R Partial Reshape Data from Long to Wide

Long to wide format in R based based on one column

Try this tidyverse approach. You data is in long format, so you have to reshape to wide. Here the code:

library(tidyverse)
#Code
df %>% group_by(ID) %>% mutate(Var=paste0('Scan_Number_',1:n())) %>%
  pivot_wider(names_from = Var,values_from=Scan_Number)

Output:

# A tibble: 2 x 3
# Groups:   ID [2]
     ID Scan_Number_1 Scan_Number_2
  <int> <chr>         <chr>        
1     1 E43           E56          
2     2 E65           E98   

Some data used:

#Data
df <- structure(list(ID = c(1L, 1L, 2L, 2L), Scan_Number = c("E43", 
"E56", "E65", "E98")), class = "data.frame", row.names = c(NA, 
-4L))

From long to wide for ALL the columns

You could tidyr::pivot_wider like so:

library(tidyr)

pivot_wider(aa, names_from = type, values_from = -c(month, type))
#> # A tibble: 2 × 7
#>   month value_most value_least NP_most NP_least NO_most NO_least
#>   <dbl>      <dbl>       <dbl>   <dbl>    <dbl>   <dbl>    <dbl>
#> 1     1        0.2         0.8       4        2       1        5
#> 2     2        1           0.1       0        6       2        4

R- How to reshape Long to Wide with multiple variables/columns

Some variables are can be better to together

df %>%
  pivot_wider(id_cols = c(UserID, Full.Name, DOB, EncounterID), names_from = c(QuestionID, QName, labelnospaces), values_from = responses)

  UserID Full.Name  DOB    EncounterID `505_Intro_Were you given any info?` `506_Care_By using this service..`
   <int> <chr>      <chr>        <int> <chr>                                <chr>                             
1      1 John Smith 1-1-90          13 yes                                  yes                               
2      2 Jane Doe   2-2-80          14 no                                   no                                
  `507_Out_How satisfied are you?`
  <chr>                           
1 vsat                            
2 unsat

r reshape data long to wide with unknown number of columns

There's probably a more efficient way to do this, but I can't think of it right now. With two variables that need to be transformed into wide format, I think you may need to cast them separately and then merge the two together. I'd love to be proved wrong though. To do this, I create two new variables which generate a column sequence for each new ID. This will allow them to be filled with NAs easily. With the new columns, it's pretty easy to cast them into the right format and merge them together.

library(plyr)
library(reshape2)

#Assumes your data is read into a variable named x
x <- ddply(x, "IndividualID", transform, 
      castPropClass = paste0("PropClass", seq(length(PropClass))),
      castProp = paste0("Prop", seq(length(Property))))

#Use these two new variables to cast into wide format. Wrap in merge to join together:
merge(dcast(IndividualID ~ castPropClass, value.var = "PropClass", data = x),
      dcast(IndividualID ~ castProp,      value.var = "Property",  data = x))
#Gives you this:
  IndividualID PropClass1 PropClass2 PropClass3 Prop1 Prop2 Prop3
1            1          A          B       <NA>     X     Y  <NA>
2            2          A       <NA>       <NA>     X  <NA>  <NA>
3            3          B          C          A     Y     W     Z

This obviously doesn't have the right "order" of columns, but the data itself is right.

changing (semi)long format to wide in R

Using dplyr and tidyr, you can try:

df %>%
 group_by(number) %>%
 mutate(variable = paste0("letter", row_number())) %>%
 spread(variable, letter)

  number letter1 letter2 letter3
   <int> <chr>   <chr>   <chr>  
1      1 A       <NA>    <NA>   
2      2 B       C       <NA>   
3      3 D       C       A   

Convert from long to wide format with multiple unique variables to other unique variables in R

We can use pivot_wider after creating a sequence column by group

library(dplyr)
library(tidyr)
df %>% 
    group_by(block, species, den) %>% 
    mutate(rn = row_number()) %>% 
    ungroup %>% 
    pivot_wider(names_from = rn, values_from = c(area, size), names_sep = ".")
# A tibble: 8 x 9
#  block species den   area.1 area.2 area.3 size.1 size.2 size.3
#  <fct> <fct>   <fct>  <int>  <int>  <int>  <int>  <int>  <int>
#1 1     A       20         1      2     NA     17     18     NA
#2 1     A       50         3      4     NA     19     20     NA
#3 1     B       20         5      6     NA     21     22     NA
#4 1     B       50         7      8     NA     23     24     NA
#5 2     A       20         9     10     NA     25     26     NA
#6 2     A       50        11     12     NA     27     28     NA
#7 2     B       20        13     14     NA     29     30     NA
#8 2     B       50        15     16     17     31     32     33

reordering my reshape: long to wide with pivot_wider, different column order

It may be easier with names_glue in pivot_wider

library(dplyr)
library(tidyr)
df %>% 
   pivot_wider(names_from = Set, values_from = c(Street, State), 
       names_glue = "{tools::toTitleCase(Set)}_{.value}") %>%   
   dplyr::select(ID, Name, order(readr::parse_number(names(.)[-(1:2)])) + 2)

-output

# A tibble: 3 × 8
  ID    Name  Set1_Street Set1_State Set2_Street Set2_State Set3_Street Set3_State
  <chr> <chr> <chr>       <chr>      <chr>       <chr>      <chr>       <chr>     
1 A     mary  123 St      al         234 St      nc         <NA>        <NA>      
2 B     berry 543 St      fl         492 st      ca         231 st      md        
3 C     paul  492 st      tx         231 st      vt         <NA>        <NA>      

Reshape data set from wide to long format grouped by variable suffix

Using reshape we can set the cutpoints with sep="".

reshape(d, idvar="ID", varying=2:5, timevar="YEAR", sep="", direction="long")
#         ID YEAR MI FRAC
# 1.1995   1 1995  2    3
# 7.1995   7 1995  3   10
# 10.1995 10 1995  1    2
# 1.1996   1 1996  2    4
# 7.1996   7 1996 12    1
# 10.1996 10 1996  1    1

Data

d <- structure(list(ID = c(1L, 7L, 10L), MI_1995 = c(2L, 3L, 1L),
                    FRAC_1995 = c(3L, 10L, 2L), MI_1996 = c(2L, 12L, 1L),
                    FRAC_1996 = c(4L, 1L, 1L)), row.names = c(NA, -3L),
               class = "data.frame")

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