R ggplot，每侧2个y轴，不同比例
我需要在一个图表中绘制一个显示计数的条形图和一个显示比率的折线图，我可以分别绘制这两个图表，但当我将它们放在一起时，我会绘制第一层的比例，即几何图形条与第二层重叠，即几何图形条R ggplot，每侧2个y轴，不同比例,r,ggplot2,r-faq,R,Ggplot2,R Faq,我需要在一个图表中绘制一个显示计数的条形图和一个显示比率的折线图，我可以分别绘制这两个图表，但当我将它们放在一起时，我会绘制第一层的比例，即几何图形条与第二层重叠，即几何图形条 我可以将几何图形线的轴向右移动吗？在ggplot2中不可能这样做，因为我认为使用单独的y比例而不是相互转换的y比例的图从根本上是有缺陷的。一些问题： 这些点是不可逆的：给定绘图空间上的点，不能将其唯一地映射回数据空间中的点 与其他选项相比，它们相对难以正确阅读。有关详细信息，请参见Petra Isenberg、Anast
我可以将几何图形线的轴向右移动吗？在ggplot2中不可能这样做，因为我认为使用单独的y比例而不是相互转换的y比例的图从根本上是有缺陷的。一些问题： 这些点是不可逆的：给定绘图空间上的点，不能将其唯一地映射回数据空间中的点 与其他选项相比，它们相对难以正确阅读。有关详细信息，请参见Petra Isenberg、Anastasia Bezerianos、Pierre Dragicevic和Jean Daniel Fekete 它们很容易被操纵来误导：没有唯一的方法来指定轴的相对比例，从而使它们易于操纵。Junkcharts博客中的两个例子： 它们是任意的：为什么只有2个刻度，而不是3、4或10
您可能还想阅读Stephen Now关于该主题的冗长讨论。有时客户需要两个y刻度。给他们有缺陷的演讲通常是毫无意义的。但我确实喜欢ggplot2坚持以正确的方式做事。我确信ggplot实际上是在教育普通用户正确的可视化技术
也许你可以使用刻面和无标度来比较这两个数据系列e、 g.看这里：以下文章帮助我将ggplot2生成的两个绘图合并到一行： 在这种情况下，代码可能是这样的：
p1 <-
ggplot() + aes(mns)+ geom_histogram(aes(y=..density..), binwidth=0.01, colour="black", fill="white") + geom_vline(aes(xintercept=mean(mns, na.rm=T)), color="red", linetype="dashed", size=1) + geom_density(alpha=.2)
p2 <-
ggplot() + aes(mns)+ geom_histogram( binwidth=0.01, colour="black", fill="white") + geom_vline(aes(xintercept=mean(mns, na.rm=T)), color="red", linetype="dashed", size=1)
multiplot(p1,p2,cols=2)
可以对变量使用facet_wrap~variable，ncol=创建新的比较。它不在同一个轴上，但它是相似的。解决这一挑战的技术骨干大约在3年前由Kohske提供[]。在Stackoverflow[IDs:18989001、29235405、21026598]的几个实例中讨论了该主题及其解决方案的技术细节。因此，我将仅使用上述解决方案提供一个特定的变体和一些解释性演练 让我们假设G1组中确实有一些数据y1，G2组中的一些数据y2以某种方式相关，例如范围/比例变换或添加了一些噪声。因此，我们希望将数据一起绘制在一个图上，左侧的比例为y1，右侧的比例为y2
df <- data.frame(item=LETTERS[1:n], y1=c(-0.8684, 4.2242, -0.3181, 0.5797, -0.4875), y2=c(-5.719, 205.184, 4.781, 41.952, 9.911 )) # made up!
> df
item y1 y2
1 A -0.8684 -19.154567
2 B 4.2242 219.092499
3 C -0.3181 18.849686
4 D 0.5797 46.945161
5 E -0.4875 -4.721973
从ggplot2 2.2.0开始，可以添加一个次轴，如下所示： 对Stephen Now的报告进行了有趣的引用 我不知道OP对计数和速率意味着什么，但快速搜索给了我一些关于北美登山事故的数据1： 然后，我尝试按照上述报告第7页的建议绘制图表，并按照OP的要求，将计数绘制为条形图，将比率绘制为折线图： 另一个不太明显的解决方案是 通过以下步骤将所有值集转换为通用定量标尺： 显示每个值和引用之间的百分比差异 或索引值。例如，选择一个特定的时间点， 例如图形中出现的第一个间隔，并表示 每个后续值作为其与 初始值。这是通过除以中每个点的值来实现的 时间乘以初始时间点的值，然后乘以 按100将比率转换为百分比，如下所示 这就是结果： 但我不太喜欢它，我不能轻易地把它的传奇 一, 威廉姆森，杰德，等。2005年北美登山事故。登山者图书，2005年。
对我来说，棘手的部分是计算两个轴之间的变换函数。我用过这个
> dput(combined_80_8192 %>% filter (time > 270, time < 280))
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configname = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
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1L, 1L), .Label = "Road-Vlc", class = "factor"), packetByteLength = c(8192L,
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), numVehicles = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
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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), 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"
))
我们完全可以用基函数图建立一个具有双Y轴的图
我承认并同意和其他人的观点，即单独的y型量表存在根本性缺陷。话虽如此，我通常希望ggplot2具备这一功能，尤其是当数据存在时，我很快就想可视化或检查数据，即仅供个人使用 虽然tidyverse库可以很容易地将数据转换为长格式，这样facet_网格就可以工作，但这个过程仍然不是很简单，如下所示：
library(tidyverse)
df.wide %>%
# Select only the columns you need for the plot.
select(date, column1, column2, column3) %>%
# Create an id column – needed in the `gather()` function.
mutate(id = n()) %>%
# The `gather()` function converts to long-format.
# In which the `type` column will contain three factors (column1, column2, column3),
# and the `value` column will contain the respective values.
# All the while we retain the `id` and `date` columns.
gather(type, value, -id, -date) %>%
# Create the plot according to your specifications
ggplot(aes(x = date, y = value)) +
geom_line() +
# Create a panel for each `type` (ie. column1, column2, column3).
# If the types have different scales, you can use the `scales="free"` option.
facet_grid(type~., scales = "free")
考虑到以上答案和一些微调，不管它值多少钱，这里有一种通过sec_轴实现两个尺度的方法： 假设一个简单且纯粹虚构的数据集dt：在五天内，它跟踪中断次数与生产率：
when numinter prod
1 2018-03-20 1 0.95
2 2018-03-21 5 0.50
3 2018-03-23 4 0.70
4 2018-03-24 3 0.75
5 2018-03-25 4 0.60
ggplot() +
geom_bar(mapping = aes(x = dt$when, y = dt$numinter), stat = "identity", fill = "grey") +
geom_line(mapping = aes(x = dt$when, y = dt$prod*5), size = 2, color = "blue") +
scale_x_date(name = "Day", labels = NULL) +
scale_y_continuous(name = "Interruptions/day",
sec.axis = sec_axis(~./5, name = "Productivity % of best",
labels = function(b) { paste0(round(b * 100, 0), "%")})) +
theme(
axis.title.y = element_text(color = "grey"),
axis.title.y.right = element_text(color = "blue"))
这两幅图像的完整代码实际上并不比上面的内容多，只是完整的并准备运行的代码如下：这里有一个更详细的解释：您可以创建一个应用于第二个几何体和右y轴的比例因子。这是从塞巴斯蒂安的解推导出来的
library(ggplot2)
scaleFactor <- max(mtcars$cyl) / max(mtcars$hp)
ggplot(mtcars, aes(x=disp)) +
geom_smooth(aes(y=cyl), method="loess", col="blue") +
geom_smooth(aes(y=hp * scaleFactor), method="loess", col="red") +
scale_y_continuous(name="cyl", sec.axis=sec_axis(~./scaleFactor, name="hp")) +
theme(
axis.title.y.left=element_text(color="blue"),
axis.text.y.left=element_text(color="blue"),
axis.title.y.right=element_text(color="red"),
axis.text.y.right=element_text(color="red")
)
注：使用ggplot2有常见的双y轴用例，例如显示月温度和降水量。下面是一个简单的解决方案，它是威震天解决方案的推广，允许您将变量的下限设置为零以外的值： 示例数据：
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)
)
这似乎是一个简单的问题，但它围绕着两个基本问题。A如何在比较图表中显示时处理多个标量数据，以及B这是否可以在不使用R编程的一些拇指规则实践的情况下完成，例如i熔化数据、ii刻面、iii在现有层上添加另一层。 下面给出的解决方案满足上述两个条件，因为它处理数据而不必重新缩放数据。其次，不使用上述技术 这是结果， 对于那些有兴趣了解更多关于这种方法的人，请点击下面的链接。 我发现这对我帮助最大，但发现有些边缘情况似乎处理不正确，尤其是负面情况，还有我的极限距离为0的情况，如果我们从最大/最小数据中获取极限，可能会发生这种情况。测试似乎表明，这种方法一直有效 我使用以下代码。这里我假设我们有[x1，x2]，我们想要转换成[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，然后将[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]变换次y轴，然后对实际值y=ylim.prim[1]+Temp ylim.sec[1]/b应用逆运算。我们还应该确保limits=ylim.prim 以下内容结合了公司的基本数据和编程，改进了公司创建转换函数的策略，以优化曲线图和数据轴的组合，并回应了公司的说明，即可以在R中创建此类函数
#Climatogram for Oslo (1961-1990)
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))
#y1 identifies the position, relative to the y1 axis,
#the locations of the minimum and maximum of the y2 graph.
#Usually this will be the min and max of y1.
#y1<-(c(max(climate$Precip), 0))
#y1<-(c(150, 55))
y1<-(c(max(climate$Precip), min(climate$Precip)))
#y2 is the Minimum and maximum of the secondary axis data.
y2<-(c(max(climate$Temp), min(climate$Temp)))
#axis combines y1 and y2 into a dataframe used for regressions.
axis<-cbind(y1,y2)
axis<-data.frame(axis)
#Regression of Temperature to Precipitation:
T2P<-lm(formula = y1 ~ y2, data = axis)
T2P_summary <- summary(lm(formula = y1 ~ y2, data = axis))
T2P_summary
#Identifies the intercept and slope of regressing Temperature to Precipitation:
T2PInt<-T2P_summary$coefficients[1, 1]
T2PSlope<-T2P_summary$coefficients[2, 1]
#Regression of Precipitation to Temperature:
P2T<-lm(formula = y2 ~ y1, data = axis)
P2T_summary <- summary(lm(formula = y2 ~ y1, data = axis))
P2T_summary
#Identifies the intercept and slope of regressing Precipitation to Temperature:
P2TInt<-P2T_summary$coefficients[1, 1]
P2TSlope<-P2T_summary$coefficients[2, 1]
#Create Plot:
ggplot(climate, aes(Month, Precip)) +
geom_col() +
geom_line(aes(y = T2PSlope*Temp + T2PInt), color = "red") +
scale_y_continuous("Precipitation", sec.axis = sec_axis(~.*P2TSlope + P2TInt, name = "Temperature")) +
scale_x_continuous("Month", breaks = 1:12) +
theme(axis.line.y.right = element_line(color = "red"),
axis.ticks.y.right = element_line(color = "red"),
axis.text.y.right = element_text(color = "red"),
axis.title.y.right = element_text(color = "red")) +
ggtitle("Climatogram for Oslo (1961-1990)")
From:y1这是我关于如何对次轴进行变换的两分钱。首先，您希望将主数据和辅助数据的范围耦合起来。这通常是混乱的，因为你不想要的变量会污染你的全球环境 为了简化此过程，我们将创建一个函数工厂，该工厂将生成两个函数，其中scales:：rescale完成所有繁重的工作。因为这些是闭包，所以它们知道创建它们的环境，所以它们“拥有”创建之前生成的to和from参数的内存 一个函数执行正向转换：将辅助数据转换为主比例。 第二个函数执行反向转换：将主要单元中的数据转换为次要单元。 图书馆GGPLOT2 图书馆天平 次轴变换的函数工厂
列车员：你能详细阐述一下你的意见吗？我不太明白，我认为这是一种绘制两个自变量的相当简洁的方法。这也是一个似乎被要求的功能，而且被广泛使用。@hadley：我基本上同意，但多个y刻度有一个真正的用途——对相同的数据使用两个不同的单位，例如温度时间序列上的摄氏度和华氏度。@hadley在您看来。我和其他许多科学家都没有。当然，这可以通过在第一个绘图上直接放置第二个具有完全透明背景的绘图来实现，因此它们看起来像一个。我只是不知道如何确保边界框X的角彼此对齐/对齐。@hadley例如，在中，通常使用两个y轴。因为有一个固定的处方，如何做到这一点，可能的混乱是最小的…@hadley，对不起，我不明白给定的气候图有什么问题。把温度和降水量按固定的公式放在一张图中，人们可以快速地首先猜测它是潮湿气候还是干旱气候。或者说方法：有什么更好的方法来可视化温度、降水量及其关系？无论如何，非常感谢您在ggplot2中所做的工作！我同意安德烈亚斯的观点——有时像现在这样，对我来说，客户希望在同一个绘图上有两组数据，而不想听到我谈论绘图理论。我要么说服他们不要再这样了，我不想发动一场战争，要么告诉他们我使用的绘图软件包不支持这一点。所以我今天要从ggplot转到这个项目=为什么绘图软件包需要在其运行方式中插入自己的个人意见？不，谢谢。我不同意你的评论。真是太棒了！尽可能地压缩信息，例如，考虑到科学期刊等施加的严格限制，以便快速传达信息。因此，无论如何，添加第二个y轴是正在进行的，在我看来，ggplot应该有助于做到这一点。令人惊讶的是，毫无疑问，像“有缺陷的和正确的方式”这样的词被抛来抛去，好像它们不是基于一个本身非常固执己见和教条主义的理论，但却被太多人不假思索地接受，从这一事实可以看出，在撰写本文时，这个完全没有帮助的答案抛出了一个链接骨骼，获得了72票。例如，在比较时间序列时，将两者放在同一张图表上是非常宝贵的，因为差异的相关性更容易发现。只要问问成千上万受过高等教育的金融专业人士，他们每天都在做这些事情。@hadley我同意。ggplot Absolutey 100%需要双轴。每天都会有成千上万的人继续使用双轴，如果能在r。这是一个令人痛苦的疏忽。我正在将数据从r中取出并输入excel。你能在这里使用shwon的方法吗？向下滚动查看scale_y_*中的本机ggplot2实现，当前称为秒轴。缺点是，它只能使用当前轴的一些公式转换，而不能使用新变量，例如，r可以做这类事情，coeflmc-70，-110~c1,0和coeflmc1,0~c-70，-110。你可以定义一个辅助函数，比如equationise yeap，我知道。。。只是觉得网站会更直观多点功能怎么了？尽管我已经安装并加载了ggplot2库，但我还是收到一个找不到函数的错误。@Danka multiplot函数是链接页面底部的自定义函数。您能添加绘图吗？最近，在编写ggplot2时，有许多软件包的选项/功能比multiplot更多，已经通过sec_轴支持了这一点。这在一些值ylim.prim和ylim.sec处中断。这非常好。双轴图表没有缺陷的好例子。一般的整洁者思维的一部分，认为他们比你更了解你的工作。当我在我的例子中选择特定的轴限制时，ylim.prim@anke：文本有点马虎，wh
它指的是ylim.prim和ylim.sec。它们不是指轴的限制，而是指数据的限制。设置ylim.prim时，这是一个干净的解决方案。
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))
)
> 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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9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 5L, 5L,
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6L, 6L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), module = 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, 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, 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, 1L, 1L, 1L,
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,
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, 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, 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,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
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,
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,
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,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
), dDistance = c(80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L), time = c(270.166006903445,
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-94.982938427575, -96.181073124017, -102.03018610927, -99.721633629806,
-97.32940323644, -97.347613268692, -100.87007386786), snr = 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), 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"
))
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") )
# pseudo dataset
df <- data.frame(x = seq(1, 1000, 1), y1 = sample.int(100, 1000, replace=T), y2 = sample(50, 1000, replace = T))
# plot first plot
with(df, plot(y1 ~ x, col = "red"))
# set new plot
par(new = T)
# plot second plot, but without axis
with(df, plot(y2 ~ x, type = "l", xaxt = "n", yaxt = "n", xlab = "", ylab = ""))
# define y-axis and put y-labs
axis(4)
with(df, mtext("y2", side = 4))
library(tidyverse)
df.wide %>%
# Select only the columns you need for the plot.
select(date, column1, column2, column3) %>%
# Create an id column – needed in the `gather()` function.
mutate(id = n()) %>%
# The `gather()` function converts to long-format.
# In which the `type` column will contain three factors (column1, column2, column3),
# and the `value` column will contain the respective values.
# All the while we retain the `id` and `date` columns.
gather(type, value, -id, -date) %>%
# Create the plot according to your specifications
ggplot(aes(x = date, y = value)) +
geom_line() +
# Create a panel for each `type` (ie. column1, column2, column3).
# If the types have different scales, you can use the `scales="free"` option.
facet_grid(type~., scales = "free")
when numinter prod
1 2018-03-20 1 0.95
2 2018-03-21 5 0.50
3 2018-03-23 4 0.70
4 2018-03-24 3 0.75
5 2018-03-25 4 0.60
ggplot() +
geom_bar(mapping = aes(x = dt$when, y = dt$numinter), stat = "identity", fill = "grey") +
geom_line(mapping = aes(x = dt$when, y = dt$prod*5), size = 2, color = "blue") +
scale_x_date(name = "Day", labels = NULL) +
scale_y_continuous(name = "Interruptions/day",
sec.axis = sec_axis(~./5, name = "Productivity % of best",
labels = function(b) { paste0(round(b * 100, 0), "%")})) +
theme(
axis.title.y = element_text(color = "grey"),
axis.title.y.right = element_text(color = "blue"))
library(ggplot2)
scaleFactor <- max(mtcars$cyl) / max(mtcars$hp)
ggplot(mtcars, aes(x=disp)) +
geom_smooth(aes(y=cyl), method="loess", col="blue") +
geom_smooth(aes(y=hp * scaleFactor), method="loess", col="red") +
scale_y_continuous(name="cyl", sec.axis=sec_axis(~./scaleFactor, name="hp")) +
theme(
axis.title.y.left=element_text(color="blue"),
axis.text.y.left=element_text(color="blue"),
axis.title.y.right=element_text(color="red"),
axis.text.y.right=element_text(color="red")
)
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)
)
ylim.prim <- c(0, 180) # in this example, precipitation
ylim.sec <- c(-4, 18) # in this example, temperature
b <- diff(ylim.prim)/diff(ylim.sec)
a <- ylim.prim[1] - b*ylim.sec[1]) # there was a bug here
ggplot(climate, aes(Month, Precip)) +
geom_col() +
geom_line(aes(y = a + Temp*b), color = "red") +
scale_y_continuous("Precipitation", sec.axis = sec_axis(~ (. - a)/b, name = "Temperature")) +
scale_x_continuous("Month", breaks = 1:12) +
ggtitle("Climatogram for Oslo (1961-1990)")
ggplot(climate, aes(Month, Precip)) +
geom_col() +
geom_line(aes(y = a + Temp*b), color = "red") +
scale_y_continuous("Precipitation", sec.axis = sec_axis(~ (. - a)/b, name = "Temperature")) +
scale_x_continuous("Month", breaks = 1:12) +
theme(axis.line.y.right = element_line(color = "red"),
axis.ticks.y.right = element_line(color = "red"),
axis.text.y.right = element_text(color = "red"),
axis.title.y.right = element_text(color = "red")
) +
ggtitle("Climatogram for Oslo (1961-1990)")
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)")
#Climatogram for Oslo (1961-1990)
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))
#y1 identifies the position, relative to the y1 axis,
#the locations of the minimum and maximum of the y2 graph.
#Usually this will be the min and max of y1.
#y1<-(c(max(climate$Precip), 0))
#y1<-(c(150, 55))
y1<-(c(max(climate$Precip), min(climate$Precip)))
#y2 is the Minimum and maximum of the secondary axis data.
y2<-(c(max(climate$Temp), min(climate$Temp)))
#axis combines y1 and y2 into a dataframe used for regressions.
axis<-cbind(y1,y2)
axis<-data.frame(axis)
#Regression of Temperature to Precipitation:
T2P<-lm(formula = y1 ~ y2, data = axis)
T2P_summary <- summary(lm(formula = y1 ~ y2, data = axis))
T2P_summary
#Identifies the intercept and slope of regressing Temperature to Precipitation:
T2PInt<-T2P_summary$coefficients[1, 1]
T2PSlope<-T2P_summary$coefficients[2, 1]
#Regression of Precipitation to Temperature:
P2T<-lm(formula = y2 ~ y1, data = axis)
P2T_summary <- summary(lm(formula = y2 ~ y1, data = axis))
P2T_summary
#Identifies the intercept and slope of regressing Precipitation to Temperature:
P2TInt<-P2T_summary$coefficients[1, 1]
P2TSlope<-P2T_summary$coefficients[2, 1]
#Create Plot:
ggplot(climate, aes(Month, Precip)) +
geom_col() +
geom_line(aes(y = T2PSlope*Temp + T2PInt), color = "red") +
scale_y_continuous("Precipitation", sec.axis = sec_axis(~.*P2TSlope + P2TInt, name = "Temperature")) +
scale_x_continuous("Month", breaks = 1:12) +
theme(axis.line.y.right = element_line(color = "red"),
axis.ticks.y.right = element_line(color = "red"),
axis.text.y.right = element_text(color = "red"),
axis.title.y.right = element_text(color = "red")) +
ggtitle("Climatogram for Oslo (1961-1990)")