我有这样的数据.有什么方法可以平滑我的情节吗？
cr <- colorRampPalette(col=c("red", "red", "red", "red"), bias=1)
linecols <- cr(3)
x<-c(-1000.000000,-900.000000,-800.000000,-700.000000,-600.000000,-500.000000,-400.000000,-300.000000,-200.000000,-100.000000,0.000000,100.000000,200.000000,300.000000,400.000000,500.000000,600.000000,700.000000,800.000000,900.000000,1000.000000)
y<-c(0.809524,1.000000,1.333333,1.333333,3.285714,7.761905,13.619048,7.571429,14.809524,3.904762,1.857143,2.285714,4.857143,8.571429,2.000000,1.523810,2.714286,0.857143,1.285714,0.857143,1.380952)
plot(x, y,type="l",main="Average",ylab="Average Profile",col=linecols[1],ylim=c(0.809524,14.809524),xaxt="s",yaxt="s",lwd=2)
R的热图和pheatmap无法通过抛出此错误来生成集群.
Error in vector("double", length) : vector size specified is too large
有谁知道我怎么能想象那个大矩阵？
我正在尝试使用等高线图绘制密度估计值并得到以下错误。
A = np.random.uniform(size=(100, 2))
#mean = np.mean(x)
#cov = np.cov(x)
mean = np.array([0.5, 0.1])
cov = np.array([[0.1, 0.0], [0.0, 1.5,]])
B = multivariate_normal.pdf(A, mean=mean, cov=cov)
# visualize
contours = plt.contour(A, B, linewidths=2)
plt.clabel(contours, inline=True, fontsize=12)
#plt.plot(x, y)
plt.colorbar();
我使用以下代码将日期时间列绘制为每天的直方图。
即使将 X 轴休息时间设置为 1 天后，我得到的默认选项可能是 1 个月。如何用“年-月-日”绘制每一天的X轴？
另外，如果可能的话，我想在每个条形的顶部显示计数。
代码
data <- read.table("input.csv", sep=",", head=T)
data$datetime <- as.Date(data$datetime)
ggplot(data, aes(x=datetime)) +
geom_histogram(binwidth=0.5, colour="black", fill="white") +
scale_x_date(date_minor_breaks = "1 day")
数据
index,datetime,values,type
55,2019-11-02 12:00:00,2.2571265131358302,x
56,2019-11-02 13:00:00,2.4175854658358227,x
57,2019-11-02 14:00:00,2.7247976096720685,x
58,2019-11-02 15:00:00,3.0477882795726146,x
59,2019-11-02 16:00:00,3.2006008421516996,x
60,2019-11-02 17:00:00,3.2116870146833625,x
61,2019-11-02 18:00:00,3.4693910132468457,x
62,2019-11-02 19:00:00,3.911869748466746,x
63,2019-11-02 20:00:00,4.00069618775975,x
64,2019-11-02 22:00:00,4.0834105399449285,x
65,2019-11-02 23:00:00,4.160321416499418,x
66,2019-11-03 00:00:00,4.098125637510356,x
67,2019-11-03 01:00:00,4.021221769171646,x
68,2019-11-03 02:00:00,4.017759023510684,x
69,2019-11-03 03:00:00,4.013082714411543,x
70,2019-11-03 04:00:00,4.057993925343051,x
71,2019-11-03 05:00:00,4.194095193140784,x
72,2019-11-03 07:00:00,4.294259567020734,x
73,2019-11-03 08:00:00,4.337746909001244,x
74,2019-11-03 09:00:00,4.384365666444803,x
75,2019-11-03 10:00:00,4.448100962130655,x
76,2019-11-03 11:00:00,4.501990074404225,x
77,2019-11-03 13:00:00,4.52830137381731,x
78,2019-11-03 14:00:00,4.647279146289785,x
79,2019-11-03 …