Chapter 3 Starting with data

Learning 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.

  • Format dates.

3.1 Presentation of the Survey Data

We are studying the species repartition and weight of animals caught in plots in our study area. The dataset is stored as a comma separated value (CSV) file. Each row holds information for a single animal, and the columns represent:

Column Description
record_id Unique id for the observation
month month of observation
day day of observation
year year of observation
plot_id ID of a particular plot
species_id 2-letter code
sex sex of animal (“M”, “F”)
hindfoot_length length of the hindfoot in mm
weight weight of the animal in grams
genus genus of animal
species species of animal
taxon e.g. Rodent, Reptile, Bird, Rabbit
plot_type type of plot
Taxonomic rank---taken from [https://en.wikipedia.org/wiki/Taxonomic_rank](https://en.wikipedia.org/wiki/Taxonomic_rank)

Figure 3.1: Taxonomic rank—taken from https://en.wikipedia.org/wiki/Taxonomic_rank

We are going to use the R function download.file() to download the CSV file that contains the survey data from Figshare, and we will use read.csv() to load into memory the content of the CSV file as an object of class data.frame. Inside the download.file command, the first entry is a character string with the source URL (“https://ndownloader.figshare.com/files/2292169”). This source URL downloads a CSV file from figshare. The text after the comma (“data_raw/portal_data_joined.csv”) is the destination of the file on your local machine. You’ll need to have a folder on your machine called “data_raw” where you’ll download the file. So this command downloads a file from Figshare, names it “portal_data_joined.csv” and adds it to a preexisting folder named “data_raw”.

url <- "https://ndownloader.figshare.com/files/2292169"
if(!file.exists("./data_raw/portal_data_joined.csv")){
    download.file(url, destfile = "./data_raw/portal_data_joined.csv")}

You are now ready to load the data:

surveys <- read.csv("data_raw/portal_data_joined.csv")

This statement doesn’t produce any output because, as you might recall, assignments don’t display anything. If we want to check that our data has been loaded, we can see the contents of the data frame by typing its name: surveys.

Wow… that was a lot of output. At least it means the data loaded properly. Let’s check the top (the first 6 lines) of this data frame using the function head():

head(surveys)
  record_id month day year plot_id species_id sex hindfoot_length weight
1         1     7  16 1977       2         NL   M              32     NA
2        72     8  19 1977       2         NL   M              31     NA
3       224     9  13 1977       2         NL                  NA     NA
4       266    10  16 1977       2         NL                  NA     NA
5       349    11  12 1977       2         NL                  NA     NA
6       363    11  12 1977       2         NL                  NA     NA
    genus  species   taxa plot_type
1 Neotoma albigula Rodent   Control
2 Neotoma albigula Rodent   Control
3 Neotoma albigula Rodent   Control
4 Neotoma albigula Rodent   Control
5 Neotoma albigula Rodent   Control
6 Neotoma albigula Rodent   Control
str(surveys)
'data.frame':   34786 obs. of  13 variables:
 $ record_id      : int  1 72 224 266 349 363 435 506 588 661 ...
 $ month          : int  7 8 9 10 11 11 12 1 2 3 ...
 $ day            : int  16 19 13 16 12 12 10 8 18 11 ...
 $ year           : int  1977 1977 1977 1977 1977 1977 1977 1978 1978 1978 ...
 $ plot_id        : int  2 2 2 2 2 2 2 2 2 2 ...
 $ species_id     : Factor w/ 48 levels "AB","AH","AS",..: 16 16 16 16 16 16 16 16 16 16 ...
 $ sex            : Factor w/ 3 levels "","F","M": 3 3 1 1 1 1 1 1 3 1 ...
 $ hindfoot_length: int  32 31 NA NA NA NA NA NA NA NA ...
 $ weight         : int  NA NA NA NA NA NA NA NA 218 NA ...
 $ genus          : Factor w/ 26 levels "Ammodramus","Ammospermophilus",..: 13 13 13 13 13 13 13 13 13 13 ...
 $ species        : Factor w/ 40 levels "albigula","audubonii",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ taxa           : Factor w/ 4 levels "Bird","Rabbit",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ plot_type      : Factor w/ 5 levels "Control","Long-term Krat Exclosure",..: 1 1 1 1 1 1 1 1 1 1 ...
## Try also
View(surveys)

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 exactly like read.csv but uses different parameters for the decimal and the field separators. If you are working with another format, they can be both specified by the user. Check out the help for read.csv() by typing ?read.csv to learn more. There is also the read.delim() for in tab separated data files. It is important to note that all of these functions are actually wrapper functions for the main read.table() function with different arguments.

As such, the surveys data above could have also been loaded by using read.table() with the separation argument as ,. The code is as follows: surveys <- read.table(file="data_raw/portal_data_joined.csv", sep = ",", header = TRUE).
The header argument has to be set to TRUE to be able to read the headers as by default read.table() has the header argument set to FALSE.

In addition to the above versions of the csv format, you should develop the habits of looking at and record some parameters of your csv files. For instance, the character encoding, control characters used for line ending, date format (if the date is not splitted 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.

Alternatives readr::read_csv() and data.table::fread() — Can you tell the difference?

surveys2 <- readr::read_csv("data_raw/portal_data_joined.csv")
surveys2
# A tibble: 34,786 x 13
   record_id month   day  year plot_id species_id sex   hindfoot_length weight
       <dbl> <dbl> <dbl> <dbl>   <dbl> <chr>      <chr>           <dbl>  <dbl>
 1         1     7    16  1977       2 NL         M                  32     NA
 2        72     8    19  1977       2 NL         M                  31     NA
 3       224     9    13  1977       2 NL         <NA>               NA     NA
 4       266    10    16  1977       2 NL         <NA>               NA     NA
 5       349    11    12  1977       2 NL         <NA>               NA     NA
 6       363    11    12  1977       2 NL         <NA>               NA     NA
 7       435    12    10  1977       2 NL         <NA>               NA     NA
 8       506     1     8  1978       2 NL         <NA>               NA     NA
 9       588     2    18  1978       2 NL         M                  NA    218
10       661     3    11  1978       2 NL         <NA>               NA     NA
# … with 34,776 more rows, and 4 more variables: genus <chr>, species <chr>,
#   taxa <chr>, plot_type <chr>
str(surveys2)
tibble [34,786 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ record_id      : num [1:34786] 1 72 224 266 349 363 435 506 588 661 ...
 $ month          : num [1:34786] 7 8 9 10 11 11 12 1 2 3 ...
 $ day            : num [1:34786] 16 19 13 16 12 12 10 8 18 11 ...
 $ year           : num [1:34786] 1977 1977 1977 1977 1977 ...
 $ plot_id        : num [1:34786] 2 2 2 2 2 2 2 2 2 2 ...
 $ species_id     : chr [1:34786] "NL" "NL" "NL" "NL" ...
 $ sex            : chr [1:34786] "M" "M" NA NA ...
 $ hindfoot_length: num [1:34786] 32 31 NA NA NA NA NA NA NA NA ...
 $ weight         : num [1:34786] NA NA NA NA NA NA NA NA 218 NA ...
 $ genus          : chr [1:34786] "Neotoma" "Neotoma" "Neotoma" "Neotoma" ...
 $ species        : chr [1:34786] "albigula" "albigula" "albigula" "albigula" ...
 $ taxa           : chr [1:34786] "Rodent" "Rodent" "Rodent" "Rodent" ...
 $ plot_type      : chr [1:34786] "Control" "Control" "Control" "Control" ...
 - attr(*, "spec")=
  .. cols(
  ..   record_id = col_double(),
  ..   month = col_double(),
  ..   day = col_double(),
  ..   year = col_double(),
  ..   plot_id = col_double(),
  ..   species_id = col_character(),
  ..   sex = col_character(),
  ..   hindfoot_length = col_double(),
  ..   weight = col_double(),
  ..   genus = col_character(),
  ..   species = col_character(),
  ..   taxa = col_character(),
  ..   plot_type = col_character()
  .. )
surveys3 <- data.table::fread("data_raw/portal_data_joined.csv")
surveys3
       record_id month day year plot_id species_id sex hindfoot_length weight
    1:         1     7  16 1977       2         NL   M              32     NA
    2:        72     8  19 1977       2         NL   M              31     NA
    3:       224     9  13 1977       2         NL                  NA     NA
    4:       266    10  16 1977       2         NL                  NA     NA
    5:       349    11  12 1977       2         NL                  NA     NA
   ---                                                                       
34782:     26966    10  25 1997       7         PL   M              20     16
34783:     27185    11  22 1997       7         PL   F              21     22
34784:     27792     5   2 1998       7         PL   F              20      8
34785:     28806    11  21 1998       7         PX                  NA     NA
34786:     30986     7   1 2000       7         PX                  NA     NA
             genus  species   taxa        plot_type
    1:     Neotoma albigula Rodent          Control
    2:     Neotoma albigula Rodent          Control
    3:     Neotoma albigula Rodent          Control
    4:     Neotoma albigula Rodent          Control
    5:     Neotoma albigula Rodent          Control
   ---                                             
34782:  Peromyscus leucopus Rodent Rodent Exclosure
34783:  Peromyscus leucopus Rodent Rodent Exclosure
34784:  Peromyscus leucopus Rodent Rodent Exclosure
34785: Chaetodipus      sp. Rodent Rodent Exclosure
34786: Chaetodipus      sp. Rodent Rodent Exclosure
str(surveys3)
Classes 'data.table' and 'data.frame':  34786 obs. of  13 variables:
 $ record_id      : int  1 72 224 266 349 363 435 506 588 661 ...
 $ month          : int  7 8 9 10 11 11 12 1 2 3 ...
 $ day            : int  16 19 13 16 12 12 10 8 18 11 ...
 $ year           : int  1977 1977 1977 1977 1977 1977 1977 1978 1978 1978 ...
 $ plot_id        : int  2 2 2 2 2 2 2 2 2 2 ...
 $ species_id     : chr  "NL" "NL" "NL" "NL" ...
 $ sex            : chr  "M" "M" "" "" ...
 $ hindfoot_length: int  32 31 NA NA NA NA NA NA NA NA ...
 $ weight         : int  NA NA NA NA NA NA NA NA 218 NA ...
 $ genus          : chr  "Neotoma" "Neotoma" "Neotoma" "Neotoma" ...
 $ species        : chr  "albigula" "albigula" "albigula" "albigula" ...
 $ taxa           : chr  "Rodent" "Rodent" "Rodent" "Rodent" ...
 $ plot_type      : chr  "Control" "Control" "Control" "Control" ...
 - attr(*, ".internal.selfref")=<externalptr>

3.2 What are data frames?

Data frames are the de facto data structure for most tabular data, and what we use for statistics and plotting.

A data frame can be created by hand, but most commonly they are generated by the functions read.csv() or read.table(); 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, Figure 3.2 depicts a data frame comprising a numeric, a character, and a logical vector.

Example Data Frame

Figure 3.2: Example Data Frame

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

str(surveys)
'data.frame':   34786 obs. of  13 variables:
 $ record_id      : int  1 72 224 266 349 363 435 506 588 661 ...
 $ month          : int  7 8 9 10 11 11 12 1 2 3 ...
 $ day            : int  16 19 13 16 12 12 10 8 18 11 ...
 $ year           : int  1977 1977 1977 1977 1977 1977 1977 1978 1978 1978 ...
 $ plot_id        : int  2 2 2 2 2 2 2 2 2 2 ...
 $ species_id     : Factor w/ 48 levels "AB","AH","AS",..: 16 16 16 16 16 16 16 16 16 16 ...
 $ sex            : Factor w/ 3 levels "","F","M": 3 3 1 1 1 1 1 1 3 1 ...
 $ hindfoot_length: int  32 31 NA NA NA NA NA NA NA NA ...
 $ weight         : int  NA NA NA NA NA NA NA NA 218 NA ...
 $ genus          : Factor w/ 26 levels "Ammodramus","Ammospermophilus",..: 13 13 13 13 13 13 13 13 13 13 ...
 $ species        : Factor w/ 40 levels "albigula","audubonii",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ taxa           : Factor w/ 4 levels "Bird","Rabbit",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ plot_type      : Factor w/ 5 levels "Control","Long-term Krat Exclosure",..: 1 1 1 1 1 1 1 1 1 1 ...

3.3 Inspecting data.frame Objects

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(surveys) - 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(surveys) - returns the number of rows
    • ncol(surveys) - returns the number of columns
  • Content:
    • head(surveys) - shows the first 6 rows
    • tail(surveys) - shows the last 6 rows
  • Names:
    • names(surveys) - returns the column names (synonym of colnames() for data.frame objects)
    • rownames(surveys) - returns the row names
  • Summary:
    • str(surveys) - structure of the object and information about the class, length and content of each column
    • summary(surveys) - 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

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

  1. What is the class of the object surveys?
  2. How many rows and how many columns are in this object?
  3. How many species have been recorded during these surveys?

Solution

3.4 Indexing and subsetting data frames

Our survey 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. However, note that different ways of specifying these coordinates lead to results with different classes.

# first element in the first column of the data frame (as a vector)
surveys[1, 1]   
# first element in the 6th column (as a vector)
surveys[1, 6]   
# first column of the data frame (as a vector)
surveys[, 1]    
# first column of the data frame (as a data.frame)
surveys[1]      
# first three elements in the 7th column (as a vector)
surveys[1:3, 7] 
# the 3rd row of the data frame (as a data.frame)
surveys[3, ]    
# equivalent to head_surveys <- head(surveys)
head_surveys <- surveys[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:

surveys[, -1]          # The whole data frame, except the first column
surveys[-c(7:34786), ] # Equivalent to head(surveys)

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

surveys["species_id"]       # Result is a data.frame
surveys[, "species_id"]     # Result is a vector
surveys[["species_id"]]     # Result is a vector
surveys$species_id          # Result is a vector

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

Challenge

  1. Create a data.frame (surveys_200) containing only the data in row 200 of the surveys dataset.

  2. 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 (surveys_last) from that last row.

  3. Use nrow() to extract the row that is in the middle of the data frame. Store the content of this row in an object named surveys_middle.

  4. Combine nrow() with the - notation above to reproduce the behavior of head(surveys), keeping just the first through 6th rows of the surveys dataset.

Solution

3.5 Factors

When we did str(surveys) we saw that several of the columns consist of integers. The columns genus, species, sex, plot_type, … are of a special class 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.

Factors represent categorical data. They 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.

Once created, factors can only contain a pre-defined set of values, known as levels. By default, R always sorts levels in alphabetical order. For instance, if you have a factor with 2 levels:

sex <- factor(c("male", "female", "female", "male"))

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

levels(sex)
[1] "female" "male"  
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] male   female female male  
Levels: female male
sex <- factor(sex, levels = c("male", "female"))
sex # after re-ordering
[1] male   female female male  
Levels: male female

In R’s memory, these factors are represented by integers (1, 2, 3), but are more informative than integers because factors are self describing: "female", "male" is more descriptive than 1, 2. Which one is “male”? 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 (like the species names in our example dataset).

3.5.1 Converting factors

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

as.character(sex)
[1] "male"   "female" "female" "male"

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

3.5.2 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 males and females captured over the course of the experiment:

## bar plot of the number of females and males captured during the experiment:
plot(surveys$sex)

## OR with ggplot2
library(ggplot2)
ggplot(data = surveys, aes(x = sex)) +
  geom_bar() + 
  theme_bw()

In addition to males and females, there are about 1700 individuals for which the sex information hasn’t been recorded. Additionally, for these individuals, there is no label to indicate that the information is missing or undetermined. Let’s rename this label to something more meaningful. Before doing that, we’re going to pull out the data on sex and work with that data, so we’re not modifying the working copy of the data frame:

sex <- surveys$sex
head(sex)
[1] M M        
Levels:  F M
levels(sex)
[1] ""  "F" "M"
levels(sex)[1] <- "undetermined"
levels(sex)
[1] "undetermined" "F"            "M"           
head(sex)
[1] M            M            undetermined undetermined undetermined
[6] undetermined
Levels: undetermined F M

Challenge

  1. Rename “F” and “M” to “female” and “male” respectively.

  2. Now that we have renamed the factor level to “undetermined”, can you recreate the barplot such that “undetermined” is last (after “male”)?

Solution

3.5.3 Using stringsAsFactors=FALSE

`

By default, when building or importing a data frame, the columns that contain characters (i.e. text) are coerced (= converted) into factors when using read.table() or its variants (this is not true for readr or data.table functions). Depending on what you want to do with the data, you may want to keep these columns as character. To do so, read.csv() and read.table() have an argument called stringsAsFactors which can be set to FALSE.

In most cases, it is preferable to set stringsAsFactors = FALSE when importing data and to convert as a factor only the columns that require this data type.

## Compare the difference between our data read as `factor` vs `character`.
surveys <- read.csv("data_raw/portal_data_joined.csv", stringsAsFactors = TRUE)
str(surveys)
'data.frame':   34786 obs. of  13 variables:
 $ record_id      : int  1 72 224 266 349 363 435 506 588 661 ...
 $ month          : int  7 8 9 10 11 11 12 1 2 3 ...
 $ day            : int  16 19 13 16 12 12 10 8 18 11 ...
 $ year           : int  1977 1977 1977 1977 1977 1977 1977 1978 1978 1978 ...
 $ plot_id        : int  2 2 2 2 2 2 2 2 2 2 ...
 $ species_id     : Factor w/ 48 levels "AB","AH","AS",..: 16 16 16 16 16 16 16 16 16 16 ...
 $ sex            : Factor w/ 3 levels "","F","M": 3 3 1 1 1 1 1 1 3 1 ...
 $ hindfoot_length: int  32 31 NA NA NA NA NA NA NA NA ...
 $ weight         : int  NA NA NA NA NA NA NA NA 218 NA ...
 $ genus          : Factor w/ 26 levels "Ammodramus","Ammospermophilus",..: 13 13 13 13 13 13 13 13 13 13 ...
 $ species        : Factor w/ 40 levels "albigula","audubonii",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ taxa           : Factor w/ 4 levels "Bird","Rabbit",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ plot_type      : Factor w/ 5 levels "Control","Long-term Krat Exclosure",..: 1 1 1 1 1 1 1 1 1 1 ...
surveys <- read.csv("data_raw/portal_data_joined.csv", stringsAsFactors = FALSE)
str(surveys)
'data.frame':   34786 obs. of  13 variables:
 $ record_id      : int  1 72 224 266 349 363 435 506 588 661 ...
 $ month          : int  7 8 9 10 11 11 12 1 2 3 ...
 $ day            : int  16 19 13 16 12 12 10 8 18 11 ...
 $ year           : int  1977 1977 1977 1977 1977 1977 1977 1978 1978 1978 ...
 $ plot_id        : int  2 2 2 2 2 2 2 2 2 2 ...
 $ species_id     : chr  "NL" "NL" "NL" "NL" ...
 $ sex            : chr  "M" "M" "" "" ...
 $ hindfoot_length: int  32 31 NA NA NA NA NA NA NA NA ...
 $ weight         : int  NA NA NA NA NA NA NA NA 218 NA ...
 $ genus          : chr  "Neotoma" "Neotoma" "Neotoma" "Neotoma" ...
 $ species        : chr  "albigula" "albigula" "albigula" "albigula" ...
 $ taxa           : chr  "Rodent" "Rodent" "Rodent" "Rodent" ...
 $ plot_type      : chr  "Control" "Control" "Control" "Control" ...
## Convert the column "plot_type" into a factor
surveys$plot_type <- factor(surveys$plot_type)

Challenge

  1. 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)
                  )
  2. 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 have been different if we had added stringsAsFactors = FALSE when creating the data frame?
    • 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

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

3.6 Formatting Dates

One of the most common issues that new (and experienced!) R users have is converting date and time information into a variable that is appropriate and usable during analyses. As a reminder from earlier in this lesson, the best practice for dealing with date data is to ensure that each component of your date is stored as a separate variable. Using str(), We can confirm that our data frame has a separate column for day, month, and year, and that each contains integer values.

str(surveys)
'data.frame':   34786 obs. of  13 variables:
 $ record_id      : int  1 72 224 266 349 363 435 506 588 661 ...
 $ month          : int  7 8 9 10 11 11 12 1 2 3 ...
 $ day            : int  16 19 13 16 12 12 10 8 18 11 ...
 $ year           : int  1977 1977 1977 1977 1977 1977 1977 1978 1978 1978 ...
 $ plot_id        : int  2 2 2 2 2 2 2 2 2 2 ...
 $ species_id     : chr  "NL" "NL" "NL" "NL" ...
 $ sex            : chr  "M" "M" "" "" ...
 $ hindfoot_length: int  32 31 NA NA NA NA NA NA NA NA ...
 $ weight         : int  NA NA NA NA NA NA NA NA 218 NA ...
 $ genus          : chr  "Neotoma" "Neotoma" "Neotoma" "Neotoma" ...
 $ species        : chr  "albigula" "albigula" "albigula" "albigula" ...
 $ taxa           : chr  "Rodent" "Rodent" "Rodent" "Rodent" ...
 $ plot_type      : Factor w/ 5 levels "Control","Long-term Krat Exclosure",..: 1 1 1 1 1 1 1 1 1 1 ...

We are going to use the ymd() function from the package lubridate (which belongs to the tidyverse; learn more here). lubridate gets installed as part as the tidyverse installation. When you load the tidyverse (library(tidyverse)), the core packages (the packages used in most data analyses) get loaded. lubridate however does not belong to the core tidyverse, so you have to load it explicitly with library(lubridate).

Start by loading the required package:

library(lubridate)

ymd() takes a vector representing year, month, and day, and converts it to a Date vector. Date is a class of data recognized by R as being a date and can be manipulated as such. The argument that the function requires is flexible, but, as a best practice, is a character vector formatted as “YYYY-MM-DD”.

Let’s create a date object and inspect the structure:

Sys.Date()
[1] "2020-08-21"
today_date <- Sys.Date()
my_bday <- ymd("1963-09-11")
str(today_date)
 Date[1:1], format: "2020-08-21"
today_date - my_bday
Time difference of 20799 days

Now let’s paste the year, month, and day separately - we get the same result:

# sep indicates the character to use to separate each component
different_date <- ymd(paste("2020", "02", "19", sep = "-")) 
str(different_date)
 Date[1:1], format: "2020-02-19"

Now we apply this function to the surveys dataset. Create a character vector from the year, month, and day columns of surveys using paste():

head(paste(surveys$year, surveys$month, surveys$day, sep = "-"))
[1] "1977-7-16"  "1977-8-19"  "1977-9-13"  "1977-10-16" "1977-11-12"
[6] "1977-11-12"

This character vector can be used as the argument for ymd():

head(ymd(paste(surveys$year, surveys$month, surveys$day, sep = "-")))
[1] "1977-07-16" "1977-08-19" "1977-09-13" "1977-10-16" "1977-11-12"
[6] "1977-11-12"

The resulting Date vector can be added to surveys as a new column called date:

surveys$date <- ymd(paste(surveys$year, surveys$month, surveys$day, sep = "-"))
str(surveys) # notice the new column, with 'date' as the class
'data.frame':   34786 obs. of  14 variables:
 $ record_id      : int  1 72 224 266 349 363 435 506 588 661 ...
 $ month          : int  7 8 9 10 11 11 12 1 2 3 ...
 $ day            : int  16 19 13 16 12 12 10 8 18 11 ...
 $ year           : int  1977 1977 1977 1977 1977 1977 1977 1978 1978 1978 ...
 $ plot_id        : int  2 2 2 2 2 2 2 2 2 2 ...
 $ species_id     : chr  "NL" "NL" "NL" "NL" ...
 $ sex            : chr  "M" "M" "" "" ...
 $ hindfoot_length: int  32 31 NA NA NA NA NA NA NA NA ...
 $ weight         : int  NA NA NA NA NA NA NA NA 218 NA ...
 $ genus          : chr  "Neotoma" "Neotoma" "Neotoma" "Neotoma" ...
 $ species        : chr  "albigula" "albigula" "albigula" "albigula" ...
 $ taxa           : chr  "Rodent" "Rodent" "Rodent" "Rodent" ...
 $ plot_type      : Factor w/ 5 levels "Control","Long-term Krat Exclosure",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ date           : Date, format: "1977-07-16" "1977-08-19" ...

Let’s make sure everything worked correctly. One way to inspect the new column is to use summary():

summary(surveys$date)
        Min.      1st Qu.       Median         Mean      3rd Qu.         Max. 
"1977-07-16" "1984-03-12" "1990-07-22" "1990-12-15" "1997-07-29" "2002-12-31" 
        NA's 
       "129"

Something went wrong: some dates have missing values. Let’s investigate where they are coming from.

We can use the functions we saw previously to deal with missing data to identify the rows in our data frame that are failing. If we combine them with what we learned about subsetting data frames earlier, we can extract the columns “year”, “month”, “day” from the records that have NA in our new column date. We will also use head() so we don’t clutter the output:

missing_dates <- surveys[is.na(surveys$date), c("year", "month", "day")]
head(missing_dates)
     year month day
3144 2000     9  31
3817 2000     4  31
3818 2000     4  31
3819 2000     4  31
3820 2000     4  31
3856 2000     9  31

Why did these dates fail to parse? If you had to use these data for your analyses, how would you deal with this situation?