I was running a procedure to make the first row as column names. Since one value is NA one column has become empty. Here is the example.
df <- data.frame(var1 = c("a","b","c","d",NA),
var2 = c(1,2,3,4,NA),
var3 = c(NA,7,8,9,10))
> df
var1 var2 var3
1 a 1 NA
2 b 2 7
3 c 3 8
4 d 4 9
5 <NA> NA 10
df1 <- df %>%
janitor::row_to_names(row_number = 1)
> df1
a 1 NA
2 b 2 7
3 c 3 8
4 d 4 9
5 <NA> NA 10
How can I remove when the column name is empty?
The desired output would be:
> df2
a 1
2 b 2
3 c 3
4 d 4
5 <NA> NA
Related
I have a data.frame (the eBird basic dataset) where many observers may upload a record from a same sighting to a database, in this case, the event is given a "group identifier"; when not from a group session, a NA will appear in the database; so I'm trying to filter out all those duplicates from group events and keep all NAs, I'm trying to do this without splitting the dataframe in two:
library(dplyr)
set.seed(1)
df <- tibble(
x = sample(c(1:6, NA), 30, replace = T),
y = sample(c(letters[1:4]), 30, replace = T)
)
df %>% count(x,y)
gives:
> df %>% count(x,y)
# A tibble: 20 x 3
x y n
<int> <chr> <int>
1 1 a 1
2 1 b 2
3 2 a 1
4 2 b 1
5 2 c 1
6 2 d 3
7 3 a 1
8 3 b 1
9 3 c 4
10 4 d 1
11 5 a 1
12 5 b 2
13 5 c 1
14 5 d 1
15 6 a 1
16 6 c 2
17 NA a 1
18 NA b 2
19 NA c 2
20 NA d 1
I want no NA at x to be grouped together, as here happened with "NA b" and "NA c" combinations; distinct function has no information on not taking NAs into the computation; is splitting the dataframe the only solution?
With distinct an option is to create a new column based on the NA elements in 'x'
library(dplyr)
df %>%
mutate(x1 = row_number() * is.na(x)) %>%
distinct %>%
select(-x1)
Or we can use duplicated with an OR (|) condition to return all NA elements in 'x' with filter
df %>%
filter(is.na(x)|!duplicated(cur_data()))
# A tibble: 20 x 2
# x y
# <int> <chr>
# 1 1 b
# 2 4 b
# 3 NA a
# 4 1 d
# 5 2 c
# 6 5 a
# 7 NA d
# 8 3 c
# 9 6 b
#10 2 b
#11 3 b
#12 1 c
#13 5 d
#14 2 d
#15 6 d
#16 2 a
#17 NA c
#18 NA a
#19 1 a
#20 5 b
I want to make value of each row of column A , NA ,where column B is 2:
data
A B
1 2
2 4
NA 5
6 2
output
A B
NA 2
2 4
NA 5
NA 2
first and last row of B was 2 so A got NA in those.
Here's a way using ifelse in base R -
df$A <- ifelse(df$B == 2, NA_real_, df$A)
set.seed(0)
df <- data.frame(A = sample(1:10, size=5, replace=T),
B = sample(1:10, size=5, replace=T))
df
A B
1 9 7
2 4 2
3 7 3
4 1 1
5 2 5
df$A[df$B == 2] <- NA
df
A B
1 9 7
2 NA 2
3 7 3
4 1 1
5 2 5
I am trying to remove the same rows with NA in df1 from df2.
eg.
df1
A
1 1
2 NA
3 7
4 NA
df2
A B C D
1 2 4 7 10
2 3 6 1 3
3 9 5 1 3
4 4 9 2 5
Intended outcome:
df1
A
1 1
3 7
df2
A B C D
1 2 4 7 10
3 9 5 1 3
I have already tried things along the lines of...
newdf <- df2[-which(rowSums(is.na(df1))),]
and
noNA <- function(x) { x[!rowSums(!is.na(df1)) == 1]}
NMR_6mos_noNA <- as.data.frame(lapply(df2, noNA))
or
noNA <- function(x) { x[,!is.na(df1)]}
newdf3 <- as.data.frame(lapply(df2, noNA))
We can use is.na to create a logical condition and use that to subset the rows of 'df1' and 'df2'
i1 <- !is.na(df1$A)
df1[i1, , drop = FALSE]
# A
#1 1
#3 7
df2[i1,]
# A B C D
# 1 2 4 7 10
#3 9 5 1 3
I have 2 data frames with different rownames, e.g.:
df1 <- data.frame(A = c(1,3,7,1,5), B = c(5,2,9,5,5), C = c(1,1,3,4,5))
df2 <- data.frame(A = c(4,3,2), B = c(4,4,9), C = c(3,9,3))
rownames(df2) <- c(1, 3, 6)
> df1
A B C
1 1 5 1
2 3 2 1
3 7 9 3
4 1 5 4
5 5 5 5
> df2
A B C
1 4 4 3
3 3 4 9
6 2 9 3
I need to insert NA-rows in both data frames for each row that does exist in only one of the data frames. In the given example:
> df1
A B C
1 1 5 1
2 3 2 1
3 7 9 3
4 1 5 4
5 5 5 5
6 NA NA NA
> df2
A B C
1 4 4 3
2 NA NA NA
3 3 4 9
4 NA NA NA
5 NA NA NA
6 2 9 3
I will have to perform this operation many times with different data frames, so I need an automatized way to do this. I was trying to solve the issue with different if/else loops, but I am sure there must be a much more automatized way.
We can use functions union, %in% or intersect to find the common rownames and assign rows of an NA dataframe with the values of the dataset if it matches the rownames
un1 <- union(rownames(df1), rownames(df2))
d1 <- as.data.frame(matrix(NA, ncol = ncol(df1),
nrow = length(un1), dimnames = list(un1, names(df1))))
d2 <- d1
d1[rownames(d1) %in% rownames(df1),] <- df1
d2[rownames(d2) %in% rownames(df2),] <- df2
d2
# A B C
#1 4 4 3
#2 NA NA NA
#3 3 4 9
#4 NA NA NA
#5 NA NA NA
#6 2 9 3
I have the following data.frame.
a <- c(rep("A", 3), rep("B", 3), rep("C",2), "D")
b <- c(NA,1,2,4,1,NA,2,NA,NA)
c <- c(1,1,2,4,1,1,2,2,2)
d <- c(1,2,3,4,5,6,7,8,9)
df <-data.frame(a,b,c,d)
a b c d
1 A NA 1 1
2 A 1 1 2
3 A 2 2 3
4 B 4 4 4
5 B 1 1 5
6 B NA 1 6
7 C 2 2 7
8 C NA 2 8
9 D NA 2 9
I want to remove duplicate rows (based on column A & C) so that the row with values in column B are kept. In this example, rows 1, 6, and 8 are removed.
One way to do this is to order by 'a', 'b' and the the logical vector based on 'b' so that all 'NA' elements will be last for each group of 'a', and 'b'. Then, apply the duplicated and keep only the non-duplicate elements
df1 <- df[order(df$a, df$b, is.na(df$b)),]
df2 <- df1[!duplicated(df1[c('a', 'c')]),]
df2
# a b c d
#2 A 1 1 2
#3 A 2 2 3
#5 B 1 1 5
#4 B 4 4 4
#7 C 2 2 7
#9 D NA 2 9
setdiff(seq_len(nrow(df)), row.names(df2) )
#[1] 1 6 8
First create two datasets, one with duplicates in column a and one without duplicate in column a using the below function :
x = df[df$a %in% names(which(table(df$a) > 1)), ]
x1 = df[df$a %in% names(which(table(df$a) ==1)), ]
Now use na.omit function on data set x to delete the rows with NA and then rbind x and x1 to the final data set.
rbind(na.omit(x),x1)
Answer:
a b c d
2 A 1 1 2
3 A 2 2 3
4 B 4 4 4
5 B 1 1 5
7 C 2 2 7
9 D NA 2 9
You can use dplyr to do this.
df %>% distinct(a, c, .keep_all = TRUE)
Output
a b c d
1 A NA 1 1
2 A 2 2 3
3 B 4 4 4
4 B 1 1 5
5 C 2 2 7
6 D NA 2 9
There are other options in dplyr, check this question for details: Remove duplicated rows using dplyr