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charanhu/Assignment: swirl Lesson 12: Looking for data

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Assignment: swirl Lesson 12: Looking for data
election: 12
|
| | 0%
| 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?
...
|
|====== | 4%
| 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).
...
|
|============= | 8%
| 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] "alongwith" "boring_function" "cls_list" "cls_vect" "cnames" "flag_colors" "flag_shapes" "flags" "ints"
[10] "my_char" "my_data" "my_div" "my_matrix" "my_matrix2" "my_na" "my_name" "my_seq" "my_sqrt"
[19] "my_vector" "num_vect" "ok" "old.dir" "patients" "plants" "shape_mat" "test_func1" "tf"
[28] "unique_vals" "vect" "vect2" "viewinfo" "x" "y" "z"
| Keep working like that and you'll get there!
|
|=================== | 12%
| 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.
> class(plants)
[1] "data.frame"
| Your dedication is inspiring!
|
|========================= | 16%
| 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.
...
|
|=============================== | 20%
| 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.
>
> dim(plants)
[1] 5166 10
| You nailed it! Good job!
|
|====================================== | 24%
| The first number you see (5166) is the number of rows (observations) and the second number (10) is the number of columns (variables).
...
|
|============================================ | 28%
| You can also use nrow(plants) to see only the number of rows. Try it out.
> nrows(plants)
Error: could not find function "nrows"
> nrow(plant)
Error in nrow(plant) : object 'plant' not found
> nrow(plants)
[1] 5166
| You got it right!
|
|================================================== | 32%
| ... And ncol(plants) to see only the number of columns.
> ncol(plants)
[1] 10
| Nice work!
|
|========================================================= | 36%
| If you are curious as to how much space the dataset is occupying in memory, you can use object.size(plants).
> object.size(plants)
538888 bytes
| You are really on a roll!
|
|=============================================================== | 40%
| 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" "pH_Min" "pH_Max" "Precip_Min"
[8] "Precip_Max" "Shade_Tolerance" "Temp_Min_F"
| Excellent work!
|
|===================================================================== | 44%
| 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.
...
|
|=========================================================================== | 48%
| The head() function allows you to preview the top of the dataset. Give it a try with only one argument.
> head()
Error in head.default() : argument "x" is missing, with no default
> head(plants)
Scientific_Name Duration Active_Growth_Period Foliage_Color pH_Min pH_Max Precip_Min Precip_Max Shade_Tolerance Temp_Min_F
1 Abelmoschus <NA> <NA> <NA> NA NA NA NA <NA> NA
2 Abelmoschus esculentus Annual, Perennial <NA> <NA> NA NA NA NA <NA> NA
3 Abies <NA> <NA> <NA> NA NA NA NA <NA> NA
4 Abies balsamea Perennial Spring and Summer Green 4 6 13 60 Tolerant -43
5 Abies balsamea var. balsamea Perennial <NA> <NA> NA NA NA NA <NA> NA
6 Abutilon <NA> <NA> <NA> NA NA NA NA <NA> NA
| That's a job well done!
|
|================================================================================== | 52%
| Take 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.
...
|
|======================================================================================== | 56%
| 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 Precip_Min Precip_Max Shade_Tolerance Temp_Min_F
1 Abelmoschus <NA> <NA> <NA> NA NA NA NA <NA> NA
2 Abelmoschus esculentus Annual, Perennial <NA> <NA> NA NA NA NA <NA> NA
3 Abies <NA> <NA> <NA> NA NA NA NA <NA> NA
4 Abies balsamea Perennial Spring and Summer Green 4 6.0 13 60 Tolerant -43
5 Abies balsamea var. balsamea Perennial <NA> <NA> NA NA NA NA <NA> NA
6 Abutilon <NA> <NA> <NA> NA NA NA NA <NA> NA
7 Abutilon theophrasti Annual <NA> <NA> NA NA NA NA <NA> NA
8 Acacia <NA> <NA> <NA> NA NA NA NA <NA> NA
9 Acacia constricta Perennial Spring and Summer Green 7 8.5 4 20 Intolerant -13
10 Acacia constricta var. constricta Perennial <NA> <NA> NA NA NA NA <NA> NA
| You got it!
|
|============================================================================================== | 60%
| The 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 Precip_Max Shade_Tolerance Temp_Min_F
5152 Zizania <NA> <NA> <NA> NA NA NA NA <NA> NA
5153 Zizania aquatica Annual Spring Green 6.4 7.4 30 50 Intolerant 32
5154 Zizania aquatica var. aquatica Annual <NA> <NA> NA NA NA NA <NA> NA
5155 Zizania palustris Annual <NA> <NA> NA NA NA NA <NA> NA
5156 Zizania palustris var. palustris Annual <NA> <NA> NA NA NA NA <NA> NA
5157 Zizaniopsis <NA> <NA> <NA> NA NA NA NA <NA> NA
5158 Zizaniopsis miliacea Perennial Spring and Summer Green 4.3 9.0 35 70 Intolerant 12
5159 Zizia <NA> <NA> <NA> NA NA NA NA <NA> NA
5160 Zizia aptera Perennial <NA> <NA> NA NA NA NA <NA> NA
5161 Zizia aurea Perennial <NA> <NA> NA NA NA NA <NA> NA
5162 Zizia trifoliata Perennial <NA> <NA> NA NA NA NA <NA> NA
5163 Zostera <NA> <NA> <NA> NA NA NA NA <NA> NA
5164 Zostera marina Perennial <NA> <NA> NA NA NA NA <NA> NA
5165 Zoysia <NA> <NA> <NA> NA NA NA NA <NA> NA
5166 Zoysia japonica Perennial <NA> <NA> NA NA NA NA <NA> NA
| That's correct!
|
|==================================================================================================== | 64%
| After 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 pH_Max Precip_Min
Abelmoschus : 1 Perennial :3031 Spring and Summer : 447 Dark Green : 82 Min. :3.000 Min. : 5.100 Min. : 4.00
Abelmoschus esculentus : 1 Annual : 682 Spring : 144 Gray-Green : 25 1st Qu.:4.500 1st Qu.: 7.000 1st Qu.:16.75
Abies : 1 Annual, Perennial: 179 Spring, Summer, Fall: 95 Green : 692 Median :5.000 Median : 7.300 Median :28.00
Abies balsamea : 1 Annual, Biennial : 95 Summer : 92 Red : 4 Mean :4.997 Mean : 7.344 Mean :25.57
Abies balsamea var. balsamea: 1 Biennial : 57 Summer and Fall : 24 White-Gray : 9 3rd Qu.:5.500 3rd Qu.: 7.800 3rd Qu.:32.00
Abutilon : 1 (Other) : 92 (Other) : 30 Yellow-Green: 20 Max. :7.000 Max. :10.000 Max. :60.00
(Other) :5160 NA's :1030 NA's :4334 NA's :4334 NA's :4327 NA's :4327 NA's :4338
Precip_Max Shade_Tolerance Temp_Min_F
Min. : 16.00 Intermediate: 242 Min. :-79.00
1st Qu.: 55.00 Intolerant : 349 1st Qu.:-38.00
Median : 60.00 Tolerant : 246 Median :-33.00
Mean : 58.73 NA's :4329 Mean :-22.53
3rd Qu.: 60.00 3rd Qu.:-18.00
Max. :200.00 Max. : 52.00
NA's :4338 NA's :4328
| Your dedication is inspiring!
|
|=========================================================================================================== | 68%
| summary() 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.
...
|
|================================================================================================================= | 72%
| 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.
...
|
|======================================================================================================================= | 76%
| 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 Spring, Summer, Fall Summer
15 144 10 447 95 92
Summer and Fall Year Round
24 5
| Perseverance, that's the answer.
|
|============================================================================================================================== | 80%
| Each 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....
...
|
|==================================================================================================================================== | 84%
| Perhaps the most useful and concise function for understanding the *str*ucture of your data is str(). Give it a try now.
> str(plants)
'data.frame': 5166 obs. of 10 variables:
$ Scientific_Name : Factor w/ 5166 levels "Abelmoschus",..: 1 2 3 4 5 6 7 8 9 10 ...
$ Duration : Factor w/ 8 levels "Annual","Annual, Biennial",..: NA 4 NA 7 7 NA 1 NA 7 7 ...
$ Active_Growth_Period: Factor w/ 8 levels "Fall, Winter and Spring",..: NA NA NA 4 NA NA NA NA 4 NA ...
$ Foliage_Color : Factor w/ 6 levels "Dark Green","Gray-Green",..: NA NA NA 3 NA NA NA NA 3 NA ...
$ 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 : Factor w/ 3 levels "Intermediate",..: NA NA NA 3 NA NA NA NA 2 NA ...
$ Temp_Min_F : int NA NA NA -43 NA NA NA NA -13 NA ...
| That's a job well done!
|
|========================================================================================================================================== | 88%
| 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.
...
|
|================================================================================================================================================ | 92%
| 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.
...
|
|======================================================================================================================================================= | 96%
| 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.
...
|
|=============================================================================================================================================================| 100%
| Would you like to receive credit for completing this course on Coursera.org?
1: No
2: Yes
Selection: 2
What is your email address? siby.thomas@capgemini.com
What is your assignment token?
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