--- 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 ``` ### By Group Processing of Multiple Variables If desired, you can pass multiple variables on the `by` parameter. When there are multiple by groups, the `first.` and `last.` automatic variables described above will represent an "or" combination of values for all by-variables. In addition, automatic variables will be created for each variable in the by group, similar to SAS®. Observe: #### Example 5: Multiple By Groups ```{r eval=FALSE, echo=TRUE} library(libr) # Create sample data df <- data.frame(HairEyeColor)[seq(2, 32, 2), ] # Sort by groups df <- sort(df, by = c("Sex", "Hair")) # Identify start and end of by-groups df2 <- datastep(df, drop = c("Eye", "Freq"), by = c("Sex", "Hair"), { fSex <- first.Sex lSex <- last.Sex fHair <- first.Hair lHair <- last.Hair }) df2 # Hair Sex fSex lSex fHair lHair # 1 Brown Male TRUE FALSE TRUE FALSE # 2 Brown Male FALSE FALSE FALSE FALSE # 3 Brown Male FALSE FALSE FALSE FALSE # 4 Brown Male FALSE FALSE FALSE TRUE # 5 Blond Male FALSE FALSE TRUE FALSE # 6 Blond Male FALSE FALSE FALSE FALSE # 7 Blond Male FALSE FALSE FALSE FALSE # 8 Blond Male FALSE TRUE FALSE TRUE # 9 Brown Female TRUE FALSE TRUE FALSE # 10 Brown Female FALSE FALSE FALSE FALSE # 11 Brown Female FALSE FALSE FALSE FALSE # 12 Brown Female FALSE FALSE FALSE TRUE # 13 Blond Female FALSE FALSE TRUE FALSE # 14 Blond Female FALSE FALSE FALSE FALSE # 15 Blond Female FALSE FALSE FALSE FALSE # 16 Blond Female FALSE TRUE FALSE TRUE ``` The above `first.Sex`, `last.Sex`, `first.Hair`, and `last.Hair` variables may also be used in conditions, functions, or any other expression inside your datastep. Note that like `first.` and `last.` they are dropped automatically at the end of the datastep. If you want to retain their values, assign them to a new variable as shown above. ### 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 6: 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 7: 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 8: 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 # # 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 9: 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 ``` In the example above, the "Tot", "Mean", and "Top" columns were all calculated using the datastep array. These types of row-wise statistics are hard to calculate otherwise. ### Filtering and Duplicating Rows The datastep provides different ways to control which rows are output. First, the function has a `where` parameter to pass a filter expression to the datastep. The where clause will be executed at the end of datastep processing. Pass in the where clause using the `expression()` function. Like so: ```{r eval=FALSE, echo=TRUE} # Prepare sample data dat <- as.data.frame(HairEyeColor) # Filter for black hair and blue eyes res <- datastep(dat, where = expression(Hair == "Black" & Eye == "Blue"), {}) res # Hair Eye Sex Freq # 1 Black Blue Male 11 # 2 Black Blue Female 9 ``` The datastep also recognizes the `delete()` and `output()` functions to remove or duplicate rows from inside the datastep. These functions give you conditional control over which rows are output. ```{r eval=FALSE, echo=TRUE} # Delete rows with frequencies less than 25 res1 <- datastep(dat, { if (Freq < 25) delete() }) res1 # Hair Eye Sex Freq # 1 Black Brown Male 32 # 2 Brown Brown Male 53 # 3 Brown Blue Male 50 # 4 Blond Blue Male 30 # 5 Brown Hazel Male 25 # 6 Black Brown Female 36 # 7 Brown Brown Female 66 # 8 Brown Blue Female 34 # 9 Blond Blue Female 64 # 10 Brown Hazel Female 29 # Only output rows for brown-eyes and frequencies over 25 res2 <- datastep(dat, { if (Eye == "Brown") { if (Freq >= 25) { output() } } }) res2 # Hair Eye Sex Freq # 1 Black Brown Male 32 # 2 Brown Brown Male 53 # 3 Black Brown Female 36 # 4 Brown Brown Female 66 ``` You can also use the `output()` function to create datasets from scratch, just like in SAS®. To create a dataset from scratch, simply pass in an empty data frame and output the desired values. ```{r eval=FALSE, echo=TRUE} # Create metadata res3 <- datastep(data.frame(), { name <- "mtcars" rows <- nrow(mtcars) cols <- ncol(mtcars) output() name <- "iris" rows <- nrow(iris) cols <- ncol(iris) output() name <- "beaver1" rows <- nrow(beaver1) cols <- ncol(beaver1) output() }) res3 # name rows cols # 1 mtcars 32 11 # 2 iris 150 5 # 3 beaver1 114 4 ``` ### Set and Merge Operations When working with data, joining datasets is an essential activity. While there are many different functions in R to perform joins, the `datastep()` "set" and "merge" parameters offer unusual flexibility. The "set" parameter stacks two or more datasets. The "merge" parameter joins two or more datasets. Together, these two parameters allow you to perform the most common types of data combinations. To illustrate, first let's create up some sample datasets. The datasets we will create include one "region" dataset, and two "stores" datasets. Note that the columns on the stores datasets are not identical. ```{r eval=FALSE, echo=TRUE} # Create sample data region <- read.table(header = TRUE, text = ' REGION NAME R01 East R02 West R03 North R04 South ', stringsAsFactors = FALSE) # First stores dataset stores1 <- read.table(header = TRUE, text = ' ID NAME SIZE REGION FRANCHISE A01 "Eastern Lumber" L R01 T A02 "Tri-City Hardwood" M R02 F A05 "Reliable Hardware" S R01 T ', stringsAsFactors = FALSE) # Extra column on this one stores2 <- read.table(header = TRUE, text = ' ID NAME SIZE REGION A03 "AAA Mills" S R05 A04 "Home and Yard" L R03 ', stringsAsFactors = FALSE) ``` Despite not having the same columns, the two stores datasets can be set using the `datastep()` function. The function will fill in the missing values automatically. Like so: ```{r eval=FALSE, echo=TRUE} # Set operation allstores <- datastep(stores1, set = stores2, {}) # Extra values filled with NA allstores # ID NAME SIZE REGION FRANCHISE # 1 A01 Eastern Lumber L R01 TRUE # 2 A02 Tri-City Hardwood M R02 FALSE # 3 A05 Reliable Hardware S R01 TRUE # 4 A03 AAA Mills S R05 NA # 5 A04 Home and Yard L R03 NA ``` Let's pretend we noticed the missing data, and decide to fill it in. We can do that by merging the missing FRANCHISE values to the second stores dataset. First let's create the missing data: ```{r eval=FALSE, echo=TRUE} # Create small dataset of missing FRANCHISE values franchises <- data.frame(FRANCHISE = c(F, F), stringsAsFactors = FALSE) franchises # FRANCHISE # 1 FALSE # 2 FALSE ``` Next we can merge in the missing data on "stores2", and set the two store datasets again: ```{r eval=FALSE, echo=TRUE} # Merge in missing FRANCHISE column stores2mod <- datastep(stores2, merge = franchises, {}) stores2mod # ID NAME SIZE REGION FRANCHISE # 1 A03 AAA Mills S R05 FALSE # 2 A04 Home and Yard L R03 FALSE # Set again allstores <- datastep(stores1, set = stores2mod, {}) # Now everything is aligned allstores # ID NAME SIZE REGION FRANCHISE # 1 A01 Eastern Lumber L R01 TRUE # 2 A02 Tri-City Hardwood M R02 FALSE # 3 A05 Reliable Hardware S R01 TRUE # 4 A03 AAA Mills S R05 FALSE # 5 A04 Home and Yard L R03 FALSE ``` Observe that we did not have to specify a join condition on the merge. When no "merge_by" is indicated, the datastep will simply append the new columns to the right - without complaining. This behavior is very convenient. Now let's do another join, but this time we will specify a join condition. We will join in the store regions by the region ID. We will also set up merge flags so we can see which rows were in which input dataset. ```{r eval=FALSE, echo=TRUE} # Merge operation - Outer Join res <- datastep(allstores, merge = region, merge_by = "REGION", merge_in = c("inA", "inB"), {}) # View results res # ID NAME.1 SIZE REGION FRANCHISE NAME.2 inA inB # 1 A01 Eastern Lumber L R01 TRUE East 1 1 # 2 A05 Reliable Hardware S R01 TRUE East 1 1 # 3 A02 Tri-City Hardwood M R02 FALSE West 1 1 # 4 A04 Home and Yard L R03 FALSE North 1 1 # 5 A03 AAA Mills S R05 FALSE 1 0 # 6 R04 NA South 0 1 ``` Notice three things: 1. The "Name" field appeared in both datasets, and was therefore appended with suffixes to distinguish them. 2. The region value "R05" is in the stores dataset, but not in the region dataset. It appears this value was coded incorrectly. 3. Region "R04" had no stores. Let's try one last time to fix the above issues. We can fix the column names with the "rename" parameter, and exclude rows with a "where" expression. Also, we can recode "R05" to "R04" inside the datastep. Finally, we'll drop the merge flags to clean up the columns. ```{r eval=FALSE, echo=TRUE} # Merge operation - Left join and clean up res <- datastep(allstores, merge = region, merge_by = "REGION", merge_in = c("inA", "inB"), rename = c(NAME.1 = "STORE_NAME", NAME.2 = "REGION_NAME"), where = expression(inA == TRUE), drop = c("inA", "inB"), { if (REGION == "R05") { REGION <- "R04" NAME.2 <- "South" } }) #' # View results res # ID STORE_NAME SIZE REGION FRANCHISE REGION_NAME # 1 A01 Eastern Lumber L R01 TRUE East # 2 A05 Reliable Hardware S R01 TRUE East # 3 A02 Tri-City Hardwood M R02 FALSE West # 4 A04 Home and Yard L R03 FALSE North # 5 A03 AAA Mills S R04 FALSE South ``` ### Datastep Performance One weakness of the **libr** `datastep()` function is performance. The function is far slower than the equivalent SAS® datastep. The performance profile may limit the number of records you are able to reasonably process with the `datastep()`. One thing you can do to increase performance is to reduce the number of rows and columns on the input data. You can perform this pre-filtering with Base R or **Tidyverse** functions. This strategy is particularly recommended if you were planning to subset the data anyway using the "where" or "keep" options. The Base R `subset()` function is convenient to use because it is always available. Here is an example showing how to reduce the size of the **iris** sample dataframe using Base R `subset()` before sending it to a datastep. #### Example 10: Increasing Performance ``` # Subset the input dataset first for only needed rows and columns dat <- subset(iris, Species == 'versicolor', c('Petal.Length', 'Petal.Width')) |> datastep({ if (Petal.Length < 3.5) Petal.Size <- "Short" else if (Petal.Length > 4.5) Petal.Size <- "Long" else Petal.Size <- "Medium" }) # View Some Results dat[1:10, ] # Petal.Length Petal.Width Petal.Size # 1 4.7 1.4 Long # 2 4.5 1.5 Medium # 3 4.9 1.5 Long # 4 4.0 1.3 Medium # 5 4.6 1.5 Long # 6 4.5 1.3 Medium # 7 4.7 1.6 Long # 8 3.3 1.0 Short # 9 4.6 1.3 Long # 10 3.9 1.4 Medium ``` Next: [Disclaimer](libr-disclaimer.html)