hdugan
commented
11 months ago I'm not sure if this is helpful. But in the glmtools package, the function plot_var_df is similar but a bit more advanced than the function in rLakeAnalyzer. You might try giving that function a shot.
>str(cr_wtr)tibble [28,548 × 13] (S3: tbl_df/tbl/data.frame) $ datetime: POSIXct[1:28548], format: "2022-12-26 00:00:00" "2022-12-26 00:15:00" "2022-12-26 00:30:00" ... $ wtr_4.5 : num [1:28548] 1.52 1.52 1.56 1.47 1.51 ... $ wtr_7.0 : num [1:28548] 1.68 1.64 1.68 1.69 1.66 ... $ wtr_8.5 : num [1:28548] 1.92 1.86 1.82 1.8 1.85 ... $ wtr_10.0: num [1:28548] 2.01 1.97 2 2.02 2.02 ... $ wtr_11.5: num [1:28548] 2.07 2.05 2.07 2.07 2.09 ... $ wtr_13.0: num [1:28548] NA 2.43 2.46 2.54 2.6 ... $ wtr_14.5: num [1:28548] 2.7 2.72 2.72 2.75 2.77 ... $ wtr_16.0: num [1:28548] 2.8 2.81 2.79 2.81 2.8 ... $ wtr_20.5: num [1:28548] 2.95 2.93 2.93 2.91 2.93 ... $ wtr_23.5: num [1:28548] 2.97 2.97 2.97 2.98 2.98 ... $ wtr_26.5: num [1:28548] 3.18 3.16 3.17 3.24 3.23 ... $ wtr_29.5: num [1:28548] 3.24 3.23 3.25 3.24 3.24 ...'``Rplot.pdf
I am having issues filling in the missing values this heatmap. I am interested in solutions for further interpolating this data to filling in the gaps in June as well as interpolating values above 4.5 m to the surface.
Thanks!