Slides adapted from datasciencebox.org by Dr. Lucy D'Agostino McGowan
NC bike crashes
- Go to RStudio Cloud and open
NC bike crashes
Slides adapted from datasciencebox.org by Dr. Lucy D'Agostino McGowan
Tidy data
Slides adapted from datasciencebox.org by Dr. Lucy D'Agostino McGowan
Tidy data
Happy families are all alike; every unhappy family is unhappy in its own way.
Leo Tolstoy
Slides adapted from datasciencebox.org by Dr. Lucy D'Agostino McGowan
Tidy data
Happy families are all alike; every unhappy family is unhappy in its own way.
Leo Tolstoy
Characteristics of tidy data:
- Each variable forms a column.
- Each observation forms a row.
- Each type of observational unit forms a table.
Slides adapted from datasciencebox.org by Dr. Lucy D'Agostino McGowan
Tidy data
Happy families are all alike; every unhappy family is unhappy in its own way.
Leo Tolstoy
Characteristics of tidy data:
- Each variable forms a column.
- Each observation forms a row.
- Each type of observational unit forms a table.
Characteristics of untidy data:
!@#$%^&*()
Slides adapted from datasciencebox.org by Dr. Lucy D'Agostino McGowan
Summary tables
Is each of the following a dataset or a summary table?
## # A tibble: 87 x 3## name height mass## <chr> <int> <dbl>## 1 Luke Skywalker 172 77## 2 C-3PO 167 75## 3 R2-D2 96 32## 4 Darth Vader 202 136## 5 Leia Organa 150 49## 6 Owen Lars 178 120## 7 Beru Whitesun lars 165 75## 8 R5-D4 97 32## 9 Biggs Darklighter 183 84## 10 Obi-Wan Kenobi 182 77## # … with 77 more rows## # A tibble: 5 x 2## gender avg_height## <chr> <dbl>## 1 <NA> 120 ## 2 female 165.## 3 hermaphrodite 175 ## 4 male 179.## 5 none 200Pipes
Slides adapted from datasciencebox.org by Dr. Lucy D'Agostino McGowan
Where does the name come from?
The pipe operator is implemented in the package magrittr, it's pronounced "and then".
Slides adapted from datasciencebox.org by Dr. Lucy D'Agostino McGowan
Review: How does a pipe work?
- You can think about the following sequence of actions - find key, unlock car, start car, drive to school, park.
- Expressed as a set of nested functions in R pseudocode this would look like:
park(drive(start_car(find("keys")), to = "campus"))- Writing it out using pipes give it a more natural (and easier to read) structure:
find("keys") %>% start_car() %>% drive(to = "campus") %>% park()What about other arguments?
To send results to a function argument other than first one or to use the previous result for multiple arguments, use .:
starwars %>% filter(species == "Human") %>% lm(mass ~ height, data = .)## ## Call:## lm(formula = mass ~ height, data = .)## ## Coefficients:## (Intercept) height ## -116.58 1.11
Slides adapted from datasciencebox.org by Dr. Lucy D'Agostino McGowan
Bike crashes in NC 2007 - 2014
The dataset is in the dsbox package:
library(dsbox)ncbikecrashVariables
View the names of variables via
names(ncbikecrash)## [1] "object_id" "city" "county" ## [4] "region" "development" "locality" ## [7] "on_road" "rural_urban" "speed_limit" ## [10] "traffic_control" "weather" "workzone" ## [13] "bike_age" "bike_age_group" "bike_alcohol" ## [16] "bike_alcohol_drugs" "bike_direction" "bike_injury" ## [19] "bike_position" "bike_race" "bike_sex" ## [22] "driver_age" "driver_age_group" "driver_alcohol" ## [25] "driver_alcohol_drugs" "driver_est_speed" "driver_injury" ## [28] "driver_race" "driver_sex" "driver_vehicle_type" ## [31] "crash_alcohol" "crash_date" "crash_day" ## [34] "crash_group" "crash_hour" "crash_location" ## [37] "crash_month" "crash_severity" "crash_time" ## [40] "crash_type" "crash_year" "ambulance_req" ## [43] "hit_run" "light_condition" "road_character" ## [46] "road_class" "road_condition" "road_configuration" ## [49] "road_defects" "road_feature" "road_surface" ## [52] "num_bikes_ai" "num_bikes_bi" "num_bikes_ci" ## [55] "num_bikes_ki" "num_bikes_no" "num_bikes_to" ## [58] "num_bikes_ui" "num_lanes" "num_units" ## [61] "distance_mi_from" "frm_road" "rte_invd_cd" ## [64] "towrd_road" "geo_point" "geo_shape"Variables
See detailed descriptions with ?ncbikecrash.
Slides adapted from datasciencebox.org by Dr. Lucy D'Agostino McGowan
Viewing your data
- In the Environment, after loading with
data(ncbikecrash), click on the name of the data frame to view it in the data viewer - Use the
glimpsefunction to take a peek
glimpse(ncbikecrash)## Observations: 7,467## Variables: 66## $ object_id <int> 1686, 1674, 1673, 1687, 1653, 1665, 1642, 1…## $ city <chr> "None - Rural Crash", "Henderson", "None - …## $ county <chr> "Wayne", "Vance", "Lincoln", "Columbus", "N…## $ region <chr> "Coastal", "Piedmont", "Piedmont", "Coastal…## $ development <chr> "Farms, Woods, Pastures", "Residential", "F…## $ locality <chr> "Rural (<30% Developed)", "Mixed (30% To 70…## $ on_road <chr> "SR 1915", "NICHOLAS ST", "US 321", "W BURK…## $ rural_urban <chr> "Rural", "Urban", "Rural", "Urban", "Urban"…## $ speed_limit <chr> "50 - 55 MPH", "30 - 35 MPH", "50 - 55 M…## $ traffic_control <chr> "No Control Present", "Stop Sign", "Double …## $ weather <chr> "Clear", "Clear", "Clear", "Rain", "Clear",…## $ workzone <chr> "No", "No", "No", "No", "No", "No", "No", "…## $ bike_age <chr> "52", "66", "33", "52", "22", "15", "41", "…## $ bike_age_group <chr> "50-59", "60-69", "30-39", "50-59", "20-24"…## $ bike_alcohol <chr> "No", "No", "No", "Yes", "No", "No", "No", …## $ bike_alcohol_drugs <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…## $ bike_direction <chr> "With Traffic", "With Traffic", "With Traff…## $ bike_injury <chr> "B: Evident Injury", "C: Possible Injury", …## $ bike_position <chr> "Bike Lane / Paved Shoulder", "Travel Lane"…## $ bike_race <chr> "Black", "Black", "White", "Black", "White"…## $ bike_sex <chr> "Male", "Male", "Male", "Male", "Female", "…## $ driver_age <chr> "34", NA, "37", "55", "25", "17", NA, "50",…## $ driver_age_group <chr> "30-39", NA, "30-39", "50-59", "25-29", "0-…## $ driver_alcohol <chr> "No", "Missing", "No", "No", "No", "No", "M…## $ driver_alcohol_drugs <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…## $ driver_est_speed <chr> "51-55 mph", "6-10 mph", "41-45 mph", "11-1…## $ driver_injury <chr> "O: No Injury", "Unknown Injury", "O: No In…## $ driver_race <chr> "White", "Unknown/Missing", "Hispanic", "Bl…## $ driver_sex <chr> "Male", NA, "Female", "Male", "Male", "Fema…## $ driver_vehicle_type <chr> "Single Unit Truck (2-Axle, 6-Tire)", NA, "…## $ crash_alcohol <chr> "No", "No", "No", "Yes", "No", "No", "No", …## $ crash_date <chr> "11DEC2013", "20NOV2013", "03NOV2013", "14D…## $ crash_day <chr> "Wednesday", "Wednesday", "Sunday", "Saturd…## $ crash_group <chr> "Motorist Overtaking Bicyclist", "Bicyclist…## $ crash_hour <int> 6, 20, 18, 18, 13, 17, 17, 7, 15, 2, 12, 22…## $ crash_location <chr> "Non-Intersection", "Intersection", "Non-In…## $ crash_month <chr> "December", "November", "November", "Decemb…## $ crash_severity <chr> "B: Evident Injury", "C: Possible Injury", …## $ crash_time <drtn> 06:10:00, 20:41:00, 18:05:00, 18:34:00, 13…## $ crash_type <chr> "Motorist Overtaking - Undetected Bicyclist…## $ crash_year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2…## $ ambulance_req <chr> "Yes", "No", "Yes", "Yes", "Yes", "Yes", "Y…## $ hit_run <chr> "No", "Yes", "No", "No", "No", "No", "Yes",…## $ light_condition <chr> "Dark - Roadway Not Lighted", NA, "Dark - R…## $ road_character <chr> "Straight - Level", "Straight - Level", "St…## $ road_class <chr> "State Secondary Route", "Local Street", "U…## $ road_condition <chr> "Dry", "Dry", "Dry", "Water (Standing, Movi…## $ road_configuration <chr> "Two-Way, Not Divided", "Two-Way, Divided, …## $ road_defects <chr> "None", NA, "None", "None", "None", "None",…## $ road_feature <chr> "No Special Feature", "T-Intersection", "No…## $ road_surface <chr> "Coarse Asphalt", "Smooth Asphalt", "Smooth…## $ num_bikes_ai <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…## $ num_bikes_bi <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…## $ num_bikes_ci <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…## $ num_bikes_ki <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…## $ num_bikes_no <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…## $ num_bikes_to <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…## $ num_bikes_ui <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…## $ num_lanes <chr> "2 lanes", "2 lanes", "2 lanes", "1 lane", …## $ num_units <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…## $ distance_mi_from <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0"…## $ frm_road <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…## $ rte_invd_cd <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…## $ towrd_road <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…## $ geo_point <chr> "35.3336070056, -77.9955023901", "36.315187…## $ geo_shape <chr> "{\"type\": \"Point\", \"coordinates\": [-7…A Grammar of Data Manipulation
dplyr is based on the concepts of functions as verbs that manipulate data frames.
filter: pick rows matching criteriaslice: pick rows using index(es)select: pick columns by namepull: grab a column as a vectorarrange: reorder rowsmutate: add new variablesdistinct: filter for unique rowssample_n/sample_frac: randomly sample rowssummarise: reduce variables to values- ... (many more)
Slides adapted from datasciencebox.org by Dr. Lucy D'Agostino McGowan
dplyr rules for functions
- First argument is always a data frame
- Subsequent arguments say what to do with that data frame
- Always return a data frame
- Doesn't modify in place
Slides adapted from datasciencebox.org by Dr. Lucy D'Agostino McGowan
A note on piping and layering
- The
%>%operator in dplyr functions is called the pipe operator. This means you "pipe" the output of the previous line of code as the first input of the next line of code.
Slides adapted from datasciencebox.org by Dr. Lucy D'Agostino McGowan
A note on piping and layering
- The
%>%operator in dplyr functions is called the pipe operator. This means you "pipe" the output of the previous line of code as the first input of the next line of code. - The
+operator in ggplot2 functions is used for "layering". This means you create the plot in layers, separated by+.
Slides adapted from datasciencebox.org by Dr. Lucy D'Agostino McGowan
filter to select a subset of rows
for crashes in Durham County
ncbikecrash %>% filter(county == "Durham")## # A tibble: 340 x 66## object_id city county region development locality on_road rural_urban## <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> ## 1 2452 Durh… Durham Piedm… Residential Urban (… <NA> Urban ## 2 2441 Durh… Durham Piedm… Commercial Urban (… <NA> Urban ## 3 2466 Durh… Durham Piedm… Commercial Urban (… <NA> Urban ## 4 549 Durh… Durham Piedm… Residential Urban (… PARK A… Urban ## 5 598 Durh… Durham Piedm… Residential Urban (… BELT S… Urban ## 6 603 Durh… Durham Piedm… Residential Urban (… HINSON… Urban ## 7 3974 Durh… Durham Piedm… Commercial Urban (… <NA> Urban ## 8 7134 Durh… Durham Piedm… Commercial Urban (… <NA> Urban ## 9 1670 Durh… Durham Piedm… Commercial Urban (… INFINI… Urban ## 10 1773 Durh… Durham Piedm… Residential Urban (… <NA> Urban ## # … with 330 more rows, and 58 more variables: speed_limit <chr>,## # traffic_control <chr>, weather <chr>, workzone <chr>, bike_age <chr>,## # bike_age_group <chr>, bike_alcohol <chr>, bike_alcohol_drugs <chr>,## # bike_direction <chr>, bike_injury <chr>, bike_position <chr>,## # bike_race <chr>, bike_sex <chr>, driver_age <chr>,## # driver_age_group <chr>, driver_alcohol <chr>,## # driver_alcohol_drugs <chr>, driver_est_speed <chr>,## # driver_injury <chr>, driver_race <chr>, driver_sex <chr>,## # driver_vehicle_type <chr>, crash_alcohol <chr>, crash_date <chr>,## # crash_day <chr>, crash_group <chr>, crash_hour <int>,## # crash_location <chr>, crash_month <chr>, crash_severity <chr>,## # crash_time <drtn>, crash_type <chr>, crash_year <int>,## # ambulance_req <chr>, hit_run <chr>, light_condition <chr>,## # road_character <chr>, road_class <chr>, road_condition <chr>,## # road_configuration <chr>, road_defects <chr>, road_feature <chr>,## # road_surface <chr>, num_bikes_ai <int>, num_bikes_bi <int>,## # num_bikes_ci <int>, num_bikes_ki <int>, num_bikes_no <int>,## # num_bikes_to <int>, num_bikes_ui <int>, num_lanes <chr>,## # num_units <int>, distance_mi_from <chr>, frm_road <chr>,## # rte_invd_cd <int>, towrd_road <chr>, geo_point <chr>, geo_shape <chr>filter for many conditions at once
for crashes in Durham County where biker was 0-5 years old
ncbikecrash %>% filter(county == "Durham", bike_age_group == "0-5")## # A tibble: 4 x 66## object_id city county region development locality on_road rural_urban## <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> ## 1 4062 Durh… Durham Piedm… Residential Urban (… <NA> Urban ## 2 414 Durh… Durham Piedm… Residential Urban (… PVA 90… Urban ## 3 3016 Durh… Durham Piedm… Residential Urban (… <NA> Urban ## 4 1383 Durh… Durham Piedm… Residential Urban (… PVA 62… Urban ## # … with 58 more variables: speed_limit <chr>, traffic_control <chr>,## # weather <chr>, workzone <chr>, bike_age <chr>, bike_age_group <chr>,## # bike_alcohol <chr>, bike_alcohol_drugs <chr>, bike_direction <chr>,## # bike_injury <chr>, bike_position <chr>, bike_race <chr>,## # bike_sex <chr>, driver_age <chr>, driver_age_group <chr>,## # driver_alcohol <chr>, driver_alcohol_drugs <chr>,## # driver_est_speed <chr>, driver_injury <chr>, driver_race <chr>,## # driver_sex <chr>, driver_vehicle_type <chr>, crash_alcohol <chr>,## # crash_date <chr>, crash_day <chr>, crash_group <chr>,## # crash_hour <int>, crash_location <chr>, crash_month <chr>,## # crash_severity <chr>, crash_time <drtn>, crash_type <chr>,## # crash_year <int>, ambulance_req <chr>, hit_run <chr>,## # light_condition <chr>, road_character <chr>, road_class <chr>,## # road_condition <chr>, road_configuration <chr>, road_defects <chr>,## # road_feature <chr>, road_surface <chr>, num_bikes_ai <int>,## # num_bikes_bi <int>, num_bikes_ci <int>, num_bikes_ki <int>,## # num_bikes_no <int>, num_bikes_to <int>, num_bikes_ui <int>,## # num_lanes <chr>, num_units <int>, distance_mi_from <chr>,## # frm_road <chr>, rte_invd_cd <int>, towrd_road <chr>, geo_point <chr>,## # geo_shape <chr>Logical operators in R
| operator | definition | operator | definition | |
|---|---|---|---|---|
< |
less than | x | y |
x OR y |
|
<= |
less than or equal to | is.na(x) |
test if x is NA |
|
> |
greater than | !is.na(x) |
test if x is not NA |
|
>= |
greater than or equal to | x %in% y |
test if x is in y |
|
== |
exactly equal to | !(x %in% y) |
test if x is not in y |
|
!= |
not equal to | !x |
not x |
|
x & y |
x AND y |
Slides adapted from datasciencebox.org by Dr. Lucy D'Agostino McGowan
select to keep variables
ncbikecrash %>% filter(county == "Durham", bike_age_group == "0-5") %>% select(locality, speed_limit)## # A tibble: 4 x 2## locality speed_limit ## <chr> <chr> ## 1 Urban (>70% Developed) 30 - 35 MPH## 2 Urban (>70% Developed) 5 - 15 MPH ## 3 Urban (>70% Developed) 20 - 25 MPH## 4 Urban (>70% Developed) 20 - 25 MPHselect to exclude variables
ncbikecrash %>% select(-object_id)## # A tibble: 7,467 x 65## city county region development locality on_road rural_urban speed_limit## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> ## 1 None… Wayne Coast… Farms, Woo… Rural (… SR 1915 Rural 50 - 55 M…## 2 Hend… Vance Piedm… Residential Mixed (… NICHOL… Urban 30 - 35 M…## 3 None… Linco… Piedm… Farms, Woo… Rural (… US 321 Rural 50 - 55 M…## 4 Whit… Colum… Coast… Commercial Urban (… W BURK… Urban 30 - 35 M…## 5 Wilm… New H… Coast… Residential Urban (… RACINE… Urban <NA> ## 6 None… Robes… Coast… Farms, Woo… Rural (… SR 1513 Rural 50 - 55 M…## 7 None… Richm… Piedm… Residential Mixed (… SR 1903 Rural 30 - 35 M…## 8 Rale… Wake Piedm… Commercial Urban (… PERSON… Urban 30 - 35 M…## 9 Whit… Colum… Coast… Residential Rural (… FLOWER… Urban 30 - 35 M…## 10 New … Craven Coast… Residential Urban (… SUTTON… Urban 20 - 25 M…## # … with 7,457 more rows, and 57 more variables: traffic_control <chr>,## # weather <chr>, workzone <chr>, bike_age <chr>, bike_age_group <chr>,## # bike_alcohol <chr>, bike_alcohol_drugs <chr>, bike_direction <chr>,## # bike_injury <chr>, bike_position <chr>, bike_race <chr>,## # bike_sex <chr>, driver_age <chr>, driver_age_group <chr>,## # driver_alcohol <chr>, driver_alcohol_drugs <chr>,## # driver_est_speed <chr>, driver_injury <chr>, driver_race <chr>,## # driver_sex <chr>, driver_vehicle_type <chr>, crash_alcohol <chr>,## # crash_date <chr>, crash_day <chr>, crash_group <chr>,## # crash_hour <int>, crash_location <chr>, crash_month <chr>,## # crash_severity <chr>, crash_time <drtn>, crash_type <chr>,## # crash_year <int>, ambulance_req <chr>, hit_run <chr>,## # light_condition <chr>, road_character <chr>, road_class <chr>,## # road_condition <chr>, road_configuration <chr>, road_defects <chr>,## # road_feature <chr>, road_surface <chr>, num_bikes_ai <int>,## # num_bikes_bi <int>, num_bikes_ci <int>, num_bikes_ki <int>,## # num_bikes_no <int>, num_bikes_to <int>, num_bikes_ui <int>,## # num_lanes <chr>, num_units <int>, distance_mi_from <chr>,## # frm_road <chr>, rte_invd_cd <int>, towrd_road <chr>, geo_point <chr>,## # geo_shape <chr>select a range of variables
ncbikecrash %>% select(city:locality)## # A tibble: 7,467 x 5## city county region development locality ## <chr> <chr> <chr> <chr> <chr> ## 1 None - Rural … Wayne Coastal Farms, Woods, Pa… Rural (<30% Develop…## 2 Henderson Vance Piedmo… Residential Mixed (30% To 70% D…## 3 None - Rural … Lincoln Piedmo… Farms, Woods, Pa… Rural (<30% Develop…## 4 Whiteville Columbus Coastal Commercial Urban (>70% Develop…## 5 Wilmington New Hanov… Coastal Residential Urban (>70% Develop…## 6 None - Rural … Robeson Coastal Farms, Woods, Pa… Rural (<30% Develop…## 7 None - Rural … Richmond Piedmo… Residential Mixed (30% To 70% D…## 8 Raleigh Wake Piedmo… Commercial Urban (>70% Develop…## 9 Whiteville Columbus Coastal Residential Rural (<30% Develop…## 10 New Bern Craven Coastal Residential Urban (>70% Develop…## # … with 7,457 more rowsslice for certain row numbers
First five
ncbikecrash %>% slice(1:5)## # A tibble: 5 x 66## object_id city county region development locality on_road rural_urban## <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> ## 1 1686 None… Wayne Coast… Farms, Woo… Rural (… SR 1915 Rural ## 2 1674 Hend… Vance Piedm… Residential Mixed (… NICHOL… Urban ## 3 1673 None… Linco… Piedm… Farms, Woo… Rural (… US 321 Rural ## 4 1687 Whit… Colum… Coast… Commercial Urban (… W BURK… Urban ## 5 1653 Wilm… New H… Coast… Residential Urban (… RACINE… Urban ## # … with 58 more variables: speed_limit <chr>, traffic_control <chr>,## # weather <chr>, workzone <chr>, bike_age <chr>, bike_age_group <chr>,## # bike_alcohol <chr>, bike_alcohol_drugs <chr>, bike_direction <chr>,## # bike_injury <chr>, bike_position <chr>, bike_race <chr>,## # bike_sex <chr>, driver_age <chr>, driver_age_group <chr>,## # driver_alcohol <chr>, driver_alcohol_drugs <chr>,## # driver_est_speed <chr>, driver_injury <chr>, driver_race <chr>,## # driver_sex <chr>, driver_vehicle_type <chr>, crash_alcohol <chr>,## # crash_date <chr>, crash_day <chr>, crash_group <chr>,## # crash_hour <int>, crash_location <chr>, crash_month <chr>,## # crash_severity <chr>, crash_time <drtn>, crash_type <chr>,## # crash_year <int>, ambulance_req <chr>, hit_run <chr>,## # light_condition <chr>, road_character <chr>, road_class <chr>,## # road_condition <chr>, road_configuration <chr>, road_defects <chr>,## # road_feature <chr>, road_surface <chr>, num_bikes_ai <int>,## # num_bikes_bi <int>, num_bikes_ci <int>, num_bikes_ki <int>,## # num_bikes_no <int>, num_bikes_to <int>, num_bikes_ui <int>,## # num_lanes <chr>, num_units <int>, distance_mi_from <chr>,## # frm_road <chr>, rte_invd_cd <int>, towrd_road <chr>, geo_point <chr>,## # geo_shape <chr>slice for certain row numbers
Last five
last_row <- nrow(ncbikecrash)ncbikecrash %>% slice((last_row - 4):last_row)## # A tibble: 5 x 66## object_id city county region development locality on_road rural_urban## <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> ## 1 6989 High… Guilf… Piedm… Residential Urban (… <NA> Urban ## 2 6991 Wilm… New H… Coast… Residential Urban (… <NA> Urban ## 3 6995 Kins… Lenoir Coast… Commercial Urban (… <NA> Urban ## 4 6998 Faye… Cumbe… Coast… Residential Urban (… <NA> Urban ## 5 7000 None… Onslow Coast… Farms, Woo… Rural (… <NA> Rural ## # … with 58 more variables: speed_limit <chr>, traffic_control <chr>,## # weather <chr>, workzone <chr>, bike_age <chr>, bike_age_group <chr>,## # bike_alcohol <chr>, bike_alcohol_drugs <chr>, bike_direction <chr>,## # bike_injury <chr>, bike_position <chr>, bike_race <chr>,## # bike_sex <chr>, driver_age <chr>, driver_age_group <chr>,## # driver_alcohol <chr>, driver_alcohol_drugs <chr>,## # driver_est_speed <chr>, driver_injury <chr>, driver_race <chr>,## # driver_sex <chr>, driver_vehicle_type <chr>, crash_alcohol <chr>,## # crash_date <chr>, crash_day <chr>, crash_group <chr>,## # crash_hour <int>, crash_location <chr>, crash_month <chr>,## # crash_severity <chr>, crash_time <drtn>, crash_type <chr>,## # crash_year <int>, ambulance_req <chr>, hit_run <chr>,## # light_condition <chr>, road_character <chr>, road_class <chr>,## # road_condition <chr>, road_configuration <chr>, road_defects <chr>,## # road_feature <chr>, road_surface <chr>, num_bikes_ai <int>,## # num_bikes_bi <int>, num_bikes_ci <int>, num_bikes_ki <int>,## # num_bikes_no <int>, num_bikes_to <int>, num_bikes_ui <int>,## # num_lanes <chr>, num_units <int>, distance_mi_from <chr>,## # frm_road <chr>, rte_invd_cd <int>, towrd_road <chr>, geo_point <chr>,## # geo_shape <chr>pull to extract a column as a vector
ncbikecrash %>% slice(1:6) %>% pull(locality)## [1] "Rural (<30% Developed)" "Mixed (30% To 70% Developed)"## [3] "Rural (<30% Developed)" "Urban (>70% Developed)" ## [5] "Urban (>70% Developed)" "Rural (<30% Developed)"vs.
ncbikecrash %>% slice(1:6) %>% select(locality)## # A tibble: 6 x 1## locality ## <chr> ## 1 Rural (<30% Developed) ## 2 Mixed (30% To 70% Developed)## 3 Rural (<30% Developed) ## 4 Urban (>70% Developed) ## 5 Urban (>70% Developed) ## 6 Rural (<30% Developed)sample_n / sample_frac for a random sample
sample_n: randomly sample 5 observations
ncbikecrash_n5 <- ncbikecrash %>% sample_n(5, replace = FALSE)dim(ncbikecrash_n5)## [1] 5 66sample_n / sample_frac for a random sample
sample_n: randomly sample 5 observations
ncbikecrash_n5 <- ncbikecrash %>% sample_n(5, replace = FALSE)dim(ncbikecrash_n5)## [1] 5 66sample_frac: randomly sample 20% of observations
ncbikecrash_perc20 <-ncbikecrash %>% sample_frac(0.2, replace = FALSE)dim(ncbikecrash_perc20)## [1] 1493 66distinct to filter for unique rows
And arrange to order alphabetically
ncbikecrash %>% select(county, city) %>% distinct() %>% arrange(county, city)## # A tibble: 391 x 2## county city ## <chr> <chr> ## 1 Alamance Alamance ## 2 Alamance Burlington ## 3 Alamance Elon ## 4 Alamance Elon College ## 5 Alamance Gibsonville ## 6 Alamance Graham ## 7 Alamance Green Level ## 8 Alamance Mebane ## 9 Alamance None - Rural Crash## 10 Alexander None - Rural Crash## # … with 381 more rowssummarise to reduce variables to values
ncbikecrash %>% summarise(avg_hr = mean(crash_hour))## # A tibble: 1 x 1## avg_hr## <dbl>## 1 14.7group_by to do calculations on groups
ncbikecrash %>% group_by(hit_run) %>% summarise(avg_hr = mean(crash_hour))## # A tibble: 2 x 2## hit_run avg_hr## <chr> <dbl>## 1 No 14.6## 2 Yes 15.0count observations in groups
ncbikecrash %>% count(driver_alcohol_drugs)## # A tibble: 6 x 2## driver_alcohol_drugs n## <chr> <int>## 1 <NA> 6654## 2 Missing 99## 3 No 695## 4 Yes-Alcohol, impairment suspected 12## 5 Yes-Alcohol, no impairment detected 3## 6 Yes-Drugs, impairment suspected 4mutate to add new variables
ncbikecrash %>% mutate(driver_alcohol_drugs_simplified = case_when( driver_alcohol_drugs == "Missing" ~ NA, str_detect(driver_alcohol_drugs, "Yes") ~ "Yes", TRUE ~ "No" ))
Slides adapted from datasciencebox.org by Dr. Lucy D'Agostino McGowan
"Save" when you mutate
Most often when you define a new variable with mutate you'll also want to save the resulting data frame, often by writing over the original data frame.
ncbikecrash <- ncbikecrash %>% mutate(driver_alcohol_drugs_simplified = case_when( str_detect(driver_alcohol_drugs, "Yes") ~ "Yes", TRUE ~ driver_alcohol_drugs ))"Save" when you mutate
Most often when you define a new variable with mutate you'll also want to save the resulting data frame, often by writing over the original data frame.
ncbikecrash %>% mutate(driver_alcohol_drugs_simplified = case_when( str_detect(driver_alcohol_drugs, "Yes") ~ "Yes", TRUE ~ driver_alcohol_drugs )) -> ncbikecrashCheck before you move on
ncbikecrash %>% count(driver_alcohol_drugs, driver_alcohol_drugs_simplified)## # A tibble: 6 x 3## driver_alcohol_drugs driver_alcohol_drugs_simplified n## <chr> <chr> <int>## 1 <NA> <NA> 6654## 2 Missing Missing 99## 3 No No 695## 4 Yes-Alcohol, impairment suspected Yes 12## 5 Yes-Alcohol, no impairment detected Yes 3## 6 Yes-Drugs, impairment suspected Yes 4ncbikecrash %>% count(driver_alcohol_drugs_simplified)## # A tibble: 4 x 2## driver_alcohol_drugs_simplified n## <chr> <int>## 1 <NA> 6654## 2 Missing 99## 3 No 695## 4 Yes 19 NC bike crashes
- Go to RStudio Cloud and open
NC bike crashes - For each question you work on, set the
evalchunk option toTRUEand knit