Exploring Data Frames

Last updated on 2023-11-21 | Edit this page

Overview

Questions

  • How can I manipulate a data frame?

Objectives

  • Remove rows with NA values.
  • Append two data frames.
  • Understand what a factor is.
  • Convert a factor to a character vector and vice versa.
  • Display basic properties of data frames including size and class of the columns, names, and first few rows.

At this point, you’ve seen it all: in the last lesson, we toured all the basic data types and data structures in R. Everything you do will be a manipulation of those tools. But most of the time, the star of the show is the data frame—the table that we created by loading information from a csv file. In this lesson, we’ll learn a few more things about working with data frames.

Realistic example

We already learned that the columns of a data frame are vectors, so that our data are consistent in type throughout the columns. So far, you have seen the basics of manipulating data frames with our nordic data; now let’s use those skills to digest a more extensive dataset. Let’s read in the gapminder dataset that we downloaded previously:

R 

gapminder <- read.csv("data/gapminder_data.csv")

Miscellaneous Tips

  • Another type of file you might encounter are tab-separated value files (.tsv). To specify a tab as a separator, use "\\t" or read.delim().

  • Files can also be downloaded directly from the Internet into a local folder of your choice onto your computer using the download.file function. The read.csv function can then be executed to read the downloaded file from the download location, for example,

R 

download.file("https://datacarpentry.org/r-intro-geospatial/data/gapminder_data.csv",
              destfile = "data/gapminder_data.csv")
gapminder <- read.csv("data/gapminder_data.csv")
  • Alternatively, you can also read in files directly into R from the Internet by replacing the file paths with a web address in read.csv. One should note that in doing this no local copy of the csv file is first saved onto your computer. For example,

R 

gapminder <- read.csv("https://datacarpentry.org/r-intro-geospatial/data/gapminder_data.csv", stringsAsFactors = TRUE) #in R version 4.0.0 the default stringsAsFactors changed from TRUE to FALSE. But because below we use some examples to show what is a factor, we need to add the stringAsFactors = TRUE to be able to perform the below examples with factor.
  • You can read directly from excel spreadsheets without converting them to plain text first by using the readxl package.

Let’s investigate the gapminder data frame a bit; the first thing we should always do is check out what the data looks like with str:

R 

str(gapminder)

OUTPUT 

'data.frame':	1704 obs. of  6 variables:
 $ country  : chr  "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
 $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
 $ pop      : num  8425333 9240934 10267083 11537966 13079460 ...
 $ continent: chr  "Asia" "Asia" "Asia" "Asia" ...
 $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
 $ gdpPercap: num  779 821 853 836 740 ...

We can also examine individual columns of the data frame with our class function:

R 

class(gapminder$year)

OUTPUT 

[1] "integer"

R 

class(gapminder$country)

OUTPUT 

[1] "character"

R 

str(gapminder$country)

OUTPUT 

 chr [1:1704] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...

We can also interrogate the data frame for information about its dimensions; remembering that str(gapminder) said there were 1704 observations of 6 variables in gapminder, what do you think the following will produce, and why?

R 

length(gapminder)

A fair guess would have been to say that the length of a data frame would be the number of rows it has (1704), but this is not the case; it gives us the number of columns.

R 

class(gapminder)

OUTPUT 

[1] "data.frame"

To get the number of rows and columns in our dataset, try:

R 

nrow(gapminder)

OUTPUT 

[1] 1704

R 

ncol(gapminder)

OUTPUT 

[1] 6

Or, both at once:

R 

dim(gapminder)

OUTPUT 

[1] 1704    6

We’ll also likely want to know what the titles of all the columns are, so we can ask for them later:

R 

colnames(gapminder)

OUTPUT 

[1] "country"   "year"      "pop"       "continent" "lifeExp"   "gdpPercap"

At this stage, it’s important to ask ourselves if the structure R is reporting matches our intuition or expectations; do the basic data types reported for each column make sense? If not, we need to sort any problems out now before they turn into bad surprises down the road, using what we’ve learned about how R interprets data, and the importance of strict consistency in how we record our data.

Once we’re happy that the data types and structures seem reasonable, it’s time to start digging into our data proper. Check out the first few lines:

R 

head(gapminder)

OUTPUT 

      country year      pop continent lifeExp gdpPercap
1 Afghanistan 1952  8425333      Asia  28.801  779.4453
2 Afghanistan 1957  9240934      Asia  30.332  820.8530
3 Afghanistan 1962 10267083      Asia  31.997  853.1007
4 Afghanistan 1967 11537966      Asia  34.020  836.1971
5 Afghanistan 1972 13079460      Asia  36.088  739.9811
6 Afghanistan 1977 14880372      Asia  38.438  786.1134

Challenge 1

It’s good practice to also check the last few lines of your data and some in the middle. How would you do this?

Searching for ones specifically in the middle isn’t too hard but we could simply ask for a few lines at random. How would you code this?

To check the last few lines it’s relatively simple as R already has a function for this:

R 

tail(gapminder)
tail(gapminder, n = 15)

What about a few arbitrary rows just for sanity (or insanity depending on your view)?

There are several ways to achieve this.

The solution here presents one form using nested functions. i.e. a function passed as an argument to another function. This might sound like a new concept but you are already using it in fact.

Remember my_dataframe[rows, cols] will print to screen your data frame with the number of rows and columns you asked for (although you might have asked for a range or named columns for example). How would you get the last row if you don’t know how many rows your data frame has? R has a function for this. What about getting a (pseudorandom) sample? R also has a function for this.

R 

gapminder[sample(nrow(gapminder), 5), ]

Challenge 2

Read the output of str(gapminder) again; this time, use what you’ve learned about factors and vectors, as well as the output of functions like colnames and dim to explain what everything that str prints out for gapminder means. If there are any parts you can’t interpret, discuss with your neighbors!

The object gapminder is a data frame with columns

  • country and continent are character vectors.
  • year is an integer vector.
  • pop, lifeExp, and gdpPercap are numeric vectors.

Adding columns and rows in data frames

We would like to create a new column to hold information on whether the life expectancy is below the world average life expectancy (70.5) or above:

R 

below_average <- gapminder$lifeExp < 70.5
head(gapminder)

OUTPUT 

      country year      pop continent lifeExp gdpPercap
1 Afghanistan 1952  8425333      Asia  28.801  779.4453
2 Afghanistan 1957  9240934      Asia  30.332  820.8530
3 Afghanistan 1962 10267083      Asia  31.997  853.1007
4 Afghanistan 1967 11537966      Asia  34.020  836.1971
5 Afghanistan 1972 13079460      Asia  36.088  739.9811
6 Afghanistan 1977 14880372      Asia  38.438  786.1134

We can then add this as a column via:

R 

cbind(gapminder, below_average)

OUTPUT 

      country year      pop continent lifeExp gdpPercap below_average
1 Afghanistan 1952  8425333      Asia  28.801  779.4453          TRUE
2 Afghanistan 1957  9240934      Asia  30.332  820.8530          TRUE
3 Afghanistan 1962 10267083      Asia  31.997  853.1007          TRUE
4 Afghanistan 1967 11537966      Asia  34.020  836.1971          TRUE
5 Afghanistan 1972 13079460      Asia  36.088  739.9811          TRUE
6 Afghanistan 1977 14880372      Asia  38.438  786.1134          TRUE

We probably don’t want to print the entire dataframe each time, so let’s put our cbind command within a call to head to return only the first six lines of the output.

R 

head(cbind(gapminder, below_average))

OUTPUT 

      country year      pop continent lifeExp gdpPercap below_average
1 Afghanistan 1952  8425333      Asia  28.801  779.4453          TRUE
2 Afghanistan 1957  9240934      Asia  30.332  820.8530          TRUE
3 Afghanistan 1962 10267083      Asia  31.997  853.1007          TRUE
4 Afghanistan 1967 11537966      Asia  34.020  836.1971          TRUE
5 Afghanistan 1972 13079460      Asia  36.088  739.9811          TRUE
6 Afghanistan 1977 14880372      Asia  38.438  786.1134          TRUE

Note that if we tried to add a vector of below_average with a different number of entries than the number of rows in the dataframe, it would fail:

R 

below_average <- c(TRUE, TRUE, TRUE, TRUE, TRUE)
head(cbind(gapminder, below_average))

ERROR 

Error in data.frame(..., check.names = FALSE): arguments imply differing number of rows: 1704, 5

Why didn’t this work? R wants to see one element in our new column for every row in the table:

R 

nrow(gapminder)

OUTPUT 

[1] 1704

R 

length(below_average)

OUTPUT 

[1] 5

So for it to work we need either to have nrow(gapminder) = length(below_average) or nrow(gapminder) to be a multiple of length(below_average):

R 

below_average <- c(TRUE, TRUE, FALSE)
head(cbind(gapminder, below_average))

OUTPUT 

      country year      pop continent lifeExp gdpPercap below_average
1 Afghanistan 1952  8425333      Asia  28.801  779.4453          TRUE
2 Afghanistan 1957  9240934      Asia  30.332  820.8530          TRUE
3 Afghanistan 1962 10267083      Asia  31.997  853.1007         FALSE
4 Afghanistan 1967 11537966      Asia  34.020  836.1971          TRUE
5 Afghanistan 1972 13079460      Asia  36.088  739.9811          TRUE
6 Afghanistan 1977 14880372      Asia  38.438  786.1134         FALSE

The sequence TRUE,TRUE,FALSE is repeated over all the gapminder rows.

Let’s overwrite the content of gapminder with our new data frame.

R 

below_average <-  as.logical(gapminder$lifeExp<70.5)
gapminder <- cbind(gapminder, below_average)

Now how about adding rows? The rows of a data frame are lists:

R 

new_row <- list('Norway', 2016, 5000000, 'Nordic', 80.3, 49400.0, FALSE)
gapminder_norway <- rbind(gapminder, new_row)
tail(gapminder_norway)

OUTPUT 

      country year      pop continent lifeExp  gdpPercap below_average
1700 Zimbabwe 1987  9216418    Africa  62.351   706.1573          TRUE
1701 Zimbabwe 1992 10704340    Africa  60.377   693.4208          TRUE
1702 Zimbabwe 1997 11404948    Africa  46.809   792.4500          TRUE
1703 Zimbabwe 2002 11926563    Africa  39.989   672.0386          TRUE
1704 Zimbabwe 2007 12311143    Africa  43.487   469.7093          TRUE
1705   Norway 2016  5000000    Nordic  80.300 49400.0000         FALSE

To understand why R is giving us a warning when we try to add this row, let’s learn a little more about factors.

Factors

Here is another thing to look out for: in a factor, each different value represents what is called a level. In our case, the factor “continent” has 5 levels: “Africa”, “Americas”, “Asia”, “Europe” and “Oceania”. R will only accept values that match one of the levels. If you add a new value, it will become NA.

The warning is telling us that we unsuccessfully added “Nordic” to our continent factor, but 2016 (a numeric), 5000000 (a numeric), 80.3 (a numeric), 49400.0 (a numeric) and FALSE (a logical) were successfully added to country, year, pop, lifeExp, gdpPercap and below_average respectively, since those variables are not factors. ‘Norway’ was also successfully added since it corresponds to an existing level. To successfully add a gapminder row with a “Nordic” continent, add “Nordic” as a level of the factor:

R 

levels(gapminder$continent)

OUTPUT 

NULL

R 

levels(gapminder$continent) <- c(levels(gapminder$continent), "Nordic")
gapminder_norway  <- rbind(gapminder,
                           list('Norway', 2016, 5000000, 'Nordic', 80.3,49400.0, FALSE))

WARNING 

Warning in `[<-.factor`(`*tmp*`, ri, value = structure(c("Asia", "Asia", :
invalid factor level, NA generated

R 

tail(gapminder_norway)

OUTPUT 

      country year      pop continent lifeExp  gdpPercap below_average
1700 Zimbabwe 1987  9216418      <NA>  62.351   706.1573          TRUE
1701 Zimbabwe 1992 10704340      <NA>  60.377   693.4208          TRUE
1702 Zimbabwe 1997 11404948      <NA>  46.809   792.4500          TRUE
1703 Zimbabwe 2002 11926563      <NA>  39.989   672.0386          TRUE
1704 Zimbabwe 2007 12311143      <NA>  43.487   469.7093          TRUE
1705   Norway 2016  5000000    Nordic  80.300 49400.0000         FALSE

Alternatively, we can change a factor into a character vector; we lose the handy categories of the factor, but we can subsequently add any word we want to the column without babysitting the factor levels:

R 

str(gapminder)

OUTPUT 

'data.frame':	1704 obs. of  7 variables:
 $ country      : chr  "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
 $ year         : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
 $ pop          : num  8425333 9240934 10267083 11537966 13079460 ...
 $ continent    : chr  "Asia" "Asia" "Asia" "Asia" ...
  ..- attr(*, "levels")= chr "Nordic"
 $ lifeExp      : num  28.8 30.3 32 34 36.1 ...
 $ gdpPercap    : num  779 821 853 836 740 ...
 $ below_average: logi  TRUE TRUE TRUE TRUE TRUE TRUE ...

R 

gapminder$continent <- as.character(gapminder$continent)
str(gapminder)

OUTPUT 

'data.frame':	1704 obs. of  7 variables:
 $ country      : chr  "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
 $ year         : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
 $ pop          : num  8425333 9240934 10267083 11537966 13079460 ...
 $ continent    : chr  "Asia" "Asia" "Asia" "Asia" ...
 $ lifeExp      : num  28.8 30.3 32 34 36.1 ...
 $ gdpPercap    : num  779 821 853 836 740 ...
 $ below_average: logi  TRUE TRUE TRUE TRUE TRUE TRUE ...

Appending to a data frame

The key to remember when adding data to a data frame is that columns are vectors and rows are lists. We can also glue two data frames together with rbind:

R 

gapminder <- rbind(gapminder, gapminder)
tail(gapminder, n=3)

OUTPUT 

      country year      pop continent lifeExp gdpPercap below_average
3406 Zimbabwe 1997 11404948    Africa  46.809  792.4500          TRUE
3407 Zimbabwe 2002 11926563    Africa  39.989  672.0386          TRUE
3408 Zimbabwe 2007 12311143    Africa  43.487  469.7093          TRUE

But now the row names are unnecessarily complicated (not consecutive numbers). We can remove the rownames, and R will automatically re-name them sequentially:

R 

rownames(gapminder) <- NULL
head(gapminder)

OUTPUT 

      country year      pop continent lifeExp gdpPercap below_average
1 Afghanistan 1952  8425333      Asia  28.801  779.4453          TRUE
2 Afghanistan 1957  9240934      Asia  30.332  820.8530          TRUE
3 Afghanistan 1962 10267083      Asia  31.997  853.1007          TRUE
4 Afghanistan 1967 11537966      Asia  34.020  836.1971          TRUE
5 Afghanistan 1972 13079460      Asia  36.088  739.9811          TRUE
6 Afghanistan 1977 14880372      Asia  38.438  786.1134          TRUE

Challenge 3

You can create a new data frame right from within R with the following syntax:

R 

df <- data.frame(id = c("a", "b", "c"),
                 x = 1:3,
                 y = c(TRUE, TRUE, FALSE))

Make a data frame that holds the following information for yourself:

  • first name
  • last name
  • lucky number

Then use rbind to add an entry for the people sitting beside you. Finally, use cbind to add a column with each person’s answer to the question, “Is it time for coffee break?”

R 

df <- data.frame(first = c("Grace"),
                 last = c("Hopper"),
                 lucky_number = c(0))
df <- rbind(df, list("Marie", "Curie", 238) )
df <- cbind(df, coffeetime = c(TRUE, TRUE))

Keypoints

  • Use cbind() to add a new column to a data frame.
  • Use rbind() to add a new row to a data frame.
  • Remove rows from a data frame.
  • Use na.omit() to remove rows from a data frame with NA values.
  • Use levels() and as.character() to explore and manipulate factors.
  • Use str(), nrow(), ncol(), dim(), colnames(), rownames(), head(), and typeof() to understand the structure of a data frame.
  • Read in a csv file using read.csv().
  • Understand what length() of a data frame represents.