如果不同，则用字符串将列分开

Question

I have a data frame, and I want to spli their columns if they contain different strings or words.

I am tryng different methods in R and it is not working

My data frame looks like this:

df <- data.frame(x = c(NA, "TAP1", "TAP1", "TAP2"), y = c("TAP1", "TAP2", "TAP2", "TAP3" ))

And, for example, I am trying with the first column this:

df <- data.frame(x = c(NA, "TAP1", "TAP1", "TAP2"))
df %>% separate(x, c("TAP1", "TAP2"), extra = "drop", fill = "right")

but is not working

I am having the next output:

TAP1 TAP2
1 <NA> <NA>
2 TAP1 <NA>
3 TAP1 <NA>
4 TAP2 <NA>

My expected output is:

 TAP1 TAP2
1 <NA> <NA>
2 TAP1 <NA>
3 TAP1 <NA>
4 <NA> TAP2

And I would like to do the same for all columns in the complete data frame where I have different combinations of words like TAP1, TAP2, TAP3 ... etc.

In this example, the final table taking in to account column x and y would be.

 df <- data.frame(x = c(NA, "TAP1", "TAP1", "TAP2"), y = c("TAP1", "TAP2",   "TAP2", "TAP3" ))

  TAP1 TAP2 TAP1.1 TAP2.2 TAP3.3
1 <NA> <NA> TAP1   <NA>   <NA>
2 TAP1 <NA> <NA>   TAP2   <NA>
3 TAP1 <NA> <NA>   TAP2   <NA>
4 <NA> TAP2 <NA>   <NA>   TAP3

Answer 1

We can do this with spread

library(tidyverse)
df %>% 
   mutate(n = row_number()) %>% 
   group_by(x) %>% 
   mutate(rn = row_number(), y = x) %>%
   spread(y, x) %>% 
   select(TAP1, TAP2)
# A tibble: 4 x 2
#  TAP1  TAP2 
#  <fct> <fct>
#1 <NA>  <NA> 
#2 TAP1  <NA> 
#3 TAP1  <NA> 
#4 <NA>  TAP2 

---

With multiple columns, we can gather and spread

rownames_to_column(df, 'rn') %>%
   gather(key, val, -rn) %>%
   mutate(val1 = val) %>% 
   unite(val, val,key) %>% 
   group_by(val) %>%    # not really need for this example
   mutate(ind = row_number()) %>% # not needed here though
   spread(val, val1) %>%
   select(starts_with("TAP"))
# A tibble: 4 x 5
# TAP1_x TAP1_y TAP2_x TAP2_y TAP3_y
#  <chr>  <chr>  <chr>  <chr>  <chr> 
#1 <NA>   TAP1   <NA>   <NA>   <NA>  
#2 TAP1   <NA>   <NA>   TAP2   <NA>  
#3 TAP1   <NA>   <NA>   TAP2   <NA>  
#4 <NA>   <NA>   TAP2   <NA>   TAP3  

Answer 2

Here's a base solution. This goes through all possible factors in your column (ie, TAP1 , TAP2 ) and checks whereabouts they're present. For places where they are present, it returns the name of the level. For places where they're absent, it returns NA . Then, I repackage the resulting list into a data frame and rename the columns.

# Original data frame
df <- data.frame(x = c(NA, "TAP1", "TAP1", "TAP2"))

# Repackage
df2 <- data.frame(lapply(levels(df$x), function(x)ifelse(df$x == x, x, NA)))

# Fix names
names(df2) <- levels(df$x)

# Check results
df2
#>   TAP1 TAP2
#> 1 <NA> <NA>
#> 2 TAP1 <NA>
#> 3 TAP1 <NA>
#> 4 <NA> TAP2

^{Created on 2019-05-29 by the reprex package (v0.3.0)}

---

In light of your update:

# Original data frame
df <- data.frame(x = c(NA, "TAP1", "TAP1", "TAP2"), 
                 y = c("TAP1", "TAP2",   "TAP2", "TAP3" ))

# Define splitter function
splitter <- function(foo){
  tmp <- data.frame(lapply(levels(foo), function(x)ifelse(foo == x, x, NA)))
  names(tmp) <- levels(foo)
  tmp
}

# Run over data frame and bind together
do.call(cbind, lapply(df, splitter))
#>   x.TAP1 x.TAP2 y.TAP1 y.TAP2 y.TAP3
#> 1   <NA>   <NA>   TAP1   <NA>   <NA>
#> 2   TAP1   <NA>   <NA>   TAP2   <NA>
#> 3   TAP1   <NA>   <NA>   TAP2   <NA>
#> 4   <NA>   TAP2   <NA>   <NA>   TAP3

^{Created on 2019-05-29 by the reprex package (v0.3.0)}

Same rationale as before, but I define a function that is applied to each column and the results are bound together using do.call and cbind .

Answer 3

A solution using the tidyverse and the dummies package. df3 is the final output.

library(tidyverse)
library(dummies)

df2 <- dummy.data.frame(df) %>% select(-ends_with("NA"))

cols <- str_remove(names(df2), regex("^x|^y"))

df3 <- modify2(df2, cols, ~ifelse(.x == 0, NA, .y))

df3
#   xTAP1 xTAP2 yTAP1 yTAP2 yTAP3
# 1  <NA>  <NA>  TAP1  <NA>  <NA>
# 2  TAP1  <NA>  <NA>  TAP2  <NA>
# 3  TAP1  <NA>  <NA>  TAP2  <NA>
# 4  <NA>  TAP2  <NA>  <NA>  TAP3