How to fill several columns of a dataframe

Ellenz · May 7, 2019, 6:23am

I want to create an empty dataframe and populate it row by row. The 1st column is latitude, the 2nd column is longitude, the 3rd is elevation, the next 12 columns represent values for 12 months. I don't know how to fill the last 12 columns at one time. There are many years, so each year takes one row in the dataframe. I think if this step has no problem, I will write it in a loop to fill all the rows. Thanks for your help.

df.year = data.frame()
for(i in 1:100){
df.year[i,1] = 20.15
df.year[i,2] = 108.10
df.year[i,3] = 5.5
df.year[i,] = cbind(df.year1[i,],c(7.97, 84.85, 0.00, 2.82, 0.00, 0.00, 24.49, 0.00, 0.00, 0.00, 0.00, 1.89))
}
Warning messages:
1: In data.frame(..., check.names = FALSE) :
row names were found from a short variable and have been discarded
2: In [<-.data.frame(*tmp*, i, , value = list(V1 = c(20.15, 20.15, :
replacement element 1 has 12 rows to replace 1 rows
...
or this line below

df.year[i,4:4+11] = cbind(df.year[i,],c(7.97, 84.85, 0.00, 2.82, 0.00, 0.00, 24.49, 0.00, 0.00, 0.00, 0.00, 1.89))

it gives the error

Error in [<-.data.frame(*tmp*, i, 4:4 + 11, value = list(V1 = c(20.15, :
new columns would leave holes after existing columns
In addition: Warning message:
In data.frame(..., check.names = FALSE) :
row names were found from a short variable and have been discarded

technocrat · May 7, 2019, 7:14am

(As an aside, give some consideration into making the gis x-y-z columns into a single column sf object.)

Each of these location is unique? Create a data column to serve as a key.

You have multiple temporal observations for each location? Then you need a single column date of length equal to length(key). Fill in any missing values with NA.

So, now you have id, x,y,z, datetime as your colnames.

Presumably, each of your observation relates to a single date. Create vector like

c(7.97, 84.85, 0.00, 2.82, 0.00, 0.00, 24.49, 0.00, 0.00, 0.00, 0.00, 1.89 .... end year))

rbind, iff the sizes are equal and you have a data field lined up with a date field.

Now your data frame colnames = id, x,y,z,date,observation

What about month?

df %>% filter(date = [extract date field]

The moral of this story is avoid creating a column that you can calculate in a pipe chain.

Ellenz · May 7, 2019, 7:20am

Sorry, I don't quite understand what you meant here.

Yes, each of these location is unique.
At each location, there are several years of data, where 12 months each year. But I want to create the datasets year by year, so each year has one dataframe. Each row represents one location, and I want to fill the rows and columns of the dataframe in a loop.
df.year represents year 1, df.year2 represents year 2, dr.year3 represents year 3, etc.
In df.year, the number of rows represents the number of locations. The columns are in the order: latitude, longitude, elevation, value.month1, value.month2, value.month3, ..., value.month12.
Thanks for your help.

technocrat · May 7, 2019, 7:50am

My suggestion is that if you create a column and populated it with the dates of each observation ad another column with the corresponding observation, you don't need to worry about years, months, days, hours or seconds. They are all in the datetime column. (If you don't have HH:MM:SS, you can always use a date object).

ONCE you have a master dataframe (preferably a tibble) in that form, it's trivial to dplyr::filter the data into years and a mutate it into months (if I can do it, trust me, it's easy.)

I do pro bono work for non-profits. Message me if you'd like more detailed help

Ellenz · May 7, 2019, 11:36am

I'm not familiar with tibble or dplyr:filter. Would you be able to provide a sample code? Thanks.

Yarnabrina · May 7, 2019, 2:45pm

I don't follow your question at all.

You've first created a data frame with 0 columns and 0 rows. Then, you're adding 3 elements to each row, and suddenly you are cbind-ing with a vector of length 12. I'm afraid that it doesn't make much sense (at least not to me). I feel the same way about the following, too.

If I try to modify your code to make it work, I'll do something like this:

df.year = data.frame()
for(i in 1:100) {
  df.year[i, 1] <- 20.15
  df.year[i, 2] <-  108.10
  df.year[i, 3] <-  5.5
  df.year[i, 3 + (1:12)] <- c(7.97, 84.85, 0.00, 2.82, 0.00, 0.00, 24.49, 0.00, 0.00, 0.00, 0.00, 1.89)
}
df.year

It creates a data frame with 100 rows and 15 columns, with all rows being same. Does that make any sense to you? To me, it doesn't.

This is indeed possible, but from your description, it seems to me that you already have the data and will not be creating it any way. Then, it's not clear to me how will you update the data frame with non-identical rows inside a for loop.
If you already have data in some other R object, surely there's a way to convert it to a data frame? Or, if you want to update the data frame manually, you can initialise a null data frame and then use rbind. But growing a data frame is probably inefficient, as hinted in this SO post.

technocrat · May 7, 2019, 6:38pm

A tibble is a superset of a data frame. The dplyr filter() function allows you to easily subset by row

newobj <- oldobj %>% filter(mass > 20)

Ellenz · May 8, 2019, 1:13am

I just have question about this line. I think after populating one row, I know how to populate the other rows. But When I use this code

df.year[i, 3 + (1:12)] <- c(7.97, 84.85, 0.00, 2.82, 0.00, 0.00, 24.49, 0.00, 0.00, 0.00, 0.00, 1.89)

it gives this error:

Error in *tmp*[[j]] : recursive indexing failed at level 2

How to solve this? Thanks.

Ellenz · May 8, 2019, 1:24am

I added a small loop and it worked.

df.year = data.frame()
list1 = c(7.97, 84.85, 0.00, 2.82, 0.00, 0.00, 24.49, 0.00, 0.00, 0.00, 0.00, 1.89)
for(i in 1:100) {
df.year[i, 1] <- 20.15
df.year[i, 2] <- 108.10
df.year[i, 3] <- 5.5
for(j in 1:12){
df.year[i,3+j] <- list1[i]
}
}
df.year

Yarnabrina · May 8, 2019, 3:09am

I don't face this error. See below:

results of my code

df.year = data.frame()
for(i in 1:100) {
  df.year[i, 1] <- 20.15
  df.year[i, 2] <-  108.10
  df.year[i, 3] <-  5.5
  df.year[i, 3 + (1:12)] <- c(7.97, 84.85, 0.00, 2.82, 0.00, 0.00, 24.49, 0.00, 0.00, 0.00, 0.00, 1.89)
}
df.year
#>        V1    V2  V3   V4    V5 V6   V7 V8 V9   V10 V11 V12 V13 V14  V15
#> 1   20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 2   20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 3   20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 4   20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 5   20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 6   20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 7   20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 8   20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 9   20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 10  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 11  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 12  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 13  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 14  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 15  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 16  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 17  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 18  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 19  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 20  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 21  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 22  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 23  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 24  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 25  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 26  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 27  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 28  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 29  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 30  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 31  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 32  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 33  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 34  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 35  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 36  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 37  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 38  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 39  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 40  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 41  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 42  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 43  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 44  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 45  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 46  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 47  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 48  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 49  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 50  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 51  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 52  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 53  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 54  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 55  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 56  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 57  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 58  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 59  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 60  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 61  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 62  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 63  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 64  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 65  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 66  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 67  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 68  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 69  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 70  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 71  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 72  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 73  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 74  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 75  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 76  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 77  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 78  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 79  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 80  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 81  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 82  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 83  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 84  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 85  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 86  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 87  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 88  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 89  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 90  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 91  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 92  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 93  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 94  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 95  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 96  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 97  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 98  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 99  20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89
#> 100 20.15 108.1 5.5 7.97 84.85  0 2.82  0  0 24.49   0   0   0   0 1.89

^{Created on 2019-05-08 by the reprex package (v0.2.1)}

If you're satisfied with your code, that's okay. But it generates only the 1st 12 rows for columns 4-15. And, the values for all these columns in the 1st 12 rows are same. Do you really want that?

results of your code

df.year = data.frame()
list1 = c(7.97, 84.85, 0.00, 2.82, 0.00, 0.00, 24.49, 0.00, 0.00, 0.00, 0.00, 1.89)
for(i in 1:100) {
  df.year[i, 1] <- 20.15
  df.year[i, 2] <- 108.10
  df.year[i, 3] <- 5.5
  for(j in 1:12){
    df.year[i,3+j] <- list1[i]
  }
}
df.year
#>        V1    V2  V3    V4    V5    V6    V7    V8    V9   V10   V11   V12
#> 1   20.15 108.1 5.5  7.97  7.97  7.97  7.97  7.97  7.97  7.97  7.97  7.97
#> 2   20.15 108.1 5.5 84.85 84.85 84.85 84.85 84.85 84.85 84.85 84.85 84.85
#> 3   20.15 108.1 5.5  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00
#> 4   20.15 108.1 5.5  2.82  2.82  2.82  2.82  2.82  2.82  2.82  2.82  2.82
#> 5   20.15 108.1 5.5  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00
#> 6   20.15 108.1 5.5  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00
#> 7   20.15 108.1 5.5 24.49 24.49 24.49 24.49 24.49 24.49 24.49 24.49 24.49
#> 8   20.15 108.1 5.5  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00
#> 9   20.15 108.1 5.5  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00
#> 10  20.15 108.1 5.5  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00
#> 11  20.15 108.1 5.5  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00
#> 12  20.15 108.1 5.5  1.89  1.89  1.89  1.89  1.89  1.89  1.89  1.89  1.89
#> 13  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 14  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 15  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 16  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 17  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 18  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 19  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 20  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 21  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 22  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 23  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 24  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 25  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 26  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 27  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 28  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 29  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 30  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 31  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 32  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 33  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 34  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 35  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 36  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 37  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 38  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 39  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 40  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 41  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 42  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 43  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 44  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 45  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 46  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 47  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 48  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 49  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 50  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 51  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 52  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 53  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 54  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 55  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 56  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 57  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 58  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 59  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 60  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 61  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 62  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 63  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 64  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 65  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 66  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 67  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 68  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 69  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 70  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 71  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 72  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 73  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 74  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 75  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 76  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 77  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 78  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 79  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 80  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 81  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 82  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 83  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 84  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 85  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 86  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 87  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 88  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 89  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 90  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 91  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 92  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 93  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 94  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 95  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 96  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 97  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 98  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 99  20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 100 20.15 108.1 5.5    NA    NA    NA    NA    NA    NA    NA    NA    NA
#>       V13   V14   V15
#> 1    7.97  7.97  7.97
#> 2   84.85 84.85 84.85
#> 3    0.00  0.00  0.00
#> 4    2.82  2.82  2.82
#> 5    0.00  0.00  0.00
#> 6    0.00  0.00  0.00
#> 7   24.49 24.49 24.49
#> 8    0.00  0.00  0.00
#> 9    0.00  0.00  0.00
#> 10   0.00  0.00  0.00
#> 11   0.00  0.00  0.00
#> 12   1.89  1.89  1.89
#> 13     NA    NA    NA
#> 14     NA    NA    NA
#> 15     NA    NA    NA
#> 16     NA    NA    NA
#> 17     NA    NA    NA
#> 18     NA    NA    NA
#> 19     NA    NA    NA
#> 20     NA    NA    NA
#> 21     NA    NA    NA
#> 22     NA    NA    NA
#> 23     NA    NA    NA
#> 24     NA    NA    NA
#> 25     NA    NA    NA
#> 26     NA    NA    NA
#> 27     NA    NA    NA
#> 28     NA    NA    NA
#> 29     NA    NA    NA
#> 30     NA    NA    NA
#> 31     NA    NA    NA
#> 32     NA    NA    NA
#> 33     NA    NA    NA
#> 34     NA    NA    NA
#> 35     NA    NA    NA
#> 36     NA    NA    NA
#> 37     NA    NA    NA
#> 38     NA    NA    NA
#> 39     NA    NA    NA
#> 40     NA    NA    NA
#> 41     NA    NA    NA
#> 42     NA    NA    NA
#> 43     NA    NA    NA
#> 44     NA    NA    NA
#> 45     NA    NA    NA
#> 46     NA    NA    NA
#> 47     NA    NA    NA
#> 48     NA    NA    NA
#> 49     NA    NA    NA
#> 50     NA    NA    NA
#> 51     NA    NA    NA
#> 52     NA    NA    NA
#> 53     NA    NA    NA
#> 54     NA    NA    NA
#> 55     NA    NA    NA
#> 56     NA    NA    NA
#> 57     NA    NA    NA
#> 58     NA    NA    NA
#> 59     NA    NA    NA
#> 60     NA    NA    NA
#> 61     NA    NA    NA
#> 62     NA    NA    NA
#> 63     NA    NA    NA
#> 64     NA    NA    NA
#> 65     NA    NA    NA
#> 66     NA    NA    NA
#> 67     NA    NA    NA
#> 68     NA    NA    NA
#> 69     NA    NA    NA
#> 70     NA    NA    NA
#> 71     NA    NA    NA
#> 72     NA    NA    NA
#> 73     NA    NA    NA
#> 74     NA    NA    NA
#> 75     NA    NA    NA
#> 76     NA    NA    NA
#> 77     NA    NA    NA
#> 78     NA    NA    NA
#> 79     NA    NA    NA
#> 80     NA    NA    NA
#> 81     NA    NA    NA
#> 82     NA    NA    NA
#> 83     NA    NA    NA
#> 84     NA    NA    NA
#> 85     NA    NA    NA
#> 86     NA    NA    NA
#> 87     NA    NA    NA
#> 88     NA    NA    NA
#> 89     NA    NA    NA
#> 90     NA    NA    NA
#> 91     NA    NA    NA
#> 92     NA    NA    NA
#> 93     NA    NA    NA
#> 94     NA    NA    NA
#> 95     NA    NA    NA
#> 96     NA    NA    NA
#> 97     NA    NA    NA
#> 98     NA    NA    NA
#> 99     NA    NA    NA
#> 100    NA    NA    NA

^{Created on 2019-05-08 by the reprex package (v0.2.1)}

If that's what you want, then your problem is solved.

Ellenz · May 8, 2019, 4:06am

No, I don't want the columns in the 1st 12 rows are the same. I just checked and corrected an error. This should be no problem? columns 4-15 take the values of list1 by order.

df.year[i,3+j] <- list1[j]

system · May 29, 2019, 4:06am

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