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README.md
In phillydaodata: Download Data from the Philadelphia District Attorney's Office
phillydaodata
phillydaodata is a collection of simpler helper functions that make it
easy to work with the publicly available data that the Philadelphia
District Attorney’s Office makes available on their Public Data
Dashboard. This package is
not an official product of the Philadelphia District Attorney’s Office -
it is merely a way to handle their already public and freely available
data.
If you are going to be using the data, please read the Limitations
page of the Philadelphia
District Attorney’s Office closely as that discusses the caveats of the
data. You should also read the Definitions
page which explains a lot
of the terms used in the data and some of the assumptions that the
District Attorney’s Office team took when it came to the data (e.g. how
the supervision time is estimated).
Installation
You can install the released version of phillydaodata from
CRAN with:
install.packages("phillydaodata")
Example
Each function in this package follows the same pattern where the
function name (e.g. get_arrests()) determines which data you will
receive and the parameter geography determines which geographic unit the
data will be in. The choices for this parameter are “citywide”,
“police_district”, “zip_code”, and “census_tract”. For
“census_tract” the data will be weekly; for the other geographies,
the data will be daily.[1]
If you don’t include a geography parameter, it’ll default to citywide.
library(phillydaodata)
data <- get_summary_charges()
dim(data)
#> [1] 2460 11
head(data)
#> date retail_theft theft fraud_theft_of_services vandalism
#> 1 2014-01-01 4 1 18 0
#> 2 2014-01-02 2 0 4 1
#> 3 2014-01-03 0 0 13 1
#> 4 2014-01-04 6 0 13 0
#> 5 2014-01-05 4 0 5 0
#> 6 2014-01-06 6 0 15 1
#> alcohol_possession_by_minor public_drunkenness trespass disorderly_conduct
#> 1 2 1 10 6
#> 2 0 1 2 13
#> 3 0 1 1 4
#> 4 0 1 2 0
#> 5 0 0 0 4
#> 6 0 0 0 5
#> minor_violations_open_container_public_urination_smoking_violation
#> 1 97
#> 2 34
#> 3 15
#> 4 44
#> 5 9
#> 6 17
#> uncategorized_offenses
#> 1 41
#> 2 25
#> 3 19
#> 4 36
#> 5 15
#> 6 26
tail(data)
#> date retail_theft theft fraud_theft_of_services vandalism
#> 2455 2020-10-26 3 0 0 0
#> 2456 2020-10-27 1 0 0 0
#> 2457 2020-10-28 0 0 0 2
#> 2458 2020-10-29 3 0 0 0
#> 2459 2020-10-30 0 0 0 0
#> 2460 2020-10-31 2 0 0 0
#> alcohol_possession_by_minor public_drunkenness trespass disorderly_conduct
#> 2455 0 0 0 0
#> 2456 0 0 0 1
#> 2457 0 0 0 0
#> 2458 0 0 0 0
#> 2459 0 0 0 0
#> 2460 0 0 0 0
#> minor_violations_open_container_public_urination_smoking_violation
#> 2455 0
#> 2456 0
#> 2457 0
#> 2458 0
#> 2459 0
#> 2460 0
#> uncategorized_offenses
#> 2455 1
#> 2456 0
#> 2457 3
#> 2458 2
#> 2459 1
#> 2460 0
Selecting a geography returns that data not in the geographic units you
select, rather than for all of Philadelphia. It also adds a column
indicating, for each row, what geography that row is for.
data <- get_summary_charges(geography = "police_district")
dim(data)
#> [1] 78799 12
head(data)
#> date police_district retail_theft theft fraud_theft_of_services
#> 1 2014-01-01 01 1 0 0
#> 2 2014-01-01 02 0 0 0
#> 3 2014-01-01 03 0 0 0
#> 4 2014-01-01 05 0 0 0
#> 5 2014-01-01 06 0 0 0
#> 6 2014-01-01 07 0 0 0
#> vandalism alcohol_possession_by_minor public_drunkenness trespass
#> 1 0 0 0 0
#> 2 0 0 1 0
#> 3 0 0 0 0
#> 4 0 0 0 0
#> 5 0 0 0 0
#> 6 0 0 0 0
#> disorderly_conduct
#> 1 0
#> 2 0
#> 3 0
#> 4 1
#> 5 0
#> 6 0
#> minor_violations_open_container_public_urination_smoking_violation
#> 1 0
#> 2 0
#> 3 0
#> 4 0
#> 5 0
#> 6 0
#> uncategorized_offenses
#> 1 1
#> 2 0
#> 3 0
#> 4 0
#> 5 0
#> 6 0
tail(data)
#> date police_district retail_theft theft fraud_theft_of_services
#> 78794 2020-10-31 49 0 0 0
#> 78795 2020-10-31 54 0 0 0
#> 78796 2020-10-31 56 0 0 0
#> 78797 2020-10-31 71 0 0 0
#> 78798 2020-10-31 77 0 0 0
#> 78799 2020-10-31 unknown 0 0 0
#> vandalism alcohol_possession_by_minor public_drunkenness trespass
#> 78794 0 0 0 0
#> 78795 0 0 0 0
#> 78796 0 0 0 0
#> 78797 0 0 0 0
#> 78798 0 0 0 0
#> 78799 0 0 0 0
#> disorderly_conduct
#> 78794 0
#> 78795 0
#> 78796 0
#> 78797 0
#> 78798 0
#> 78799 0
#> minor_violations_open_container_public_urination_smoking_violation
#> 78794 0
#> 78795 0
#> 78796 0
#> 78797 0
#> 78798 0
#> 78799 0
#> uncategorized_offenses
#> 78794 0
#> 78795 0
#> 78796 0
#> 78797 0
#> 78798 0
#> 78799 0
- According to the District Attorney’s Office’s website this is
because the Census tract file gets very large so they aggregate it
weekly to save space.
Try the phillydaodata package in your browser
Any scripts or data that you put into this service are public.
phillydaodata documentation built on Jan. 28, 2021, 1:09 a.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.
phillydaodata
phillydaodata is a collection of simpler helper functions that make it
easy to work with the publicly available data that the Philadelphia
District Attorney’s Office makes available on their Public Data
Dashboard. This package is
not an official product of the Philadelphia District Attorney’s Office -
it is merely a way to handle their already public and freely available
data.
If you are going to be using the data, please read the Limitations page of the Philadelphia District Attorney’s Office closely as that discusses the caveats of the data. You should also read the Definitions page which explains a lot of the terms used in the data and some of the assumptions that the District Attorney’s Office team took when it came to the data (e.g. how the supervision time is estimated).
Installation
You can install the released version of phillydaodata from
CRAN with:
install.packages("phillydaodata")
Example
Each function in this package follows the same pattern where the
function name (e.g. get_arrests()) determines which data you will
receive and the parameter geography determines which geographic unit the
data will be in. The choices for this parameter are “citywide”,
“police_district”, “zip_code”, and “census_tract”. For
“census_tract” the data will be weekly; for the other geographies,
the data will be daily.[1]
If you don’t include a geography parameter, it’ll default to citywide.
library(phillydaodata)
data <- get_summary_charges()
dim(data)
#> [1] 2460 11
head(data)
#> date retail_theft theft fraud_theft_of_services vandalism
#> 1 2014-01-01 4 1 18 0
#> 2 2014-01-02 2 0 4 1
#> 3 2014-01-03 0 0 13 1
#> 4 2014-01-04 6 0 13 0
#> 5 2014-01-05 4 0 5 0
#> 6 2014-01-06 6 0 15 1
#> alcohol_possession_by_minor public_drunkenness trespass disorderly_conduct
#> 1 2 1 10 6
#> 2 0 1 2 13
#> 3 0 1 1 4
#> 4 0 1 2 0
#> 5 0 0 0 4
#> 6 0 0 0 5
#> minor_violations_open_container_public_urination_smoking_violation
#> 1 97
#> 2 34
#> 3 15
#> 4 44
#> 5 9
#> 6 17
#> uncategorized_offenses
#> 1 41
#> 2 25
#> 3 19
#> 4 36
#> 5 15
#> 6 26
tail(data)
#> date retail_theft theft fraud_theft_of_services vandalism
#> 2455 2020-10-26 3 0 0 0
#> 2456 2020-10-27 1 0 0 0
#> 2457 2020-10-28 0 0 0 2
#> 2458 2020-10-29 3 0 0 0
#> 2459 2020-10-30 0 0 0 0
#> 2460 2020-10-31 2 0 0 0
#> alcohol_possession_by_minor public_drunkenness trespass disorderly_conduct
#> 2455 0 0 0 0
#> 2456 0 0 0 1
#> 2457 0 0 0 0
#> 2458 0 0 0 0
#> 2459 0 0 0 0
#> 2460 0 0 0 0
#> minor_violations_open_container_public_urination_smoking_violation
#> 2455 0
#> 2456 0
#> 2457 0
#> 2458 0
#> 2459 0
#> 2460 0
#> uncategorized_offenses
#> 2455 1
#> 2456 0
#> 2457 3
#> 2458 2
#> 2459 1
#> 2460 0
Selecting a geography returns that data not in the geographic units you select, rather than for all of Philadelphia. It also adds a column indicating, for each row, what geography that row is for.
data <- get_summary_charges(geography = "police_district")
dim(data)
#> [1] 78799 12
head(data)
#> date police_district retail_theft theft fraud_theft_of_services
#> 1 2014-01-01 01 1 0 0
#> 2 2014-01-01 02 0 0 0
#> 3 2014-01-01 03 0 0 0
#> 4 2014-01-01 05 0 0 0
#> 5 2014-01-01 06 0 0 0
#> 6 2014-01-01 07 0 0 0
#> vandalism alcohol_possession_by_minor public_drunkenness trespass
#> 1 0 0 0 0
#> 2 0 0 1 0
#> 3 0 0 0 0
#> 4 0 0 0 0
#> 5 0 0 0 0
#> 6 0 0 0 0
#> disorderly_conduct
#> 1 0
#> 2 0
#> 3 0
#> 4 1
#> 5 0
#> 6 0
#> minor_violations_open_container_public_urination_smoking_violation
#> 1 0
#> 2 0
#> 3 0
#> 4 0
#> 5 0
#> 6 0
#> uncategorized_offenses
#> 1 1
#> 2 0
#> 3 0
#> 4 0
#> 5 0
#> 6 0
tail(data)
#> date police_district retail_theft theft fraud_theft_of_services
#> 78794 2020-10-31 49 0 0 0
#> 78795 2020-10-31 54 0 0 0
#> 78796 2020-10-31 56 0 0 0
#> 78797 2020-10-31 71 0 0 0
#> 78798 2020-10-31 77 0 0 0
#> 78799 2020-10-31 unknown 0 0 0
#> vandalism alcohol_possession_by_minor public_drunkenness trespass
#> 78794 0 0 0 0
#> 78795 0 0 0 0
#> 78796 0 0 0 0
#> 78797 0 0 0 0
#> 78798 0 0 0 0
#> 78799 0 0 0 0
#> disorderly_conduct
#> 78794 0
#> 78795 0
#> 78796 0
#> 78797 0
#> 78798 0
#> 78799 0
#> minor_violations_open_container_public_urination_smoking_violation
#> 78794 0
#> 78795 0
#> 78796 0
#> 78797 0
#> 78798 0
#> 78799 0
#> uncategorized_offenses
#> 78794 0
#> 78795 0
#> 78796 0
#> 78797 0
#> 78798 0
#> 78799 0
- According to the District Attorney’s Office’s website this is because the Census tract file gets very large so they aggregate it weekly to save space.
Try the phillydaodata package in your browser
Any scripts or data that you put into this service are public.
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.
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