Exploring Data Frames
Overview
Teaching: 20 min
Exercises: 10 minQuestions
How can I manipulate a data frame?
Objectives
Add and remove rows or columns.
Remove rows with
NAvalues.Append two data frames.
Understand what a
factoris.Convert a
factorto acharactervector 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.
Adding columns and rows in data frames
We already learned that the columns of a data frame are vectors, so that our data are consistent in type throughout the columns. As such, if we want to add a new column, we can start by making a new vector:
age <- c(2, 3, 5)
cats
coat weight likes_string
1 calico 2.1 1
2 black 5.0 0
3 tabby 3.2 1
We can then add this as a column via:
cbind(cats, age)
coat weight likes_string age
1 calico 2.1 1 2
2 black 5.0 0 3
3 tabby 3.2 1 5
Note that if we tried to add a vector of ages with a different number of entries than the number of rows in the data frame, it would fail:
age <- c(2, 3, 5, 12)
cbind(cats, age)
Error in data.frame(..., check.names = FALSE): arguments imply differing number of rows: 3, 4
age <- c(2, 3)
cbind(cats, age)
Error in data.frame(..., check.names = FALSE): arguments imply differing number of rows: 3, 2
Why didn’t this work? Of course, R wants to see one element in our new column for every row in the table:
nrow(cats)
[1] 3
length(age)
[1] 2
So for it to work we need to have nrow(cats) = length(age). Let’s overwrite the content of cats with our new data frame.
age <- c(2, 3, 5)
cats <- cbind(cats, age)
Now how about adding rows? We already know that the rows of a data frame are lists:
newRow <- list("tortoiseshell", 3.3, TRUE, 9)
cats <- rbind(cats, newRow)
Warning in `[<-.factor`(`*tmp*`, ri, value = "tortoiseshell"): invalid factor
level, NA generated
Looks like our attempt to use the rbind() function returns a warning. Recall that, unlike errors, warnings do not necessarily stop a function from performing its intended action. You can confirm this by taking a look at the cats data frame.
cats
coat weight likes_string age
1 calico 2.1 1 2
2 black 5.0 0 3
3 tabby 3.2 1 5
4 <NA> 3.3 1 9
Notice that not only did we successfully add a new row, but there is NA in the column coats where we expected “tortoiseshell” to be. Why did this happen?
Factors
For an object containing the data type factor, each different value represents what is called a level. In our case, the factor “coat” has 3 levels: “black”, “calico”, and “tabby”. 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 “tortoiseshell” to our coat factor, but 3.3 (a numeric), TRUE (a logical), and 9 (a numeric) were successfully added to weight, likes_string, and age, respectively, since those variables are not factors. To successfully add a cat with a “tortoiseshell” coat, add “tortoiseshell” as a possible level of the factor:
levels(cats$coat)
[1] "black" "calico" "tabby"
levels(cats$coat) <- c(levels(cats$coat), "tortoiseshell")
cats <- rbind(cats, list("tortoiseshell", 3.3, TRUE, 9))
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:
str(cats)
'data.frame': 5 obs. of 4 variables:
$ coat : Factor w/ 4 levels "black","calico",..: 2 1 3 NA 4
$ weight : num 2.1 5 3.2 3.3 3.3
$ likes_string: int 1 0 1 1 1
$ age : num 2 3 5 9 9
cats$coat <- as.character(cats$coat)
str(cats)
'data.frame': 5 obs. of 4 variables:
$ coat : chr "calico" "black" "tabby" NA ...
$ weight : num 2.1 5 3.2 3.3 3.3
$ likes_string: int 1 0 1 1 1
$ age : num 2 3 5 9 9
Challenge 1
Let’s imagine that 1 cat year is equivalent to 7 human years.
- Create a vector called
human_ageby multiplyingcats$ageby 7.- Convert
human_ageto a factor.- Convert
human_ageback to a numeric vector using theas.numeric()function. Now divide it by 7 to get the original ages back. Explain what happened.Solution to Challenge 1
human_age <- cats$age * 7human_age <- factor(human_age).as.factor(human_age)works just as well.as.numeric(human_age)yields1 2 3 4 4because factors are stored as integers (here, 1:4), each of which is associated with a label (here, 28, 35, 56, and 63). Converting the factor to a numeric vector gives us the underlying integers, not the labels. If we want the original numbers, we need to converthuman_ageto a character vector (usingas.character(human_age)) and then to a numeric vector (why does this work?). This comes up in real life when we accidentally include a character somewhere in a column of a .csv file supposed to only contain numbers, and forget to setstringsAsFactors=FALSEwhen we read in the data.
Removing rows
We now know how to add rows and columns to our data frame in R—but in our first attempt to add a “tortoiseshell” cat to the data frame we have accidentally added a garbage row:
cats
coat weight likes_string age
1 calico 2.1 1 2
2 black 5.0 0 3
3 tabby 3.2 1 5
4 <NA> 3.3 1 9
5 tortoiseshell 3.3 1 9
We can ask for a data frame minus this offending row:
cats[-4, ]
coat weight likes_string age
1 calico 2.1 1 2
2 black 5.0 0 3
3 tabby 3.2 1 5
5 tortoiseshell 3.3 1 9
Notice the comma with nothing after it to indicate that we want to drop the entire fourth row.
Note: we could also remove both new rows at once by putting the row numbers
inside of a vector: cats[c(-4,-5), ]
Alternatively, we can drop all rows with NA values:
na.omit(cats)
coat weight likes_string age
1 calico 2.1 1 2
2 black 5.0 0 3
3 tabby 3.2 1 5
5 tortoiseshell 3.3 1 9
Let’s reassign the output to cats, so that our changes will be permanent:
cats <- na.omit(cats)
Removing columns
We can also remove columns in our data frame. What if we want to remove the column “age”. We can remove it in two ways, by variable number or by index.
cats[,-4]
coat weight likes_string
1 calico 2.1 1
2 black 5.0 0
3 tabby 3.2 1
5 tortoiseshell 3.3 1
Notice the comma with nothing before it, indicating we want to keep all of the rows.
Alternatively, we can drop the column by using the index name and the %in% operator. The %in% operator goes through each element of its left argument, in this case the names of cats, and asks, “Does this element occur in the second argument?”
drop <- names(cats) %in% c("age")
cats[,!drop]
coat weight likes_string
1 calico 2.1 1
2 black 5.0 0
3 tabby 3.2 1
5 tortoiseshell 3.3 1
We will cover subsetting with logical operators like %in% in more detail in the next episode. See the section Subsetting through other logical operations
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:
cats <- rbind(cats, cats)
cats
coat weight likes_string age
1 calico 2.1 1 2
2 black 5.0 0 3
3 tabby 3.2 1 5
5 tortoiseshell 3.3 1 9
11 calico 2.1 1 2
21 black 5.0 0 3
31 tabby 3.2 1 5
51 tortoiseshell 3.3 1 9
But now the row names are unnecessarily complicated. We can remove the rownames, and R will automatically re-name them sequentially:
rownames(cats) <- NULL
cats
coat weight likes_string age
1 calico 2.1 1 2
2 black 5.0 0 3
3 tabby 3.2 1 5
4 tortoiseshell 3.3 1 9
5 calico 2.1 1 2
6 black 5.0 0 3
7 tabby 3.2 1 5
8 tortoiseshell 3.3 1 9
Challenge 2
You can create a new data frame right from within R with the following syntax:
df <- data.frame(id = c("a", "b", "c"), x = 1:3, y = c(TRUE, TRUE, FALSE), stringsAsFactors = FALSE)Make a data frame that holds the following information for yourself:
- first name
- last name
- lucky number
Then use
rbindto add an entry for the people sitting beside you. Finally, usecbindto add a column with each person’s answer to the question, “Is it time for coffee break?”Solution to Challenge 2
df <- data.frame(first = c("Grace"), last = c("Hopper"), lucky_number = c(0), stringsAsFactors = FALSE) df <- rbind(df, list("Marie", "Curie", 238) ) df <- cbind(df, coffeetime = c(TRUE,TRUE))
Realistic example
So far, you have seen the basics of manipulating data frames with our cat data;
now let’s use those skills to digest a more realistic dataset. Let’s read in the
gapminder dataset that we downloaded previously:
gapminder <- read.csv("data/gapminder_data.csv", stringsAsFactors = TRUE)
Miscellaneous Tips
Another type of file you might encounter are tab-separated value files (.tsv). To specify a tab as a separator, use
"\\t"orread.delim().Files can also be downloaded directly from the Internet into a local folder of your choice onto your computer using the
download.filefunction. Theread.csvfunction can then be executed to read the downloaded file from the download location, for example,download.file("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder_data.csv", destfile = "data/gapminder_data.csv") gapminder <- read.csv("data/gapminder_data.csv", stringsAsFactors = TRUE)
- 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,gapminder <- read.csv("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder_data.csv", stringsAsFactors = TRUE)
- You can read directly from excel spreadsheets without converting them to plain text first by using the readxl package.
Let’s investigate gapminder a bit; the first thing we should always do is check
out what the data looks like with str:
str(gapminder)
'data.frame': 1704 obs. of 6 variables:
$ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
$ pop : num 8425333 9240934 10267083 11537966 13079460 ...
$ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
$ lifeExp : num 28.8 30.3 32 34 36.1 ...
$ gdpPercap: num 779 821 853 836 740 ...
An additional method for examining the structure of gapminder is to use the summary function. This function can be used on various objects in R. For data frames, summary yields a numeric, tabular, or descriptive summary of each column. Factor columns are summarized by the number of items in each level, numeric or integer columns by the descriptive statistics (quartiles and mean), and character columns by its length, class, and mode.
summary(gapminder$country)
Afghanistan Albania Algeria
12 12 12
Angola Argentina Australia
12 12 12
Austria Bahrain Bangladesh
12 12 12
Belgium Benin Bolivia
12 12 12
Bosnia and Herzegovina Botswana Brazil
12 12 12
Bulgaria Burkina Faso Burundi
12 12 12
Cambodia Cameroon Canada
12 12 12
Central African Republic Chad Chile
12 12 12
China Colombia Comoros
12 12 12
Congo Dem. Rep. Congo Rep. Costa Rica
12 12 12
Cote d'Ivoire Croatia Cuba
12 12 12
Czech Republic Denmark Djibouti
12 12 12
Dominican Republic Ecuador Egypt
12 12 12
El Salvador Equatorial Guinea Eritrea
12 12 12
Ethiopia Finland France
12 12 12
Gabon Gambia Germany
12 12 12
Ghana Greece Guatemala
12 12 12
Guinea Guinea-Bissau Haiti
12 12 12
Honduras Hong Kong China Hungary
12 12 12
Iceland India Indonesia
12 12 12
Iran Iraq Ireland
12 12 12
Israel Italy Jamaica
12 12 12
Japan Jordan Kenya
12 12 12
Korea Dem. Rep. Korea Rep. Kuwait
12 12 12
Lebanon Lesotho Liberia
12 12 12
Libya Madagascar Malawi
12 12 12
Malaysia Mali Mauritania
12 12 12
Mauritius Mexico Mongolia
12 12 12
Montenegro Morocco Mozambique
12 12 12
Myanmar Namibia Nepal
12 12 12
Netherlands New Zealand Nicaragua
12 12 12
Niger Nigeria Norway
12 12 12
Oman Pakistan Panama
12 12 12
(Other)
516
Along with the str and summary functions, we can examine individual columns of the data frame with our typeof function:
typeof(gapminder$year)
[1] "integer"
typeof(gapminder$country)
[1] "integer"
str(gapminder$country)
Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
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?
length(gapminder)
[1] 6
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; remember, a data frame is a list of vectors and factors:
typeof(gapminder)
[1] "list"
When length gave us 6, it’s because gapminder is built out of a list of 6
columns. To get the number of rows and columns in our dataset, try:
nrow(gapminder)
[1] 1704
ncol(gapminder)
[1] 6
Or, both at once:
dim(gapminder)
[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:
colnames(gapminder)
[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:
head(gapminder)
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 3
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 ask for a few lines at random. How would you code this?
Solution to Challenge 3
To check the last few lines it’s relatively simple as R already has a function for this:
tail(gapminder) tail(gapminder, n = 15)What about a few arbitrary rows just in case something is odd in the middle?
Tip: There are several ways to achieve this.
The solution here presents one form of 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! 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.
gapminder[sample(nrow(gapminder), 5), ]
To make sure our analysis is reproducible, we should put the code into a script file so we can come back to it later.
Challenge 4
Go to file -> new file -> R script, and write an R script to load in the gapminder dataset. Put it in the
scripts/directory and add it to version control.Run the script using the
sourcefunction, using the file path as its argument (or by pressing the “source” button in RStudio).Solution to Challenge 4
The
sourcefunction can be used to use a script within a script. Assume you would like to load the same type of file over and over again and therefore you need to specify the arguments to fit the needs of your file. Instead of writing the necessary argument again and again you could just write it once and save it as a script. Then, you can usesource("Your_Script_containing_the_load_function")in a new script to use the function of that script without writing everything again. Check out?sourceto find out more.download.file("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder_data.csv", destfile = "data/gapminder_data.csv") gapminder <- read.csv(file = "data/gapminder_data.csv", stringsAsFactors = TRUE)To run the script and load the data into the
gapmindervariable:source(file = "scripts/load-gapminder.R")
Challenge 5
Read the output of
str(gapminder)again; this time, use what you’ve learned about factors, lists and vectors, as well as the output of functions likecolnamesanddimto explain what everything thatstrprints out for gapminder means. If there are any parts you can’t interpret, discuss with your neighbors!Solution to Challenge 5
The object
gapminderis a data frame with columns
countryandcontinentare factors.yearis an integer vector.pop,lifeExp, andgdpPercapare numeric vectors.
Key Points
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 withNAvalues.Use
levels()andas.character()to explore and manipulate factors.Use
str(),summary(),nrow(),ncol(),dim(),colnames(),rownames(),head(), andtypeof()to understand the structure of a data frame.Read in a csv file using
read.csv().Understand what
length()of a data frame represents.