Overview

Teaching: 30 min
Exercises: 10 min

Questions

How do I get started with tabular data (e.g. spreadsheets) in R?

What are some good practices for reading data into R?

Objectives

Load external data from a .csv file into a data frame.

Describe what a data frame is.

Summarize the contents of a data frame.

Use indexing to subset specific portions of data frames.

Describe what a factor is.

Convert between strings and factors.

Reorder and rename factors.

Change how character strings are handled in a data frame.

Loading the hawks data

We we will use here a dataset with measurements on three hawk species. The data was collected during bird capture sessions by students and faculty at Cornell College in Mount Vernon, Iowa, USA. The dataset include birds of the following species: Red-tailed, Sharp-shinned, and Cooper’s hawks.

Column	Description
Month	8=September to 12=December
Day	Date in the month
Year	Year: 1992-2003
CaptureTime	Time of capture (HH:MM)
ReleaseTime	Time of release (HH:MM)
BandNumber	ID band code
Species	`CH`=Cooper’s, `RT`=Red-tailed, `SS`=Sharp-shinned
Age	`A`=Adult or `I`=Immature
Sex	`F`=Female or `M`=Male
Wing	Length (in mm) of primary wing feather from tip to wrist it attaches to
Weight	Body weight (in gram)
Culmen	Length (in mm) of the upper bill from the tip to where it bumps into the fleshy part of the bird
Hallux	Length (in mm) of the killing talon
Tail	Measurement (in mm) related to the length of the tail (invented at the MacBride Raptor Center)
StandardTail	Standard measurement of tail length (in mm)
Tarsus	Length of the basic foot bone (in mm)
WingPitFat	Amount of fat in the wing pit
KeelFat	Amount of fat on the breastbone (measured by feel)
Crop	Amount of material in the crop, coded from 1=full to 0=empty

The “Hawks” dataset is available from the RDatasets website.

Downloading the data

We will store the data inside the project folder that we created during the introduction to RStudio. If you have set up the folder structure properly, you should have a folder data_raw inside your project folder.

We are going to use the R function download.file() to download the CSV file that contains the Hawks dataset, and we will use read_csv() to load the content of the CSV file into R.

Inside the download.file command, the first entry is a character string with the source URL (“https://nbisweden.github.io/module-r-intro-dm-practices/data/Hawks.csv”). This source URL downloads a CSV file from GitHub. The text after the comma (“data_raw/Hawks.csv”) is the destination of the file on your local machine. You’ll need the folder “raw” inside a folder named “data” in your project folder (which should be your working directory). So this command downloads the file from GitHub, names it “hawks.csv” and adds it to a preexisting folder named “data_raw”.

download.file(
  url = "https://nbisweden.github.io/module-r-intro-dm-practices/data/Hawks.csv",
  destfile = "data_raw/Hawks.csv"
)

If you wish, you can also download the the data dictionary describing the dataset. This code will download the file (in markdown) to the data_raw folder:

download.file(
  url = "https://nbisweden.github.io/module-r-intro-dm-practices/data/Hawks-data-dictionary.md",
  destfile = "data_raw/Hawks-data-dictionary.md"
)

Reading the data into R

The file has now been downloaded to the destination you specified, but R has not yet loaded the data from the file into memory. To do this, we can use the read_csv() function from the tidyverse package.

Packages in R are basically sets of additional functions that let you do more stuff. The functions we’ve been using so far, like round(), sqrt(), or c() come built into R. Packages give you access to additional functions beyond base R. A similar function to read_csv() from the tidyverse package is read.csv() from base R. We don’t have time to cover their differences but notice that the exact spelling determines which function is used. Before you use a package for the first time you need to install it on your machine, and then you should import it in every subsequent R session when you need it.

To install the tidyverse package, we can type install.packages("tidyverse") straight into the console. In fact, it’s better to write this in the console than in our script for any package, as there’s no need to re-install packages every time we run the script. Then, to load the package type:

## load the tidyverse packages, incl. dplyr
library(tidyverse)

Now we can use the functions from the tidyverse package. Let’s use read_csv() to read the data into a data frame (we will learn more about data frames later):

Rows: 908 Columns: 19

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (5): CaptureTime, BandNumber, Species, Age, Sex
dbl  (13): Month, Day, Year, Wing, Weight, Culmen, Hallux, Tail, StandardTai...
time  (1): ReleaseTime

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

hawks <- read_csv("data_raw/Hawks.csv")

When you execute read_csv on a data file, it looks through the first 1000 rows of each column and guesses its data type. Use the function spec() to view the full column specification:

spec(hawks)

cols(
  Month = col_double(),
  Day = col_double(),
  Year = col_double(),
  CaptureTime = col_character(),
  ReleaseTime = col_time(format = ""),
  BandNumber = col_character(),
  Species = col_character(),
  Age = col_character(),
  Sex = col_character(),
  Wing = col_double(),
  Weight = col_double(),
  Culmen = col_double(),
  Hallux = col_double(),
  Tail = col_double(),
  StandardTail = col_double(),
  Tarsus = col_double(),
  WingPitFat = col_double(),
  KeelFat = col_double(),
  Crop = col_double()
)

For example, in this dataset, read_csv() reads CaptureTime and BandNumber as col_character, Month and Weight as col_double (a numeric data type), and ReleaseTime as col_time. You can use the col_types argument in read_csv() to manually specify the data type when reading the data.

You might have noticed that read_csv() interpreted CaptureTime as col_character and not as col_time (like was done for RealeaseTime). The reason for this is one malformed value (“9.12” instead of “9:12”) in the column. A common way of dealing with such situations is to read the column as col_character, modify any erroneous character strings, and then convert the column to the appropriate data type. In the next episode, we will learn how to use some tidyverse functions to modify data.

Note

read_csv() assumes that fields are delineated by commas. However, in several countries, the comma is used as a decimal separator and the semicolon (;) is used as a field delineator. If you want to read in this type of files in R, you can use the read_csv2() function. It behaves like read_csv() but uses different parameters for the decimal and the field separators. There is also the read_tsv() for tab separated data files and read_delim() for less common formats. Check out the help for read_csv() by typing ?read_csv to learn more.

In addition to the above versions of the csv format, you should develop the habits of looking at and recording some parameters of your csv files. For instance, the character encoding, control characters used for line ending, date format (if the date is not split into three variables), and the presence of unexpected newlines are important characteristics of your data files. Those parameters will ease up the import step of your data in R.

We can see the contents of the first few lines of the data by typing its name: hawks. By default, this will show you as many rows and columns of the data as fit on your screen. If you wanted the first 50 rows, you could type print(hawks, n = 50)

We can also extract the first few lines of this dataset using the function head():

head(hawks)

# A tibble: 6 × 19
  Month   Day  Year CaptureTime ReleaseTime BandNumber Species Age   Sex    Wing
  <dbl> <dbl> <dbl> <chr>       <time>      <chr>      <chr>   <chr> <chr> <dbl>
1     9    19  1992 13:30          NA       877-76317  RT      I     <NA>    385
2     9    22  1992 10:30          NA       877-76318  RT      I     <NA>    376
3     9    23  1992 12:45          NA       877-76319  RT      I     <NA>    381
4     9    23  1992 10:50          NA       745-49508  CH      I     F       265
5     9    27  1992 11:15          NA       1253-98801 SS      I     F       205
6     9    28  1992 11:25          NA       1207-55910 RT      I     <NA>    412
# … with 9 more variables: Weight <dbl>, Culmen <dbl>, Hallux <dbl>,
#   Tail <dbl>, StandardTail <dbl>, Tarsus <dbl>, WingPitFat <dbl>,
#   KeelFat <dbl>, Crop <dbl>

Unlike the print() function, head() returns the extracted data. You could use it to assign the first 100 rows of hawks to an object using hawks_sample <- head(hawks, 100). This can be useful if you want to try out complex computations on a subset of your data before you apply them to the whole dataset. There is a similar function that lets you extract the last few lines of the data set. It is called (you might have guessed it) tail().

To open the dataset in RStudio’s Data Viewer, use the view() function:

view(hawks)

Note

There are two functions for viewing which are case-sensitive. Using view() with a lowercase ‘v’ is part of tidyverse, whereas using View() with an uppercase ‘V’ is loaded through base R in the utils package.

What are data frames?

When we loaded the data into R, it got stored as an object of class tibble, which is a special kind of data frame (the difference is not important for our purposes, but you can learn more about tibbles here). Data frames are the de facto data structure for most tabular data, and what we use for statistics and plotting. Data frames can be created by hand, but most commonly they are generated by functions like read_csv(); in other words, when importing spreadsheets from your hard drive or the web.

A data frame is the representation of data in the format of a table where the columns are vectors that all have the same length. Because columns are vectors, each column must contain a single type of data (e.g., characters, integers, factors). For example, here is a figure depicting a data frame comprising a numeric, a character, and a logical vector.

We can see this also when inspecting the structure of a data frame with the function str():

str(hawks)

spec_tbl_df [908 × 19] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ Month       : num [1:908] 9 9 9 9 9 9 9 9 9 9 ...
 $ Day         : num [1:908] 19 22 23 23 27 28 28 29 29 30 ...
 $ Year        : num [1:908] 1992 1992 1992 1992 1992 ...
 $ CaptureTime : chr [1:908] "13:30" "10:30" "12:45" "10:50" ...
 $ ReleaseTime : 'hms' num [1:908] NA NA NA NA ...
  ..- attr(*, "units")= chr "secs"
 $ BandNumber  : chr [1:908] "877-76317" "877-76318" "877-76319" "745-49508" ...
 $ Species     : chr [1:908] "RT" "RT" "RT" "CH" ...
 $ Age         : chr [1:908] "I" "I" "I" "I" ...
 $ Sex         : chr [1:908] NA NA NA "F" ...
 $ Wing        : num [1:908] 385 376 381 265 205 412 370 375 412 405 ...
 $ Weight      : num [1:908] 920 930 990 470 170 1090 960 855 1210 1120 ...
 $ Culmen      : num [1:908] 25.7 NA 26.7 18.7 12.5 28.5 25.3 27.2 29.3 26 ...
 $ Hallux      : num [1:908] 30.1 NA 31.3 23.5 14.3 32.2 30.1 30 31.3 30.2 ...
 $ Tail        : num [1:908] 219 221 235 220 157 230 212 243 210 238 ...
 $ StandardTail: num [1:908] NA NA NA NA NA NA NA NA NA NA ...
 $ Tarsus      : num [1:908] NA NA NA NA NA NA NA NA NA NA ...
 $ WingPitFat  : num [1:908] NA NA NA NA NA NA NA NA NA NA ...
 $ KeelFat     : num [1:908] NA NA NA NA NA NA NA NA NA NA ...
 $ Crop        : num [1:908] NA NA NA NA NA NA NA NA NA NA ...
 - attr(*, "spec")=
  .. cols(
  ..   Month = col_double(),
  ..   Day = col_double(),
  ..   Year = col_double(),
  ..   CaptureTime = col_character(),
  ..   ReleaseTime = col_time(format = ""),
  ..   BandNumber = col_character(),
  ..   Species = col_character(),
  ..   Age = col_character(),
  ..   Sex = col_character(),
  ..   Wing = col_double(),
  ..   Weight = col_double(),
  ..   Culmen = col_double(),
  ..   Hallux = col_double(),
  ..   Tail = col_double(),
  ..   StandardTail = col_double(),
  ..   Tarsus = col_double(),
  ..   WingPitFat = col_double(),
  ..   KeelFat = col_double(),
  ..   Crop = col_double()
  .. )
 - attr(*, "problems")=<externalptr> 

Inspecting data frames

We already saw how the functions head() and str() can be useful to check the content and the structure of a data frame. Here is a non-exhaustive list of functions to get a sense of the content/structure of the data. Let’s try them out!

Size:
- dim(hawks) - returns a vector with the number of rows in the first element, and the number of columns as the second element (the dimensions of the object)
- nrow(hawks) - returns the number of rows
- ncol(hawks) - returns the number of columns
Content:
- head(hawks) - shows the first 6 rows
- tail(hawks) - shows the last 6 rows
Names:
- names(hawks) - returns the column names (synonym of colnames() for data.frame objects)
- rownames(hawks) - returns the row names
Summary:
- str(hawks) - structure of the object and information about the class, length and content of each column
- summary(hawks) - summary statistics for each column

Note: most of these functions are “generic”, they can be used on other types of objects besides data.frame.

Challenge 2.1

Based on the output of str(hawks), can you answer the following questions?

What is the class of the object hawks?
How many rows and how many columns are in this object?

Solution

str(hawks)

spec_tbl_df [908 × 19] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ Month       : num [1:908] 9 9 9 9 9 9 9 9 9 9 ...
 $ Day         : num [1:908] 19 22 23 23 27 28 28 29 29 30 ...
 $ Year        : num [1:908] 1992 1992 1992 1992 1992 ...
 $ CaptureTime : chr [1:908] "13:30" "10:30" "12:45" "10:50" ...
 $ ReleaseTime : 'hms' num [1:908] NA NA NA NA ...
  ..- attr(*, "units")= chr "secs"
 $ BandNumber  : chr [1:908] "877-76317" "877-76318" "877-76319" "745-49508" ...
 $ Species     : chr [1:908] "RT" "RT" "RT" "CH" ...
 $ Age         : chr [1:908] "I" "I" "I" "I" ...
 $ Sex         : chr [1:908] NA NA NA "F" ...
 $ Wing        : num [1:908] 385 376 381 265 205 412 370 375 412 405 ...
 $ Weight      : num [1:908] 920 930 990 470 170 1090 960 855 1210 1120 ...
 $ Culmen      : num [1:908] 25.7 NA 26.7 18.7 12.5 28.5 25.3 27.2 29.3 26 ...
 $ Hallux      : num [1:908] 30.1 NA 31.3 23.5 14.3 32.2 30.1 30 31.3 30.2 ...
 $ Tail        : num [1:908] 219 221 235 220 157 230 212 243 210 238 ...
 $ StandardTail: num [1:908] NA NA NA NA NA NA NA NA NA NA ...
 $ Tarsus      : num [1:908] NA NA NA NA NA NA NA NA NA NA ...
 $ WingPitFat  : num [1:908] NA NA NA NA NA NA NA NA NA NA ...
 $ KeelFat     : num [1:908] NA NA NA NA NA NA NA NA NA NA ...
 $ Crop        : num [1:908] NA NA NA NA NA NA NA NA NA NA ...
 - attr(*, "spec")=
  .. cols(
  ..   Month = col_double(),
  ..   Day = col_double(),
  ..   Year = col_double(),
  ..   CaptureTime = col_character(),
  ..   ReleaseTime = col_time(format = ""),
  ..   BandNumber = col_character(),
  ..   Species = col_character(),
  ..   Age = col_character(),
  ..   Sex = col_character(),
  ..   Wing = col_double(),
  ..   Weight = col_double(),
  ..   Culmen = col_double(),
  ..   Hallux = col_double(),
  ..   Tail = col_double(),
  ..   StandardTail = col_double(),
  ..   Tarsus = col_double(),
  ..   WingPitFat = col_double(),
  ..   KeelFat = col_double(),
  ..   Crop = col_double()
  .. )
 - attr(*, "problems")=<externalptr> 

The object hawks is of class data.frame, or more specifically a tibble (spec_tbl_df/tbl_df/tbl/data.frame)
Rows and columns: 908 rows and 19 columns

Indexing and subsetting data frames

Our hawks data frame has rows and columns (it has 2 dimensions), if we want to extract some specific data from it, we need to specify the “coordinates” we want from it. Row numbers come first, followed by column numbers.

# first element in the first column of the data frame
hawks[1, 1]   

# A tibble: 1 × 1
  Month
  <dbl>
1     9

# first element in the 6th column
hawks[1, 6]   

# A tibble: 1 × 1
  BandNumber
  <chr>     
1 877-76317 

# first column of the data frame
hawks[, 1]    

# A tibble: 908 × 1
   Month
   <dbl>
   9
   9
   9
   9
   9
   9
   9
   9
   9
   9
# … with 898 more rows

# first column of the data frame
hawks[1]      

# A tibble: 908 × 1
   Month
   <dbl>
   9
   9
   9
   9
   9
   9
   9
   9
   9
   9
# … with 898 more rows

# first three rows of the 6th column
hawks[1:3, 6] 

# A tibble: 3 × 1
  BandNumber
  <chr>     
1 877-76317 
2 877-76318 
3 877-76319 

# the 3rd row of the data frame
hawks[3, ]    

# A tibble: 1 × 19
  Month   Day  Year CaptureTime ReleaseTime BandNumber Species Age   Sex    Wing
  <dbl> <dbl> <dbl> <chr>       <time>      <chr>      <chr>   <chr> <chr> <dbl>
1     9    23  1992 12:45          NA       877-76319  RT      I     <NA>    381
# … with 9 more variables: Weight <dbl>, Culmen <dbl>, Hallux <dbl>,
#   Tail <dbl>, StandardTail <dbl>, Tarsus <dbl>, WingPitFat <dbl>,
#   KeelFat <dbl>, Crop <dbl>

# equivalent to head_hawks <- head(hawks)
head_hawks <- hawks[1:6, ] 

: is a special function that creates numeric vectors of integers in increasing or decreasing order, test 1:10 and 10:1 for instance.

You can also exclude certain indices of a data frame using the “-” sign:

hawks[, -1]  # the whole data frame, except the first column

# A tibble: 908 × 18
     Day  Year CaptureTime ReleaseTime BandNumber Species Age   Sex    Wing
   <dbl> <dbl> <chr>       <time>      <chr>      <chr>   <chr> <chr> <dbl>
 1    19  1992 13:30          NA       877-76317  RT      I     <NA>    385
 2    22  1992 10:30          NA       877-76318  RT      I     <NA>    376
 3    23  1992 12:45          NA       877-76319  RT      I     <NA>    381
 4    23  1992 10:50          NA       745-49508  CH      I     F       265
 5    27  1992 11:15          NA       1253-98801 SS      I     F       205
 6    28  1992 11:25          NA       1207-55910 RT      I     <NA>    412
 7    28  1992 13:30          NA       877-76320  RT      I     <NA>    370
 8    29  1992 11:45          NA       877-76321  RT      A     <NA>    375
 9    29  1992 15:35          NA       877-76322  RT      A     <NA>    412
10    30  1992 13:45          NA       1207-55911 RT      I     <NA>    405
# … with 898 more rows, and 9 more variables: Weight <dbl>, Culmen <dbl>,
#   Hallux <dbl>, Tail <dbl>, StandardTail <dbl>, Tarsus <dbl>,
#   WingPitFat <dbl>, KeelFat <dbl>, Crop <dbl>

hawks[-(7:908), ]  # equivalent to head(hawks)

# A tibble: 6 × 19
  Month   Day  Year CaptureTime ReleaseTime BandNumber Species Age   Sex    Wing
  <dbl> <dbl> <dbl> <chr>       <time>      <chr>      <chr>   <chr> <chr> <dbl>
1     9    19  1992 13:30          NA       877-76317  RT      I     <NA>    385
2     9    22  1992 10:30          NA       877-76318  RT      I     <NA>    376
3     9    23  1992 12:45          NA       877-76319  RT      I     <NA>    381
4     9    23  1992 10:50          NA       745-49508  CH      I     F       265
5     9    27  1992 11:15          NA       1253-98801 SS      I     F       205
6     9    28  1992 11:25          NA       1207-55910 RT      I     <NA>    412
# … with 9 more variables: Weight <dbl>, Culmen <dbl>, Hallux <dbl>,
#   Tail <dbl>, StandardTail <dbl>, Tarsus <dbl>, WingPitFat <dbl>,
#   KeelFat <dbl>, Crop <dbl>

Data frames can be subset by calling indices (as shown previously), but also by calling their column names directly:

hawks["Year"]

# A tibble: 908 × 1
    Year
   <dbl>
1992
1992
1992
1992
1992
1992
1992
1992
1992
1992
# … with 898 more rows

hawks[, "Year"]

# A tibble: 908 × 1
    Year
   <dbl>
1992
1992
1992
1992
1992
1992
1992
1992
1992
1992
# … with 898 more rows

When we extract a subset from a data frame of class tibble, we normally get back an object of the same class. To get one-column subsets returned as vectors, we can use double square brackets:

hawks[[1]]  # first column as vector

  [1]  9  9  9  9  9  9  9  9  9  9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
 [26] 10 10 10 10 10 10 10 11 11 11 11 11 11 11  9  9  9  9  9  9  9  9  9  9  9
 [51]  9  9  9  9  9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
 [76] 10 11 11  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  8  9  9  9  9  9  9
[101]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9 10 10 10 10 10
[126] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[151] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[176] 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[201] 11 11 11 11 11 11 11  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[226]  9  9  9  9  9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[251] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[276] 10 10 11 11 11 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 11 11  9
[301]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[326]  9  9  9  9  9  9  9  9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[351] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11
[376] 11 11 11 11  9  9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[401] 11 11 11 11 11 11 11 11 11 11 11 11 11  9  9  9  9  9  9  9  9  9  9  9  9
[426]  9  9  9  9  9  9  9  9  9  9  9  9  9  9 10 10 10 10 10 10 10 10 10 10 10
[451] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[476] 10 10 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11  9
[501]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[526]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9 10 10 10 10 10 10 10 10 10
[551] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[576] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[601] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11  9  9  9  9  9  9  9  9  9
[626]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9 10 10 10 10 10 10 10 10 10
[651] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[676] 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[701]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[726]  9  9  9  9  9  9  9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[751] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[776] 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11  9
[801]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[826]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9 10 10 10 10 10 10 10 10 10
[851] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[876] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 11
[901] 11 11 11 11 11 11 11 11

hawks[["Year"]]  # named column as vector

  [1] 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992
 [16] 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992
 [31] 1992 1992 1992 1992 1992 1992 1992 1992 1992 1993 1993 1993 1993 1993 1993
 [46] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993
 [61] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993
 [76] 1993 1993 1993 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994
 [91] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994
[106] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994
[121] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994
[136] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994
[151] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994
[166] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994
[181] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994
[196] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1995 1995 1995
[211] 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995
[226] 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995
[241] 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995
[256] 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995
[271] 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1996 1996 1996 1996
[286] 1996 1996 1996 1996 1996 1996 1996 1996 1996 1996 1996 1996 1996 1996 1997
[301] 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997
[316] 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997
[331] 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997
[346] 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997
[361] 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997
[376] 1997 1997 1997 1997 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998
[391] 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998
[406] 1998 1998 1998 1998 1998 1998 1998 1998 1999 1999 1999 1999 1999 1999 1999
[421] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
[436] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
[451] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
[466] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
[481] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
[496] 1999 1999 1999 1999 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000
[511] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000
[526] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000
[541] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000
[556] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000
[571] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000
[586] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000
[601] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000
[616] 2000 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001
[631] 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001
[646] 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001
[661] 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001
[676] 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001
[691] 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2002 2002 2002 2002 2002
[706] 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002
[721] 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002
[736] 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002
[751] 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002
[766] 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002
[781] 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002
[796] 2002 2002 2002 2002 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003
[811] 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003
[826] 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003
[841] 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003
[856] 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003
[871] 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003
[886] 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003
[901] 2003 2003 2003 2003 2003 2003 2003 2003

hawks[[1, 1]]  # first element in the first column as vector        

[1] 9

hawks[[1, "Year"]]  # first element in the named column as vector

[1] 1992

We can also access an individual column as a vector by using a dollar sign, $:

hawks$Year  # named column as vector

  [1] 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992
 [16] 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992
 [31] 1992 1992 1992 1992 1992 1992 1992 1992 1992 1993 1993 1993 1993 1993 1993
 [46] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993
 [61] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993
 [76] 1993 1993 1993 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994
 [91] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994
[106] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994
[121] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994
[136] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994
[151] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994
[166] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994
[181] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994
[196] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1995 1995 1995
[211] 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995
[226] 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995
[241] 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995
[256] 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995
[271] 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1996 1996 1996 1996
[286] 1996 1996 1996 1996 1996 1996 1996 1996 1996 1996 1996 1996 1996 1996 1997
[301] 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997
[316] 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997
[331] 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997
[346] 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997
[361] 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997
[376] 1997 1997 1997 1997 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998
[391] 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998
[406] 1998 1998 1998 1998 1998 1998 1998 1998 1999 1999 1999 1999 1999 1999 1999
[421] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
[436] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
[451] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
[466] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
[481] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
[496] 1999 1999 1999 1999 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000
[511] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000
[526] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000
[541] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000
[556] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000
[571] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000
[586] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000
[601] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000
[616] 2000 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001
[631] 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001
[646] 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001
[661] 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001
[676] 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001
[691] 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2002 2002 2002 2002 2002
[706] 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002
[721] 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002
[736] 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002
[751] 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002
[766] 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002
[781] 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002
[796] 2002 2002 2002 2002 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003
[811] 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003
[826] 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003
[841] 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003
[856] 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003
[871] 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003
[886] 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003
[901] 2003 2003 2003 2003 2003 2003 2003 2003

In RStudio, you can use the autocompletion feature to get the full and correct names of the columns.

Challenge 2.2

Create a data.frame (hawks_20) containing only the data in row 20 of the hawks dataset.

Notice how nrow() gave you the number of rows in a data.frame?

Use that number to pull out just that last row in the data frame.

Compare that with what you see as the last row using tail() to make sure it’s meeting expectations.

Pull out that last row using nrow() instead of the row number.

Create a new data frame (hawks_last) from that last row.

Combine nrow() with the - notation above to reproduce the behavior of head(hawks), keeping just the first 6 rows of the hawks dataset.
Solution
## 1.
hawks_20 <- hawks[20, ]
## 2.
# Saving `n_rows` to improve readability and reduce duplication
n_rows <- nrow(hawks)
hawks_last <- hawks[n_rows, ]
## 3.
hawks_head<- hawks[-(7:n_rows), ]

Factors

When we did str(hawks) we saw that the e.g. the columns Month, Wing and Tail consist of numeric values while other columns (e.g. CaptureTime, Species and Sex), were of class character. Arguably, some of these columns (like Species and Sex) contain categorical data, which means that they can only take on a limited number of values.

R has a special class for working with categorical data, called factor. Factors are very useful and actually contribute to making R particularly well suited to working with data. So we are going to spend a little time introducing them.

Once created, factors can only contain a pre-defined set of values, known as levels. Factors are stored as integers associated with labels and they can be ordered or unordered. While factors look (and often behave) like character vectors, they are actually treated as integer vectors by R. So you need to be very careful when treating them as strings.

When importing a data frame with read_csv(), the columns that contain text are not automatically coerced (=converted) into the factor data type, but once we have loaded the data we can do the conversion using the factor() function:

hawks$Sex <- factor(hawks$Sex)

We can see that the conversion has worked by using the summary() function again. This produces a table with the counts for each factor level:

summary(hawks$Sex)

   F    M NA's 
 174  158  576 

By default, R always sorts levels in alphabetical order. For instance, if you have a factor with 2 levels:

sex <- factor(c("M", "F", "F", "M"))

R will assign 1 to the level "F" and 2 to the level "M" (because F comes before M, even though the first element in this vector is "M"). You can see this by using the function levels() and you can find the number of levels using nlevels():

levels(sex)

[1] "F" "M"

nlevels(sex)

[1] 2

Sometimes, the order of the factors does not matter, other times you might want to specify the order because it is meaningful (e.g., “low”, “medium”, “high”), it improves your visualization, or it is required by a particular type of analysis. Here, one way to reorder our levels in the sex vector would be:

sex # current order

[1] M F F M
Levels: F M

sex <- factor(sex, levels = c("M", "F"))
sex # after re-ordering

[1] M F F M
Levels: M F

In R’s memory, these factors are represented by integers (1, 2), but are more informative than integers because factors are self describing: "M", "F" is more descriptive than 1, 2. Which one is “M”? You wouldn’t be able to tell just from the integer data. Factors, on the other hand, have this information built in. It is particularly helpful when there are many levels.

Challenge 2.3

Change the columns Species and Age in the hawks data frame into factors.

Using the functions you have learnt so far, can you find out…

How many levels are there in the Age column?

How many observed birds are listed as females?
Solution
hawks$Species <- factor(hawks$Species)
hawks$Age <- factor(hawks$Age)
nlevels(hawks$Age)
[1] 2
summary(hawks$Sex)
   F    M NA's 
 174  158  576 
How many levels in the Age column? There are 2 levels.

How many are listed as females? There are 174 listed as females.

Converting factors

If you need to convert a factor to a character vector, you use as.character(x).

as.character(sex)

[1] "M" "F" "F" "M"

In some cases, you may have to convert factors where the levels appear as numbers (such as concentration levels or years) to a numeric vector. For instance, in one part of your analysis the years might need to be encoded as factors (e.g., comparing average weights across years) but in another part of your analysis they may need to be stored as numeric values (e.g., doing math operations on the years). This conversion from factor to numeric is a little trickier. The as.numeric() function returns the index values of the factor, not its levels, so it will result in an entirely new (and unwanted in this case) set of numbers. One method to avoid this is to convert factors to characters, and then to numbers.

Another method is to use the levels() function. Compare:

year_fct <- factor(c(1990, 1983, 1977, 1998, 1990))
as.numeric(year_fct)  # Wrong! And there is no warning...

[1] 3 2 1 4 3

as.numeric(as.character(year_fct))  # Works...

[1] 1990 1983 1977 1998 1990

as.numeric(levels(year_fct))[year_fct]  # The recommended way.

[1] 1990 1983 1977 1998 1990

Notice that in the levels() approach, three important steps occur:

We obtain all the factor levels using levels(year_fct)
We convert these levels to numeric values using as.numeric(levels(year_fct))
We then access these numeric values using the underlying integers of the vector year_fct inside the square brackets

Renaming factors

When your data is stored as a factor, you can use the plot() function to get a quick glance at the number of observations represented by each factor level. Let’s look at the number of captured males and females:

plot(hawks$Sex)

plot of chunk barplot-1

However, as we saw when we used summary(hawks$Sex), there are 576 individuals for which the information hasn’t been recorded. To show them in the plot, we can turn the missing values into a factor level with the addNA() function. We will also have to give the new factor level a label. We are going to work with a copy of the Sex column, so we’re not modifying the working copy of the data frame:

sex <- hawks$Sex
levels(sex)

[1] "F" "M"

We see that the missing values are not among the levels, so let’s add them into a new level:

sex <- addNA(sex)
levels(sex)

[1] "F" "M" NA

Let us then label the new level with something more sensible:

levels(sex)[3] <- "unknown"
levels(sex)

[1] "F"       "M"       "unknown"

Now we can plot the data again, using plot(sex).

plot of chunk barplot-2

Challenge 2.4

Store a copy of the factor column Age to a new object named age.

In the new object, rename “A” and “I” to “Adult” and “Immature”, respectively.

Reorder the factor levels so that “Immature” comes before “Adult”.

Create a bar plot of the factor.
Solution
age <- factor(hawks$Age)
levels(age)[1:2] <- c("Adult", "Immature")
age <- factor(age, levels = c("Immature", "Adult"))
plot(age)

Challenge 2.5 (optional)
We have seen how data frames are created when using read_csv(), but they can also be created by hand with the data.frame() function. There are a few mistakes in this hand-crafted data.frame. Can you spot and fix them? Don’t hesitate to experiment!
animal_data <- data.frame(
          animal = c(dog, cat, sea cucumber, sea urchin),
          feel = c("furry", "squishy", "spiny"),
          weight = c(45, 8 1.1, 0.8)
          )
Can you predict the class for each of the columns in the following example? Check your guesses using str(country_climate):

Are they what you expected? Why? Why not?

What would you need to change to ensure that each column had the accurate data type?
 country_climate <- data.frame(
        country = c("Canada", "Panama", "South Africa", "Australia"),
        climate = c("cold", "hot", "temperate", "hot/temperate"),
        temperature = c(10, 30, 18, "15"),
        northern_hemisphere = c(TRUE, TRUE, FALSE, "FALSE"),
        has_kangaroo = c(FALSE, FALSE, FALSE, 1)
        )
Solution

missing quotations around the names of the animals

missing one entry in the feel column (probably for one of the furry animals)

missing one comma in the weight column

country, climate, temperature, and northern_hemisphere are characters; has_kangaroo is numeric

using factor() one could replace character columns with factors columns

removing the quotes in temperature and northern_hemisphere and replacing 1 by TRUE in the has_kangaroo column would give what was probably intended

The automatic conversion of data type is sometimes a blessing, sometimes an annoyance. Be aware that it exists, learn the rules, and double check that data you import in R are of the correct type within your data frame. If not, use it to your advantage to detect mistakes that might have been introduced during data entry (for instance, a letter in a column that should only contain numbers).

Learn more in this RStudio tutorial

Starting with data

Overview

Loading the hawks data

Downloading the data

Reading the data into R

Note

Note

What are data frames?

Inspecting data frames

Challenge 2.1

Solution

Indexing and subsetting data frames

Challenge 2.2

Solution

Factors

Challenge 2.3

Solution

Converting factors

Renaming factors

Challenge 2.4

Solution

Challenge 2.5 (optional)

Solution