Change Values in Row Based on a Column Value R

Change values in row based on a column value and replacing specified column range

Try:

x[x$Month %in% c("March", "April"), c("VAR2", "VAR3")] = NA

Change values in row based on a column value r

how about:

x[x$C <10 ,] <- NA

Update row values based on condition in R

A super easy base solution

df <- data.frame(cue=sample(c(1:3),10,replace = T),c2=sample(c(0,1),10,replace = T),c3=sample(c(0,1),10,replace = T))

df$c2 <- ifelse(df$cue==2,1,0)
df$c3 <- ifelse(df$cue==3,1,0)

EDIT

to add another dplyr solution

df <- dplyr::mutate(df,c2= ifelse(cue==2,1,0),c3= ifelse(cue==3,1,0))

Change row value based on another column in DataFrame using dplyr

Instead of filtering and then joining we can directly use mutate_at with case_when

library(dplyr)
df %>% 
    mutate_at(vars(col2, col3), ~ 
       case_when(col1 == 'b' ~  .* 10, col1  == 'c' ~ .* 20, TRUE  ~ .))
#  col1 col2 col3
#1    a    2    5
#2    b   40  100
#3    a    6   15
#4    c  160  400
#5    b  100  250
#6    c  240  600

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Or in the dplyr 1.0.0, it can be done with mutate/across

df %>%
   mutate(across(c(col2, col3), ~ 
       case_when(col1 == 'b' ~  .* 10, col1  == 'c' ~ .* 20, TRUE  ~ .)))
#  col1 col2 col3
#1    a    2    5
#2    b   40  100
#3    a    6   15
#4    c  160  400
#5    b  100  250
#6    c  240  600

Update column rows based on rows in another column

There are several options:

Base R - Option 1

mydf$Description[mydf$Value == 0] <- "TEST"

Base R - Option 2

mydf$Description <- ifelse(mydf$Value == 0, "TEST", mydf$Description)

dplyr - if_else()

library("dplyr")
mydf <- mydf %>% mutate(Description = if_else(Value == 0, "TEST", Description))

dplyr - case_when()

Useful when a number of if statement need to be evaluated.

library("dplyr")
mydf <- mydf %>% mutate(
  Description = case_when(
    Value == 0 ~ "TEST",
    TRUE ~ Description
    )
  )

R - change values in multiple selected rows based on multiple conditional column

Here is an alternative way without loop:

have <- have %>% mutate(cond = col1 == "A" & col2 == "B", id = 1:nrow(have))
met  <- have %>% filter(cond == TRUE) %>% 
        mutate(col3 = "C", col4 = paste(cyl, hp, sep = ","))
have[met$id,] <- met

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