Question

我真的很R，这可能是一个非常基本的问题。请参阅我的示例代码。我想代表在24小时内每周执行工作的人数百分比。如何将y轴更改为百分比而不是总计？

我尝试了此代码，但不确定：

df2 <- df3 %>% 
  group_by(day,time) %>% 
  summarise(Total=sum(value)) 
df2$Pct <- df2$Total/ sum(df2$Total)

df2<-structure(list(`Day of the week` = structure(c(1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L), .Label = c("Monday", 
    "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"
    ), class = "factor"), time = c(4, 4.25, 4.5, 4.75, 5, 5.25, 5.5, 
    5.75, 6, 6.25, 6.5, 6.75, 7, 7.25, 7.5, 7.75, 8, 8.25, 8.5, 8.75, 
    9, 9.25, 9.5, 9.75, 10, 10.25, 10.5, 10.75, 11, 11.25, 11.5, 
    11.75, 12, 12.25, 12.5, 12.75, 13, 13.25, 13.5, 13.75, 14, 14.25, 
    14.5, 14.75, 15, 15.25, 15.5, 15.75, 16, 16.25, 16.5, 16.75, 
    17, 17.25, 17.5, 17.75, 18, 18.25, 18.5, 18.75, 19, 19.25, 19.5, 
    19.75, 20, 20.25, 20.5, 20.75, 21, 21.25, 21.5, 21.75, 22, 22.25, 
    22.5, 22.75, 23, 23.25, 23.5, 23.75, 24, 24.25, 24.5, 24.75, 
    25, 25.25, 25.5, 25.75, 26, 26.25, 26.5, 26.75, 27, 27.25, 27.5, 
    27.75, 4, 4.25, 4.5, 4.75, 5, 5.25, 5.5, 5.75, 6, 6.25, 6.5, 
    6.75, 7, 7.25, 7.5, 7.75, 8, 8.25, 8.5, 8.75, 9, 9.25, 9.5, 9.75, 
    10, 10.25, 10.5, 10.75, 11, 11.25, 11.5, 11.75, 12, 12.25, 12.5, 
    12.75, 13, 13.25, 13.5, 13.75, 14, 14.25, 14.5, 14.75, 15, 15.25, 
    15.5, 15.75, 16, 16.25, 16.5, 16.75, 17, 17.25, 17.5, 17.75, 
    18, 18.25, 18.5, 18.75, 19, 19.25, 19.5, 19.75, 20, 20.25, 20.5, 
    20.75, 21, 21.25, 21.5, 21.75, 22, 22.25, 22.5, 22.75, 23, 23.25, 
    23.5, 23.75, 24, 24.25, 24.5, 24.75, 25, 25.25, 25.5, 25.75, 
    26, 26.25, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 4, 4.25, 4.5, 
    4.75, 5, 5.25, 5.5, 5.75, 6, 6.25, 6.5, 6.75, 7, 7.25, 7.5, 7.75, 
    8, 8.25, 8.5, 8.75, 9, 9.25, 9.5, 9.75, 10, 10.25, 10.5, 10.75, 
    11, 11.25, 11.5, 11.75, 12, 12.25, 12.5, 12.75, 13, 13.25, 13.5, 
    13.75, 14, 14.25, 14.5, 14.75, 15, 15.25, 15.5, 15.75, 16, 16.25, 
    16.5, 16.75, 17, 17.25, 17.5, 17.75, 18, 18.25, 18.5, 18.75, 
    19, 19.25, 19.5, 19.75, 20, 20.25, 20.5, 20.75, 21, 21.25, 21.5, 
    21.75, 22, 22.25, 22.5, 22.75, 23, 23.25, 23.5, 23.75, 24, 24.25, 
    24.5, 24.75, 25, 25.25, 25.5, 25.75, 26, 26.25, 26.5, 26.75, 
    27, 27.25, 27.5, 27.75, 4, 4.25, 4.5, 4.75, 5, 5.25, 5.5, 5.75, 
    6, 6.25, 6.5, 6.75, 7, 7.25, 7.5, 7.75, 8, 8.25, 8.5, 8.75, 9, 
    9.25, 9.5, 9.75, 10, 10.25, 10.5, 10.75, 11, 11.25, 11.5, 11.75, 
    12, 12.25, 12.5, 12.75, 13, 13.25, 13.5, 13.75, 14, 14.25, 14.5, 
    14.75, 15, 15.25, 15.5, 15.75, 16, 16.25, 16.5, 16.75, 17, 17.25, 
    17.5, 17.75, 18, 18.25, 18.5, 18.75, 19, 19.25, 19.5, 19.75, 
    20, 20.25, 20.5, 20.75, 21, 21.25, 21.5, 21.75, 22, 22.25, 22.5, 
    22.75, 23, 23.25, 23.5, 23.75, 24, 24.25, 24.5, 24.75, 25, 25.25, 
    25.5, 25.75, 26, 26.25, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 
    4, 4.25, 4.5, 4.75, 5, 5.25, 5.5, 5.75, 6, 6.25, 6.5, 6.75, 7, 
    7.25, 7.5, 7.75, 8, 8.25, 8.5, 8.75, 9, 9.25, 9.5, 9.75, 10, 
    10.25, 10.5, 10.75, 11, 11.25, 11.5, 11.75, 12, 12.25, 12.5, 
    12.75, 13, 13.25, 13.5, 13.75, 14, 14.25, 14.5, 14.75, 15, 15.25, 
    15.5, 15.75, 16, 16.25, 16.5, 16.75, 17, 17.25, 17.5, 17.75, 
    18, 18.25, 18.5, 18.75, 19, 19.25, 19.5, 19.75, 20, 20.25, 20.5, 
    20.75, 21, 21.25, 21.5, 21.75, 22, 22.25, 22.5, 22.75, 23, 23.25, 
    23.5, 23.75, 24, 24.25, 24.5, 24.75, 25, 25.25, 25.5, 25.75, 
    26, 26.25, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 4, 4.25, 4.5, 
    4.75, 5, 5.25, 5.5, 5.75, 6, 6.25, 6.5, 6.75, 7, 7.25, 7.5, 7.75, 
    8, 8.25, 8.5, 8.75, 9, 9.25, 9.5, 9.75, 10, 10.25, 10.5, 10.75, 
    11, 11.25, 11.5, 11.75, 12, 12.25, 12.5, 12.75, 13, 13.25, 13.5, 
    13.75, 14, 14.25, 14.5, 14.75, 15, 15.25, 15.5, 15.75, 16, 16.25, 
    16.5, 16.75, 17, 17.25, 17.5, 17.75, 18, 18.25, 18.5, 18.75, 
    19, 19.25, 19.5, 19.75, 20, 20.25, 20.5, 20.75, 21, 21.25, 21.5, 
    21.75, 22, 22.25, 22.5, 22.75, 23, 23.25, 23.5, 23.75, 24, 24.25, 
    24.5, 24.75, 25, 25.25, 25.5, 25.75, 26, 26.25, 26.5, 26.75, 
    27, 27.25, 27.5, 27.75, 4, 4.25, 4.5, 4.75, 5, 5.25, 5.5, 5.75, 
    6, 6.25, 6.5, 6.75, 7, 7.25, 7.5, 7.75, 8, 8.25, 8.5, 8.75, 9, 
    9.25, 9.5, 9.75, 10, 10.25, 10.5, 10.75, 11, 11.25, 11.5, 11.75, 
    12, 12.25, 12.5, 12.75, 13, 13.25, 13.5, 13.75, 14, 14.25, 14.5, 
    14.75, 15, 15.25, 15.5, 15.75, 16, 16.25, 16.5, 16.75, 17, 17.25, 
    17.5, 17.75, 18, 18.25, 18.5, 18.75, 19, 19.25, 19.5, 19.75, 
    20, 20.25, 20.5, 20.75, 21, 21.25, 21.5, 21.75, 22, 22.25, 22.5, 
    22.75, 23, 23.25, 23.5, 23.75, 24, 24.25, 24.5, 24.75, 25, 25.25, 
    25.5, 25.75, 26, 26.25, 26.5, 26.75, 27, 27.25, 27.5, 27.75), 
        Total = c(6, 6, 6, 6, 7, 8, 10, 11, 19, 22, 27, 28, 44, 47, 
        56, 59, 100, 106, 135, 136, 173, 184, 191, 197, 200, 199, 
        203, 201, 198, 199, 202, 202, 193, 189, 182, 183, 155, 153, 
        153, 157, 183, 185, 185, 185, 185, 182, 173, 172, 158, 158, 
        140, 139, 125, 118, 108, 101, 68, 66, 54, 50, 37, 38, 32, 
        30, 26, 26, 26, 25, 24, 23, 23, 23, 25, 23, 21, 20, 15, 14, 
        14, 15, 11, 11, 10, 10, 10, 9, 9, 9, 10, 10, 10, 10, 10, 
        10, 9, 9, 8, 8, 8, 8, 10, 10, 14, 15, 20, 20, 27, 27, 45, 
        47, 59, 62, 104, 110, 137, 140, 179, 186, 202, 203, 206, 
        209, 209, 210, 205, 207, 211, 210, 200, 199, 194, 197, 169, 
        166, 176, 180, 193, 196, 197, 197, 192, 190, 180, 176, 162, 
        162, 153, 148, 124, 122, 106, 97, 64, 61, 57, 54, 38, 37, 
        38, 34, 32, 33, 31, 28, 24, 24, 22, 21, 20, 20, 17, 16, 13, 
        12, 10, 10, 9, 9, 8, 8, 8, 7, 8, 9, 9, 8, 8, 8, 8, 7, 7, 
        8, 7, 7, 7, 7, 10, 11, 14, 16, 22, 24, 27, 28, 45, 48, 63, 
        66, 104, 116, 141, 145, 191, 198, 209, 210, 215, 216, 218, 
        216, 216, 218, 221, 221, 206, 204, 194, 194, 180, 179, 184, 
        186, 206, 209, 208, 207, 204, 203, 196, 194, 179, 182, 168, 
        164, 131, 127, 115, 106, 66, 60, 57, 52, 39, 36, 36, 33, 
        32, 31, 29, 29, 22, 21, 18, 17, 16, 15, 14, 14, 12, 12, 12, 
        11, 9, 9, 8, 8, 7, 7, 7, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 
        5, 5, 7, 7, 9, 9, 18, 20, 21, 21, 38, 39, 58, 61, 108, 116, 
        138, 141, 179, 185, 196, 196, 200, 205, 205, 201, 202, 204, 
        204, 202, 191, 188, 184, 188, 170, 172, 180, 178, 190, 191, 
        196, 195, 193, 194, 184, 180, 165, 166, 150, 149, 128, 123, 
        108, 99, 66, 66, 60, 55, 36, 36, 33, 34, 35, 35, 31, 31, 
        22, 22, 22, 22, 17, 17, 15, 14, 12, 12, 11, 10, 10, 10, 10, 
        10, 9, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 6, 6, 7, 7, 8, 8, 
        12, 12, 21, 21, 25, 27, 43, 44, 56, 59, 100, 110, 129, 132, 
        166, 172, 187, 189, 189, 191, 193, 192, 188, 194, 193, 192, 
        173, 173, 172, 176, 159, 154, 157, 163, 166, 167, 170, 169, 
        162, 161, 157, 156, 141, 142, 130, 125, 92, 91, 73, 68, 46, 
        47, 40, 35, 24, 23, 21, 19, 20, 20, 20, 21, 19, 19, 17, 17, 
        20, 19, 18, 18, 11, 11, 12, 11, 10, 10, 10, 10, 9, 7, 7, 
        7, 6, 6, 7, 7, 7, 7, 7, 6, 5, 6, 7, 7, 10, 9, 11, 12, 13, 
        14, 14, 15, 20, 20, 20, 21, 26, 26, 28, 29, 32, 33, 40, 40, 
        38, 37, 43, 43, 44, 43, 43, 44, 43, 41, 40, 39, 39, 40, 39, 
        38, 37, 37, 39, 41, 33, 34, 37, 36, 34, 34, 35, 33, 28, 28, 
        24, 24, 19, 19, 19, 18, 18, 19, 17, 15, 15, 15, 15, 15, 14, 
        14, 15, 15, 14, 13, 13, 13, 12, 11, 10, 9, 8, 8, 8, 6, 4, 
        4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 8, 8, 
        7, 8, 8, 9, 14, 14, 15, 16, 18, 18, 17, 16, 18, 18, 20, 20, 
        21, 22, 25, 25, 30, 30, 29, 28, 25, 24, 23, 23, 22, 21, 21, 
        21, 20, 21, 23, 23, 23, 22, 21, 23, 19, 18, 19, 18, 19, 19, 
        21, 21, 16, 17, 16, 16, 17, 17, 19, 19, 19, 19, 20, 20, 15, 
        15, 17, 17, 18, 17, 16, 16, 13, 12, 12, 12, 11, 11, 10, 10, 
        9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 8)), row.names = c(NA, -672L
    ), class = c("grouped_df", "tbl_df", "tbl", "data.frame"), groups = structure(list(
        day = structure(1:7, .Label = c("Monday", "Tuesday", "Wednesday", 
        "Thursday", "Friday", "Saturday", "Sunday"), class = "factor"), 
        .rows = list(1:96, 97:192, 193:288, 289:384, 385:480, 481:576, 
            577:672)), row.names = c(NA, -7L), class = c("tbl_df", 
    "tbl", "data.frame"), .drop = TRUE))

Answer 1

我建议采用下一种方法。如果按日期和时间分组，则所有百分比均为1。如果按日期分组，则得到以下信息：

<?xml version="1.0" encoding="UTF-8"?>
<persistence version="2.1"
             xmlns="http://xmlns.jcp.org/xml/ns/persistence"
             xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
             xsi:schemaLocation="http://xmlns.jcp.org/xml/ns/persistence http://xmlns.jcp.org/xml/ns/persistence/persistence_2_1.xsd">

  <persistence-unit name="org.jbpm.persistence.jpa" transaction-type="JTA">
    <provider>org.hibernate.jpa.HibernatePersistenceProvider</provider>
    <jta-data-source>jdbc/testDS1</jta-data-source>
    <mapping-file>META-INF/JBPMorm.xml</mapping-file>
    <mapping-file>META-INF/Taskorm.xml</mapping-file>
    <class>org.jbpm.persistence.processinstance.ProcessInstanceInfo</class>
    <class>org.drools.persistence.info.SessionInfo</class>
    <class>org.drools.persistence.info.WorkItemInfo</class>
    <class>org.drools.persistence.info.SessionInfo</class>
    <class>org.drools.persistence.info.WorkItemInfo</class>
    <class>org.jbpm.process.audit.ProcessInstanceLog</class>
    <class>org.jbpm.process.audit.NodeInstanceLog</class>
    <class>org.jbpm.process.audit.VariableInstanceLog</class>
    <class>org.jbpm.task.Attachment</class>
    <class>org.jbpm.task.Content</class>
    <class>org.jbpm.task.BooleanExpression</class>
    <class>org.jbpm.task.Comment</class>
    <class>org.jbpm.task.Deadline</class>
    <class>org.jbpm.task.Comment</class>
    <class>org.jbpm.task.Deadline</class>
    <class>org.jbpm.task.Delegation</class>
    <class>org.jbpm.task.Escalation</class>
    <class>org.jbpm.task.Group</class>
    <class>org.jbpm.task.I18NText</class>
    <class>org.jbpm.task.Notification</class>
    <class>org.jbpm.task.EmailNotification</class>
    <class>org.jbpm.task.EmailNotificationHeader</class>
    <class>org.jbpm.task.PeopleAssignments</class>
    <class>org.jbpm.task.Reassignment</class>
    <class>org.jbpm.task.Status</class>
    <class>org.jbpm.task.Task</class>
    <class>org.jbpm.task.TaskData</class>
    <class>org.jbpm.task.SubTasksStrategy</class>
    <class>org.jbpm.task.OnParentAbortAllSubTasksEndStrategy</class>
    <class>org.jbpm.task.OnAllSubTasksEndParentEndStrategy</class>
    <class>org.jbpm.task.User</class>
 <properties>
      <property name="hibernate.max_fetch_depth" value="3"/>
      <property name="hibernate.hbm2ddl.auto" value="create"/>
      <property name="hibernate.show_sql" value="false"/>
      <property name="hibernate.dialect" value="${maven.hibernate.dialect}"/>
      <property name="hibernate.default_schema" value="${maven.jdbc.schema}"/>

      <!-- BZ 841786: AS7/EAP 6/Hib 4 uses new (sequence) generators which seem to cause problems -->
      <property name="hibernate.id.new_generator_mappings" value="false"/>
      <property name="hibernate.transaction.jta.platform" value="org.hibernate.service.jta.platform.internal.JBossStandAloneJtaPlatform"/>
      <property name="hibernate.connection.handling_mode" value="DELAYED_ACQUISITION_AND_RELEASE_AFTER_TRANSACTION"/>
    </properties>
  </persistence-unit>

输出：

Answer 2

另一个答案中的图表说明了一天中每15分钟增量的总工作量百分比。

如果y轴代表一周内以15分钟为增量的工作百分比，则分母应为max(Total)，即7天时间内以15分钟为增量的最大工作人数。数据中的句点。

另一种方法是每天使用最大值，因此在15分钟的增量内，当日工作的最多人员将在图表中显示为100％。

通过汇总数据，我们可以看到如何计算分母。

df2 %>% 
    group_by(`Day of the week`) %>% 
    summarise(.,max = max(Total))

...以及输出：

`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 7 x 2
  `Day of the week`   max
  <fct>             <dbl>
1 Monday              203
2 Tuesday             211
3 Wednesday           221
4 Thursday            205
5 Friday              194
6 Saturday             44
7 Sunday               30

根据原始答案中发布的图表，将图表标准化为每天最大工人人数的图表如下所示：

df2 %>% group_by(`Day of the week`) %>%
     mutate(Percent = Total / max(Total)) -> df3

ggplot(df3,aes(x=time,y=Percent,color=`Day of the week`))+
     geom_line() +
     scale_y_continuous(labels = scales::percent)

...以及输出：

标准化为全天最大工人人数的图表如下所示。

df2  %>% ungroup() %>%
     mutate(Percent = Total / max(Total)) -> df3

ggplot(df3,aes(x=time,y=Percent,color=`Day of the week`))+
     geom_line() +
     scale_y_continuous(labels = scales::percent)

...以及输出，我们可以清楚地看到周末工作的人更少。

如何将计数转换为百分比？

2 个答案: