R: Adding Nas into Data Frame

R: Adding NAs into Data Frame

You can use the reshape2 package:

# make sample data frame
df <- read.table(text = "Name   Position   Value
a         1        0.2
a         3        0.4
a         4        0.3
b         1        0.5
b         2        0.4
b         5        0.3
c         2        0.3
c         3        0.4
c         5        0.1
d         1        0.2
d         2        0.4
d         3        0.5", header = TRUE, stringsAsFactors = FALSE)

library('reshape2')
df2 <- dcast(df, Name ~ Position)
df3 <- melt(df2, value.name = "Value", variable.name = "Position")
df3[order(df3$Name), ]
#    Name Position Value
# 1     a        1   0.2
# 5     a        2    NA
# 9     a        3   0.4
# 13    a        4   0.3
# 17    a        5    NA
# 2     b        1   0.5
# 6     b        2   0.4
# 10    b        3    NA
# 14    b        4    NA
# 18    b        5   0.3
# 3     c        1    NA
# 7     c        2   0.3
# 11    c        3   0.4
# 15    c        4    NA
# 19    c        5   0.1
# 4     d        1   0.2
# 8     d        2   0.4
# 12    d        3   0.5
# 16    d        4    NA
# 20    d        5    NA

Assigning NAs into a dataframe in R

We can first copy Value column to Desired_output column and find out the indices (inds) where Value is greater than 1 and add NA to that row and next two rows as well.

A$Desired_output <- A$Value
inds <- which(A$Value > 1)
A$Desired_output[unique(c(inds, inds + 1, inds + 2))] <- NA
A

#   Event Value Desired_output
#1      1     5             NA
#2      2     3             NA
#3      2     0             NA
#4      2     0             NA
#5      2     0              0
#6      2     2             NA
#7      2     0             NA
#8      3     1             NA
#9      3    10             NA
#10     4     0             NA
#11     4     0             NA
#12     4    NA             NA
#13     4    NA             NA
#14     4    NA             NA
#15     5     1              1
#16     6     0              0
#17     6     8             NA
#18     6     0             NA
#19     7     0             NA

How can I add several NA's as rows to data frame?

Similar to GKi's solution but without rbind():

df[c(rep(NA, 4L), seq_len(nrow(df))), ]
#      X1.3 X4.6 X7.9
# NA     NA   NA   NA
# NA.1   NA   NA   NA
# NA.2   NA   NA   NA
# NA.3   NA   NA   NA
# 1       1    4    7
# 2       2    5    8
# 3       3    6    9

Replace NAs in dataframe with values from second dataframe based on multiple criteria

You can create a unique key to update df2.

unique_key1 <- paste(df1$A, df1$B)
unique_key2 <- paste(df2$A, df2$B)
inds <- is.na(df2$C)
df2$C[inds] <- df1$C[match(unique_key2[inds], unique_key1)]
df2

#         A     B  C    E
#1  20210901 15:00 74 A 74
#2  20210903 17:00 27 C 27
#3  20210904 18:00 60 D 60
#4  20210906 20:00  7  F 7
#5  20210907 21:00 96 G 96
#6  20210908 22:00 98 H 98
#7  20210909 23:00 38 I 38
#8  20210910 00:00 89 J 89
#9  20210912 02:00 69 L 69
#10 20210913 03:00 72 M 72
#11 20210914 04:00 76 N 76
#12 20210915 05:00 63 O 63
#13 20210916 06:00 13 P 13
#14 20210918 08:00 25 R 25
#15 20210919 09:00 92 S 92
#16 20210920 10:00 21 T 21
#17 20210921 11:00 79 U 79
#18 20210922 12:00 41 V 41
#19 20210924 14:00 97 X 97
#20 20210925 15:00 16 Y 16

data

cbind creates a matrix, use data.frame to create dataframes.

df1 <- data.frame(A, B, C, D)
df2 <- data.frame(A, B, C, E)

How do I add random `NA`s into a data frame

Return x within your function:

> df <- apply (df, 2, function(x) {x[sample( c(1:n), floor(n/10))] <- NA; x} )
> tail(df)
      id   age  sex
[45,] "45" "41" NA 
[46,] "46" NA   "f"
[47,] "47" "38" "f"
[48,] "48" "32" "f"
[49,] "49" "53" NA 
[50,] "50" "74" "f"

Randomly insert NAs into dataframe proportionaly

df <- data.frame(A = 1:10, B = 11:20, c = 21:30)
head(df)
##   A  B  c
## 1 1 11 21
## 2 2 12 22
## 3 3 13 23
## 4 4 14 24
## 5 5 15 25
## 6 6 16 26

as.data.frame(lapply(df, function(cc) cc[ sample(c(TRUE, NA), prob = c(0.85, 0.15), size = length(cc), replace = TRUE) ]))
##     A  B  c
## 1   1 11 21
## 2   2 12 22
## 3   3 13 23
## 4   4 14 24
## 5   5 NA 25
## 6   6 16 26
## 7  NA 17 27
## 8   8 18 28
## 9   9 19 29
## 10 10 20 30

It's a random process, so it might not give 15% every time.

Filling data across a data frame when there are NAs that should be left NAs

Here's a way with dplyr -

df %>% 
  mutate_at(-1, ~replace(., is.na(.) & cumsum(!is.na(.)) > 0, 0))

        dates item_1 item_2 item_3 item_4
1  2019-01-01     NA     NA     NA     NA
2  2019-01-02      1     NA     NA     NA
3  2019-01-03      2     NA     NA      1
4  2019-01-04      3     NA      8      2
5  2019-01-05      4      1      9      3
6  2019-01-06      5      2     10      4
7  2019-01-07      6      3     11      5
8  2019-01-08      7      0      0      6
9  2019-01-09      0      0      0      0
10 2019-01-10      8      9      2      0

A slightly shorter version of replace condition, thanks to @Frank: is.na(.) & cummax(!is.na(.))

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