Replace all Na with False in selected columns in R (2023)

In selected columns in R, replace all NA with FALSE

If you want to perform substitution on a subset of variables, you can still use thees.na(*) <-trick as follows:

df[c("x1", "x2")][en.in(df[c("x1", "x2")])] <- FALSE

In my opinion, using temporary variables makes the logic easier:

variables.a.replace <- c("x1", "x2")
df2 <- df[vars.to.replace]
df2[en.na(df2)] <- FALSE
df[vars.to.replace] <- df2

How to replace NA values ​​in a table for selected columns

You can do:

x[, 1:2][es.na(x[, 1:2])] <- 0

or better (IMHO) use the variable names:

x[c("a", "b")][es.na(x[c("a", "b")])] <- 0

In both cases,1:2ÖTaxi")can be replaced by a predefined array.

Replace only some NA values ​​for selected rows and only for one column in R

df$type[!df$Ask & is.na(df$type)] <- "Antworten"takes you to the desired table:

> enter <-
+ c(NA, rep("Question",3), NA, NA, rep("Answer",4), rep(NA,3), rep("Answer",2),
+ AND, "Frage", AND, rep("Answer",2), AND, AND)
> Asked <- c(
+ V, repeat (F, 9), V, repeat (F, 4), V, repeat (F, 4), V, F
+)
> df <- data.frame(title=1:22,comments=1:22,type,asked)
> df$type[!df$Asked & is.na(df$type)] <- "Antworten"
> d.f.
Title Comment Type Requested
1 1 1 <NA> TRUE
2 2 2 Question WRONG
3 3 3 question WRONG
4 4 4 question WRONG
5 5 5 answers WRONG
6 6 6 answers WRONG
7 7 7 Answer WRONG
8 8 8 Answer WRONG
9 9 9 Answer WRONG
10 10 10 Answer WRONG
11 11 11 <NA> TRUE
12 12 12 answers WRONG
13 13 13 answers WRONG
14 14 14 Answer WRONG
15 15 15 Answer WRONG
16 16 16 <NA> TRUE
17 17 17 Question WRONG
18 18 18 answers WRONG
19 19 19 Answer WRONG
20 20 20 Answer WRONG
21 21 21 <NA> TRUE
22 22 22 answers WRONG

R Replace NA for all columns except *

You can usemutate_in:

library (dplyr)

Delete them by name

df %>% mutate_at(vars(-c(Date, thatCol)), ~replace(., is.na(.), 0))

Delete them by position

df %>% mutate_at(-c(1,4), ~replace(., is.na(.), 0))

Select them by name

df %>% mutate_at(vars(col1, thisCol, col999), ~replace(., is.na(.), 0))

Select them by position

df %>% mutate_at(c(2, 3, 5), ~replace(., is.na(.), 0))

if you want to usereplace_in

df %>% mutate_at(vars(-c(Datum, thatCol)), alignr::replace_na, 0)

look at thatmutate_inwill soon be replaced bybetweenShedplyr 1.0.0.

How to represent NA as false in R

For me I think the most beneficial way would be to use adplyrvoncase_whenFunction and explicitly declare asTHEThe cases you mentioned need to be processed.

Replicating your example (note that I'm explicitly defining NAs here. Your NAs were the result of R failing to handle a string ("NA") in a numeric array.

col1 = as.numeric(c(10, 2, 15, 2, NA_real_, 15))
col2 = as.numeric(c(15, 15, 2, 2, 15, NA_real_))
heads <- data.frame(column1, column2)

so much for himspielenfunction andcase_whenfunction I loaddplyr. If you are not familiarcase_whenIt's like an ifelse with multiple conditions. Each condition is followed by a tilde "~". What comes after the tilde is what gets assigned if the condition is true. To set "everything else" to an X value, typeTRUE ~ "x"since this will obviously evaluate to true for all other cases not met in the above conditions.

This should do what you want:

library (dplyr)
tests <-moved (.data = tests,
G5 = case_when(col1 > 5 & col2 > 5 ~ "Sim", #Original
(es.na(Split1) & Split2 > 5) | (col1 > 5 & is.na(col2)) ~ "Sim",
TRUE ~ "No")) # Everything else is set to "No".
prove
#> col1 col2 G5
#> 1 10 15 SIM Card
#> 2 2 15 No
#> 3 15 2 No
#> 4 2 2 No
#> 5 OF 15 Yes
#> 6 15 E Yes

Replace NA with an interpolated value for specific column fields in r

It is not specified?na.ca.

An object with a structure similar to the object with NAs replaced by interpolation. see you approx.only internal NAs are replacedand leading or trailing NAs are omitted if na.rm = TRUE, or not replaced if na.rm = FALSE.

By default, theca.EUna.rm = TRUE

na.approx(objeto, x = index(objeto), xout, ..., na.rm = TRUE, maxgap = Inf, Along)

So we can change the code to

mis_datos[, 42] <- na.approx(mis_datos[, 42], na.rm = FALSE)

In a large dataset it is possible to have leading/trailing NAs and using the op-code will result in an output vector with fewer elements likena.rm = TRUE, which triggers the length difference error when replacing

Replace selected column values ​​based on another dataframe with a different size

Data:

dfa <- read.table(text="Accesión Columna1 Columna2 Columna3 Id. de raíz
2000_1 0 0,2 14 2000 1
2000_2 0,01 0,2 17 2000 2
2001_1 0,012 0,22 11 2001 1
2001_2 0.011 0.231 17 2001 2", head = T)

Libraries and Functions:

library (ordered)
cv <- function(x) 100 * (sd(x) / mean(x))

Solution:

Basically, if we get straight to the point and look at the end result, you want to replace the values ​​inColuna1: Coluna3comTHEif CV is greater than 30. Otherwise you want to keep the original values. The following code does this.

DFA %>%
group_by(Root) %>%
move_at(vars(Column1:Column3),
list ( ~ if ( cv ( . ) > 30 ) ONLY . ))

Result:

#> #Erm Tibble: 4 x 6
#> Membership Column 1 Column 2 Column 3 Root ID
#> <fct> <dbl> <dbl> <dbl> <int> <int>
#> 1 2000_1 n/a 0.2 14 2000 1
#> 2 2000_2 n/a 0.2 17 2000 2
#> 3 2001_1 0.012 0.22 n/a 2001 1
#> 4 2001_2 0.011 0.231 n/a 2001 2nd

More complicated approaches:

If we follow his train of thought, we end up with more complicated code, shown below;

DFA %>%
select_if(function(col) is.numeric(col) & all(col != .$ID)) %>%
group_by(Root) %>%
summarize_each(list(resume)) %>%
move_at(vars(Column1:Column3),
list(~ if not(. > 30, NA, 0))) %>%
left_join(dfa[,c("Root", "ID")], . , by = "Root") %>%
bind_rows(dfa, .) %>%
group_by(Raíz, ID) %>%
summarise_each(list(~ if(is.numeric(.)) sum(., na.rm = FALSE) else first(.))) %>%
Ungroup %>%
select(-ID, -Root, tudo())

Explanation:

1. Selection of numeric columns exceptI WANTED.
2. group byFuente.
3. CV calculation for all columns.
4. Replace CV values ​​greater than 30 withTHEand the rest with 0. I plan on summing with the original values ​​since it seems OP is interested in keeping the NAs (i.e. greater than 30) of this CV matrix but leaving the other values ​​in the original data set unchanged . Then add 0, leaving the last one unchanged while those NA(na.rm = F) affects the values.
5. Add the ID column again so the CV array is the same size (per row) as the original record. Also, it will be used later for grouping.
6. Link records row by row.
7. group byFuentemiI WANTED.
8. Summarize numeric columns (i.e.coluna1,coluna2etc.) by adding the values ​​of the original data frame and the modified CV matrix and keeping the first value of other columns (since the original data frame was the first inbind_rowsthis means that the original values ​​are preserved).
9. Ungroup to avoid future conflicts.
10. Rearranging the columns in the order specified by the OP.

another solutionIt would be very similar to the above, but instead of joining to get the ID column and expanding the CV array, you could keep them from the start by summarizing them as a list column and then nesting them.

DFA %>%
mutate(ID = as.factor(ID)) %>%
group_by(Root) %>%
summarise_each(lista(~ if(es.numeric(.)) cv(.) else lista(.))) %>%
move_at(vars(Column1:Column3),
list(~ if not(. > 30, NA, 0))) %>%
unsest(cols = c(Accessed, ID)) %>%
mutate(ID = as.integer(ID)) %>%
bind_rows(dfa, .) %>%
group_by(Raíz, ID) %>%
summarise_each(list(~ if(is.numeric(.)) sum(., na.rm = FALSE) else first(.))) %>%
Ungroup %>%
select(-ID, -Root, tudo())

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