Merging Two Columns into One in R

Merge 2 columns into one in dataframe

Some alternative way with function unite in tidyr:

library(tidyr)
df = data.frame(year=2009:2013, customerID=20227:20231) # using akrun's data

unite(df, newcol, c(year, customerID), remove=FALSE)

#      newcol year customerID
#1 2009_20227 2009      20227
#2 2010_20228 2010      20228
#3 2011_20229 2011      20229
#4 2012_20230 2012      20230
#5 2013_20231 2013      20231

Combine the contents of two columns into one column using R

We can use pivot_longer and this should be more general as it can also do reshaping based on other patterns and multiple columns as well. Note that pivot_longer succeeds the reshape2 function melt with more enhanced capabilities and bug fixes

library(dplyr)
library(tidyr)
pivot_longer(df1, cols = time1:time2, values_to = 'time') %>%
      select(-name)

-output

# A tibble: 6 x 2
#     id  time
#  <dbl> <dbl>
#1     1    10
#2     1    15
#3     1    20
#4     1    25
#5     1    30
#6     1    35

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Or using base R with stack

transform(stack(df1[-1])[1], id = rep(df1$id, 2))[2:1]

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Or can use data.frame with unlist

data.frame(id = df1$id, value = unlist(df1[-1], use.names = FALSE))

How can I combine multiple columns into one in an R dataset?

A solution using tidyverse. dat4 is the final output.

library(tidyverse)

dat2 <- dat %>%
  mutate(ID = 1:n())

dat3 <- dat2 %>%
  pivot_longer(a:f, names_to = "value", values_to = "number") %>%
  filter(number == 1) %>%
  select(-number)

dat4 <- dat2 %>%
  left_join(dat3) %>%
  select(-ID, -c(a:f)) %>%
  replace_na(list(value = "none"))

dat4
#   age gender  race insured value
# 1  13 Female white       0  none
# 2  12 Female white       1  none
# 3  19   Male other       0     f
# 4  19 Female white       0     b
# 5  13 Female white       0     a
# 6  13 Female white       0     b
# 7  13 Female white       0     f

DATA

dat <- read.table(text = "      age gender a     b     c     d     e     f     race  insured 
 1     13 Female 0     0     0     0     0     0     white 0      
 2     12 Female 0     0     0     0     0     0     white 1      
 3     19 Male   0     0     0     0     0     1     other 0      
 4     19 Female 0     1     0     0     0     0     white 0      
 5     13 Female 1     1     0     0     0     1     white 0",
                  header = TRUE)

How can I combine several columns into one variable, tacking each onto the end of the other and grouping by values in an ID variable?

Try to set the inputs of the function pivot_longer()correctly as cols and values_to. cols=... defines the columns which you are taking the values from. values_to = ... defines the new name of the column where you are writing the values you took from 'cols'. Actually I think you were doing good, just pivot_longer returns always the names of the columns which values you are taking from, unless you try other trickier things.

library(tidyverse)

df = data.frame(
  a = c("string1","string2"),
  b= c("string11","string12"),
  c = c("string21", "string22"),
  ID = c("1111","2222")
)

df %>% 
  pivot_longer(cols = names(df)[1:3],
                    values_to = "newvar") %>% 
  select(newvar, ID)

Output:

# A tibble: 6 x 2
  newvar   ID   
  <chr>    <chr>
1 string1  1111 
2 string11 1111 
3 string21 1111 
4 string2  2222 
5 string12 2222 
6 string22 2222

combine two similar columns in r

I guess you can use coalesce here which finds the first non-missing value at each position.

library(dplyr)
gadd.us %>% mutate(w1iq = coalesce(w1iq, wasiIQw1))

This will select values from w1iq if present or if w1iq is NA then it would select value from wasiIQw1. You can switch the position of w1iq and wasiIQw1 if you want to give priority to wasiIQw1.

How to combine two columns into one in R, so that each value in the second column becomes every other value in the first column?

Not fully sure what your logic is for the ymin/ymax but this is the general idea, run it by line to see what's happening.

percent_car %>%
  pivot_longer(names_to = "key", values_to = "value", -position) %>%
  mutate(
    yes_no = str_extract(key, "yes|no"),
    key = str_remove_all(key, "yes|no|_")
  ) %>%
  pivot_wider(names_from = key, values_from = value) %>%
  arrange(position) %>%
  mutate(
    ymax = if_else(yes_no == "yes", perc, 1),
    ymin = if_else(yes_no == "yes", 0, 1-perc)
      
    )
  )

case_when will be your friend if the if_else needs to be nested

How to combine two columns of different datasets in R?

You could use rbind after ensuring that they both have the same names:

 C <- rbind(setNames(A, 'X'), setNames(B, 'X'))

Another way is to concatenate the two:

 C <- data.frame(X = c(A$X1, B$term))

R Merging multiple columns into one depending on if the cell is empty

Try replacing the "" in your columns with NA and your code should work.

df <- data.frame(
  ResponseID = c (1:6),
  ZER_Condition = c ("Low","Med",NA,NA,NA,NA),
  LOW_Condition = c (NA,NA,"High","Low",NA,NA),
  MED_Condition = c (NA,NA,NA,NA,"High",NA),
  HIG_Condition = c (NA,NA,NA,NA,NA,"Low")
)

df %>% mutate (Merged_Condition = coalesce(ZER_Condition,LOW_Condition,MED_Condition,HIG_Condition)) %>%
  select(ResponseID, Merged_Condition)

Combining two columns in order to get one column in R

One solution is using dplyr's coalesce function

lebanon$test <- dplyr::coalesce(lebanon$income_under_median, lebanon$income_above_median)

or, within a pipeline

library(dplyr)
lebanon %>%
  mutate(test = coalesce(income_under_median, income_above_median))

Output

#    income_under_median   income_above_median                  test
# 1                 <NA> 2.501.000 - 3.000.000 2.501.000 - 3.000.000
# 2  751.000 - 1.000.000                  <NA>   751.000 - 1.000.000
# 3  751.000 - 1.000.000                  <NA>   751.000 - 1.000.000
# 4        Below 451.000                  <NA>         Below 451.000
# 5                 <NA>       Below 1.501.000       Below 1.501.000
# 6                 <NA>       Below 1.501.000       Below 1.501.000
# 7                 <NA> 2.001.000 - 2.500.000 2.001.000 - 2.500.000
# 8                 <NA> 1.501.000 - 2.000.000 1.501.000 - 2.000.000
# 9    451.000 - 750.000                  <NA>     451.000 - 750.000
# 10                <NA> 3.001.000 - 4.000.000 3.001.000 - 4.000.000

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