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reference/redcap_wider.md

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+# Transforms list of REDCap data.frames to a single wide data.frame
+
+Converts a list of REDCap data.frames from long to wide format. In
+essence it is a wrapper for the
+[pivot_wider](https://tidyr.tidyverse.org/reference/pivot_wider.html)
+function applied on a REDCap output (from
+[read_redcap_tables](https://agdamsbo.github.io/REDCapCAST/reference/read_redcap_tables.md))
+or manually split by
+[REDCap_split](https://agdamsbo.github.io/REDCapCAST/reference/REDCap_split.md).
+
+## Usage
+
+``` r
+redcap_wider(
+  data,
+  event.glue = "{.value}____{redcap_event_name}",
+  inst.glue = "{.value}____{redcap_repeat_instance}"
+)
+```
+
+## Arguments
+
+- data:
+
+  A list of data frames
+
+- event.glue:
+
+  A [glue](https://glue.tidyverse.org/reference/glue.html) string for
+  repeated events naming
+
+- inst.glue:
+
+  A [glue](https://glue.tidyverse.org/reference/glue.html) string for
+  repeated instruments naming
+
+## Value
+
+data.frame in wide format
+
+## Examples
+
+``` r
+# Longitudinal
+list1 <- list(
+  data.frame(
+    record_id = c(1, 2, 1, 2),
+    redcap_event_name = c("baseline", "baseline", "followup", "followup"),
+    age = c(25, 26, 27, 28)
+  ),
+  data.frame(
+    record_id = c(1, 2),
+    redcap_event_name = c("baseline", "baseline"),
+    gender = c("male", "female")
+  )
+)
+redcap_wider(list1)
+#> Joining with `by = join_by(record_id)`
+#> # A tibble: 2 × 4
+#>   record_id age____baseline age____followup gender
+#>       <dbl>           <dbl>           <dbl> <chr> 
+#> 1         1              25              27 male  
+#> 2         2              26              28 female
+# Simpel with two instruments
+list2 <- list(
+  data.frame(
+    record_id = c(1, 2),
+    age = c(25, 26)
+  ),
+  data.frame(
+    record_id = c(1, 2),
+    gender = c("male", "female")
+  )
+)
+redcap_wider(list2)
+#> Joining with `by = join_by(record_id)`
+#>   record_id age gender
+#> 1         1  25   male
+#> 2         2  26 female
+# Simple with single instrument
+list3 <- list(data.frame(
+  record_id = c(1, 2),
+  age = c(25, 26)
+))
+redcap_wider(list3)
+#>   record_id age
+#> 1         1  25
+#> 2         2  26
+# Longitudinal with repeatable instruments
+list4 <- list(
+  data.frame(
+    record_id = c(1, 2, 1, 2),
+    redcap_event_name = c("baseline", "baseline", "followup", "followup"),
+    age = c(25, 26, 27, 28)
+  ),
+  data.frame(
+    record_id = c(1, 1, 1, 1, 2, 2, 2, 2),
+    redcap_event_name = c(
+      "baseline", "baseline", "followup", "followup",
+      "baseline", "baseline", "followup", "followup"
+    ),
+    redcap_repeat_instrument = "walk",
+    redcap_repeat_instance = c(1, 2, 1, 2, 1, 2, 1, 2),
+    dist = c(40, 32, 25, 33, 28, 24, 23, 36)
+  ),
+  data.frame(
+    record_id = c(1, 2),
+    redcap_event_name = c("baseline", "baseline"),
+    gender = c("male", "female")
+  )
+)
+redcap_wider(list4)
+#> Joining with `by = join_by(record_id)`
+#> Joining with `by = join_by(record_id)`
+#> # A tibble: 2 × 8
+#>   record_id age____baseline age____followup dist____1____baseline
+#>       <dbl>           <dbl>           <dbl>                 <dbl>
+#> 1         1              25              27                    40
+#> 2         2              26              28                    28
+#> # ℹ 4 more variables: dist____1____followup <dbl>, dist____2____baseline <dbl>,
+#> #   dist____2____followup <dbl>, gender <chr>
+
+list5 <- list(
+  data.frame(
+    record_id = c(1, 2, 1, 2),
+    redcap_event_name = c("baseline", "baseline", "followup", "followup")
+  ),
+  data.frame(
+    record_id = c(1, 1, 1, 1, 2, 2, 2, 2),
+    redcap_event_name = c(
+      "baseline", "baseline", "followup", "followup",
+      "baseline", "baseline", "followup", "followup"
+    ),
+    redcap_repeat_instrument = "walk",
+    redcap_repeat_instance = c(1, 2, 1, 2, 1, 2, 1, 2),
+    dist = c(40, 32, 25, 33, 28, 24, 23, 36)
+  ),
+  data.frame(
+    record_id = c(1, 2),
+    redcap_event_name = c("baseline", "baseline"),
+    gender = c("male", "female")
+  )
+)
+redcap_wider(list5)
+#> Joining with `by = join_by(record_id)`
+#> # A tibble: 2 × 6
+#>   record_id dist____1____baseline dist____1____followup dist____2____baseline
+#>       <dbl>                 <dbl>                 <dbl>                 <dbl>
+#> 1         1                    40                    25                    32
+#> 2         2                    28                    23                    24
+#> # ℹ 2 more variables: dist____2____followup <dbl>, gender <chr>
+```