Data manipulation is central to data analysis and is often the most time consuming portion of an analysis. The dplyr package contains a suite of functions to make data manipulation easier. The core functions of the dplyr package can be thought of as verbs for data manipulation.
| Verb(s) | Meaning |
|---|---|
filter and slice |
pick specific observations (i.e. specific rows) |
arrange |
reorder the rows |
select |
pick variables by their names (i.e. specific columns) |
mutate |
add new calculated columns to a data frame |
summarize |
aggregate many rows into a single row |
In this example we will explore how to use each of these functions, as well as how to combine them with the group_by function for groupwise manipulations.
To begin, let’s make sure that our data set and the dplyr package are loaded
# NOTE THAT IF YOU USE THE "read_csv" FUNCTION INSTEAD OF "read.csv" SOME COLUMN NAMES WILL BE CHANGED.
colleges <- read.csv("data/colleges2015.csv")
library(dplyr)Data: The file college2015.csv contains information on predominantly bachelor’s-degree granting institutions from 2015 that might be of interest to a college applicant.
To get a feel for what data are available, look at the first six rows
head(colleges)## unitid college type city state
## 1 100654 Alabama A & M University public Normal AL
## 2 100663 University of Alabama at Birmingham public Birmingham AL
## 3 100690 Amridge University private Montgomery AL
## 4 100706 University of Alabama in Huntsville public Huntsville AL
## 5 100724 Alabama State University public Montgomery AL
## 6 100751 The University of Alabama public Tuscaloosa AL
## region admissionRate ACTmath ACTenglish undergrads cost gradRate
## 1 Southeast 0.8989 17 17 4051 18888 0.2914
## 2 Southeast 0.8673 23 26 11200 19990 0.5377
## 3 Southeast NA NA NA 322 12300 0.6667
## 4 Southeast 0.8062 25 26 5525 20306 0.4835
## 5 Southeast 0.5125 17 17 5354 17400 0.2517
## 6 Southeast 0.5655 25 27 28692 26717 0.6665
## FYretention fedloan debt
## 1 0.6314 0.8204 33611.5
## 2 0.8016 0.5397 23117.0
## 3 0.3750 0.7629 26995.0
## 4 0.8098 0.4728 24738.0
## 5 0.6219 0.8735 33452.0
## 6 0.8700 0.4148 24000.0the last six rows
tail(colleges)and the structure of the data frame.
str(colleges)To extract the rows only for colleges and universities in a specific state we use the filter function. For example, we can extract the colleges in Wisconsin from the colleges data set using the following code:
wi <- filter(colleges, state == "WI")
head(wi)## unitid college type city state region
## 1 238193 Alverno College private Milwaukee WI Great Lakes
## 2 238324 Bellin College private Green Bay WI Great Lakes
## 3 238333 Beloit College private Beloit WI Great Lakes
## 4 238430 Cardinal Stritch University private Milwaukee WI Great Lakes
## 5 238458 Carroll University private Waukesha WI Great Lakes
## 6 238476 Carthage College private Kenosha WI Great Lakes
## admissionRate ACTmath ACTenglish undergrads cost gradRate FYretention
## 1 0.7887 19 19 1833 30496 0.3852 0.7173
## 2 0.5556 25 24 285 NA 0.6786 0.7500
## 3 0.6769 26 28 1244 48236 0.7815 0.9228
## 4 0.8423 22 22 2680 37563 0.4162 0.7099
## 5 0.8114 24 24 3024 37963 0.5629 0.7598
## 6 0.7018 24 24 2874 44910 0.6501 0.7841
## fedloan debt
## 1 0.8784 33110
## 2 0.8145 18282
## 3 0.5784 26500
## 4 0.7178 27875
## 5 0.7108 27000
## 6 0.8048 27000Remarks
filter is always the data frame (this is true for all the core functions in dplyr), followed by logical tests that the returned cases must pass. In our example, the test was whether the school was in Wisconsin, which is written as state == "WI".== to indicate equality because = is equivalent to <-.To specify multiple tests, use a comma to separate the tests (think of the comma as the word “and”). For example,
smallWI <- filter(colleges, state == "WI", undergrads < 2000)
returns only those rows corresponding to schools in Wisconsin with fewer than 2,000 undergraduate students.
To specify that at least one test must be passed, use the | character instead of the comma. For example, the below test checks whether a college is in Wisconsin or Minnesota or Iowa, so it returns all of the colleges in Wisconsin, Minnesota, and Iowa.
WiMnIa <- filter(colleges, state == "WI" | state == "MN" | state == "IA")
You can use both | and , to specify multiple tests. For example, we can return all colleges with fewer than 2,000 undergraduate students in Wisconsin, Minnesota, and Iowa.
smallWIM <- filter(colleges, state == "WI" | state == "MN" | state == "IA", undergrads < 2000)
>, >=, <, <=, != (not equal), and == (equal).To remove rows with missing values, use the R command na.omit. For example,
colleges <- na.omit(colleges)
will reduce the data set to only rows with no missing values.
colleges <- filter(colleges, !is.na(cost))
will eliminate only rows with NA in the cost column.
Questions:
To extract rows 10 through 16 from the colleges data frame we use the slice function.
slice(colleges, 10:16)## unitid college type city state
## 1 100937 Birmingham Southern College private Birmingham AL
## 2 101073 Concordia College Alabama private Selma AL
## 3 101189 Faulkner University private Montgomery AL
## 4 101435 Huntingdon College private Montgomery AL
## 5 101453 Heritage Christian University private Florence AL
## 6 101480 Jacksonville State University public Jacksonville AL
## 7 101541 Judson College private Marion AL
## region admissionRate ACTmath ACTenglish undergrads cost gradRate
## 1 Southeast 0.6422 25 27 1181 44512 0.6192
## 2 Southeast NA NA NA 523 17655 0.2115
## 3 Southeast NA NA NA 2358 28485 0.2287
## 4 Southeast 0.6279 20 22 1100 31433 0.4319
## 5 Southeast NA NA NA 67 21160 0.0000
## 6 Southeast 0.8326 21 22 7195 19202 0.3083
## 7 Southeast 0.7388 20 23 331 27815 0.4051
## FYretention fedloan debt
## 1 0.8037 0.4939 27000
## 2 0.4103 0.9100 26500
## 3 0.5000 0.7427 23750
## 4 0.6196 0.7227 27000
## 5 1.0000 0.4839 NA
## 6 0.7112 0.6811 23500
## 7 0.5974 0.7110 26000Remarks
:.c(). For example, to select the 2nd, 18th, and 168th rows use slice(colleges, c(2, 18, 168)).To sort the rows by total cost, from the least expensive to the most expensive, we use the arrange function.
costDF <- arrange(colleges, cost)
head(costDF)## unitid college type city
## 1 197027 United States Merchant Marine Academy public Kings Point
## 2 176336 Southeastern Baptist College private Laurel
## 3 241951 Escuela de Artes Plasticas de Puerto Rico public San Juan
## 4 241216 Atlantic University College private Guaynabo
## 5 241377 Caribbean University-Bayamon private Bayamon
## 6 243221 University of Puerto Rico-Rio Piedras public San Juan
## state region admissionRate ACTmath ACTenglish undergrads
## 1 NY U.S. Service Schools NA NA NA 958
## 2 MS Southeast NA NA NA 37
## 3 PR Outlying Areas 0.7154 NA NA 529
## 4 PR Outlying Areas NA NA NA 1365
## 5 PR Outlying Areas NA NA NA 1572
## 6 PR Outlying Areas 0.5248 NA NA 11834
## cost gradRate FYretention fedloan debt
## 1 6603 0.7365 0.9733 0.0780 4211
## 2 6753 0.6875 1.0000 0.0000 NA
## 3 7248 0.4127 0.8382 0.0000 NA
## 4 7695 0.3891 0.7200 0.1053 5000
## 5 8006 0.2166 0.7951 0.2210 9000
## 6 8020 0.4748 0.8968 0.0966 5500Remarks
arrange assumes that we want the data arranged in ascending order by the specified variable(s).To arrange the rows in descending order, wrap the variable name in the desc function. For example, to arrange the data frame from most to least expensive we would use the following command:
costDF <- arrange(colleges, desc(cost))
To arrange a data frame by the values of multiple variables, list the variables in a comma separated list. The order of the variables specifies the order in which the data frame will be arranged. For example,
actDF <- arrange(colleges, desc(ACTmath), desc(ACTenglish))
reorders colleges first by the median ACT math score (in descending order) and then by the ACT english score (in descending order)
Questions
Suppose that you are only interested in a subset of the columns in the data set—say, college, city, state, undergrads, and cost—and want to create a data frame with only these columns. To do this, we select the desired columns:
lessCols <- select(colleges, college, city, state, undergrads, cost)
head(lessCols)## college city state undergrads cost
## 1 Alabama A & M University Normal AL 4051 18888
## 2 University of Alabama at Birmingham Birmingham AL 11200 19990
## 3 Amridge University Montgomery AL 322 12300
## 4 University of Alabama in Huntsville Huntsville AL 5525 20306
## 5 Alabama State University Montgomery AL 5354 17400
## 6 The University of Alabama Tuscaloosa AL 28692 26717Remarks
select to select all but the negated variables. For example, if we only wished to drop the unitid variable we run the following command:drop_unitid <- select(colleges, -unitid)
head(drop_unitid)## college type city state region
## 1 Alabama A & M University public Normal AL Southeast
## 2 University of Alabama at Birmingham public Birmingham AL Southeast
## 3 Amridge University private Montgomery AL Southeast
## 4 University of Alabama in Huntsville public Huntsville AL Southeast
## 5 Alabama State University public Montgomery AL Southeast
## 6 The University of Alabama public Tuscaloosa AL Southeast
## admissionRate ACTmath ACTenglish undergrads cost gradRate FYretention
## 1 0.8989 17 17 4051 18888 0.2914 0.6314
## 2 0.8673 23 26 11200 19990 0.5377 0.8016
## 3 NA NA NA 322 12300 0.6667 0.3750
## 4 0.8062 25 26 5525 20306 0.4835 0.8098
## 5 0.5125 17 17 5354 17400 0.2517 0.6219
## 6 0.5655 25 27 28692 26717 0.6665 0.8700
## fedloan debt
## 1 0.8204 33611.5
## 2 0.5397 23117.0
## 3 0.7629 26995.0
## 4 0.4728 24738.0
## 5 0.8735 33452.0
## 6 0.4148 24000.0Data sets often do not contain the exact variables we need, but contain all of the information necessary to calculate the needed variables. In this case, we can use the mutate function to add a new column to a data frame that is calculated from other variables. For example, we may wish to report percentages rather than proportions for the admissions rate.
colleges <- mutate(colleges, admissionPct = 100 * admissionRate)Remarks
= to assign the value of the new variable.FYretention and gradRate.colleges <- mutate(colleges, FYretentionPct = 100 * FYretention,
gradPct = 100 * gradRate)To create summary statistics for columns within the data set we must aggregate all of the rows using the summarize command. (Note that you can also use the British spelling: summarise.) For example, to calculate the median cost of all 1776 colleges in our data set we run the following command:
summarize(colleges, medianCost = median(cost, na.rm = TRUE))## medianCost
## 1 29849Remarks
na.rm = TRUE here to remove any missing values in the cost column before the calculation. Many functions, including this summarize function, will return an error if there are missing values (blanks, NAs or NaNs) in your data.summarize returns a data frame, with one row and one column.cost for all 1776 colleges in our data setsummarize(colleges,
min = min(cost, na.rm = TRUE),
Q1 = quantile(cost, .25, na.rm = TRUE),
median = median(cost, na.rm = TRUE),
Q3 = quantile(cost, .75, na.rm = TRUE),
max = max(cost, na.rm = TRUE))## min Q1 median Q3 max
## 1 6603 19831 29849 41180 62636Question
na.rm = TRUE from the code above?Often it is of interest to manipulate data within groups. For example, we might be more interested in creating separate summaries for each state, or for private and public colleges. To do this we must first tell R what groups are of interest using the group_by function, and then we can use any of the above functions. Most often group_by is paired with summarise or mutate.
Let’s first consider comparing the cost of private and public colleges. First, we must specify that the variable type defines the groups of interest.
colleges_by_type <- group_by(colleges, type)Remarks
Multiple variables can be used to specify the groups. For example, to specify groups by state and type, we would run the following command:
colleges_state_type <- group_by(colleges, state, type)
group_by with other commandsOnce we have a grouped data frame, we can obtain summaries by group via summarize. For example, the five number summary of cost by institution type is obtained below
summarize(colleges_by_type,
min = min(cost, na.rm = TRUE),
Q1 = quantile(cost, .25, na.rm = TRUE),
median = median(cost, na.rm = TRUE),
Q3 = quantile(cost, .75, na.rm = TRUE),
max = max(cost, na.rm = TRUE))## # A tibble: 2 x 6
## type min Q1 median Q3 max
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 private 6753 28788. 37302 45728. 62636
## 2 public 6603 16822 19303 21909 33208We can also calculate new variables within groups, such as the standardized cost of attendance within each state:
colleges_by_state <- group_by(colleges, state)
colleges_by_state <- mutate(colleges_by_state,
mean.cost = mean(cost, na.rm = TRUE),
sd.cost = sd(cost, na.rm = TRUE),
std.cost = (cost - mean.cost) / sd.cost)
head(colleges_by_state)## # A tibble: 6 x 21
## # Groups: state [1]
## unitid college type city state region admissionRate ACTmath ACTenglish
## <int> <fct> <fct> <fct> <fct> <fct> <dbl> <int> <int>
## 1 100654 Alabam… publ… Norm… AL South… 0.899 17 17
## 2 100663 Univer… publ… Birm… AL South… 0.867 23 26
## 3 100690 Amridg… priv… Mont… AL South… NA NA NA
## 4 100706 Univer… publ… Hunt… AL South… 0.806 25 26
## 5 100724 Alabam… publ… Mont… AL South… 0.512 17 17
## 6 100751 The Un… publ… Tusc… AL South… 0.566 25 27
## # ... with 12 more variables: undergrads <int>, cost <int>,
## # gradRate <dbl>, FYretention <dbl>, fedloan <dbl>, debt <dbl>,
## # admissionPct <dbl>, FYretentionPct <dbl>, gradPct <dbl>,
## # mean.cost <dbl>, sd.cost <dbl>, std.cost <dbl>Remarks
mutate allows you to use variables defined earlier to calculate a new variable. This is how std.cost was calculated.group_by function returns an object of class c("grouped_df", "tbl_df", "tbl", "data.frame"), which looks confusing, but essentially allows the data frame to be printed neatly. Notice that only the first 10 rows print when we print the data frame in the console by typing colleges_by_state, and the width of the console determines how many variables are shown.data.frame using the as.data.frame function. Try running head(as.data.frame(colleges_by_state)).View(colleges_by_state).select a reduced number of columns to print.total.avg.cost4.RStudio’s data wrangling cheat sheet provides a nice summary of the functions in the dplyr package, including those covered in this tutorial.
The introductory vignette to dplyr provides an example of wrangling a data set consisting of 336,776 flights that departed from New York City in 2013.
Roger Peng’s video overview of the dplyr package.