---
title: "Data Step Operations"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Data Step Operations}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```
Normally, R processes data column-by-column. The data step allows you 
to process data row-by-row.  Row-by-row processing of data is useful when you 
have related columns, and wish to perform conditional logic on those 
columns. The `datastep()` function allows you to realize this style of 
data processing. It is particularly advantageous when you wish to perform deeply 
nested conditional logic. It is also very useful for by-group
processing.

#### Example 1: Simple Data Step
Here is an example of a simple data step:
```{r eval=FALSE, echo=TRUE}
library(libr)

# Add some columns to mtcars using data step logic
df <- datastep(mtcars[1:10, 1:3], {

    if (mpg >= 20) 
      mpgcat <- "High"
    else 
      mpgcat <- "Low"
      
    recdt <- as.Date("1974-06-10")
    
    if (cyl == 8)
      is8cyl <- TRUE

  })
  
# View results  
df
#                    mpg cyl  disp      recdt mpgcat is8cyl
# Mazda RX4         21.0   6 160.0 1974-06-10   High     NA
# Mazda RX4 Wag     21.0   6 160.0 1974-06-10   High     NA
# Datsun 710        22.8   4 108.0 1974-06-10   High     NA
# Hornet 4 Drive    21.4   6 258.0 1974-06-10   High     NA
# Hornet Sportabout 18.7   8 360.0 1974-06-10    Low   TRUE
# Valiant           18.1   6 225.0 1974-06-10    Low     NA
# Duster 360        14.3   8 360.0 1974-06-10    Low   TRUE
# Merc 240D         24.4   4 146.7 1974-06-10   High     NA
# Merc 230          22.8   4 140.8 1974-06-10   High     NA
# Merc 280          19.2   6 167.6 1974-06-10    Low     NA

```
### Keep, Drop, and Rename 

The data step has parameters to perform basic shaping of the resulting
data frame.  These parameters are 'keep', 'drop', and 'rename'.  For example,
the above data step could have been performed by sending all columns into 
the data step, and keeping only the desired columns.  Using the `keep` 
parameter also allows you to order the resulting columns.

#### Example 2: Keeping Data Step Variables
```{r eval=FALSE, echo=TRUE}
library(libr)

# Keep and order output columns 
df <- datastep(mtcars[1:10,], 
  keep = c("mpg", "cyl", "disp", "mpgcat", "recdt"), {

    if (mpg >= 20) 
      mpgcat <- "High"
    else 
      mpgcat <- "Low"
      
    recdt <- as.Date("1974-06-10")
    
    if (cyl == 8)
      is8cyl <- TRUE

  })
  
df
#                    mpg cyl  disp mpgcat      recdt
# Mazda RX4         21.0   6 160.0   High 1974-06-10
# Mazda RX4 Wag     21.0   6 160.0   High 1974-06-10
# Datsun 710        22.8   4 108.0   High 1974-06-10
# Hornet 4 Drive    21.4   6 258.0   High 1974-06-10
# Hornet Sportabout 18.7   8 360.0    Low 1974-06-10
# Valiant           18.1   6 225.0    Low 1974-06-10
# Duster 360        14.3   8 360.0    Low 1974-06-10
# Merc 240D         24.4   4 146.7   High 1974-06-10
# Merc 230          22.8   4 140.8   High 1974-06-10
# Merc 280          19.2   6 167.6    Low 1974-06-10

```
### The Retain Parameter

The retain parameter allows you to define variables
that will be seeded with the value from the previous step. The retain
option is useful for creating cumulative values or for performing 
conditions based on the value of the previous row.

#### Example 3: Drop, Retain, and Rename Parameters
```{r eval=FALSE, echo=TRUE}
library(libr)

df <- datastep(mtcars[1:10, ],
               drop = c("disp", "hp", "drat", "qsec",
                        "vs", "am", "gear", "carb"),
               retain = list(cumwt = 0 ),
               rename = c(mpg = "MPG", cyl = "Cylinders", wt = "Wgt",
                          cumwt = "Cumulative Wgt"), {

  cumwt <- cumwt + wt

 })

df
#                    MPG Cylinders   Wgt Cumulative Wgt
# Mazda RX4         21.0         6 2.620          2.620
# Mazda RX4 Wag     21.0         6 2.875          5.495
# Datsun 710        22.8         4 2.320          7.815
# Hornet 4 Drive    21.4         6 3.215         11.030
# Hornet Sportabout 18.7         8 3.440         14.470
# Valiant           18.1         6 3.460         17.930
# Duster 360        14.3         8 3.570         21.500
# Merc 240D         24.4         4 3.190         24.690
# Merc 230          22.8         4 3.150         27.840
# Merc 280          19.2         6 3.440         31.280

```
### By Group Processing
The `datastep()` function also has the capabilities of performing by-group
processing.  A by-group is accomplished using the `by` parameter, and passing
a vector of column names that define the group.  Once a by-group is 
defined, the `first.` and `last.` automatic variables become active, which
allow you to identify the boundaries between groups.  Note that, by default,
your data must be sorted properly before sending it into the data step. To 
turn the sort check off, set the `sort_check` parameter to FALSE.

#### Example 4: By Groups
```{r eval=FALSE, echo=TRUE}
library(libr)

# Identify start and end of by-groups
df <- datastep(mtcars[1:10,], 
  keep = c("mpg", "cyl", "gear", "grp"), 
  by = c("gear"), sort_check = FALSE, {

    if (first. & last.)
      grp <- "Start - End"
    else if (first.)
      grp <- "Start"
    else if (last.)
      grp <- "End"
    else 
      grp <- "-"

  })
  
df
#                    mpg cyl gear   grp
# Mazda RX4         21.0   6    4 Start
# Mazda RX4 Wag     21.0   6    4     -
# Datsun 710        22.8   4    4   End
# Hornet 4 Drive    21.4   6    3 Start
# Hornet Sportabout 18.7   8    3     -
# Valiant           18.1   6    3     -
# Duster 360        14.3   8    3   End
# Merc 240D         24.4   4    4 Start
# Merc 230          22.8   4    4     -
# Merc 280          19.2   6    4   End

```
### Using Summary Functions
There may be times when you want to combine row-by-row conditional 
processing with column-by-column vector operations.  For example, 
let's say you want to calculate a mean and then perform conditional 
processing on that mean.  This 
situation can be handled using the `calculate` parameter on the `datastep()`
function. The function will execute the `calculate` block first, add any
assigned variables to the data frame, and then execute the data step.  Below 
is an example of such a scenario:

#### Example 5: Calculate Block
```{r eval=FALSE, echo=TRUE}
library(libr)

# Categorize mpg as above or below the mean
df <- datastep(mtcars, 
  keep = c("mpg", "cyl", "mean_mpg", "mpgcat"), 
  calculate = { mean_mpg = mean(mpg) },
  {

    if (mpg >= mean_mpg)
      mpgcat <- "High"
    else 
      mpgcat <- "Low"

  })
  
df[1:10,]
#                    mpg cyl mean_mpg mpgcat
# Mazda RX4         21.0   6 20.09062   High
# Mazda RX4 Wag     21.0   6 20.09062   High
# Datsun 710        22.8   4 20.09062   High
# Hornet 4 Drive    21.4   6 20.09062   High
# Hornet Sportabout 18.7   8 20.09062    Low
# Valiant           18.1   6 20.09062    Low
# Duster 360        14.3   8 20.09062    Low
# Merc 240D         24.4   4 20.09062   High
# Merc 230          22.8   4 20.09062   High
# Merc 280          19.2   6 20.09062    Low

```
### Data Steps with `dplyr`
Note that the `datastep()` function is pipe-friendly, and can be combined
with **dplyr** functions in a data pipeline.  Also note that the `datastep()`
function will recognize any group attributes added by the `group_by()` 
function.  Therefore, within a **dplyr** pipeline, it is not necessary to 
use any `datastep` parameters.  The following example recreates the above 
data frame from Example 5, but with a **dplyr** pipeline.

#### Example 6: Data Pipeline
```{r eval=FALSE, echo=TRUE}
library(libr)
library(dplyr)
library(magrittr)

# Add datastep to dplyr pipeline
df <- mtcars %>% 
  select(mpg, cyl, gear) %>% 
  mutate(mean_mpg = mean(mpg)) %>% 
  datastep({

    if (mpg >= mean_mpg)
      mpgcat <- "High"
    else 
      mpgcat <- "Low"

  }) %>% 
  filter(row_number() <= 10)
  
df
#     mpg cyl gear mean_mpg mpgcat
# 1  21.0   6    4 20.09062   High
# 2  21.0   6    4 20.09062   High
# 3  22.8   4    4 20.09062   High
# 4  21.4   6    3 20.09062   High
# 5  18.7   8    3 20.09062    Low
# 6  18.1   6    3 20.09062    Low
# 7  14.3   8    3 20.09062    Low
# 8  24.4   4    4 20.09062   High
# 9  22.8   4    4 20.09062   High
# 10 19.2   6    4 20.09062    Low

```
### Data Attributes
The **libr** package recognizes several useful data attributes that
are not normally recognized by other R functions.  For example, it is 
very convenient to assign _label_ and _description_ attributes to your columns, 
so other people can understand what data the columns contain.  

For this reason,
the `datastep()` function provides an _attrib_ parameter that allows you 
to supply such attributes as part of a data step.  Attributes are 
assigned with a named list and the `dsattr()` object. 

#### Example 7: Attributes
```{r eval=FALSE, echo=TRUE}
library(libr)

# Assign label attributes to all columns
df <- datastep(mtcars[1:10, ], 
               keep = c("mpg", "cyl", "mpgcat"),
               calculate = { mean_mpg = mean(mpg) },
               attrib = list(mpg = dsattr(label = "Miles Per Gallon"),
                             cyl = dsattr(label = "Cylinders"),
                             mpgcat = dsattr(label = "Mileage Category")), {

    if (mpg >= mean_mpg)
      mpgcat <- "High"
    else 
      mpgcat <- "Low"

  }) 

# View attributes in dictionary
dictionary(df)
# # A tibble: 3 x 10
#   Name  Column Class     Label            Description Format Width Justify  Rows   NAs
#   <chr> <chr>  <chr>     <chr>            <chr>       <lgl>  <int> <chr>   <int> <int>
# 1 df    mpg    numeric   Miles Per Gallon NA          NA        NA NA         10     0
# 2 df    cyl    numeric   Cylinders        NA          NA        NA NA         10     0
# 3 df    mpgcat character Mileage Category NA          NA         4 NA         10     0

```
### Data Step Array
As mentioned previously, R typically operates in a column-wise manner.  That is, 
R processes data column-by-column.  But what if you need to get a sum or 
mean across a row?

This situation is what led to the development of the **data step array**.  The 
data step array allows you to define a list of columns and  
iterate over the list inside a data step.  Data step arrays are defined with the 
_arrays_ parameter, which accepts a named list of `dsarray()` objects.

To see the array in action, we'll use the _AirPassengers_ sample 
data.  This data shows international airline passengers by month between
1949 and 1960.  The data looks like this:  
```{r eval=FALSE, echo=TRUE}
AirPassengers
#      Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
# 1949 112 118 132 129 121 135 148 148 136 119 104 118
# 1950 115 126 141 135 125 149 170 170 158 133 114 140
# 1951 145 150 178 163 172 178 199 199 184 162 146 166
# 1952 171 180 193 181 183 218 230 242 209 191 172 194
# 1953 196 196 236 235 229 243 264 272 237 211 180 201
# 1954 204 188 235 227 234 264 302 293 259 229 203 229
# 1955 242 233 267 269 270 315 364 347 312 274 237 278
# 1956 284 277 317 313 318 374 413 405 355 306 271 306
# 1957 315 301 356 348 355 422 465 467 404 347 305 336
# 1958 340 318 362 348 363 435 491 505 404 359 310 337
# 1959 360 342 406 396 420 472 548 559 463 407 362 405
# 1960 417 391 419 461 472 535 622 606 508 461 390 432

```
This example illustrates how to create row totals, row means, and find the 
top month using a data step array. The array has an indexer 
to extract values.  You can use the indexer to extract a single value or
a subset of values.  An empty indexer will return all the values in the
array.

#### Example 8: Using a Data Step Array
```{r eval=FALSE, echo=TRUE}
library(libr)

# Create AirPassengers Data Frame
df <- as.data.frame(t(matrix(AirPassengers, 12,
                    dimnames = list(month.abb, seq(1949, 1960)))), 
                    stringsAsFactors = FALSE)

# Use datastep array to get year tot, mean, and top month
dat <- datastep(df,
                arrays = list(months = dsarray(names(df))),
                attrib = list(Tot = 0, Mean = 0, Top = ""),
                drop = "mth",
                {

                  Tot <- sum(months[])
                  Mean <- mean(months[])

                  for (mth in months) {
                    if (months[mth] == max(months[])) {
                      Top <- mth
                    }
                  }
                })


dat
#      Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec  Tot     Mean Top
# 1949 112 118 132 129 121 135 148 148 136 119 104 118 1520 126.6667 Aug
# 1950 115 126 141 135 125 149 170 170 158 133 114 140 1676 139.6667 Aug
# 1951 145 150 178 163 172 178 199 199 184 162 146 166 2042 170.1667 Aug
# 1952 171 180 193 181 183 218 230 242 209 191 172 194 2364 197.0000 Aug
# 1953 196 196 236 235 229 243 264 272 237 211 180 201 2700 225.0000 Aug
# 1954 204 188 235 227 234 264 302 293 259 229 203 229 2867 238.9167 Jul
# 1955 242 233 267 269 270 315 364 347 312 274 237 278 3408 284.0000 Jul
# 1956 284 277 317 313 318 374 413 405 355 306 271 306 3939 328.2500 Jul
# 1957 315 301 356 348 355 422 465 467 404 347 305 336 4421 368.4167 Aug
# 1958 340 318 362 348 363 435 491 505 404 359 310 337 4572 381.0000 Aug
# 1959 360 342 406 396 420 472 548 559 463 407 362 405 5140 428.3333 Aug
# 1960 417 391 419 461 472 535 622 606 508 461 390 432 5714 476.1667 Jul

```
Next: [Enhanced Equality](libr-eq.html)


