• Data Frames are the work horse of R objects

• Structured by rows and columns and can be indexed

• Each column is a specified variable type

• Columns names can be used to index a variable

• Advice for naming variable applys to editing columns names

• Can be specified by grouping vectors of equal length as columns

• Follow along (copy & paste the code into the console):

curl::curl_download(
  "https://raw.githubusercontent.com/heike/summerschool-2022/master/01-Introduction-to-R/code/3-r-objects.R",
  "3-r-objects.R"
)
file.edit("3-r-objects.R")

• Elements indexed similar to a vector using [ ]
• df[i,j] will select the element in the \(i^{th}\) row and \(j^{th}\) column
• df[ ,j] will select the entire \(j^{th}\) column and treat it as a vector
• df[i ,] will select the entire \(i^{th}\) row and treat it as a vector
• Logical vectors can be used in place of i and j used to subset the row and columns

• Create a new vector that is the same length as other columns
• Append new column to the data frame using the $ operator
• The new data frame column will adopt the name of the vector

This is what we did before in the tips data set:

# create rate variable in the tips data set:
tips$rate <- tips$tip / tips$total_bill

Use Edgar Anderson’s Iris Data:

head(iris)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

Select Species column (5th column):

iris[,5]

##   [1] setosa     setosa     setosa     setosa     setosa     setosa    
##   [7] setosa     setosa     setosa     setosa     setosa     setosa    
##  [13] setosa     setosa     setosa     setosa     setosa     setosa    
##  [19] setosa     setosa     setosa     setosa     setosa     setosa    
##  [25] setosa     setosa     setosa     setosa     setosa     setosa    
##  [31] setosa     setosa     setosa     setosa     setosa     setosa    
##  [37] setosa     setosa     setosa     setosa     setosa     setosa    
##  [43] setosa     setosa     setosa     setosa     setosa     setosa    
##  [49] setosa     setosa     versicolor versicolor versicolor versicolor
##  [55] versicolor versicolor versicolor versicolor versicolor versicolor
##  [61] versicolor versicolor versicolor versicolor versicolor versicolor
##  [67] versicolor versicolor versicolor versicolor versicolor versicolor
##  [73] versicolor versicolor versicolor versicolor versicolor versicolor
##  [79] versicolor versicolor versicolor versicolor versicolor versicolor
##  [85] versicolor versicolor versicolor versicolor versicolor versicolor
##  [91] versicolor versicolor versicolor versicolor versicolor versicolor
##  [97] versicolor versicolor versicolor versicolor virginica  virginica 
## [103] virginica  virginica  virginica  virginica  virginica  virginica 
## [109] virginica  virginica  virginica  virginica  virginica  virginica 
## [115] virginica  virginica  virginica  virginica  virginica  virginica 
## [121] virginica  virginica  virginica  virginica  virginica  virginica 
## [127] virginica  virginica  virginica  virginica  virginica  virginica 
## [133] virginica  virginica  virginica  virginica  virginica  virginica 
## [139] virginica  virginica  virginica  virginica  virginica  virginica 
## [145] virginica  virginica  virginica  virginica  virginica  virginica 
## Levels: setosa versicolor virginica

Select Species column with the $ operator:

iris$Species

##   [1] setosa     setosa     setosa     setosa     setosa     setosa    
##   [7] setosa     setosa     setosa     setosa     setosa     setosa    
##  [13] setosa     setosa     setosa     setosa     setosa     setosa    
##  [19] setosa     setosa     setosa     setosa     setosa     setosa    
##  [25] setosa     setosa     setosa     setosa     setosa     setosa    
##  [31] setosa     setosa     setosa     setosa     setosa     setosa    
##  [37] setosa     setosa     setosa     setosa     setosa     setosa    
##  [43] setosa     setosa     setosa     setosa     setosa     setosa    
##  [49] setosa     setosa     versicolor versicolor versicolor versicolor
##  [55] versicolor versicolor versicolor versicolor versicolor versicolor
##  [61] versicolor versicolor versicolor versicolor versicolor versicolor
##  [67] versicolor versicolor versicolor versicolor versicolor versicolor
##  [73] versicolor versicolor versicolor versicolor versicolor versicolor
##  [79] versicolor versicolor versicolor versicolor versicolor versicolor
##  [85] versicolor versicolor versicolor versicolor versicolor versicolor
##  [91] versicolor versicolor versicolor versicolor versicolor versicolor
##  [97] versicolor versicolor versicolor versicolor virginica  virginica 
## [103] virginica  virginica  virginica  virginica  virginica  virginica 
## [109] virginica  virginica  virginica  virginica  virginica  virginica 
## [115] virginica  virginica  virginica  virginica  virginica  virginica 
## [121] virginica  virginica  virginica  virginica  virginica  virginica 
## [127] virginica  virginica  virginica  virginica  virginica  virginica 
## [133] virginica  virginica  virginica  virginica  virginica  virginica 
## [139] virginica  virginica  virginica  virginica  virginica  virginica 
## [145] virginica  virginica  virginica  virginica  virginica  virginica 
## Levels: setosa versicolor virginica

iris$Species == "setosa"

##   [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [13]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [25]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [37]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [49]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE

iris[iris$Species=="setosa", ]

##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1           5.1         3.5          1.4         0.2  setosa
## 2           4.9         3.0          1.4         0.2  setosa
## 3           4.7         3.2          1.3         0.2  setosa
## 4           4.6         3.1          1.5         0.2  setosa
## 5           5.0         3.6          1.4         0.2  setosa
## 6           5.4         3.9          1.7         0.4  setosa
## 7           4.6         3.4          1.4         0.3  setosa
## 8           5.0         3.4          1.5         0.2  setosa
## 9           4.4         2.9          1.4         0.2  setosa
## 10          4.9         3.1          1.5         0.1  setosa
## 11          5.4         3.7          1.5         0.2  setosa
## 12          4.8         3.4          1.6         0.2  setosa
## 13          4.8         3.0          1.4         0.1  setosa
## 14          4.3         3.0          1.1         0.1  setosa
## 15          5.8         4.0          1.2         0.2  setosa
## 16          5.7         4.4          1.5         0.4  setosa
## 17          5.4         3.9          1.3         0.4  setosa
## 18          5.1         3.5          1.4         0.3  setosa
## 19          5.7         3.8          1.7         0.3  setosa
## 20          5.1         3.8          1.5         0.3  setosa
## 21          5.4         3.4          1.7         0.2  setosa
## 22          5.1         3.7          1.5         0.4  setosa
## 23          4.6         3.6          1.0         0.2  setosa
## 24          5.1         3.3          1.7         0.5  setosa
## 25          4.8         3.4          1.9         0.2  setosa
## 26          5.0         3.0          1.6         0.2  setosa
## 27          5.0         3.4          1.6         0.4  setosa
## 28          5.2         3.5          1.5         0.2  setosa
## 29          5.2         3.4          1.4         0.2  setosa
## 30          4.7         3.2          1.6         0.2  setosa
## 31          4.8         3.1          1.6         0.2  setosa
## 32          5.4         3.4          1.5         0.4  setosa
## 33          5.2         4.1          1.5         0.1  setosa
## 34          5.5         4.2          1.4         0.2  setosa
## 35          4.9         3.1          1.5         0.2  setosa
## 36          5.0         3.2          1.2         0.2  setosa
## 37          5.5         3.5          1.3         0.2  setosa
## 38          4.9         3.6          1.4         0.1  setosa
## 39          4.4         3.0          1.3         0.2  setosa
## 40          5.1         3.4          1.5         0.2  setosa
## 41          5.0         3.5          1.3         0.3  setosa
## 42          4.5         2.3          1.3         0.3  setosa
## 43          4.4         3.2          1.3         0.2  setosa
## 44          5.0         3.5          1.6         0.6  setosa
## 45          5.1         3.8          1.9         0.4  setosa
## 46          4.8         3.0          1.4         0.3  setosa
## 47          5.1         3.8          1.6         0.2  setosa
## 48          4.6         3.2          1.4         0.2  setosa
## 49          5.3         3.7          1.5         0.2  setosa
## 50          5.0         3.3          1.4         0.2  setosa

Create our own data frame using data.frame function

mydf <- data.frame(NUMS = 1:5, 
                   lets = letters[1:5],
                   vehicle = c("car", "boat", "car", "car", "boat"))
mydf

##   NUMS lets vehicle
## 1    1    a     car
## 2    2    b    boat
## 3    3    c     car
## 4    4    d     car
## 5    5    e    boat

expand.grid allows to quickly create all combinations of levels

dframe <- data.frame(expand.grid(
  reps = 1:3, Type = c("Control", "Treatment")))
dframe

##   reps      Type
## 1    1   Control
## 2    2   Control
## 3    3   Control
## 4    1 Treatment
## 5    2 Treatment
## 6    3 Treatment

The names function can be used on the left hand side and the right hand side of an assignment:

names(dframe)

## [1] "reps" "Type"

names(dframe)[1] <- "Reps"
dframe

##   Reps      Type
## 1    1   Control
## 2    2   Control
## 3    3   Control
## 4    1 Treatment
## 5    2 Treatment
## 6    3 Treatment

• head(x) - View top 6 rows of a data frame
• tail(x) - View bottom 6 rows of a data frame
• summary(x) - Summary statistics
  
• str(x) - View structure of object
  
• dim(x) - View dimensions of object
• length(x) - Returns the length of a vector

We can examine the first two values of an object by passing the n parameter to the head function:

head(iris, n = 2)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa

What’s its structure?

str(iris)

## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  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 ...

• Can save output from a function as an object
• Object is generally a list of output objects
• Can pull off items from the output for further computing
• Examine object using functions like str(x)

• t-test using iris data to see if petal lengths for setosa and versicolor are the same
• t.test function can only handle two groups, so we subset out the virginica species

t.test(Petal.Length ~ Species, data = iris[iris$Species != "virginica", ])

## 
##  Welch Two Sample t-test
## 
## data:  Petal.Length by Species
## t = -39.493, df = 62.14, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group setosa and group versicolor is not equal to 0
## 95 percent confidence interval:
##  -2.939618 -2.656382
## sample estimates:
##     mean in group setosa mean in group versicolor 
##                    1.462                    4.260

Save the output of the t-test function to an object

tout <- t.test(Petal.Length ~ Species, data = iris[iris$Species != "virginica", ])

Let’s look at the structure of this object:

str(tout)

## List of 10
##  $ statistic  : Named num -39.5
##   ..- attr(*, "names")= chr "t"
##  $ parameter  : Named num 62.1
##   ..- attr(*, "names")= chr "df"
##  $ p.value    : num 9.93e-46
##  $ conf.int   : num [1:2] -2.94 -2.66
##   ..- attr(*, "conf.level")= num 0.95
##  $ estimate   : Named num [1:2] 1.46 4.26
##   ..- attr(*, "names")= chr [1:2] "mean in group setosa" "mean in group versicolor"
##  $ null.value : Named num 0
##   ..- attr(*, "names")= chr "difference in means between group setosa and group versicolor"
##  $ stderr     : num 0.0708
##  $ alternative: chr "two.sided"
##  $ method     : chr "Welch Two Sample t-test"
##  $ data.name  : chr "Petal.Length by Species"
##  - attr(*, "class")= chr "htest"

Since this is simply a list, we can use our regular indexing:

tout$p.value

## [1] 9.934433e-46

tout[[3]]

## [1] 9.934433e-46

We often need to import in our own data rather than just using built-in datasets.

• First need to tell R where the data is saved (either by specifying the path or navigating the working directory by setting setwd())
• For finding a file/path can use a file browser/search by calling file.choose()
• Data read in using R functions such as:
  • read.table() for reading in .txt files
  • read.csv() for reading in .csv files
• Assign the data to new R object when reading in the file

write.csv(iris, "iris.csv", row.names=FALSE)

• Similarly to importing, exporting is supported using functions:

  • for data frames use write.csv or write.table
  • for more general objects such as lists use save (see ?save). Objects saved with save can be loaded using the function load()
  • save writes the object in an rda format (short for R data) - this is a R specific binary format (it is small, keeps types and loads fast)

iris_rda <- iris
save(iris_rda, file="iris-data.rda")
rm(iris_rda) # object is gone from environment

load("iris-data.rda") # and now it is back!