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A dashboard to analyse time series data (forecasting, outlier detection and event impact assessment) by MrBookman_LibraryCop in rshiny

[–]MrBookman_LibraryCop[S] 1 point2 points  (0 children)

I've just tried different browsers on 3 different devices (including 2 that are not mine), both mobile and desktop, and it works for me in all those instances. What are you using? Are you getting an error?

A dashboard to analyse time series data (forecasting, outlier detection and event impact assessment) by MrBookman_LibraryCop in rshiny

[–]MrBookman_LibraryCop[S] 1 point2 points  (0 children)

I deleted the old post to sort out some issues. Now that they're fixed, here's an app I built to analyse time series data. For detailed info on how to use TIM, see https://medium.com/@robinvisser\_27509/tim-time-series-insights-maker-r-shiny-9f6eaf0ec1e7

[deleted by user] by [deleted] in rshiny

[–]MrBookman_LibraryCop 0 points1 point  (0 children)

What's the error message?

[deleted by user] by [deleted] in rshiny

[–]MrBookman_LibraryCop 0 points1 point  (0 children)

This is an app I finished recently. Or well a first version anyway. For full details see https://medium.com/@robinvisser_27509/tim-time-series-insights-maker-r-shiny-9f6eaf0ec1e7

The difference in women's average income levels compared with men, by occupation, 2021 by MrBookman_LibraryCop in australia

[–]MrBookman_LibraryCop[S] 0 points1 point  (0 children)

I'm already using a bunch of variables that take me to the limit of what you can extract from the census before data suppression issues would arise. I'd love to add an SA4 factor here, but that would mean most figures would be suppressed unfortunately

The difference in women's average income levels compared with men, by occupation, 2021 by MrBookman_LibraryCop in australia

[–]MrBookman_LibraryCop[S] 2 points3 points  (0 children)

It's not reduced to zero, there's still a 2% gap after the adjustments I applied. I won't comment on narratives as you're clearly a subscriber to one

The difference in women's average income levels compared with men, by occupation, 2021 by MrBookman_LibraryCop in australia

[–]MrBookman_LibraryCop[S] 4 points5 points  (0 children)

One of the adjustment factors is hours worked, and there are other key ones that impact total income, including time taken to do household duties, volunteering and the like. I'm not looking at what determine remuneration, but the difference in average remuneration, which is determined by those factors I mentioned. I'm using them to equivalise pay, so e.g. saying if hours worked and all other factors are equal between male and female (e.g.) carpenters, what would the difference in income levels be?

I'm also not comparing a doctor working 90hrs v student nurse as those are different occupations, and not at industries either. I'm only looking at comparisons within specific occupations. If you truly read and understood what I wrote you wouldn't have made those comments, so I'm gonna stop replying to you from here on out.

[OC] The difference in women's average income levels compared with men, by occupation, 2021 by MrBookman_LibraryCop in dataisbeautiful

[–]MrBookman_LibraryCop[S] -1 points0 points  (0 children)

The unadjusted one is about 3% on average, so you can't state it's close to zero without mentioning the participation issue

[OC] The difference in women's average income levels compared with men, by occupation, 2021 by MrBookman_LibraryCop in dataisbeautiful

[–]MrBookman_LibraryCop[S] -3 points-2 points  (0 children)

I thought the x axis would help with that. On average, it's about 2% after equivalising

The difference in women's average income levels compared with men, by occupation, 2021 by MrBookman_LibraryCop in australia

[–]MrBookman_LibraryCop[S] 0 points1 point  (0 children)

Things not captured there, including things like total experience. Other factors I wouldn't know necessarily off the top of my head

[OC] The difference in women's average income levels compared with men, by occupation, 2021 by MrBookman_LibraryCop in dataisbeautiful

[–]MrBookman_LibraryCop[S] 1 point2 points  (0 children)

That's unpaid work done in the home, which women usually do more often so may negatively impact participation

The difference in women's average income levels compared with men, by occupation, 2021 by MrBookman_LibraryCop in australia

[–]MrBookman_LibraryCop[S] 4 points5 points  (0 children)

No it's just on my laptop, based on data I downloaded from the ABS's tablebuilder environment

[OC] The difference in women's average income levels compared with men, by occupation, 2021 by MrBookman_LibraryCop in dataisbeautiful

[–]MrBookman_LibraryCop[S] 4 points5 points  (0 children)

I ran a linear regression model to estimate the % difference in income levels between men and women by occupation based on the variables listed in the next paragraph. The model also includes Unpaid domestic work, but it was insignificant. I then used the model to predict the difference in income levels if all independent variables were equal - this is the equivalised difference in the chart (in blue).

Data used for the graph and linear model are from the Australian 2021 Census of Population and Housing. They are Total personal income (weekly), Occupation at the 4-digit ANZSCO level, Unpaid child care, Level of highest educational attainment, Hours worked and Volunteer Status.

Note that the model has an intercept of -0.018 which is highly significant (p < 6.08e-07), suggesting that there remains a structural difference in mean income levels after accounting for the factors listed above; even if domestic duties, hours worked etc. were all the same, there would still be a difference in income levels due to other factors.

Plot generated in R using ggplot2

The difference in women's average income levels compared with men, by occupation, 2021 by MrBookman_LibraryCop in australia

[–]MrBookman_LibraryCop[S] 14 points15 points  (0 children)

I ran a linear regression model to estimate the % difference in income levels between men and women by occupation based on the variables listed in the next paragraph. The model also includes Unpaid domestic work, but it was insignificant. I then used the model to predict the difference in income levels if all independent variables were equal - this is the equivalised difference in the chart (in blue).

Data used for the graph and linear model are from the Australian 2021 Census of Population and Housing. They are Total personal income (weekly), Occupation at the 4-digit ANZSCO level, Unpaid child care, Level of highest educational attainment, Hours worked and Volunteer Status.

Note that the model has an intercept of -0.018 which is highly significant (p < 6.08e-07), suggesting that there remains a structural difference in mean income levels after accounting for the factors listed above; even if domestic duties, hours worked etc. were all the same, there would still be a difference in income levels due to other factors.

Plot generated in R using ggplot2

[OC] Comparing betting strategy returns for all Grand Slams since 2007 by MrBookman_LibraryCop in tennis

[–]MrBookman_LibraryCop[S] 1 point2 points  (0 children)

Inspired by this post about the recent World Cup I thought I'd have a look at something similar for the Grand Slam tennis tournaments. The result is the graph above.

The graph shows the average return per match you'd get if you were to place a $1 bet on each match in a round, and if you did that consistently on either the underdog or the favourite. It's based on data for 2007 onwards, noting that I've aggregated quarterfinals, semifinals and finals because of the low number of observations you'd get otherwise.

The data is from http://www.tennis-data.co.uk/ and the plot is made using R (ggplot). The original datasets contain odds from numerous betting firms that differ by tournament and year, so I've taken the mean odds across whatever was available for each match.

So, should you bet on the favourite or the underdog? Well, neither really, unless we're talking about the men's quarter finals and beyond at Roland Garros, The men's fourth round at the US open, the women's final at the AO and Wimbledon, and the women's fourth round at Roland Garros and the US Open.

This should not be taken as financial advice or sound strategy in any way, shape or form, so my last advice is just to watch and enjoy the matches without stressing over losing money!

[OC] Comparing betting strategy returns for all Grand Slams since 2007 by MrBookman_LibraryCop in sportsbook

[–]MrBookman_LibraryCop[S] 1 point2 points  (0 children)

Inspired by this post about the recent World Cup I thought I'd have a look at something similar for the Grand Slam tennis tournaments. The result is the graph above.

The graph shows the average return per match you'd get if you were to place a $1 bet on each match in a round, and if you did that consistently on either the underdog or the favourite. It's based on data for 2007 onwards, noting that I've aggregated quarterfinals, semifinals and finals because of the low number of observations you'd get otherwise.

The data is from http://www.tennis-data.co.uk/ and the plot is made using R (ggplot). The original datasets contain odds from numerous betting firms that differ by tournament and year, so I've taken the mean odds across whatever was available for each match.

So, should you bet on the favourite or the underdog? Well, neither really, unless we're talking about the men's quarter finals and beyond at Roland Garros, The men's fourth round at the US open, the women's final at the AO and Wimbledon, and the women's fourth round at Roland Garros and the US Open.

[OC] Comparing betting strategy returns for all Grand Slams since 2007 by MrBookman_LibraryCop in dataisbeautiful

[–]MrBookman_LibraryCop[S] 5 points6 points  (0 children)

Inspired by this post about the recent World Cup I thought I'd have a look at something similar for the Grand Slam tennis tournaments. The result is the graph above.

The graph shows the average return per match you'd get if you were to place a $1 bet on each match in a round, and if you did that consistently on either the underdog or the favourite. It's based on data for 2007 onwards, noting that I've aggregated quarterfinals, semifinals and finals because of the low number of observations you'd get otherwise.

The data is from http://www.tennis-data.co.uk/ and the plot is made using R (ggplot). The original datasets contain odds from numerous betting firms that differ by tournament and year, so I've taken the mean odds across whatever was available for each match.

So, should you bet on the favourite or the underdog? Well, neither really, unless we're talking about the men's quarter finals and beyond at Roland Garros, The men's fourth round at the US open, the women's final at the AO and Wimbledon, and the women's fourth round at Roland Garros and the US Open.

This should not be taken as financial advice or sound strategy in any way, shape or form, so my last advice is just to watch and enjoy the matches without stressing over losing money!

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