Peak_status = c("Off-Peak", "Off-Peak", "Off-Peak", "Off-Peak", ), tzone = "Asia/Bangkok"), DayNight = c("Day", "Day", "Day", Df1 is how much 'stuff' was detected in the water whereas df2 is how much fish was physically pulled from the water in the same location. In this instance, the y axes data from both datasets are reflective of one another, so I believe it would be useful here. I do want to add that I understand that this visualisation method is often considered 'flawed' and I agree with this sentiment in the vast majority of cases. Is there a way to plot the data on the same graph using different scales on the y-axes that will actually visualise them properly? Scale_y_continuous(name="Sv", sec.axis = sec_axis(~ 1*., name="kg"))Īs you can see, both axes follow the scaling for df2, leaving the points for df1 unable to be really seen. Geom_point(aes(x=date_column, y=total_catch), data=Dai15E) + I am able to make a rudimentary version of the desired graph using the following code: ggplot(aes(x=date_column, y=Sv_log), data=daily_avg_stn1) + geom_point() + Df1 is in decibels so the values are negative, df2 has a scale in kg's. Both df's have similar x-axes of dates but have totally different y axes scales. Df1 is acoustic backscatter over time whereas df2 is total catch over time. Points(x = gender_data$ "Olympic year", y = Plot(x = gender_data$ "Olympic year", y = Can anyone help? #Clear out old variables Now I would just like to add two lines of best fit. I'm getting it to plot on a scatter plot. I have a data set with men's and women's race times on it.
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