ggplot 直方图中计数的排序数
Ordering number of counts in ggplot histogram
我有以下数据table dt.test:
dput(dt.test)
structure(list(Year = c(2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L), GPNRPlan = c(100236L,
111328L, 128003L, 130058L, 133874L, 135886L, 138153L, 142309L,
151988L, 157024L, 158101L, 158757L, 163260L, 200324L, 200792L,
207342L, 214062L, 217331L, 218005L, 218454L, 219836L, 238154L,
254868L, 254869L, 254874L, 259947L, 264205L, 267717L, 282089L,
283151L, 292166L, 306032L, 307995L, 330523L, 335787L, 348047L,
348664L, 355409L, 363008L, 365676L, 368657L, 392114L, 394894L,
395993L, 404010L, 500269L, 502879L, 515783L, 516704L, 613479L
), TB.Info = c("Below TB", "In TB", "Over TB", "Below TB", "Over TB",
"Below TB", "Over TB", "In TB", "In TB", "In TB", "In TB", "In TB",
"Below TB", "In TB", "Over TB", "In TB", "Below TB", "In TB",
"Below TB", "Over TB", "Over TB", "Below TB", "Over TB", "Over TB",
"In TB", "Over TB", "Over TB", "Below TB", "Below TB", "In TB",
"Below TB", "Below TB", "In TB", "In TB", "Over TB", "In TB",
"Below TB", "Over TB", "Below TB", "Below TB", "Over TB", "Below TB",
"Below TB", "Below TB", "Over TB", "Over TB", "Below TB", "In TB",
"Below TB", "Below TB"), EV = c(0, 8991.71216732506, 0, 0, 952495.536027306,
15787.4125389769, 5881.79901225216, 10020.1539658745, 29679.260860874,
6623.90844477817, 19956.607049949, 10795.0898132095, 20110.9214012254,
154265.744138562, 0, 18022.1733304456, 0, 46930.0874076566, 5031.92796650957,
14766.994353086, 17428.2699502266, 1139.95904700027, 0, 0, 74480.5539097248,
0, 0, 3125.39890360564, 41135.8019532497, 45455.901141461, 18068.8194807139,
1574.19941919776, 71695.2819940052, 840619.522035336, 19137.7135190254,
17462.2344585882, 6001.7267195835, 14056.3435516429, 0, 0, 51034.1108681587,
2680.21093607705, 0, 0, 0, 0, 0, 897749.506753749, 7272.99092335283,
-772.886617083522), EK = c(0, 8727.76940892211, 0, 0, 968654.154020939,
15784.2477645437, 5852.58295283679, 10126.9467047725, 30178.7537734498,
6452.5979909353, 19917.497780561, 10505.985181701, 20386.342324393,
157344.902967186, 0, 18979.9946111749, 0, 45191.3139241601, 5152.46448254231,
14606.1474532383, 17485.4003681122, 956.14494674998, 0, 0, 76242.2008458597,
0, 0, 3271.78810230747, 43153.4577000728, 46305.3394606706, 18865.559964053,
1676.38074270217, 73792.5743431249, 816694.588902057, 19259.8905579868,
17273.9513316854, 5558.58747405537, 14081.8692372851, 0, 0, 53417.9083227921,
2766.49603915519, 0, 0, 0, 0, 0, 925462.446710217, 7411.27949339667,
-2269.10945132687), EH = c(0, -263.94275840295, 0, 0, 16158.6179936329,
-3.16477443318076, -29.216059415372, 106.792738898008, 499.492912575829,
-171.310453842869, -39.1092693880457, -289.10463150853, 275.420923167619,
3079.15882862376, 0, 957.821280729258, 0, -1738.77348349646,
120.536516032746, -160.84689984772, 57.13041788553, -183.814100250285,
0, 0, 1761.64693613498, 0, 0, 146.38919870183, 2017.65574682313,
849.438319209584, 796.740483339144, 102.181323504405, 2097.29234911977,
-23924.9331332788, 122.177038961415, -188.283126902762, -443.139245528132,
25.5256856422447, 0, 0, 2383.79745463335, 86.2851030781408, 0,
0, 0, 0, 0, 27712.9399564679, 138.288570043838, -1496.22283424335
), pBu = c(0.1, 0.05, 0.1, 0.1, 0.05, 0.1, 0.05, 0.15, 0.05,
0.1, 0.05, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.15, 0.1, 0.1, 0.1, 0.1, 0.1, 0.05,
0.1, 0.1, 0.2, 0.1, 0.1, 0.2, 0.1, 0.05, 0.1, 0.1, 0.1, 0.05,
0.05, 0.1, 0.1, 0.1, 0.1)), class = c("data.table", "data.frame"
), row.names = c(NA, -50L))
现在我已经针对不同的公差带 pBu:
为列 EV 构造了一个 ggplot 直方图 geom_histogram()
EV <- (dt.test %>% dplyr::mutate(pBu = as.factor(pBu)) %>%
ggplot(aes(x = EV)) +
geom_histogram(aes(color = pBu, binwidth = 0.3), fill = "transparent") +
ggtitle("Histogram EV 2020") +
ylab("Counts") +
theme(panel.background = element_blank(), axis.line = element_line(colour = "black"),
plot.title = element_text(lineheight = .8, hjust = 0.5, face = "bold"),
legend.title = element_blank(),
legend.background = element_rect(fill = "white", size = 0.5, linetype = "solid", colour ="black"),
legend.box.background = element_rect(colour = "black"), legend.box.margin = margin(t = 1, l = 1))
) %>%
ggplotly()
这个情节看起来像这样：
现在我想知道为什么 pBu = 0.05 的条形图比绿色的条形图高？因为当我使用 plotly 的交互工具时，我可以看到，红色的 (pBu=0.05) 计数为 4，绿色的 (pBu=0.1) 计数为 25.
如何正确订购？看情节时，每个人都会说 EV=0 我们的 0.05 比 0.1 多，这是不正确的。
希望这些版本对您有所帮助。
未堆叠直方图
(dt.test %>% dplyr::mutate(pBu = as.factor(pBu)) %>%
ggplot(aes(x = EV)) +
geom_histogram(aes(color = factor(pBu), binwidth = 0.3), fill = "transparent", alpha = 0.7, position = "dodge") +
ggtitle("Histogram EV 2020") +
ylab("Counts") +
theme(panel.background = element_blank(), axis.line = element_line(colour = "black"),
plot.title = element_text(lineheight = .8, hjust = 0.5, face = "bold"),
legend.title = element_blank(),
legend.background = element_rect(fill = "white", size = 0.5, linetype = "solid", colour ="black"),
legend.box.background = element_rect(colour = "black"), legend.box.margin = margin(t = 1, l = 1))
) %>%
ggplotly()
使用facet_wrap
(dt.test %>% dplyr::mutate(pBu = as.factor(pBu)) %>%
ggplot(aes(x = EV)) +
geom_histogram(aes(color = factor(pBu), binwidth = 0.3), fill = "transparent", alpha = 0.7, position = "dodge") +
ggtitle("Histogram EV 2020") +
ylab("Counts") +
theme(panel.background = element_blank(), axis.line = element_line(colour = "black"),
plot.title = element_text(lineheight = .8, hjust = 0.5, face = "bold"),
legend.title = element_blank(),
legend.background = element_rect(fill = "white", size = 0.5, linetype = "solid", colour ="black"),
legend.box.background = element_rect(colour = "black"), legend.box.margin = margin(t = 1, l = 1)) +
facet_wrap(.~)
) %>%
ggplotly()
我有以下数据table dt.test:
dput(dt.test)
structure(list(Year = c(2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L), GPNRPlan = c(100236L,
111328L, 128003L, 130058L, 133874L, 135886L, 138153L, 142309L,
151988L, 157024L, 158101L, 158757L, 163260L, 200324L, 200792L,
207342L, 214062L, 217331L, 218005L, 218454L, 219836L, 238154L,
254868L, 254869L, 254874L, 259947L, 264205L, 267717L, 282089L,
283151L, 292166L, 306032L, 307995L, 330523L, 335787L, 348047L,
348664L, 355409L, 363008L, 365676L, 368657L, 392114L, 394894L,
395993L, 404010L, 500269L, 502879L, 515783L, 516704L, 613479L
), TB.Info = c("Below TB", "In TB", "Over TB", "Below TB", "Over TB",
"Below TB", "Over TB", "In TB", "In TB", "In TB", "In TB", "In TB",
"Below TB", "In TB", "Over TB", "In TB", "Below TB", "In TB",
"Below TB", "Over TB", "Over TB", "Below TB", "Over TB", "Over TB",
"In TB", "Over TB", "Over TB", "Below TB", "Below TB", "In TB",
"Below TB", "Below TB", "In TB", "In TB", "Over TB", "In TB",
"Below TB", "Over TB", "Below TB", "Below TB", "Over TB", "Below TB",
"Below TB", "Below TB", "Over TB", "Over TB", "Below TB", "In TB",
"Below TB", "Below TB"), EV = c(0, 8991.71216732506, 0, 0, 952495.536027306,
15787.4125389769, 5881.79901225216, 10020.1539658745, 29679.260860874,
6623.90844477817, 19956.607049949, 10795.0898132095, 20110.9214012254,
154265.744138562, 0, 18022.1733304456, 0, 46930.0874076566, 5031.92796650957,
14766.994353086, 17428.2699502266, 1139.95904700027, 0, 0, 74480.5539097248,
0, 0, 3125.39890360564, 41135.8019532497, 45455.901141461, 18068.8194807139,
1574.19941919776, 71695.2819940052, 840619.522035336, 19137.7135190254,
17462.2344585882, 6001.7267195835, 14056.3435516429, 0, 0, 51034.1108681587,
2680.21093607705, 0, 0, 0, 0, 0, 897749.506753749, 7272.99092335283,
-772.886617083522), EK = c(0, 8727.76940892211, 0, 0, 968654.154020939,
15784.2477645437, 5852.58295283679, 10126.9467047725, 30178.7537734498,
6452.5979909353, 19917.497780561, 10505.985181701, 20386.342324393,
157344.902967186, 0, 18979.9946111749, 0, 45191.3139241601, 5152.46448254231,
14606.1474532383, 17485.4003681122, 956.14494674998, 0, 0, 76242.2008458597,
0, 0, 3271.78810230747, 43153.4577000728, 46305.3394606706, 18865.559964053,
1676.38074270217, 73792.5743431249, 816694.588902057, 19259.8905579868,
17273.9513316854, 5558.58747405537, 14081.8692372851, 0, 0, 53417.9083227921,
2766.49603915519, 0, 0, 0, 0, 0, 925462.446710217, 7411.27949339667,
-2269.10945132687), EH = c(0, -263.94275840295, 0, 0, 16158.6179936329,
-3.16477443318076, -29.216059415372, 106.792738898008, 499.492912575829,
-171.310453842869, -39.1092693880457, -289.10463150853, 275.420923167619,
3079.15882862376, 0, 957.821280729258, 0, -1738.77348349646,
120.536516032746, -160.84689984772, 57.13041788553, -183.814100250285,
0, 0, 1761.64693613498, 0, 0, 146.38919870183, 2017.65574682313,
849.438319209584, 796.740483339144, 102.181323504405, 2097.29234911977,
-23924.9331332788, 122.177038961415, -188.283126902762, -443.139245528132,
25.5256856422447, 0, 0, 2383.79745463335, 86.2851030781408, 0,
0, 0, 0, 0, 27712.9399564679, 138.288570043838, -1496.22283424335
), pBu = c(0.1, 0.05, 0.1, 0.1, 0.05, 0.1, 0.05, 0.15, 0.05,
0.1, 0.05, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.15, 0.1, 0.1, 0.1, 0.1, 0.1, 0.05,
0.1, 0.1, 0.2, 0.1, 0.1, 0.2, 0.1, 0.05, 0.1, 0.1, 0.1, 0.05,
0.05, 0.1, 0.1, 0.1, 0.1)), class = c("data.table", "data.frame"
), row.names = c(NA, -50L))
现在我已经针对不同的公差带 pBu:
EV 构造了一个 ggplot 直方图 geom_histogram()
EV <- (dt.test %>% dplyr::mutate(pBu = as.factor(pBu)) %>%
ggplot(aes(x = EV)) +
geom_histogram(aes(color = pBu, binwidth = 0.3), fill = "transparent") +
ggtitle("Histogram EV 2020") +
ylab("Counts") +
theme(panel.background = element_blank(), axis.line = element_line(colour = "black"),
plot.title = element_text(lineheight = .8, hjust = 0.5, face = "bold"),
legend.title = element_blank(),
legend.background = element_rect(fill = "white", size = 0.5, linetype = "solid", colour ="black"),
legend.box.background = element_rect(colour = "black"), legend.box.margin = margin(t = 1, l = 1))
) %>%
ggplotly()
这个情节看起来像这样：
现在我想知道为什么 pBu = 0.05 的条形图比绿色的条形图高？因为当我使用 plotly 的交互工具时，我可以看到，红色的 (pBu=0.05) 计数为 4，绿色的 (pBu=0.1) 计数为 25.
如何正确订购？看情节时，每个人都会说 EV=0 我们的 0.05 比 0.1 多，这是不正确的。
希望这些版本对您有所帮助。
未堆叠直方图
(dt.test %>% dplyr::mutate(pBu = as.factor(pBu)) %>%
ggplot(aes(x = EV)) +
geom_histogram(aes(color = factor(pBu), binwidth = 0.3), fill = "transparent", alpha = 0.7, position = "dodge") +
ggtitle("Histogram EV 2020") +
ylab("Counts") +
theme(panel.background = element_blank(), axis.line = element_line(colour = "black"),
plot.title = element_text(lineheight = .8, hjust = 0.5, face = "bold"),
legend.title = element_blank(),
legend.background = element_rect(fill = "white", size = 0.5, linetype = "solid", colour ="black"),
legend.box.background = element_rect(colour = "black"), legend.box.margin = margin(t = 1, l = 1))
) %>%
ggplotly()
使用facet_wrap
(dt.test %>% dplyr::mutate(pBu = as.factor(pBu)) %>%
ggplot(aes(x = EV)) +
geom_histogram(aes(color = factor(pBu), binwidth = 0.3), fill = "transparent", alpha = 0.7, position = "dodge") +
ggtitle("Histogram EV 2020") +
ylab("Counts") +
theme(panel.background = element_blank(), axis.line = element_line(colour = "black"),
plot.title = element_text(lineheight = .8, hjust = 0.5, face = "bold"),
legend.title = element_blank(),
legend.background = element_rect(fill = "white", size = 0.5, linetype = "solid", colour ="black"),
legend.box.background = element_rect(colour = "black"), legend.box.margin = margin(t = 1, l = 1)) +
facet_wrap(.~)
) %>%
ggplotly()