I have a matrix which I wanted to convert to a data frame. As I could not
succeed and resorted to export to csv and reimport it again. Why did I fail
in the attempt and how can I achieve what I wanted without this
roundabouts?
The original matrix:
> str(comb_model0)
num [1:90, 1:4] 3.5938 0.0274 0.0342 0.0135 0.0207 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:90] "(Intercept)" "as.factor(dow)1" "as.factor(dow)2"
"as.factor(dow)3" ...
..$ : chr [1:4] "Estimate" "Std. Error" "t value" "Pr(>|t|)"
> dim(comb_model0)
[1] 90 4
> class(comb_model0)
[1] "matrix"
>
>
> head(comb_model0,10)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.593793 0.012537 286.656 0.000000000
as.factor(dow)1 0.027378 0.007348 3.726 0.000196109
as.factor(dow)2 0.034211 0.007348 4.656 0.000003288
as.factor(dow)3 0.013481 0.007393 1.823 0.068281371
as.factor(dow)4 0.020708 0.007358 2.814 0.004903774
as.factor(dow)5 0.030164 0.007327 4.117 0.000038886
as.factor(dow)6 0.021723 0.007327 2.965 0.003039448
as.factor(fluepi) 0.044272 0.014697 3.012 0.002601912
o3max 0.003441 0.001723 1.998 0.045785798
One of the many conversion attempts:
> xx<-as.data.frame(comb_model0)
> xx
Error in data.frame(Estimate = c(" 3.59379258", " 0.02737812", "
0.03421067", :
duplicate row.names: (Intercept), as.factor(dow)1, as.factor(dow)2,
as.factor(dow)3, as.factor(dow)4, as.factor(dow)5, as.factor(dow)6,
as.factor(fluepi)
write.csv(comb_model0, "test.csv")
test<-read.csv("test.csv")
class(test)
[1] "data.frame"
> dim(test)
[1] 90 5
head(test,9)
X Estimate Std..Error t.value
Pr...t..
1 (Intercept) 3.59379258 0.012537 286.65611
0.000000000
2 as.factor(dow)1 0.02737812 0.007348 3.72596
0.000196109
3 as.factor(dow)2 0.03421067 0.007348 4.65575
0.000003288
4 as.factor(dow)3 0.01348142 0.007393 1.82343
0.068281371
5 as.factor(dow)4 0.02070761 0.007358 2.81419
0.004903774
6 as.factor(dow)5 0.03016411 0.007327 4.11665
0.000038886
7 as.factor(dow)6 0.02172334 0.007327 2.96476
0.003039448
8 as.factor(fluepi) 0.04427231 0.014697 3.01232 0.002601912
9 o3max 0.00344111 0.001723 1.99772
0.045785798
--
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