Replacing a Whole Column in R

Replacing a whole column in R

With assignment:

data$B <- whatever
# or
data[, "B"] <- whatever
# or
data[["B"]] <- whatever

Replace all occurrences of a string in a data frame

If you are only looking to replace all occurrences of "< " (with space) with "<" (no space), then you can do an lapply over the data frame, with a gsub for replacement:

> data <- data.frame(lapply(data, function(x) {
+                  gsub("< ", "<", x)
+              }))
> data
  name var1 var2
1    a   <2   <3
2    a   <2   <3
3    a   <2   <3
4    b   <2   <3
5    b   <2   <3
6    b   <2   <3
7    c   <2   <3
8    c   <2   <3
9    c   <2   <3

replacing values in a column with another column R

You can use merge to match by id, then replace in column swl1 those items from datB which exist:

datC <- merge(datA, datB, all.x=TRUE)
datC
##   id swl1 swl2
## 1  1  0.8  0.8
## 2  2  0.7   NA
## 3  3  0.4  0.6
## 4  4  0.7   NA
## 5  5  0.0  0.7

This matches up the rows. Now to replace those values in column swl1 with the non-NA values from column swl2:

datC$swl1 <- ifelse(is.na(datC$swl2), datC$swl1, datC$swl2)
datC$swl2 <- NULL
datC
##   id swl1
## 1  1  0.8
## 2  2  0.7
## 3  3  0.6
## 4  4  0.7
## 5  5  0.7

R: How do I replace a column with another column from a different dataframe?

Using base R,

ped[,6] = bipolar_ctl$Phenotype

Replacing values from a column using a condition in R

# reassign depth values under 10 to zero
df$depth[df$depth<10] <- 0

(For the columns that are factors, you can only assign values that are factor levels. If you wanted to assign a value that wasn't currently a factor level, you would need to create the additional level first:

levels(df$species) <- c(levels(df$species), "unknown") 
df$species[df$depth<10]  <- "unknown" 

Replace all row values according to a value in a specific column in R

We can use across in dplyr - loop across the 'c2', 'c3' columns, and use the logical column from 'c1' to return the values of the column, by default the last condition i.e. TRUE will be all NA

library(dplyr)
data <- data %>% 
   mutate(across(c2:c3, ~ case_when(c1 ~ .x)))

-output

data
# A tibble: 4 × 4
  var   c1    c2    c3   
  <chr> <lgl> <lgl> <lgl>
1 a     TRUE  TRUE  TRUE 
2 b     FALSE NA    NA   
3 c     TRUE  TRUE  TRUE 
4 d     FALSE NA    NA   

Replace matching column values in multiple dataframes with NA in R

As you have the data frames in this format, you can set the values of the required data to NA in the new data frames, and then update the rows of the original data frames with these values using dplyr::rows_update(). This assumes you have at least dplyr v1.0.0.

library(dplyr)

outliers1$eucDist  <- NA
outliers2$eucDist2  <- NA
dat  |>
    rows_update(outliers1, by = c("participant", "target"))  |>
    rows_update(select(outliers2, -eucDist), by = c("participant", "target")) 

#   participant target eucDist eucDist2
# 1          01      1      NA       NA
# 2          01      2    0.16     0.04
# 3          01      3    0.89     0.03
# 4          02      1    0.10     0.05
# 5          02      2    0.11     1.45
# 6          02      3      NA     0.09

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