Challenge 2

Challenge 5

Dataset australian_marriage_tidy.csv

Today we’ll be looking at the Australian marriage dataset.

territory	resp	count	percent
New South Wales	yes	2374362	57.8
New South Wales	no	1736838	42.2
Victoria	yes	2145629	64.9
Victoria	no	1161098	35.1
Queensland	yes	1487060	60.7
Queensland	no	961015	39.3

Here we have preview of the data available to us. The territory of the respondent is given, as well as the respondent’s answer and the count of people who answered either yes or no. We’re going to create two graphs, one uni-variate and one bivariate.

As you can see here, I have generated three graphs of the available data by manipulating them carefully. The first table titled aum1 is the original table. aum2 is the table I pivoted with the data to more accurately compare percents of people’s responses per territory.

Territory	Response	Count	Percent
New South Wales	yes	2374362	57.8
New South Wales	no	1736838	42.2
Victoria	yes	2145629	64.9
Victoria	no	1161098	35.1
Queensland	yes	1487060	60.7
Queensland	no	961015	39.3
South Australia	yes	592528	62.5
South Australia	no	356247	37.5
Western Australia	yes	801575	63.7
Western Australia	no	455924	36.3
Tasmania	yes	191948	63.6
Tasmania	no	109655	36.4
Northern Territory(b)	yes	48686	60.6
Northern Territory(b)	no	31690	39.4
Australian Capital Territory(c)	yes	175459	74.0
Australian Capital Territory(c)	no	61520	26.0

Territory	No	Yes
New South Wales	42.2	57.8
Victoria	35.1	64.9
Queensland	39.3	60.7
South Australia	37.5	62.5
Western Australia	36.3	63.7
Tasmania	36.4	63.6
Northern Territory(b)	39.4	60.6
Australian Capital Territory(c)	26.0	74.0

Using these tables I generated a histogram showing the number of respondents who particicpated, a bar graph comparing the respondent’s answers based on territory, and a boxplot of the percent of people’s responses. Overall, the boxplot shows that the mean percent of respondents for an answer from each territory is roughly 50%.

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.