Want to load commonly used packages automatically? Have a suite of random functions you use all the time? No problem. R gives you several options to meet your needs.
First, you can create a custom R profile that loads at startup. This is simply a text file (called .Rprofile) placed in your home directory. Don’t know where your home directory is? Start RStudio, close any open projects, and type getwd().
Consider the following (simple) Rprofile:
# Feeling creative? Customize the R interactive prompt
options(prompt = "aRRR> ")
# Change some R defaults
options(stringsAsFactors = FALSE, show.signif.stars = FALSE)
# Create a new invisible environment for functions so as not to clutter your
# workspace.
.env <- new.env()
# Some useful aliases
.env$s <- base::summary
.env$cd <- base::setwd
.env$pwd <- base::getwd
# Some useful functions
## Return names(df) in single column, numbered matrix format.
.env$n <- function(df) matrix(names(df))
## Head and tail; show the first and last 10 items of an object
.env$ht <- function(d) rbind(head(d, 10), tail(d, 10))
# This function runs when R starts
.First <- function() {
toLoad = c("plyr", "lubridate", "reshape2", "ggplot2", "devtools")
sapply(toLoad, require, character.only = TRUE)
rm(toLoad)
cat("\nBack for more, eh?\n")
}
# This function runs when R closes
.Last <- function() {
cat("\nLeaving so soon? You'll be back. I know it.\n")
}
Inspiration for Rprofile modifications abound online (e.g., here and here). Importantly, notice that we’ve included aliases and utility functions in a hidden environment. This keeps our workspace clean and avoids their accidental removal.
While a custom Rprofile generally works well, you may run into problems when sharing scripts with colleagues if they operate R with different defaults. A more reproducible approach is to keep your customizations in a version-controlled script on, e.g., Github and source it at the beginning of every script. Your collaborators then have access to it as well. For example, assuming the devtools package is loaded, ypu can call in an custom script saved as a gist using source_gist. Voila! Here’s how I load my R profile:
source_gist(9216051, quiet = TRUE)
## SHA-1 hash of file is 88c05eb67d371880b7c774e32eb495cb9c22f16a
One final alternative, but beyond our scope, is to keep your functions in an R package (see, e.g., here).
OK, I’ll admit it - Rcmdr houses a wider breadth of methods than I recalled (or expected). But you’ll have to throw away the training wheels (i.e., GUI) at some point. You can only hide from the console for so long. Data reorganization, reshaping, and cleaning are a set of common tasks for which this is likely to be the case, and one for which R is very capable. Moreover, it’s good practice to bring your raw data into R before you perform any manipulations or analysis. Not only is R very capable of these manipulations, but coding them in R makes the manipulations transparent and reproducible; the same can’t be said of button clicks in Excel.
We’ll skip getting data into R and instead focus on some useful data manipulation functions. Jeff covered this in bootcamp. But, to summarize your data input options, R can handle nearly any data format including .csv, .sas, .spss, and even .xls and .xlsx if you’re patient. To summarize the summary: make .csv (Excel can output these easily) and the read.csv command your friends.
Looking at your raw data before analysis is tremendously important. I hesitate to skip it, but we could spend an entire semester on the many ways R gives you to look at your raw data. On the other hand, there is lots of useful guidance online for both base graphics and gglot2 (e.g., here and here). The lattice package is another viable option. In the interest of time, we’ll simply refer you to those links and the Getting help section below.
R likewise gives you many options to perform common data sorting and manipulation tasks. We’ll focus on some of the more intuitive convenience functions to perform these tasks. Most of them reside in Hadley Wickham’s plyr and reshape2 packages.
We’ll use the diamonds data set in the ggplot2 package. diamonds is a large (50000+) data set with multiple numeric and categorical variables.
# Look at the description of the diamonds data
`?`(diamonds)
## starting httpd help server ... done
str(diamonds)
## 'data.frame': 53940 obs. of 10 variables:
## $ carat : num 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
## $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
## $ depth : num 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
## $ table : num 55 61 65 58 58 57 57 55 61 61 ...
## $ price : int 326 326 327 334 335 336 336 337 337 338 ...
## $ x : num 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
## $ y : num 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
## $ z : num 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...
head(diamonds, 10)
## carat cut color clarity depth table price x y z
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
summary(diamonds) # notice no missing values (NAs); very uncommon
## carat cut color clarity depth
## Min. :0.200 Fair : 1610 D: 6775 SI1 :13065 Min. :43.0
## 1st Qu.:0.400 Good : 4906 E: 9797 VS2 :12258 1st Qu.:61.0
## Median :0.700 Very Good:12082 F: 9542 SI2 : 9194 Median :61.8
## Mean :0.798 Premium :13791 G:11292 VS1 : 8171 Mean :61.8
## 3rd Qu.:1.040 Ideal :21551 H: 8304 VVS2 : 5066 3rd Qu.:62.5
## Max. :5.010 I: 5422 VVS1 : 3655 Max. :79.0
## J: 2808 (Other): 2531
## table price x y z
## Min. :43.0 Min. : 326 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:56.0 1st Qu.: 950 1st Qu.: 4.71 1st Qu.: 4.72 1st Qu.: 2.91
## Median :57.0 Median : 2401 Median : 5.70 Median : 5.71 Median : 3.53
## Mean :57.5 Mean : 3933 Mean : 5.73 Mean : 5.73 Mean : 3.54
## 3rd Qu.:59.0 3rd Qu.: 5324 3rd Qu.: 6.54 3rd Qu.: 6.54 3rd Qu.: 4.04
## Max. :95.0 Max. :18823 Max. :10.74 Max. :58.90 Max. :31.80
##
Notice that this data set is in wide format. That is, each of our units of observation (each diamond, in this case) is associated with multiple columns representing several measured variable. The alternative, long format, puts each variable on its own line, in which case each observation (diamond) would be associated with multiple rows. Most analyses in R require data in the wide format, but long format is useful in certain situations. We’ll come back to the utility of the long format later.
Let’s start with simple operations. One of the most basic data frame manipulations is sorting by one or more columns. In base R, the order function is the function of choice, but its syntax is a bit painful. For example, to sort the diamond data by increasing size (carat) and decreasing price:
dsort <- diamonds[with(diamonds, order(carat, -price)), ]
# dsort <- diamonds[order(diamonds$carat, -diamonds$price), ] # equivalent sort
head(dsort, 10)
## carat cut color clarity depth table price x y z
## 31592 0.2 Premium E VS2 59.8 62 367 3.79 3.77 2.26
## 31593 0.2 Premium E VS2 59.0 60 367 3.81 3.78 2.24
## 31594 0.2 Premium E VS2 61.1 59 367 3.81 3.78 2.32
## 31595 0.2 Premium E VS2 59.7 62 367 3.84 3.80 2.28
## 31596 0.2 Ideal E VS2 59.7 55 367 3.86 3.84 2.30
## 31597 0.2 Premium F VS2 62.6 59 367 3.73 3.71 2.33
## 31598 0.2 Ideal D VS2 61.5 57 367 3.81 3.77 2.33
## 31599 0.2 Very Good E VS2 63.4 59 367 3.74 3.71 2.36
## 31600 0.2 Ideal E VS2 62.2 57 367 3.76 3.73 2.33
## 31601 0.2 Premium D VS2 62.3 60 367 3.73 3.68 2.31
Yikes. That’s pretty ugly. The arrange function in plyr makes it far more intuitive, and should take care of your sorting needs.
dsort2 <- arrange(diamonds, carat, -price)
head(dsort2, 10)
## carat cut color clarity depth table price x y z
## 1 0.2 Premium E VS2 59.8 62 367 3.79 3.77 2.26
## 2 0.2 Premium E VS2 59.0 60 367 3.81 3.78 2.24
## 3 0.2 Premium E VS2 61.1 59 367 3.81 3.78 2.32
## 4 0.2 Premium E VS2 59.7 62 367 3.84 3.80 2.28
## 5 0.2 Ideal E VS2 59.7 55 367 3.86 3.84 2.30
## 6 0.2 Premium F VS2 62.6 59 367 3.73 3.71 2.33
## 7 0.2 Ideal D VS2 61.5 57 367 3.81 3.77 2.33
## 8 0.2 Very Good E VS2 63.4 59 367 3.74 3.71 2.36
## 9 0.2 Ideal E VS2 62.2 57 367 3.76 3.73 2.33
## 10 0.2 Premium D VS2 62.3 60 367 3.73 3.68 2.31
In many analyses, variables of interest derive from modifications or combinations of raw measurements. R again gives you multiple options to create and calculate new variables from existing columns. We’ll look at two - base R’s within function and plyr’s mutate function.
# Creating two new variables:
# 1. logP: natural log of diamond price
# 2. volume: silly estimate of diamond volume (mm<sup>3</sup>)
# First, use base R 'within' function
d <- within(diamonds, {
logP <- log(price) # no comma here; compare 'mutate'
volume <- x * y * z
})
head(d)
## carat cut color clarity depth table price x y z volume logP
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 38.20 5.787
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 34.51 5.787
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 38.08 5.790
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63 46.72 5.811
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 51.92 5.814
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 38.69 5.817
# Now use plyr 'mutate' function
d2 <- mutate(diamonds,
logP = log(price), # comma here
volume = x * y * z
)
head(d2)
## carat cut color clarity depth table price x y z logP volume
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 5.787 38.20
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 5.787 34.51
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 5.790 38.08
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63 5.811 46.72
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 5.814 51.92
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 5.817 38.69
Typically, our data sets comprise a mixture of categorical (factor) and numeric variables. Functions applied to the different levels of one or more of these factor variables (e.g., calculating group averages) is often a useful aspect of data exploration.
plyr contains functions that allow users to evaluate functions over one or more grouping variables (i.e., the levels of a categorical variable). The ddply function simplifies this process. The dd simply indicates that the function takes a data frame as an input and outputs a data frame. There are other versions (e.g., ldply, which takes a list as an input and outputs a data frame), but ddply is the one you’ll use most regularly.
An example will hopefully clarify. To calculate the average size (carats) for each combination of diamond cut, clarity, and color:
# First, what inputs does the function require?
?ddply
# How many groups are we talking about here?
with(diamonds, length(levels(cut)) * length(levels(clarity)) * length(levels(color)))
## [1] 280
# Now, create a new data frame that takes the diamond data and calculates mean carat size by cut, clarity, and color combinations
dcut <- ddply(diamonds, .(cut, clarity, color), summarize,
meancarat = mean(carat, na.rm = TRUE), # Don't need na.rm in this case, but often will
ndiamonds = length(carat)) # # diamonds in each calculation
head(dcut, 10)
## cut clarity color meancarat ndiamonds
## 1 Fair I1 D 1.8775 4
## 2 Fair I1 E 0.9689 9
## 3 Fair I1 F 1.0234 35
## 4 Fair I1 G 1.2264 53
## 5 Fair I1 H 1.4987 52
## 6 Fair I1 I 1.3229 34
## 7 Fair I1 J 1.9935 23
## 8 Fair SI2 D 1.0170 56
## 9 Fair SI2 E 1.0156 78
## 10 Fair SI2 F 1.0801 89
# Note that any function can be applied over the grouping variable(s)
dcut2 <- ddply(diamonds, .(cut, clarity, color), summarize,
sdcarat = sd(carat, na.rm = TRUE),
nonsense = sqrt(median(x * y / z, na.rm = TRUE)))
A cross-tabulation (~ pivot table) with the xtab function can make ddply’s output a little easier on the eyes. With three or more grouping variables, flattening the cross-tabulation with ftable may keep your head from exploding.
# Two grouping variable example
xtabs(meancarat ~ cut + clarity, data = dcut)
## clarity
## cut I1 SI2 SI1 VS2 VS1 VVS2 VVS1 IF
## Fair 9.911 8.567 6.956 6.293 6.058 5.179 4.995 1.390
## Good 8.554 7.748 6.125 6.132 5.329 4.520 4.062 4.497
## Very Good 9.237 7.776 6.188 5.950 5.305 4.512 3.734 4.368
## Premium 9.149 8.361 6.665 6.230 5.746 4.954 4.299 4.687
## Ideal 9.038 7.440 5.904 5.198 4.889 4.344 3.525 3.443
# The order determines the arrangement of the table
xtabs(meancarat ~ clarity + cut, data = dcut)
## cut
## clarity Fair Good Very Good Premium Ideal
## I1 9.911 8.554 9.237 9.149 9.038
## SI2 8.567 7.748 7.776 8.361 7.440
## SI1 6.956 6.125 6.188 6.665 5.904
## VS2 6.293 6.132 5.950 6.230 5.198
## VS1 6.058 5.329 5.305 5.746 4.889
## VVS2 5.179 4.520 4.512 4.954 4.344
## VVS1 4.995 4.062 3.734 4.299 3.525
## IF 1.390 4.497 4.368 4.687 3.443
# Three grouping variables without flattening... Not run... xtabs(meancarat ~
# cut + clarity + color, data = dcut)
# Try it if you dare, but it's a bit painful to read. Flattening helps.
ftable(xtabs(meancarat ~ cut + clarity + color, data = dcut))
## color D E F G H I J
## cut clarity
## Fair I1 1.8775 0.9689 1.0234 1.2264 1.4987 1.3229 1.9935
## SI2 1.0170 1.0156 1.0801 1.2620 1.3644 1.5116 1.3167
## SI1 0.9138 0.8671 0.8641 0.9096 1.1123 1.1080 1.1811
## VS2 0.8436 0.6902 0.7587 0.9778 1.0368 0.9531 1.0326
## VS1 0.6300 0.6329 0.8048 0.7742 0.9759 1.0104 1.2294
## VVS2 0.5911 0.6008 0.6270 0.6647 0.8409 0.8450 1.0100
## VVS1 0.6067 0.6400 0.6680 0.5700 0.9100 0.9000 0.7000
## IF 0.3800 0.0000 0.5550 0.4550 0.0000 0.0000 0.0000
## Good I1 1.0400 1.3309 0.9763 1.1742 1.2521 1.4100 1.3700
## SI2 0.8583 0.8826 1.0025 1.0869 1.1735 1.4248 1.3189
## SI1 0.7008 0.7239 0.7683 0.8838 0.9067 1.0153 1.1258
## VS2 0.7025 0.7394 0.7521 0.8157 0.8780 1.1296 1.1143
## VS1 0.6633 0.6807 0.6246 0.7792 0.7799 0.9266 0.8750
## VVS2 0.4812 0.5602 0.6076 0.6261 0.5884 0.7196 0.9369
## VVS1 0.4908 0.4181 0.4657 0.5471 0.4735 0.6664 1.0000
## IF 0.7867 0.3733 0.5333 0.6482 0.9350 0.5300 0.6900
## Very Good I1 0.9500 1.0695 1.2108 1.1238 1.6542 1.7663 1.4625
## SI2 0.9317 0.9304 0.9512 1.0328 1.2346 1.3343 1.3609
## SI1 0.7078 0.7231 0.7965 0.7858 0.9740 1.0659 1.1353
## VS2 0.6337 0.6644 0.7420 0.8103 0.8932 1.0664 1.1405
## VS1 0.5834 0.6098 0.6881 0.7013 0.7723 0.9854 0.9649
## VVS2 0.4657 0.4267 0.5714 0.6507 0.5937 0.7018 1.1021
## VVS1 0.4746 0.4001 0.4937 0.5247 0.5043 0.5709 0.7653
## IF 0.8030 0.5793 0.6069 0.6004 0.5583 0.7647 0.4550
## Premium I1 1.1550 1.0430 1.1353 1.2913 1.3398 1.6058 1.5785
## SI2 0.9189 0.9577 1.0352 1.1427 1.3283 1.4240 1.5545
## SI1 0.6917 0.7263 0.8421 0.8827 1.0840 1.1804 1.2578
## VS2 0.5846 0.6189 0.7310 0.8095 1.0046 1.2359 1.2457
## VS1 0.6871 0.6432 0.7673 0.7502 0.7796 0.9826 1.1362
## VVS2 0.5805 0.5116 0.6576 0.6927 0.5775 0.6822 1.2521
## VVS1 0.5383 0.4623 0.6062 0.5351 0.4163 0.5162 1.2246
## IF 0.7080 0.5763 0.5255 0.5640 0.5980 0.5730 1.1417
## Ideal I1 0.9600 1.0378 1.1079 1.1687 1.4755 1.2982 1.9900
## SI2 0.7503 0.8744 0.9322 0.9761 1.1438 1.3785 1.3845
## SI1 0.5948 0.6706 0.7696 0.7603 0.9372 1.0276 1.1439
## VS2 0.4993 0.5211 0.6322 0.7697 0.7960 0.9279 1.0513
## VS1 0.5335 0.5036 0.6440 0.7171 0.7065 0.8061 0.9784
## VVS2 0.5448 0.4839 0.5773 0.6461 0.5673 0.6538 0.8706
## VVS1 0.4601 0.4265 0.4762 0.5391 0.4935 0.5513 0.5783
## IF 0.6157 0.4577 0.4115 0.4547 0.4746 0.4515 0.5768
Combining multiple data frames based on shared variables is another common data management task. The base R merge function will work, but we’ll stick with the plyr version - join. To keep it simple, we’ll illustrate only basic join usage; check the documentation for other options (e.g., data frames with incomplete matching).
`?`(join)
# Join two diamond data frames Three shared columns (cut, clarity, and color)
head(dcut)
## cut clarity color meancarat ndiamonds
## 1 Fair I1 D 1.8775 4
## 2 Fair I1 E 0.9689 9
## 3 Fair I1 F 1.0234 35
## 4 Fair I1 G 1.2264 53
## 5 Fair I1 H 1.4987 52
## 6 Fair I1 I 1.3229 34
head(dcut2)
## cut clarity color sdcarat nonsense
## 1 Fair I1 D 1.0690 3.351
## 2 Fair I1 E 0.2070 3.121
## 3 Fair I1 F 0.5525 2.972
## 4 Fair I1 G 0.6381 3.081
## 5 Fair I1 H 0.7340 3.228
## 6 Fair I1 I 0.5025 3.185
# Join by cut, clarity, and color combination
djoin <- join(dcut, dcut2, by = c("cut", "clarity", "color"))
head(djoin)
## cut clarity color meancarat ndiamonds sdcarat nonsense
## 1 Fair I1 D 1.8775 4 1.0690 3.351
## 2 Fair I1 E 0.9689 9 0.2070 3.121
## 3 Fair I1 F 1.0234 35 0.5525 2.972
## 4 Fair I1 G 1.2264 53 0.6381 3.081
## 5 Fair I1 H 1.4987 52 0.7340 3.228
## 6 Fair I1 I 1.3229 34 0.5025 3.185
ddply with mutate to create observation-level variables relevant to group valuesSometimes it may be relevant to construct a new variable for your data set relative to a group-level measure. For example, the absolute price of a diamond may be less important than the price of that diamond relative to similar diamonds. In these cases, you can use ddply with mutate rather than summarize to calculate these relative variables.
# Notice that using 'mutate' preserves all other variables in the data frame;
# using 'summarize' would preserve only the newly-created variable and the
# grouping variable(s).
diamonds2 <- ddply(diamonds, .(cut, color, clarity), mutate, priceDev = price - mean(price,
na.rm = TRUE), priceRes = resid(lm(price ~ carat)))
head(diamonds2)
## carat cut color clarity depth table price x y z priceDev priceRes
## 1 1.50 Fair D I1 64.7 62 5460 7.19 7.04 4.60 -1923 151.3
## 2 1.70 Fair D I1 64.7 56 5617 7.46 7.37 4.80 -1766 -790.7
## 3 3.40 Fair D I1 66.8 52 15964 9.42 9.34 6.27 8581 215.0
## 4 0.91 Fair D I1 66.2 57 2491 6.00 5.94 3.95 -4892 424.3
## 5 0.75 Fair D SI2 64.6 57 2848 5.74 5.72 3.70 -1507 706.0
## 6 0.90 Fair D SI2 66.9 57 2885 6.02 5.90 3.99 -1470 -500.5
ddply is quite useful and intuitive for variable creation and data manipulation, but it can be a bit slow when you’re dealing with very large data frames (e.g., hundreds of thousands or millions of records). Nonetheless, modifications are available to speed it up in these instances (e.g., here), and plyr functions can run in parallel on multiple core machines. Finally, for R 3.0.2 or later, Hadley has created a new incarnation of ddply. The dplyr package is even more intuitive, allows you to chain commands together in a logical order, and much, much faster. Hopefully we can revisit dplyr later this semester.
We’ve noted that most analyses require data in wide format with a single record (row) for each sample and multiple columns for the variables measured on/during that sample. A reminder of what wide format data looks like:
## carat cut color clarity depth table price x y z
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
In some instances, however, it may be useful to convert wide format data into long format data. For example, ggplot2 is usually happier with data in long format. The melt function in the reshape2 package makes this process easy, although the base R reshape function can be useful in more complicated situations, but I’ve never had to use it.
dmelt <- melt(diamonds, id.vars = c("cut", "clarity", "color"))
rbind(head(dmelt), tail(dmelt))
## cut clarity color variable value
## 1 Ideal SI2 E carat 0.23
## 2 Premium SI1 E carat 0.21
## 3 Good VS1 E carat 0.23
## 4 Premium VS2 I carat 0.29
## 5 Good SI2 J carat 0.31
## 6 Very Good VVS2 J carat 0.24
## 377575 Premium SI1 D z 3.58
## 377576 Ideal SI1 D z 3.50
## 377577 Good SI1 D z 3.61
## 377578 Very Good SI1 D z 3.56
## 377579 Premium SI2 H z 3.74
## 377580 Ideal SI2 D z 3.64
With this particularly data set in long format, we can easily visualize certain aspects of the data in ggplot2.
dplot <- ggplot(dmelt, aes(x = value, fill = cut)) + geom_histogram(alpha = 0.2,
position = "identity") + facet_wrap(~variable, scales = "free_x")
suppressMessages(print(dplot)) # Preventing binwidth messages
Additionally, melting the data provides an additional way to reshape the data (with the dcast function in reshape2) by applying functions to different groups of the data, but that functionality is largely, if not entirely, available in ddply and dplyr. Not surprisingly, there are many ways to tackle these problems using base R and other packages.
Invariably, you’re going to run into problems (e.g., error messages, warnings). This is true whether you’re using R, SAS, SPSS, or whatever. But there is lots of R help at your fingertips, and much of it is useful! For example:
?function will give you the help page of a function or data set (e.g., ?ddply, ?mtcars)example(function) asks R to run an example(s) of the function??searchterm will look for a search term in all packages on CRAN (e.g., ??split)RSiteSearch("search terms") will search the R site for your search terms (e.g., `RSiteSearch(“plyr reshape2”))RSiteSearch("{search terms}") will search for an exact phrase (e.g., RSiteSearch("{linear mixed model}"))