Package 'mdsr' reference manual

Title:	Complement to 'Modern Data Science with R'
Description:	A complement to all editions of Modern Data Science with R (ISBN: 978-0367191498, publisher URL: <https://www.routledge.com/Modern-Data-Science-with-R/Baumer-Kaplan-Horton/p/book/9780367191498>). This package contains data and code to complete exercises and reproduce examples from the text. It also facilitates connections to the SQL database server used in the book. All editions of the book are supported by this package.
Authors:	Benjamin S. Baumer [aut, cre] , Nicholas Horton [aut] , Daniel Kaplan [aut]
Maintainer:	Benjamin S. Baumer <[email protected]>
License:	CC0
Version:	0.2.9
Built:	2024-06-25 04:48:02 UTC
Source:	https://github.com/mdsr-book/mdsr

Cherry Blossom runs

Description

Cherry Blossom runs

Usage

Cherry
Cherry

Format

An object of class tbl_df (inherits from tbl, data.frame) with 41,248 rows and 8 columns. Each row refers to an individual runner in one race of the Cherry Blossom Ten Miler. The data cover the years 1999 to 2008. All of the runners listed ran at least two of the races in that period, some ran many more than that.

name.yob: a unique identifier for each runner composed of the runner's full name and year of birth.
age: integer giving the runner's age in the race whose result is being reported.
gun: the number of minutes elapsed from the starter's gun to the person crossing the finish line
net: the number of minutes elapsed from the runner's crossing the start line to crossing the finish line.
sex: the runner's sex
year: the year of that race
previous: integer specifying how many times previous to this race the runner had participated in the years 1999 to 2008.
nruns: integer giving the total number of times that runner participated in the years from 1999 to 2008. The smallest is 2, the largest is 10.
nruns: integer giving the total number of times that runner participated in the years from 1999 to 2008. The smallest is 2, the largest is 10.

Details

The Cherry Blossom 10 Mile Run is a road race held in Washington, D.C. in April each year. (The name comes from the famous cherry trees that are in bloom in April in Washington.) The results of this race are published at https://www.cherryblossom.org/post-race/race-results/.

Examples

if (require(dplyr)) {
  Cherry %>%
    group_by(name.yob) %>%
    count() %>%
    group_by(n) %>%
    count(name = "appearances")
}
if (require(dplyr)) {
  Cherry %>%
    group_by(name.yob) %>%
    count() %>%
    group_by(n) %>%
    count(name = "appearances")
}

Deaths and Pumps from 1854 London cholera outbreak

Description

Deaths and Pumps from 1854 London cholera outbreak

Usage

CholeraDeaths

CholeraPumps
CholeraDeaths

CholeraPumps

Format

An object of class sf whose data attribute has 250 rows and 2 columns.

An object of class sf.

Details

Both spatial objects are projected in EPSG:27700, aka the British National Grid.

Source

https://blog.rtwilson.com/john-snows-cholera-data-in-more-formats/

Examples

if (require(sf)) {
  plot(st_geometry(CholeraDeaths))
}
if (require(sf)) {
  plot(st_geometry(CholeraDeaths))
}

Several variables on countries from the CIA Factbook, 2014.

Description

The CIA Factbook has geographic, demographic, and economic data on a country-by-country basis. In the description of the variables, the 4-digit number indicates the code used to specify that variable on the data and documentation web site.

Usage

CIACountries
CIACountries

Format

A data frame with the following variables for each of the Countries in the World. (236 countries are given.)

country: Name of the country
pop: number of people, 2119
area: area (sq km), 2147
oil_prod: Crude oil - production (bbl/day), 2241
gdp: Gross Domestic Product per capita ($/person), 2001
educ: education spending (% of GDP), 2206
roadways: Roadways per unit area (km/sq km), 2085
net_users: Fraction of Internet users (% of population), 2153

Source

From the CIA World Factbook, https://www.cia.gov/the-world-factbook/

References

https://github.com/factbook/factbook/blob/master/CATEGORIES.md

Examples

str(CIACountries)

str(CIACountries)

Data Science Papers from arXiv.org

Description

Papers matching the search string "Data Science" on arXiv.org in August, 2020

Usage

DataSciencePapers
DataSciencePapers

Format

A data frame with 1089 observations on the following 15 variables.

id: unique arXiv.org identifier for the paper
submitted: date submitted
updated: date last updated
title: title of the paper
abstract: contents of the abstract
authors: authors of the paper
affiliations: affiliations of the authors
link_abstract: direct link to the abstract
link_pdf: direct link to the pdf
link_doi: direct link to the digital object identifier (doi)
comment: commentary
journal_ref: reference to the journal (if published)
doi: digital object identifier
primary_category: arXiv.org primary category
categories: arXiv.org categories

Source

https://arxiv.org/

Examples


data(DataSciencePapers)
str(DataSciencePapers)

data(DataSciencePapers)
str(DataSciencePapers)

Election Statistics

Description

Election Statistics

Usage

Elections
Elections

Format

An object of class \codetbl_df (inherits from \codetbl, \codedata.frame) with 117 rows and 13 columns.

Ward: Name of the country
Precinct: number of people, 2119
Registered.Voters.at.7am: area (sq km), 2147
Voters.Registering.at.Polls: Crude oil - production (bbl/day), 2241
gdp: Gross Domestic Product per capita ($/person), 2001
educ: education spending (% of GDP), 2206
roadways: Roadways per unit area (km/sq km), 2085
net_users: Fraction of Internet users (% of population), 2153

Email Train

Description

The training dataset includes a set of email subject lines used for classification of whether the message is spam (unsolicited commercial content) or not. Many subject lines include subject matter inappropriate for classroom use. Given the volume of headlines containing such language (especially for spam == TRUE), user discretion is advised. This dataset is a random sample of 80% of the emails data.

The testing dataset is a random sample of 20% of the emails data.

Usage

Emails_train

Emails_test
Emails_train

Emails_test

Format

A data frame with 5,526 rows and 3 variables:

ids: an integer vector
subjectline: a character vector
type: a character vector

A data frame with 1,382 rows and 3 variables:

Source

Originally retrieved from http://www.rdatasciencecases.org/Data.html

Examples

nrow(Emails_train)
nrow(Emails_test)
nrow(Emails_train)
nrow(Emails_test)

Load the NCI60 data from GitHub

Description

Load the NCI60 data from GitHub

Usage

etl_NCI60()
etl_NCI60()

Examples


## Not run: 
NCI60 <- etl_NCI60()

## End(Not run)
## Not run: 
NCI60 <- etl_NCI60()

## End(Not run)

Headlines_train

Description

This data comes from Chakraborty et. al., which combines headlines from a variety of news and clickbait sources. Some headlines contain subject matter inappropriate for classroom use. Given the volume of headlines containing such language (especially for clickbait == TRUE), this filtering might not catch all problematic headlines. User discretion is advised. The training dataset is a random sample of approximately 80% of the observations from the original dataset.

The testing dataset is a random sample of the remaining 20% of the observations not found in the training set.

Usage

Headlines_train

Headlines_test
Headlines_train

Headlines_test

Format

A data frame with 18,360 rows and 3 variables:

title: a character vector
clickbait: a logical vector
ids: an integer vector

A data frame with 4,589 rows and 3 variables:

Source

https://github.com/bhargaviparanjape/clickbait/

References

doi:10.1109/ASONAM.2016.7752207

Examples

nrow(Headlines_train)
nrow(Headlines_test)
nrow(Headlines_train)
nrow(Headlines_test)

Text of Macbeth

Description

The entire text of Macbeth, stored in a character vector of length 1.

Usage

Macbeth_raw
Macbeth_raw

Format

A character vector of length 1

Source

Project Gutenberg, https://www.gutenberg.org/ebooks/1129/

Replacements for LaTeX macros

Description

Replacements for LaTeX macros

Usage

func(x, ...)

sql_func(x)

sql_word(x)

argument(x)

variable(x)

pkg(x, ...)

mdsr_data(x)

mdsr_person(x, ...)

vocab(x, ...)

index_entry(
  index_label = "subject",
  x,
  emph = FALSE,
  index = TRUE,
  .f = NULL,
  alt = NULL
)
func(x, ...)

sql_func(x)

sql_word(x)

argument(x)

variable(x)

pkg(x, ...)

mdsr_data(x)

mdsr_person(x, ...)

vocab(x, ...)

index_entry(
  index_label = "subject",
  x,
  emph = FALSE,
  index = TRUE,
  .f = NULL,
  alt = NULL
)

Arguments

`x`	text to wrap in macro
`...`	arguments passed to `index_entry`
`index_label`	the name of the index
`emph`	Display the LaTeX entry in italics
`index`	add LaTeX indexing?
`.f`	function to apply to `x` during indexing
`alt`	alternate character string to use for indexing

Examples

func("mutate")
func("mutate", index = FALSE)
func("left_join")
pkg("dplyr")
mdsr_person("Ben Baumer")
mdsr_person("Ben Baumer", emph = TRUE)
mdsr_person("Richard De Veaux")
mdsr_person("Richard De Veaux", alt = "De Veaux, Richard")
vocab(x = "Big data", .f = tolower)
index_entry(x = "Barack Obama")
index_entry(x = "Barack Obama", index = FALSE)
index_entry(x = "Big data", .f = tolower)
index_entry(x = "Twilight", emph = TRUE)
index_entry(x = "Richard De Veaux", alt = "De Veaux, Richard")
index_entry(x = "left_join")
func("mutate")
func("mutate", index = FALSE)
func("left_join")
pkg("dplyr")
mdsr_person("Ben Baumer")
mdsr_person("Ben Baumer", emph = TRUE)
mdsr_person("Richard De Veaux")
mdsr_person("Richard De Veaux", alt = "De Veaux, Richard")
vocab(x = "Big data", .f = tolower)
index_entry(x = "Barack Obama")
index_entry(x = "Barack Obama", index = FALSE)
index_entry(x = "Big data", .f = tolower)
index_entry(x = "Twilight", emph = TRUE)
index_entry(x = "Richard De Veaux", alt = "De Veaux, Richard")
index_entry(x = "left_join")

Wrangle babynames data

Description

Wrangle babynames data

Usage

make_babynames_dist()
make_babynames_dist()

Value

a tbl_df similar to babynames with a column for the estimated number of people alive in 2014.

Examples


BabynameDist <- make_babynames_dist()
if (require(dplyr)) {
  BabynameDist %>%
    filter(name == "Benjamin")
}
BabynameDist <- make_babynames_dist()
if (require(dplyr)) {
  BabynameDist %>%
    filter(name == "Benjamin")
}

Custom table output

Description

Custom table output

Usage

mdsr_table(x, ...)

mdsr_sql_explain_table(x, ...)

mdsr_sql_keys_table(x, ...)
mdsr_table(x, ...)

mdsr_sql_explain_table(x, ...)

mdsr_sql_keys_table(x, ...)

Arguments

`x`	A data.frame
`...`	arguments passed to `kbl`

Examples

mdsr_table(faithful)
mdsr_table(faithful)

Charges to and Payments from Medicare

Description

These data for 2011, released in May 2013, describe how much hospitals charged Medicare for various inpatient procedures, how many were performed, and how much Medicare actually paid.

Usage

MedicareCharges
MedicareCharges

Format

A data frame with 5,025 observations on the following 4 variables.

drg: Code for the Diagnosis Related Group: a character string that looks like a number.
stateProvider: the state providing the care.
num_charges: the total number of charges.
mean_charge: the average charge for each drg across each state

Details

These data are part of a set with DiagnosisRelatedGroup, which gives a description of the medical procedure associated with each DRG, and MedicareProviders, which translates idProvider into a name, address, state, Zip, etc..

These data have been pre-aggregated by state.

Source

Data from the Centers for Medicare and Medicaid Services. See https://data.cms.gov/provider-summary-by-type-of-service/medicare-inpatient-hospitals/

Examples


data(MedicareCharges)

data(MedicareCharges)

Medicare Providers

Description

Name and location data for the medicare providers in the MedicareCharges data table.

Usage

MedicareProviders
MedicareProviders

Format

A data frame with 3337 observations on the following 7 variables.

idProvider: a unique number assigned to each provider
nameProvider: Name of the provider. (text string)
addressProvider: Street address of the provider. (text string)
cityProvider: The name of the city in which the provider is located. (factor)
stateProvider: The two-letter postal code of the state in which the provider is located. (factor)
zipProvider: The provider's ZIP code. (factor)
referralRegion: An identifier for the region serviced by the provider.

Details

This data table is related to MedicareCharges data.

Source

Extracted from the highly repetitive table provided by the Centers for Medicare and Medicaid Services. See https://data.cms.gov/provider-summary-by-type-of-service/medicare-inpatient-hospitals/

Examples


data(MedicareProviders)

data(MedicareProviders)

Ballots in the 2013 Mayoral election in Minneapolis

Description

The choices marked on each (valid) ballot for the election, which was run using a rank-choice, instant runoff system.

Usage

Minneapolis2013
Minneapolis2013

Format

A data frame with 80,101 observations on the following 5 variables. All are stored as character strings.

Precinct: Precincts are sub-divisions within Wards
First: The voter's first choice
Second: The voter's second choice
Third: The voter's third choice
Ward: The city is divided spatially into districts or 'wards'. These are further subdivided into precincts.

Details

Ballot information for the 2013 Minneapolis Mayoral election, which was run as a rank-choice election. In rank-choice, a voter can indicate first, second, and third choices. If a voter's first choice is eliminated (by being last in the count across voters), the second choice is promoted to that voter's first choice, and similarly third -> second. Eliminations are done successively until one candidate has a majority of the first-choice votes.

Source

Ballot data from the Minneapolis city government: https://vote.minneapolismn.gov/results-data/election-results/2013/mayor/

References

Description of ranked-choice voting: https://vote.minneapolismn.gov/ranked-choice-voting/

A Minnesota Public Radio story about the election ballot tallying process: https://www.mprnews.org/2013/11/22/politics/ranked-choice-vote-count-programmers/

The Wikipedia article about the election: https://en.wikipedia.org/wiki/2013_Minneapolis_mayoral_election

Examples


data(Minneapolis2013)
data(Minneapolis2013)

Data about recent major league baseball teams

Description

A dataset containing information about Major League Baseball teams from 2008-2014.

Usage

MLB_teams
MLB_teams

Format

A tbl_df object.

yearID: season in which the team played
teamID: the team's three character identifier
lgID: the league in which the team played
W: number of wins
L: number of losses
WPct: winning percentage
attendance: number of fans in attendance
normAttend: number of fans in attendance, relative to the team with the highest attendance in this sample (the 2008 New York Yankees)
payroll: the sum of the salaries of the players on each team. Note that this number is only an estimate of the actual team payroll – and may not even be a very good one. Salaries are accumulated from Salaries
metroPop: the size of the team's home city's metropolitan population, according to Wikipedia and the 2010 US Census
name: the full name of the team

Source

The Teams table from Lahman-package and https://en.wikipedia.org/wiki/List_of_Metropolitan_Statistical_Areas

Gene expression in cancer

Description

The data come from a National Cancer Institute study of gene expression in cell lines drawn from various sorts of cancer.

Usage

NCI60_tiny

Cancer
NCI60_tiny

Cancer

Format

The expression data, NCI60_tiny is a dataframe of 41,078 gene probes (rows) and 60 cell lines (columns). The first column, Probe gives the name of the Agilent microarray probe. Each of the remaining columns is named for a cell line. The value is the log-2 expression associated with that probe for the cell line.

Probe: the name of the Agilent microarray probe

For Cancer:

otherCellLine: a character vector giving the name of one cell line
cellLine: a character vector giving the name of another cell line
correlation: the correlation between the two cell lines. See cor

An object of class tbl_df (inherits from tbl, data.frame) with 1770 rows and 3 columns.

Details

Cancer gives information about each cell line.

References

Staunton et al. (2001), PNAS (doi:10.1073/pnas.191368598)

D.T. Ross et al. (2000) Nature Genetics, 24(3):227-234 (doi:10.1038/73432)

CellMiner

Examples


data(NCI60_tiny) 

data(NCI60_tiny)

Birds captured and released at Ordway, complete and uncleaned

Description

The historical record of birds captured and released at the Katharine Ordway Natural History Study Area, a 278-acre preserve in Inver Grove Heights, Minnesota, owned and managed by Macalester College.

Usage

ordway_birds
ordway_birds

Format

A data frame with 15,829 observations on the bird's species, size, date found, and band number.

bogus: a character vector
Timestamp: Timestamp indicates when the data were entered into an electronic record, not anything about the bird being described
Year: a character vector
Day: a character vector
Month: a character vector
CaptureTime: a character vector
SpeciesName: a character vector
Sex: a character vector
Age: a character vector
BandNumber: a character vector
TrapID: a character vector
Weather: a character vector
BandingReport: a character vector
RecaptureYN: a character vector
RecaptureMonth: a character vector
RecaptureDay: a character vector
Condition: a character vector
Release: a character vector
Comments: a character vector
DataEntryPerson: a character vector
Weight: a character vector
WingChord: a character vector
Temperature: a character vector
RecaptureOriginal: a character vector
RecapturePrevious: a character vector
TailLength: a character vector

Timestamp indicates when the data were entered into an electronic record, not anything about the bird being described.

Details

There are many extraneous levels of variables such as species. Part of the purpose of this data set is to teach about data cleaning.

Source

Jerald Dosch, Dept. of Biology, Macalester College: the manager of the Study Area.

References

https://www.macalester.edu/ordway/

Examples


ordway_birds

ordway_birds

Convert Rnw to Rmd

Description

Convert Rnw to Rmd

Usage

Rnw2Rmd(path, new_path = NULL)
Rnw2Rmd(path, new_path = NULL)

Arguments

path

A character vector of one or more paths.

new_path

New file path. If new_path is existing directory, the file will be moved into that directory; otherwise it will be moved/renamed to the full path.

Should either be the same length as path, or a single directory.

Saratoga Houses

Description

Saratoga Houses

Usage

saratoga_houses

saratoga_codes
saratoga_houses

saratoga_codes

Format

A tibble with 1728 rows and 16 variables:

price

lot_size

waterfront

age

land_value

construction

air_cond

fuel

heat

sewer

living_area

pct_college

bedrooms

fireplaces

bathrooms

rooms

@examples saratoga_houses

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 13 rows and 3 columns.

State SAT scores from 2010

Description

SAT results by state for 2010

Usage

SAT_2010
SAT_2010

Format

A data.frame with 50 rows and 9 variables.

state: a factor with levels for each state
expenditure: average expenditure per student (in each state)
pupil_teacher_ratio: pupil to teacher ratio in that state
salary: teacher salary (in 2010 US $)
read: state average Reading SAT score
math: state average Math SAT score
write: state average Writing SAT score
total: state average Total SAT score
sat_pct: percent of students taking SAT in that state

Details

Embedded webshot of leaflet map

Description

Embedded webshot of leaflet map

Usage

save_webshot(
  map,
  path_to_img,
  overwrite = FALSE,
  vwidth = 800,
  vheight = 600,
  cliprect = "viewport",
  ...
)
save_webshot(
  map,
  path_to_img,
  overwrite = FALSE,
  vwidth = 800,
  vheight = 600,
  cliprect = "viewport",
  ...
)

Arguments

`map`	A leaflet map object
`path_to_img`	A path to the image file to save
`overwrite`	Do you want to clobber any existing file?
`vwidth`	see `webshot`
`vheight`	see `webshot`
`cliprect`	see `webshot`
`...`	arguments passed to `webshot`

Value

a path to a PNG file

Examples

## Not run: 
if (require(leaflet)) {
  map <- leaflet() |>
    addTiles() |>
    addMarkers(lng = 174.768, lat = -36.852, popup = "The birthplace of R")
  save_webshot(map, tempfile())
}

## End(Not run)
## Not run: 
if (require(leaflet)) {
  map <- leaflet() |>
    addTiles() |>
    addMarkers(lng = 174.768, lat = -36.852, popup = "The birthplace of R")
  save_webshot(map, tempfile())
}

## End(Not run)

Custom skimmer

Description

Custom skimmer

Usage

skim(data, ...)
skim(data, ...)

Arguments

`data`	A tibble, or an object that can be coerced into a tibble.
`...`	Columns to select for skimming. When none are provided, the default is to skim all columns.

Examples

skim(faithful)
skim(faithful)

src_scidb

Description

Connect to the scidb server on Amazon Web Services.

Usage

src_scidb(dbname, ...)

dbConnect_scidb(dbname, ...)

mysql_scidb(dbname, ...)
src_scidb(dbname, ...)

dbConnect_scidb(dbname, ...)

mysql_scidb(dbname, ...)

Arguments

`dbname`	the name of the database to which you want to connect
`...`	arguments passed to `src_dbi` or `dbConnect`

Details

This is a public, read-only account. Any abuse will be considered a hostile act.

Value

For src_scidb, a src_dbi object

For dbConnect_scidb, a MariaDBConnection-class object

For mysql_scidb, a character vector of length 1 to be used as an engine.ops argument, or on the command line.

Examples

## Not run: 
db_air <- src_scidb("airlines")
db_air

## End(Not run)
## Not run: 
db_air <- dbConnect_scidb("airlines")
db_air
if (require(DBI)) {
  dbListTables(db_air)
}

## End(Not run)

if (require(knitr)) {
  opts_chunk$set(engine.opts = mysql_scidb("airlines"))
}
## Not run: 
db_air <- src_scidb("airlines")
db_air

## End(Not run)
## Not run: 
db_air <- dbConnect_scidb("airlines")
db_air
if (require(DBI)) {
  dbListTables(db_air)
}

## End(Not run)

if (require(knitr)) {
  opts_chunk$set(engine.opts = mysql_scidb("airlines"))
}

MDSR themes

Description

Graphical themes used in MDSR book

Usage

theme_mdsr(base_size = 12, base_family = "Bookman")
theme_mdsr(base_size = 12, base_family = "Bookman")

Arguments

`base_size`	base font size, given in pts.
`base_family`	base font family

Examples

if (require(ggplot2)) {
  p <- ggplot(mtcars, aes(x = hp, y = mpg, color = factor(cyl))) + 
    geom_point() + facet_wrap(~ am) + geom_smooth()
  p + theme_grey()
  p + theme_mdsr()
 }
if (require(ggplot2)) {
  p <- ggplot(mtcars, aes(x = hp, y = mpg, color = factor(cyl))) + 
    geom_point() + facet_wrap(~ am) + geom_smooth()
  p + theme_grey()
  p + theme_mdsr()
 }

NYC Restaurant Health Violations

Description

NYC Restaurant Health Violations

Usage

Violations

ViolationCodes

Cuisines
Violations

ViolationCodes

Cuisines

Format

A data frame with 480,621 observations on the following 16 variables.

camis: unique identifier
dba: full name doing business as
boro: borough of New York
building: building name
street: street address
zipcode: zipcode
phone: phone number
inspection_date: inspection date
action: action taken
violation_code: violation code, see ViolationCodes
score: inspection score
grade: inspection grade
grade_date: grade date
record_date: recording date
inspection_type: inspect type
cuisine_code: cuisine code, see Cuisines

A data frame with 174 observations on the following 3 variables.

violation_code: a factor with many levels
critical_flag: is violation critical: a factor with levels N Y
violation_description: violation description

A data frame with 84 observations on the following 2 variables.

cuisine_code: a character vector
cuisine_description: a character vector

Source

NYC Open Data, https://data.cityofnewyork.us/Health/DOHMH-New-York-City-Restaurant-Inspection-Results/43nn-pn8j/

Examples

data(Violations)
if (require(dplyr)) {
  Violations %>%
    inner_join(Cuisines, by = "cuisine_code") %>%
    filter(cuisine_description == "American") %>%
    arrange(grade_date) %>%
    head()
 }
data(Violations)
if (require(dplyr)) {
  Violations %>%
    inner_join(Cuisines, by = "cuisine_code") %>%
    filter(cuisine_description == "American") %>%
    arrange(grade_date) %>%
    head()
 }

Votes from Scottish Parliament

Description

Votes recorded on each ballot by each member of the Scottish Parliament in 2008 along with information about party affiliation.

Usage

Votes

Parties
Votes

Parties

Format

Votes is a data.frame with 103582 rows and 3 variables.

bill: an identifier for the bill
name: the name of the member of parliament
vote: 1 means a vote for, -1 a vote against. 0 is an abstention.

Parties is a data.frame with 134 rows, one for each member of parliament, and 2 variables.

party: the name of the political party the member belongs to
name: the name of the member of parliament

An object of class data.frame with 134 rows and 2 columns.

Details

Almost all of the members of parliament belongs to a political party. This table identifies that party. These data were provided by Caroline Ettinger and form part of her senior honor's project at Macalester College. Prof. Andrew Beveridge supervised the thesis. Ms. Ettinger used the vote data to explore how to extract the party association of members purely from voting records. The Parties data was used to evaluate the success of methods.

Cities and their populations

Description

A list of cities

Usage

world_cities
world_cities

Format

A data frame with 4,428 observations on the following 10 variables.

geoname_id: integer id of record in geonames database
name: name of geographical point in plain ascii characters
latitude: latitude in decimal degrees (wgs84)
longitude: longitude in decimal degrees (wgs84)
country: ISO-3166 2-letter country code
country_region: fipscode
population: Population
timezone: the iana timezone id
modification_date: date of last modification

Source

GeoNames: http://download.geonames.org/export/dump/

Examples


world_cities

world_cities

Package 'mdsr'

Help Index

Cherry Blossom runs

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Deaths and Pumps from 1854 London cholera outbreak

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Several variables on countries from the CIA Factbook, 2014.

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Data Science Papers from arXiv.org

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Election Statistics

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Email Train

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Load the NCI60 data from GitHub

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Headlines_train

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Text of Macbeth

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Replacements for LaTeX macros

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Wrangle babynames data

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Custom table output

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Charges to and Payments from Medicare

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Medicare Providers

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