How can I average separate variables by minute

Question

I want to find the average value of Var1, Var2, and Var3 by minute.

Here is the structure of my data:

> dput(df)
structure(list(Date_Time = structure(c(1609602780, 1609602780, 
1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 
1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 
1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 
1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 
1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 
1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 
1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 
1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 
1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 1609602780, 
1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 
1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 
1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 
1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 
1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 
1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 
1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 
1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 
1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 
1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 1609602840, 
1609602840, 1609602840, 1609602840, 1609602840, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 1609602900, 
1609602900, NA), class = c("POSIXct", "POSIXt"), tzone = ""), 
    Var1 = c(3, 3, 3, 3, 3, 3, 15, 3, 4, 3, 3, 3, 3, 3, 3, 3, 
    2, 3, 3, 3, 3, 3, 3, 3, 1.4, 3, 3, 3, 3, 3, 3, 2.7, 3, 3, 
    3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
    3, 3, 3, 3, 1.3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
    3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
    3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 1.7, 3, 3, 
    3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
    3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
    3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
    3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
    3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
    3, 3, 3, 3, 3, 3, 3, 3, 2.7, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
    3, NA), Var2 = c(1.3, 1.3, 1.3, 1.3, 1.3, 0, 1.3, 1.3, 1.3, 
    1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 3, 1.3, 1.3, 1.3, 1.3, 
    2, 1.6, 1.3, 1.3, 1.3, 5.6, 1.3, 1.1, 1.3, 1.3, 1.3, 1.3, 
    1.3, 1.3, 4, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 
    1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 
    5.1, 1.3, 1.3, 1.3, 1.3, 1.3, 2.8, 1.3, 1.3, 1.3, 1.3, 1.3, 
    1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 
    1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 
    1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 
    1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 
    1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 
    1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 
    1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 
    1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 
    1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 
    1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 
    1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 
    1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 
    1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 
    NA), Var3 = c(4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 1.3, 4.5, 4.5, 
    4.5, 4.5, 4.5, 1.2, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 
    4.5, 4.5, 4.5, 7.9, 4.5, 4.5, 4.5, 4.5, 4.5, 3.3, 4.5, 4.5, 
    4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 
    4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 3.3, 
    4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 
    4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 
    4.5, 4.5, 4.5, 4.3, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 
    4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 
    4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 
    4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 1.3, 
    4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 8.3, 4.5, 4.5, 4.5, 4.5, 
    4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 
    4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 
    4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 
    4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 
    4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 
    4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 5, 4.5, 4.5, 4.5, 4.5, 
    4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 
    NA)), row.names = c(NA, -226L), class = "data.frame")

Also, my Date_Time variable is indeed a date/time variable, but it looks like when I use dput() it converts it to a numeric string. Please let me know if I need to fix this.

In case the Date_Time variable from dput() does not work, please use this instead:

> print(df$Date_Time)
  [1] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
  [4] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
  [7] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
 [10] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
 [13] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
 [16] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
 [19] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
 [22] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
 [25] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
 [28] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
 [31] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
 [34] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
 [37] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
 [40] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
 [43] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
 [46] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
 [49] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
 [52] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST"
 [55] "2021-01-02 07:53:00 PST" "2021-01-02 07:53:00 PST" "2021-01-02 07:54:00 PST"
 [58] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
 [61] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
 [64] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
 [67] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
 [70] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
 [73] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
 [76] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
 [79] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
 [82] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
 [85] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
 [88] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
 [91] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
 [94] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
 [97] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
[100] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
[103] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
[106] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
[109] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
[112] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
[115] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
[118] "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST" "2021-01-02 07:54:00 PST"
[121] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[124] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[127] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[130] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[133] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[136] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[139] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[142] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[145] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[148] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[151] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[154] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[157] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[160] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[163] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[166] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[169] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[172] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[175] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[178] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[181] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[184] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[187] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[190] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[193] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[196] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[199] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[202] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[205] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[208] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[211] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[214] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[217] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[220] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"
[223] "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST" "2021-01-02 07:55:00 PST"

If your data is already in minutes, `df %>% group_by(Date_Time) %>% summarise(across(Var1:Var3, ~ mean(.x, na.rm = TRUE)))` — akrun, Dec 08 '22 at 18:11
I've closed your question as a duplicate of the Sum By Group FAQ, because it looks like akrun's shared code for a normal sum by group should work. If you need more help, please edit the question accordingly to highlight what doesn't work. (Not that usually 10-20 rows of data are **plenty**, and more rows just make it harder to see what's going on. If there are problems, cutting down the sample data rather than adding more sample data would be appreciated.) — Gregor Thomas, Dec 08 '22 at 19:04

How can I average separate variables by minute

0 Answers0