I have a small dataset written in SPSS syntax which comes from Table 5.3 p. 189 of this book (type 210 in the page slot to see the table).
I was wondering if there might be a way to convert this data to .csv file? (I want to use the data in R afterwards)
# SPSS Code:
DATA LIST FREE/gpid anx socskls assert.
BEGIN DATA.
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
END DATA.
EDIT - in order to check answers I am adding here the actual way the data looks after reading it in SPSS :
gpid anx socskls assert
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
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
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
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.
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.