Hi all! I have this kind of DataFrame containing data:
julia> df_filtered = filter(:mnemonic => m -> m ∈ selected_instructions, df_temp)
1990×4 DataFrame
Row │ mnemonic instruction forwarding value
│ String String Bool Quantity…
──────┼───────────────────────────────────────────────────
1 │ add add_r0_r2_#0 false 0.0829053 W
2 │ add add_r0_r2_#1 false 0.0830969 W
3 │ add add_r0_r2_#10 false 0.0831797 W
4 │ add add_r0_r2_#100 false 0.083226 W
5 │ add add_r0_r2_#101 false 0.0834463 W
6 │ add add_r0_r2_#102 false 0.0834131 W
7 │ add add_r0_r2_#103 false 0.0836367 W
8 │ add add_r0_r2_#104 false 0.0830944 W
9 │ add add_r0_r2_#105 false 0.0832901 W
10 │ add add_r0_r2_#106 false 0.0832738 W
11 │ add add_r0_r2_#107 false 0.0834709 W
12 │ add add_r0_r2_#108 false 0.0833354 W
13 │ add add_r0_r2_#109 false 0.0835491 W
14 │ add add_r0_r2_#11 false 0.083368 W
⋮ │ ⋮ ⋮ ⋮ ⋮
1977 │ mvn mvn_r6_r2 false 0.0863886 W
1978 │ mvn mvn_r6_r3 false 0.0866079 W
1979 │ mvn mvn_r6_r4 false 0.0864734 W
1980 │ mvn mvn_r6_r5 false 0.0866562 W
1981 │ mvn mvn_r6_r6 true 0.108087 W
1982 │ mvn mvn_r6_r7 false 0.0870755 W
1983 │ mvn mvn_r7_r0 false 0.0864812 W
1984 │ mvn mvn_r7_r1 false 0.0866842 W
1985 │ mvn mvn_r7_r2 false 0.0866603 W
1986 │ mvn mvn_r7_r3 false 0.086884 W
1987 │ mvn mvn_r7_r4 false 0.0867469 W
1988 │ mvn mvn_r7_r5 false 0.0869432 W
1989 │ mvn mvn_r7_r6 false 0.0869205 W
1990 │ mvn mvn_r7_r7 true 0.108796 W
1962 rows omitted
I want to produce a boxplot comparing the effect of the activation of the column “forwarding”. For now the best I could get is using groupedboxplot(), like this:
p = @df df_filtered groupedboxplot(:mnemonic, :value;
group = :forwarding,
# xlabel = "Instructions",
ylabel = "Measured power",
label = ["not active" "active"],
bar_width = 0.7,
left_margin = 5mm,
bottom_margin = 3mm,
size = (1000, 500),
tickfontsize = 14,
guidefontsize = 16
)
producing
Now I’d like to show the mean for each group on the plot, like a scatter point. I see this can be done for single boxplots, however for grouped ones I don’t know how to do it.
The best I could reach is grouping the DF so to get the mean for each group:
julia> df_grouped = groupby(df_temp, [:mnemonic, :forwarding]; sort = true);
julia> df_means = combine(df_grouped, :value => mean)
90×3 DataFrame
Row │ mnemonic forwarding value_mean
│ String Bool Quantity…
─────┼───────────────────────────────────
1 │ adc false 0.0836535 W
2 │ adc true 0.0863453 W
3 │ add false 0.0839741 W
4 │ add true 0.0828457 W
5 │ and false 0.0834052 W
6 │ and true 0.082732 W
7 │ asr false 0.0861718 W
8 │ asr true 0.0865888 W
9 │ b false 0.170137 W
10 │ bfc false 0.0876756 W
11 │ bfi false 0.0854044 W
12 │ bic false 0.0830427 W
13 │ bl false 0.195068 W
14 │ blx false 0.0943027 W
⋮ │ ⋮ ⋮ ⋮
77 │ teq false 0.0844381 W
78 │ tst false 0.0842573 W
79 │ ubfx false 0.0855511 W
80 │ ubfx true 0.0843686 W
81 │ udiv false 0.0689451 W
82 │ udiv true 0.073053 W
83 │ umlal false 0.0848933 W
84 │ umlal true 0.0915934 W
85 │ umull false 0.083016 W
86 │ umull true 0.0831577 W
87 │ uxtb false 0.0744491 W
88 │ uxtb true 0.0745435 W
89 │ uxth false 0.0742926 W
90 │ uxth true 0.0743984 W
62 rows omitted
Is there a way I can do this? Thanks.
