Convert a small dataset written in SPSS to CSV

This requires some manual clean-up in Notepad or similar to place the data in the right format. But essentially, this could be imported using the following

df <- data.frame(
  gpid = c(1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,
                 2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3),
  anx = c(5,5,4,4,3,4,4,4,5,5,4,6,6,5,6,
                 4,7,5,5,5,5,6,4,4,4,4,4,4,4,4,4,5,4),
  socskls = c(3,4,5,5,5,5,5,4,4,4,4,2,2,2,2,
                 4,1,4,2,3,4,2,4,3,4,5,5,4,5,6,4,3,4),
  assert = c(3,3,4,4,5,4,5,4,3,3,4,1,2,3,2,
                 4,1,3,3,3,3,3,4,3,4,5,5,4,4,5,4,3,4)
)

write.csv(df, "df.csv", row.names = F)

Note that the first 4 values (1, 5, 3, 3) are the gpid, anx, socskls, and assert values for row 1. Whereas the values 1, 5, 4, 3 which appear to be in the next column of the pasted data in SPSS syntax (i.e. the next 4 values reading the syntax left to right) are actually the values for participant 10.

Note: I'm assuming you don't have SPSS installed. If you did the easiest option would using SPSS syntax to create the dataset in SPSS and then just export to R.

Using readLines and some string manipulating tools.

tmp <- readLines("spss1.txt")  ## read from .txt
tmp <- trimws(gsub("[A-Z/.]", "", tmp))  ## remove caps and specials
nm <- strsplit(tmp[[1]], " ")[[1]]  ## split names
tmp <- unlist(strsplit(tmp[3:11], "\\s{2,}") )  ## split data blocks

Finally, splitting at the spaces gives the result.

dat <- setNames(
  type.convert(do.call(rbind.data.frame, strsplit(tmp, "\\s"))), 
  nm)

Result

dat
#    gpid anx socskls assert
# 1     1   5       3      3
# 2     1   5       4      3
# 3     1   4       5      4
# 4     1   4       5      4
# 5     1   3       5      5
# 6     1   4       5      4
# 7     1   4       5      5
# 8     1   4       4      4
# 9     1   5       4      3
# 10    1   5       4      3
# 11    1   4       4      4
# 12    2   6       2      1
# 13    2   6       2      2
# 14    2   5       2      3
# 15    2   6       2      2
# 16    2   4       4      4
# 17    2   7       1      1
# 18    2   5       4      3
# 19    2   5       2      3
# 20    2   5       3      3
# 21    2   5       4      3
# 22    2   6       2      3
# 23    3   4       4      4
# 24    3   4       3      3
# 25    3   4       4      4
# 26    3   4       5      5
# 27    3   4       5      5
# 28    3   4       4      4
# 29    3   4       5      4
# 30    3   4       6      5
# 31    3   4       4      4
# 32    3   5       3      3
# 33    3   4       4      4

Note: Results in the same Wilks' lambda as @emily-kothe's method. Maybe the authors used different data or your manova method is flawed?

If I understand correctly, the 1st, 5th, 9th, and 13th column of the dataset belong to variable gpid, the 2nd, 6th, 10th, and 14th column belong to variable anx, and so on. So, we need to

• reshape from wide to long format
• with multiple measure variables
• where each measure variable spans several columns
• and where some values are missing.

Many roads lead to Rome.

This is what I would do using my favourite tools. In particular, this approach uses the feature of data.table::melt() to reshape multiple measure columns simultaneously. There is no manual cleanup of the data section in a text editor required.

The resulting dataset result can be used directly afterwards in any subsequent R code as requested by the OP. There is no need to take a detour using a .csv file (However, feel free to save result as a .csv file).

library(data.table)
library(magrittr)
cols <- c("gpid", "anx", "socskls", "assert")
raw <- fread(text = "
1 5 3 3     1 5 4 3     1 4 5 4     1 4 5 4
1 3 5 5     1 4 5 4     1 4 5 5     1 4 4 4
1 5 4 3     1 5 4 3     1 4 4 4
2 6 2 1     2 6 2 2     2 5 2 3     2 6 2 2
2 4 4 4     2 7 1 1     2 5 4 3     2 5 2 3
2 5 3 3     2 5 4 3     2 6 2 3
3 4 4 4     3 4 3 3     3 4 4 4     3 4 5 5
3 4 5 5     3 4 4 4     3 4 5 4     3 4 6 5
3 4 4 4     3 5 3 3     3 4 4 4",
fill = TRUE)
mv <- colnames(raw) %>% 
  matrix(ncol = 4L, byrow = TRUE) %>% 
  as.data.table() %>% 
  setnames(new = cols)
result <- melt(raw, measure.vars = mv, na.rm = TRUE)[
  order(rowid(variable))][
    , variable := NULL]
result

    gpid anx socskls assert
 1:    1   5       3      3
 2:    1   5       4      3
 3:    1   4       5      4
 4:    1   4       5      4
 5:    1   3       5      5
 6:    1   4       5      4
 7:    1   4       5      5
 8:    1   4       4      4
 9:    1   5       4      3
10:    1   5       4      3
11:    1   4       4      4
12:    2   6       2      1
13:    2   6       2      2
14:    2   5       2      3
15:    2   6       2      2
16:    2   4       4      4
17:    2   7       1      1
18:    2   5       4      3
19:    2   5       2      3
20:    2   5       3      3
21:    2   5       4      3
22:    2   6       2      3
23:    3   4       4      4
24:    3   4       3      3
25:    3   4       4      4
26:    3   4       5      5
27:    3   4       5      5
28:    3   4       4      4
29:    3   4       5      4
30:    3   4       6      5
31:    3   4       4      4
32:    3   5       3      3
33:    3   4       4      4
    gpid anx socskls assert

Some explanations

fread() returns a data.table raw with default column names V1, V2, ... V16 and with missing values filled with NA

mv is a data.table which indicates which columns of raw belong to each target variable:

mv

   gpid anx socskls assert
1:   V1  V2      V3     V4
2:   V5  V6      V7     V8
3:   V9 V10     V11    V12
4:  V13 V14     V15    V16

This informations is used by melt(). melt() also removes rows with missing values from the resulting long format.

After reshaping, the rows are ordered by the variable number but need to be reordered in the original row order by using rowid(variable). Finally, the variable column is removed.

EDIT: Improved version

Giving a second thought, here is a streamlined version of the code which skips the creation of mv and uses data.table chaining:

library(data.table)
cols <- c("gpid", "anx", "socskls", "assert")
result <- fread(
  text = "
1 5 3 3     1 5 4 3     1 4 5 4     1 4 5 4
1 3 5 5     1 4 5 4     1 4 5 5     1 4 4 4
1 5 4 3     1 5 4 3     1 4 4 4
2 6 2 1     2 6 2 2     2 5 2 3     2 6 2 2
2 4 4 4     2 7 1 1     2 5 4 3     2 5 2 3
2 5 3 3     2 5 4 3     2 6 2 3
3 4 4 4     3 4 3 3     3 4 4 4     3 4 5 5
3 4 5 5     3 4 4 4     3 4 5 4     3 4 6 5
3 4 4 4     3 5 3 3     3 4 4 4",
  fill = TRUE, col.names = rep(cols, 4L))[
    , melt(.SD, measure.vars = patterns(cols), value.name = cols, na.rm = TRUE)][
      order(rowid(variable))][
        , variable := NULL][]
result

Here, the columns are renamed within the call to fread(). In this case, duplicated column names are desirable (as opposed to the usual use case) because the patterns() function in the subsequent call to melt() use the duplicated column names to combine the columns which belong to one measure variable.