I need to find the average of every 6 months, starting from v1 to v15. Now that i know that there are v15 columns hence its working with my below code. But there will more than 15 columns and I need a generic code that can solve the purpose.
Logic i am using is: taking the average of columns - 1:6 and printing, then 2:7 and so on- till 15, as i know there are 15 columns. But there will more columns in actual.
csv file:
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15
1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
2 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.11 0.04 0.04 0.04 0.04 0.04 0.04 0.04
3 3.29 3.56 3.97 3.23 2.96 2.35 0.06 1.72 2.19 1.92 1.84 2.87 2.57 2.24 3.06
4 11.79 15.01 14.76 13.19 18.29 4.51 16.24 11.92 10.49 13.05 12.74 12.95 12.25 14.46 14.27
5 20.11 21.76 21.92 23.67 19.87 25.59 23.04 16.67 22.78 21.32 20.85 21.57 21.99 22.69 22.96
6 24.85 26.56 29.45 24.96 25.91 16.31 27.51 22.56 28.35 26.96 26.53 28.23 28.24 29.85 29.79
7 29.02 32.75 29.95 27.7 29.6 17.91 32.08 25.71 33.16 31.56 30.89 32.68 34.05 36.26 33.27
8 32.83 33.09 17.03 33.23 31.22 39.71 35.43 28.77 37.09 34.18 34.05 36.98 37.16 38.74 37.32
9 32.86 36.34 35.47 33.6 35 42.79 37.22 30.62 38.74 35.83 36.17 39.48 39.18 42.87 39.54
10 36.02 37.66 36.15 34.79 36.84 22.19 38.9 32.62 40.28 37.87 38.09 41.04 41.62 44.94 42.18
11 36.96 39.22 19.13 36.68 37.43 46.26 40.84 33.88 41.31 39.09 39.14 43.46 42.75 47.2 43.8
12 37.34 40.87 35.91 37.66 39.22 46.95 42.26 35.19 42.93 41 40.61 44.73 45.2 48.14 44.49
13 38.92 38.37 41.01 39.01 41 48.89 43.8 37.16 44.1 42.46 41.3 45.47 46.65 50.48 47.6
14 21.67 43.16 20.98 39.84 42 49.62 44.35 37.46 44.63 43.15 42.64 48.48 48.53 53.55 48.57
a <- t(apply(mat,1,function(x){ c(mean(x[1:6]),mean(x[2:7]),mean(x[3:8]),mean(x[4:9]),mean(x[5:10]),mean(x[6:11]),mean(x[7:12]),mean(x[8:13]),mean(x[9:14]),mean(x[10:15])) }))
Please help. thanks in Advance.
We can do this with a rolling mean (rollmean
library(zoo)
t(apply(df1, 1, function(x) rollmean(x, 6)))
Using base R:
n=6
d=lapply(1:(ncol(data)-(n-1)),function(x) x:(x+n-1))
sapply(d,function(w) rowMeans(data[,w]))
another base solution:
rowlingRowMeans <- function(matrix, n_meanrows){
out <- NULL
for(z in 1:(nrow(matrix)-n_meanrows+2)){
out <- cbind(out, rowMeans(matrix[,z:(z+n_meanrows-1)]))
}
return(out)
}
mat <- matrix(rnorm(15*14, 1,10), ncol=15, nrow=14)
rowlingRowMeans(mat, 6)
Related
everyone. I am using DLNM in R to analyze to lag-effect of climatic conditions on the prevalence of the disease.
I followed somebody else's program strictly
, and it worked in avg.temp and max.speed, but showed err "coef/vcov not consistent with basis matrix" in avg.ap and avg.hum. However, i just changed the variables set in code, and never changed other code.
I have a hypothesis that maybe DLNM doesn't like wet weather. T T
I don't know what to do, can you help me?
Part 1 was the Successfully run code, part 2 was the code that showed err, and part 3 was the data I used.
Thank you very much. I hope you can help me
Part 1. Successfully run code
attach(cpdlnm)
cb.temp = crossbasis(avg.temp, lag=1 ,
argvar=list(fun="ns",
knots= c(10)),
arglag=list(fun="lin"))
modeltemp = glm(pre1 ~ cb.temp +
ns(no,1*1),
family=quasipoisson(), cpdlnm)
pred1.temp = crosspred(cb.temp,
modelhum,
cen=round(median(avg.temp)),
bylag=1)
Part 2. Error code
attach(cpdlnm)
cb.hum = crossbasis(avg.hum, lag=1 ,
argvar=list(fun="ns",
knots= c(10)),
arglag=list(fun="lin"))
modelhum = glm(pre1 ~ cb.hum +
ns(no,1*1),
family=quasipoisson(), cpdlnm)
pred1.hum = crosspred(cb.hum, # This step shows "coef/vcov not consistent with basis matrix"
modelhum,
cen=round(median(avg.hum)),
bylag=0.1)
Part 3. the data are as following:
no pre1 date year month avg.ap avg.temp avg.hum max.speed
1 3.23 12-Jan 2012 1 996.60 9.00 81.60 5.30
2 6.04 12-Feb 2012 2 993.20 10.90 80.80 6.20
3 5.18 12-Mar 2012 3 991.00 16.40 78.70 7.60
4 4.07 12-Apr 2012 4 985.40 23.50 73.50 7.40
5 4.88 12-May 2012 5 982.60 26.30 77.20 7.00
6 5.11 12-Jun 2012 6 978.10 27.00 81.30 6.20
7 6.18 12-Jul 2012 7 979.50 28.10 77.70 6.40
8 6.17 12-Aug 2012 8 980.40 28.00 75.60 7.90
9 5.18 12-Sep 2012 9 987.60 25.30 73.60 6.30
10 5.16 12-Oct 2012 10 990.70 23.60 72.20 6.20
11 4.61 12-Nov 2012 11 991.70 18.00 79.70 6.90
12 5.26 12-Dec 2012 12 995.00 13.20 74.90 6.50
13 3.79 13-Jan 2013 1 997.10 11.20 78.40 5.70
14 3.87 13-Feb 2013 2 993.50 15.30 82.20 6.50
15 3.37 13-Mar 2013 3 989.90 20.20 74.20 8.00
16 2.85 13-Apr 2013 4 987.00 21.50 78.50 7.70
17 4.38 13-May 2013 5 983.30 25.60 79.20 6.80
18 5.67 13-Jun 2013 6 980.60 27.40 76.90 6.60
19 6.45 13-Jul 2013 7 981.30 28.00 77.50 7.10
20 6.95 13-Aug 2013 8 980.50 27.90 78.20 7.90
21 6.51 13-Sep 2013 9 985.90 25.40 77.60 6.00
22 8.16 13-Oct 2013 10 992.20 22.10 68.80 5.30
23 5.34 13-Nov 2013 11 994.50 18.70 72.30 6.20
24 6.18 13-Dec 2013 12 997.30 11.70 67.20 5.30
25 5.69 14-Jan 2014 1 996.70 12.70 70.30 6.00
26 6.44 14-Feb 2014 2 993.00 12.10 76.90 6.40
27 4.16 14-Mar 2014 3 991.60 16.50 83.90 7.30
28 4.13 14-Apr 2014 4 987.60 22.60 82.40 6.70
29 3.96 14-May 2014 5 983.60 25.70 78.80 7.70
30 4.72 14-Jun 2014 6 979.20 27.70 81.40 7.90
31 5.21 14-Jul 2014 7 980.70 28.30 80.20 9.40
32 5.29 14-Aug 2014 8 982.40 27.50 81.30 7.50
33 6.74 14-Sep 2014 9 984.70 27.10 77.70 8.50
34 4.80 14-Oct 2014 10 991.20 23.90 73.10 5.90
35 4.31 14-Nov 2014 11 993.30 18.60 79.60 6.20
36 4.35 14-Dec 2014 12 998.70 12.30 67.30 5.90
37 2.95 15-Jan 2015 1 996.70 13.30 76.30 6.20
38 4.63 15-Feb 2015 2 993.50 15.50 78.30 6.50
39 4.00 15-Mar 2015 3 991.70 17.70 83.40 6.30
40 4.16 15-Apr 2015 4 988.40 22.80 70.20 7.30
41 4.67 15-May 2015 5 982.40 26.70 80.50 8.00
42 5.62 15-Jun 2015 6 980.90 28.20 81.00 7.40
43 5.04 15-Jul 2015 7 980.20 27.30 79.40 6.70
44 5.79 15-Aug 2015 8 982.40 27.60 80.10 6.50
45 5.28 15-Sep 2015 9 986.30 26.00 84.60 6.50
46 4.39 15-Oct 2015 10 991.20 23.00 78.30 6.90
47 4.13 15-Nov 2015 11 993.50 19.40 85.30 6.90
48 3.30 15-Dec 2015 12 997.80 13.00 80.90 5.70
49 5.30 16-Jan 2016 1 996.00 11.80 82.30 6.40
50 4.57 16-Feb 2016 2 997.80 12.20 68.90 7.00
51 4.66 16-Mar 2016 3 991.70 17.00 78.90 7.00
52 4.01 16-Apr 2016 4 984.60 23.40 80.90 9.80
53 4.90 16-May 2016 5 983.80 25.50 78.70 8.30
54 3.75 16-Jun 2016 6 981.70 28.20 78.80 7.70
55 3.13 16-Jul 2016 7 981.10 28.90 77.60 7.60
56 3.25 16-Aug 2016 8 979.00 28.00 79.80 8.70
57 2.93 16-Sep 2016 9 984.30 26.60 75.20 6.40
58 2.93 16-Oct 2016 10 987.90 24.40 72.90 7.00
59 3.08 16-Nov 2016 11 993.40 18.10 79.60 6.70
60 2.99 16-Dec 2016 12 995.70 15.40 71.70 6.80
61 3.10 17-Jan 2017 1 994.70 14.50 79.20 6.50
62 3.75 17-Feb 2017 2 994.80 14.70 71.50 8.30
63 3.49 17-Mar 2017 3 990.20 16.50 83.60 8.50
64 3.36 17-Apr 2017 4 986.80 21.90 76.70 7.80
65 3.69 17-May 2017 5 985.00 24.80 77.50 10.00
66 3.76 17-Jun 2017 6 980.20 26.90 84.80 8.50
67 2.69 17-Jul 2017 7 981.00 27.50 83.60 9.80
68 3.05 17-Aug 2017 8 980.50 27.70 83.40 9.00
69 3.05 17-Sep 2017 9 984.20 27.60 81.50 7.10
70 2.46 17-Oct 2017 10 990.00 22.80 75.90 7.90
71 2.08 17-Nov 2017 11 993.00 17.80 79.50 7.00
72 2.32 17-Dec 2017 12 996.90 13.30 69.30 6.90
73 2.53 18-Jan 2018 1 992.10 12.00 78.40 8.10
74 3.29 18-Feb 2018 2 992.90 13.40 68.70 7.20
75 3.03 18-Mar 2018 3 988.30 19.20 78.20 9.10
76 2.30 18-Apr 2018 4 986.50 21.80 77.30 8.70
77 1.75 18-May 2018 5 982.60 26.70 79.40 8.90
78 2.03 18-Jun 2018 6 978.30 26.90 81.60 9.00
79 2.79 18-Jul 2018 7 976.80 27.90 82.10 9.20
80 2.32 18-Aug 2018 8 976.40 27.50 83.40 9.60
81 1.88 18-Sep 2018 9 983.50 26.10 80.10 8.90
82 2.76 18-Oct 2018 10 990.50 21.10 78.70 7.10
83 2.14 18-Nov 2018 11 991.50 18.20 80.30 7.10
84 1.78 18-Dec 2018 12 994.50 13.00 84.00 7.80
85 2.77 19-Jan 2019 1 995.20 11.70 84.50 7.30
86 4.60 19-Feb 2019 2 990.50 13.70 84.80 8.10
87 2.32 19-Mar 2019 3 987.70 17.30 85.90 9.90
88 2.07 19-Apr 2019 4 983.60 23.10 84.80 9.80
89 2.97 19-May 2019 5 981.80 24.30 83.20 7.70
90 2.48 19-Jun 2019 6 977.80 27.50 84.80 9.00
91 2.32 19-Jul 2019 7 977.20 27.80 85.00 8.90
92 2.06 19-Aug 2019 8 977.20 28.30 81.20 10.30
93 2.10 19-Sep 2019 9 984.60 26.40 72.70 8.20
94 2.89 19-Oct 2019 10 989.10 22.70 78.00 7.00
My guess is that when you specify "knots= c(10)", 10 is within the range of temperature but not the same for humidity (if the min>10, then the lag can't be defined).
I have loop to analysis of 25 time series, In the analysis I need to get the mean of the individual columns for each station , for example the mean of all the Januaries, the same for all the Februaries, etc., etc. To complicate matters the time series are not all the same length, for example station 1 might run from 1900 to 1955 while station 2 might run from 1881 to 1945. So I need the mean of a Januaries of station 1, the mean of all Februaries of Station 1, etc., etc. and the same process for station 2 etc., etc. My times do not all start in January or end in December, but can start and finish in any month, each time series is individual. To get the colMeans, I need to change the time series to a matrix, but I need to pad out the empty spaces with NAs. Now how can I do that and put the function into the loop
Below is an example of my data
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1951 15.50 18.74 22.75 25.90 25.43 27.61
1952 27.60 27.72 27.63 24.38 20.34 17.74 17.90 20.57 23.13 25.60 26.41 26.98
1953 25.80 26.19 24.99 23.23 19.59 15.78 14.85 18.97 20.44 25.78 26.65 27.00
1954 26.25 26.97 25.33 23.16 20.47 15.47 15.64 18.33 22.71 26.71 25.77 25.94
1955 26.69 25.36 24.19 23.42 19.65 17.10 17.36 18.67 20.95 24.41 24.93 26.12
1956 26.02 26.48 25.81 23.91 20.78 17.40 17.48 19.96 21.06 25.44 26.16 25.92
1957 26.67 28.03 25.24 24.40 19.89 16.54 17.99 19.01 24.81 26.18 28.38 26.96
1958 25.90 26.49 24.90 24.67 21.36 16.19 16.29 17.20 22.18 24.52 29.13 26.65
1959 26.53 26.65 25.17 24.26 20.67 17.56 18.11 18.49 21.50 26.21 26.48 27.52
1960 27.25 26.04 26.58 22.80 19.41 17.16 15.57 20.24 22.86 26.68 25.71 27.58
1961 26.79 25.88 26.19 24.22 22.09 17.77 17.91 18.56 23.27 24.94 25.68 26.66
1962 27.03 28.11 26.05 23.81 18.79 17.32 16.04 19.23 23.14 27.57 27.37 27.09
1963 26.91 26.68 24.97 22.87 18.71 16.79 16.05 18.25 23.52 25.73 27.08 26.86
1964 28.63 28.04 28.16 23.98 19.78 15.40 14.98 18.32 22.88 25.60 26.55 25.23
1965 27.77 28.87 26.62 23.40 19.49 15.62 17.14 19.79 22.09 23.44 26.32 28.40
1966 29.68 26.63 25.50 23.13 19.49 18.65 17.69 19.32 22.12 23.88 27.37 27.75
1967 27.84 26.46 25.75 24.20 20.15 17.22 15.64 18.39 22.41 25.38 27.42 27.62
1968 30.27 27.91 26.32 22.56 19.86 14.16 17.07 19.76 22.42 26.05 24.61 26.38
1969 28.72 30.04 25.85 23.68 20.09 18.32 16.85 19.61 22.10 24.97 27.28 25.46
1970 29.62 27.24 26.62 23.34 20.20 16.95 17.40 20.23 24.21 24.74 27.25 28.71
1971 26.25 26.44 28.15 25.31 19.14 16.21 16.92 19.12 23.09 24.28 24.43 27.19
1972 26.64 26.07 25.17 24.13 19.45 15.83 16.14 18.45 22.74 24.98 25.54 30.09
1973 30.44 28.54 28.10 22.80 20.36 18.05 16.74 19.16 23.56 24.64 25.97 25.50
1974 27.46 26.52 25.44 22.36 19.79 16.83 16.70 19.89 21.39 26.22 25.84 25.93
1975 27.84 25.91 24.42 22.87 20.90 16.42 16.25 18.79 23.24 24.54 27.07 26.97
1976 26.05 26.33 24.95 22.07 18.42 16.88 15.79 17.24 23.01 25.26 27.65 29.15
1977 30.00 26.31 23.47 23.17 20.21 17.23 16.12 18.80 23.55 25.93 26.97 26.68
1978 25.99 26.36 26.31 22.49 19.72 15.88 15.99 21.64 23.57 24.68 24.78 26.47
1979 26.98 28.17 25.36 24.06 20.65 17.64 15.98 19.89 22.63 25.40 25.31 27.03
1980 27.74 28.05 25.47 23.72 20.55 15.96
I obtain the following data set in Azure. Each row is a parameter that is relevant to a forecasting model.
I am relatively new to R. I tried the following code but it does not give me the expected output. After I transpose the data set, I want to add an additional column "Month-Year".
Can someone help me? Thanks.
Data set
features V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12
A 28.21 42.03 48.56 46.85 46.03 54.6 63.87 50 53.34 43.47 34.66 27.48
B 1333 1348.64 1364.28 1379.92 1395.56 1411.2 1426.84 1442.48 1458.11 1473.75 1489.39 1505.03
C 10.05 5.46 4.82 5.27 5.07 4.07 9.53 1.95 6.95 6.54 5.91 0.56
D 18.22 18.41 14.31 30.28 18.16 15.52 12.52 13.14 15.05 8.89 12.51 25.25
R code
# Map 1-based optional input ports to variables
dataset <- maml.mapInputPort(1)
a <- c("A", "B", "C", "D")
data.set <- cbind(a, dataset)
names(data.set)[1] <- c("features")
# first remember the names
n <- dataset$features
# transpose all but the first column (name)
df.aree <- as.data.frame(t(data.set[,-1]))
names(data.set)[1] <- n
df.aree$myfactor <- factor(row.names(df.aree))
maml.mapOutputPort("df.aree")
Expected result
Month-Year A B C D
01-01-15 28.21 1333 10.05 18.22
01-02-15 42.03 1348.64 5.46 18.41
01-03-15 48.56 1364.28 4.82 14.31
01-04-15 46.85 1379.92 5.27 30.28
01-05-15 46.03 1395.56 5.07 18.16
01-06-15 54.6 1411.2 4.07 15.52
01-07-15 63.87 1426.84 9.53 12.52
01-08-15 50 1442.48 1.95 13.14
01-09-15 53.34 1458.11 6.95 15.05
01-10-15 43.47 1473.75 6.54 8.89
01-11-15 34.66 1489.39 5.91 12.51
01-12-15 27.48 1505.03 0.56 25.25
Create "MonYear" using seq with from and to dates.
MonYear <- format(seq(as.Date('2015-01-01'), as.Date('2015-12-01'),
by = 'month'), '%d-%m-%y')
Transpose the non-numeric columns in the original dataset (the output will be a matrix. We create a data.frame by combining 'MonYear' and the matrix output.
df2 <- data.frame(MonYear,t(df1[-1]))
Change the column names and row names accordingly
colnames(df2)[-1] <- LETTERS[1:4]
row.names(df2) <- NULL
df2
MonYear A B C D
1 01-01-15 28.21 1333.00 10.05 18.22
2 01-02-15 42.03 1348.64 5.46 18.41
3 01-03-15 48.56 1364.28 4.82 14.31
4 01-04-15 46.85 1379.92 5.27 30.28
5 01-05-15 46.03 1395.56 5.07 18.16
6 01-06-15 54.60 1411.20 4.07 15.52
7 01-07-15 63.87 1426.84 9.53 12.52
8 01-08-15 50.00 1442.48 1.95 13.14
9 01-09-15 53.34 1458.11 6.95 15.05
10 01-10-15 43.47 1473.75 6.54 8.89
11 01-11-15 34.66 1489.39 5.91 12.51
12 01-12-15 27.48 1505.03 0.56 25.25
I'm trying to use cor() to return the most correlated elements in order of their correlation. I wrote this function adapting cor() to do it and it works perfectly, but only when I run it on a big input. When I try and run it on a small input, I get a missing value where TRUE/FALSE needed error and I don't understand why?
Here is an example of my input data:
This can be directly copied into R(printed via write.table):
"Col2" "Col3" "Col4" "Col5" "Col6"
"Market Capitalization" NA NA 17082.69 17879.8 16266.11
"Cash & Equivalents" NA NA 747 132 394
"Preferred & Other" NA NA 0 0 0
"Total Debt" NA NA 12379 11982 11309
"Enterprise Value" NA NA 28714.69 29729.8 27181.11
"Total Revenue" 2896.75 3461.25 2818 3184 2901
"Growth % YoY" -0.15 0.68 1.7 3.44 -0.48
"Gross Profit" NA NA 1874 2080 1981
"Margin %" NA NA 66.5 65.33 68.29
"EBITDA" 758 1074 641 777 699
"Margin %1" 26.17 31.03 22.75 24.4 24.1
"Net Income Before XO" 214.5 410 172 192 207
"Margin %2" 7.4 11.85 6.1 6.03 7.14
"Adjusted EPS" 0.7 1.42 0.59 1.07 0.69
"Growth % YoY1" 0.72 -1.67 -3.28 5.94 -6.76
"Cash from Operations" 375.79 812.21 991 -84 961
"Capital Expenditures" NA NA -660 -676 -608
"Free Cash Flow" NA NA 331 -760 353
"Adjusted Price" 2094.66 3689.2 3805.62 3588.42 3582.4
This is the mycor() function I wrote
mycor<-function(dataset, relative.to=19, neg.cor=0){
#This takes the dataset (as a matrix) and computes the best correleted value
#and returns the row (variable ID) that is the most strongly correlated
#to the variable row referenced by relative.to. Use neg.cor = 1 for neg correlation
if(neg.cor == 0){
best.cor <- -1.0 #Have to get better correlation then this
best.cor.row <- integer() #The row with the best correlation
all.cor <- numeric() #The correlation for everything else
index <- 1 #The index for the all.cor array
for(i in 1:nrow(dataset)){
if(i != relative.to){ #No self correlation
temp.cor <- cor(dataset[i,], dataset[relative.to,], use = "na.or.complete")
all.cor[index] <- temp.cor
index <- index+1 #I wish the ++ opperator worked in R...
cat(best.cor)
pause()
if(temp.cor > best.cor){ #This remembers the best seen cor value
best.cor <- temp.cor
best.cor.row <- i
} #End inner if
} #End outter if
} #End for loop
}else{
best.cor <- 1.0 #Have to get better correlation then this
best.cor.row <- integer() #The row with the best correlation
all.cor <- numeric() #The correlation for everything else
index <- 1 #The index for the all.cor array
for(i in 1:nrow(dataset)){
if(i != relative.to){ #No self correlation
temp.cor <- cor(dataset[i,], dataset[relative.to,], use = "na.or.complete")
all.cor[index] <- temp.cor
index <- index+1 #I wish the ++ opperator worked in R...
if(temp.cor < best.cor){ #This remembers the worst seen cor value
best.cor <- temp.cor
best.cor.row <- i
} #End inner if
} #End outter if
} #End for loop
} #End else
return(list(all.cor = all.cor, best.cor.row = best.cor.row))
)
When I try and run this I get: Error in if (temp.cor > best.cor) { : missing value where TRUE/FALSE needed. The part about this that is strange, is that the mycor function works perfectly and gives no error when I give it a larger chunk of the same data set.
This is the larger chunk of the same data set.
This can also be copied into R(printed via write.table):
"Col2" "Col3" "Col4" "Col5" "Col6" "Col7" "Col8" "Col9" "Col10" "Col11" "Col12" "Col13" "Col14" "Col15" "Col16" "Col17" "Col18" "Col19" "Col20" "Col21" "Col22" "Col23" "Col24" "Col25" "Col26" "Col27" "Col28" "Col29" "Col30" "Col31" "Col32" "Col33" "Col34" "Col35" "Col36" "Col37" "Col38" "Col39" "Col40" "Col41" "Col42" "Col43" "Col44" "Col45" "Col46" "Col47" "Col48" "Col49" "Col50" "Col51" "Col52" "Col53" "Col54" "Col55" "Col56" "Col57" "Col58" "Col59" "Col60" "Col61" "Col62" "Col63" "Col64" "Col65" "Col66" "Col67" "Col68" "Col69" "Col70" "Col71" "Col72" "Col73" "Col74" "Col75" "Col76" "Col77" "Col78" "Col79" "Col80" "Col81" "Col82" "Col83" "Col84" "Col85" "Col86" "Col87" "Col88" "Col89" "Col90" "Col91" "Col92" "Col93" "Col94" "Col95" "Col96" "Col97" "Col98" "Col99" "Col100" "Col101" "Col102" "Col103" "Col104" "Col105" "Col106" "Col107" "Col108" "Col109" "Col110" "Col111"
"Market Capitalization" NA NA 17082.69 17879.8 16266.11 17540.1 18214.39 17110.13 18167.87 16700.24 15592.71 14824.06 14455.42 13685.56 12168.31 12550.1 12771.45 11273.2 10284.48 10863.21 10655.99 11750.74 10671.37 10818.32 13288.42 12558.8 12221.79 13213.51 12375.92 11854.12 10942.65 10689.79 11364.1 11887.9 11426.1 10249.34 10609.99 10167.51 9600.1 10001.68 9713.38 9184.3 9730.33 8249.64 9160.61 8586.38 8894.55 8908.81 11887.9 11426.1 10249.34 10609.99 10167.51 9600.1 10001.68 9713.38 9184.3 9730.33 8249.64 9160.61 8586.38 8894.55 8908.81 8566.69 8641.04 8444.84 7867.83 8163.04 7238.2 6279.55 6173.33 7376.47 9048.75 10095.35 10351.52 12311.04 12006.02 10785.58 11009.16 9655.09 7990.1 6918.52 7050.24 6844.2 6520.75 6873.11 7489.61 7459.85 7136.58 6930.38 6401.43 6048.8 5843.01 6224.43 6840.76 7529.23 8452.46 8247.48 8132.72 7632.03 7339.11 6549.2 6165.26 6535.8 5793.52 5621.57 5877.31 5391.98 4792.51 5362.35
"Cash & Equivalents" NA NA 747 132 394 69 1381 769 648 398 492 516 338 198 178 87 260 75 311 651 74 68 1757 144 210 192 186 157 94 234 63 177 81 119 818 477 26 70 487 55 49 49 60 62 117.86 83.4 59.2 108.34 119 818 477 26 70 487 55 49 49 60 62 117.86 83.4 59.2 108.34 271.35 432.14 41.63 59.57 94.83 72.81 37.66 73.6 485.05 188.94 291.14 57.5 102.29 153.82 105.01 198.26 183.46 269.87 12.23 94.9 106.88 117.28 57.37 103.23 342.29 429.89 48.49 111.39 245.22 360.74 80.65 205.1 36.76 203.96 143.32 74.33 282.45 349.66 384.84 238.24 317.86 315.65 291.01 185.21 353.33 160.33 160.31
"Preferred & Other" NA NA 0 0 0 0 0 0 213 213 213 213 213 213 213 213 213 213 213 213 213 213 213 257 256 255 255 254 254 254 255 255 255 254 255 255 252 252 253 254 255 221 222 221 221.47 221.13 221.2 220.79 254 255 255 252 252 253 254 255 221 222 221 221.47 221.13 221.2 220.79 222.09 212.56 249.61 212.56 249.61 212.56 212.56 212.56 249.61 212.56 212.56 212.56 249.61 318.02 318.02 318.02 318.02 322.34 322.42 322.54 322.65 322.74 322.77 322.84 639.92 639.98 640.13 640.24 640.31 640.39 640.47 640.54 640.73 640.89 640.95 641.09 641.25 645.87 634.99 635.05 635.18 637.51 637.73 638.05 638.15 640.53 640.77
"Total Debt" NA NA 12379 11982 11309 11111 11873 11073 10675 10676 10678 11144 10683 11526 11020 11027 10599 10773 10366 10699 10094 9751 9480 9363 9282 9213 8653 8943 8815 8968 8487 8162 8205 7687 7868 7498 7219 7245 7336 7432 7094 6968 6682 7000 6841.23 6584.25 6374.14 6264.74 7687 7868 7498 7219 7245 7336 7432 7094 6968 6682 7000 6841.23 6584.25 6374.14 6264.74 6234.03 6249.6 6448.51 6100.6 6011.55 5693.56 5536.13 5276.01 5449.52 4792.08 4881.68 4471.08 4312.4 4410.61 4480.08 4437.33 4758.17 4432.04 4532.28 4466.59 4387.54 4313.86 4316.43 4316.66 4146.02 4175.36 4082.33 4085.09 4089.16 4116.98 3970.11 3972.46 3827.89 3850.12 3927.94 3722.68 3709.36 3804.58 3658.69 3885.52 3667.45 3734.29 3737 3615.16 3492.38 3374.62 3229.81
"Enterprise Value" NA NA 28714.69 29729.8 27181.11 28582.1 28706.39 27414.13 28407.87 27191.24 25991.71 25665.06 25013.42 25226.56 23223.31 23703.1 23323.45 22184.2 20552.48 21124.21 20888.99 21646.74 18607.37 20294.32 22616.42 21834.8 20943.79 22253.51 21350.92 20842.12 19621.65 18929.79 19743.1 19709.9 18731.1 17525.34 18054.99 17594.51 16702.1 17632.68 17013.38 16324.3 16574.33 15408.64 16105.45 15308.35 15430.68 15286 19709.9 18731.1 17525.34 18054.99 17594.51 16702.1 17632.68 17013.38 16324.3 16574.33 15408.64 16105.45 15308.35 15430.68 15286 14751.46 14671.06 15101.34 14121.44 14329.37 13071.51 11990.59 11588.31 12590.55 13864.46 14898.46 14977.66 16770.77 16580.82 15478.67 15566.25 14547.82 12474.62 11760.98 11744.46 11447.51 11040.07 11454.93 12025.88 11903.5 11522.02 11604.35 11015.38 10533.05 10239.65 10754.35 11248.66 11961.09 12739.51 12673.05 12422.15 11700.18 11439.9 10458.04 10447.58 10520.58 9849.67 9705.29 9945.31 9169.17 8647.34 9072.61
"Total Revenue" 2896.75 3461.25 2818 3184 2901 3438 2771 3078 2915 3629 2993 3349 3140 3707 3017 3462 3273 3489 2845 3423 2998 3858 3149 3577 3228 3579 2957 3357 2649 3441 2555 3317 3107 3337 2395 2800 2181 2734 2164 2685 2279 2801 2176 2570 2057.03 2539.49 1848 2056 3337 2395 2800 2181 2734 2164 2685 2279 2801 2176 2570 2057.03 2539.49 1848 2056 1942.6 2627.56 2112.22 2886.26 2250.13 2820.78 2041.89 2318.59 1963.38 2346.24 1479.08 1776.59 1617.34 2061.62 1561.04 1853.05 1720.06 2011.03 1504.01 1886.15 1632.3 1920.34 1539.73 1867.36 1528.38 1879.88 1459.85 1668.79 1461.25 1821.99 1392.09 1697.76 1483.61 1799.69 1396.01 1586.08 1478.81 1717.88 1280.11 1456.11 1342.73 1720.3 1330.65 1479.39 1367.21 1613.83 1263.27
"Growth % YoY" -0.15 0.68 1.7 3.44 -0.48 -5.26 -7.42 -8.09 -7.17 -2.1 -0.8 -3.26 -4.06 6.25 6.05 1.14 9.17 -9.56 -9.65 -4.31 -7.13 7.8 6.49 6.55 21.86 4.01 15.73 1.21 -14.74 3.12 6.68 18.46 42.46 22.06 10.67 4.28 -4.3 -2.39 -0.55 4.47 10.79 10.3 17.75 25 5.89 -3.35 -12.51 -28.77 22.06 10.67 4.28 -4.3 -2.39 -0.55 4.47 10.79 10.3 17.75 25 5.89 -3.35 -12.51 -28.77 -13.67 -6.85 3.44 24.48 14.6 20.23 38.05 30.51 21.4 13.81 -5.25 -4.13 -5.97 2.52 3.79 -1.75 5.38 4.72 -2.32 1.01 6.8 2.15 5.47 11.9 4.59 3.18 4.87 -1.71 -1.51 1.24 -0.28 7.04 0.32 4.76 9.05 8.93 10.13 -0.14 -3.8 -1.57 -1.79 6.6 5.33 -1.02 NA NA NA
"Gross Profit" NA NA 1874 2080 1981 2393 1934 1993 1846 2244 1794 2000 1942 2103 1723 1826 1700 1979 1558 1551 1459 1531 1420 1588 1478 1595 1317 1506 1273 1554 1202 1322 1179 1460 1097 1217 916 1285 980 1169 1066 1349 975 1157 1024.93 1317.57 980 1091 1460 1097 1217 916 1285 980 1169 1066 1349 975 1157 1024.93 1317.57 980 1091 1052.71 1368.8 1091.61 1236.41 991.8 1374.86 1043.29 1236.87 1129.87 1507.31 998.19 1190.69 1151.22 1475.08 1025.84 1170.8 1115.9 1438.56 981.96 1159.37 1094.25 1401.25 1001.2 1198.64 1079.65 1405.45 984.46 1196.22 1086.13 1415.37 998.06 1177.1 1086.53 1381.01 971.41 1118.91 1055.19 1331.37 947.22 1036.88 991.58 1301.1 921.48 994.97 967.89 1217.32 848.39
"Margin %" NA NA 66.5 65.33 68.29 69.6 69.79 64.75 63.33 61.84 59.94 59.72 61.85 56.73 57.11 52.74 51.94 56.72 54.76 45.31 48.67 39.68 45.09 44.39 45.79 44.57 44.54 44.86 48.06 45.16 47.05 39.86 37.95 43.75 45.8 43.46 42 47 45.29 43.54 46.77 48.16 44.81 45.02 49.83 51.88 53.03 53.06 43.75 45.8 43.46 42 47 45.29 43.54 46.77 48.16 44.81 45.02 49.83 51.88 53.03 53.06 54.19 52.09 51.68 42.84 44.08 48.74 51.09 53.35 57.55 64.24 67.49 67.02 71.18 71.55 65.72 63.18 64.88 71.53 65.29 61.47 67.04 72.97 65.02 64.19 70.64 74.76 67.44 71.68 74.33 77.68 71.7 69.33 73.24 76.74 69.58 70.55 71.35 77.5 74 71.21 73.85 75.63 69.25 67.26 70.79 75.43 67.16
"EBITDA" 758 1074 641 777 699 1091 711 794 684 978 617 844 708 916 640 696 625 885 569 611 567 586 520 702 596 715 510 694 547 670 467 564 423 717 411 533 274 624 367 497 458 669 334 485 388.44 693.3 384 487 717 411 533 274 624 367 497 458 669 334 485 388.44 693.3 384 487 445 695.27 439.32 538.75 377.16 666.39 492.65 526.86 446.87 748.34 331.51 492.91 430.87 760.5 313.33 474.78 434.79 751.92 280.96 463.41 390.79 712.97 313.14 490.27 368.26 711.24 307.36 506.85 383.64 721.41 317.3 474.34 363.04 678.27 279.09 400.41 320.03 637.82 281.47 340.21 297.39 610.07 247.48 300.27 305.15 561.67 203.06
"Margin %1" 26.17 31.03 22.75 24.4 24.1 31.73 25.66 25.8 23.46 26.95 20.61 25.2 22.55 24.71 21.21 20.1 19.1 25.37 20 17.85 18.91 15.19 16.51 19.63 18.46 19.98 17.25 20.67 20.65 19.47 18.28 17 13.61 21.49 17.16 19.04 12.56 22.82 16.96 18.51 20.1 23.88 15.35 18.87 18.88 27.3 20.78 23.69 21.49 17.16 19.04 12.56 22.82 16.96 18.51 20.1 23.88 15.35 18.87 18.88 27.3 20.78 23.69 22.91 26.46 20.8 18.67 16.76 23.62 24.13 22.72 22.76 31.9 22.41 27.74 26.64 36.89 20.07 25.62 25.28 37.39 18.68 24.57 23.94 37.13 20.34 26.25 24.09 37.83 21.05 30.37 26.25 39.59 22.79 27.94 24.47 37.69 19.99 25.25 21.64 37.13 21.99 23.36 22.15 35.46 18.6 20.3 22.32 34.8 16.07
"Net Income Before XO" 214.5 410 172 192 207 440 214 280 193 386 168 314 236 353 186 229 205 339 153 183 163 185 283 303 209 313 154 261 205 234 129 183 148 290 121 184 55 253 92 158 50 260 69 157 123.03 286.54 101 169 290 121 184 55 253 92 158 50 260 69 157 123.03 286.54 101 169 128.51 280.74 104.07 182.51 49.48 283.27 72.14 191.53 124.96 339.41 69.8 180.05 135.23 351.55 66.51 176.45 143.61 355.04 47.56 166.61 120.15 327.99 71.42 188.48 113.12 333.3 76.4 201.03 117.88 339.87 87.21 189.31 117.29 324.84 62.45 153.94 100.63 309.44 77.54 116.48 92.2 303.36 64.65 106.7 121.1 263.26 49.06
"Margin %2" 7.4 11.85 6.1 6.03 7.14 12.8 7.72 9.1 6.62 10.64 5.61 9.38 7.52 9.52 6.17 6.61 6.26 9.72 5.38 5.35 5.44 4.8 8.99 8.47 6.47 8.75 5.21 7.77 7.74 6.8 5.05 5.52 4.76 8.69 5.05 6.57 2.52 9.25 4.25 5.88 2.19 9.28 3.17 6.11 5.98 11.28 5.47 8.22 8.69 5.05 6.57 2.52 9.25 4.25 5.88 2.19 9.28 3.17 6.11 5.98 11.28 5.47 8.22 6.62 10.68 4.93 6.32 2.2 10.04 3.53 8.26 6.36 14.47 4.72 10.13 8.36 17.05 4.26 9.52 8.35 17.65 3.16 8.83 7.36 17.08 4.64 10.09 7.4 17.73 5.23 12.05 8.07 18.65 6.26 11.15 7.91 18.05 4.47 9.71 6.8 18.01 6.06 8 6.87 17.63 4.86 7.21 8.86 16.31 3.88
"Adjusted EPS" 0.7 1.42 0.59 1.07 0.69 1.44 0.61 1.01 0.74 1.33 0.57 0.99 0.69 1.32 0.51 0.93 0.67 1.16 0.48 0.78 0.72 0.98 0.42 0.87 0.71 1.2 0.58 1.03 0.78 0.92 0.51 0.86 0.59 1.17 0.48 0.75 0.49 1.08 0.38 0.69 0.65 1.16 0.29 0.72 0.56 1.33 0.46 0.78 1.17 0.48 0.75 0.49 1.08 0.38 0.69 0.65 1.16 0.29 0.72 0.56 1.33 0.46 0.78 0.59 1.3 0.48 0.84 0.52 1.4 0.33 0.88 0.57 1.5 0.3 0.76 0.56 1.49 0.26 0.73 0.59 1.49 0.18 0.69 0.49 1.38 0.28 0.78 0.44 1.38 0.29 0.82 0.47 1.41 0.33 0.77 0.46 1.35 0.23 0.62 0.39 1.3 0.3 0.47 0.36 1.29 0.24 0.43 0.49 1.11 0.18
"Growth % YoY1" 0.72 -1.67 -3.28 5.94 -6.76 8.27 7.02 2.02 7.25 0.76 11.76 6.45 2.99 13.79 6.25 19.23 -6.94 18.37 14.29 -10.34 1.41 -18.33 -27.59 -15.53 -8.97 30.43 13.73 19.77 32.2 -21.37 6.25 14.67 20.41 8.33 26.32 8.7 -24.62 -6.9 31.03 -4.17 16.07 -12.78 -36.96 -7.69 -5.08 2.31 -4.17 -7.14 8.33 26.32 8.7 -24.62 -6.9 31.03 -4.17 16.07 -12.78 -36.96 -7.69 -5.08 2.31 -4.17 -7.14 13.46 -7.14 45.45 -4.55 -8.77 -6.67 10 15.79 1.79 0.67 13.64 4.11 -5.08 -0.07 44.44 5.89 20.41 8.05 -34.72 -11.62 11.36 0 -3.45 -4.88 -6.38 -2.13 -12.12 6.49 2.17 4.44 43.48 24.19 17.95 3.85 -23.33 31.91 8.33 0.78 25 9.3 -26.53 16.22 33.33 -23.21 NA NA NA
"Cash from Operations" 375.79 812.21 991 -84 961 391 845 402 976 572 1227 362 1407 179 794 1 997 26 798 645 581 -1237 733 563 630 109 346 481 710 -162 224 593 177 581 -346 389 525 164 490 152 766 218 492 -58 735.49 285 369 146 581 -346 389 525 164 490 152 766 218 492 -58 735.49 285 369 146 490.18 387.73 254.59 141.41 215.82 279.84 489.5 199.17 -325.31 -66.66 280.22 256.65 718.82 438.66 302.05 244.37 -52.38 647.78 53.19 258.9 294.29 359.1 267.8 184.51 310.07 585.52 233.75 145.31 426.63 480.57 187.86 270.34 236.08 472.92 243.13 69.8 261.19 291.41 285.57 77.33 283.64 328.4 309.68 11.95 357.21 141.59 357.15
"Capital Expenditures" NA NA -660 -676 -608 -478 -635 -523 -542 -503 -629 -460 -599 -548 -551 -465 -719 -531 -595 -529 -785 -584 -608 -547 -638 -519 -485 -482 -583 -480 -537 -420 -619 -385 -426 -390 -431 -439 -308 -373 -448 -356 -404 -317 -593.69 -310 -392 -340 -385 -426 -390 -431 -439 -308 -373 -448 -356 -404 -317 -593.69 -310 -392 -340 -302.22 -394.08 -274.8 -228.02 -75.57 -274.36 -684.94 -207.41 -211.95 -218.98 -157.07 -127.56 -210.59 -156.81 -150.58 -127.3 -226.32 -145.55 -171.37 -140.37 -244.12 -167.92 -185.35 -142.94 -239.55 -165.98 -166.25 -147.38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
"Free Cash Flow" NA NA 331 -760 353 -87 210 -121 434 69 598 -98 808 -369 243 -464 278 -505 203 116 -204 -1821 125 16 -8 -410 -139 -1 127 -642 -313 173 -442 196 -772 -1 94 -275 182 -221 318 -138 88 -375 141.79 -25 -23 -194 196 -772 -1 94 -275 182 -221 318 -138 88 -375 141.79 -25 -23 -194 187.96 -6.35 -20.21 -86.61 140.26 5.47 -195.45 -8.24 -537.26 -285.64 123.15 129.09 508.23 281.85 151.46 117.07 -278.7 502.23 -118.18 118.53 50.17 191.18 82.45 41.57 70.51 419.54 67.49 -2.08 426.63 480.57 187.86 270.34 236.08 472.92 243.13 69.8 261.19 291.41 285.57 77.33 283.64 328.4 309.68 11.95 357.21 141.59 357.15
"Adjusted Price" 2094.66 3689.2 3805.62 3588.42 3582.4 3885.75 3523.13 3554.9 3420.27 3141.36 2984.19 2838.81 2760.09 2517.44 2447.56 2403.89 2188.98 1960.8 1952.2 2033.87 2099.97 1993.98 2043.36 2296.42 2201.73 2277.15 2301.5 2203.47 2086.87 1938.95 2019.34 2002.47 2048.12 1881.97 1817.17 1807.02 1664.57 1659.78 1717.25 1585.27 1589.9 1506.13 1534.98 1531.24 1498.21 1528.96 1418.46 1431.1 1343.43 1244.04 1194.62 1076.93 1058.66 960.76 1112.69 1322.69 1414.59 1442.28 1545.6 1364.27 1305.46 1231.15 1022.23 869.37 796.9 820.22 762.84 715.9 756.11 816.37 731.97 705.73 657.84 628.55 571.47 624.67 651.89 676.63 759.77 742.27 734.39 657.44 619.61 569.84 524.2 510.26 475.43 449.8 441.27 409.34 383 413.34 441.72 435.71 419.07 385.87 356.85 346.15 326.97 318.45 323.72 314.18 313.22 300.88 329.3 315.1 312.34 279.11 163.47 NA
The larger chunk works perfectly, but I need to be able to check the correlation on the smaller sections. I'm really new to R so it might be easy, but I've read the boards here and the r manuals and can't find it.
In your example above, your code fails on the first (smaller) data set because row 3 consists only of 0's and NA's, so it has a standard deviation of 0 and so its correlation with any other row will return NA, since computing correlation involves dividing the sample covariance by the sample standard deviation of each vector. It doesn't happen in the larget example because row 3 has sufficient variation to have a non-zero standard deviation.
However, your approach seems a bit convoluted. If you want to compute the correlation between a single row in the matrix and all other rows, sorted by correlation, then you can use cor() on the transposed matrix and sort the result, for example:
mycor <- function(dataset, relative.to=19) {
mat <- t(dataset)
cors <- cor(mat, mat[, relative.to], use="na.or.complete")
cors[order(drop(cors)), ]
}
mycor(dataset)
Quantmod version 0.4.0
Function getSymbols returns empty dates when using Google as source, not using Yahoo.
Google data seems fine, checking http://www.google.com/finance/historical?cid=700196&startdate=Sep+30%2C+2010&enddate=Nov+1%2C+2010&num=30&ei=6sqlUoieA5SLsgfq5gE&output=csv
Reproducable with followong code:
library(quantmod)
Sys.setenv(TZ="UTC")
DataG <- getSymbols('XLF',src="google",auto.assign=FALSE, from = '2010-09-30', to = '2010-11-01')
DataG
DataY <- getSymbols('XLF',src="yahoo",auto.assign=FALSE, from = '2010-09-30', to = '2010-11-01')
DataY
Retested in version 0.4.1, still NA's in date!
DataG <- getSymbols('XLF',src="google",auto.assign=FALSE, from = '2010-09-30', to = '2010-11-01')
DataG
XLF.Open XLF.High XLF.Low XLF.Close XLF.Volume
2010-09-30 14.46 14.63 14.34 14.34 107539828
<NA> 14.49 14.55 14.34 14.50 132131830
<NA> 14.48 14.59 14.33 14.40 85547602
<NA> 14.53 14.78 14.42 14.73 133006599
<NA> 14.76 14.80 14.67 14.72 64754368
<NA> 14.79 14.84 14.58 14.66 71795649
<NA> 14.68 14.73 14.62 14.70 62422677
<NA> 14.71 14.73 14.62 14.68 41265794
<NA> 14.60 14.87 14.57 14.84 65831033
<NA> 14.94 15.00 14.82 14.86 112666954
<NA> 14.72 14.75 14.46 14.60 169232668
<NA> 14.64 14.72 14.25 14.34 132860239
<NA> 14.30 14.69 14.30 14.67 78307701
<NA> 14.56 14.77 14.41 14.47 146739470
<NA> 14.48 14.69 14.35 14.61 96600861
<NA> 14.67 14.78 14.49 14.61 73596983
<NA> 14.64 14.69 14.56 14.60 41264255
<NA> 14.73 14.75 14.53 14.55 45766940
<NA> 14.49 14.60 14.46 14.57 47408863
<NA> 14.52 14.63 14.47 14.58 62701109
<NA> 14.66 14.70 14.49 14.58 57911184
<NA> 14.54 14.59 14.48 14.56 39827062
2010-11-01 14.59 14.69 14.42 14.56 65746592
DataY <- getSymbols('XLF',src="yahoo",auto.assign=FALSE, from = '2010-09-30', to = '2010-11-01')
DataY
XLF.Open XLF.High XLF.Low XLF.Close XLF.Volume XLF.Adjusted
2010-09-30 14.46 14.63 14.34 14.35 107532900 13.65
2010-10-01 14.49 14.55 14.34 14.50 132129000 13.80
2010-10-04 14.48 14.59 14.33 14.40 85547600 13.70
2010-10-05 14.53 14.79 14.42 14.73 133006600 14.01
2010-10-06 14.76 14.80 14.67 14.72 64754400 14.01
2010-10-07 14.79 14.84 14.58 14.66 71794600 13.95
2010-10-08 14.68 14.73 14.62 14.70 62412700 13.99
2010-10-11 14.71 14.73 14.62 14.68 41265800 13.97
2010-10-12 14.60 14.87 14.57 14.85 65831100 14.13
2010-10-13 14.94 15.00 14.82 14.86 112667000 14.14
2010-10-14 14.72 14.75 14.46 14.60 169232700 13.89
2010-10-15 14.65 14.72 14.25 14.35 132854700 13.65
2010-10-18 14.30 14.69 14.30 14.67 78305300 13.96
2010-10-19 14.56 14.77 14.41 14.47 146739500 13.77
2010-10-20 14.48 14.69 14.35 14.61 96600900 13.90
2010-10-21 14.67 14.78 14.49 14.61 73588900 13.90
2010-10-22 14.64 14.69 14.56 14.60 41264300 13.89
2010-10-25 14.73 14.75 14.53 14.55 45766800 13.84
2010-10-26 14.49 14.60 14.46 14.57 47400100 13.86
2010-10-27 14.52 14.63 14.47 14.58 62701200 13.87
2010-10-28 14.66 14.70 14.49 14.58 57907600 13.87
2010-10-29 14.54 14.59 14.48 14.56 39826600 13.85
2010-11-01 14.59 14.69 14.42 14.56 65743100 13.85
installed.packages()["quantmod", "Version"]
[1] "0.4-1"
There is a problem with localization and date format. This works for me.
invisible(Sys.setlocale("LC_MESSAGES", "C"))
invisible(Sys.setlocale("LC_TIME", "C"))
Now try getSymbols with google as data source.