Chapter 3 All about Data in R

3.1 Using data that is built into R

Some times we can just use data that is built into an R package. This is mostly for instructional purposes since this data has already been analyzed. For example, the mtcars data set has a number of

str(mtcars)
## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...
head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

3.2 Importing Data from .CSV file

  1. If your data is in your working directory (the same directory where your program is):
library(data.table)
## 
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
## 
##     between, first, last
# This command creates a dataframe called “df”
df <- fread("GummyBears.csv")

str(df)
## Classes 'data.table' and 'data.frame':   96 obs. of  4 variables:
##  $ Group   : chr  "A" "A" "A" "A" ...
##  $ Blocks  : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Ramp    : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Distance: num  65 78 45.5 50 46 23.5 13 35.5 83 74 ...
##  - attr(*, ".internal.selfref")=<externalptr>
  1. If your data is on the web somewhere:
library(data.table)

df <- fread("http://citadel.sjfc.edu/faculty/ageraci/data/GummyBears.csv")

str(df)
## Classes 'data.table' and 'data.frame':   216 obs. of  4 variables:
##  $ Group   : chr  "A" "A" "A" "A" ...
##  $ Blocks  : int  1 1 1 1 1 1 1 1 5 5 ...
##  $ Ramp    : chr  "top" "top" "top" "top" ...
##  $ Distance: num  40 56 48 54 59 49 53 58 70 76 ...
##  - attr(*, ".internal.selfref")=<externalptr>

3.3 Creating Raw Data from Summary Data

Let’s say we only have summary level data, as in this table of counts:

In order to create a data table with individual rows for each subject, we use the data.table command.

library(data.table)
df <- data.table(Group = c(rep("dolphin", 15),
                           rep("control",15)),
                 Improve = c(rep("yes",10), rep("no",5), 
                             rep("yes",3), rep("no", 12)))

3.4 Creating output from R

Occasionally, you may need to save your R data to an external file so that you can share it with someone else. In this case, we will use the fwrite function, which is a good complement to fread.

library(data.table)
fwrite(mtcars,"mtcars.csv")

3.5 Data frames

Data sets in R are usually stored in objects that we call data frames. A dataframe is a rectangular arrangement of data with rows corresponding to observational units and columns corresponding to variables.

For example, the mtcars data frame is an arrangment of data where each row pertains to one model of car, and the columns contain measurements or other characteristics about that model.

head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1