The following question is about tidy data. The table below contains looks at crime occurrence in different locations across Victoria:

If you would like to calculate the proportion of the different crime types by location which code do you need to use? Hint: Missing values is typically denoted by "NA" in the dataset, we can ignore these values by passing the option "na.rm = TRUE" to the appropriate R command.
Incorrect answers will be penalised.

Question

The following question is about tidy data. The table below contains looks at crime occurrence in different locations across Victoria: 
  
 If you would like to calculate the proportion of the different crime types by location which code do you need to use? Hint: Missing values is typically denoted by "NA" in the dataset, we can ignore these values by passing the option "na.rm = TRUE" to the appropriate R command. 
 Incorrect answers will be penalised.

Accepted Answer

crime_data %>% group_by(crime_type, Location) %>% summarise(n = sum(count)) %>% mutate(prop = n / sum(n, na.rm = TRUE))

Answer

crime_data %>% group_by(crime_type, Location) %>% summarise(n = sum(count)) %>% mutate(prop = n / sum(n))

Answer

crime_data %>% group_by(Location) %>% summarise(n = sum(count)) %>% mutate(prop = n / sum(n, na.rm = TRUE))

Answer

crime_data %>% group_by(crime_type) %>% summarise(n = sum(count)) %>% mutate(prop = n / sum(n, na.rm = TRUE))

entry_point	Location	crime_type	count
FRONT DOOR	Oakleigh	violent	67
FRONT DOOR	Clayton	violent	53
WINDOW	Oakleigh	burglary	NA
WINDOW	Clayton	burglary	6
Roof	Oakleigh	Others	17
Roof	Clayton	Others	22

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The following question is about tidy data. The table below contains looks at c...

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