Working in small groups or pairs, complete the following exercises.
The following packages will be required or may come in handy.
library(readr)
library(xlsx)
library(readxl)
library(foreign)
library(gdata)
library(rvest)Display your working directory using getwd()
command.
Create a folder and name it “Week2” on your
desktop and set this as your working directory. (Hint: You can set your
working directory using setwd() command.)
The following exercises (exercise 3-5) are based on Population density and regional dataset
(population.csv) which contains 2245 rows of time series data
between 2009 to 2016. This data is taken from OECD located at http://stats.oecd.org/Index.aspx?DataSetCode=REGION_DEMOGR
where you can find more about the data structure. The variables for
population.csv dataset consists of:
Region [Character]: Region name
Territory Level and Typology [Factor]: Description of
the region (Country, Large regions, Small regions,… )
2009, 2010, ...,2016 [Numeric]: Population density
growth index per referenced time.
Import csv version of Population dataset (population.csv) using Base R functions.
Repeat exercise 3, this time use readr
functions.
Import spss version of Population data (population.sav) using
foreign package.
Exercise 6 is based on population-migration dataset (population-migration.xls) which contains two sheets named “Population Density and Regional” and “Inter-regional Migration” in .xls format. “Inter-regional Migration” sheet contains 1564 rows of timeseries data between 2009 to 2016. This data set is also taken from OECD located at http://stats.oecd.org/Index.aspx?DataSetCode=REGION_DEMOGR. The variables for population-migration data (“Inter-regional Migration” sheet) contains:
Region [Character]: Region name
Territory Level and Typology [Factor]: Description of
the region (Country, Large regions, Small regions,… )
2009, 2010, ...,2016 [Numeric]: Migration (All persons
inflows minus outflows)
readxl functions. (Hint: Use sheet
argument.)The following exercises (exercise 7-9) is based on Popular Baby Names, NYC dataset which contains 22,035 rows of data recorded in 2011. This data set is taken from NYC Open Data which is located at https://data.cityofnewyork.us/Health/Most-Popular-Baby-Names-by-Sex-and-Mother-s-Ethnic/25th-nujf containing the following variables:
BIRTHYEAR [Integer]: Year of birth
GENDER [Character]: Gender
ETHNICITY [Character]: Mother’s ethnicity (categories:
HISPANIC, ASIAN AND PACIFIC ISLANDER, WHITE NON HISPANIC, BLACK NON
HISPANIC)
NAME [Character]: Child first name
COUNT [Integer]: Count
RANK [Integer]: Rank
The url for the csv file is located at https://data.cityofnewyork.us/api/views/25th-nujf/rows.csv?accessType=DOWNLOAD
Use the url for Popular Baby Names, NYC dataset to import in R.
After you import the dataset save it as a .csv file.
Repeat exercise 8, this time save it as .Rdata.
The following exercise is based on the html data table taken from Wikipedia located at https://en.wikipedia.org/wiki/List_of_cities_in_Australia_by_population containing following variables:
RANK [Integer]: Rank
GCCSA/SUA [Character]: Greater Capital City Statistical
Areas/Significant Urban Areas
State/Territory [Character]: State/Territory names
June 2016[2] population [Numeric]: Population
Percentage of national population [Character]: Ratio of
the population as a percentage
If you have finished the above tasks, work through the weekly list of tasks posted on the Canvas announcement page.