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(tidyverse)
library(dplyr)
library(lubridate)
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

Get all the differet types of bike types from the type column. Use sort(unique()). Assign this to an object btypes. Type dput(btypes)

head(factor(bike$type))

## [1] BIKE BOULEVARD SIDEPATH       SIGNED ROUTE   SIDEPATH      
## [5] BIKE LANE      SIGNED ROUTE  
## 7 Levels: BIKE BOULEVARD BIKE LANE CONTRAFLOW ... SIGNED ROUTE

btypes = sort(unique(bike$type))
x = c("SIDEPATH","BIKE BOULEVARD", "BIKE LANE", "CONTRAFLOW",
        "SHARED BUS BIKE",  "SHARROW",  "SIGNED ROUTE")
dput(btypes)

## c("BIKE BOULEVARD", "BIKE LANE", "CONTRAFLOW", "SHARED BUS BIKE", 
## "SHARROW", "SIDEPATH", "SIGNED ROUTE")

dput(btypes)[c(6,1:5,7)]

## c("BIKE BOULEVARD", "BIKE LANE", "CONTRAFLOW", "SHARED BUS BIKE", 
## "SHARROW", "SIDEPATH", "SIGNED ROUTE")

## [1] "SIDEPATH"        "BIKE BOULEVARD"  "BIKE LANE"      
## [4] "CONTRAFLOW"      "SHARED BUS BIKE" "SHARROW"        
## [7] "SIGNED ROUTE"

dput(btypes[c(6,1:5,7)])

## c("SIDEPATH", "BIKE BOULEVARD", "BIKE LANE", "CONTRAFLOW", "SHARED BUS BIKE", 
## "SHARROW", "SIGNED ROUTE")

lev = c( "SIDEPATH", "BIKE BOULEVARD", "BIKE LANE", "CONTRAFLOW", "SHARED BUS BIKE", 
        "SHARROW", "SIGNED ROUTE")

1. Recode type as a factor that has the SIDEPATH level first. Print head(bike$type). Note what you see. Run table(bike$type) afterwards and note the order

bike$type = factor(bike$type)
bike$type = relevel(bike$type, "SIDEPATH")
bike$type = factor(bike$type,
          levels = dput(btypes[c(6,1:5,7)]))

## c("SIDEPATH", "BIKE BOULEVARD", "BIKE LANE", "CONTRAFLOW", "SHARED BUS BIKE", 
## "SHARROW", "SIGNED ROUTE")

bike = bike %>% mutate(type = factor(type, 
                levels = dput(btypes[c(6,1:5,7)])))

## c("SIDEPATH", "BIKE BOULEVARD", "BIKE LANE", "CONTRAFLOW", "SHARED BUS BIKE", 
## "SHARROW", "SIGNED ROUTE")

table(bike$type)

## 
##        SIDEPATH  BIKE BOULEVARD       BIKE LANE      CONTRAFLOW 
##               7              49             621              13 
## SHARED BUS BIKE         SHARROW    SIGNED ROUTE 
##              39             589             304

Make a column type2, which is a factor of type, with the levels: c( "SIDEPATH", "BIKE BOULEVARD", "BIKE LANE"). Run table(bike$type2), with the option useNA = "always". Note, we do not have to make type a character again before doing this

bike = bike %>% 
  mutate(type2 = factor(type, 
             levels = c( "SIDEPATH", "BIKE BOULEVARD", 
                         "BIKE LANE") ) )
table(bike$type)

## 
##        SIDEPATH  BIKE BOULEVARD       BIKE LANE      CONTRAFLOW 
##               7              49             621              13 
## SHARED BUS BIKE         SHARROW    SIGNED ROUTE 
##              39             589             304

table(bike$type2)

## 
##       SIDEPATH BIKE BOULEVARD      BIKE LANE 
##              7             49            621

table(bike$type2, useNA = "always")

## 
##       SIDEPATH BIKE BOULEVARD      BIKE LANE           <NA> 
##              7             49            621            954

Reassign dateInstalled into a character using as.character. Run head(bike$dateInstalled).

bike = bike %>% 
  mutate(dateInstalled = 
           as.character(dateInstalled)
  )
head(bike$dateInstalled)

## [1] "0"    "2010" "2010" "0"    "2011" "2007"

Reassign dateInstalled a factor, using the default levels. Run head(bike$dateInstalled).

bike = bike %>% 
  mutate(dateInstalled = 
           factor(dateInstalled)
  )
head(bike$dateInstalled)

## [1] 0    2010 2010 0    2011 2007
## Levels: 0 2006 2007 2008 2009 2010 2011 2012 2013

table(factor(bike$dateInstalled, levels = 2005:2017))

## 
## 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 
##    0    2  368  206   86  625  101  107   10    0    0    0    0

table(factor(bike$dateInstalled, levels = 2005:2017), 
        useNA="ifany")

## 
## 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 <NA> 
##    0    2  368  206   86  625  101  107   10    0    0    0    0  126

Do not reassign dateInstalled, but simply run head(as.numeric(bike$dateInstalled)) we are looking to see what happens when we try to go from factor to numeric

head(as.numeric(bike$dateInstalled))

## [1] 1 6 6 1 7 3

Do not reassign dateInstalled, but simply run head(as.numeric(as.character(bike$dateInstalled))) this is how you get “numeric” values back if they were “incorrectly” factors

head(as.numeric(as.character(
    bike$dateInstalled)))

## [1]    0 2010 2010    0 2011 2007

Part 2

Convert type back to a character. Make a column type2 (replacing the old one) where if the type is one of these categories: c("CONTRAFLOW", "SHARED BUS BIKE", "SHARROW", "SIGNED ROUTE") call it "OTHER". Use %in% and ifelse. Make type2 a factor with the levels c( "SIDEPATH", "BIKE BOULEVARD", "BIKE LANE", "OTHER")

bike = bike %>% mutate(
    type = as.character(type),
    type2 = ifelse(type %in% c("CONTRAFLOW", "SHARED BUS BIKE", 
                               "SHARROW", "SIGNED ROUTE"), "OTHER", type),
    type2 = factor(type2, levels = c( "SIDEPATH", "BIKE BOULEVARD", 
                               "BIKE LANE", "OTHER") ))

table(bike$type2)

## 
##       SIDEPATH BIKE BOULEVARD      BIKE LANE          OTHER 
##              7             49            621            945

bike2 = bike %>% 
  mutate(
    type = factor(type,
                  levels = c( "SIDEPATH", "BIKE BOULEVARD", 
                              "BIKE LANE", "CONTRAFLOW", 
                              "SHARED BUS BIKE", 
                              "SHARROW", "SIGNED ROUTE")
                  ),
    type2 = recode_factor(type, 
                          "CONTRAFLOW" = "OTHER",
                          "SHARED BUS BIKE" = "OTHER",
                          "SHARROW" = "OTHER",
                          "SIGNED ROUTE" = "OTHER")
  )
table(bike2$type2)

## 
##          OTHER       SIDEPATH BIKE BOULEVARD      BIKE LANE 
##            945              7             49            621

Part 3

Parse the following dates with the correct lubridate function:

“2014/02-14”

ymd("2014/02-14")

## [1] "2014-02-14"

“04/22/14 03:20”

mdy_hm("04/22/14 03:20")

## [1] "2014-04-22 03:20:00 UTC"

“4/5/2016 03:2:22” assume mdy

mdy_hms("4/5/2016 03:2:22")

## [1] "2016-04-05 03:02:22 UTC"

Data Class Lab Key

Data used

Part 1

Part 2

Part 3