README.md
In haliyan/fars.functions: Process, Summarize, and Map FARS Data
fars.functions
The goal of fars.functions is to read in, summarize, and map FARS
(Fatality Analysis Reporting System) data on car accidents in the United
States. This package was created as part of the Coursera course
“Building R Packages” by JHU.
VERY IMPORTANT NOTES FOR PEER REVIEWERS
PLEASE READ (2 NOTES BELOW)
NOTE 1
This package has a R-CMD-Check badge provided by GitHub Actions and
not a Travis badge.
Since the time of the creation of the course, Travis CI has changed
significantly. It is no longer as straightforward as outlined in the
course materials to use Travis for open-source projects. My
understanding is that using their services for open-source projects such
as this one requires signing up for a plan/trial (which in turn requires
disclosing credit card information to Travis, which I wish to avoid if
at all possible) and applying for open source credits. See Travis’s
statements on open source credits
here
and trial plan
here.
Due to these issues and as per the suggestion of R, I have used GitHub
Actions instead of Travis to check my package.
You can verify that the log contains NO errors, warnings, or notes by
going to the “Actions” tab in my GitHub repo and clicking on the TOP
workflow run (it should have a blue check mark beside it).
I am asking that you please, please be considerate of this situation
when grading my work!
NOTE 2
In order to pass the tests I set up in the tests sub directory, I had to
make some changes to the original fars_read and fars_summarize_years
functions.
-
Changing the dplyr::tbl_df() function (which has been deprecated)
to the tibble::as_tibble() function in fars.read.
-
Adding the line
filename <- system.file("extdata", filename, package= "fars.functions"
to fars.read.
-
Changing n() to dplyr::n() in fars_summarize_years.
Omitting these changes causes problems with running examples and tests,
building the vignette, creating the README file, and passing checks.
No other changes have been made to the original code.
Installation
You can install the development version of fars.functions in R by
entering install_github("haliyan/fars.functions") into the console.
Note: Using the install_github function requires that the
devtools package be installed and loaded. devtools can be
installed using install.packages("devtools") and loaded using
library(devtools)
Once the fars.functions package is installed, load it using
library(fars.functions).
Examples
The fars.functions package function make_filename creates a FARS file
name of the correct format for a certain year supplied by the user. This
format is accident_X.csv.bz2, where X is the user-supplied year:
make_filename("2013")
#> [1] "accident_2013.csv.bz2"
The function fars_read reads in a FARS file of a certain file name and
creates a tibble of the data.
fars_read("accident_2013.csv.bz2")
#> # A tibble: 30,202 x 50
#> STATE ST_CASE VE_TOTAL VE_FORMS PVH_INVL PEDS PERNOTMVIT PERMVIT PERSONS
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 10001 1 1 0 0 0 8 8
#> 2 1 10002 2 2 0 0 0 2 2
#> 3 1 10003 1 1 0 0 0 1 1
#> 4 1 10004 1 1 0 0 0 3 3
#> 5 1 10005 2 2 0 0 0 3 3
#> 6 1 10006 2 2 0 0 0 3 3
#> 7 1 10007 1 1 0 0 0 1 1
#> 8 1 10008 2 2 0 0 0 2 2
#> 9 1 10009 1 1 0 0 0 1 1
#> 10 1 10010 2 2 0 0 0 4 4
#> # ... with 30,192 more rows, and 41 more variables: COUNTY <dbl>, CITY <dbl>,
#> # DAY <dbl>, MONTH <dbl>, YEAR <dbl>, DAY_WEEK <dbl>, HOUR <dbl>,
#> # MINUTE <dbl>, NHS <dbl>, ROAD_FNC <dbl>, ROUTE <dbl>, TWAY_ID <chr>,
#> # TWAY_ID2 <chr>, MILEPT <dbl>, LATITUDE <dbl>, LONGITUD <dbl>, SP_JUR <dbl>,
#> # HARM_EV <dbl>, MAN_COLL <dbl>, RELJCT1 <dbl>, RELJCT2 <dbl>, TYP_INT <dbl>,
#> # WRK_ZONE <dbl>, REL_ROAD <dbl>, LGT_COND <dbl>, WEATHER1 <dbl>,
#> # WEATHER2 <dbl>, WEATHER <dbl>, SCH_BUS <dbl>, RAIL <chr>, NOT_HOUR <dbl>,
#> # NOT_MIN <dbl>, ARR_HOUR <dbl>, ARR_MIN <dbl>, HOSP_HR <dbl>, HOSP_MN <dbl>,
#> # CF1 <dbl>, CF2 <dbl>, CF3 <dbl>, FATALS <dbl>, DRUNK_DR <dbl>
This function can also be used together with make_filename and the
pipe operator %>% from the magrittr package, especially if the
correct file name format is not known.
library(magrittr)
make_filename("2013") %>%
fars_read()
#> # A tibble: 30,202 x 50
#> STATE ST_CASE VE_TOTAL VE_FORMS PVH_INVL PEDS PERNOTMVIT PERMVIT PERSONS
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 10001 1 1 0 0 0 8 8
#> 2 1 10002 2 2 0 0 0 2 2
#> 3 1 10003 1 1 0 0 0 1 1
#> 4 1 10004 1 1 0 0 0 3 3
#> 5 1 10005 2 2 0 0 0 3 3
#> 6 1 10006 2 2 0 0 0 3 3
#> 7 1 10007 1 1 0 0 0 1 1
#> 8 1 10008 2 2 0 0 0 2 2
#> 9 1 10009 1 1 0 0 0 1 1
#> 10 1 10010 2 2 0 0 0 4 4
#> # ... with 30,192 more rows, and 41 more variables: COUNTY <dbl>, CITY <dbl>,
#> # DAY <dbl>, MONTH <dbl>, YEAR <dbl>, DAY_WEEK <dbl>, HOUR <dbl>,
#> # MINUTE <dbl>, NHS <dbl>, ROAD_FNC <dbl>, ROUTE <dbl>, TWAY_ID <chr>,
#> # TWAY_ID2 <chr>, MILEPT <dbl>, LATITUDE <dbl>, LONGITUD <dbl>, SP_JUR <dbl>,
#> # HARM_EV <dbl>, MAN_COLL <dbl>, RELJCT1 <dbl>, RELJCT2 <dbl>, TYP_INT <dbl>,
#> # WRK_ZONE <dbl>, REL_ROAD <dbl>, LGT_COND <dbl>, WEATHER1 <dbl>,
#> # WEATHER2 <dbl>, WEATHER <dbl>, SCH_BUS <dbl>, RAIL <chr>, NOT_HOUR <dbl>,
#> # NOT_MIN <dbl>, ARR_HOUR <dbl>, ARR_MIN <dbl>, HOSP_HR <dbl>, HOSP_MN <dbl>,
#> # CF1 <dbl>, CF2 <dbl>, CF3 <dbl>, FATALS <dbl>, DRUNK_DR <dbl>
The fars_read_years takes a list or vector of years and produces a
list of tibbles. Each tibble contains the months in one of the years in
the supplied vector of years. The number of rows for each month
represents the number of accidents that occurred in that month.
fars_read_years(c("2013","2014"))
#> [[1]]
#> # A tibble: 30,202 x 2
#> MONTH year
#> <dbl> <chr>
#> 1 1 2013
#> 2 1 2013
#> 3 1 2013
#> 4 1 2013
#> 5 1 2013
#> 6 1 2013
#> 7 1 2013
#> 8 1 2013
#> 9 1 2013
#> 10 1 2013
#> # ... with 30,192 more rows
#>
#> [[2]]
#> # A tibble: 30,056 x 2
#> MONTH year
#> <dbl> <chr>
#> 1 1 2014
#> 2 1 2014
#> 3 1 2014
#> 4 1 2014
#> 5 1 2014
#> 6 1 2014
#> 7 1 2014
#> 8 1 2014
#> 9 1 2014
#> 10 1 2014
#> # ... with 30,046 more rows
The fars_summarize_years function also takes a list or vector of years
and produces a single tibble. The rows of the tibble represent the 12
months in a year. The columns of the tibble are the years that were
supplied to the function. The values contained in the tibble are the
numbers of accidents in a particular month of a particular year.
fars_summarize_years(c("2013","2014"))
#> # A tibble: 12 x 3
#> MONTH `2013` `2014`
#> <dbl> <int> <int>
#> 1 1 2230 2168
#> 2 2 1952 1893
#> 3 3 2356 2245
#> 4 4 2300 2308
#> 5 5 2532 2596
#> 6 6 2692 2583
#> 7 7 2660 2696
#> 8 8 2899 2800
#> 9 9 2741 2618
#> 10 10 2768 2831
#> 11 11 2615 2714
#> 12 12 2457 2604
The fars_map_state function creates a map of accidents in a given
state in a given year. For example, the following code creates a map of
the accidents in the state of Wisconsin in 2014:
fars_map_state(55, 2014)
Note: for some state numbers, the fars_map_state function will not
work, for various reasons. A quick summary table of these is included
below. For more details plus a full table of all 56 state numbers and
corresponding state names, please see the package vignette. You can
instruct R to build the vignette when installing from GitHub by
specifying build_vignettes=TRUE when installing with
install_github.
| Numbers | Reasons |
|--------:|:------------------------|
| 2 | Alaska; out of bounds |
| 3 | American Samoa; no data |
| 7 | Canal Zone; no data |
| 14 | Guam; no data |
| 15 | Hawaii; out of bounds |
| 43 | Puerto Rico; no data |
| 52 | Virgin Islands; no data |
License
GPL
haliyan/fars.functions documentation built on May 9, 2022, 8:30 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.
fars.functions
The goal of fars.functions is to read in, summarize, and map FARS (Fatality Analysis Reporting System) data on car accidents in the United States. This package was created as part of the Coursera course “Building R Packages” by JHU.
VERY IMPORTANT NOTES FOR PEER REVIEWERS
PLEASE READ (2 NOTES BELOW)
NOTE 1
This package has a R-CMD-Check badge provided by GitHub Actions and not a Travis badge.
Since the time of the creation of the course, Travis CI has changed significantly. It is no longer as straightforward as outlined in the course materials to use Travis for open-source projects. My understanding is that using their services for open-source projects such as this one requires signing up for a plan/trial (which in turn requires disclosing credit card information to Travis, which I wish to avoid if at all possible) and applying for open source credits. See Travis’s statements on open source credits here and trial plan here.
Due to these issues and as per the suggestion of R, I have used GitHub Actions instead of Travis to check my package.
You can verify that the log contains NO errors, warnings, or notes by going to the “Actions” tab in my GitHub repo and clicking on the TOP workflow run (it should have a blue check mark beside it).
I am asking that you please, please be considerate of this situation when grading my work!
NOTE 2
In order to pass the tests I set up in the tests sub directory, I had to
make some changes to the original fars_read and fars_summarize_years
functions.
-
Changing the
dplyr::tbl_df()function (which has been deprecated) to thetibble::as_tibble()function infars.read. -
Adding the line
filename <- system.file("extdata", filename, package= "fars.functions"tofars.read. -
Changing
n()todplyr::n()infars_summarize_years.
Omitting these changes causes problems with running examples and tests, building the vignette, creating the README file, and passing checks. No other changes have been made to the original code.
Installation
You can install the development version of fars.functions in R by
entering install_github("haliyan/fars.functions") into the console.
Note: Using the install_github function requires that the
devtools package be installed and loaded. devtools can be
installed using install.packages("devtools") and loaded using
library(devtools)
Once the fars.functions package is installed, load it using
library(fars.functions).
Examples
The fars.functions package function make_filename creates a FARS file
name of the correct format for a certain year supplied by the user. This
format is accident_X.csv.bz2, where X is the user-supplied year:
make_filename("2013")
#> [1] "accident_2013.csv.bz2"
The function fars_read reads in a FARS file of a certain file name and
creates a tibble of the data.
fars_read("accident_2013.csv.bz2")
#> # A tibble: 30,202 x 50
#> STATE ST_CASE VE_TOTAL VE_FORMS PVH_INVL PEDS PERNOTMVIT PERMVIT PERSONS
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 10001 1 1 0 0 0 8 8
#> 2 1 10002 2 2 0 0 0 2 2
#> 3 1 10003 1 1 0 0 0 1 1
#> 4 1 10004 1 1 0 0 0 3 3
#> 5 1 10005 2 2 0 0 0 3 3
#> 6 1 10006 2 2 0 0 0 3 3
#> 7 1 10007 1 1 0 0 0 1 1
#> 8 1 10008 2 2 0 0 0 2 2
#> 9 1 10009 1 1 0 0 0 1 1
#> 10 1 10010 2 2 0 0 0 4 4
#> # ... with 30,192 more rows, and 41 more variables: COUNTY <dbl>, CITY <dbl>,
#> # DAY <dbl>, MONTH <dbl>, YEAR <dbl>, DAY_WEEK <dbl>, HOUR <dbl>,
#> # MINUTE <dbl>, NHS <dbl>, ROAD_FNC <dbl>, ROUTE <dbl>, TWAY_ID <chr>,
#> # TWAY_ID2 <chr>, MILEPT <dbl>, LATITUDE <dbl>, LONGITUD <dbl>, SP_JUR <dbl>,
#> # HARM_EV <dbl>, MAN_COLL <dbl>, RELJCT1 <dbl>, RELJCT2 <dbl>, TYP_INT <dbl>,
#> # WRK_ZONE <dbl>, REL_ROAD <dbl>, LGT_COND <dbl>, WEATHER1 <dbl>,
#> # WEATHER2 <dbl>, WEATHER <dbl>, SCH_BUS <dbl>, RAIL <chr>, NOT_HOUR <dbl>,
#> # NOT_MIN <dbl>, ARR_HOUR <dbl>, ARR_MIN <dbl>, HOSP_HR <dbl>, HOSP_MN <dbl>,
#> # CF1 <dbl>, CF2 <dbl>, CF3 <dbl>, FATALS <dbl>, DRUNK_DR <dbl>
This function can also be used together with make_filename and the
pipe operator %>% from the magrittr package, especially if the
correct file name format is not known.
library(magrittr)
make_filename("2013") %>%
fars_read()
#> # A tibble: 30,202 x 50
#> STATE ST_CASE VE_TOTAL VE_FORMS PVH_INVL PEDS PERNOTMVIT PERMVIT PERSONS
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 10001 1 1 0 0 0 8 8
#> 2 1 10002 2 2 0 0 0 2 2
#> 3 1 10003 1 1 0 0 0 1 1
#> 4 1 10004 1 1 0 0 0 3 3
#> 5 1 10005 2 2 0 0 0 3 3
#> 6 1 10006 2 2 0 0 0 3 3
#> 7 1 10007 1 1 0 0 0 1 1
#> 8 1 10008 2 2 0 0 0 2 2
#> 9 1 10009 1 1 0 0 0 1 1
#> 10 1 10010 2 2 0 0 0 4 4
#> # ... with 30,192 more rows, and 41 more variables: COUNTY <dbl>, CITY <dbl>,
#> # DAY <dbl>, MONTH <dbl>, YEAR <dbl>, DAY_WEEK <dbl>, HOUR <dbl>,
#> # MINUTE <dbl>, NHS <dbl>, ROAD_FNC <dbl>, ROUTE <dbl>, TWAY_ID <chr>,
#> # TWAY_ID2 <chr>, MILEPT <dbl>, LATITUDE <dbl>, LONGITUD <dbl>, SP_JUR <dbl>,
#> # HARM_EV <dbl>, MAN_COLL <dbl>, RELJCT1 <dbl>, RELJCT2 <dbl>, TYP_INT <dbl>,
#> # WRK_ZONE <dbl>, REL_ROAD <dbl>, LGT_COND <dbl>, WEATHER1 <dbl>,
#> # WEATHER2 <dbl>, WEATHER <dbl>, SCH_BUS <dbl>, RAIL <chr>, NOT_HOUR <dbl>,
#> # NOT_MIN <dbl>, ARR_HOUR <dbl>, ARR_MIN <dbl>, HOSP_HR <dbl>, HOSP_MN <dbl>,
#> # CF1 <dbl>, CF2 <dbl>, CF3 <dbl>, FATALS <dbl>, DRUNK_DR <dbl>
The fars_read_years takes a list or vector of years and produces a
list of tibbles. Each tibble contains the months in one of the years in
the supplied vector of years. The number of rows for each month
represents the number of accidents that occurred in that month.
fars_read_years(c("2013","2014"))
#> [[1]]
#> # A tibble: 30,202 x 2
#> MONTH year
#> <dbl> <chr>
#> 1 1 2013
#> 2 1 2013
#> 3 1 2013
#> 4 1 2013
#> 5 1 2013
#> 6 1 2013
#> 7 1 2013
#> 8 1 2013
#> 9 1 2013
#> 10 1 2013
#> # ... with 30,192 more rows
#>
#> [[2]]
#> # A tibble: 30,056 x 2
#> MONTH year
#> <dbl> <chr>
#> 1 1 2014
#> 2 1 2014
#> 3 1 2014
#> 4 1 2014
#> 5 1 2014
#> 6 1 2014
#> 7 1 2014
#> 8 1 2014
#> 9 1 2014
#> 10 1 2014
#> # ... with 30,046 more rows
The fars_summarize_years function also takes a list or vector of years
and produces a single tibble. The rows of the tibble represent the 12
months in a year. The columns of the tibble are the years that were
supplied to the function. The values contained in the tibble are the
numbers of accidents in a particular month of a particular year.
fars_summarize_years(c("2013","2014"))
#> # A tibble: 12 x 3
#> MONTH `2013` `2014`
#> <dbl> <int> <int>
#> 1 1 2230 2168
#> 2 2 1952 1893
#> 3 3 2356 2245
#> 4 4 2300 2308
#> 5 5 2532 2596
#> 6 6 2692 2583
#> 7 7 2660 2696
#> 8 8 2899 2800
#> 9 9 2741 2618
#> 10 10 2768 2831
#> 11 11 2615 2714
#> 12 12 2457 2604
The fars_map_state function creates a map of accidents in a given
state in a given year. For example, the following code creates a map of
the accidents in the state of Wisconsin in 2014:
fars_map_state(55, 2014)
Note: for some state numbers, the fars_map_state function will not
work, for various reasons. A quick summary table of these is included
below. For more details plus a full table of all 56 state numbers and
corresponding state names, please see the package vignette. You can
instruct R to build the vignette when installing from GitHub by
specifying build_vignettes=TRUE when installing with
install_github.
| Numbers | Reasons | |--------:|:------------------------| | 2 | Alaska; out of bounds | | 3 | American Samoa; no data | | 7 | Canal Zone; no data | | 14 | Guam; no data | | 15 | Hawaii; out of bounds | | 43 | Puerto Rico; no data | | 52 | Virgin Islands; no data |
License
GPL
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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