Chapter 11 Looking at Data
Whenever you’re working with a new dataset, the first thing you should do is look at it! What is the format of the data? What are the dimensions? What are the variable names? How are the variables stored? Are there missing data? Are there any flaws in the data?
This lesson will teach you how to answer these questions and more using R’s built-in functions. We’ll be using a dataset constructed from the United States Department of Agriculture’s PLANTS Database (http://plants.usda.gov/adv_search.html).
I’ve stored the data for you in a variable called plants. Type ls() to list the variables in your workspace, among which should be plants.
ls()
## [1] "boring_function" "cnames" "evaluate" "i" "ints"
## [6] "j" "my_char" "my_data" "my_div" "my_matrix"
## [11] "my_matrix2" "my_mean" "my_na" "my_name" "my_seq"
## [16] "my_sqrt" "my_vector" "myFun" "nrRepetitions" "num_vect"
## [21] "patients" "plants" "remainder" "tf" "vect"
## [26] "vect2" "x" "y" "z"Let’s begin by checking the class of the plants variable with class(plants). This will give us a clue as to the overall structure of the data.
It’s very common for data to be stored in a data frame. It is the default class for data read into R using functions like read.csv() and read.table(), which you’ll learn about in another lesson.
Since the dataset is stored in a data frame, we know it is rectangular. In other words, it has two dimensions (rows and columns) and fits neatly into a table or spreadsheet. Use dim(plants) to see exactly how many rows and columns we’re dealing with.
The first number you see (5166) is the number of rows (observations) and the second number (10) is the number of columns (variables).
You can also use nrow(plants) to see only the number of rows. Try it out.
… And ncol(plants) to see only the number of columns.
If you are curious as to how much space the dataset is occupying in memory, you can use object.size(plants).
Now that we have a sense of the shape and size of the dataset, let’s get a feel for what’s inside. names(plants) will return a character vector of column (i.e. variable) names. Give it a shot.
names(plants)
## [1] "Scientific_Name" "Duration" "Active_Growth_Period" "Foliage_Color"
## [5] "pH_Min" "pH_Max" "Precip_Min" "Precip_Max"
## [9] "Shade_Tolerance" "Temp_Min_F"We’ve applied fairly descriptive variable names to this dataset, but that won’t always be the case. A logical next step is to peek at the actual data. However, our dataset contains over 5000 observations (rows), so it’s impractical to view the whole thing all at once.
The head() function allows you to preview the top of the dataset. Give it a try with only one argument.
head(plants)
## Scientific_Name Duration Active_Growth_Period Foliage_Color pH_Min pH_Max Precip_Min
## 1 Abelmoschus <NA> <NA> <NA> NA NA NA
## 2 Abelmoschus esculentus Annual, Perennial <NA> <NA> NA NA NA
## 3 Abies <NA> <NA> <NA> NA NA NA
## 4 Abies balsamea Perennial Spring and Summer Green 4 6 13
## 5 Abies balsamea var. balsamea Perennial <NA> <NA> NA NA NA
## 6 Abutilon <NA> <NA> <NA> NA NA NA
## Precip_Max Shade_Tolerance Temp_Min_F
## 1 NA <NA> NA
## 2 NA <NA> NA
## 3 NA <NA> NA
## 4 60 Tolerant -43
## 5 NA <NA> NA
## 6 NA <NA> NATake a minute to look through and understand the output above. Each row is labeled with the observation number and each column with the variable name. Your screen is probably not wide enough to view all 10 columns side-by-side, in which case R displays as many columns as it can on each line before continuing on the next.
By default, head() shows you the first six rows of the data. You can alter this behavior by passing as a second argument the number of rows you’d like to view. Use head() to preview the first 10 rows of plants.
head(plants, 10)
## Scientific_Name Duration Active_Growth_Period Foliage_Color pH_Min pH_Max
## 1 Abelmoschus <NA> <NA> <NA> NA NA
## 2 Abelmoschus esculentus Annual, Perennial <NA> <NA> NA NA
## 3 Abies <NA> <NA> <NA> NA NA
## 4 Abies balsamea Perennial Spring and Summer Green 4 6.0
## 5 Abies balsamea var. balsamea Perennial <NA> <NA> NA NA
## 6 Abutilon <NA> <NA> <NA> NA NA
## 7 Abutilon theophrasti Annual <NA> <NA> NA NA
## 8 Acacia <NA> <NA> <NA> NA NA
## 9 Acacia constricta Perennial Spring and Summer Green 7 8.5
## 10 Acacia constricta var. constricta Perennial <NA> <NA> NA NA
## Precip_Min Precip_Max Shade_Tolerance Temp_Min_F
## 1 NA NA <NA> NA
## 2 NA NA <NA> NA
## 3 NA NA <NA> NA
## 4 13 60 Tolerant -43
## 5 NA NA <NA> NA
## 6 NA NA <NA> NA
## 7 NA NA <NA> NA
## 8 NA NA <NA> NA
## 9 4 20 Intolerant -13
## 10 NA NA <NA> NAThe same applies for using tail() to preview the end of the dataset. Use tail() to view the last 15 rows.
tail(plants, 15)
## Scientific_Name Duration Active_Growth_Period Foliage_Color pH_Min pH_Max Precip_Min
## 5152 Zizania <NA> <NA> <NA> NA NA NA
## 5153 Zizania aquatica Annual Spring Green 6.4 7.4 30
## 5154 Zizania aquatica var. aquatica Annual <NA> <NA> NA NA NA
## 5155 Zizania palustris Annual <NA> <NA> NA NA NA
## 5156 Zizania palustris var. palustris Annual <NA> <NA> NA NA NA
## 5157 Zizaniopsis <NA> <NA> <NA> NA NA NA
## 5158 Zizaniopsis miliacea Perennial Spring and Summer Green 4.3 9.0 35
## 5159 Zizia <NA> <NA> <NA> NA NA NA
## 5160 Zizia aptera Perennial <NA> <NA> NA NA NA
## 5161 Zizia aurea Perennial <NA> <NA> NA NA NA
## 5162 Zizia trifoliata Perennial <NA> <NA> NA NA NA
## 5163 Zostera <NA> <NA> <NA> NA NA NA
## 5164 Zostera marina Perennial <NA> <NA> NA NA NA
## 5165 Zoysia <NA> <NA> <NA> NA NA NA
## 5166 Zoysia japonica Perennial <NA> <NA> NA NA NA
## Precip_Max Shade_Tolerance Temp_Min_F
## 5152 NA <NA> NA
## 5153 50 Intolerant 32
## 5154 NA <NA> NA
## 5155 NA <NA> NA
## 5156 NA <NA> NA
## 5157 NA <NA> NA
## 5158 70 Intolerant 12
## 5159 NA <NA> NA
## 5160 NA <NA> NA
## 5161 NA <NA> NA
## 5162 NA <NA> NA
## 5163 NA <NA> NA
## 5164 NA <NA> NA
## 5165 NA <NA> NA
## 5166 NA <NA> NAAfter previewing the top and bottom of the data, you probably noticed lots of NAs, which are R’s placeholders for missing values. Use summary(plants) to get a better feel for how each variable is distributed and how much of the dataset is missing.
summary(plants)
## Scientific_Name Duration Active_Growth_Period Foliage_Color pH_Min
## Length:5166 Length:5166 Length:5166 Length:5166 Min. :3.000
## Class :character Class :character Class :character Class :character 1st Qu.:4.500
## Mode :character Mode :character Mode :character Mode :character Median :5.000
## Mean :4.997
## 3rd Qu.:5.500
## Max. :7.000
## NA's :4327
## pH_Max Precip_Min Precip_Max Shade_Tolerance Temp_Min_F
## Min. : 5.100 Min. : 4.00 Min. : 16.00 Length:5166 Min. :-79.00
## 1st Qu.: 7.000 1st Qu.:16.75 1st Qu.: 55.00 Class :character 1st Qu.:-38.00
## Median : 7.300 Median :28.00 Median : 60.00 Mode :character Median :-33.00
## Mean : 7.344 Mean :25.57 Mean : 58.73 Mean :-22.53
## 3rd Qu.: 7.800 3rd Qu.:32.00 3rd Qu.: 60.00 3rd Qu.:-18.00
## Max. :10.000 Max. :60.00 Max. :200.00 Max. : 52.00
## NA's :4327 NA's :4338 NA's :4338 NA's :4328summary() provides different output for each variable, depending on its class. For numeric data such as Precip_Min, summary() displays the minimum, 1st quartile, median, mean, 3rd quartile, and maximum. These values help us understand how the data are distributed.
For categorical variables (called ‘factor’ variables in R), summary() displays the number of times each value (or ‘level’) occurs in the data. For example, each value of Scientific_Name only appears once, since it is unique to a specific plant. In contrast, the summary for Duration (also a factor variable) tells us that our dataset contains 3031 Perennial plants, 682 Annual plants, etc.
You can see that R truncated the summary for Active_Growth_Period by including a catch-all category called ‘Other’. Since it is a categorical/factor variable, we can see how many times each value actually occurs in the data with table(plants$Active_Growth_Period).
table(plants$Active_Growth_Period)
##
## Fall, Winter and Spring Spring Spring and Fall Spring and Summer
## 15 144 10 447
## Spring, Summer, Fall Summer Summer and Fall Year Round
## 95 92 24 5Each of the functions we’ve introduced so far has its place in helping you to better understand the structure of your data. However, we’ve left the best for last….
Perhaps the most useful and concise function for understanding the structure of your data is str(). Give it a try now.
str(plants)
## 'data.frame': 5166 obs. of 10 variables:
## $ Scientific_Name : chr "Abelmoschus" "Abelmoschus esculentus" "Abies" "Abies balsamea" ...
## $ Duration : chr NA "Annual, Perennial" NA "Perennial" ...
## $ Active_Growth_Period: chr NA NA NA "Spring and Summer" ...
## $ Foliage_Color : chr NA NA NA "Green" ...
## $ pH_Min : num NA NA NA 4 NA NA NA NA 7 NA ...
## $ pH_Max : num NA NA NA 6 NA NA NA NA 8.5 NA ...
## $ Precip_Min : int NA NA NA 13 NA NA NA NA 4 NA ...
## $ Precip_Max : int NA NA NA 60 NA NA NA NA 20 NA ...
## $ Shade_Tolerance : chr NA NA NA "Tolerant" ...
## $ Temp_Min_F : int NA NA NA -43 NA NA NA NA -13 NA ...The beauty of str() is that it combines many of the features of the other functions you’ve already seen, all in a concise and readable format. At the very top, it tells us that the class of plants is ‘data.frame’ and that it has 5166 observations and 10 variables. It then gives us the name and class of each variable, as well as a preview of its contents.
str() is actually a very general function that you can use on most objects in R. Any time you want to understand the structure of something (a dataset, function, etc.), str() is a good place to start.
In this lesson, you learned how to get a feel for the structure and contents of a new dataset using a collection of simple and useful functions. Taking the time to do this upfront can save you time and frustration later on in your analysis.
You’ve successfully completed this lesson!