使用 `transition_reveal` 时，将保留不需要的层的 `gganimate` 过去数据
`gganimate` past data of unwanted layers are kept when using `transition_reveal`
我正在使用 gganimate，我需要在绘图区域中使用 4 个元素：
- 一个
geom_line()
- 一个
geom_point()
- A
geom_abline() 静态 slope 和 color，变化 intercept
- 另一个
geom_abline() slope、color 和 intercept
前 3 个元素按照我的意愿进行了动画处理：
其中只有geom_line()逐渐显露出来，其中geom_line()和第一个geom_abline()只显示该帧的数据
但是当我这样添加第四层时（需要手动调色），过去的geom_abline()并没有消失：
我尝试添加参数 transition_reveal(keep_last = F)，并尝试使用多个 shadow_mark() 为不同的层定义不同的规则，但到目前为止没有任何效果。
要确保第四层只显示当前帧吗？
当前代码：
temp_ani <-
ggplot(tab_temp, aes(tot, new_loess)) +
geom_line(size = 1.25) +
geom_point(size = 2) +
geom_abline(aes(slope = ref_log10m, intercept = ref_log10c), size = 1.1, linetype = "dashed", alpha = 0.3) +
geom_abline(aes(slope = trend_log10m, intercept = trend_log10c, color = ID_factor), size = 1.1, linetype = "dashed", alpha = 0.7,
show.legend = F) +
scale_color_manual(values = tab_temp[, trend_color]) +
transition_reveal(t) +
scale_x_continuous(name = "tot", trans = "log10", limits = c(24, 786.5)) +
scale_y_continuous(name = "new_loess", trans = "log10", limits = c(21, 601.7)) +
theme_classic() +
theme(axis.title = element_text(size = 18),
axis.text = element_text(size = 16))
animate(temp_ani)
示例数据（由dput生成）：
tab_temp <-
structure(list(tot = c(24L, 26L, 26L, 36L, 42L, 49L, 50L, 53L,
56L, 56L, 57L, 60L, 62L, 65L, 69L, 69L, 69L, 75L, 81L, 85L, 91L,
93L, 94L, 95L, 100L, 101L, 101L, 105L, 105L, 108L, 110L, 115L,
116L, 121L, 130L, 132L, 138L, 142L, 149L, 158L, 168L, 193L, 209L,
257L, 274L, 318L, 357L, 387L, 411L, 454L, 519L, 583L, 642L, 683L,
715L),
new = c(22L, 21L, 21L, 28L, 34L, 41L, 40L, 41L, 43L, 42L,
42L, 44L, 45L, 44L, 45L, 43L, 43L, 39L, 39L, 36L, 41L, 40L, 38L,
39L, 43L, 41L, 39L, 40L, 36L, 39L, 41L, 40L, 35L, 36L, 39L, 39L,
44L, 47L, 49L, 57L, 67L, 88L, 104L, 149L, 164L, 203L, 241L, 266L,
281L, 322L, 381L, 441L, 493L, 525L, 547L),
t = 1:55,
new_loess = c(20.4718824559981,
21.7890308359201, 21.7890308359201, 29.230438378301, 34.1819985150492,
39.5079831823911, 40.0755079303131, 41.4099675429299, 42.370529571607,
42.370529571607, 42.8201044925118, 43.8252925663542, 44.3125327660056,
44.5559375847947, 43.3892881478697, 43.3892881478697, 43.3892881478697,
40.6214119319565, 38.4905820730386, 37.9720281923537, 39.2781675720742,
39.6852276451529, 39.9358369585547, 40.1843988106463, 39.903890056883,
39.8282043659178, 39.8282043659178, 39.5872985292962, 39.5872985292962,
39.1703139824658, 38.8033157618615, 37.6950388015054, 37.5718391291946,
37.3871340774544, 38.9365752067175, 39.8424253760653, 42.8908232704117,
45.2358046101902, 49.8927271106793, 56.9673605996446, 65.3666082472634,
88.2333199821777, 103.004387148103, 148.220525358668, 163.942873635871,
204.371579248934, 238.230045236249, 264.200021490433, 285.078351674107,
323.515728645106, 381.682707746736, 438.213805930832, 489.137706154487,
523.921712190664, 550.558894652666),
ID_factor = structure(1:55, .Label = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24",
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35",
"36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46",
"47", "48", "49", "50", "51", "52", "53", "54", "55"), class = "factor"),
ref_log10m = c(NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1),
ref_log10c = c(NA, -0.0767354344741067, -0.0767354344741067,
-0.0904671721067551, -0.0894518394244057, -0.09351121980074,
-0.096090968882282, -0.107170979497638, -0.12112413490539,
-0.12112413490539, -0.124227132918044, -0.136426426370142,
-0.145865115840727, -0.16400776974052, -0.201466565659624,
-0.201466565659624, -0.201466565659624, -0.266306248714822,
-0.323130540124996, -0.349955131070593, -0.364890173692428,
-0.369854072675033, -0.371765062973866, -0.373666129954109,
-0.398984764767179, -0.404130646875006, -0.404130646875006,
-0.423633432928999, -0.423633432928999, -0.440466702695637,
-0.452523847307499, -0.484413645662749, -0.489595535070859,
-0.510063194866713, -0.523585603228512, -0.520228163317121,
-0.507514703991367, -0.496806024856726, -0.475149025512369,
-0.443030988676679, -0.409953330914954, -0.339924688141996,
-0.307290563603587, -0.239024775067351, -0.223058019569702,
-0.192006619096867, -0.17567168290562, -0.165778116414225,
-0.15887758283895, -0.147160452867149, -0.133464873299646,
-0.123982498866305, -0.118103885501835, -0.115154306972075,
-0.113502258448684),
trend_log10m = c(NA, 0.779013043966055,
0, 0.902838605051119, 1.01516626395308, 0.939364077071771,
0.705975457374834, 0.562156421073622, 0.41648278860048, 0,
0.596322729780414, 0.452366794075921, 0.337191122084057,
0.115926899508026, -0.444292873406387, 0, 0, -0.790549373381475,
-0.700116895372846, -0.281394397911645, 0.495820425082167,
0.474249915989644, 0.588583559834837, 0.586342537885251,
-0.136567884586587, -0.190797733928004, 0, -0.156205385180462,
0, -0.375890288287152, -0.513019327466591, -0.651879927019231,
-0.378107511884984, -0.116780389727616, 0.566005001030829,
1.50635698066896, 1.6585525574429, 1.86295886106405, 2.03632300098696,
2.26097387810847, 2.24108569839695, 2.16233918241475, 1.94348336584128,
1.76030953589316, 1.57398365165524, 1.48010286623756, 1.32513096747946,
1.28232849305659, 1.26407605433669, 1.27114179598159, 1.23567759206272,
1.18776534049548, 1.14041338808179, 1.10970817963632, 1.08307872430731
),
trend_log10c = c(NA, 0.235955218563125, 1.33823791349671,
0.0607453498302259, -0.114070466624236, 0.00897537944146443,
0.403447909576491, 0.647792138304764, 0.898973667615296,
1.62706389210081, 0.584579635667037, 0.837348243495363, 1.04214800866147,
1.43874016239004, 2.45437007136201, 1.63738252507763, 1.63738252507763,
3.09108352150306, 2.92151708503656, 2.1223914715644, 0.622818482734461,
0.665076002900152, 0.440012174544851, 0.444433997374107,
1.874151004406, 1.98261070308883, 1.60019072690764, 1.91327651912479,
1.59755586614094, 2.35730129445125, 2.63614274028582, 2.91961175246926,
2.35544952785373, 1.81595066271427, 0.393855239776779, -1.59399557640782,
-1.91673754895948, -2.35414232320695, -2.72727194089704,
-3.21548014224428, -3.17175285497492, -2.99651750205615,
-2.49630999086779, -2.07131990960092, -1.62228698944198,
-1.39342905195201, -1.00562316966424, -0.896362653634025,
-0.849130617820197, -0.867599348834218, -0.773368978244672,
-0.643279196748284, -0.512319390951289, -0.426113442696479,
-0.350634363184159),
trend_color = c(NA, "#a7ad06", "#00b51b",
"#c1ac03", "#d7a800", "#c9ac02", "#97ae08", "#78af0c", "#59b110",
"#00b51b", "#80af0b", "#61b00f", "#48b212", "#19b418", "#00a980",
"#00b51b", "#00b51b", "#009fcf", "#00a1bb", "#00ad5b", "#6ab00e",
"#65b00e", "#7eaf0b", "#7daf0b", "#00b13a", "#00b047", "#00b51b",
"#00b13f", "#00b51b", "#00aa71", "#00a790", "#00a3b0", "#00aa71",
"#00b236", "#79af0c", "#eb5400", "#f13a00", "#f91700", "#fb0006",
"#df002b", "#e20028", "#eb001b", "#fd0a00", "#f52900", "#ee4900",
"#ea5900", "#e37300", "#e27b00", "#e17e00", "#e17d00", "#e08300",
"#de8b00", "#dc9300", "#da9800", "#d99d00")),
row.names = c(NA, -55L), class = c("data.table", "data.frame"))
假设 tab_temp$trend_color 与 tab_ani$trend_color 相同（您未提供），我通过将 color 参数从美学中移出得到了您想要的结果第二个 geom_abline 并将颜色设置为 trend_color。那么 scale_color_manual 是不必要的：
temp_ani <-
ggplot(tab_temp, aes(tot, new_loess)) +
geom_line(size = 1.25) +
geom_point(size = 2) +
geom_abline(aes(slope = ref_log10m, intercept = ref_log10c), size = 1.1, linetype = "dashed", alpha = 0.3) +
geom_abline(aes(slope = trend_log10m, intercept = trend_log10c), color = tab_temp$trend_color, size = 1.1, linetype = "dashed", alpha = 0.7,
show.legend = F) +
transition_reveal(t) +
scale_x_continuous(name = "tot", trans = "log10", limits = c(24, 786.5)) +
scale_y_continuous(name = "new_loess", trans = "log10", limits = c(21, 601.7)) +
theme_classic() +
theme(axis.title = element_text(size = 18),
axis.text = element_text(size = 16))
我正在使用 gganimate，我需要在绘图区域中使用 4 个元素：
- 一个
geom_line() - 一个
geom_point() - A
geom_abline()静态slope和color，变化intercept - 另一个
geom_abline()slope、color和intercept
前 3 个元素按照我的意愿进行了动画处理：
其中只有geom_line()逐渐显露出来，其中geom_line()和第一个geom_abline()只显示该帧的数据
但是当我这样添加第四层时（需要手动调色），过去的geom_abline()并没有消失：
我尝试添加参数 transition_reveal(keep_last = F)，并尝试使用多个 shadow_mark() 为不同的层定义不同的规则，但到目前为止没有任何效果。
要确保第四层只显示当前帧吗？
当前代码：
temp_ani <-
ggplot(tab_temp, aes(tot, new_loess)) +
geom_line(size = 1.25) +
geom_point(size = 2) +
geom_abline(aes(slope = ref_log10m, intercept = ref_log10c), size = 1.1, linetype = "dashed", alpha = 0.3) +
geom_abline(aes(slope = trend_log10m, intercept = trend_log10c, color = ID_factor), size = 1.1, linetype = "dashed", alpha = 0.7,
show.legend = F) +
scale_color_manual(values = tab_temp[, trend_color]) +
transition_reveal(t) +
scale_x_continuous(name = "tot", trans = "log10", limits = c(24, 786.5)) +
scale_y_continuous(name = "new_loess", trans = "log10", limits = c(21, 601.7)) +
theme_classic() +
theme(axis.title = element_text(size = 18),
axis.text = element_text(size = 16))
animate(temp_ani)
示例数据（由dput生成）：
tab_temp <-
structure(list(tot = c(24L, 26L, 26L, 36L, 42L, 49L, 50L, 53L,
56L, 56L, 57L, 60L, 62L, 65L, 69L, 69L, 69L, 75L, 81L, 85L, 91L,
93L, 94L, 95L, 100L, 101L, 101L, 105L, 105L, 108L, 110L, 115L,
116L, 121L, 130L, 132L, 138L, 142L, 149L, 158L, 168L, 193L, 209L,
257L, 274L, 318L, 357L, 387L, 411L, 454L, 519L, 583L, 642L, 683L,
715L),
new = c(22L, 21L, 21L, 28L, 34L, 41L, 40L, 41L, 43L, 42L,
42L, 44L, 45L, 44L, 45L, 43L, 43L, 39L, 39L, 36L, 41L, 40L, 38L,
39L, 43L, 41L, 39L, 40L, 36L, 39L, 41L, 40L, 35L, 36L, 39L, 39L,
44L, 47L, 49L, 57L, 67L, 88L, 104L, 149L, 164L, 203L, 241L, 266L,
281L, 322L, 381L, 441L, 493L, 525L, 547L),
t = 1:55,
new_loess = c(20.4718824559981,
21.7890308359201, 21.7890308359201, 29.230438378301, 34.1819985150492,
39.5079831823911, 40.0755079303131, 41.4099675429299, 42.370529571607,
42.370529571607, 42.8201044925118, 43.8252925663542, 44.3125327660056,
44.5559375847947, 43.3892881478697, 43.3892881478697, 43.3892881478697,
40.6214119319565, 38.4905820730386, 37.9720281923537, 39.2781675720742,
39.6852276451529, 39.9358369585547, 40.1843988106463, 39.903890056883,
39.8282043659178, 39.8282043659178, 39.5872985292962, 39.5872985292962,
39.1703139824658, 38.8033157618615, 37.6950388015054, 37.5718391291946,
37.3871340774544, 38.9365752067175, 39.8424253760653, 42.8908232704117,
45.2358046101902, 49.8927271106793, 56.9673605996446, 65.3666082472634,
88.2333199821777, 103.004387148103, 148.220525358668, 163.942873635871,
204.371579248934, 238.230045236249, 264.200021490433, 285.078351674107,
323.515728645106, 381.682707746736, 438.213805930832, 489.137706154487,
523.921712190664, 550.558894652666),
ID_factor = structure(1:55, .Label = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24",
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35",
"36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46",
"47", "48", "49", "50", "51", "52", "53", "54", "55"), class = "factor"),
ref_log10m = c(NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1),
ref_log10c = c(NA, -0.0767354344741067, -0.0767354344741067,
-0.0904671721067551, -0.0894518394244057, -0.09351121980074,
-0.096090968882282, -0.107170979497638, -0.12112413490539,
-0.12112413490539, -0.124227132918044, -0.136426426370142,
-0.145865115840727, -0.16400776974052, -0.201466565659624,
-0.201466565659624, -0.201466565659624, -0.266306248714822,
-0.323130540124996, -0.349955131070593, -0.364890173692428,
-0.369854072675033, -0.371765062973866, -0.373666129954109,
-0.398984764767179, -0.404130646875006, -0.404130646875006,
-0.423633432928999, -0.423633432928999, -0.440466702695637,
-0.452523847307499, -0.484413645662749, -0.489595535070859,
-0.510063194866713, -0.523585603228512, -0.520228163317121,
-0.507514703991367, -0.496806024856726, -0.475149025512369,
-0.443030988676679, -0.409953330914954, -0.339924688141996,
-0.307290563603587, -0.239024775067351, -0.223058019569702,
-0.192006619096867, -0.17567168290562, -0.165778116414225,
-0.15887758283895, -0.147160452867149, -0.133464873299646,
-0.123982498866305, -0.118103885501835, -0.115154306972075,
-0.113502258448684),
trend_log10m = c(NA, 0.779013043966055,
0, 0.902838605051119, 1.01516626395308, 0.939364077071771,
0.705975457374834, 0.562156421073622, 0.41648278860048, 0,
0.596322729780414, 0.452366794075921, 0.337191122084057,
0.115926899508026, -0.444292873406387, 0, 0, -0.790549373381475,
-0.700116895372846, -0.281394397911645, 0.495820425082167,
0.474249915989644, 0.588583559834837, 0.586342537885251,
-0.136567884586587, -0.190797733928004, 0, -0.156205385180462,
0, -0.375890288287152, -0.513019327466591, -0.651879927019231,
-0.378107511884984, -0.116780389727616, 0.566005001030829,
1.50635698066896, 1.6585525574429, 1.86295886106405, 2.03632300098696,
2.26097387810847, 2.24108569839695, 2.16233918241475, 1.94348336584128,
1.76030953589316, 1.57398365165524, 1.48010286623756, 1.32513096747946,
1.28232849305659, 1.26407605433669, 1.27114179598159, 1.23567759206272,
1.18776534049548, 1.14041338808179, 1.10970817963632, 1.08307872430731
),
trend_log10c = c(NA, 0.235955218563125, 1.33823791349671,
0.0607453498302259, -0.114070466624236, 0.00897537944146443,
0.403447909576491, 0.647792138304764, 0.898973667615296,
1.62706389210081, 0.584579635667037, 0.837348243495363, 1.04214800866147,
1.43874016239004, 2.45437007136201, 1.63738252507763, 1.63738252507763,
3.09108352150306, 2.92151708503656, 2.1223914715644, 0.622818482734461,
0.665076002900152, 0.440012174544851, 0.444433997374107,
1.874151004406, 1.98261070308883, 1.60019072690764, 1.91327651912479,
1.59755586614094, 2.35730129445125, 2.63614274028582, 2.91961175246926,
2.35544952785373, 1.81595066271427, 0.393855239776779, -1.59399557640782,
-1.91673754895948, -2.35414232320695, -2.72727194089704,
-3.21548014224428, -3.17175285497492, -2.99651750205615,
-2.49630999086779, -2.07131990960092, -1.62228698944198,
-1.39342905195201, -1.00562316966424, -0.896362653634025,
-0.849130617820197, -0.867599348834218, -0.773368978244672,
-0.643279196748284, -0.512319390951289, -0.426113442696479,
-0.350634363184159),
trend_color = c(NA, "#a7ad06", "#00b51b",
"#c1ac03", "#d7a800", "#c9ac02", "#97ae08", "#78af0c", "#59b110",
"#00b51b", "#80af0b", "#61b00f", "#48b212", "#19b418", "#00a980",
"#00b51b", "#00b51b", "#009fcf", "#00a1bb", "#00ad5b", "#6ab00e",
"#65b00e", "#7eaf0b", "#7daf0b", "#00b13a", "#00b047", "#00b51b",
"#00b13f", "#00b51b", "#00aa71", "#00a790", "#00a3b0", "#00aa71",
"#00b236", "#79af0c", "#eb5400", "#f13a00", "#f91700", "#fb0006",
"#df002b", "#e20028", "#eb001b", "#fd0a00", "#f52900", "#ee4900",
"#ea5900", "#e37300", "#e27b00", "#e17e00", "#e17d00", "#e08300",
"#de8b00", "#dc9300", "#da9800", "#d99d00")),
row.names = c(NA, -55L), class = c("data.table", "data.frame"))
假设 tab_temp$trend_color 与 tab_ani$trend_color 相同（您未提供），我通过将 color 参数从美学中移出得到了您想要的结果第二个 geom_abline 并将颜色设置为 trend_color。那么 scale_color_manual 是不必要的：
temp_ani <-
ggplot(tab_temp, aes(tot, new_loess)) +
geom_line(size = 1.25) +
geom_point(size = 2) +
geom_abline(aes(slope = ref_log10m, intercept = ref_log10c), size = 1.1, linetype = "dashed", alpha = 0.3) +
geom_abline(aes(slope = trend_log10m, intercept = trend_log10c), color = tab_temp$trend_color, size = 1.1, linetype = "dashed", alpha = 0.7,
show.legend = F) +
transition_reveal(t) +
scale_x_continuous(name = "tot", trans = "log10", limits = c(24, 786.5)) +
scale_y_continuous(name = "new_loess", trans = "log10", limits = c(21, 601.7)) +
theme_classic() +
theme(axis.title = element_text(size = 18),
axis.text = element_text(size = 16))