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Add NCDS reshape page
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‎docs/ncds-data_discovery.md

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---
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layout: default
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title: Data Discovery
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nav_order: 2
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parent: NCDS
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format: docusaurus-md
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---

‎docs/ncds-merging_across_sweeps.md

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---
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layout: default
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title: Combining Data Across Sweeps
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nav_order: 3
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parent: NCDS
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format: docusaurus-md
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---

‎docs/ncds-reshape_long_wide.md

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---
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layout: default
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title: Reshaping Data from Long to Wide (or Wide to Long)
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nav_order: 4
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parent: NCDS
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format: docusaurus-md
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---
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# Introduction
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In this section, we show how to reshape data from long to wide (and vice
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versa). To demonstrate, we use data from Sweeps 4 (23y) and 8 (50y) on
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cohort member’s height and weight collected.
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The packages we use are:
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```r
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# Load Packages
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library(tidyverse) # For data manipulation
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library(haven) # For importing .dta files
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```
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# Reshaping Raw Data from Wide to Long
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We begin by loading the data from each sweep and merging these together
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into a single wide format data frame; see [Combining Data Across
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Sweeps](https://cls-data.github.io/docs/ncds-merging_across_sweeps.html)
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for further explanation on how this is achieved. Note, the names of the
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height and weight variables in Sweep 4 and Sweep 8 follow a similar
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convention, which is the exception rather than the rule in NCDS data.
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Below, we convert the variable names in the Sweep 4 data frame to upper
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case so that they closely match those in the Sweep 8 data frame. This
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will make reshaping easier.
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```r
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df_23y <- read_dta("23y/ncds4.dta",
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col_select = c("ncdsid", "dvwt23", "dvht23")) %>%
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rename_with(str_to_upper)
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df_50y <- read_dta("50y/ncds_2008_followup.dta",
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col_select = c("NCDSID", "DVWT50", "DVHT50"))
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df_wide <- df_23y %>%
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full_join(df_50y, by = "NCDSID")
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```
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`df_wide` has 5 columns. Besides, the identifier, `NCDSID`, there are 4
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columns for height and weight measurements at each sweep. Each of these
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4 columns is suffixed by two numbers indicating the age at assessment.
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We can reshape the dataset into long format (one row per person x sweep
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combination) using the `pivot_longer()` function so that the resulting
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data frame has four columns: one person identifier, a variable for age
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of assessment (`fup`), and variables for height and weight. We specify
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the columns to be reshaped using the `cols` argument, provide the new
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variable names in the `names_to` argument, and the pattern the existing
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column names take using the `names_pattern` argument. For
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`names_pattern` we specify `"^(.*)(\\d\\d)$"`, which breaks the column
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name into two pieces: the first characters (`"(.*)"`) and two digits at
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the end of the name (`"(\\d\\d)$"`). `names_pattern` uses regular
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expressions. `.` matches single characters, and `.*` modifies this to
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make zero or more characters. `\\d` is a special character denoting a
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digit. As noted, the final two digits character hold information on age
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of assessment; in the reshaped data frame the character is stored as a
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value in a new column `fup`. `.value` is a placeholder for the new
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columns in the reshaped data frame that store the values from the
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columns selected by `cols`; these new columns are named using the first
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piece from `names_pattern` - in this case `DVHT` (height) and `DVWT`
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(weight).
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```r
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df_long <- df_wide %>%
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pivot_longer(cols = matches("DV(HT|WT)\\d\\d"),
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names_to = c(".value", "fup"),
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names_pattern = "^(.*)(\\d\\d)$")
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df_long
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```
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``` text
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# A tibble: 28,028 × 4
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NCDSID fup DVHT DVWT
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<chr> <chr> <dbl+lbl> <dbl+lbl>
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1 N10001N 23 1.63 59.4
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2 N10001N 50 NA 66.7
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3 N10002P 23 1.90 73.5
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4 N10002P 50 NA 79.4
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5 N10004R 23 1.65 76.2
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6 N10004R 50 NA NA
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7 N10007U 23 1.63 52.2
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8 N10007U 50 NA 72.1
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9 N10009W 23 1.73 66.7
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10 N10009W 50 1.7 78
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# ℹ 28,018 more rows
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```
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# Reshaping Raw Data from Long to Wide
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We can also reshape the data from long to wide format using the
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`pivot_wider()` function. In this case, we want to create two new
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columns for each sweep: one for height and one for weight. We specify
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the columns to be reshaped using the `values_from` argument, provide the
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old column names in the `names_from` argument, and use the `names_glue`
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argument to specify the convention to follow for the new column names.
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The `names_glue` argument uses curly braces (`{}`) to reference the
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values from the `names_from` and `.value` arguments. As we are
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specifying multiple columns in `values_from`, `.value` is a placeholder
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for the names of the variables selected in `values_from`.
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```r
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df_long %>%
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pivot_wider(names_from = fup,
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values_from = matches("DV(HT|WT)"),
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names_glue = "{.value}{fup}")
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```
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``` text
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# A tibble: 14,014 × 5
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NCDSID DVHT23 DVHT50 DVWT23 DVWT50
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<chr> <dbl+lbl> <dbl+lbl> <dbl+lbl> <dbl+lbl>
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1 N10001N 1.63 NA 59.4 66.7
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2 N10002P 1.90 NA 73.5 79.4
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3 N10004R 1.65 NA 76.2 NA
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4 N10007U 1.63 NA 52.2 72.1
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5 N10009W 1.73 1.7 66.7 78
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6 N10011Q 1.68 1.7 63.5 95
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7 N10012R 1.96 NA 114. 133.
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8 N10013S 1.78 NA 83.5 95.2
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9 N10014T 1.55 NA 57.2 63.5
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10 N10015U 1.80 NA 73.0 78
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# ℹ 14,004 more rows
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```
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Note, in the original `df_wide` tibble, `DVHT23` and `DVWT23` were
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labelled numeric vectors - this class allows users to add metadata to
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variables (value labels, etc.). `DVHT50` and `DVWT50`, on the other
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hand, were standard numeric vectors. When reshaping to long format,
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multiple variables are effectively appended together. The final reshape
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variables can only have one set of properties. `pivot_longer()` merges
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variables together to preserve variables attributes, but in some cases
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will throw an error (where variables are of inconsistent types) or print
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a warning (where value labels are inconsistent). Note above, where we
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reshape `df_long` back to wide format, all weight and height variables
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now have labelled numeric type.

‎quarto/ncds-data_discovery.qmd

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---
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layout: default
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title: "Data Discovery"
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nav_order: 1
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nav_order: 2
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parent: NCDS
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format: docusaurus-md
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---

‎quarto/ncds-merging_across_sweeps.qmd

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---
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layout: default
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title: "Combining Data Across Sweeps"
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nav_order: 2
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nav_order: 3
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parent: NCDS
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format: docusaurus-md
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---

‎quarto/ncds-reshape_long_wide.qmd

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---
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layout: default
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title: "Reshaping Data from Long to Wide (or Wide to Long)"
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nav_order: 4
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parent: NCDS
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format: docusaurus-md
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---
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# Introduction
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In this section, we show how to reshape data from long to wide (and vice versa). To demonstrate, we use data from Sweeps 4 (23y) and 8 (50y) on cohort member's height and weight collected.
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The packages we use are:
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```{r}
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#| warning: false
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# Load Packages
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library(tidyverse) # For data manipulation
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library(haven) # For importing .dta files
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```
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```{r}
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#| include: false
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# setwd(Sys.getenv("mcs_fld"))
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```
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# Reshaping Raw Data from Wide to Long
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We begin by loading the data from each sweep and merging these together into a single wide format data frame; see [Combining Data Across Sweeps](https://cls-data.github.io/docs/ncds-merging_across_sweeps.html) for further explanation on how this is achieved. Note, the names of the height and weight variables in Sweep 4 and Sweep 8 follow a similar convention, which is the exception rather than the rule in NCDS data. Below, we convert the variable names in the Sweep 4 data frame to upper case so that they closely match those in the Sweep 8 data frame. This will make reshaping easier.
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```{r}
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df_23y <- read_dta("23y/ncds4.dta",
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col_select = c("ncdsid", "dvwt23", "dvht23")) %>%
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rename_with(str_to_upper)
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df_50y <- read_dta("50y/ncds_2008_followup.dta",
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col_select = c("NCDSID", "DVWT50", "DVHT50"))
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df_wide <- df_23y %>%
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full_join(df_50y, by = "NCDSID")
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```
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`df_wide` has 5 columns. Besides, the identifier, `NCDSID`, there are 4 columns for height and weight measurements at each sweep. Each of these 4 columns is suffixed by two numbers indicating the age at assessment. We can reshape the dataset into long format (one row per person x sweep combination) using the `pivot_longer()` function so that the resulting data frame has four columns: one person identifier, a variable for age of assessment (`fup`), and variables for height and weight. We specify the columns to be reshaped using the `cols` argument, provide the new variable names in the `names_to` argument, and the pattern the existing column names take using the `names_pattern` argument. For `names_pattern` we specify `"^(.*)(\\d\\d)$"`, which breaks the column name into two pieces: the first characters (`"(.*)"`) and two digits at the end of the name (`"(\\d\\d)$"`). `names_pattern` uses regular expressions. `.` matches single characters, and `.*` modifies this to make zero or more characters. `\\d` is a special character denoting a digit. As noted, the final two digits character hold information on age of assessment; in the reshaped data frame the character is stored as a value in a new column `fup`. `.value` is a placeholder for the new columns in the reshaped data frame that store the values from the columns selected by `cols`; these new columns are named using the first piece from `names_pattern` - in this case `DVHT` (height) and `DVWT` (weight).
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```{r}
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#| warning: false
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df_long <- df_wide %>%
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pivot_longer(cols = matches("DV(HT|WT)\\d\\d"),
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names_to = c(".value", "fup"),
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names_pattern = "^(.*)(\\d\\d)$")
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df_long
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```
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# Reshaping Raw Data from Long to Wide
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We can also reshape the data from long to wide format using the `pivot_wider()` function. In this case, we want to create two new columns for each sweep: one for height and one for weight. We specify the columns to be reshaped using the `values_from` argument, provide the old column names in the `names_from` argument, and use the `names_glue` argument to specify the convention to follow for the new column names. The `names_glue` argument uses curly braces (`{}`) to reference the values from the `names_from` and `.value` arguments. As we are specifying multiple columns in `values_from`, `.value` is a placeholder for the names of the variables selected in `values_from`.
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```{r}
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df_long %>%
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pivot_wider(names_from = fup,
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values_from = matches("DV(HT|WT)"),
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names_glue = "{.value}{fup}")
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```
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Note, in the original `df_wide` tibble, `DVHT23` and `DVWT23` were labelled numeric vectors - this class allows users to add metadata to variables (value labels, etc.). `DVHT50` and `DVWT50`, on the other hand, were standard numeric vectors. When reshaping to long format, multiple variables are effectively appended together. The final reshape variables can only have one set of properties. `pivot_longer()` merges variables together to preserve variables attributes, but in some cases will throw an error (where variables are of inconsistent types) or print a warning (where value labels are inconsistent). Note above, where we reshape `df_long` back to wide format, all weight and height variables now have labelled numeric type.

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