The course provides a brief overview of R data structures followed by the following topics:
Loops in R
Vectorized functions (apply family of functions)
How R functions work
Function writing
First up, recap and streamline repeated sections of code!
What do we want to be able to do?
Review of Data Structures in R
Five major types
- Atomic Vector (1d)
- Matrix (2d)
- Array (nd)
- Data Frame (2d)
- List (1d)
- Atomic Vector (1d)
| Dimension | Homogeneous | Heterogeneous |
|---|---|---|
| 1d | Atomic Vector | List |
| 2d | Matrix | Data Frame |
Vector
Atomic Vector (1D group of elements with an ordering)
Elements must be same ‘type’
- numeric (integer or double), character, or logical
Accessing Parts of an Atomic Vector (1D)
Return elements using square brackets
[]Can ‘feed’ in a vector of indices to
[]
letters
## [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" ## [20] "t" "u" "v" "w" "x" "y" "z"
letters[1:4]
## [1] "a" "b" "c" "d"
x <- c(1, 2, 5); letters[x]
## [1] "a" "b" "e"
Data Frames
- Best R object for data sets
- Collection (list) of vectors of the same length
Data Frames
Consider the built in
irisdata setCan see info about object with
str()
myIris <- as_tibble(iris) str(myIris)
## tibble [150 x 5] (S3: tbl_df/tbl/data.frame) ## $ Sepal.Length: num [1:150] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... ## $ Sepal.Width : num [1:150] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... ## $ Petal.Length: num [1:150] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... ## $ Petal.Width : num [1:150] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... ## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
Accessing Parts of Data Frame (2D)
- Returning rectangular portions of a data frame
myIris[1:4, 2:4]
## # A tibble: 4 x 3 ## Sepal.Width Petal.Length Petal.Width ## <dbl> <dbl> <dbl> ## 1 3.5 1.4 0.2 ## 2 3 1.4 0.2 ## 3 3.2 1.3 0.2 ## 4 3.1 1.5 0.2
myIris[1, ]
## # A tibble: 1 x 5 ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## <dbl> <dbl> <dbl> <dbl> <fct> ## 1 5.1 3.5 1.4 0.2 setosa
Accessing Parts of Data Frame (2D)
- Grabbing a column (same result for both)
myIris$Sepal.Length
dplyr::pull(myIris, Sepal.Length)
## [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1 ## [19] 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.0 ## [37] 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0 6.4 6.9 5.5 ## [55] 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1 ## [73] 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4 6.0 6.7 6.3 5.6 5.5 ## [91] 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3 ## [109] 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.0 6.9 5.6 7.7 6.3 6.7 7.2 ## [127] 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8 ## [145] 6.7 6.7 6.3 6.5 6.2 5.9
Data Frames
- Grabbing multiple columns
dplyr::select(myIris, starts_with("Sepal"))
## # A tibble: 150 x 2 ## Sepal.Length Sepal.Width ## <dbl> <dbl> ## 1 5.1 3.5 ## 2 4.9 3 ## 3 4.7 3.2 ## 4 4.6 3.1 ## 5 5 3.6 ## # ... with 145 more rows
Packages & tidyverse
“TidyVerse” - collection of R packages that share common philosophies and are designed to work together!
If not installed (downloaded) on computer
install.packages("tidyverse")
- Once installed,
library()orrequire()to load
library(tidyverse)
dplrypackage made for most standard data manipulation tasks
tidyrhandles most of the rest
%>%operator allows coding from left to right
Pipe
- Generically, pipe does the following
x %>% f(y) turns into f(x,y)
x %>% f(y) %>% g(z) turns into g(f(x, y), z)
library(Lahman) #Install pacakage if needed
Batting %>%
as_tibble() %>%
select(starts_with("X"), ends_with("ID"), G) %>%
rename("Doubles" = X2B, "Triples" = X3B)
## # A tibble: 105,861 x 7 ## Doubles Triples playerID yearID teamID lgID G ## <int> <int> <chr> <int> <fct> <fct> <int> ## 1 0 0 abercda01 1871 TRO NA 1 ## 2 6 0 addybo01 1871 RC1 NA 25 ## 3 4 5 allisar01 1871 CL1 NA 29 ## 4 10 2 allisdo01 1871 WS3 NA 27 ## 5 11 3 ansonca01 1871 RC1 NA 25 ## # ... with 105,856 more rows
Streamlining Code
On to the main attraction! Improving R code!
Often a repetitive task must be done
Task requires a small change each time it is done
Example:
- Summarize each column of a dataset
Summarizing columns
Consider wine data from UCI machine learning repository
wineData <- read_csv("../datasets/winequality-full.csv")
wineData
## # A tibble: 6,497 x 13 ## `fixed acidity` `volatile acidity` `citric acid` `residual sugar` chlorides ## <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 7.4 0.7 0 1.9 0.076 ## 2 7.8 0.88 0 2.6 0.098 ## 3 7.8 0.76 0.04 2.3 0.092 ## 4 11.2 0.28 0.56 1.9 0.075 ## 5 7.4 0.7 0 1.9 0.076 ## # ... with 6,492 more rows, and 8 more variables: free sulfur dioxide <dbl>, ## # total sulfur dioxide <dbl>, density <dbl>, pH <dbl>, sulphates <dbl>, ## # alcohol <dbl>, quality <dbl>, type <chr>
Summarizing Columns
- Of course R has some nice functions to help us out…
summary(wineData)
## fixed acidity volatile acidity citric acid residual sugar ## Min. : 3.800 Min. :0.0800 Min. :0.0000 Min. : 0.600 ## 1st Qu.: 6.400 1st Qu.:0.2300 1st Qu.:0.2500 1st Qu.: 1.800 ## Median : 7.000 Median :0.2900 Median :0.3100 Median : 3.000 ## Mean : 7.215 Mean :0.3397 Mean :0.3186 Mean : 5.443 ## 3rd Qu.: 7.700 3rd Qu.:0.4000 3rd Qu.:0.3900 3rd Qu.: 8.100 ## Max. :15.900 Max. :1.5800 Max. :1.6600 Max. :65.800 ## chlorides free sulfur dioxide total sulfur dioxide density ## Min. :0.00900 Min. : 1.00 Min. : 6.0 Min. :0.9871 ## 1st Qu.:0.03800 1st Qu.: 17.00 1st Qu.: 77.0 1st Qu.:0.9923 ## Median :0.04700 Median : 29.00 Median :118.0 Median :0.9949 ## Mean :0.05603 Mean : 30.53 Mean :115.7 Mean :0.9947 ## 3rd Qu.:0.06500 3rd Qu.: 41.00 3rd Qu.:156.0 3rd Qu.:0.9970 ## Max. :0.61100 Max. :289.00 Max. :440.0 Max. :1.0390 ## pH sulphates alcohol quality ## Min. :2.720 Min. :0.2200 Min. : 8.00 Min. :3.000 ## 1st Qu.:3.110 1st Qu.:0.4300 1st Qu.: 9.50 1st Qu.:5.000 ## Median :3.210 Median :0.5100 Median :10.30 Median :6.000 ## Mean :3.219 Mean :0.5313 Mean :10.49 Mean :5.818 ## 3rd Qu.:3.320 3rd Qu.:0.6000 3rd Qu.:11.30 3rd Qu.:6.000 ## Max. :4.010 Max. :2.0000 Max. :14.90 Max. :9.000 ## type ## Length:6497 ## Class :character ## Mode :character ## ## ##
Custom Column Summary - Hard Way
- Print out mean, median, and trimmed mean for each (numeric) column
#fixed acidity c(Mean = mean(wineData$`fixed acidity`), Median = median(wineData$`fixed acidity`), TrimmedMean = mean(wineData$`fixed acidity`, 0.05))
## Mean Median TrimmedMean ## 7.215307 7.000000 7.104796
#volatile acidity c(Mean = mean(wineData$`volatile acidity`), Median = median(wineData$`volatile acidity`), TrimmedMean = mean(wineData$`volatile acidity`, 0.05))
## Mean Median TrimmedMean ## 0.3396660 0.2900000 0.3255864
#...
Custom Column Summary
Instead use a Loop!
for loopsorwhile loopscommonly used in Rfor loopsyntax
for(index in values){
code to be run
}
For Loops - Index
- index defines ‘counter’ or variable that varies
for (index in 1:10){
print(index)
}
## [1] 1 ## [1] 2 ## [1] 3 ## [1] 4 ## [1] 5 ## [1] 6 ## [1] 7 ## [1] 8 ## [1] 9 ## [1] 10
for (i in c("cat", "dog", "wolf")){
print(i)
}
## [1] "cat" ## [1] "dog" ## [1] "wolf"
For Loops - Values
- ‘values’ define which values index takes on
values <- 1:10
for (index in values){
print(index)
}
## [1] 1 ## [1] 2 ## [1] 3 ## [1] 4 ## [1] 5 ## [1] 6 ## [1] 7 ## [1] 8 ## [1] 9 ## [1] 10
for (i in seq_along(iris)){
print(names(iris)[i])
}
## [1] "Sepal.Length" ## [1] "Sepal.Width" ## [1] "Petal.Length" ## [1] "Petal.Width" ## [1] "Species"
Custom Column Summary
- Print out mean, median, and trimmed mean for each (numeric) column
for(i in 1:12){ #first 12 columns are numeric
colData <- pull(wineData, i)
print(names(wineData)[i])
print(c(Mean = mean(colData),
Median = median(colData),
TrimmedMean = mean(colData, 0.05))
)
}
## [1] "fixed acidity" ## Mean Median TrimmedMean ## 7.215307 7.000000 7.104796 ## [1] "volatile acidity" ## Mean Median TrimmedMean ## 0.3396660 0.2900000 0.3255864 ## [1] "citric acid" ## Mean Median TrimmedMean ## 0.3186332 0.3100000 0.3160780 ## [1] "residual sugar" ## Mean Median TrimmedMean ## 5.443235 3.000000 5.027039 ## [1] "chlorides" ## Mean Median TrimmedMean ## 0.05603386 0.04700000 0.05197538 ## [1] "free sulfur dioxide" ## Mean Median TrimmedMean ## 30.52532 29.00000 29.64473 ## [1] "total sulfur dioxide" ## Mean Median TrimmedMean ## 115.7446 118.0000 115.2693 ## [1] "density" ## Mean Median TrimmedMean ## 0.9946966 0.9948900 0.9946826 ## [1] "pH" ## Mean Median TrimmedMean ## 3.218501 3.210000 3.214850 ## [1] "sulphates" ## Mean Median TrimmedMean ## 0.5312683 0.5100000 0.5208189 ## [1] "alcohol" ## Mean Median TrimmedMean ## 10.4918 10.3000 10.4434 ## [1] "quality" ## Mean Median TrimmedMean ## 5.818378 6.000000 5.810737
Custom Column Summary
- Might store the results for later use
ncols <- ncol(wineData)
sumDF <- data.frame(varName = names(wineData)[-ncols],
mean = numeric(ncols-1),
median = numeric(ncols-1),
trimmedMean = numeric(ncols-1)
)
sumDF
## varName mean median trimmedMean ## 1 fixed acidity 0 0 0 ## 2 volatile acidity 0 0 0 ## 3 citric acid 0 0 0 ## 4 residual sugar 0 0 0 ## 5 chlorides 0 0 0 ## 6 free sulfur dioxide 0 0 0 ## 7 total sulfur dioxide 0 0 0 ## 8 density 0 0 0 ## 9 pH 0 0 0 ## 10 sulphates 0 0 0 ## 11 alcohol 0 0 0 ## 12 quality 0 0 0
Custom Column Summary
- Fill in each row as you go
for(i in seq_along(wineData)[-ncols]){
colData <- pull(wineData, i)
sumDF[i, 2:4] <- c(mean(colData), median(colData), mean(colData, 0.05))
}
sumDF
## varName mean median trimmedMean ## 1 fixed acidity 7.21530706 7.00000 7.10479569 ## 2 volatile acidity 0.33966600 0.29000 0.32558643 ## 3 citric acid 0.31863322 0.31000 0.31607796 ## 4 residual sugar 5.44323534 3.00000 5.02703881 ## 5 chlorides 0.05603386 0.04700 0.05197538 ## 6 free sulfur dioxide 30.52531938 29.00000 29.64472559 ## 7 total sulfur dioxide 115.74457442 118.00000 115.26927680 ## 8 density 0.99469663 0.99489 0.99468259 ## 9 pH 3.21850085 3.21000 3.21485040 ## 10 sulphates 0.53126828 0.51000 0.52081894 ## 11 alcohol 10.49180083 10.30000 10.44339944 ## 12 quality 5.81837771 6.00000 5.81073688
while Loops
whileloop similar toforloops
while(condition) {
expression to evaluate
modify condition to FALSE?
}
break Out of a Loop
breakexits a loop
for (i in 1:5){
if (i == 4){
break
}
print(i)
}
## [1] 1 ## [1] 2 ## [1] 3
next to Skip
nextjumps to the next iteration of the loop
for (i in 1:5){
if (i == 3){
next
}
print(i)
}
## [1] 1 ## [1] 2 ## [1] 4 ## [1] 5
Quick Examples
- Go to the course files page and try Exercise 1 - Loops
Looping in R
For loops inefficient in R
- R interpreted language
- Must figure out how to evaluate code at each iteration of loop
- Slows it down
Looping in R
For loops inefficient in R
R interpreted language
Must figure out how to evaluate code at each iteration of loop
Slows it down
Vectorized functions much faster!
- Vectorized function: works on entire vector at once
- Avoids costly computation time
Efficient Code
Some ‘built-in’ vectorized functions
colMeans(),rowMeans()colSums(),rowSums()colSds(),colVars(),colMedians()(matrixStatspackage)ifelse(),dplyr::if_else()apply()family
- Create your own with
Vectorize()
colMeans - Find Column Means
colMeans()just requires a numeric data frame (array)
wineData %>% select(-type) %>% colMeans()
## fixed acidity volatile acidity citric acid ## 7.21530706 0.33966600 0.31863322 ## residual sugar chlorides free sulfur dioxide ## 5.44323534 0.05603386 30.52531938 ## total sulfur dioxide density pH ## 115.74457442 0.99469663 3.21850085 ## sulphates alcohol quality ## 0.53126828 10.49180083 5.81837771
Compare computational time
microbenchmarkpackage allows for easy recording of computing time
install.packages("microbenchmarK")
library(microbenchmark)
Compare computational time
wineData2 <- wineData %>% select(-type) microbenchmark(colMeans(wineData2), unit = "ms")
## Unit: milliseconds ## expr min lq mean median uq max neval ## colMeans(wineData2) 0.2975 0.30905 0.416344 0.3237 0.34855 5.8771 100
microbenchmark(for(i in 1:12){mean(wineData[[i]])}, unit = "ms")
## Unit: milliseconds
## expr min lq mean median
## for (i in 1:12) { mean(wineData[[i]]) } 1.5886 1.67655 1.961633 1.7918
## uq max neval
## 2.0541 4.5707 100
colMedians - column medians
matrixStats::colMedians()just requires a numeric data frame (array)
library(matrixStats) wineData %>% select(-type) %>% as.matrix() %>% colMedians()
## [1] 7.00000 0.29000 0.31000 3.00000 0.04700 29.00000 118.00000 ## [8] 0.99489 3.21000 0.51000 10.30000 6.00000
Creating a New Variable
Want to code a new categorical quality variable
- Poor (quality \(\leq\) 3)
- Ok (3 \(<\) quality \(\leq 5\))
- Good (5 \(<\) quality \(\leq\) 7)
- Great (7 \(<\) quality \(\leq\) 10)
- Initial plan: loop through each observation and use
ifthenelse
Inefficient Code
#initialize vector to save results
qualityCat <- character()
for (i in 1:(dim(wineData)[1])){
if(wineData$quality[i] <= 3){
qualityCat[i] <- "Poor"
} else if(wineData$quality[i] <= 5){
qualityCat[i] <- "Ok"
} else if(wineData$quality[i] <= 7){
qualityCat[i] <- "Good"
} else if(wineData$quality[i] <= 10){
qualityCat[i] <- "Great"
} else {
qualityCat[i] <- "Error"
}
}
Inefficient Code
wineData$qualityCat <- qualityCat wineData %>% select(qualityCat, quality, everything())
## # A tibble: 6,497 x 14 ## qualityCat quality `fixed acidity` `volatile acidity` `citric acid` ## <chr> <dbl> <dbl> <dbl> <dbl> ## 1 Ok 5 7.4 0.7 0 ## 2 Ok 5 7.8 0.88 0 ## 3 Ok 5 7.8 0.76 0.04 ## 4 Good 6 11.2 0.28 0.56 ## 5 Ok 5 7.4 0.7 0 ## # ... with 6,492 more rows, and 9 more variables: residual sugar <dbl>, ## # chlorides <dbl>, free sulfur dioxide <dbl>, total sulfur dioxide <dbl>, ## # density <dbl>, pH <dbl>, sulphates <dbl>, alcohol <dbl>, type <chr>
Efficiency
Know for loops not great
if_else()(orifelse()) is vectorized version ofif then elseSyntax
if_else(vector_condition, if_true_do_this, if_false_do_this)
Back to the Code
Know for loops not great
if_else()(orifelse()) is vectorized version ofif then elseSyntax
if_else(vector_condition, if_true_do_this, if_false_do_this)
qualityCat <- if_else(wineData$quality <= 3, "Poor",
if_else(wineData$quality <= 5, "Ok",
if_else(wineData$quality <= 7, "Good",
if_else(wineData$quality <= 10, "Great", "Error"))))
Compare speed
loopTime<-microbenchmark(
for (i in 1:(dim(wineData)[1])){
if(wineData$quality[i] <= 3){
qualityCat[i] <- "Poor"
} else if(wineData$quality[i] <= 5){
qualityCat[i] <- "Ok"
} else if(wineData$quality[i] <= 7){
qualityCat[i] <- "Good"
} else if(wineData$quality[i] <= 10){
qualityCat[i] <- "Great"
} else {
qualityCat[i] <- "Error"
}
}
, unit = "us")
Compare speed
vectorTime <- microbenchmark(
if_else(wineData$quality <= 3, "Poor",
if_else(wineData$quality <= 5, "Ok",
if_else(wineData$quality <= 7, "Good",
if_else(wineData$quality <= 10, "Great", "Error"))))
, unit = "us")
Efficient Code
loopTime
## Unit: microseconds
## expr
## for (i in 1:(dim(wineData)[1])) { if (wineData$quality[i] <= 3) { qualityCat[i] <- "Poor" } else if (wineData$quality[i] <= 5) { qualityCat[i] <- "Ok" } else if (wineData$quality[i] <= 7) { qualityCat[i] <- "Good" } else if (wineData$quality[i] <= 10) { qualityCat[i] <- "Great" } else { qualityCat[i] <- "Error" } }
## min lq mean median uq max neval
## 31811.9 35022.7 36533.4 36424.6 38195 42296.4 100
vectorTime
## Unit: microseconds ## expr ## if_else(wineData$quality <= 3, "Poor", if_else(wineData$quality <= 5, "Ok", if_else(wineData$quality <= 7, "Good", if_else(wineData$quality <= 10, "Great", "Error")))) ## min lq mean median uq max neval ## 762.3 802.15 2110.173 841.05 951.15 110538 100
dplyr to Summarize Data
group_by()andsummarize()great for quick summariesFind mean alcohol for each quality category
wineData %>% group_by(qualityCat, type) %>% summarize(meanAlcohol = mean(alcohol))
## `summarise()` has grouped output by 'qualityCat'. You can override using the `.groups` argument.
## # A tibble: 8 x 3 ## # Groups: qualityCat [4] ## qualityCat type meanAlcohol ## <chr> <chr> <dbl> ## 1 Good Red 10.8 ## 2 Good White 10.8 ## 3 Great Red 12.1 ## 4 Great White 11.7 ## 5 Ok Red 9.93 ## 6 Ok White 9.84 ## 7 Poor Red 9.96 ## 8 Poor White 10.3
dplyr to Summarize Data
group_by()andmutate()provide a nice way to add to a dataframe
wineData %>% group_by(qualityCat, type) %>% mutate(meanAlcoholCat = mean(alcohol)) %>% select(meanAlcoholCat, qualityCat, type, alcohol, everything())
## # A tibble: 6,497 x 15 ## # Groups: qualityCat, type [8] ## meanAlcoholCat qualityCat type alcohol `fixed acidity` `volatile acidity` ## <dbl> <chr> <chr> <dbl> <dbl> <dbl> ## 1 9.93 Ok Red 9.4 7.4 0.7 ## 2 9.93 Ok Red 9.8 7.8 0.88 ## 3 9.93 Ok Red 9.8 7.8 0.76 ## 4 10.8 Good Red 9.8 11.2 0.28 ## 5 9.93 Ok Red 9.4 7.4 0.7 ## # ... with 6,492 more rows, and 9 more variables: citric acid <dbl>, ## # residual sugar <dbl>, chlorides <dbl>, free sulfur dioxide <dbl>, ## # total sulfur dioxide <dbl>, density <dbl>, pH <dbl>, sulphates <dbl>, ## # quality <dbl>
Quick Examples
- Go to the course files page and try Exercise 2 - if else
Efficient Code
Some ‘built-in’ vectorized functions
colMeans(),rowMeans()colSums(),rowSums()colSds(),colVars(),colMedians()(matrixStatspackage)ifelse(),dplyr::if_else()apply()familyCreate your own with
Vectorize()
apply() family
apply()family of functions pretty fast
- We’ll look at
apply(),lapply(),sapply(), andreplicate()
apply() family
- Use
apply()to find summary for columns of wine data
apply(X = wineData %>% select(-type, -qualityCat),
MARGIN = 2,
FUN = summary,
na.rm = TRUE)
## fixed acidity volatile acidity citric acid residual sugar chlorides ## Min. 3.800000 0.080000 0.0000000 0.600000 0.00900000 ## 1st Qu. 6.400000 0.230000 0.2500000 1.800000 0.03800000 ## Median 7.000000 0.290000 0.3100000 3.000000 0.04700000 ## Mean 7.215307 0.339666 0.3186332 5.443235 0.05603386 ## 3rd Qu. 7.700000 0.400000 0.3900000 8.100000 0.06500000 ## Max. 15.900000 1.580000 1.6600000 65.800000 0.61100000 ## free sulfur dioxide total sulfur dioxide density pH sulphates ## Min. 1.00000 6.0000 0.9871100 2.720000 0.2200000 ## 1st Qu. 17.00000 77.0000 0.9923400 3.110000 0.4300000 ## Median 29.00000 118.0000 0.9948900 3.210000 0.5100000 ## Mean 30.52532 115.7446 0.9946966 3.218501 0.5312683 ## 3rd Qu. 41.00000 156.0000 0.9969900 3.320000 0.6000000 ## Max. 289.00000 440.0000 1.0389800 4.010000 2.0000000 ## alcohol quality ## Min. 8.0000 3.000000 ## 1st Qu. 9.5000 5.000000 ## Median 10.3000 6.000000 ## Mean 10.4918 5.818378 ## 3rd Qu. 11.3000 6.000000 ## Max. 14.9000 9.000000
lapply
lapply()to apply a function to a listCreate a list object
myList <- list( norm = rnorm(100), unif = runif(25), gamma = rgamma(500, rate = 1, shape = 1) )
lapply
- Apply
mean()function to each list element
lapply(X = myList, FUN = mean)
## $norm ## [1] 0.007024213 ## ## $unif ## [1] 0.5315503 ## ## $gamma ## [1] 1.026491
sapply
- Use
sapply()similar but returns a vector if possible
sapply(X = myList, FUN = mean)
## norm unif gamma ## 0.007024213 0.531550253 1.026491492
replicate
replicate()function great for repeatedly running codeEstimate a probability using repeated simulations
Suppose you select five letters at random. What is the probability none are repeated?
sample(size = 5, letters, replace = TRUE)
## [1] "s" "x" "o" "e" "z"
sample(size = 5, letters, replace = TRUE)
## [1] "p" "e" "k" "c" "w"
replicate
- Suppose you select five letters at random. What is the probability none are repeated?
set.seed(1) sample(size = 5, letters, replace = TRUE) %>% unique()
## [1] "y" "d" "g" "a" "b"
set.seed(1) sample(size = 5, letters, replace = TRUE) %>% unique() %>% length()
## [1] 5
replicate
- Suppose you select five letters at random. What is the probability none are repeated?
set.seed(1) sample(size = 5, letters, replace = TRUE) %>% unique() %>% length() == 5
## [1] TRUE
replicate
- Suppose you select five letters at random. What is the probability none are repeated?
replicate(5,
sample(size = 5, letters, replace = TRUE) %>%
unique() %>%
length() == 5
)
## [1] TRUE FALSE TRUE TRUE FALSE
replicate
- Suppose you select five letters at random. What is the probability none are repeated?
replicate(50000,
sample(size = 5, letters, replace = TRUE) %>%
unique() %>%
length() == 5
) %>%
mean()
## [1] 0.66264
Recap!
Vectorized functions fast!
‘Built-in’ vectorized functions
colMeans(),rowMeans()colSums(),rowSums()colSds(),colVars(),colMedians()(matrixStatspackage)ifelse()apply()family
Quick Examples
- Go to the course files page and try Exercise 3 - apply