
df %>%
ggplot(aes(x = tuitionfee_in, y = year, fill = iclevel)) +
ggjoy::geom_joy(scale = 2, alpha = .8, colour = "white") +
ggjoy::theme_joy() +
facet_grid(iclevel~control, scales = "free") +
labs(x = NULL, y = NULL,
title = "In-State Tuition and Fees, 2000-2017") +
scale_x_continuous(labels = scales::dollar) +
scale_y_discrete(breaks = seq(2017, 2000, -3),
expand = c(0.01, 0)) +
theme(axis.text = element_text(size = 8),
legend.position = "none")
Compareing in-state and out-of-state tuition and fees
Distribution of Average Age of Entry
jjchern/scorecard documentation built on Jan. 1, 2020, 12:59 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
About scorecard
The scorecard package includes processed datasets from the College
Scorecard, 1996-2017.
The Scorecard datasets are imbalance panels at the colleges-by-school-year level. The data was last updated in 2019. See the changelog for more details.
The following datasets are available:
scorecard::mf1996_97scorecard::mf1997_98scorecard::mf1998_99scorecard::mf1999_00scorecard::mf2000_01scorecard::mf2001_02scorecard::mf2002_03scorecard::mf2003_04scorecard::mf2004_05scorecard::mf2005_06scorecard::mf2006_07scorecard::mf2007_08scorecard::mf2008_09scorecard::mf2009_10scorecard::mf2010_11scorecard::mf2011_12scorecard::mf2012_13scorecard::mf2013_14scorecard::mf2014_15scorecard::mf2015_16scorecard::mf2016_17scorecard::mf2017_18scorecard::codebookscorecard::cohort_map
The following functions are implemented:
scorecard::attach_var_label(): Assign variable labels.scorecard::attach_val_label(): Assign value labels for certain variables
Related R Package
Benjamin Skinner has created a wonderful
R client rscorecard for the
College Scorecard GET
API. If you’re
interested in getting some specific variables quickly, I suggest using
the rscorecard package.
Installation
You can also download the datasets as an R package. It might take a while to install and load into memory. To download the most recent stable release, use
# install.packages("devtools")
devtools::install_github("jjchern/scorecard@v0.4.0")
# To uninstall the package, use:
# remove.packages("scorecard")
Examples
Loading the merged file for school year 2014-15
All datasets are tibbles:
library(tidyverse)
scorecard::mf2014_15
#> # A tibble: 7,703 x 1,660
#> mf_year unitid opeid opeid6 instnm city stabbr zip sch_deg main
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl+l>
#> 1 2014-15 100654 0010… 001002 Alaba… Norm… AL 35762 3 1 [Mai…
#> 2 2014-15 100663 0010… 001052 Unive… Birm… AL 3529… 3 1 [Mai…
#> 3 2014-15 100690 0250… 025034 Amrid… Mont… AL 3611… 3 1 [Mai…
#> 4 2014-15 100706 0010… 001055 Unive… Hunt… AL 35899 3 1 [Mai…
#> 5 2014-15 100724 0010… 001005 Alaba… Mont… AL 3610… 3 1 [Mai…
#> 6 2014-15 100751 0010… 001051 The U… Tusc… AL 3548… 3 1 [Mai…
#> 7 2014-15 100760 0010… 001007 Centr… Alex… AL 35010 2 1 [Mai…
#> 8 2014-15 100812 0010… 001008 Athen… Athe… AL 35611 3 1 [Mai…
#> 9 2014-15 100830 0083… 008310 Aubur… Mont… AL 3611… 3 1 [Mai…
#> 10 2014-15 100858 0010… 001009 Aubur… Aubu… AL 36849 3 1 [Mai…
#> # … with 7,693 more rows, and 1,650 more variables: numbranch <dbl>,
#> # preddeg <dbl+lbl>, highdeg <dbl+lbl>, control <dbl+lbl>,
#> # st_fips <dbl+lbl>, region <dbl+lbl>, adm_rate <dbl>,
#> # adm_rate_all <dbl>, satvr25 <dbl>, satvr75 <dbl>, satmt25 <dbl>,
#> # satmt75 <dbl>, satwr25 <dbl>, satwr75 <dbl>, satvrmid <dbl>,
#> # satmtmid <dbl>, satwrmid <dbl>, actcm25 <dbl>, actcm75 <dbl>,
#> # acten25 <dbl>, acten75 <dbl>, actmt25 <dbl>, actmt75 <dbl>,
#> # actwr25 <dbl>, actwr75 <dbl>, actcmmid <dbl>, actenmid <dbl>,
#> # actmtmid <dbl>, actwrmid <dbl>, sat_avg <dbl>, sat_avg_all <dbl>,
#> # pcip01 <dbl>, pcip03 <dbl>, pcip04 <dbl>, pcip05 <dbl>, pcip09 <dbl>,
#> # pcip10 <dbl>, pcip11 <dbl>, pcip12 <dbl>, pcip13 <dbl>, pcip14 <dbl>,
#> # pcip15 <dbl>, pcip16 <dbl>, pcip19 <dbl>, pcip22 <dbl>, pcip23 <dbl>,
#> # pcip24 <dbl>, pcip25 <dbl>, pcip26 <dbl>, pcip27 <dbl>, pcip29 <dbl>,
#> # pcip30 <dbl>, pcip31 <dbl>, pcip38 <dbl>, pcip39 <dbl>, pcip40 <dbl>,
#> # pcip41 <dbl>, pcip42 <dbl>, pcip43 <dbl>, pcip44 <dbl>, pcip45 <dbl>,
#> # pcip46 <dbl>, pcip47 <dbl>, pcip48 <dbl>, pcip49 <dbl>, pcip50 <dbl>,
#> # pcip51 <dbl>, pcip52 <dbl>, pcip54 <dbl>, cip01cert1 <dbl+lbl>,
#> # cip01cert2 <dbl+lbl>, cip01assoc <dbl+lbl>, cip01cert4 <dbl>,
#> # cip01bachl <dbl>, cip03cert1 <dbl>, cip03cert2 <dbl>,
#> # cip03assoc <dbl>, cip03cert4 <dbl>, cip03bachl <dbl>,
#> # cip04cert1 <dbl>, cip04cert2 <dbl>, cip04assoc <dbl>,
#> # cip04cert4 <dbl>, cip04bachl <dbl>, cip05cert1 <dbl>,
#> # cip05cert2 <dbl>, cip05assoc <dbl>, cip05cert4 <dbl>,
#> # cip05bachl <dbl>, cip09cert1 <dbl>, cip09cert2 <dbl>,
#> # cip09assoc <dbl>, cip09cert4 <dbl>, cip09bachl <dbl>,
#> # cip10cert1 <dbl>, cip10cert2 <dbl>, cip10assoc <dbl>,
#> # cip10cert4 <dbl>, cip10bachl <dbl>, cip11cert1 <dbl>, …
Working with variable and value labels
All the datasets have variable labels attached, which can be viewed in RStudio’s Data Viewer:
View(scorecard::mf2014_15)
You can also use the labelled package:
scorecard::mf2014_15 %>%
select(1:8) %>%
labelled::var_label()
#> $mf_year
#> NULL
#>
#> $unitid
#> [1] "Unit ID for institution"
#>
#> $opeid
#> [1] "8-digit OPE ID for institution"
#>
#> $opeid6
#> [1] "6-digit OPE ID for institution"
#>
#> $instnm
#> [1] "Institution name"
#>
#> $city
#> [1] "City"
#>
#> $stabbr
#> [1] "State postcode"
#>
#> $zip
#> [1] "ZIP code"
Or work with the codebook directly:
## Show variable labels
scorecard::codebook %>%
select(var_name, var_label)
#> # A tibble: 2,231 x 2
#> var_name var_label
#> <chr> <chr>
#> 1 unitid Unit ID for institution
#> 2 opeid 8-digit OPE ID for institution
#> 3 opeid6 6-digit OPE ID for institution
#> 4 instnm Institution name
#> 5 city City
#> 6 stabbr State postcode
#> 7 zip ZIP code
#> 8 accredagency Accreditor for institution
#> 9 insturl URL for institution's homepage
#> 10 npcurl URL for institution's net price calculator
#> # … with 2,221 more rows
## Build a small function to shown value labels
show_val_label = . %>% {
filter(scorecard::codebook, var_name == .) %>%
mutate(val_label = glue::glue("{val_label} = {value}")) %>%
distinct(val_label)}
## Show value labels:
show_val_label("curroper")
#> # A tibble: 2 x 1
#> val_label
#> <glue>
#> 1 Not currently certified as an operating institution = 0
#> 2 Currently certified as operating = 1
show_val_label("distanceonly")
#> # A tibble: 2 x 1
#> val_label
#> <glue>
#> 1 Not distance-education only = 0
#> 2 Distance-education only = 1
Exploring codebook and plot distributions of in-state tuition
vars = c("mf_year", "iclevel", "control", "tuitionfee_in")
scorecard::codebook %>%
select(var_name, var_label, value, val_label) %>%
filter(var_name %in% vars) %>%
knitr::kable()
| var_name | var_label | value | val_label | | :------------- | :------------------------ | ----: | :----------------- | | control | Control of institution | 1 | Public | | control | Control of institution | 2 | Private nonprofit | | control | Control of institution | 3 | Private for-profit | | tuitionfee_in | In-state tuition and fees | NA | NA | | iclevel | Level of institution | 1 | 4-year | | iclevel | Level of institution | 2 | 2-year | | iclevel | Level of institution | 3 | Less-than-2-year |
dplyr_seq = . %>%
select(one_of(vars)) %>%
haven::as_factor() %>%
filter(iclevel %in% c("4-year", "2-year")) %>%
mutate(year = mf_year %>% parse_number() %>% as.factor()) %>%
group_by(iclevel, control) %>%
mutate_at(c("tuitionfee_in"),
~statar::winsorise(., probs = c(0.02, 0.98), verbose = FALSE)) %>%
ungroup()
## Test the functional sequence
scorecard::mf2014_15 %>% dplyr_seq()
#> # A tibble: 5,530 x 5
#> mf_year iclevel control tuitionfee_in year
#> <chr> <fct> <fct> <dbl> <fct>
#> 1 2014-15 4-year Public 9096 2014
#> 2 2014-15 4-year Public 7510 2014
#> 3 2014-15 4-year Private nonprofit 6900 2014
#> 4 2014-15 4-year Public 9158 2014
#> 5 2014-15 4-year Public 8720 2014
#> 6 2014-15 4-year Public 9826 2014
#> 7 2014-15 2-year Public 3491 2014
#> 8 2014-15 4-year Public NA 2014
#> 9 2014-15 4-year Public 9080 2014
#> 10 2014-15 4-year Public 10200 2014
#> # … with 5,520 more rows
bind_rows(
scorecard::mf2017_18 %>% dplyr_seq(),
scorecard::mf2016_17 %>% dplyr_seq(),
scorecard::mf2015_16 %>% dplyr_seq(),
scorecard::mf2014_15 %>% dplyr_seq(),
scorecard::mf2013_14 %>% dplyr_seq(),
scorecard::mf2012_13 %>% dplyr_seq(),
scorecard::mf2011_12 %>% dplyr_seq(),
scorecard::mf2010_11 %>% dplyr_seq(),
scorecard::mf2009_10 %>% dplyr_seq(),
scorecard::mf2008_09 %>% dplyr_seq(),
scorecard::mf2007_08 %>% dplyr_seq(),
scorecard::mf2006_07 %>% dplyr_seq(),
scorecard::mf2005_06 %>% dplyr_seq(),
scorecard::mf2004_05 %>% dplyr_seq(),
scorecard::mf2003_04 %>% dplyr_seq(),
scorecard::mf2002_03 %>% dplyr_seq(),
scorecard::mf2001_02 %>% dplyr_seq(),
scorecard::mf2000_01 %>% dplyr_seq()
) -> df
df %>%
ggplot(aes(x = tuitionfee_in, y = year, fill = iclevel)) +
ggjoy::geom_joy(scale = 2, alpha = .8, colour = "white") +
ggjoy::theme_joy() +
facet_grid(iclevel~control, scales = "free") +
labs(x = NULL, y = NULL,
title = "In-State Tuition and Fees, 2000-2017") +
scale_x_continuous(labels = scales::dollar) +
scale_y_discrete(breaks = seq(2017, 2000, -3),
expand = c(0.01, 0)) +
theme(axis.text = element_text(size = 8),
legend.position = "none")
Compareing in-state and out-of-state tuition and fees
Distribution of Average Age of Entry
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.