我正在尝试使用使用crosstalk::filter_select(). 但是，当我选择一个组时，热图似乎是空的，我不知道为什么。我已经包含了生成热图的代码、下面的一些图像以更好地演示问题，以及重现结果的数据。
运行代码以生成热图：
library(tidyverse)
library(crosstalk)
library(plotly)
# The data frame 'df' can be retrieved from the code below, if needed
interactive <- SharedData$new(df, ~comb_name, group = "Combination")
g <- interactive %>%
ggplot(aes(x = day,
y = hour,
fill = diff)) +
geom_tile(colour = "white",
size = 0.1) +
scale_fill_viridis(name= "Temperature", option = "B") +
facet_wrap(~ month_abb) +
scale_x_continuous(breaks =c(1,10,20,31)) +
scale_y_continuous(breaks = 0:23) +
theme_minimal() +
theme(legend.position = "bottom")
filter <- bscols(
filter_select(id = "id",
label = "Select Sites",
sharedData = interactive,
group = ~comb_name,
multiple = FALSE),
ggplotly(g,
tooltip = c("text"),
dynamicTicks = TRUE) %>%
config(displayModeBar = F),
widths = c(10, 10)
)
bscols(filter)
默认图像显示所有组的热图：
在选择输入中选择一个组时，它会显示一个空的热图
我在下面包含了一个数据框的小样本。
structure(list(date = structure(c(18231, 18231, 18231, 18231,
18231, 18231, 18231, 18231, 18231, 18231, 18231, 18231, 18231,
18231, 18231, 18231, 18231, 18231, 18231, 18231, 18231, 18231,
18231, 18231, 18231, 18231, 18231, 18231, 18231, 18231, 18262,
18262, 18262, 18262, 18262, 18262, 18262, 18262, 18262, 18262,
18262, 18262, 18262, 18262, 18262, 18262, 18262, 18262, 18262,
18262, 18262, 18262, 18262, 18262, 18262, 18262, 18262, 18262,
18262, 18262, 18293, 18293, 18293, 18293, 18293, 18293, 18293,
18293, 18293, 18293, 18293, 18293, 18293, 18293, 18293, 18293,
18293, 18293, 18322, 18322, 18322, 18322, 18322, 18322, 18322,
18322, 18322, 18322, 18322, 18322, 18322, 18322, 18322, 18322,
18322, 18322, 18322, 18322, 18322, 18322, 18322, 18322, 18322,
18322, 18322, 18322, 18322, 18322, 18353, 18353, 18353, 18353,
18353, 18353, 18353, 18353, 18353, 18353, 18353, 18353, 18353,
18353, 18353, 18353, 18353, 18353, 18353, 18353, 18353, 18353,
18353, 18353, 18353, 18353, 18353, 18353, 18353, 18353, 18414,
18414, 18414, 18414, 18414, 18414, 18414, 18414, 18414, 18414,
18414, 18414, 18414, 18414, 18414, 18414, 18414, 18414, 18414,
18414, 18414, 18414, 18414, 18414, 18414, 18414, 18414, 18414,
18414, 18414, 18444, 18444, 18444, 18444, 18444, 18444, 18444,
18444, 18444, 18444, 18444, 18444, 18444, 18444, 18444, 18444,
18444, 18444, 18475, 18475, 18475, 18475, 18475, 18475, 18475,
18475, 18475, 18475, 18475, 18475, 18475, 18475, 18475, 18475,
18475, 18475, 18475, 18475, 18475, 18475, 18475, 18475, 18475,
18475, 18475, 18475, 18475, 18475, 18506, 18506, 18506, 18506,
18506, 18506, 18506, 18506, 18506, 18506, 18506, 18506, 18506,
18506, 18506, 18506, 18506, 18506, 18383, 18383, 18383), class = "Date"),
hour = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L
), comb_name = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 2L, 3L, 4L, 8L,
9L, 10L, 2L, 3L, 4L, 8L, 9L, 10L, 2L, 3L, 4L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 2L, 3L, 4L, 8L, 9L, 10L, 2L, 3L,
4L, 8L, 9L, 10L, 2L, 3L, 4L, 8L, 9L, 10L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L,
4L, 5L, 7L, 9L, 1L, 2L, 4L, 5L, 7L, 9L, 1L, 2L, 4L, 5L, 7L,
9L, 9L, 9L, 9L), .Label = c("arc1045 - arc1046", "arc1045 - arc1047",
"arc1045 - arc1048", "arc1045 - arc1050", "arc1046 - arc1047",
"arc1046 - arc1048", "arc1046 - arc1050", "arc1047 - arc1048",
"arc1047 - arc1050", "arc1048 - arc1050"), class = "factor"),
diff = c(1.58, 1.79, 1.8, 1.78, 0.2, 0.21, 0.2, 0.01, 0,
-0.01, 1.03, 1.21, 1.28, 1.29, 0.18, 0.25, 0.26, 0.07, 0.08,
0.01, 0.84, 1.02, 0.99, 0.99, 0.18, 0.15, 0.15, -0.02, -0.03,
0, 1.48, 1.69, 1.5, 1.32, 0.22, 0.03, -0.15, -0.19, -0.37,
-0.18, 1.17, 1.5, 1.17, 0.98, 0.33, 0, -0.19, -0.33, -0.52,
-0.19, 1.01, 1.25, 1, 0.85, 0.24, -0.02, -0.16, -0.26, -0.4,
-0.14, 0.65, 0.65, 0.73, 0, 0.08, 0.08, 0.67, 0.54, 0.56,
-0.14, -0.11, 0.03, 0.82, 0.62, 0.59, -0.2, -0.24, -0.04,
1.31, 1.35, 1.24, 0.94, 0.04, -0.07, -0.37, -0.11, -0.41,
-0.3, 1.54, 1.52, 1.42, 1.28, -0.02, -0.12, -0.26, -0.11,
-0.24, -0.14, 1.55, 1.5, 1.44, 1.32, -0.05, -0.11, -0.23,
-0.05, -0.18, -0.12, 1.45, 1.67, 1.52, 1.17, 0.22, 0.07,
-0.28, -0.15, -0.49, -0.35, 1.29, 1.36, 1.19, 0.85, 0.07,
-0.1, -0.44, -0.17, -0.51, -0.34, 1.41, 1.7, 1.18, 0.97,
0.29, -0.23, -0.44, -0.52, -0.73, -0.21, 0.09, 0.18, 0.26,
0.21, 0.09, 0.17, 0.12, 0.08, 0.03, -0.05, 0.04, 0.12, 0.21,
0.17, 0.08, 0.17, 0.12, 0.09, 0.04, -0.05, -0.03, -0.1, 0.13,
0.1, -0.07, 0.16, 0.13, 0.23, 0.19, -0.03, 0.13, 1.19, 0.71,
1.06, 0.58, -0.48, 0.06, 0.76, 0.39, 0.7, 0.34, -0.37, 0.08,
0.64, 0.25, 0.56, 0.18, -0.38, 0.49, 0.44, 0.76, 0.6, -0.05,
0.27, 0.11, 0.32, 0.16, -0.16, 1.1, 0.69, 1.25, 0.94, -0.41,
0.15, -0.16, 0.56, 0.25, -0.31, 0.59, 0.21, 0.84, 0.7, -0.38,
0.25, 0.1, 0.63, 0.48, -0.15, 0.75, 0.81, 0.95, 0.06, 0.21,
0.15, 0.76, 0.82, 0.87, 0.06, 0.11, 0.04, 1.02, 1.03, 1.16,
0.01, 0.14, 0.13, 0.03, 0.02, -0.03), day = 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, 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, 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), month_abb = structure(c(12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 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, 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, 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, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 5L, 5L, 5L), .Label = c("Jan", "Feb", "Mar", "Apr", "May",
"Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"), class = c("ordered",
"factor"))), row.names = c(NA, -237L), class = c("tbl_df",
"tbl", "data.frame"))
#> date hour comb_name diff day month_abb
#> 1 2019-12-01 1 arc1045 - arc1046 1.58 1 Dec
#> 2 2019-12-01 1 arc1045 - arc1047 1.79 1 Dec
#> 3 2019-12-01 1 arc1045 - arc1048 1.80 1 Dec
#> 4 2019-12-01 1 arc1045 - arc1050 1.78 1 Dec
#> 5 2019-12-01 1 arc1046 - arc1047 0.20 1 Dec
#> 6 2019-12-01 1 arc1046 - arc1048 0.21 1 Dec
#> 7 2019-12-01 1 arc1046 - arc1050 0.20 1 Dec
#> 8 2019-12-01 1 arc1047 - arc1048 0.01 1 Dec
#> 9 2019-12-01 1 arc1047 - arc1050 0.00 1 Dec
#> 10 2019-12-01 1 arc1048 - arc1050 -0.01 1 Dec
#> 11 2019-12-01 2 arc1045 - arc1046 1.03 1 Dec
#> 12 2019-12-01 2 arc1045 - arc1047 1.21 1 Dec
#> 13 2019-12-01 2 arc1045 - arc1048 1.28 1 Dec
#> 14 2019-12-01 2 arc1045 - arc1050 1.29 1 Dec
#> 15 2019-12-01 2 arc1046 - arc1047 0.18 1 Dec
#> 16 2019-12-01 2 arc1046 - arc1048 0.25 1 Dec
#> 17 2019-12-01 2 arc1046 - arc1050 0.26 1 Dec
#> 18 2019-12-01 2 arc1047 - arc1048 0.07 1 Dec
#> 19 2019-12-01 2 arc1047 - arc1050 0.08 1 Dec
#> 20 2019-12-01 2 arc1048 - arc1050 0.01 1 Dec
#> 21 2019-12-01 3 arc1045 - arc1046 0.84 1 Dec
#> 22 2019-12-01 3 arc1045 - arc1047 1.02 1 Dec
#> 23 2019-12-01 3 arc1045 - arc1048 0.99 1 Dec
#> 24 2019-12-01 3 arc1045 - arc1050 0.99 1 Dec
#> 25 2019-12-01 3 arc1046 - arc1047 0.18 1 Dec
#> 26 2019-12-01 3 arc1046 - arc1048 0.15 1 Dec
#> 27 2019-12-01 3 arc1046 - arc1050 0.15 1 Dec
#> 28 2019-12-01 3 arc1047 - arc1048 -0.02 1 Dec
#> 29 2019-12-01 3 arc1047 - arc1050 -0.03 1 Dec
#> 30 2019-12-01 3 arc1048 - arc1050 0.00 1 Dec
#> 31 2020-01-01 1 arc1045 - arc1046 1.48 1 Jan
#> 32 2020-01-01 1 arc1045 - arc1047 1.69 1 Jan
#> 33 2020-01-01 1 arc1045 - arc1048 1.50 1 Jan
#> 34 2020-01-01 1 arc1045 - arc1050 1.32 1 Jan
#> 35 2020-01-01 1 arc1046 - arc1047 0.22 1 Jan
#> 36 2020-01-01 1 arc1046 - arc1048 0.03 1 Jan
#> 37 2020-01-01 1 arc1046 - arc1050 -0.15 1 Jan
#> 38 2020-01-01 1 arc1047 - arc1048 -0.19 1 Jan
#> 39 2020-01-01 1 arc1047 - arc1050 -0.37 1 Jan
#> 40 2020-01-01 1 arc1048 - arc1050 -0.18 1 Jan
#> 41 2020-01-01 2 arc1045 - arc1046 1.17 1 Jan
#> 42 2020-01-01 2 arc1045 - arc1047 1.50 1 Jan
#> 43 2020-01-01 2 arc1045 - arc1048 1.17 1 Jan
#> 44 2020-01-01 2 arc1045 - arc1050 0.98 1 Jan
#> 45 2020-01-01 2 arc1046 - arc1047 0.33 1 Jan
#> 46 2020-01-01 2 arc1046 - arc1048 0.00 1 Jan
#> 47 2020-01-01 2 arc1046 - arc1050 -0.19 1 Jan
#> 48 2020-01-01 2 arc1047 - arc1048 -0.33 1 Jan
#> 49 2020-01-01 2 arc1047 - arc1050 -0.52 1 Jan
#> 50 2020-01-01 2 arc1048 - arc1050 -0.19 1 Jan
#> 51 2020-01-01 3 arc1045 - arc1046 1.01 1 Jan
#> 52 2020-01-01 3 arc1045 - arc1047 1.25 1 Jan
#> 53 2020-01-01 3 arc1045 - arc1048 1.00 1 Jan
#> 54 2020-01-01 3 arc1045 - arc1050 0.85 1 Jan
#> 55 2020-01-01 3 arc1046 - arc1047 0.24 1 Jan
#> 56 2020-01-01 3 arc1046 - arc1048 -0.02 1 Jan
#> 57 2020-01-01 3 arc1046 - arc1050 -0.16 1 Jan
#> 58 2020-01-01 3 arc1047 - arc1048 -0.26 1 Jan
#> 59 2020-01-01 3 arc1047 - arc1050 -0.40 1 Jan
#> 60 2020-01-01 3 arc1048 - arc1050 -0.14 1 Jan
#> 61 2020-02-01 1 arc1045 - arc1047 0.65 1 Feb
#> 62 2020-02-01 1 arc1045 - arc1048 0.65 1 Feb
#> 63 2020-02-01 1 arc1045 - arc1050 0.73 1 Feb
#> 64 2020-02-01 1 arc1047 - arc1048 0.00 1 Feb
#> 65 2020-02-01 1 arc1047 - arc1050 0.08 1 Feb
#> 66 2020-02-01 1 arc1048 - arc1050 0.08 1 Feb
#> 67 2020-02-01 2 arc1045 - arc1047 0.67 1 Feb
#> 68 2020-02-01 2 arc1045 - arc1048 0.54 1 Feb
#> 69 2020-02-01 2 arc1045 - arc1050 0.56 1 Feb
#> 70 2020-02-01 2 arc1047 - arc1048 -0.14 1 Feb
#> 71 2020-02-01 2 arc1047 - arc1050 -0.11 1 Feb
#> 72 2020-02-01 2 arc1048 - arc1050 0.03 1 Feb
#> 73 2020-02-01 3 arc1045 - arc1047 0.82 1 Feb
#> 74 2020-02-01 3 arc1045 - arc1048 0.62 1 Feb
#> 75 2020-02-01 3 arc1045 - arc1050 0.59 1 Feb
#> 76 2020-02-01 3 arc1047 - arc1048 -0.20 1 Feb
#> 77 2020-02-01 3 arc1047 - arc1050 -0.24 1 Feb
#> 78 2020-02-01 3 arc1048 - arc1050 -0.04 1 Feb
#> 79 2020-03-01 1 arc1045 - arc1046 1.31 1 Mar
#> 80 2020-03-01 1 arc1045 - arc1047 1.35 1 Mar
#> 81 2020-03-01 1 arc1045 - arc1048 1.24 1 Mar
#> 82 2020-03-01 1 arc1045 - arc1050 0.94 1 Mar
#> 83 2020-03-01 1 arc1046 - arc1047 0.04 1 Mar
#> 84 2020-03-01 1 arc1046 - arc1048 -0.07 1 Mar
#> 85 2020-03-01 1 arc1046 - arc1050 -0.37 1 Mar
#> 86 2020-03-01 1 arc1047 - arc1048 -0.11 1 Mar
#> 87 2020-03-01 1 arc1047 - arc1050 -0.41 1 Mar
#> 88 2020-03-01 1 arc1048 - arc1050 -0.30 1 Mar
#> 89 2020-03-01 2 arc1045 - arc1046 1.54 1 Mar
#> 90 2020-03-01 2 arc1045 - arc1047 1.52 1 Mar
#> 91 2020-03-01 2 arc1045 - arc1048 1.42 1 Mar
#> 92 2020-03-01 2 arc1045 - arc1050 1.28 1 Mar
#> 93 2020-03-01 2 arc1046 - arc1047 -0.02 1 Mar
#> 94 2020-03-01 2 arc1046 - arc1048 -0.12 1 Mar
#> 95 2020-03-01 2 arc1046 - arc1050 -0.26 1 Mar
#> 96 2020-03-01 2 arc1047 - arc1048 -0.11 1 Mar
#> 97 2020-03-01 2 arc1047 - arc1050 -0.24 1 Mar
#> 98 2020-03-01 2 arc1048 - arc1050 -0.14 1 Mar
#> 99 2020-03-01 3 arc1045 - arc1046 1.55 1 Mar
#> 100 2020-03-01 3 arc1045 - arc1047 1.50 1 Mar
#> 101 2020-03-01 3 arc1045 - arc1048 1.44 1 Mar
#> 102 2020-03-01 3 arc1045 - arc1050 1.32 1 Mar
#> 103 2020-03-01 3 arc1046 - arc1047 -0.05 1 Mar
#> 104 2020-03-01 3 arc1046 - arc1048 -0.11 1 Mar
#> 105 2020-03-01 3 arc1046 - arc1050 -0.23 1 Mar
#> 106 2020-03-01 3 arc1047 - arc1048 -0.05 1 Mar
#> 107 2020-03-01 3 arc1047 - arc1050 -0.18 1 Mar
#> 108 2020-03-01 3 arc1048 - arc1050 -0.12 1 Mar
#> 109 2020-04-01 1 arc1045 - arc1046 1.45 1 Apr
#> 110 2020-04-01 1 arc1045 - arc1047 1.67 1 Apr
#> 111 2020-04-01 1 arc1045 - arc1048 1.52 1 Apr
#> 112 2020-04-01 1 arc1045 - arc1050 1.17 1 Apr
#> 113 2020-04-01 1 arc1046 - arc1047 0.22 1 Apr
#> 114 2020-04-01 1 arc1046 - arc1048 0.07 1 Apr
#> 115 2020-04-01 1 arc1046 - arc1050 -0.28 1 Apr
#> 116 2020-04-01 1 arc1047 - arc1048 -0.15 1 Apr
#> 117 2020-04-01 1 arc1047 - arc1050 -0.49 1 Apr
#> 118 2020-04-01 1 arc1048 - arc1050 -0.35 1 Apr
#> 119 2020-04-01 2 arc1045 - arc1046 1.29 1 Apr
#> 120 2020-04-01 2 arc1045 - arc1047 1.36 1 Apr
#> 121 2020-04-01 2 arc1045 - arc1048 1.19 1 Apr
#> 122 2020-04-01 2 arc1045 - arc1050 0.85 1 Apr
#> 123 2020-04-01 2 arc1046 - arc1047 0.07 1 Apr
#> 124 2020-04-01 2 arc1046 - arc1048 -0.10 1 Apr
#> 125 2020-04-01 2 arc1046 - arc1050 -0.44 1 Apr
#> 126 2020-04-01 2 arc1047 - arc1048 -0.17 1 Apr
#> 127 2020-04-01 2 arc1047 - arc1050 -0.51 1 Apr
#> 128 2020-04-01 2 arc1048 - arc1050 -0.34 1 Apr
#> 129 2020-04-01 3 arc1045 - arc1046 1.41 1 Apr
#> 130 2020-04-01 3 arc1045 - arc1047 1.70 1 Apr
#> 131 2020-04-01 3 arc1045 - arc1048 1.18 1 Apr
#> 132 2020-04-01 3 arc1045 - arc1050 0.97 1 Apr
#> 133 2020-04-01 3 arc1046 - arc1047 0.29 1 Apr
#> 134 2020-04-01 3 arc1046 - arc1048 -0.23 1 Apr
#> 135 2020-04-01 3 arc1046 - arc1050 -0.44 1 Apr
#> 136 2020-04-01 3 arc1047 - arc1048 -0.52 1 Apr
#> 137 2020-04-01 3 arc1047 - arc1050 -0.73 1 Apr
#> 138 2020-04-01 3 arc1048 - arc1050 -0.21 1 Apr
#> 139 2020-06-01 1 arc1045 - arc1046 0.09 1 Jun
#> 140 2020-06-01 1 arc1045 - arc1047 0.18 1 Jun
#> 141 2020-06-01 1 arc1045 - arc1048 0.26 1 Jun
#> 142 2020-06-01 1 arc1045 - arc1050 0.21 1 Jun
#> 143 2020-06-01 1 arc1046 - arc1047 0.09 1 Jun
#> 144 2020-06-01 1 arc1046 - arc1048 0.17 1 Jun
#> 145 2020-06-01 1 arc1046 - arc1050 0.12 1 Jun
#> 146 2020-06-01 1 arc1047 - arc1048 0.08 1 Jun
#> 147 2020-06-01 1 arc1047 - arc1050 0.03 1 Jun
#> 148 2020-06-01 1 arc1048 - arc1050 -0.05 1 Jun
#> 149 2020-06-01 2 arc1045 - arc1046 0.04 1 Jun
#> 150 2020-06-01 2 arc1045 - arc1047 0.12 1 Jun
#> 151 2020-06-01 2 arc1045 - arc1048 0.21 1 Jun
#> 152 2020-06-01 2 arc1045 - arc1050 0.17 1 Jun
#> 153 2020-06-01 2 arc1046 - arc1047 0.08 1 Jun
#> 154 2020-06-01 2 arc1046 - arc1048 0.17 1 Jun
#> 155 2020-06-01 2 arc1046 - arc1050 0.12 1 Jun
#> 156 2020-06-01 2 arc1047 - arc1048 0.09 1 Jun
#> 157 2020-06-01 2 arc1047 - arc1050 0.04 1 Jun
#> 158 2020-06-01 2 arc1048 - arc1050 -0.05 1 Jun
#> 159 2020-06-01 3 arc1045 - arc1046 -0.03 1 Jun
#> 160 2020-06-01 3 arc1045 - arc1047 -0.10 1 Jun
#> 161 2020-06-01 3 arc1045 - arc1048 0.13 1 Jun
#> 162 2020-06-01 3 arc1045 - arc1050 0.10 1 Jun
#> 163 2020-06-01 3 arc1046 - arc1047 -0.07 1 Jun
#> 164 2020-06-01 3 arc1046 - arc1048 0.16 1 Jun
#> 165 2020-06-01 3 arc1046 - arc1050 0.13 1 Jun
#> 166 2020-06-01 3 arc1047 - arc1048 0.23 1 Jun
#> 167 2020-06-01 3 arc1047 - arc1050 0.19 1 Jun
#> 168 2020-06-01 3 arc1048 - arc1050 -0.03 1 Jun
#> 169 2020-07-01 1 arc1045 - arc1047 0.13 1 Jul
#> 170 2020-07-01 1 arc1045 - arc1048 1.19 1 Jul
#> 171 2020-07-01 1 arc1045 - arc1050 0.71 1 Jul
#> 172 2020-07-01 1 arc1047 - arc1048 1.06 1 Jul
#> 173 2020-07-01 1 arc1047 - arc1050 0.58 1 Jul
#> 174 2020-07-01 1 arc1048 - arc1050 -0.48 1 Jul
#> 175 2020-07-01 2 arc1045 - arc1047 0.06 1 Jul
#> 176 2020-07-01 2 arc1045 - arc1048 0.76 1 Jul
#> 177 2020-07-01 2 arc1045 - arc1050 0.39 1 Jul
#> 178 2020-07-01 2 arc1047 - arc1048 0.70 1 Jul
#> 179 2020-07-01 2 arc1047 - arc1050 0.34 1 Jul
#> 180 2020-07-01 2 arc1048 - arc1050 -0.37 1 Jul
#> 181 2020-07-01 3 arc1045 - arc1047 0.08 1 Jul
#> 182 2020-07-01 3 arc1045 - arc1048 0.64 1 Jul
#> 183 2020-07-01 3 arc1045 - arc1050 0.25 1 Jul
#> 184 2020-07-01 3 arc1047 - arc1048 0.56 1 Jul
#> 185 2020-07-01 3 arc1047 - arc1050 0.18 1 Jul
#> 186 2020-07-01 3 arc1048 - arc1050 -0.38 1 Jul
#> 187 2020-08-01 1 arc1045 - arc1046 0.49 1 Aug
#> 188 2020-08-01 1 arc1045 - arc1047 0.44 1 Aug
#> 189 2020-08-01 1 arc1045 - arc1048 0.76 1 Aug
#> 190 2020-08-01 1 arc1045 - arc1050 0.60 1 Aug
#> 191 2020-08-01 1 arc1046 - arc1047 -0.05 1 Aug
#> 192 2020-08-01 1 arc1046 - arc1048 0.27 1 Aug
#> 193 2020-08-01 1 arc1046 - arc1050 0.11 1 Aug
#> 194 2020-08-01 1 arc1047 - arc1048 0.32 1 Aug
#> 195 2020-08-01 1 arc1047 - arc1050 0.16 1 Aug
#> 196 2020-08-01 1 arc1048 - arc1050 -0.16 1 Aug
#> 197 2020-08-01 2 arc1045 - arc1046 1.10 1 Aug
#> 198 2020-08-01 2 arc1045 - arc1047 0.69 1 Aug
#> 199 2020-08-01 2 arc1045 - arc1048 1.25 1 Aug
#> 200 2020-08-01 2 arc1045 - arc1050 0.94 1 Aug
#> 201 2020-08-01 2 arc1046 - arc1047 -0.41 1 Aug
#> 202 2020-08-01 2 arc1046 - arc1048 0.15 1 Aug
#> 203 2020-08-01 2 arc1046 - arc1050 -0.16 1 Aug
#> 204 2020-08-01 2 arc1047 - arc1048 0.56 1 Aug
#> 205 2020-08-01 2 arc1047 - arc1050 0.25 1 Aug
#> 206 2020-08-01 2 arc1048 - arc1050 -0.31 1 Aug
#> 207 2020-08-01 3 arc1045 - arc1046 0.59 1 Aug
#> 208 2020-08-01 3 arc1045 - arc1047 0.21 1 Aug
#> 209 2020-08-01 3 arc1045 - arc1048 0.84 1 Aug
#> 210 2020-08-01 3 arc1045 - arc1050 0.70 1 Aug
#> 211 2020-08-01 3 arc1046 - arc1047 -0.38 1 Aug
#> 212 2020-08-01 3 arc1046 - arc1048 0.25 1 Aug
#> 213 2020-08-01 3 arc1046 - arc1050 0.10 1 Aug
#> 214 2020-08-01 3 arc1047 - arc1048 0.63 1 Aug
#> 215 2020-08-01 3 arc1047 - arc1050 0.48 1 Aug
#> 216 2020-08-01 3 arc1048 - arc1050 -0.15 1 Aug
#> 217 2020-09-01 1 arc1045 - arc1046 0.75 1 Sep
#> 218 2020-09-01 1 arc1045 - arc1047 0.81 1 Sep
#> 219 2020-09-01 1 arc1045 - arc1050 0.95 1 Sep
#> 220 2020-09-01 1 arc1046 - arc1047 0.06 1 Sep
#> 221 2020-09-01 1 arc1046 - arc1050 0.21 1 Sep
#> 222 2020-09-01 1 arc1047 - arc1050 0.15 1 Sep
#> 223 2020-09-01 2 arc1045 - arc1046 0.76 1 Sep
#> 224 2020-09-01 2 arc1045 - arc1047 0.82 1 Sep
#> 225 2020-09-01 2 arc1045 - arc1050 0.87 1 Sep
#> 226 2020-09-01 2 arc1046 - arc1047 0.06 1 Sep
#> 227 2020-09-01 2 arc1046 - arc1050 0.11 1 Sep
#> 228 2020-09-01 2 arc1047 - arc1050 0.04 1 Sep
#> 229 2020-09-01 3 arc1045 - arc1046 1.02 1 Sep
#> 230 2020-09-01 3 arc1045 - arc1047 1.03 1 Sep
#> 231 2020-09-01 3 arc1045 - arc1050 1.16 1 Sep
#> 232 2020-09-01 3 arc1046 - arc1047 0.01 1 Sep
#> 233 2020-09-01 3 arc1046 - arc1050 0.14 1 Sep
#> 234 2020-09-01 3 arc1047 - arc1050 0.13 1 Sep
#> 235 2020-05-01 1 arc1047 - arc1050 0.03 1 May
#> 236 2020-05-01 2 arc1047 - arc1050 0.02 1 May
#> 237 2020-05-01 3 arc1047 - arc1050 -0.03 1 May
由reprex 包于 2020-11-04 创建(v0.3.0)
devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.0.2 (2020-06-22)
#> os macOS Catalina 10.15.7
#> system x86_64, darwin17.0
#> ui X11
#> language (EN)
#> collate en_AU.UTF-8
#> ctype en_AU.UTF-8
#> tz Australia/Melbourne
#> date 2020-11-04
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date lib source
#> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.2)
#> backports 1.1.10 2020-09-15 [1] CRAN (R 4.0.2)
#> callr 3.5.1 2020-10-13 [1] CRAN (R 4.0.2)
#> cli 2.1.0 2020-10-12 [1] CRAN (R 4.0.2)
#> crayon 1.3.4 2017-09-16 [1] CRAN (R 4.0.2)
#> desc 1.2.0 2018-05-01 [1] CRAN (R 4.0.2)
#> devtools 2.3.2 2020-09-18 [1] CRAN (R 4.0.2)
#> digest 0.6.27 2020-10-24 [1] CRAN (R 4.0.2)
#> ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.2)
#> evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.1)
#> fansi 0.4.1 2020-01-08 [1] CRAN (R 4.0.2)
#> fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.2)
#> glue 1.4.2 2020-08-27 [1] CRAN (R 4.0.2)
#> highr 0.8 2019-03-20 [1] CRAN (R 4.0.2)
#> htmltools 0.5.0 2020-06-16 [1] CRAN (R 4.0.2)
#> knitr 1.30 2020-09-22 [1] CRAN (R 4.0.2)
#> magrittr 1.5 2014-11-22 [1] CRAN (R 4.0.2)
#> memoise 1.1.0 2017-04-21 [1] CRAN (R 4.0.2)
#> pkgbuild 1.1.0 2020-07-13 [1] CRAN (R 4.0.2)
#> pkgload 1.1.0 2020-05-29 [1] CRAN (R 4.0.2)
#> prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.0.2)
#> processx 3.4.4 2020-09-03 [1] CRAN (R 4.0.2)
#> ps 1.4.0 2020-10-07 [1] CRAN (R 4.0.2)
#> R6 2.5.0 2020-10-28 [1] CRAN (R 4.0.2)
#> remotes 2.2.0 2020-07-21 [1] CRAN (R 4.0.2)
#> rlang 0.4.8 2020-10-08 [1] CRAN (R 4.0.2)
#> rmarkdown 2.5 2020-10-21 [1] CRAN (R 4.0.2)
#> rprojroot 1.3-2 2018-01-03 [1] CRAN (R 4.0.2)
#> sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.0.2)
#> stringi 1.5.3 2020-09-09 [1] CRAN (R 4.0.2)
#> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.0.2)
#> testthat 2.3.2 2020-03-02 [1] CRAN (R 4.0.2)
#> usethis 1.6.3 2020-09-17 [1] CRAN (R 4.0.2)
#> withr 2.3.0 2020-09-22 [1] CRAN (R 4.0.2)
#> xfun 0.19.1 2020-10-31 [1] Github (yihui/xfun@621896e)
#> yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.2)
#>
#> [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library