Chapter 1 Data Preparation
Before you can visualize your data, you have to get it into R. This involves importing the data from an external source and massaging it into a useful format.
1.1 Importing data
R can import data from almost any source, including text files, excel spreadsheets, statistical packages, and database management systems. We’ll illustrate these techniques using the Salaries dataset, containing the 9 month academic salaries of college professors at a single institution in 2008-2009.
1.1.1 Text files
readr package provides functions for importing delimited text files into R data frames.
library(readr) # import data from a comma delimited file Salaries <- read_csv("salaries.csv") # import data from a tab delimited file Salaries <- read_tsv("salaries.txt")
These function assume that the first line of data contains the variable names, values are separated by commas or tabs respectively, and that missing data are represented by blanks. For example, the first few lines of the comma delimited file looks like this.
"rank","discipline","yrs.since.phd","yrs.service","sex","salary" "Prof","B",19,18,"Male",139750 "Prof","B",20,16,"Male",173200 "AsstProf","B",4,3,"Male",79750 "Prof","B",45,39,"Male",115000 "Prof","B",40,41,"Male",141500 "AssocProf","B",6,6,"Male",97000
Options allow you to alter these assumptions. See the documentation for more details.
1.1.2 Excel spreadsheets
readxl package can import data from Excel workbooks. Both xls and xlsx formats are supported.
library(readxl) # import data from an Excel workbook Salaries <- read_excel("salaries.xlsx", sheet=1)
Since workbooks can have more than one worksheet, you can specify the one you want with the
sheet option. The default is
1.1.3 Statistical packages
haven package provides functions for importing data from a variety of statistical packages.
library(haven) # import data from Stata Salaries <- read_dta("salaries.dta") # import data from SPSS Salaries <- read_sav("salaries.sav") # import data from SAS Salaries <- read_sas("salaries.sas7bdat")
Importing data from a database requires additional steps and is beyond the scope of this book. Depending on the database containing the data, the following packages can help:
RMongo. In the newest versions of RStudio, you can use the Connections pane to quickly access the data stored in database management systems.
1.2 Cleaning data
The processes of cleaning your data can be the most time-consuming part of any data analysis. The most important steps are considered below. While there are many approaches, those using the
tidyr packages are some of the quickest and easiest to learn.
|dplyr||mutate||transform or recode variables|
|dplyr||group_by||identify subgroups for further processing|
|tidyr||gather||convert wide format dataset to long format|
|tidyr||spread||convert long format dataset to wide format|
Examples in this section will use the starwars dataset from the
dplyr package. The dataset provides descriptions of 87 characters from the Starwars universe on 13 variables. (I actually prefer StarTrek, but we work with what we have.)
1.2.1 Selecting variables
select function allows you to limit your dataset to specified variables (columns).
library(dplyr) # keep the variables name, height, and gender newdata <- select(starwars, name, height, gender) # keep the variables name and all variables # between mass and species inclusive newdata <- select(starwars, name, mass:species) # keep all variables except birth_year and gender newdata <- select(starwars, -birth_year, -gender)
1.2.2 Selecting observations
filter function allows you to limit your dataset to observations (rows) meeting a specific criteria. Multiple criteria can be combined with the
& (AND) and
| (OR) symbols.
library(dplyr) # select females newdata <- filter(starwars, gender == "female") # select females that are from Alderaan newdata <- filter(starwars, gender == "female" & homeworld == "Alderaan") # select individuals that are from # Alderaan, Coruscant, or Endor newdata <- filter(starwars, homeworld == "Alderaan" | homeworld == "Coruscant" | homeworld == "Endor") # this can be written more succinctly as newdata <- filter(starwars, homeworld %in% c("Alderaan", "Coruscant", "Endor"))
1.2.3 Creating/Recoding variables
mutate function allows you to create new variables or transform existing ones.
library(dplyr) # convert height in centimeters to inches, # and mass in kilograms to pounds newdata <- mutate(starwars, height = height * 0.394, mass = mass * 2.205)
ifelse function (part of base R) can be used for recoding data. The format is
ifelse(test, return if TRUE, return if FALSE).
library(dplyr) # if height is greater than 180 # then heightcat = "tall", # otherwise heightcat = "short" newdata <- mutate(starwars, heightcat = ifelse(height > 180, "tall", "short") # convert any eye color that is not # black, blue or brown, to other newdata <- mutate(starwars, eye_color = ifelse(eye_color %in% c("black", "blue", "brown"), eye_color, "other") # set heights greater than 200 or # less than 75 to missing newdata <- mutate(starwars, height = ifelse(height < 75 | height > 200, NA, height)
1.2.4 Summarizing data
summarize function can be used to reduce multiple values down to a single value (such as a mean). It is often used in conjunction with the
by_group function, to calculate statistics by group. In the code below, the
na.rm=TRUE option is used to drop missing values before calculating the means.
library(dplyr) # calculate mean height and mass newdata <- summarize(starwars, mean_ht = mean(height, na.rm=TRUE), mean_mass = mean(mass, na.rm=TRUE)) newdata
## # A tibble: 1 x 2 ## mean_ht mean_mass ## <dbl> <dbl> ## 1 174. 97.3
# calculate mean height and weight by gender newdata <- group_by(starwars, gender) newdata <- summarize(newdata, mean_ht = mean(height, na.rm=TRUE), mean_wt = mean(mass, na.rm=TRUE)) newdata
## # A tibble: 5 x 3 ## gender mean_ht mean_wt ## <chr> <dbl> <dbl> ## 1 female 165. 54.0 ## 2 hermaphrodite 175. 1358. ## 3 male 179. 81.0 ## 4 none 200. 140. ## 5 <NA> 120. 46.3
1.2.5 Using pipes
tidyr allow you to write your code in a compact format using the pipe
%>% operator. Here is an example.
library(dplyr) # calculate the mean height for women by species newdata <- filter(starwars, gender == "female") newdata <- group_by(species) newdata <- summarize(newdata, mean_ht = mean(height, na.rm = TRUE)) # this can be written as newdata <- starwars %>% filter(gender == "female") %>% group_by(species) %>% summarize(mean_ht = mean(height, na.rm = TRUE))
%>% operator passes the result on the left to the first parameter of the function on the right.
1.2.6 Reshaping data
Some graphs require the data to be in wide format, while some graphs require the data to be in long format.
You can convert a wide dataset to a long dataset using
library(tidyr) long_data <- gather(wide_data, key="variable", value="value", sex:income)
Conversely, you can convert a long dataset to a wide dataset using
library(tidyr) wide_data <- spread(long_data, variable, value)
1.2.7 Missing data
Real data are likely to contain missing values. There are three basic approaches to dealing with missing data: feature selection, listwise deletion, and imputation. Let’s see how each applies to the msleep dataset from the
ggplot2 package. The
msleep dataset describes the sleep habits of mammals and contains missing values on several variables.
22.214.171.124 Feature selection
In feature selection, you delete variables (columns) that contain too many missing values.
data(msleep, package="ggplot2") # what is the proportion of missing data for each variable? pctmiss <- colSums(is.na(msleep))/nrow(msleep) round(pctmiss, 2)
## name genus vore order conservation ## 0.00 0.00 0.08 0.00 0.35 ## sleep_total sleep_rem sleep_cycle awake brainwt ## 0.00 0.27 0.61 0.00 0.33 ## bodywt ## 0.00
Sixty-one percent of the sleep_cycle values are missing. You may decide to drop it.
126.96.36.199 Listwise deletion
Listwise deletion involves deleting observations (rows) that contain missing values on any of the variables of interest.
# Create a dataset containing genus, vore, and conservation. # Delete any rows containing missing data. newdata <- select(msleep, genus, vore, conservation) newdata <- na.omit(newdata)
Imputation involves replacing missing values with “reasonable” guesses about what the values would have been if they had not been missing. There are several approaches, as detailed in such packages as
missForest. Here we will use the
kNN function from the
VIM package to replace missing values with imputed values.
# Impute missing values using the 5 nearest neighbors library(VIM) newdata <- kNN(msleep, k=5)
Basically, for each case with a missing value, the k most similar cases not having a missing value are selected. If the missing value is numeric, the mean of those k cases is used as the imputed value. If the missing value is categorical, the most frequent value from the k cases is used. The process iterates over cases and variables until the results converge (become stable). This is a bit of an oversimplification - see Imputation with R Package VIM for the actual details.
Important caveate: Missing values can bias the results of studies (sometimes severely). If you have a significant amount of missing data, it is probably a good idea to consult a statistician or data scientist before deleting cases or imputing missing values.