# Reshape Multiple Scores From Long to Wide `long2wide` reshapes data from long to wide. This if often necessary to do with multilevel data where variables in the long format seek to be reshaped to multiple sets of variables in the wide format. If only one column needs to be reshaped, then you can use `unstack2` or `cast` - but that does not work for *multiple* columns. ```r long2wide( data, vrb.nm, grp.nm, obs.nm, sep = ".", colnames.by.obs = TRUE, keep.attr = FALSE ) ``` ## Arguments - `data`: data.frame of data. - `vrb.nm`: character vector of colnames from `data` specifying the variables to be reshaped. In longitudinal panel data, this would be the scores. - `grp.nm`: character vector of colnames from `data` specifying the groups. In longitudnal panel data, this would be the participant ID variable. - `obs.nm`: character vector of length 1 with a colname from `data` specifying the observation within each group. In longitudinal panel data, this would be the time variable. - `sep`: character vector of length 1 specifying the string that separates the name prefix (e.g., score) from it's number suffix (e.g., timepoint). If `sep` = "", then that implies there is no string separating the name prefix and the number suffix (e.g., "outcome1"). - `colnames.by.obs`: logical vector of length 1 specifying whether to sort the return object colnames by the observation label (TRUE) or by the order of `vrb.nm`. See the example at the end of the "MULTIPLE GROUPING VARIABLES" section of the examples. - `keep.attr`: logical vector of length 1 specifying whether to keep the "reshapeWide" attribute (from `reshape`) in the return object. ## Returns data.frame with nrow equal to `nrow(unique(data[grp.nm]))` and number of reshaped columns equal to `length(vrb.nm) * unique(data[[obs.nm]])`. The colnames will have the structure `paste0(vrb.nm, sep, unique(data[[obs.nm]]))`. The reshaped colnames are sorted by the observation labels if `colnames.by.obs` = TRUE and sorted by `vrb.nm` if `colnames.by.obs` = FALSE. Overall, the columns are in the following order: 1) `grp.nm` of the groups, 2) reshaped columns, 3) additional columns that were not reshaped. ## Details `long2wide` uses `reshape(direction = "wide")` to reshape the data. It attempts to streamline the task of reshaping long to wide as the `reshape` arguments can be confusing because the same arguments are used for wide vs. long reshaping. See `reshape` if you are curious. ## Examples ```r # SINGLE GROUPING VARIABLE dat_long <- as.data.frame(ChickWeight) # b/c groupedData class does weird things... w1 <- long2wide(data = dat_long, vrb.nm = "weight", grp.nm = "Chick", obs.nm = "Time") # NAs inserted for missing observations in some groups w2 <- long2wide(data = dat_long, vrb.nm = "weight", grp.nm = "Chick", obs.nm = "Time", sep = "_") head(w1); head(w2) w3 <- long2wide(data = dat_long, vrb.nm = "weight", grp.nm = "Chick", obs.nm = "Time", sep = "_T", keep.attr = TRUE) attributes(w3) # MULTIPLE GROUPING VARIABLE tmp <- psychTools::sai grps <- interaction(tmp[1:3], drop = TRUE) dups <- duplicated(grps) dat_long <- tmp[!(dups), ] # for some reason there are duplicate groups in the data vrb_nm <- str2str::pick(names(dat_long), val = c("study","time","id"), not = TRUE) w4 <- long2wide(data = dat_long, vrb.nm = vrb_nm, grp.nm = c("study","id"), obs.nm = "time") w5 <- long2wide(data = dat_long, vrb.nm = vrb_nm, grp.nm = c("study","id"), obs.nm = "time", colnames.by.obs = FALSE) # colnames sorted by `vrb.nm` instead head(w4); head(w5) ``` ## See Also `wide2long` `reshape` `unstack2`

long2wide function