This page introduces the basics of working with data sets having multiple variables, often of several types. The focus here is on data frames, which are the most convenient data objects in R. Others you will run across are matrices and lists, which I introduce briefly at the end.
Tibbles are a type of data frame that can be slightly easier to work with than the base R version. To use them here, load the
dplyr packages (you might need to install them first).
library(readr) library(dplyr) # or library(tidyverse) # load both packages and many others including ggplot2
dplyr functions will work on both types of data frames. It is also easy to convert back and forth between the two types of data frame.
mydata <- as_tibble(mydata) # convert data frame to tibble type mydata <- as.data.frame(mydata, stringsAsFactors = FALSE) # do the reverse
Enter your data with a spreadsheet program
Enter your data using a spreadsheet program. Use columns for variables and rows for individual sampling units.
Long vs wide layouts
Keep data that you want analyzed together in a single worksheet. A "long" layout is recommended, rather than a "wide" layout. Here is an example of a wide layout of data on the numbers of individuals of 3 species recorded in plots and sites.
Plot Site species1 species2 species3 1 A 0 12 4 2 A 88 2 0 3 B 12 4 1 ...
The equivalent long layout will be easier to analyze.
Plot Site Species Number 1 A 1 0 1 A 2 12 1 A 3 4 2 A 1 88 2 A 2 2 2 A 3 0 3 B 1 12 3 B 2 4 3 B 3 1 ...
What to put in columns
These will save you frustration when it comes time to read into R.
- Use brief, informative variable names in plain text. Keep more detailed explanations of variables in a separate text file.
- Avoid spaces in variable names -- use a dot or underscore instead (e.g.,
- Leave missing cells blank.
- Avoid non-numeric characters in columns of numeric data. R will assume that the entire column is non-numeric. For example, avoid using a question mark "12.67?" to indicate a number you are not sure about. Put the question mark and other comments into a separate column just for comments.
- Use the international format (YYYY-MM-DD) or use separate columns for year, month and day.
- Keep commas out of your data set entirely, because they are column delimiters in your .csv file.
- R is case-sensitive: "Hi" and "hi" are distinct entries.
Save data to a comma-separated text file
Save the data to an ordinary text file, such as a
.csv (comma separated text) file. A text file is never obsolete and can be read by any computer package now and in the future. Data in a proprietary format may not be readable 10 years from now.
The following command in base R reads a data file named "filename.csv" into a data frame
stringsAsFactors = FALSE argument tells R to keep each character variable as-is rather than convert to factors, which are a little harder to work with (I explain what factors are further below).
# base R mydata <- read.csv(file.choose(), stringsAsFactors = FALSE) mydata <- read.csv("/directoryname/filename.csv", stringsAsFactors = FALSE) mydata <- read.csv(url("http://www.zoology.ubc.ca/~bio501/data/filename.csv"), stringsAsFactors = FALSE) # using readr package mydata <- read_csv("/directoryname/filename.csv")
A few options can save frustration if your data file has imperfections.
# base R method: mydata <- read.csv("filename.csv", stringsAsFactors = FALSE, strip.white = TRUE, na.strings = c("NA","") ) # using readr package mydata <- read_csv("/directoryname/filename.csv", na = c("NA",""))
strip.white = TRUE removes spaces at the start and end of character elements. Spaces are often introduced accidentally during data entry. R treats "word" and " word" differently, which is not usually desired.
na.strings = c("NA","") and
na = c("NA","") tells R to treat both
NA and empty strings in columns of character data to missing. This is actually the default, but I include it because it is possible to change the code for missing data when you read a data file into R.
R automatically calls variable types
As it reads your data, R will classify your variables into types.
- Columns with only numbers are made into numeric or integer variables.
read_csv()keeps columns having non-numeric characters as characters by default.
- By default,
read.csv()converts character variables into factors, which can be annoying to work with. Circumvent this by specifying
stringsAsFactors = FALSE.
- A factor is a categorical variable whose categories represent levels. These levels have names, but they additionally have a numeric interpretation. If a variable
Ahas 3 categories "a", "b", and "c", R will order the levels alphabetically, by default, and give them the corresponding numerical interpretations 1, 2, and 3. This will determine the order that the categories appear in graphs and tables. You can always change the order of the levels. For example, if you want "c" to be first (e.g., because it refers to the control group), set the order as follows:
A <- factor(A, levels = c("c","a","b"))
To check on how R has classified all your variables, enter
str(mydata) # structure glimpse(mydata) # command from dplyr package
To check on R's classification of just one variable, x,
class(mydata$x) # integer, character, factor, numeric, etc is.factor(mydata$x) # result: TRUE or FALSE is.character(mydata$x) # result: TRUE or FALSE is.integer(mydata$x) # result: TRUE or FALSE
Convert variable to another type
You can always convert variables between types. The following should work well:
mydata$x <- as.factor(mydata$x) # character to factor mydata$x <- as.character(mydata$x) # factor to character
Warning: To convert factors to numeric or integer, first convert to character. Converting factors directly to numeric or integer data can lead to unwanted outcomes.
Always check the results of a conversion to make sure R did what you wanted.
To write the data frame
mydata to a comma delimited text file, use either of the following commands. The first is from the
readr package and is slightly easier than the base R method.
write_csv(mydata, path = "/directoryname/filename.csv") # readr write.csv(mydata, file="/directoryname/filename.csv", rownames = FALSE) # base R
The following commands are useful for viewing aspects of a data frame.
mydata # if a tibble, prints the first few rows; otherwise prints all print(mydata, n=5) # prints the first 5 rows head(mydata) # prints the first few rows tail(mydata) # prints the last few rows names(mydata) # see the variable names rownames(mydata) # view row names (numbers, if you haven't assigned names)
These functions are applied to the whole data frame.
str(mydata) # summary of variables in frame is.data.frame(mydata) # TRUE or FALSE ncol(mydata) # number of columns in data nrow(mydata) # number of rows names(mydata) # variable names names(mydata) <- c("quad") # change 1st variable name to quad rownames(mydata) # row names
Some vector functions can be applied to whole data frames too, but with different outcomes:
length(mydata) # number of variables var(mydata) # covariances between all variables
The columns of the data frame are vectors representing variables. They can be accessed several ways.
mydata$site # the variable named "site" select(mydata, site) # same, using the dplyr package mydata[ , 2] # the second variable (column) of the data frame mydata[5, 2] # the 5th element (row) of the second variable
For example, log transform a variable named
size.mm and save the result as a new variable named
logsize in the data frame. (
log yields the natural log, whereas the function
log10 yields log base 10.)
mydata$logsize <- log(mydata$size.mm) # as described mydata <- mutate(mydata, logsize = log(size.mm)) # using the dplyr package
For example, to delete the variable
mydata$site <- NULL # NULL must be upper case mydata <- select(mydata, -site) # dplyr method
There are several ways. One is to use indicators inside square brackets using the following format:
newdata <- mydata[ , c(2,3)] # all rows, columns 2 and 3 only; newdata <- mydata[ , -1] # all rows, leave out first column newdata <- mydata[1:3, 1:2] # first three rows, first two columns
Logical statements and variable names within the square brackets also work.
newdata <- mydata[mydata$sex == "f" & mydata$size.mm > 25, c("site","id","weight")]
subset command in base R is easy to use to extract rows and columns. Use the
select argument to select columns (variables). For example, to pull out rows corresponding to females with size > 25, and the three variables, site, id, and weight, use the following.
newdata <- subset(mydata, sex == "f" & size.mm > 25, select = c(site,id,weight))
You can also use
select commands. Use
select to extract variables (columns), and use
filter to select rows, as in the following examples.
temp <- filter(mydata, sex == "f") # extract only these rows newdata <- select(temp, site, id, weight) # extract these columns from "temp" data frame
To re-order the rows of a data frame
mydata to correspond to the sorted order of one of its variables, say
mydata.x <- mydata[order(mydata$x), ] # base R mydata.x <- arrange(mydata, x) # dplyr method
See the sections on frequency tables and tables of summary (descriptive) statistics on the Graphs/Tables R tips page.
Measurements stored in two data frames might relate to one another. For example, one data frame might contain measurements of individuals of a bird species (e.g., weight, age, sex) caught at multiple sites. A second data frame might contain physical measurements made at those sites (e.g., elevation, rainfall). If the site names in both data frames correspond, then it is possible to bring one or all the variables from the second data frame to the first.
For example, to bring the site variable "elevation" from the
sites data frame to the
birds data frame,
birds$elevation <- sites$elevation[match(birds$siteno, sites$siteno)]
To bring all the variables from the sites data set to the bird data set, corresponding to the same sites in both data frames, use the
birds2 <- left_join(birds, sites, by="siteno")
Always check the results to make sure R did what you wanted.
Some functions will give a matrix as output, which is not as convenient for data as a data frame. For example, all columns of a matrix must be of the same data type. Briefly, here's how to manipulate matrices and convert them to data frames.
Reshape a vector to a matrix
matrix to reshape a vector into a matrix. For example, if
x <- c(1,2,3,4,5,6) xmat <- matrix(x,nrow=2)
yields the matrix
[,1] [,2] [,3] [1,] 1 3 5 [2,] 2 4 6
xmat <- matrix(x,nrow=2, byrow=TRUE)
yields the matrix
[,1] [,2] [,3] [1,] 1 2 3 [2,] 4 5 6
Bind vectors to make a matrix
cbind to bind vectors in columns of equal length, and use
rbind to bind them by rows instead. For example,
x <- c(1,2,3) y <- c(4,5,6) xmat <- cbind(x,y)
yields the matrix
x y [1,] 1 4 [2,] 2 5 [3,] 3 6
Access subsets of a matrix
Use integers in square brackets to access subsets of a matrix. Within square brackets, integers before the comma refer to rows, whereas integers after the comma indicate columns: [rows, columns].
xmat[2,3] # value in the 2nd row, 3rd column of matrix xmat[, 2] # 2nd column of matrix (result is a vector) xmat[2, ] # 2nd row of matrix (result is a vector) xmat[ ,c(2,3)] # matrix subset containing columns 2 and 3 only xmat[-1, ] # matrix subset leaving out first row xmat[1:3,1:2] # submatrix containing first 3 rows and first 2 columns only
Useful matrix functions
dim(xmat) # dimensions (rows & columns) of a matrix ncol(xmat) # number of columns in matrix nrow(xmat) # number of rows t(xmat) # transpose a matrix
Convert a matrix to a data.frame
mydata <- as.data.frame(xmat, stringsAsFactors = FALSE)
stringsAsFactors=FALSE is optional but recommended to preserve character data. Otherwise character variables are converted to factors.
Some R functions will output results as a list. A list is a collection of R objects bundled together in a single object. The component objects can be anything at all: vectors, matrices, data frames, and even other lists. The different objects needn't have the same length or number of rows and columns.
list command to create a list of multiple objects. For example, here two vectors are bundled into a list
x <- c(1,2,3,4,5,6,7) y <- c("a","b","c","d","e") mylist <- list(x,y) # simple version mylist <- list(name1 = x, name2 = y) # names each list object
mylist in the R command window shows the contents of the list, which is
[]  1 2 3 4 5 6 7 []  "a" "b" "c" "d" "e"
if the components were left unnamed, or
$name1  1 2 3 4 5 6 7 $name2  "a" "b" "c" "d" "e"
if you named the list components.
Add an object to an existing list
Use the "$" symbol to name a new object in the list.
z <- c("A","C","G","T") mylist$name3 <- z
Access list components
Use the "$" to grab a named object in a list. Or, use an integer between double square brackets,
mylist$name2 # the 2nd list object mylist[] # the 2nd list component, here a vector mylist[] # the 4th element of the 1st list component, here "4"
Useful list functions
names(mylist) # NULL if components are unnamed unlist(mylist) # collapse list to a single vector
Convert a list of vectors to a data frame
This is advised only if all list objects are vectors of equal length.
x <- c(1,2,3,4,5,6,7) y <- c("a","b","c","d","e","f","g") mylist <- list(x = x, y = y) mydata <- do.call("cbind.data.frame", list(mylist, stringsAsFactors=FALSE))
Notice how the option
stringsAsFactors=FALSE for the command
cbind.data.frame is contained inside the
list() argument of