CS代考 Data Summarization Lab Key

Data Summarization Lab Key

Data Summarization Lab Key

Copyright By PowCoder代写 加微信 powcoder

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

library(readr)
library(dplyr)

## Attaching package: ‘dplyr’

## The following objects are masked from ‘package:stats’:
## filter, lag

## The following objects are masked from ‘package:base’:
## intersect, setdiff, setequal, union

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.5 ✓ stringr 1.4.0
## ✓ tidyr 1.1.4 ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()

bike = read_csv(
“data/Bike_Lanes.csv”)

## Rows: 1631 Columns: 9

## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: “,”
## chr (6): subType, name, block, type, project, route
## dbl (3): numLanes, length, dateInstalled
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

How many bike “lanes” are currently in Baltimore? You can assume each observation/row is a different bike “lane”

nrow(bike)

## [1] 1631

## [1] 1631 9

## [1] 1631

How many (a) feet and (b) miles of bike “lanes” are currently in Baltimore?

sum(bike$length)

## [1] 439447.6

sum(bike$length)/5280

## [1] 83.22871

sum(bike$length/5280)

## [1] 83.22871

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 SHARROW
## 49 621 13 39 589
## SIDEPATH SIGNED ROUTE
## 7 304 9

unique(bike$type)

## [1] “BIKE BOULEVARD” “SIDEPATH” “SIGNED ROUTE” “BIKE LANE”
## [5] “SHARROW” NA “CONTRAFLOW” “SHARED BUS BIKE”

length(table(bike$type))

length(unique(bike$type))

is.na(unique(bike$type))

## [1] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE

counts = bike %>%
count(type)

group_by(type) %>%
summarise(number_of_rows = n(),
mean = mean(length)) %>%
arrange(mean)

## # A tibble: 8 × 3
## type number_of_rows mean
##
## 1 CONTRAFLOW 13 136.
## 2 BIKE BOULEVARD 49 197.
## 3 SHARROW 589 244.
## 4 9 260.
## 5 SIGNED ROUTE 304 264.
## 6 SHARED BUS BIKE 39 277.
## 7 BIKE LANE 621 300.
## 8 SIDEPATH 7 666.

How many different projects do the “bike” lanes fall into? Which project category has the longest average bike lane?

length(unique(bike$project))

group_by(project) %>%
summarise(n = n(),
mean = mean(length)) %>%
arrange(desc(mean))

## # A tibble: 13 × 3
## project n mean
##
## 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 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.

group_by(project, type) %>%
summarise(n = n(),
mean = mean(length)) %>%
arrange(desc(mean)) %>%
ungroup() %>%
slice(1) %>%
magrittr::extract(“project”)

## `summarise()` has grouped output by ‘project’. You can override using the
## `.groups` argument.

## # A tibble: 1 × 1
## project
##
## 1 MAINTENANCE

arrange(summarize(group_by(bike, project, type),
n = n(), mean = mean(length)),
desc(mean))

## `summarise()` has grouped output by ‘project’. You can override using the
## `.groups` argument.

## # A tibble: 32 × 4
## # Groups: project [13]
## project type n mean
##
## 1 MAINTENANCE BIKE LANE 4 1942.
## 2 TRAFFIC SIDEPATH 1 1848.
## 3 ENGINEERING CONSTRUCTION BIKE LANE 8 537.
## 4 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 BIKE LANE 12 357.
## # … with 22 more rows

avg = bike %>%
group_by(type) %>%
summarize(mn = mean(length, na.rm = TRUE)) %>%
filter(mn == max(mn))

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,
dateInstalled)
mean(bike$length[ !is.na(bike$dateInstalled)])

## [1] 273.9943

is.na(bike$dateInstalled)

## [1] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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!is.na(bike$dateInstalled)

## [1] FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [13] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [25] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [37] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [49] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [73] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [85] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [97] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE
## [109] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [133] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [145] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [157] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [169] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [193] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [205] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [217] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [229] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [253] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [265] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [277] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [289] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [301] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [313] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [325] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [337] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [349] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [361] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
## [385] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [397] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [433] TRUE TRUE TRUE TRUE TRU

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