Data used

Bike Lanes Dataset: BikeBaltimore is the Department of Transportation’s bike program. The data is from http://data.baltimorecity.gov/Transportation/Bike-Lanes/xzfj-gyms

You can Download as a CSV in your current working directory. Note its also available at: http://johnmuschelli.com/intro_to_r/data/Bike_Lanes.csv

library(readr)
library(dplyr)
library(tidyverse)
library(jhur)

bike = read_csv(
  "http://johnmuschelli.com/intro_to_r/data/Bike_Lanes.csv")
## Parsed with column specification:
## cols(
##   subType = col_character(),
##   name = col_character(),
##   block = col_character(),
##   type = col_character(),
##   numLanes = col_double(),
##   project = col_character(),
##   route = col_character(),
##   length = col_double(),
##   dateInstalled = col_double()
## )

or use

library(jhur)
bike = read_bike()
## Parsed with column specification:
## cols(
##   subType = col_character(),
##   name = col_character(),
##   block = col_character(),
##   type = col_character(),
##   numLanes = col_double(),
##   project = col_character(),
##   route = col_character(),
##   length = col_double(),
##   dateInstalled = col_double()
## )

Part 1

  1. How many bike “lanes” are currently in Baltimore? You can assume each observation/row is a different bike “lane”
nrow(bike)
## [1] 1631
dim(bike)
## [1] 1631    9
bike %>% 
  nrow()
## [1] 1631
  1. How many (a) feet and (b) miles of bike “lanes” are currently in Baltimore?
sum(bike$length)
## [1] 439447.586561
sum(bike$length)/5280
## [1] 83.2287095759
sum(bike$length/5280)
## [1] 83.2287095759

Part 2

  1. How many types of bike lanes are there? Which type has (a) the most number of and (b) longest average bike lane length?
table(bike$type, useNA = "ifany")
## 
##  BIKE BOULEVARD       BIKE LANE      CONTRAFLOW SHARED BUS BIKE 
##              49             621              13              39 
##         SHARROW        SIDEPATH    SIGNED ROUTE            <NA> 
##             589               7             304               9
unique(bike$type)
## [1] "BIKE BOULEVARD"  "SIDEPATH"        "SIGNED ROUTE"   
## [4] "BIKE LANE"       "SHARROW"         NA               
## [7] "CONTRAFLOW"      "SHARED BUS BIKE"
length(table(bike$type))
## [1] 7
length(unique(bike$type))
## [1] 8
is.na(unique(bike$type))
## [1] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
counts = bike %>% 
  count(type)

bike %>% 
  group_by(type) %>% 
  summarise(number_of_rows = n(),
            mean = mean(length)) %>% 
  arrange(mean)
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 8 x 3
##   type            number_of_rows  mean
##   <chr>                    <int> <dbl>
## 1 CONTRAFLOW                  13  136.
## 2 BIKE BOULEVARD              49  197.
## 3 SHARROW                    589  244.
## 4 <NA>                         9  260.
## 5 SIGNED ROUTE               304  264.
## 6 SHARED BUS BIKE             39  277.
## 7 BIKE LANE                  621  300.
## 8 SIDEPATH                     7  666.
  1. How many different projects do the “bike” lanes fall into? Which project category has the longest average bike lane?
length(unique(bike$project))
## [1] 13
bike %>% 
  group_by(project) %>% 
  summarise(n = n(),
            mean = mean(length)) %>% 
  arrange(desc(mean)) 
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 13 x 3
##    project                       n  mean
##    <chr>                     <int> <dbl>
##  1 MAINTENANCE                   4 1942.
##  2 ENGINEERING CONSTRUCTION     12  512.
##  3 TRAFFIC                      51  420.
##  4 COLLEGETOWN                 339  321.
##  5 PARK HEIGHTS BIKE NETWORK   172  283.
##  6 CHARM CITY CIRCULATOR        39  277.
##  7 TRAFFIC CALMING              79  269.
##  8 OPERATION ORANGE CONE       458  250.
##  9 <NA>                         74  214.
## 10 COLLEGETOWN NETWORK          13  214.
## 11 SOUTHEAST BIKE NETWORK      323  211.
## 12 PLANNING TRAFFIC             18  209.
## 13 GUILFORD AVE BIKE BLVD       49  197.
bike %>% 
  group_by(project, type) %>% 
  summarise(n = n(),
            mean = mean(length)) %>% 
  arrange(desc(mean)) %>% 
  ungroup() %>% 
  slice(1) %>% 
  magrittr::extract("project")
## `summarise()` regrouping output by 'project' (override with `.groups` argument)
## # A tibble: 1 x 1
##   project    
##   <chr>      
## 1 MAINTENANCE
arrange(summarize(group_by(bike, project, type), 
          n = n(), mean = mean(length)),
        desc(mean))
## `summarise()` regrouping output by 'project' (override with `.groups` argument)
## # A tibble: 32 x 4
## # Groups:   project [13]
##    project                   type             n  mean
##    <chr>                     <chr>        <int> <dbl>
##  1 MAINTENANCE               BIKE LANE        4 1942.
##  2 TRAFFIC                   SIDEPATH         1 1848.
##  3 ENGINEERING CONSTRUCTION  BIKE LANE        8  537.
##  4 <NA>                      SIDEPATH         2  512.
##  5 ENGINEERING CONSTRUCTION  SIDEPATH         3  481.
##  6 ENGINEERING CONSTRUCTION  SHARROW          1  403.
##  7 PARK HEIGHTS BIKE NETWORK SIGNED ROUTE    27  398.
##  8 TRAFFIC                   BIKE LANE       50  391.
##  9 PLANNING TRAFFIC          BIKE LANE        6  363.
## 10 <NA>                      BIKE LANE       12  357.
## # … with 22 more rows
avg = bike %>% 
  group_by(type) %>% 
  summarize(mn = mean(length, na.rm = TRUE)) %>% 
  filter(mn == max(mn))
## `summarise()` ungrouping output (override with `.groups` argument)

Part 3

  1. What was the average bike lane length per year that they were installed? Set bike$dateInstalled to NA if it is equal to zero.
bike = bike %>% mutate(
  dateInstalled = ifelse(
    dateInstalled == 0, 
    NA,
    dateInstalled)
)
mean(bike$length[ !is.na(bike$dateInstalled)])
## [1] 273.994253525
is.na(bike$dateInstalled)
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!is.na(bike$dateInstalled)
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b2 = bike %>% 
  mutate(dateInstalled = ifelse(dateInstalled == "0", NA, 
                                dateInstalled))
b2 = bike %>% 
  mutate(dateInstalled = if_else(dateInstalled == "0", 
                                 NA_real_,
                                 dateInstalled))
bike$dateInstalled[bike$dateInstalled == "0"] = NA

bike %>% 
  mutate(length = ifelse(length == 0, NA, length)) %>% 
  group_by(dateInstalled) %>% 
  summarise(n = n(),
            mean_of_the_bike = mean(length, na.rm = TRUE),
            n_missing = sum(is.na(length)))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 9 x 4
##   dateInstalled     n mean_of_the_bike n_missing
##           <dbl> <int>            <dbl>     <int>
## 1          2006     2            1469.         0
## 2          2007   368             310.         0
## 3          2008   206             249.         0
## 4          2009    86             407.         0
## 5          2010   625             246.         0
## 6          2011   101             233.         0
## 7          2012   107             271.         0
## 8          2013    10             290.         0
## 9            NA   126             217.         1

Part 3

    1. Numerically [hint: quantile()] and
      1. graphically [hint: hist() or plot(density())] describe the distribution of bike “lane” lengths.
quantile(bike$length)
##            0%           25%           50%           75%          100% 
##    0.00000000  124.04071740  200.30268446  341.02238099 3749.32263773
qplot(x = length, data = bike, geom = "histogram")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

qplot(x = log10(length), data = bike, geom = "histogram")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1 rows containing non-finite values (stat_bin).

hist(bike$length)

hist(bike$length,breaks=100)

hist(log2(bike$length),breaks=100)

hist(log10(bike$length),breaks=100)