Exploring Data Frames
OverviewTeaching: 20 min
Exercises: 10 minQuestions
How can I manipulate a data frame?Objectives
Be able to add and remove rows and columns.
Be able to remove rows with
Be able to append two data frames
Be able to articulate what a
factoris and how to convert between
Be able to find basic properties of a data frames including size, class or type of the columns, names, and first few rows.
At this point, you’ve see 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 a whole lot of the time, the star of the show is going to be 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 frame
We learned last time that the columns in a data frame were vectors, so that our data are consistent in type throughout the column. As such, if we want to add a new column, we need to 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:
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 dataframe, 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:
So for it to work we have to have
length(age). Let’s store it into cats and overwite the contents of that data frame.
age <- c(2, 3, 5) cats <- cbind(cats, age)
Now how about adding rows - in this case, we saw last time that the rows of a data frame are made of lists:
newRow <- list("tortoiseshell", 3.3, TRUE, 9) cats <- rbind(cats, newRow)
Warning in `[<-.factor`(`*tmp*`, ri, value = "tortoiseshell"): invalid factor level, NA generated
Another thing to look out for has emerged - when R creates a factor, it only allows whatever is originally there when our data was first loaded, which was ‘black’, ‘calico’ and ‘tabby’ in our case. Anything new that doesn’t fit into one of these categories is rejected as nonsense (becomes 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 values are not factors. To successfully add a cat with a ‘tortoiseshell’ coat, explicitly add ‘tortoiseshell’ as a level in the factor:
 "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 column to a character vector; we lose the handy categories of the factor, but can subsequently add any word we want to the column without babysitting the factor levels:
'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
Let’s imagine that, like dogs, 1 human year is equivalent to 7 cat years. (The Purina company uses a more sophisticated alogrithm).
- Create a vector called
human.ageto a factor.
human.ageback to a numeric vector using the
as.numeric()function. Now divide it by 7 to get back the original ages. Explain what happened.
Solution to Challenge 1
human.age <- cats$age * 7
human.age <- factor(human.age).
as.factor(human.age)works just as well.
1 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 convert
human.ageto a character vector 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 that is supposed to only contain numbers, and forget to set
stringsAsFactors=FALSEwhen we read in the data.
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’ve accidentally added a garbage row:
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:
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 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:
Alternatively, we can drop all rows with
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)
Appending to a data frame
The key to remember when adding data to a data frame is that columns are
vectors or factors, and rows are lists. We can also glue two data frames
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
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
rbindto add an entry for the people sitting beside you. Finally, use
cbindto 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))
So far, you’ve 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-FiveYearData.csv")
Another type of file you might encounter are tab-separated value files (.tsv). To specify a tab as a separator, use
Files can also be downloaded directly from the Internet into a local folder of your choice onto your computer using the
read.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-FiveYearData.csv", destfile = "data/gapminder-FiveYearData.csv") gapminder <- read.csv("data/gapminder-FiveYearData.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,
gapminder <- read.csv("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder-FiveYearData.csv")
- 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
'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 ...
We can also examine individual columns of the data frame with our
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;
str(gapminder) said there were 1704 observations of 6
variables in gapminder, what do you think the following will produce, and why?
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:
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:
Or, both at once:
 1704 6
We’ll also likely want to know what the titles of all the columns are, so we can ask for them later:
 "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:
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
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?
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 for sanity (or insanity depending on your view)?
Tip: 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.
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.
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 contents of
download.file("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder-FiveYearData.csv", destfile = "data/gapminder-FiveYearData.csv") gapminder <- read.csv(file = "data/gapminder-FiveYearData.csv")
To run the script and load the data into the
source(file = "scripts/load-gapminder.R")
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 like
dimto explain what everything that
strprints out for gapminder means. If there are any parts you can’t interpret, discuss with your neighbors!
Solution to Challenge 5
gapminderis a data frame with columns
yearis an integer vector.
gdpPercapare numeric vectors.
cbind()to add a new column to a data frame.
rbind()to add a new row to a data frame.
Remove rows from a data frame.
na.omit()to remove rows from a data frame with
as.character()to explore and manipulate factors
typeof()to understand structure of the data frame
Read in a csv file using
length()of a data frame