我需要在一个图表中绘制一个显示计数的柱状图和一个显示率的折线图，我可以分别做这两个，但当我把它们放在一起时，我的第一层(即geom_bar)的比例被第二层(即geom_line)重叠。
我可以将geom_line的轴向右移动吗?
当前回答
我发现这个答案对我帮助最大，但发现有一些边缘情况，它似乎不能正确处理，特别是消极的情况，以及极限距离为0的情况(如果我们从最大/最小数据中获取极限，就会发生这种情况)。测试似乎表明，这是一致的
我使用以下代码。这里我假设我们有[x1,x2]我们想把它变换成[y1,y2]。我处理这个问题的方法是将[x1,x2]转换为[0,1](一个足够简单的转换)，然后[0,1]转换为[y1,y2]。
climate <- tibble(
Month = 1:12,
Temp = c(-4,-4,0,5,11,15,16,15,11,6,1,-3),
Precip = c(49,36,47,41,53,65,81,89,90,84,73,55)
)
#Set the limits of each axis manually:
ylim.prim <- c(0, 180) # in this example, precipitation
ylim.sec <- c(-4, 18) # in this example, temperature
b <- diff(ylim.sec)/diff(ylim.prim)
#If all values are the same this messes up the transformation, so we need to modify it here
if(b==0){
ylim.sec <- c(ylim.sec[1]-1, ylim.sec[2]+1)
b <- diff(ylim.sec)/diff(ylim.prim)
}
if (is.na(b)){
ylim.prim <- c(ylim.prim[1]-1, ylim.prim[2]+1)
b <- diff(ylim.sec)/diff(ylim.prim)
}
ggplot(climate, aes(Month, Precip)) +
geom_col() +
geom_line(aes(y = ylim.prim[1]+(Temp-ylim.sec[1])/b), color = "red") +
scale_y_continuous("Precipitation", sec.axis = sec_axis(~((.-ylim.prim[1]) *b + ylim.sec[1]), name = "Temperature"), limits = ylim.prim) +
scale_x_continuous("Month", breaks = 1:12) +
ggtitle("Climatogram for Oslo (1961-1990)")
这里的关键部分是，我们用~((.-ylim.prim[1]) *b + ylim.sec[1])转换次要y轴，然后对实际值y = ylim.prim[1]+(Temp-ylim.sec[1])/b)应用逆。我们还应该确保limits = ylim.prim。
其他回答
有时客户想要两个y刻度。给他们“有缺陷”的演讲通常是毫无意义的。但是我喜欢ggplot2坚持以正确的方式做事。我确信ggplot实际上是在向普通用户传授正确的可视化技术。
也许你可以使用面形和无比例来比较两个数据序列?看这里:https://github.com/hadley/ggplot2/wiki/Align-two-plots-on-a-page
Hadley的回答参考了Stephen Few的报告《双缩放轴在图中是最好的解决方案吗?》
我不知道OP中的“counts”和“rate”是什么意思，但快速搜索会给我counts和Rates，所以我得到了一些关于北美登山事故的数据:
Years<-c("1998","1999","2000","2001","2002","2003","2004")
Persons.Involved<-c(281,248,301,276,295,231,311)
Fatalities<-c(20,17,24,16,34,18,35)
rate=100*Fatalities/Persons.Involved
df<-data.frame(Years=Years,Persons.Involved=Persons.Involved,Fatalities=Fatalities,rate=rate)
print(df,row.names = FALSE)
Years Persons.Involved Fatalities rate
1998 281 20 7.117438
1999 248 17 6.854839
2000 301 24 7.973422
2001 276 16 5.797101
2002 295 34 11.525424
2003 231 18 7.792208
2004 311 35 11.254019
然后，我尝试按照Few在上述报告第7页建议的那样绘制图表(并按照OP的要求将计数绘制为柱状图，将率绘制为折线图):
The other less obvious solution, which works only for time series, is to convert all sets of values to a common quantitative scale by displaying percentage differences between each value and a reference (or index) value. For instance, select a particular point in time, such as the first interval that appears in the graph, and express each subsequent value as the percentage difference between it and the initial value. This is done by dividing the value at each point in time by the value for the initial point in time and then multiplying it by 100 to convert the rate to a percentage, as illustrated below.
df2<-df
df2$Persons.Involved <- 100*df$Persons.Involved/df$Persons.Involved[1]
df2$rate <- 100*df$rate/df$rate[1]
plot(ggplot(df2)+
geom_bar(aes(x=Years,weight=Persons.Involved))+
geom_line(aes(x=Years,y=rate,group=1))+
theme(text = element_text(size=30))
)
这就是结果:
但我不是很喜欢它，我不能轻易地给它加上一个传奇……
1 威廉森，杰德，等人。2005年北美登山事故。The Mountaineers Books, 2005。
对我来说，棘手的部分是计算出两个轴之间的变换函数。我用的是我的curvefit。
> dput(combined_80_8192 %>% filter (time > 270, time < 280))
structure(list(run = c(268L, 268L, 268L, 268L, 268L, 268L, 268L,
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264L, 264L, 264L, 264L, 264L, 264L, 260L, 260L, 260L, 260L, 260L,
260L, 260L, 260L, 260L, 260L), repetition = c(8L, 8L, 8L, 8L,
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), module = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
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1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "scenario.node[0].nicVLCTail.phyVLC", class = "factor"),
configname = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
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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), .Label = "Road-Vlc", class = "factor"), packetByteLength = c(8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
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8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L
), numVehicles = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
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2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
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2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
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24.19227425387, 10.537686730745, 10.699414795917, 49.84017267426,
53.139646558768, 60.160512118809, 41.509660845114, 31.42665220053,
8.1846370024428, 14.231126423354, 31.584125885363, 6.2494585568733,
10.654622041348, 49.854827699773, 57.49079026127, 60.192705735317,
41.55465351989, 31.509340361646, 6.2867464196657, 13.941251828322,
24.140336174865, 4.765718874642, 10.679016976694, 49.856439162736,
57.49079026127, 60.196678846453, 41.55465351989, 31.509340361646,
6.2867464196657, 13.941251828322, 24.140336174865, 4.7666691818074,
10.679016976694, 49.867624820879, 57.412299088098, 60.224172277442,
41.630930975211, 24.074540962859, 6.279972363168, 10.644529778044,
24.19227425387, 10.546845071479, 10.699414795917, 49.862851240855,
57.397787176282, 60.212457625018, 41.61637603957, 31.529239767749,
6.2952688513108, 10.640565481982, 24.178672145334, 8.0771089950663,
10.694731030907, 53.262541905639, 57.43627424514, 61.382796189332,
31.747253311549, 24.093100244121, 6.2658701281075, 10.661949889074,
18.495227442305, 18.417839037171, 8.1845086722809), frameId = c(15051,
15106, 15165, 15220, 15279, 15330, 15385, 15452, 15511, 15566,
15019, 15074, 15129, 15184, 15239, 15298, 15353, 15412, 15471,
15526, 14947, 14994, 15057, 15112, 15171, 15226, 15281, 15332,
15391, 15442, 14971, 15030, 15085, 15144, 15203, 15262, 15321,
15380, 15435, 15490, 14915, 14978, 15033, 15092, 15147, 15198,
15257, 15312, 15371, 15430, 14975, 15034, 15089, 15140, 15195,
15254, 15313, 15368, 15427, 15478, 14987, 15046, 15105, 15160,
15215, 15274, 15329, 15384, 15447, 15506, 14943, 15002, 15061,
15116, 15171, 15230, 15285, 15344, 15399, 15454, 14971, 15026,
15081, 15136, 15195, 15258, 15313, 15368, 15423, 15478, 15039,
15094, 15149, 15204, 15263, 15314, 15369, 15428, 15487, 15546
), packetOkSinr = c(0.99999999314881, 0.9999999998736, 0.99999999996428,
0.99999952114066, 0.99991568416005, 3.00628034688444e-08,
0.51497487795954, 0.99627877136019, 0, 0.011303253101957,
0.99999999314881, 0.99999999987726, 0.99999999996428, 0.99999952114066,
0.99991568416005, 3.00628034688444e-08, 0.51530974419663,
0.99627877136019, 0, 0.011269851265775, 0.9999999931708,
0.99999999985986, 0.99999999996428, 0.99999952599145, 0.99991770469509,
0, 0.45861812482641, 0.99629897628155, 0, 0.011403119534097,
0.99999999321568, 0.99999999985437, 0.99999999996519, 0.99999954639936,
0.99618434878558, 0, 0.010513119213425, 0.99641022914441,
0.00801687746446111, 0.012011103529927, 0.9999999931195,
0.99999999871861, 0.99999999996428, 0.99999951617905, 0.99991456738049,
2.6525298291169e-08, 0.51328066587104, 0.9999212220316, 0,
0.010777054258914, 0.9999999931708, 0.99999999985986, 0.99999999996428,
0.99999952718674, 0.99991812902805, 0, 0.45929307038653,
0.99631228046814, 0, 0.011436292559188, 0.99999999317629,
0.99999999985986, 0.99999999996428, 0.99999952718674, 0.99991812902805,
0, 0.45929307038653, 0.99631228046814, 0, 0.011436292559188,
0.99999999321568, 0.99999999985437, 0.99999999996519, 0.99999954527918,
0.99618434878558, 0, 0.010513119213425, 0.99641022914441,
0.00821047996950475, 0.012011103529927, 0.99999999319919,
0.99999999985345, 0.99999999996519, 0.99999954188106, 0.99991896371849,
0, 0.010410830482692, 0.996384831822, 9.12484388049251e-09,
0.011877185067536, 0.99999999879646, 0.9999999998562, 0.99999999998077,
0.99992756868677, 0.9962208785486, 0, 0.010971897073662,
0.93214999078663, 0.92943956665979, 2.64925478221656e-08),
snir = c(49.848348091678, 57.698190927109, 60.17669971462,
41.529809724535, 31.452202106925, 8.1976890851341, 14.240447804094,
24.122884195464, 6.2202875499406, 10.674183333671, 49.848348091678,
57.746270018264, 60.17669971462, 41.529809724535, 31.452202106925,
8.1976890851341, 14.242292077376, 24.122884195464, 6.2202875499406,
10.672962852322, 49.854827699773, 57.49079026127, 60.192705735317,
41.549715223147, 31.499301851462, 6.2853718719014, 13.937702343688,
24.133388256416, 6.2028757927148, 10.677815810561, 49.867624820879,
57.417115267867, 60.224172277442, 41.635752021705, 24.074540962859,
6.2847854917092, 10.644529778044, 24.19227425387, 10.537686730745,
10.699414795917, 49.84017267426, 53.139646558768, 60.160512118809,
41.509660845114, 31.42665220053, 8.1846370024428, 14.231126423354,
31.584125885363, 6.2494585568733, 10.654622041348, 49.854827699773,
57.49079026127, 60.192705735317, 41.55465351989, 31.509340361646,
6.2867464196657, 13.941251828322, 24.140336174865, 4.765718874642,
10.679016976694, 49.856439162736, 57.49079026127, 60.196678846453,
41.55465351989, 31.509340361646, 6.2867464196657, 13.941251828322,
24.140336174865, 4.7666691818074, 10.679016976694, 49.867624820879,
57.412299088098, 60.224172277442, 41.630930975211, 24.074540962859,
6.279972363168, 10.644529778044, 24.19227425387, 10.546845071479,
10.699414795917, 49.862851240855, 57.397787176282, 60.212457625018,
41.61637603957, 31.529239767749, 6.2952688513108, 10.640565481982,
24.178672145334, 8.0771089950663, 10.694731030907, 53.262541905639,
57.43627424514, 61.382796189332, 31.747253311549, 24.093100244121,
6.2658701281075, 10.661949889074, 18.495227442305, 18.417839037171,
8.1845086722809), ookSnirBer = c(8.8808636558081e-24, 3.2219795637026e-27,
2.6468895519653e-28, 3.9807779074715e-20, 1.0849324265615e-15,
2.5705217057696e-05, 4.7313805615763e-08, 1.8800438086075e-12,
0.00021005320203921, 1.9147343768384e-06, 8.8808636558081e-24,
3.0694773489537e-27, 2.6468895519653e-28, 3.9807779074715e-20,
1.0849324265615e-15, 2.5705217057696e-05, 4.7223753038869e-08,
1.8800438086075e-12, 0.00021005320203921, 1.9171738578051e-06,
8.8229427230445e-24, 3.9715925056443e-27, 2.6045198111088e-28,
3.9014083702734e-20, 1.0342658440386e-15, 0.00019591630514278,
6.4692014108683e-08, 1.8600094209271e-12, 0.0002140067535655,
1.9074922485477e-06, 8.7096574467175e-24, 4.2779443633862e-27,
2.5231916788231e-28, 3.5761615214425e-20, 1.9750692814982e-12,
0.0001960392878411, 1.9748966344895e-06, 1.7515881895994e-12,
2.2078334799411e-06, 1.8649940680806e-06, 8.954486301678e-24,
3.2021085732779e-25, 2.690441113724e-28, 4.0627628846548e-20,
1.1134484878561e-15, 2.6061691733331e-05, 4.777159157954e-08,
9.4891388749738e-16, 0.00020359398491544, 1.9542110660398e-06,
8.8229427230445e-24, 3.9715925056443e-27, 2.6045198111088e-28,
3.8819641115984e-20, 1.0237769828158e-15, 0.00019562832342849,
6.4455095380046e-08, 1.8468752030971e-12, 0.0010099091367628,
1.9051035165106e-06, 8.8085966897635e-24, 3.9715925056443e-27,
2.594108048185e-28, 3.8819641115984e-20, 1.0237769828158e-15,
0.00019562832342849, 6.4455095380046e-08, 1.8468752030971e-12,
0.0010088638355194, 1.9051035165106e-06, 8.7096574467175e-24,
4.2987746909572e-27, 2.5231916788231e-28, 3.593647329558e-20,
1.9750692814982e-12, 0.00019705170257492, 1.9748966344895e-06,
1.7515881895994e-12, 2.1868296425817e-06, 1.8649940680806e-06,
8.7517439682173e-24, 4.3621551072316e-27, 2.553168170837e-28,
3.6469582463164e-20, 1.0032983660212e-15, 0.00019385229409318,
1.9830820164805e-06, 1.7760568361323e-12, 2.919419915209e-05,
1.8741284335866e-06, 2.8285944348148e-25, 4.1960751547207e-27,
7.8468215407139e-29, 8.0407329049747e-16, 1.9380328071065e-12,
0.00020004849911333, 1.9393279417733e-06, 5.9354475879597e-10,
6.4258355913627e-10, 2.6065221215415e-05), ookSnrBer = c(8.8808636558081e-24,
3.2219795637026e-27, 2.6468895519653e-28, 3.9807779074715e-20,
1.0849324265615e-15, 2.5705217057696e-05, 4.7313805615763e-08,
1.8800438086075e-12, 0.00021005320203921, 1.9147343768384e-06,
8.8808636558081e-24, 3.0694773489537e-27, 2.6468895519653e-28,
3.9807779074715e-20, 1.0849324265615e-15, 2.5705217057696e-05,
4.7223753038869e-08, 1.8800438086075e-12, 0.00021005320203921,
1.9171738578051e-06, 8.8229427230445e-24, 3.9715925056443e-27,
2.6045198111088e-28, 3.9014083702734e-20, 1.0342658440386e-15,
0.00019591630514278, 6.4692014108683e-08, 1.8600094209271e-12,
0.0002140067535655, 1.9074922485477e-06, 8.7096574467175e-24,
4.2779443633862e-27, 2.5231916788231e-28, 3.5761615214425e-20,
1.9750692814982e-12, 0.0001960392878411, 1.9748966344895e-06,
1.7515881895994e-12, 2.2078334799411e-06, 1.8649940680806e-06,
8.954486301678e-24, 3.2021085732779e-25, 2.690441113724e-28,
4.0627628846548e-20, 1.1134484878561e-15, 2.6061691733331e-05,
4.777159157954e-08, 9.4891388749738e-16, 0.00020359398491544,
1.9542110660398e-06, 8.8229427230445e-24, 3.9715925056443e-27,
2.6045198111088e-28, 3.8819641115984e-20, 1.0237769828158e-15,
0.00019562832342849, 6.4455095380046e-08, 1.8468752030971e-12,
0.0010099091367628, 1.9051035165106e-06, 8.8085966897635e-24,
3.9715925056443e-27, 2.594108048185e-28, 3.8819641115984e-20,
1.0237769828158e-15, 0.00019562832342849, 6.4455095380046e-08,
1.8468752030971e-12, 0.0010088638355194, 1.9051035165106e-06,
8.7096574467175e-24, 4.2987746909572e-27, 2.5231916788231e-28,
3.593647329558e-20, 1.9750692814982e-12, 0.00019705170257492,
1.9748966344895e-06, 1.7515881895994e-12, 2.1868296425817e-06,
1.8649940680806e-06, 8.7517439682173e-24, 4.3621551072316e-27,
2.553168170837e-28, 3.6469582463164e-20, 1.0032983660212e-15,
0.00019385229409318, 1.9830820164805e-06, 1.7760568361323e-12,
2.919419915209e-05, 1.8741284335866e-06, 2.8285944348148e-25,
4.1960751547207e-27, 7.8468215407139e-29, 8.0407329049747e-16,
1.9380328071065e-12, 0.00020004849911333, 1.9393279417733e-06,
5.9354475879597e-10, 6.4258355913627e-10, 2.6065221215415e-05
)), class = "data.frame", row.names = c(NA, -100L), .Names = c("run",
"repetition", "module", "configname", "packetByteLength", "numVehicles",
"dDistance", "time", "distanceToTx", "headerNoError", "receivedPower_dbm",
"snr", "frameId", "packetOkSinr", "snir", "ookSnirBer", "ookSnrBer"
))
求变换函数
Y1 -> y2 该函数用于将次要y轴的数据按照第一个y轴进行“归一化”
变换函数:f(y1) = 0.025*x + 2.75
Y2 -> y1 该函数用于将第一个y轴的断点转换为第二个y轴的值。注意，现在坐标轴互换了。
变换函数:f(y1) = 40*x - 110
策划
注意如何在ggplot调用中使用转换函数来“动态地”转换数据
ggplot(data=combined_80_8192 %>% filter (time > 270, time < 280), aes(x=time) ) +
stat_summary(aes(y=receivedPower_dbm ), fun.y=mean, geom="line", colour="black") +
stat_summary(aes(y=packetOkSinr*40 - 110 ), fun.y=mean, geom="line", colour="black", position = position_dodge(width=10)) +
scale_x_continuous() +
scale_y_continuous(breaks = seq(-0,-110,-10), "y_first", sec.axis=sec_axis(~.*0.025+2.75, name="y_second") )
第一个stat_summary调用是为第一个y轴设置基数的调用。 第二个stat_summary调用用于转换数据。请记住，所有数据将以第一个y轴为基础。第一个y轴的数据需要标准化。为此，我使用数据上的转换函数:y=packetOkSinr*40 - 110
现在要转换第二个轴，我在scale_y_continuous调用中使用相反的函数:sec.axis=sec_axis(~.*0.025+2.75, name="y_second")。
这在ggplot2中是不可能的，因为我认为具有单独y尺度的图(不是相互转换的y尺度)从根本上是有缺陷的。一些问题:
The are not invertible: given a point on the plot space, you can not uniquely map it back to a point in the data space. They are relatively hard to read correctly compared to other options. See A Study on Dual-Scale Data Charts by Petra Isenberg, Anastasia Bezerianos, Pierre Dragicevic, and Jean-Daniel Fekete for details. They are easily manipulated to mislead: there is no unique way to specify the relative scales of the axes, leaving them open to manipulation. Two examples from the Junkcharts blog: one, two They are arbitrary: why have only 2 scales, not 3, 4 or ten?
你也可能想要阅读Stephen Few关于双缩放轴在图形中的主题的冗长讨论，它们是最好的解决方案吗?
It seemingly appears to be a simple question but it boggles around 2 fundamental questions. A) How to deal with a multi-scalar data while presenting in a comparative chart, and secondly, B) whether this can be done without some thumb rule practices of R programming such as i) melting data, ii) faceting, iii) adding another layer to existing one. The solution given below satisfies both the above conditions as it deals data without having to rescale it and secondly, the techniques mentioned are not used.
这是结果，
如果有兴趣了解更多关于此方法的信息，请点击下面的链接。 如何绘制一个2 y轴图表与条形并排而不重新缩放数据
