Transpose/Reshape Dataframe Without "Timevar" from Long to Wide Format

Transpose / reshape dataframe without timevar from long to wide format

Assuming your data is in the object dataset:

library(plyr)
## Add a medication index
data_with_index <- ddply(dataset, .(Name), mutate, 
                         index = paste0('medication', 1:length(Name)))    
dcast(data_with_index, Name ~ index, value.var = 'MedName')

##    Name   medication1    medication2      medication3
## 1 Name1 atenolol 25mg   aspirin 81mg sildenafil 100mg
## 2 Name2 atenolol 50mg enalapril 20mg             <NA>

Reshape dataframe without “timevar” and multiple value columns from long to wide format

Third option using dcast from data.table. We create the missing 'time variable' with rowid(key):

library(data.table)
# convert data to a data.table object
setDT(data)
# reshape
dcast(data, key  ~ rowid(key), value.var = c("acitity", "intervall"))

Result

#    key acitity_1    acitity_2 acitity_3   acitity_4 intervall_1 intervall_2 intervall_3 intervall_4
#1:   A  watering remove weeds       cut remove leaf           5           7           6           1
#2:   B  watering remove weeds       cut   fertilize           8           4           2           3

reshape R data frame long to wide

You can use pivot_wider. Also, I added a more compact form of your toy data set using expand.grid.

library(tidyr)

df <- data.frame(y=y, expand.grid(t=c(1,2,3), g=c("g1", "g2"), x=c("A","B")))
pivot_wider(df, values_from = y, names_from = c(x,t), names_sep = ".")

  g        A.1   A.2    A.3    B.1   B.2   B.3
  <fct>  <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl>
1 g1    -0.626 0.184 -0.836  0.487 0.738 0.576
2 g2     1.60  0.330 -0.820 -0.305 1.51  0.390

How to reshape data from long to wide format

Using reshape function:

reshape(dat1, idvar = "name", timevar = "numbers", direction = "wide")

R Reshape data frame from long to wide format?

A possible solution is this

library(tidyverse)

df = read.table(text = "
                year prod value
                2015    PRODA  test1
                2015    PRODA  blue
                2015    PRODA  50
                2015    PRODA  66
                2015    PRODA  66
                2018    PRODB  test2
                2018    PRODB  yellow
                2018    PRODB  70
                2018    PRODB  88.8
                2018    PRODB  88.8
                2018    PRODA  test3
                2018    PRODA  red
                2018    PRODA  55
                2018    PRODA  88
                2018    PRODA  90
                ", header=T, stringsAsFactors=F)

df %>%
  group_by(year, prod) %>%                           # for each year and prod combination
  mutate(id = paste0("new_col_", row_number())) %>%  # enumerate rows (this will be used as column names in the reshaped version)
  ungroup() %>%                                      # forget the grouping
  spread(id, value)                                  # reshape

# # A tibble: 3 x 7
#    year prod  new_col_1 new_col_2 new_col_3 new_col_4 new_col_5
#   <int> <chr> <chr>     <chr>     <chr>     <chr>     <chr>    
# 1  2015 PRODA test1     blue      50        66        66       
# 2  2018 PRODA test3     red       55        88        90       
# 3  2018 PRODB test2     yellow    70        88.8      88.8

Problem when reshaping data from long to wide format in R

Reshaping data with stats::reshape can be tedious. Hadley Wickham and
his team have spent quite some time on creating a comprehensive solution.
First there was the reshape2 package, then tidyr had spread() and gather(),
those are now ~~replaced~~ complemented by pivot_wider() and pivot_longer().

This is how you can use tidyr::pivot_wider() to achieve the result, you seem to
be going for.

library(tidyr)
pivot_wider(
  my_df,
  id_cols = c(transcript, response),
  names_from = hours,
  values_from = exp.change,
  names_prefix = "exp.change_"
)
#> # A tibble: 6 x 7
#>   transcript response exp.change_0 exp.change_2 exp.change_8 exp.change_24
#>   <chr>      <chr>           <dbl>        <dbl>        <dbl>         <dbl>
#> 1 TR100743-… Primary            NA        -43.2       -61.3          965. 
#> 2 TR100987-… Primary            NA        -46.3         3.29        -100. 
#> 3 TR101301-… Primary            NA        -29.6       522.            40.5
#> 4 TR102190-… Tertiary           NA        -18.8         5.49          55.1
#> 5 TR102346-… Primary            NA       -100.  789697313.            18.6
#> 6 TR102352-… Primary            NA        -31.3         9.65          28.5
#> # … with 1 more variable: exp.change_48 <dbl>

I think having dedicated commands with dedicated documentation for the two transformations (wide/long) makes the tidyr commands much easier to use, compared to stats::reshape().

EDIT:
stats::reshape() is giving weird results, because it seems to be having issues dealing with my_df being a tibble. Other than that your command was just fine. Just add in a as.data.frame() and you are good to go.

reshape(
  as.data.frame(my_df),
  idvar = c("transcript", "response"),
  timevar   = "hours",
  v.names = "exp.change",
  direction = "wide"
)
#>            transcript response exp.change.0 exp.change.2  exp.change.8
#> 1   TR100743-c0_g1_i3  Primary           NA    -43.19583 -6.130140e+01
#> 6   TR100987-c0_g1_i2  Primary           NA    -46.25638  3.293969e+00
#> 11 TR101301-c4_g1_i16  Primary           NA    -29.63413  5.222249e+02
#> 16  TR102190-c1_g1_i1 Tertiary           NA    -18.76708  5.494728e+00
#> 21  TR102346-c0_g2_i1  Primary           NA    -99.99996  7.896973e+08
#> 26  TR102352-c4_g2_i5  Primary           NA    -31.33341  9.647458e+00
#>    exp.change.24 exp.change.48
#> 1      964.92512 -5.270607e+01
#> 6      -99.99947  1.067105e+08
#> 11      40.47377 -1.343882e+00
#> 16      55.10727  3.358246e+01
#> 21      18.63375  5.244430e+01
#> 26      28.48553  7.058088e+01

But since it seems that you are already using the tidyverse tidyr::pivot_wider() seems like the best fit.

How to Reshape Long to Wide While Preserving Some Variables in R

Another option only using tidyverse:

library(tidyverse)
#Code
df %>% group_by(id) %>% mutate(idv=paste0('type.',1:n())) %>%
  pivot_wider(names_from = idv,values_from=type)

Output:

# A tibble: 2 x 5
# Groups:   id [2]
  id    dates     type.1 type.2 type.3
  <chr> <chr>     <chr>  <chr>  <chr> 
1 1000  10/5/2019 A      B      B     
2 1001  9/17/2020 C      C      A

Or using row_number() (credits to @r2evans):

#Code 2
df %>% group_by(id) %>% mutate(idv=paste0('type.',row_number())) %>%
  pivot_wider(names_from = idv,values_from=type)

Output:

# A tibble: 2 x 5
# Groups:   id [2]
  id    dates     type.1 type.2 type.3
  <chr> <chr>     <chr>  <chr>  <chr> 
1 1000  10/5/2019 A      B      B     
2 1001  9/17/2020 C      C      A

Reshape long to wide with two columns to expand in R data.table [R]

You may use dcast -

library(data.table)

setDT(data_sample)
dcast(data_sample, code~rowid(code), value.var = c('name', 'numberdata'))

#   code name_1 name_2 numberdata_1 numberdata_2
#1:    1   bill    bob          100          400
#2:    2    rob   john          300         -500
#3:    3    max    joe         -200         -400
#4:    4  mitch   bart          300          100

how to transpose a dataset from wide format to long format in R

Using tidyr::pivot_longer :

tidyr::pivot_longer(df, cols = -c(ID, SEX), 
                   names_to = c('.value', 'n'), 
                   names_pattern = '(.*)(\\d+)')

# A tibble: 6 x 5
#     ID   SEX n     bloodpressure weight
#  <int> <int> <chr>         <int>  <int>
#1     1     1 1                90    100
#2     1     1 2               100    105
#3     1     1 3                NA    112
#4     2     0 1               101    140
#5     2     0 2               120     NA
#6     2     0 3               115    150

Transpose/Reshape Dataframe Without "Timevar" from Long to Wide Format