Overview
Teaching: 20 min Exercises: 10 minQuestions
How can I manipulate a dataframe?
Objectives
To learn how to manipulate a data.frame in memory
To tour some best practices of exploring and understanding a data frame when it is first loaded.
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 - that table that we started with that information from a CSV gets dumped into when we load it. In this lesson, we’ll learn a few more things about working with 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(2,3,5,12)
cats
coat weight likes_string
1 calico 2.1 TRUE
2 black 5.0 FALSE
3 tabby 3.2 TRUE
We can then add this as a column via:
cats <- cbind(cats, age)
Error in data.frame(..., check.names = FALSE): arguments imply differing number of rows: 3, 4
Why didn’t this work? Of course, R wants to see one element in our new column for every row in the table:
cats
coat weight likes_string
1 calico 2.1 TRUE
2 black 5.0 FALSE
3 tabby 3.2 TRUE
age <- c(4,5,8)
cats <- cbind(cats, age)
cats
coat weight likes_string age
1 calico 2.1 TRUE 4
2 black 5.0 FALSE 5
3 tabby 3.2 TRUE 8
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 its categories is rejected as nonsense, until we explicitly add that as a level in 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 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:
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: logi TRUE FALSE TRUE TRUE TRUE
$ age : num 4 5 8 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: logi TRUE FALSE TRUE TRUE TRUE
$ age : num 4 5 8 9 9
We now know how to add rows and columns to our data.frame in R - but in our work we’ve accidentally added a garbage row:
cats
coat weight likes_string age
1 calico 2.1 TRUE 4
2 black 5.0 FALSE 5
3 tabby 3.2 TRUE 8
4 <NA> 3.3 TRUE 9
5 tortoiseshell 3.3 TRUE 9
We can ask for a data.frame minus this offending row:
cats[-4,]
coat weight likes_string age
1 calico 2.1 TRUE 4
2 black 5.0 FALSE 5
3 tabby 3.2 TRUE 8
5 tortoiseshell 3.3 TRUE 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: 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 TRUE 4
2 black 5.0 FALSE 5
3 tabby 3.2 TRUE 8
5 tortoiseshell 3.3 TRUE 9
Let’s reassign the output to cats, so that our changes will be permanent:
cats <- na.omit(cats)
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 dataframes
together with rbind:
cats <- rbind(cats, cats)
cats
coat weight likes_string age
1 calico 2.1 TRUE 4
2 black 5.0 FALSE 5
3 tabby 3.2 TRUE 8
5 tortoiseshell 3.3 TRUE 9
11 calico 2.1 TRUE 4
21 black 5.0 FALSE 5
31 tabby 3.2 TRUE 8
51 tortoiseshell 3.3 TRUE 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 TRUE 4
2 black 5.0 FALSE 5
3 tabby 3.2 TRUE 8
4 tortoiseshell 3.3 TRUE 9
5 calico 2.1 TRUE 4
6 black 5.0 FALSE 5
7 tabby 3.2 TRUE 8
8 tortoiseshell 3.3 TRUE 9
Challenge 1
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 1
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. Lets read in the gapminder dataset that we downloaded previously:
gapminder <- read.csv("data/gapminder-FiveYearData.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"orread.delim().- You can also read in files from the Internet by replacing the file paths with a web address. For example,
gapminder <- read.csv("https://swcarpentry.github.io/r-novice-gapminder/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 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 ...
We can also examine individual columns of the data.frame with our typeof function:
typeof(gapminder$year)
[1] "integer"
typeof(gapminder$lifeExp)
[1] "double"
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
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 2
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 2
The contents of
script/load-gapminder.R:gapminder <- read.csv(file = "data/gapminder-FiveYearData.csv")To run the script and load the data into the
gapmindervariable:source(file = "scripts/load-gapminder.R")
Challenge 3
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 3
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 dataframe.Use
rbind()to add a new row to a dataframe.Use
omit()to remove rows from a dataframe.