Dplyr Replacing Na Values in a Column Based on Multiple Conditions

dplyr: Replace multiple values based on condition in a selection of columns

A dplyr solution:

library(dplyr)
dt %>%
  mutate(across(3:5, ~ ifelse(measure == "led", stringr::str_replace_all(
    as.character(.),
    c("2" = "X", "3" = "Y")
  ), .)))

Result:

   measure site space qty qty.exit cf
 1:     led    4     1   4        6  3
 2:    exit    4     2   1        4  6
 3:     cfl    1     4   6        2  3
 4:  linear    3     4   1        3  5
 5:     cfl    5     1   6        1  6
 6:    exit    4     3   2        6  4
 7:    exit    5     1   4        2  5
 8:    exit    1     4   3        6  4
 9:  linear    3     1   5        4  1
10:     led    4     1   1        1  1
11:    exit    5     4   3        5  2
12:     cfl    4     2   4        5  5
13:     led    4     X   Y        Y  4
...

How to replace the value with multiple conditions in R

Update: thank to valuable comment of Onyambu:

jimma3n<-jimma3 %>%
    select(Enterdateofexam,Enterdateofexam2, Enterdayofexam, UniqueKey,MEDICALRECORD)%>%
    mutate(
        day= case_when(UniqueKey == 1234 | UniqueKey= 1321 & day==80 ~ 8,
                       UniqueKey == 1234 | UniqueKey= 1321 & day==70 ~ 7,
                       UniqueKey == 1484 & day==79 ~ 7,
                       TRUE ~ day),
        month = case_when(UniqueKey== 1484 & month==79 ~ 09,
                       TRUE ~ month)
        )

OK try this and tell me please:

jimma3n<-jimma3 %>%
    select(Enterdateofexam,Enterdateofexam2, Enterdayofexam, UniqueKey,MEDICALRECORD)%>%
    mutate(
        day= case_when(UniqueKey == 1234 | UniqueKey= 1321 & day==80 ~ 8,
                       UniqueKey == 1234 | UniqueKey= 1321 & day==70 ~ 7,
                       UniqueKey == 1484 & day==79 ~ 7,
                       TRUE ~ day)
        ) %>%
    mutate(
        month = case_when(UniqueKey== 1484 & month==79 ~ 09,
        TRUE ~ month)
        )

Replace NA values with preexisting value based on multiple conditions

Try using ifelse() to apply the same condition.

df$Box_Height <- ifelse(is.na(df$Box_Height) & !is.na(df$Item_Height), df$Item_Height, df$Box_Height)

The ifelse() function requires you to provide values for cases where the condition is true and false separately which ensures that the vector lengths will match. Subsetting df$Box_Height with [ probably results in a vector that is shorter than df$Item_Height, which is unsubsetted.

R replace_na values conditionally by column with multiple conditions

First compute a logical vector, all.na having one component per row which is TRUE if that row's numeric data is all NAs and FALSE otherwise. Then use na.aggregate to fill in all-NA rows. Also use na.aggregate on 2007. Then convert to long form and apply na.locf0 by country and convert back to wide form.

library(dplyr)
library(tidyr)
library(zoo)

all.na <- sample %>%
  select_if(is.numeric) %>%
  { rowSums(is.na(.)) == ncol(.) }

sample %>% 
  mutate_at(-(1:3), ~ if_else(all.na, na.aggregate(.x), .x)) %>%
  mutate(`2007` = na.aggregate(`2007`)) %>%
  gather(key, value, -`Country Name`, -`Country Code`) %>%
  group_by(`Country Name`, `Country Code`) %>%
  mutate(value = na.locf0(value)) %>%
  ungroup %>%
  spread(key, value)

or using only zoo:

library(zoo)

all.na <- apply(is.na(sample[grep("^2", names(sample))]), 1, all)

ix <- -(1:3)
sample.out <- sample
Fill <- function(x) ifelse(all.na, na.aggregate(x), x)
sample.out[ix] <- lapply(sample[ix], Fill)
sample.out$"2007" <- na.aggregate(sample.out$"2007")
sample.out[ix] <- t(apply(sample.out[ix], 1, na.locf0))

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)

R replace multiple columns based on multiple conditions

Try this:

test %>% 
   mutate(substart = ifelse(value <= -1 & is.na(substart),start,substart),
          subend = ifelse(value <= -1 & is.na(subend),end,subend))

Manually replace missing value in a column based on another column

Does this work. Not sure if in your data you'd have NA for all values of geo == ny. Hence I've added & is.na(mark).

library(dplyr)
df %>% mutate(mark = case_when(geo == 'ny' & is.na(mark) ~ 'toyota', TRUE ~ mark))
# A tibble: 5 x 3
  geo   mark   value
  <chr> <chr>  <dbl>
1 texas nissan     2
2 texas nissan    78
3 ny    toyota    65
4 ny    toyota    15
5 ca    audi      22

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