R: Insert a Vector as a Row in Data.Frame

R: Insert a vector as a row in data.frame

I wouldn't claim this to be the most elegant and pretty solution out there, but it gets the job done.
Notice that each dataframe row carries its own row name, which becomes a problem when inserting new lines. That being said, you can mend this with row.names (see below).

my.df <- data.frame(a = runif(10), b = runif(10), c = runif(10))
my.vec <- c(1, 1, 1)
new.df <- rbind(my.df[1:5, ], my.vec, my.df[6:nrow(my.df), ])
new.df
            a         b          c
1  0.45433791 0.3798105 0.84514864
2  0.07074529 0.4985765 0.53912585
3  0.09645574 0.5441647 0.96636213
4  0.60788436 0.6070706 0.53791603
5  0.01593911 0.1697248 0.62697924
6  1.00000000 1.0000000 1.00000000
61 0.98455694 0.2206702 0.85500531
7  0.85356834 0.5279596 0.27462326
8  0.48028935 0.6689572 0.05428349
9  0.95675901 0.6875491 0.77642924
10 0.24691330 0.7980741 0.24013096

row.names(new.df) <- 1:nrow(new.df)  # make row names pretty again

Add a row in a dataframe taking values from a vector R

You may add a list column to the dataframe and then use tidyr::unnest to get them as separate rows.

inds <- df$w2 == "eat"
df$new_col[!inds] <- 'no correspondance'
df$new_col[inds] <- list(vec)
tidyr::unnest(df, new_col)

#     id w1    w2    new_col          
#  <dbl> <chr> <chr> <chr>            
#1   123 abc   eat   value1           
#2   123 abc   eat   value2           
#3   123 fgh   drink no correspondance
#4   456 kit   ty    no correspondance

Insert Values of Vector into Data Frame on Nearest Value

Use findInterval() to identify valid indexes

aidx = findInterval(c, df$a)
bidx = findInterval(c, df$b) + 1
keep = aidx == bidx

then update the original data frame

df[aidx[keep], "c"] = c[keep]

I would expect this to be fast for up to 100's of millions of rows, provided the data.frame rows are already sorted.

How to add a named vector as a row to a data frame, reordered according to column name order?

Make a data frame out of v2 prior to the rbind:

rbind(df, as.data.frame(t(v2)))
##   id va vb vc
## 1  1 11 21 31
## 2  2 12 22 32
## 3  4 14 25 NA
## 4  9 19 NA 34

Here is why this works:

v2 has names, but it acts like a column vector to as.data.frame:

as.data.frame(v2)
##    v2
## va 19
## id  9
## vc 34
## vb NA

Thus, you must transpose the data to put it into the correct form:

as.data.frame(t(v2))
##   va id vc vb
## 1 19  9 34 NA

How to create a one-row data frame from a vector in R?

We could use unnest_wider after returning the output in a list in summarise

library(dplyr)
library(tidyr)
mtcars %>%
    group_by(cyl) %>%
    summarise(out = list(boxplot.stats(wt)$stats)) %>% 
    unnest_wider(out) %>% 
    rename_at(-1, ~ str_replace(., '\\.+', 'x'))
# A tibble: 3 x 6
#    cyl    x1    x2    x3    x4    x5
#   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1     4  1.51  1.88  2.2   2.62  3.19
#2     6  2.62  2.82  3.22  3.44  3.46
#3     8  3.17  3.52  3.76  4.07  4.07

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Or if we want to use the OP's method, then set the names for that vector and use as_tibble_row

library(purrr)
library(stringr)
mtcars %>% 
  group_by(cyl) %>% 
  group_map(~ tibble(cyl = first(.x$cyl), 
          setNames(boxplot.stats(.$wt)$stats, str_c('x', 1:5)) %>% 
             as_tibble_row) , .keep = TRUE) %>%
  bind_rows
# A tibble: 3 x 6
#    cyl    x1    x2    x3    x4    x5
#  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1     4  1.51  1.88  2.2   2.62  3.19
#2     6  2.62  2.82  3.22  3.44  3.46
#3     8  3.17  3.52  3.76  4.07  4.07

---

As the output of group_map is always a list, it may be better to use group_modify to return a tbl thus avoiding the last map_dfr/bind_rows

mtcars %>% 
  group_by(cyl) %>% 
  group_modify(~ setNames(boxplot.stats(.$wt)$stats, str_c('x', 1:5)) %>%
         as_tibble_row , .keep = TRUE) %>% 
  ungroup
# A tibble: 3 x 6
#    cyl    x1    x2    x3    x4    x5
#  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1     4  1.51  1.88  2.2   2.62  3.19
#2     6  2.62  2.82  3.22  3.44  3.46
#3     8  3.17  3.52  3.76  4.07  4.07

Add vector to each row of a dataframe

There's a specific function for this purpose:

sweep(data, 2, to_add, "+")
                         X0 X3 X6 X9 X12 X18 X21
chr5_89951600_89954799   24 26 23 23  34  33  29
chr16_70874600_70876999  21 26 29 15  23  26  22
chr2_51953000_51955199   13 26 11 15  31  20  39
chr3_143120600_143123799 31 40 39 24  30  31  37
chr15_34771400_34774599  33 42 35 22  29  18  40
chr2_13077000_13083999   42 73 56 49  78  57  59

It applies a particular function across all items of a "margin" using the values in the "statistic". It's the mean of that margin (and here you want columns) by default, but it can be any vector of the same length as the margin.

R: add character vector to data.frame row

Suppose the initial dataset ('DF') have some 'factor' columns as in the post. Before rbind the vector ('v'), change the 'factor' columns to 'character'. For that create a logical index ('indx') of the factor columns, loop through the factor columns and convert to character (lapply(DF[indx],..)). rbind the vector with the dataset and convert the column classes with type.convert.

indx <- sapply(DF, is.factor)
DF[indx] <- lapply(DF[indx], as.character)
DF1 <- rbind(v, DF)
DF1[] <- lapply(DF1, type.convert)
DF1
#  V1 V2 V3 V4
#1  g  H  7  8
#2  a  e  1  4
#3  d  f  2  5
#4  c  g  3  6

str(DF1)
#'data.frame':  4 obs. of  4 variables:
# $ V1: Factor w/ 4 levels "a","c","d","g": 4 1 3 2
# $ V2: Factor w/ 4 levels "e","f","g","H": 4 1 2 3
# $ V3: int  7 1 2 3
# $ V4: int  8 4 5 6

data

DF <- structure(list(V1 = structure(c(1L, 3L, 2L), .Label = c("a", 
 "c", "d"), class = "factor"), V2 = structure(1:3, .Label = c("e", 
 "f", "g"), class = "factor"), V3 = 1:3, V4 = 4:6), .Names = c("V1", 
"V2", "V3", "V4"), class = "data.frame", row.names = c(NA, -3L))

v <- c('g', 'H', 7, 8)

Add vector as a column to a data.frame with fill = NA

We create a NA column and then assign the 'my_vector' based on the length of the vector. Here seq_along(my_vector) return 1:3, thus the first 3 elements are replaced with 'my_vector' values

my_df$new_column <- NA_character_
my_df$new_column[seq_along(my_vector)] <- my_vector

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Or this can be done in a single step if we pad NA at the end by making use of length<-

my_df$new_column <-  `length<-`(my_vector, nrow(my_df))

-output

my_df
#   person                    role                             new_column
#1    Oleg         EDITOR-IN-CHIEF                   Lomonosov University
#2   Yurii DEPUTY EDITORS-IN-CHIEF            Russian Academy of Sciences
#3    Igor DEPUTY EDITORS-IN-CHIEF Institute of Acoustics, Moscow, Russia
#4 Mikhail     Coordinating Editor                                   <NA>

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