In jariou/MachCalc: A Machinist Calculator

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title: "Machinist Calculator Vignette" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Main vignette for MachCalc} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc}

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This vignette presents an overview of the main functions implemented in the MachCalc package.

Overview

This package was writtent mainly for my own use, but I hope others will also find a use for them and let me know if they see errors.

List of main R functions and objects

| Function | Summary Description | |-------------------------------|---------------------------------------------------------------------| | bestGearRatio | Finds pairs of gears producing ratios close to the one required | | bore3P | Size of the bore of a hole in which 3 given pins fit cotangentially | | gearRatio | Support function for bestGearRatio | linesCircle | Support function for bore3P | plotCircle | Support function for bore3P | spindleSpeed | Calculates spindle speed

To begin, we'll load machcalc and call the bestGearRatio function.

library(MachCalc)
bestGearRatio(1.27,100)

arrange(): Arrange rows by variables

dplyr::arrange() orders the rows of a data frame by the values of one or more columns:

mtcars %>% arrange(cyl, disp)

The desc() helper allows you to order selected variables in descending order:

mtcars %>% arrange(desc(cyl), desc(disp))

We can replicate in base R by using [ with order():

mtcars[order(mtcars$cyl, mtcars$disp), , drop = FALSE]

Note the use of drop = FALSE. If you forget this, and the input is a data frame with a single column, the output will be a vector, not a data frame. This is a source of subtle bugs.

Base R does not provide a convenient and general way to sort individual variables in descending order, so you have two options:

• For numeric variables, you can use -x.
• You can request order() to sort all variables in descending order.

mtcars[order(mtcars$cyl, mtcars$disp, decreasing = TRUE), , drop = FALSE]
mtcars[order(-mtcars$cyl, -mtcars$disp), , drop = FALSE]

distinct(): Select distinct/unique rows

dplyr::distinct() selects unique rows:

df <- tibble(
  x = sample(10, 100, rep = TRUE),
  y = sample(10, 100, rep = TRUE)
)
df %>% distinct(x) # selected columns
df %>% distinct(x, .keep_all = TRUE) # whole data frame

There are two equivalents in base R, depending on whether you want the whole data frame, or just selected variables:

unique(df["x"]) # selected columns
df[!duplicated(df$x), , drop = FALSE] # whole data frame

filter(): Return rows with matching conditions

dplyr::filter() selects rows where an expression is TRUE:

starwars %>% filter(species == "Human")
starwars %>% filter(mass > 1000)
starwars %>% filter(hair_color == "none" & eye_color == "black")

The closest base equivalent (and the inspiration for filter()) is subset():

subset(starwars, species == "Human")
subset(starwars, mass > 1000)
subset(starwars, hair_color == "none" & eye_color == "black")

You can also use [ but this also requires the use of which() to remove NAs:

starwars[which(starwars$species == "human"), , drop = FALSE]
starwars[which(starwars$mass > 1000), , drop = FALSE]
starwars[which(starwars$hair_color == "none" & starwars$eye_color == "black"), , drop = FALSE]

mutate(): Create or transform variables

dplyr::mutate() creates new variables from existing variables:

df %>% mutate(z = x + y, z2 = z ^ 2)

The closest base equivalent is transform(), but note that it cannot use freshly created variables:

head(transform(df, z = x + y, z2 = (x + y) ^ 2))

Alternatively, you can use $<-:

mtcars$cyl2 <- mtcars$cyl * 2
mtcars$cyl4 <- mtcars$cyl2 * 2

When applied to a grouped data frame, dplyr::mutate() computes new variable once per group:

gf <- tibble(g = c(1, 1, 2, 2), x = c(0.5, 1.5, 2.5, 3.5))
gf %>% 
  group_by(g) %>% 
  mutate(x_mean = mean(x), x_rank = rank(x))

To replicate this in base R, you can use ave():

transform(gf, 
  x_mean = ave(x, g, FUN = mean), 
  x_rank = ave(x, g, FUN = rank)
)

pull(): Pull out a single variable

dplyr::pull() extracts a variable either by name or position:

mtcars %>% pull(1)
mtcars %>% pull(cyl)

This equivalent to [[ for positions and $ for names:

mtcars[[1]]
mtcars$cyl

rename(): Rename variables by name

dplyr::rename() allows you to rename variables by name or position:

iris %>% rename(sepal_length = Sepal.Length, sepal_width = 2)

Renaming variables by position is straight forward in base R:

iris2 <- iris
names(iris2)[2] <- "sepal_width"

Renaming variables by name requires a bit more work:

names(iris2)[names(iris2) == "Sepal.Length"] <- "sepal_length"

Subsetting variables by position is straightforward in base R:

iris[1:3] # single argument selects columns; never drops
iris[1:3, , drop = FALSE]

You have two options to subset by name:

iris[c("Species", "Sepal.Length")]
subset(iris, select = c(Species, Sepal.Length))

Subsetting by function of name requires a bit of work with grep():

iris[grep("^Petal", names(iris))]

And you can use Filter() to subset by type:

Filter(is.factor, iris)

summarise(): Reduce multiple values down to a single value

dplyr::summarise() computes one or more summaries for each group:

mtcars %>% 
  group_by(cyl) %>% 
  summarise(mean = mean(disp), n = n())

I think the closest base R equivalent uses by(). Unfortunately by() returns a list of data frames, but you can combine them back together again with do.call() and rbind():

mtcars_by <- by(mtcars, mtcars$cyl, function(df) {
  with(df, data.frame(cyl = cyl[[1]], mean = mean(disp), n = nrow(df)))
})
do.call(rbind, mtcars_by)

aggregate() comes very close to providing an elegant answer:

agg <- aggregate(disp ~ cyl, mtcars, function(x) c(mean = mean(x), n = length(x)))
agg

But unfortunately while it looks like there are disp.mean and disp.n columns, it's actually a single matrix column:

str(agg)

You can see a variety of other options at https://gist.github.com/hadley/c430501804349d382ce90754936ab8ec, many generated by the R community inspired by a tweet.

slice(): Choose rows by position

slice() selects rows with their location:

slice(mtcars, 25:n())

This is straightforward to replicate with [:

mtcars[25:nrow(mtcars), , drop = FALSE]

Two-table verbs

When we want to merge two data frames, x and y), we have a variety of different ways to bring them together. Various base R merge() calls are replaced by a variety of dplyr join() functions.

| dplyr | base | |------------------------|-----------------------------------------| | inner_join(df1, df2) |merge(df1, df2) | | left_join(df1, df2) |merge(df1, df2, all.x = TRUE) | | right_join(df1, df2) |merge(df1, df2, all.y = TRUE) | | full_join(df1, df2) |merge(df1, df2, all = TRUE) | | semi_join(df1, df2) |df1[df1xx, , drop = FALSE] | | anti_join(df1, df2) |df1[!df1xx, , drop = FALSE] |

For more information about two-table verbs, see vignette("two-table").

Mutating joins

dplyr's inner_join(), left_join(), right_join(), and full_join() add new columns from y to x, matching rows based on a set of "keys", and differ only in how missing matches are handled. They are equivalent to calls to merge() with various settings of the all, all.x, and all.y arguments. The main difference is the order of the rows:

• dplyr preserves the order of the x data frame.
• merge() sorts the key columns.

Filtering joins

dplyr's semi_join() and anti_join() affect only the rows, not the columns:

band_members %>% semi_join(band_instruments)
band_members %>% anti_join(band_instruments)

They can be replicated in base R with [ and %in%:

band_members[band_members$name %in% band_instruments$name, , drop = FALSE]
band_members[!band_members$name %in% band_instruments$name, , drop = FALSE]

Semi and anti joins with multiple key variables are considerably more challenging to implement.

jariou/MachCalc documentation built on March 1, 2020, 2:38 a.m.