Required Packages

The following packages will be required or may come in handy.

library(tidyr)
library(dplyr)
library(readr)
library(readxl)
library(knitr)

Exercises

Family Incidents data set

The following exercises (exercise 1-4) are based on family incidents (family_incidents.csv) data set. This data set is taken from Crime Statistics Agency https://www.crimestatistics.vic.gov.au/family-violence-data-portal/download-data-tables containing 84 observation from Local government areas about family incident rate per 100,000 population by police region from 2012 to 2017. The variables for family_incidents.csv data set consists of:

Police Region [Character]: Police regions (levels: North West Metro, Eastern, Southern Metro, Western and where Total2 is for unknown geographical location).

Local Government Area [Character]: 79 Local government areas of Victoria there are totals per region.

2012-2013, ..., 2016-2017: The year that data was collected.

First six observations of the family_incidents data set:

Police region Local government area 2012-13 2013-14 2014-15 2015-16 2016-17
North West Metro Banyule 940.9 974.1 1060.8 1030.6 1014.3
North West Metro Brimbank 986.8 1084.3 1240.5 1343.3 1105.0
North West Metro Darebin 1020.8 1131.3 1063.6 1071.9 1052.4
North West Metro Hobsons Bay 899.5 1065.7 1137.0 1205.7 1075.9
North West Metro Hume 1390.7 1540.3 1554.9 1538.0 1478.6
North West Metro Maribyrnong 956.8 1042.4 997.6 1089.4 891.8

  1. Read in the family_incidents.csv data set using a suitable function. Check out the classes of each variable and convert Police Region column into factor.

  2. Choose the suitable action for family_incidents data set.

 a) We need to combine multiple columns into a single column using unite() function

 b) We need to gather those columns into a new pair of variables using pivot_longer or gather() function

 c) We need to split the variables since multiple variables are stored in one column using pivot_wider or separate() function

 d) We need to transform data from long format to wide format using spread() function

  1. Tidy the family_incidents data set into the form below.
Police region Local government area year cases
North West Metro Banyule 2012-13 940.9
North West Metro Brimbank 2012-13 986.8
North West Metro Darebin 2012-13 1020.8
North West Metro Hobsons Bay 2012-13 899.5
  1. Using family_incidents data set, change the separator of the year column from "-" to "/" using separate() and unite() functions.

Primary Health Network data set

The following exercises (exercise 5-8) are based on the PHN data set (PHN.xlsx). This data set has the percentage of children who are immunised against Polio and Hepatitis B in Australia by age group and location in October 2017 - September 2017. PHN data set is taken from https://beta.health.gov.au/resources/publications/2017-phn-childhood-immunisation-coverage-data with 93 observations, containing variables:

PHN Number [Character]: ID of the Primary Health Network Area

PHN Name [Character]: Name of the Primary Health Network Area

Age Group [Character]: Factor with Levels 12-<15 Months, 24-<27 Months, 60-<63 Months

%Polio [Numeric]: Percentage of children immunised against Polio

%HEP [Numeric]: Percentage of children immunised against Hepatitis B

First six observations of the PHN data set:

PHN Number PHN Name Age Group %Polio %HEP
PHN101 Central and Eastern Sydney 12-<15 Months 94.20931 94.09876
PHN102 Northern Sydney 12-<15 Months 94.75332 94.58254
PHN103 Western Sydney 12-<15 Months 94.07182 93.99051
PHN104 Nepean Blue Mountains 12-<15 Months 94.94118 94.94118
PHN105 South Western Sydney 12-<15 Months 93.85654 93.92611
PHN106 South Eastern NSW 12-<15 Months 95.44135 95.54297

  1. Read in the PHN.xlsx data set using a suitable function. Check out the classes of each variable and convert Age Group column into factor, use ordered argument then check its levels.

  2. Assume that we want to calculate the mean percentage as a column of all age groups of children immunised against Polio and Hepatitis B. Before calculating the mean, select the tidy functions that we need in order to form the data set as:

PHN Number PHN Name Vaccination Type 12-<15 Months 24-<27 Months 60-<63 Months
PHN101 Central and Eastern Sydney %HEP 94.09876 95.37775 0.00000
PHN101 Central and Eastern Sydney %Polio 94.20931 95.54502 92.50768
PHN102 Northern Sydney %HEP 94.58254 95.11925 0.00000
PHN102 Northern Sydney %Polio 94.75332 95.56295 92.16040
PHN103 Western Sydney %HEP 93.99051 95.89433 0.00000
PHN103 Western Sydney %Polio 94.07182 95.99414 94.36424

 a) unite(), gather()

 b) split(), unite()

 c) gather(), spread()

 d) separate(), split()

  1. Tidy the PHN data set into the form in exercise 6. Once you tidy, use rowMeans function from baseR package to calculate the mean percentage of children immunised and save this average as a vector. (Hint: Remember how to select columns as a sequence i.e., df[,3:5].) Repeat this calculation by combining your tidy code and calculating the mean in one line whilst saving it as a vector.

  2. Bonus exercise: Use round function from base R package to round the values to 2 decimals places in PHN data set.

Finished?

If you have finished the above tasks, work through the weekly list of tasks posted on the Canvas announcement page.

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