How to Replace Certain Values in a Specific Rows and Columns with Na in R

How to replace certain values in a specific rows and columns with NA in R?

Since your data structure is 2 dimensional, you can find the indices of the rows containing a specific value first and then use this information.

which(DF$Fruits == "Pineapple")
[1]  3
DF$Weight[which(DF$Fruits == "Pineapple")] <- NA

You should be aware of that which will return a vector, so if you have multiple fruits called "Pineapple" then the previous command will return all indices of them.

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

df$type[!df$Asked & is.na(df$type)] <- "Replies" gets you to your desired table:

> type <-
+   c(NA, rep("Question",3), NA, NA,  rep("Answer",4), rep(NA, 3), rep("Answer",2),
+     NA, "Question", NA, rep("Answer",2), NA,NA)
> Asked <- c(
+   T, rep(F, 9), T, rep(F, 4), T, rep(F, 4), T,F
+ )
> df <- data.frame(title = 1:22, comments = 1:22, type, Asked)
> df$type[!df$Asked & is.na(df$type)] <- "Replies"
> df
   title comments     type Asked
1      1        1     <NA>  TRUE
2      2        2 Question FALSE
3      3        3 Question FALSE
4      4        4 Question FALSE
5      5        5  Replies FALSE
6      6        6  Replies FALSE
7      7        7   Answer FALSE
8      8        8   Answer FALSE
9      9        9   Answer FALSE
10    10       10   Answer FALSE
11    11       11     <NA>  TRUE
12    12       12  Replies FALSE
13    13       13  Replies FALSE
14    14       14   Answer FALSE
15    15       15   Answer FALSE
16    16       16     <NA>  TRUE
17    17       17 Question FALSE
18    18       18  Replies FALSE
19    19       19   Answer FALSE
20    20       20   Answer FALSE
21    21       21     <NA>  TRUE
22    22       22  Replies FALSE

How to change NA value in a specific row and column?

Try something like this. Add in a rowID and then create a lookup data frame with your replacements. Then you can just left_join() in the new values.

The lookup_df replaces your "if this company and this row then new value" logic.

library(dplyr)

df <- tibble(companyID = c(rep(1, 5), rep(2, 6)), value = NA_character_)

lookup_df <- tibble(companyID = c(1, 2), rowID = c(3, 4), valueNew = c("F", "D"))

df %>% 
  group_by(companyID) %>% 
  mutate(rowID = row_number()) %>% 
  left_join(lookup_df, by = c("companyID", "rowID")) %>% 
  mutate(value = coalesce(value, valueNew)) %>% 
  select(companyID, value)

result:

# A tibble: 11 x 2
# Groups:   companyID [2]
   companyID value
       <dbl> <chr>
 1         1 NA   
 2         1 NA   
 3         1 F    
 4         1 NA   
 5         1 NA   
 6         2 NA   
 7         2 NA   
 8         2 NA   
 9         2 D    
10         2 NA   
11         2 NA  

Replacing certain NA values with a specific value

We can use ifelse (or case_when) with your conditions:

your_data %>%
  mutate(Cq = ifelse(is.na(Cq) & Dilution %in% c("1", "1:1"), 31, Cq))
#   Sample Dilution Cq
# 1      1        1 31
# 2      1      1:1 31
# 3      1      1:5 27
# 4      2        1 13
# 5      2      1:1 14
# 6      2      1:5 15

---

Using this data:

your_data = read.table(text = 'Sample   Dilution    Cq
1   1   NA
1   1:1     NA
1   1:5     27
2   1   13
2   1:1     14
2   1:5     15', header = T)

replace values with NA in several columns

We may do this in two steps - loop across the columns that have 'VAR' followed by digits (\\d+) in column names, replace the values where the first two characters are not AA or DD to NA, then replace the corresponding DATE column to NA based on the NA in the 'VAR1', 'VAR2' columns

library(dplyr)
library(stringr)
DF %>%
    mutate(across(matches("^VAR\\d+$"),
        ~ replace(., !substr(., 1, 2)  %in% c("AA", "DD"), NA)), 
      across(ends_with("DATE"), 
     ~ replace(., is.na(get(str_remove(cur_column(), "DATE"))), NA)))

-output

# A tibble: 5 × 5
     ID VAR1  VAR1DATE   VAR2  VAR2DATE  
  <int> <chr> <chr>      <chr> <chr>     
1     1 AABB  2001-01-01 <NA>  <NA>      
2     2 AACC  2001-01-02 AACC  2001-01-02
3     3 <NA>  <NA>       DDCC  2001-01-03
4     4 DDAA  2001-01-04 <NA>  <NA>      
5     5 <NA>  <NA>       <NA>  <NA>      

---

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