Chapter 6 Timeline 的应用
# fake data
df <- data.frame(
year = c(
rep(2016, 25),
rep(2017, 25),
rep(2018, 25),
rep(2019, 25)
),
x = rnorm(100),
y = rnorm(100),
grp = c(
rep("A", 50),
rep("B", 50)
)
)
df## year x y grp
## 1 2016 -0.296782623 0.718985265 A
## 2 2016 0.312793305 0.677681461 A
## 3 2016 1.817075245 -1.104529717 A
## 4 2016 0.238115698 -0.006028082 A
## 5 2016 1.350354507 -0.808396383 A
## 6 2016 0.118038429 -0.243130048 A
## 7 2016 0.050326459 1.197025864 A
## 8 2016 -1.037839953 -1.176511611 A
## 9 2016 1.020651149 0.005653544 A
## 10 2016 -0.524535306 1.274133550 A
## 11 2016 -0.565236275 1.437384284 A
## 12 2016 -1.291292641 2.597664494 A
## 13 2016 -0.270646262 2.281484805 A
## 14 2016 1.219938867 -0.097324732 A
## 15 2016 -0.901850050 -0.494629622 A
## 16 2016 -1.062584092 -0.465067677 A
## 17 2016 0.000570845 0.312191443 A
## 18 2016 0.419383946 -0.702976334 A
## 19 2016 0.289888222 0.246592144 A
## 20 2016 0.234681967 -1.486379952 A
## 21 2016 -0.999642762 -0.598242739 A
## 22 2016 -0.106152245 1.470907051 A
## 23 2016 -0.005350965 0.423576567 A
## 24 2016 0.935846847 0.646514821 A
## 25 2016 -0.029422237 -0.856781516 A
## 26 2017 1.632636209 -1.344829678 A
## 27 2017 -0.197441407 -0.794504261 A
## 28 2017 1.092323453 2.021271984 A
## 29 2017 1.583604321 0.591413528 A
## 30 2017 0.644523190 -0.382952293 A
## 31 2017 2.208418916 -1.759460582 A
## 32 2017 -0.663672097 1.163947064 A
## 33 2017 1.004204049 0.511043740 A
## 34 2017 -0.075935451 -0.156142616 A
## 35 2017 -0.207016525 -2.257377565 A
## 36 2017 0.701250690 -0.410791783 A
## 37 2017 -0.068560873 -0.116472806 A
## 38 2017 0.740448741 -0.591395856 A
## 39 2017 -0.541359687 -0.524522219 A
## 40 2017 -1.399168389 -0.037624141 A
## 41 2017 -0.669938865 1.467127602 A
## 42 2017 0.455138452 -2.242708376 A
## 43 2017 -0.264072274 0.053960919 A
## 44 2017 1.384445567 1.404407274 A
## 45 2017 -0.555434957 -0.894484722 A
## 46 2017 -1.710709394 1.205762662 A
## 47 2017 -0.627077016 0.113297004 A
## 48 2017 -1.481589795 0.560303971 A
## 49 2017 -1.170346444 0.816909398 A
## 50 2017 -1.143187678 -0.341919468 A
## 51 2018 0.251725974 0.288196244 B
## 52 2018 1.618166167 0.565898911 B
## 53 2018 0.330343357 0.587254934 B
## 54 2018 -0.387089294 -0.249830381 B
## 55 2018 -0.203429469 1.518763408 B
## 56 2018 -0.353292946 -0.670490604 B
## 57 2018 -0.933930611 -1.238365741 B
## 58 2018 -1.526409672 0.488875380 B
## 59 2018 -0.503692449 -1.465528820 B
## 60 2018 1.865257330 3.029477021 B
## 61 2018 0.667498117 -1.926520364 B
## 62 2018 0.212699614 -0.771956884 B
## 63 2018 -0.853166896 1.180006960 B
## 64 2018 -1.010269135 0.288282745 B
## 65 2018 0.747310451 -0.361486477 B
## 66 2018 1.481153996 -1.415105562 B
## 67 2018 -0.140419074 0.643557225 B
## 68 2018 -0.605700438 -0.021985744 B
## 69 2018 1.295516709 0.246057156 B
## 70 2018 -1.384787454 -1.018143554 B
## 71 2018 -0.915316498 -1.685467400 B
## 72 2018 -0.423887379 -1.334321526 B
## 73 2018 1.793131286 1.382982274 B
## 74 2018 -0.227629808 0.119007662 B
## 75 2018 -0.810225424 -0.613609946 B
## 76 2019 0.178838356 0.748382197 B
## 77 2019 0.107159238 -0.304321839 B
## 78 2019 2.212073491 -0.758382377 B
## 79 2019 -0.796318561 0.251677897 B
## 80 2019 1.815386659 0.724524796 B
## 81 2019 0.346248382 -0.639431921 B
## 82 2019 0.535964787 0.318571510 B
## 83 2019 -0.645245715 -0.022188096 B
## 84 2019 -0.351599736 -0.740556649 B
## 85 2019 0.115510581 1.531348381 B
## 86 2019 -0.545631155 1.926177007 B
## 87 2019 -0.432295049 -0.442586302 B
## 88 2019 1.220507120 1.461153205 B
## 89 2019 0.852704640 1.559490496 B
## 90 2019 0.228705251 1.619696795 B
## 91 2019 0.409849456 1.880508765 B
## 92 2019 0.209431976 -0.235924967 B
## 93 2019 0.179461875 0.393598332 B
## 94 2019 -1.034125923 -0.108949748 B
## 95 2019 0.183910812 -0.892360308 B
## 96 2019 -0.789447234 0.351797758 B
## 97 2019 -1.723376053 1.135834258 B
## 98 2019 -2.035568854 -0.916957319 B
## 99 2019 0.077780134 0.771851422 B
## 100 2019 -0.640441867 1.893584653 Bdf %>%
group_by(year) %>%
e_charts(x, timeline = T) %>%
e_scatter(y, symbol_size = 5)df %>%
group_by(year) %>%
e_charts(x, timeline = T) %>%
e_scatter(y, symbol_size = 5) %>%
e_lm(y ~ x)df %>%
group_by(year) %>%
e_charts(x, timeline = T) %>%
e_scatter(y, symbol_size = 5) %>%
e_datazoom() %>%
e_loess(y ~ x)6.1 map timeline
library(echarts4r.maps)
df <- USArrests
# scale 0 to 1
.scl <- function(x){
(x - min(x)) / (max(x) - min(x))
}
df %>%
mutate(
State = row.names(.),
Rape = .scl(Rape),
Murder = .scl(Murder),
Assault = .scl(Assault)
) %>%
select(State, Rape, Murder, Assault) %>%
# group_by(State) %>%
# tidyr::gather("Key", "Value", Murder, Rape, Assault) %>%
pivot_longer(
-c(State),
names_to = "Key",
values_to = "Value"
) %>%
group_by(Key) %>%
e_charts(State, timeline = TRUE) %>%
em_map("USA") %>%
e_map(Value, map = "USA") %>%
e_visual_map(min = 0, max = 1) %>%
e_timeline_opts(autoPlay = TRUE) %>%
e_timeline_serie(
title = list(
list(text = "Assault", subtext = "Percentage based on arrests"),
list(text = "Murder", subtext = "Percentage based on arrests"),
list(text = "Rape", subtext = "Percentage based on arrests")
)
)