数据（下面）有两个包含因子的名为“日期”和“独立变量（IV）”的列，还有两个包含连续值的名为“ Independent_value”和“ Sapflow”的额外列。
列说明：
日期= 5个月（6月至10月）内独立变量的测量值。
独立变量= 3个独立变量（即温度，湿度和辐射）。
独立值=表示从6月到10月的每天时间段内的温度，辐射和湿度的读数。
树液的流量（因变量）=从6月到10月的每天时间记录的树种树液的流量，以及自变量如何影响这些树液的流量。
目标
在这种情况下，我想使用 cast（） 或按组（即日期和自变量）汇总数据（在下面找到） ddply（） 生成一个新的数据框，该数据框显示每月每个自变量（温度，湿度和辐射）的平均记录值以及每月自变量的平均树胶流量率格式如下：-
*键
* IV =自变量（即温度，湿度和辐射）
* Mean_IV =自变量的平均值
* Mean_Sapflow（因变量）=每个自变量每月的平均Sapflow率
Date IV Mean_IV Mean_Sapflow
1 June Humidity 19.67 14.97
2 June Humidity 18.82 16.31
3 June Humidity 20.38 17.52
4 June Humidity 14.94 7.45
5 June Humidity 12.92 12.18
6 June Humidity 15.28 15.82
问题：
我尝试使用 ddply（）和 cast（），但无法生成上面显示的数据框格式。如果有人可以提供帮助，我将非常感激。
* ddply
library(plyr)
summarised_Sapflow<-ddply(Sapflow_new, c("Date", "Independent_Variable"), summarise,
N=length(Independent_Value),
mean("Independent Value","Sapflow"))
输出是一系列警告：
Warning messages:
1: In mean.default("Independent Value", "Sapflow") :
argument is not numeric or logical: returning NA
2: In mean.default("Independent Value", "Sapflow") :
argument is not numeric or logical: returning NA
3: In mean.default("Independent Value", "Sapflow") :
argument is not numeric or logical: returning NA
cast（）
library(reshape)
Sapflow.Summary<-cast(Sapflow_new,
Date~Independent_Variable, mean,
value=c('Independent_Value','Sapflow'))
此输出非常接近我的目标，但是由于没有正确的代码排列，因此意味着缺少sapflow，并且“日期”中包含的月份的排列顺序错误。
Date Humidity Radiation Temperature
1 August 18.38968 178.9806 71.73355
2 July 21.80065 270.9065 61.33065
3 June 17.60733 263.6733 70.56133
4 October 11.34867 93.6000 81.74300
5 September 14.82200 152.2333 72.21367
数据：
structure(list(Date = structure(c(3L, 3L, 3L, 3L, 3L,
3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 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, 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, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L,
5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L,
4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 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,
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, 5L,
5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L,
4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L,
3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 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, 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, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L,
5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L,
4L), .Label = c("August", "July", "June", "October",
"September"
), class = "factor"), Independent_Variable =
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, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 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, 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), .Label = c("Humidity",
"Radiation",
"Temperature"), class = "factor"), Independent_Value =
c(19.67,
18.82, 20.38, 14.94, 12.92, 15.28, 15.12, 16.05, 15.19,
16.67,
18.69, 14.61, 16.71, 17.35, 16.98, 15.44, 15.21, 18.62,
20.11,
18.64, 15.66, 17.2, 18.21, 19.32, 23.02, 21.69, 18.03,
18.46,
18.45, 20.78, 23.04, 22.05, 19.71, 20.59, 24.89, 23.34,
24.7,
24.2, 22.43, 18.21, 17.66, 18.23, 20.36, 22.83, 23.52,
22.88,
19.59, 21.51, 22.25, 21.47, 22.03, 22.51, 25.54, 24.01,
24.28,
26.21, 23.72, 17.63, 17.27, 19.19, 19.97, 19.84, 22.78,
24.46,
23.05, 23.31, 24.75, 23.23, 18.91, 15.56, 13.51, 15.8,
17.67,
19.18, 18.93, 20.05, 17.1, 16.87, 18.77, 20.49, 21.5,
18.04,
18.82, 17.38, 13.05, 13.13, 13.48, 16.32, 16.74, 16.11,
15.77,
15.48, 18.17, 18.16, 18.44, 16.63, 16.64, 14.47, 13.07,
14.14,
17.27, 16.71, 18.22, 12.9, 13.95, 14.7, 15.78, 17.52,
19.66,
18.87, 18.07, 16.4, 12.92, 10.57, 10.04, 9.78, 10.24,
14.25,
15.92, 11.59, 9.25, 10.33, 11.22, 15.03, 13.67, 14.26,
15.42,
8.34, 8.56, 12.37, 14.38, 15.47, 16.4, 17.15, 20.05,
11.08, 10.63,
14.34, 13.27, 9.33, 8.1, 10.95, 12.79, 8.64, 11.42,
12.12, 9.91,
7.86, 3.51, 4.97, 3.63, 5.59, 85.07, 79.72, 72.83, 90.1,
83.02,
73.34, 77.11, 74.79, 81.66, 77.71, 66.14, 78.15, 69.33,
68.13,
60.31, 69.47, 81.86, 78.63, 77.69, 77.56, 52.88, 53.32,
53.74,
55.85, 49.56, 55.3, 69.25, 74.96, 69.29, 60.07, 54.31,
48.6,
55.73, 56.74, 47.66, 60.51, 55.64, 58.39, 63.8, 63.16,
73.65,
71.08, 64.34, 60.1, 51.61, 54.87, 58.23, 52.49, 52.56,
59.64,
67.85, 64.42, 60.08, 59.71, 57.12, 58.7, 68.85, 72.44,
89.13,
77.67, 62.17, 61.3, 63.58, 66.26, 60.09, 56.63, 53.11,
59.84,
60.06, 80.76, 79.51, 73.96, 84.58, 78.77, 71.65, 72.59,
77.52,
69.04, 78.26, 77.22, 73.75, 81.95, 82.04, 78.14, 73.41,
72.76,
90.68, 74.24, 71.3, 74.4, 60.26, 66.08, 65.18, 57.17,
66.88,
75.53, 71.52, 74.97, 66.02, 78.06, 73.58, 68.18, 83.55,
80.4,
66.28, 72.32, 72.39, 77.74, 69.81, 74.21, 77.37, 88.28,
65.33,
87.54, 80.49, 69.58, 68.18, 69.25, 60.06, 66.38, 68.51,
71.65,
63.29, 76.63, 80.46, 85.56, 81.25, 94.48, 73.87, 76.8,
72.83,
77.55, 81.5, 77.7, 75.79, 94.38, 99.55, 94.14, 87.29,
84.81,
82.63, 85.27, 84.52, 71.13, 76.28, 78.06, 82.83, 75.18,
83.8,
85.38, 84, 85.33, 197.8, 195.5, 288, 72, 160.5, 337.1,
176.9,
242.3, 189.4, 295.7, 363.2, 158, 290, 251.2, 297.3,
192.6, 163.5,
274.5, 210.7, 243.4, 287.4, 375.7, 290.5, 336.4, 361.6,
369.2,
302.6, 295.2, 348.5, 343.5, 327.6, 358.9, 358.6, 288.9,
325.6,
307.8, 321.3, 321.5, 280.6, 264.9, 253, 279.5, 318.1,
285.1,
330.8, 252, 201, 229.9, 259.3, 230.4, 265.5, 214.1, 307,
311.1,
282.5, 256.9, 227.2, 263.4, 68.2, 130.8, 276.6, 299.2,
276.5,
243.9, 291, 289.3, 290.6, 259.6, 220.5, 72.7, 158.9,
233.8, 105.9,
164.2, 168.1, 188.7, 120.1, 217.7, 111.2, 114.7, 143.6,
55.2,
108.5, 162.2, 185, 197.7, 54.1, 126.3, 111.2, 135.4,
228.3, 214.3,
240.1, 247.6, 173, 172.4, 131.9, 149.4, 203.1, 92.3,
168.5, 146.6,
65.9, 103.6, 200.2, 131.3, 183.5, 128.3, 140.6, 124.1,
125.9,
75.8, 173.2, 47.9, 111.7, 205.8, 188.3, 175.6, 193.7,
170.4,
188.3, 108, 171.1, 59.5, 87.7, 142.2, 111.8, 26.3,
129.9, 103.1,
158.7, 147.9, 109.8, 67.8, 106.6, 12.3, 15.8, 53, 63.4,
86.2,
123.3, 112.9, 128.2, 141.9, 81.6, 102, 86.8, 83.9, 50,
96.8,
100.5, 47), Sapflow = c(14.97, 16.31, 17.52, 7.45,
12.18, 15.82,
11.79, 14.45, 10.95, 13.62, 16.28, 11.42, 16.13, 15.09,
17.28,
14.43, 11.7, 16.06, 17.66, 16.33, 17.79, 18.58, 19.41,
19.8,
21.63, 21.35, 17.81, 17.56, 19.37, 21.27, 23.26, 23.67,
22.64,
21.85, 24.81, 22.36, 24.72, 23.87, 23.67, 22.01, 19.23,
19.92,
21.99, 23.6, 24.9, 24.46, 22.22, 23.95, 24.81, 23.88,
22.98,
24.47, 26.09, 25.97, 25.82, 26.24, 25.09, 22, 16.91,
21.35, 25.32,
25.76, 26.38, 25.78, 25.77, 25.15, 26.29, 26.22,
24.59, 18.26,
18.91, 21.57, 21.37, 21.29, 23.96, 24.85, 21.02, 23.05,
22.69,
23.9, 25.24, 25.4, 23.19, 22.8, 22.08, 21.86, 13.82,
22.05, 23.21,
20.12, 22.73, 21.88, 23.33, 24.76, 23.5, 22.06, 22.01,
20.65,
21.54, 19.9, 21.67, 21.84, 18.82, 17.99, 21.41, 23.53,
23.39,
25.75, 22.62, 22.25, 21.81, 16.81, 20.42, 12.08, 12.36,
15.31,
14.14, 15.48, 15.18, 14.19, 12.09, 12.39, 12.34, 12.61,
10.79,
10.53, 11.29, 9.92, 9.79, 10.86, 10.98, 10.58, 12.54,
12.52,
12.25, 6.38, 0.91, 5.24, 6.56, 5.72, 4.55, 4.99, 2.88,
0.99,
1.03, 1.57, 2.07, 2.3, 2.22, 2.11, 2.21, 2.29, 14.97,
16.31,
17.52, 7.45, 12.18, 15.82, 11.79, 14.45, 10.95, 13.62,
16.28,
11.42, 16.13, 15.09, 17.28, 14.43, 11.7, 16.06, 17.66,
16.33,
17.79, 18.58, 19.41, 19.8, 21.63, 21.35, 17.81, 17.56,
19.37,
21.27, 23.26, 23.67, 22.64, 21.85, 24.81, 22.36,
24.72, 23.87,
23.67, 22.01, 19.23, 19.92, 21.99, 23.6, 24.9, 24.46,
22.22,
23.95, 24.81, 23.88, 22.98, 24.47, 26.09, 25.97, 25.82,
26.24,
25.09, 22, 16.91, 21.35, 25.32, 25.76, 26.38, 25.78,
25.77, 25.15,
26.29, 26.22, 24.59, 18.26, 18.91, 21.57, 21.37, 21.29,
23.96,
24.85, 21.02, 23.05, 22.69, 23.9, 25.24, 25.4, 23.19,
22.8, 22.08,
21.86, 13.82, 22.05, 23.21, 20.12, 22.73, 21.88, 23.33,
24.76,
23.5, 22.06, 22.01, 20.65, 21.54, 19.9, 21.67, 21.84,
18.82,
17.99, 21.41, 23.53, 23.39, 25.75, 22.62, 22.25, 21.81,
16.81,
20.42, 12.08, 12.36, 15.31, 14.14, 15.48, 15.18, 14.19,
12.09,
12.39, 12.34, 12.61, 10.79, 10.53, 11.29, 9.92, 9.79,
10.86,
10.98, 10.58, 12.54, 12.52, 12.25, 6.38, 0.91, 5.24,
6.56, 5.72,
4.55, 4.99, 2.88, 0.99, 1.03, 1.57, 2.07, 2.3, 2.22,
2.11, 2.21,
2.29, 14.97, 16.31, 17.52, 7.45, 12.18, 15.82, 11.79,
14.45,
10.95, 13.62, 16.28, 11.42, 16.13, 15.09, 17.28, 14.43,
11.7,
16.06, 17.66, 16.33, 17.79, 18.58, 19.41, 19.8, 21.63,
21.35,
17.81, 17.56, 19.37, 21.27, 23.26, 23.67, 22.64, 21.85,
24.81,
22.36, 24.72, 23.87, 23.67, 22.01, 19.23, 19.92, 21.99,
23.6,
24.9, 24.46, 22.22, 23.95, 24.81, 23.88, 22.98, 24.47,
26.09,
25.97, 25.82, 26.24, 25.09, 22, 16.91, 21.35, 25.32,
25.76, 26.38,
25.78, 25.77, 25.15, 26.29, 26.22, 24.59, 18.26, 18.91,
21.57,
21.37, 21.29, 23.96, 24.85, 21.02, 23.05, 22.69, 23.9,
25.24,
25.4, 23.19, 22.8, 22.08, 21.86, 13.82, 22.05, 23.21,
20.12,
22.73, 21.88, 23.33, 24.76, 23.5, 22.06, 22.01, 20.65,
21.54,
19.9, 21.67, 21.84, 18.82, 17.99, 21.41, 23.53, 23.39,
25.75,
22.62, 22.25, 21.81, 16.81, 20.42, 12.08, 12.36, 15.31,
14.14,
15.48, 15.18, 14.19, 12.09, 12.39, 12.34, 12.61, 10.79,
10.53,
11.29, 9.92, 9.79, 10.86, 10.98, 10.58, 12.54, 12.52,
12.25,
6.38, 0.91, 5.24, 6.56, 5.72, 4.55, 4.99, 2.88, 0.99,
1.03, 1.57,
2.07, 2.3, 2.22, 2.11, 2.21, 2.29)), class =
"data.frame", row.names = c(NA,
-456L))
答案 0 :(得分：1)
这不是ddply()或cast()解决方案，但可以使用tidyverse和reshape2来做到：
df %>%
group_by(Date, Independent_Variable) %>%
summarise(Independent_Value = mean(Independent_Value)) %>%
mutate(Independent_Variable = paste(Independent_Variable, "IV", sep = "_")) %>%
dcast(Date~Independent_Variable, value.var = "Independent_Value") %>%
arrange(factor(Date, levels = month.name)) %>%
left_join(df %>%
group_by(Date, Independent_Variable) %>%
summarise(Sapflow = mean(Sapflow)) %>%
mutate(Independent_Variable = paste(Independent_Variable, "Sapflow", sep = "_")) %>%
dcast(Date~Independent_Variable, value.var = "Sapflow") %>%
arrange(factor(Date, levels = month.name)),
by = c("Date" = "Date"))
Date Humidity_IV Radiation_IV Temperature_IV Humidity_Sapflow
1 June 17.60733 263.6733 70.56133 16.067000
2 July 21.80065 270.9065 61.33065 23.356774
3 August 18.38968 178.9806 71.73355 22.941613
4 September 14.82200 152.2333 72.21367 19.309333
5 October 11.34867 93.6000 81.74300 6.700667
Radiation_Sapflow Temperature_Sapflow
1 16.067000 16.067000
2 23.356774 23.356774
3 22.941613 22.941613
4 19.309333 19.309333
5 6.700667 6.700667
首先，它按“日期”和“ Independent_Variable”分组并汇总“ Independent_Value”。其次，它将“ _IV”添加到Independent_Variable中的值。第三，它正在重塑数据并按照几个月的真实顺序进行安排。第四，它正在执行“ Sapflow”的前三个步骤。最后，它将两者合并。
或者仅使用tidyverse：
df %>%
group_by(Date, Independent_Variable) %>% #Grouping
summarise_all(funs(mean = mean(.))) %>% #Summarising all variables and adding "_mean" to the new variables
arrange(factor(Date, levels = month.name)) #Arranging according the real order of months
Date Independent_Variable Independent_Value_mean Sapflow_mean
<fct> <fct> <dbl> <dbl>
1 June Humidity 17.6 16.1
2 June Radiation 264. 16.1
3 June Temperature 70.6 16.1
4 July Humidity 21.8 23.4
5 July Radiation 271. 23.4
6 July Temperature 61.3 23.4