Here is a solution using dplyr to transform your table into the requested format:

library(dplyr)

mydata<- reshape(mydata, direction='long', 
                varying=c('Morning_1_id', 'Var1', 'Morning_2_id', 'Var2', 'Afternoon_1_id', 'Var3', 'Afternoon_2_id', 'Var4'), 
                timevar='Var',
                times=c('Morning1', 'Morning2', 'Afternoon1', 'Afternoon2'),
                v.names=c('Id', 'Var'),
                idvar='Day')

mydata<- tibble::rownames_to_column(mydata)
mydata$rowname<- gsub("^.*\\.","", mydata$rowname)
names(mydata)<- c("Time", "Day", "Var", "Id")
mydata<- mydata[,c(2,4,3,1)]

An Introduction to reshape2, Wide- to long-format data: the melt function. For this example we'll work with the airquality dataset that is built into R . First we'll change the  The melt function takes data in wide format and stacks a set of columns into a single column of data. To make use of the function we need to specify a data frame, the id variables (which will be left at their settings) and the measured variables (columns of data) to be stacked.

Consider base R by building a list of every 2nd column of a sequence and then row binding all df elements:

df_list <- lapply(seq(3, length(df), 2), function(i) {
  sub <- df[c(1, (i-1):i)]                                      # SUBSET BY COLS
  sub <- transform(sub, Time = sub("_id", "", names(df)[i-1]))  # ADD TIME VAR
  setNames(sub, c("Day", "Id", "Var", "Time"))                  # RENAME COLS  
})

long_df <- do.call(rbind, df_list)

head(long_df, 20)    
#    Day                  Id      Var      Time
# 1    1 20180501-033-000001 3.156667 Morning_1
# 2    2 20180502-033-000001 2.986667 Morning_1
# 3    3 20180503-033-000001 3.073333 Morning_1
# 4    4 20180507-033-000001 3.236667 Morning_1
# 5    5 20180508-033-000001 3.030000 Morning_1
# 6    6 20180509-033-000001 3.010000 Morning_1
# 7    7 20180510-033-000001 2.916667 Morning_1
# 8    8 20180511-033-000001 3.293333 Morning_1
# 9    9 20180514-033-000001 3.136667 Morning_1
# 10  10 20180516-033-000001 3.116667 Morning_1
# 11  11 20180517-033-000003 2.843333 Morning_1
# 12  12 20180518-033-000001 3.033333 Morning_1
# 13  13 20180521-033-000002 3.173333 Morning_1
# 14  14 20180523-033-000001 3.336667 Morning_1
# 15   1 20180501-033-000002 2.866667 Morning_2
# 16   2 20180502-033-000002 2.930000 Morning_2
# 17   3 20180503-033-000002 3.070000 Morning_2
# 18   4 20180507-033-000002 2.990000 Morning_2
# 19   5 20180508-033-000002 3.150000 Morning_2
# 20   6 20180509-033-000002 3.020000 Morning_2

Efficient reshaping using data.tables, Introduction. The melt and dcast functions for data.table s are for reshaping wide-​to-long and long-to  This the generic melt function. See the following functions for the details about different data structures: further arguments passed to or from other methods. Should NA values be removed from the data set? This will convert explicit missings to implicit missings. lixx5423@d.umn.edu at Feb 27, 2019 reshape2 v1.4.3.

Here is a tidyverse option

corrected per comments from @Calum You

df %>%
  gather(Time, Var, -Day, -c(Var1, Var2, Var3, Var4)) %>%
  mutate(Time = gsub('.{3}$', '',Time),
         start = substr(Time, 1, 1),
         end = substr(Time, nchar(Time), nchar(Time)),
         id = paste0(start,end),
         Val = case_when(id=='M1' ~ Var1,
                         id=='M2' ~ Var2,
                         id=='A1' ~ Var3,
                         id=='A2' ~ Var4)) %>% 
  dplyr::select(Day, Id=Var, Val, Time)

Original incorrect code

df %>%
   gather(Time, Var, -Day, -c(Var1, Var2, Var3, Var4)) %>%
   gather( key, value, -Day, -Time, -Var) %>% 
   mutate(Time = gsub('.{3}$', '',Time)) %>% 
   dplyr::select(Day, Id=Var, Var=value, Time)

melt function, Arguments. data. Data set to melt further arguments passed to or from other methods. na.rm. Should NA values be removed from the data set? This will convert  One of the most interesting aspects of R programming is about changing the shape of the data to get a desired shape.Melting and casting in R, are the functions that can be used efficiently to reshape the data. The functions used to do this are called melt() and cast(). Melt Function in R:

Here's another tidyverse method. This is complicated by the fact that the different Var columns correspond to a particular time, but the indication of the time is different from the way it is represented in the id columns. So you need to have some way of matching the two. Here I do that with a named list inside var_renamer. Once the columns are consistently named, it becomes possible to use gather and separate to generate the right variables to be spread back out into the desired format. Note that I mutate Time into an ordered factor so it can be sorted by time rather than alphabetically with arrange.

df <- structure(list(Day = 1:14, Morning_1_id = structure(1:14, .Label = c("20180501-033-000001", "20180502-033-000001", "20180503-033-000001", "20180507-033-000001", "20180508-033-000001", "20180509-033-000001", "20180510-033-000001", "20180511-033-000001", "20180514-033-000001", "20180516-033-000001", "20180517-033-000003", "20180518-033-000001", "20180521-033-000002", "20180523-033-000001"), class = "factor"), Var1 = c(3.156666667, 2.986666667, 3.073333333, 3.236666667, 3.03, 3.01, 2.916666667, 3.293333333, 3.136666667, 3.116666667, 2.843333333, 3.033333333, 3.173333333, 3.336666667), Morning_2_id = structure(1:14, .Label = c("20180501-033-000002", "20180502-033-000002", "20180503-033-000002", "20180507-033-000002", "20180508-033-000002", "20180509-033-000002", "20180510-033-000002", "20180511-033-000002", "20180514-033-000002", "20180516-033-000002", "20180517-033-000004", "20180518-033-000002", "20180521-033-000003", "20180523-033-000002"), class = "factor"), Var2 = c(2.866666667, 2.93, 3.07, 2.99, 3.15, 3.02, 3.103333333, 3.163333333, 3.186666667, 3.283333333, 3.12, 3.29, 2.993333333, 3.026666667), Afternoon_1_id = structure(1:14, .Label = c("20180501-033-000008", "20180502-033-000020", "20180503-033-000011", "20180507-033-000015", "20180508-033-000015", "20180509-033-000007", "20180510-033-000007", "20180511-033-000013", "20180514-033-000007", "20180516-033-000008", "20180517-033-000008", "20180518-033-000007", "20180521-033-000008", "20180523-033-000007"), class = "factor"), Var3 = c(2.946666667, 3.076666667, 3.106666667, 3.043333333, 3.156666667, 3, 3.336666667, 2.98, 2.766666667, 3.133333333, 3.06, 3.006666667, 2.983333333, 3.3), Afternoon_2_id = structure(1:14, .Label = c("20180501-033-000009", "20180502-033-000021", "20180503-033-000012", "20180507-033-000016", "20180508-033-000017", "20180509-033-000008", "20180510-033-000008", "20180511-033-000014", "20180514-033-000008", "20180516-033-000009", "20180517-033-000009", "20180518-033-000008", "20180521-033-000009", "20180523-033-000008"), class = "factor"), Var4 = c(3.133333333, 3.013333333, 2.9, 3.116666667, 3.343333333, 3.156666667, 3.066666667, 2.94, 3.1, 3.04, 3.033333333, 2.973333333, 3.02, 3.21)), class = "data.frame", row.names = c(NA, -14L))

library(tidyverse)
var_renamer <- function(name) {
  time_list <- list(
    "1" = "Morning_1", "2" = "Morning_2", "3" = "Afternoon_1", "4" = "Afternoon_2"
  )
  timenum = str_remove(name, "Var")
  timestr = map_chr(timenum, ~ time_list[[.x]])
  str_c(timestr, "-Var")
}

df %>%
  rename_at(vars(starts_with("Var")), var_renamer) %>%
  rename_all(funs(str_replace(., "_id", "-Id"))) %>%
  gather(colname, val, -Day) %>%
  separate(colname, c("Time", "id_var"), sep = "-") %>%
  mutate(Time = factor(
    x = Time,
    levels = c("Morning_1", "Morning_2", "Afternoon_1", "Afternoon_2"),
    ordered = TRUE
  )) %>%
  spread(id_var, val) %>%
  arrange(Time, Day)
#> Warning: attributes are not identical across measure variables;
#> they will be dropped
#>    Day        Time                  Id         Var
#> 1    1   Morning_1 20180501-033-000001 3.156666667
#> 2    2   Morning_1 20180502-033-000001 2.986666667
#> 3    3   Morning_1 20180503-033-000001 3.073333333
#> 4    4   Morning_1 20180507-033-000001 3.236666667
#> 5    5   Morning_1 20180508-033-000001        3.03
#> 6    6   Morning_1 20180509-033-000001        3.01
#> 7    7   Morning_1 20180510-033-000001 2.916666667
#> 8    8   Morning_1 20180511-033-000001 3.293333333
#> 9    9   Morning_1 20180514-033-000001 3.136666667
#> 10  10   Morning_1 20180516-033-000001 3.116666667
#> 11  11   Morning_1 20180517-033-000003 2.843333333
#> 12  12   Morning_1 20180518-033-000001 3.033333333
#> 13  13   Morning_1 20180521-033-000002 3.173333333
#> 14  14   Morning_1 20180523-033-000001 3.336666667
#> 15   1   Morning_2 20180501-033-000002 2.866666667
#> 16   2   Morning_2 20180502-033-000002        2.93
#> 17   3   Morning_2 20180503-033-000002        3.07
#> 18   4   Morning_2 20180507-033-000002        2.99
#> 19   5   Morning_2 20180508-033-000002        3.15
#> 20   6   Morning_2 20180509-033-000002        3.02
#> 21   7   Morning_2 20180510-033-000002 3.103333333
#> 22   8   Morning_2 20180511-033-000002 3.163333333
#> 23   9   Morning_2 20180514-033-000002 3.186666667
#> 24  10   Morning_2 20180516-033-000002 3.283333333
#> 25  11   Morning_2 20180517-033-000004        3.12
#> 26  12   Morning_2 20180518-033-000002        3.29
#> 27  13   Morning_2 20180521-033-000003 2.993333333
#> 28  14   Morning_2 20180523-033-000002 3.026666667
#> 29   1 Afternoon_1 20180501-033-000008 2.946666667
#> 30   2 Afternoon_1 20180502-033-000020 3.076666667
#> 31   3 Afternoon_1 20180503-033-000011 3.106666667
#> 32   4 Afternoon_1 20180507-033-000015 3.043333333
#> 33   5 Afternoon_1 20180508-033-000015 3.156666667
#> 34   6 Afternoon_1 20180509-033-000007           3
#> 35   7 Afternoon_1 20180510-033-000007 3.336666667
#> 36   8 Afternoon_1 20180511-033-000013        2.98
#> 37   9 Afternoon_1 20180514-033-000007 2.766666667
#> 38  10 Afternoon_1 20180516-033-000008 3.133333333
#> 39  11 Afternoon_1 20180517-033-000008        3.06
#> 40  12 Afternoon_1 20180518-033-000007 3.006666667
#> 41  13 Afternoon_1 20180521-033-000008 2.983333333
#> 42  14 Afternoon_1 20180523-033-000007         3.3
#> 43   1 Afternoon_2 20180501-033-000009 3.133333333
#> 44   2 Afternoon_2 20180502-033-000021 3.013333333
#> 45   3 Afternoon_2 20180503-033-000012         2.9
#> 46   4 Afternoon_2 20180507-033-000016 3.116666667
#> 47   5 Afternoon_2 20180508-033-000017 3.343333333
#> 48   6 Afternoon_2 20180509-033-000008 3.156666667
#> 49   7 Afternoon_2 20180510-033-000008 3.066666667
#> 50   8 Afternoon_2 20180511-033-000014        2.94
#> 51   9 Afternoon_2 20180514-033-000008         3.1
#> 52  10 Afternoon_2 20180516-033-000009        3.04
#> 53  11 Afternoon_2 20180517-033-000009 3.033333333
#> 54  12 Afternoon_2 20180518-033-000008 2.973333333
#> 55  13 Afternoon_2 20180521-033-000009        3.02
#> 56  14 Afternoon_2 20180523-033-000008        3.21

Created on 2018-08-07 by the reprex package (v0.2.0).

Chapter 8: Melting & Casting, 1 Melting and Casting. 1.1 Why Reshape Your Data. Reshape2 is a package that allows us to easily transform our data into whatever structure we  How can I reshape my data in R? | R FAQ When there are multiple measurements of the same subject, across time or using different tools, the data is often described as being in "wide" format if there is one observation row per subject with each measurement present as a different variable and "long" format if there is one observation row per

How to reshape data in R: tidyr vs reshape2, Reshape your data from long to wide, split a column, aggregate: a from tidyr, with the functions melt() , colsplit() and dcast() , from reshape2. Reshape in R from wide to long and from long to wide Reshape function in R transforms the data from wide to long and also transforms back the data from long to wide. Reshape in R – reshape(), is one of the efficient function to transform the data.

Reshape R package, reshape2, melt and cast, In this tutorial on data frame we saw the reshape function that can be used to convert multiple columns of a dataframe to multiple rows. Reshape your data from long to wide, split a column, aggregate: a comparison between tidyr and reshape2 R packages to tidy data The post How to reshape data in R: tidyr vs reshape2 appeared first on MilanoR.

Melting and Casting in R, Melting and casting in R, are the functions that can be used efficiently to reshape the data. The functions used to do this are called melt() and cast(). Reshaping Data . R provides a variety of methods for reshaping data prior to analysis. Transpose . Use the t() function to transpose a matrix or a data frame. In the later case, rownames become variable (column) names. # example using built-in dataset mtcars t(mtcars) The Reshape Package

Comments

• reshape is a base R function. You are not using reshape2 which uses melt for long format. You could even remove the tibble call: my_data$Time <- gsub(".*\\.", "", row.names(mydata)); row.names(mydata) <- NULL
• I think the two gather makes a lot of a incorrect ID-Var combinations here. The output should have 56 rows only