R 重复数据的热图
我能够用一些金融时间序列数据生成热图，但我不认为它显示了我希望它显示的内容（每天每小时的平均价格变动） 当我检查某一小时的平均价格变动时，它与图表上显示的不同 我使用的代码如下：R 重复数据的热图,r,ggplot2,heatmap,R,Ggplot2,Heatmap,我能够用一些金融时间序列数据生成热图，但我不认为它显示了我希望它显示的内容（每天每小时的平均价格变动） 当我检查某一小时的平均价格变动时，它与图表上显示的不同 我使用的代码如下： ggplot(data, aes(x=Time, y=Weekday, fill = Change)) + geom_tile(color = "white", size = 0.1) + scale_x_time(expand=c(0,0)) + scale_y_discret
ggplot(data, aes(x=Time, y=Weekday, fill = Change)) +
geom_tile(color = "white", size = 0.1) +
scale_x_time(expand=c(0,0)) +
scale_y_discrete(expand=c(0,0)) +
scale_fill_viridis_c(name="Price change", option = "plasma") +
theme_bw(base_family="Helvetica") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
Date Time Change Weekday
09/03/2016 16:00 1.980786372 Wednesday
09/03/2016 17:00 0.000000000 Wednesday
09/03/2016 18:00 0.000000000 Wednesday
09/03/2016 20:00 -4.778919160 Wednesday
09/03/2016 21:00 -2.920425114 Wednesday
10/03/2016 16:00 6.123554913 Thursday
10/03/2016 17:00 0.851063830 Thursday
11/03/2016 10:00 0.000000000 Friday
11/03/2016 17:00 0.000000000 Friday
11/03/2016 18:00 0.000000000 Friday
11/03/2016 21:00 0.843881857 Friday
11/03/2016 22:00 0.000000000 Friday
11/03/2016 23:00 0.000000000 Friday
12/03/2016 02:00 1.924686192 Saturday
12/03/2016 03:00 4.448267388 Saturday
12/03/2016 04:00 -1.233616037 Saturday
12/03/2016 07:00 0.000000000 Saturday
12/03/2016 09:00 -5.263157895 Saturday
12/03/2016 12:00 -0.142322097 Saturday
12/03/2016 18:00 0.000000000 Saturday
13/03/2016 00:00 2.973977695 Sunday
13/03/2016 01:00 0.722021661 Sunday
13/03/2016 10:00 0.000000000 Sunday
13/03/2016 12:00 0.000000000 Sunday
13/03/2016 15:00 0.000000000 Sunday
13/03/2016 19:00 0.000000000 Sunday
13/03/2016 20:00 1.010101010 Sunday
13/03/2016 21:00 0.000000000 Sunday
13/03/2016 23:00 -2.587601078 Sunday
14/03/2016 00:00 -17.094487647 Monday
14/03/2016 01:00 1.209992194 Monday
14/03/2016 02:00 0.000000000 Monday
14/03/2016 09:00 -1.132075472 Monday
14/03/2016 10:00 -0.763358779 Monday
14/03/2016 11:00 -0.222222222 Monday
14/03/2016 12:00 4.797047970 Monday
14/03/2016 13:00 -1.761403509 Monday
14/03/2016 14:00 -2.857142857 Monday
14/03/2016 15:00 1.098739031 Monday
14/03/2016 16:00 -0.277008310 Monday
14/03/2016 17:00 0.870827286 Monday
14/03/2016 18:00 6.057553957 Monday
14/03/2016 19:00 -7.430004745 Monday
14/03/2016 20:00 1.054183561 Monday
14/03/2016 21:00 -2.976364167 Monday
14/03/2016 22:00 -3.306650707 Monday
14/03/2016 23:00 -4.149922720 Monday
15/03/2016 00:00 1.964415103 Tuesday
15/03/2016 01:00 0.493198632 Tuesday
15/03/2016 02:00 -1.473007913 Tuesday
15/03/2016 03:00 -3.752988048 Tuesday
15/03/2016 04:00 4.662525880 Tuesday
15/03/2016 05:00 -2.856238626 Tuesday
15/03/2016 06:00 1.950553861 Tuesday
15/03/2016 07:00 0.466698307 Tuesday
15/03/2016 08:00 1.648222275 Tuesday
15/03/2016 09:00 -0.702648444 Tuesday
15/03/2016 10:00 0.023116043 Tuesday
15/03/2016 11:00 0.145716696 Tuesday
15/03/2016 12:00 -1.045671229 Tuesday
15/03/2016 13:00 -0.656117329 Tuesday
15/03/2016 14:00 -5.497076023 Tuesday
15/03/2016 15:00 1.952420016 Tuesday
15/03/2016 16:00 -1.252408478 Tuesday
15/03/2016 17:00 0.984665052 Tuesday
15/03/2016 18:00 3.764386189 Tuesday
15/03/2016 19:00 -0.330769231 Tuesday
15/03/2016 20:00 -0.309310238 Tuesday
15/03/2016 21:00 -0.445069103 Tuesday
15/03/2016 22:00 -1.046511628 Tuesday
15/03/2016 23:00 0.699515720 Tuesday
16/03/2016 00:00 3.053435115 Wednesday
16/03/2016 01:00 0.506253722 Wednesday
16/03/2016 02:00 -0.985185185 Wednesday
16/03/2016 03:00 -1.847834219 Wednesday
16/03/2016 04:00 0.495426829 Wednesday
16/03/2016 05:00 0.792682927 Wednesday
16/03/2016 06:00 1.427169070 Wednesday
16/03/2016 07:00 -1.421064318 Wednesday
16/03/2016 08:00 -0.534196073 Wednesday
16/03/2016 09:00 2.426487263 Wednesday
16/03/2016 10:00 1.609434102 Wednesday
16/03/2016 11:00 -0.789473684 Wednesday
16/03/2016 12:00 0.877840071 Wednesday
16/03/2016 13:00 -2.807195706 Wednesday
16/03/2016 14:00 -0.514963803 Wednesday
16/03/2016 15:00 -1.849779966 Wednesday
16/03/2016 16:00 -2.112622540 Wednesday
16/03/2016 17:00 -0.679641643 Wednesday
16/03/2016 18:00 1.213573471 Wednesday
16/03/2016 19:00 0.000000000 Wednesday
16/03/2016 20:00 -1.509146341 Wednesday
16/03/2016 21:00 1.137066832 Wednesday
16/03/2016 22:00 -2.185541800 Wednesday
16/03/2016 23:00 -1.968750000 Wednesday
17/03/2016 00:00 -0.557857826 Thursday
17/03/2016 01:00 -1.062300319 Thursday
17/03/2016 02:00 0.347138129 Thursday
17/03/2016 03:00 0.602022797 Thursday
17/03/2016 04:00 0.393859015 Thursday
17/03/2016 05:00 0.240192154 Thursday
17/03/2016 06:00 0.731570062 Thursday
17/03/2016 07:00 -4.329073482 Thursday
17/03/2016 08:00 -5.912426816 Thursday
17/03/2016 09:00 2.107081174 Thursday
17/03/2016 10:00 0.684468481 Thursday
17/03/2016 11:00 -8.531163493 Thursday
17/03/2016 12:00 2.435005492 Thursday
17/03/2016 13:00 -3.139013453 Thursday
17/03/2016 14:00 1.421140628 Thursday
17/03/2016 15:00 2.541508209 Thursday
17/03/2016 16:00 -4.054054054 Thursday
17/03/2016 17:00 0.469483568 Thursday
17/03/2016 18:00 0.046433878 Thursday
17/03/2016 19:00 3.880184332 Thursday
17/03/2016 20:00 0.246218783 Thursday
17/03/2016 21:00 -1.745200698 Thursday
17/03/2016 22:00 1.030927835 Thursday
17/03/2016 23:00 -1.276785714 Thursday
18/03/2016 00:00 0.361010830 Friday
18/03/2016 01:00 -2.666064166 Friday
18/03/2016 02:00 0.649953575 Friday
18/03/2016 03:00 1.390176089 Friday
18/03/2016 04:00 0.548446069 Friday
18/03/2016 05:00 0.569538857 Friday
18/03/2016 06:00 -3.467889908 Friday
18/03/2016 07:00 -1.692015209 Friday
18/03/2016 08:00 -0.988180585 Friday
18/03/2016 09:00 -5.900195695 Friday
18/03/2016 10:00 1.409457665 Friday
18/03/2016 11:00 -13.000873193 Friday
18/03/2016 12:00 -4.120443740 Friday
18/03/2016 13:00 7.302062736 Friday
18/03/2016 14:00 9.649122807 Friday
18/03/2016 15:00 2.581039021 Friday
18/03/2016 16:00 -2.198124267 Friday
18/03/2016 17:00 -2.137648587 Friday
18/03/2016 18:00 -0.050236110 Friday
18/03/2016 19:00 1.920744036 Friday
18/03/2016 20:00 1.344032096 Friday
18/03/2016 21:00 -0.215876754 Friday
18/03/2016 22:00 6.667989680 Friday
18/03/2016 23:00 2.042711235 Friday
19/03/2016 00:00 -4.858962693 Saturday
19/03/2016 01:00 -0.861008610 Saturday
19/03/2016 02:00 -0.907787864 Saturday
19/03/2016 03:00 2.030947776 Saturday
19/03/2016 04:00 -0.511848341 Saturday
19/03/2016 05:00 -5.607566837 Saturday
19/03/2016 06:00 2.516676774 Saturday
19/03/2016 07:00 0.167570232 Saturday
19/03/2016 08:00 6.335900185 Saturday
19/03/2016 09:00 -0.632505271 Saturday
19/03/2016 10:00 -2.608213097 Saturday
19/03/2016 11:00 1.277528840 Saturday
19/03/2016 12:00 2.742999140 Saturday
19/03/2016 13:00 -0.270522388 Saturday
19/03/2016 14:00 -0.981481481 Saturday
19/03/2016 15:00 2.432179607 Saturday
19/03/2016 16:00 0.182648402 Saturday
19/03/2016 17:00 -1.276207840 Saturday
19/03/2016 18:00 -2.123730379 Saturday
19/03/2016 19:00 -3.161350844 Saturday
19/03/2016 20:00 0.862068966 Saturday
19/03/2016 21:00 -3.133903134 Saturday
19/03/2016 22:00 -0.039215686 Saturday
19/03/2016 23:00 2.920276453 Saturday
20/03/2016 00:00 -1.312682973 Sunday
20/03/2016 01:00 0.293282876 Sunday
20/03/2016 02:00 -2.159624413 Sunday
20/03/2016 03:00 2.097565669 Sunday
20/03/2016 04:00 -0.459705413 Sunday
20/03/2016 05:00 -0.961813161 Sunday
20/03/2016 06:00 0.508149569 Sunday
20/03/2016 07:00 -1.104707012 Sunday
20/03/2016 08:00 0.000000000 Sunday
20/03/2016 09:00 0.009765625 Sunday
20/03/2016 10:00 0.726532985 Sunday
20/03/2016 11:00 0.260869565 Sunday
20/03/2016 12:00 0.019426906 Sunday
20/03/2016 13:00 -2.682997959 Sunday
20/03/2016 14:00 -1.027667984 Sunday
20/03/2016 15:00 0.519117500 Sunday
20/03/2016 16:00 -1.900099900 Sunday
20/03/2016 17:00 1.530612245 Sunday
20/03/2016 18:00 -3.028479743 Sunday
20/03/2016 19:00 1.715597936 Sunday
20/03/2016 20:00 1.208459215 Sunday
20/03/2016 21:00 0.591133005 Sunday
20/03/2016 22:00 -2.233009709 Sunday
20/03/2016 23:00 1.302288871 Sunday
21/03/2016 00:00 -0.743712692 Monday
21/03/2016 01:00 1.972386588 Monday
21/03/2016 02:00 0.493230174 Monday
21/03/2016 03:00 2.149496514 Monday
21/03/2016 04:00 -0.367924528 Monday
21/03/2016 05:00 -0.056812802 Monday
21/03/2016 06:00 -0.056882821 Monday
21/03/2016 07:00 3.591736164 Monday
21/03/2016 08:00 -1.445430427 Monday
21/03/2016 09:00 1.592268753 Monday
21/03/2016 10:00 -1.746844705 Monday
21/03/2016 11:00 0.853445903 Monday
21/03/2016 12:00 -0.809900810 Monday
21/03/2016 13:00 0.036697248 Monday
21/03/2016 14:00 -1.091743119 Monday
21/03/2016 15:00 0.000000000 Monday
21/03/2016 16:00 2.357798165 Monday
21/03/2016 17:00 2.494451842 Monday
21/03/2016 18:00 0.806451613 Monday
21/03/2016 19:00 -1.846808511 Monday
21/03/2016 20:00 1.014568158 Monday
21/03/2016 21:00 0.583590800 Monday
21/03/2016 22:00 1.339590444 Monday
21/03/2016 23:00 -0.706832716 Monday
22/03/2016 00:00 -2.952872493 Tuesday
22/03/2016 01:00 0.697471665 Tuesday
22/03/2016 02:00 -0.296425458 Tuesday
22/03/2016 03:00 4.156738152 Tuesday
22/03/2016 04:00 -0.220675607 Tuesday
22/03/2016 05:00 0.110262935 Tuesday
22/03/2016 06:00 -0.025695931 Tuesday
22/03/2016 07:00 0.877796902 Tuesday
22/03/2016 08:00 -0.304594297 Tuesday
22/03/2016 09:00 0.644942295 Tuesday
22/03/2016 10:00 0.521797677 Tuesday
22/03/2016 11:00 0.000000000 Tuesday
22/03/2016 12:00 0.167448091 Tuesday
22/03/2016 13:00 -1.089689858 Tuesday
22/03/2016 14:00 0.508905852 Tuesday
22/03/2016 15:00 -5.001275836 Tuesday
22/03/2016 16:00 1.843565420 Tuesday
22/03/2016 17:00 -0.720372485 Tuesday
22/03/2016 18:00 0.639886242 Tuesday
22/03/2016 19:00 1.256859621 Tuesday
22/03/2016 20:00 -0.611353712 Tuesday
22/03/2016 21:00 0.122914838 Tuesday
22/03/2016 22:00 1.224632610 Tuesday
22/03/2016 23:00 -2.361038508 Tuesday
23/03/2016 00:00 0.757440741 Wednesday
23/03/2016 01:00 0.716941052 Wednesday
23/03/2016 02:00 1.004759387 Wednesday
23/03/2016 03:00 0.532332664 Wednesday
23/03/2016 04:00 2.246313964 Wednesday
23/03/2016 05:00 1.034775233 Wednesday
23/03/2016 06:00 -0.426813959 Wednesday
23/03/2016 07:00 -1.050861707 Wednesday
23/03/2016 08:00 0.160622200 Wednesday
23/03/2016 09:00 -0.016934801 Wednesday
23/03/2016 10:00 -0.594227504 Wednesday
23/03/2016 11:00 0.162393162 Wednesday
23/03/2016 12:00 0.509337861 Wednesday
23/03/2016 13:00 0.481418919 Wednesday
23/03/2016 14:00 -1.065078833 Wednesday
23/03/2016 15:00 0.771186441 Wednesday
23/03/2016 16:00 3.075630252 Wednesday
23/03/2016 17:00 -0.171204957 Wednesday
23/03/2016 18:00 -1.019991840 Wednesday
23/03/2016 19:00 0.476816837 Wednesday
23/03/2016 20:00 0.818665575 Wednesday
23/03/2016 21:00 0.390370852 Wednesday
23/03/2016 22:00 0.668405608 Wednesday
23/03/2016 23:00 0.574945340 Wednesday
24/03/2016 00:00 -3.086816720 Thursday
24/03/2016 01:00 -3.166666667 Thursday
24/03/2016 02:00 -0.223003688 Thursday
24/03/2016 03:00 0.837392122 Thursday
24/03/2016 04:00 0.000000000 Thursday
24/03/2016 05:00 -1.709401709 Thursday
24/03/2016 06:00 -0.786026201 Thursday
24/03/2016 07:00 0.070422535 Thursday
24/03/2016 08:00 -0.594977688 Thursday
24/03/2016 09:00 2.130094182 Thursday
24/03/2016 10:00 -1.924410541 Thursday
24/03/2016 11:00 -3.431984311 Thursday
24/03/2016 12:00 2.121771218 Thursday
24/03/2016 13:00 -1.261374899 Thursday
24/03/2016 14:00 1.012866137 Thursday
24/03/2016 15:00 2.333333333 Thursday
24/03/2016 16:00 -0.985915493 Thursday
24/03/2016 17:00 0.693529980 Thursday
24/03/2016 18:00 -0.915431561 Thursday
24/03/2016 19:00 -2.390613973 Thursday
24/03/2016 20:00 1.218631522 Thursday
24/03/2016 21:00 -1.218092253 Thursday
24/03/2016 22:00 1.074007220 Thursday
24/03/2016 23:00 0.566699649 Thursday
25/03/2016 00:00 0.071231413 Friday
25/03/2016 01:00 -1.539005838 Friday
我认为您目前在同一位置上过度拼贴了许多瓷砖。你所看到的都是最后一次策划的。您可能希望使用
stat\u summary\u 2dggplot(d, aes(x = Time, y = Weekday, z = Change)) +
geom_tile(color = "white", size = 0.1, stat = 'summary_2d') +
scale_x_discrete(expand=c(0,0)) + # or scale_x_time if formatted correctly
scale_y_discrete(expand=c(0,0)) +
scale_fill_viridis_c(name="Price change", option = "plasma")
您当前正在重复打印数据。如果您想了解方法，则需要将代码修改为以下内容：
ggplot(data %>%group_by(Time, Weekday) %>% summarize_all(mean) %>%ungroup(), aes(x=Time, y=Weekday, fill = Change)) +
geom_tile(color = "white", size = 0.1) +
scale_x_time(expand=c(0,0)) +
scale_y_discrete(expand=c(0,0)) +
scale_fill_viridis_c(name="Price change", option = "plasma") +
theme_bw(base_family="Helvetica") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
您是否在寻找历年平均每天每小时的平均价格变动？是的，因此，我在寻找每个方块，以显示特定时间和日期所有数据的平均价格变动。请让您的问题重现，以便其他人可以帮助OK根据您使用的金融数据类型（例如股票价格），使用数据片段更新问题，如果你试着找出几年内价值的平均变化，它的平均值为0，因为在任何一天，数据都有很大的波动，而且肯定是在一年内。按月份将数据分离出来，然后生成热图，或者用折线图将其可视化，可能会更有效。这会给你大量的图表，但会帮助你对数据有一个更有意义的理解。