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The fapply2() function applies two formats to two
different vectors, and combines them when complete. This function can be
used to collapse two columns into one. The fapply2()
function is convenient to use when preparing your data for reporting.
Here is an example:
# Create sample vectors
v1 <- c(27, 43, 22, 56)
v2 <- c(18.24324, 29.05405, 14.86486, 37.83784)
# Create data frame
dat <- data.frame("Counts" = v1, "Percents" = v2)
# Format and Combine
dat$CntPct <- fapply2(dat$Counts, dat$Percents, "%d", "(%.1f%%)")
# View results
dat
# Counts Percents CntPct
# 1 27 18.24324 27 (18.2%)
# 2 43 29.05405 43 (29.1%)
# 3 22 14.86486 22 (14.9%)
# 4 56 37.83784 56 (37.8%)The fapply2() function is suitable for use with the
dplyr package. Here is the same example as above, but using
dplyr mutate() instead of Base R:
library(dplyr)
# Create sample vectors
v1 <- c(27, 43, 22, 56)
v2 <- c(18.24324, 29.05405, 14.86486, 37.83784)
# Create data frame
dat <- data.frame("Counts" = v1, "Percents" = v2)
# Format and Combine
dat <- dat |>
mutate(CntPct = fapply2(dat$Counts, dat$Percents, "%d", "(%.1f%%)"))
# View results
dat
# Counts Percents CntPct
# 1 27 18.24324 27 (18.2%)
# 2 43 29.05405 43 (29.1%)
# 3 22 14.86486 22 (14.9%)
# 4 56 37.83784 56 (37.8%)The fapply2() function is also compatible with the
datastep() function from the libr package. Here is
the example again with the datastep():
library(libr)
# Create sample vectors
v1 <- c(27, 43, 22, 56)
v2 <- c(18.24324, 29.05405, 14.86486, 37.83784)
# Create data frame
dat <- data.frame("Counts" = v1, "Percents" = v2)
# Format and Combine
dat <- datastep(dat,
{
CntPct <- fapply2(Counts, Percents, "%d", "(%.1f%%)")
})
# View results
dat
# Counts Percents CntPct
# 1 27 18.24324 27 (18.2%)
# 2 43 29.05405 43 (29.1%)
# 3 22 14.86486 22 (14.9%)
# 4 56 37.83784 56 (37.8%)Note that fapply2() will use formats assigned to the
data frame columns if they are available. Assigning the formats to the
columns first can simplify use of the function and promote format reuse.
To assign the formats to the columns, use the formats()
function, like so:
# Create sample vectors
v1 <- c(27, 43, 22, 56)
v2 <- c(18.24324, 29.05405, 14.86486, 37.83784)
# Create data frame
dat <- data.frame("Counts" = v1, "Percents" = v2)
formats(dat) <- list(Counts = "%d", Percents = "(%.1f%%)")
# Format and Combine - Formats already assigned
dat$CntPct <- fapply2(dat$Counts, dat$Percents)
# View results
dat
# Counts Percents CntPct
# 1 27 18.24324 27 (18.2%)
# 2 43 29.05405 43 (29.1%)
# 3 22 14.86486 22 (14.9%)
# 4 56 37.83784 56 (37.8%)datastep()The ability to use any formats assigned to the columns makes the
fapply() function very useful when combined with format
catalogs and the datastep() function. When the format
catalog is assigned to the datastep(), it will
automatically assign the formats in the catalog to any corresponding
columns on the input data frame. This feature allows you to quickly
assign saved formats to a new dataset, and use those formats to combine
columns in the desired way. Observe:
library(libr)
# Create sample vectors
grp <- c("Group1", "Group2", "Group3", "Group4")
v1 <- c(27, 43, 22, 56)
v2 <- c(18.24324, 29.05405, 14.86486, 37.83784)
v3 <- c(5.24883, 8.83724, 2.39483, 9.12542)
v4 <- c(2.97632, 3.32845, 0.29784, 4.22156)
# Create data frame
dat <- data.frame("Group" = grp, "Counts" = v1, "Percents" = v2,
"Mean" = v3, "SD" = v4)
# View original data
dat
# Group Counts Percents Mean SD
# 1 Group1 27 18.24324 5.24883 2.97632
# 2 Group2 43 29.05405 8.83724 3.32845
# 3 Group3 22 14.86486 2.39483 0.29784
# 4 Group4 56 37.83784 9.12542 4.22156
# Create format catalog
fc <- fcat(Counts = "%d", Percents = "(%03.1f%%)",
Mean = "%.1f", SD = "(%04.2f)")
# Format and Combine columns using Format catalog
dat2 <- datastep(dat, format = fc,
keep = v(Group, CntPct, MeanSD),
{
CntPct <- fapply2(Counts, Percents)
MeanSD <- fapply2(Mean, SD)
})
# View results
dat2
# Group CntPct MeanSD
# 1 Group1 27 (18.2%) 5.2 (2.98)
# 2 Group2 43 (29.1%) 8.8 (3.33)
# 3 Group3 22 (14.9%) 2.4 (0.30)
# 4 Group4 56 (37.8%) 9.1 (4.22)The above technique points to a method for sharing formats between programs and ensuring that statistical results are formatted consistently across programs.
Note that the fapply2() function will accept any type of
format supported by the fmtr package. That means you can use
numeric formats, date formats, vector lookups, user-defined formats, and
vectorized functions. The combination of these format types allows you
to format and combine data in a powerful way that will enhance the
impact of your analysis.
Next: Convenience Functions
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