A good understanding of data is one of the key essentials to designing effective Machine Learning (ML) algorithms. Realizing the structure and properties of the data that you are working with is crucial in devising new methods that can solve your problem. Visualizing data includes the following:
R, one of the few widely-used programming languages for ML, has many data-visualization libraries.
In this article, we will explore two of the most commonly used packages in R for analyzing data?—dplyr and tidyr.
Using dplyr and tidyr
dplyr and tidyr, which were created by Hadley Wickham, are maintained by Hadley Wickham and the RStudio team. An impressive advantage with these libraries is the pipeline operator “%>%” that can be used with any of the functions in these libraries. It acts as a pipeline by supplying the output of the left argument as an input to the right-side function thus giving us the flexibility to apply any no. of functions in sequence.
tidyr
The operations in the tidyr library include the following:
gather()
This operation is used to make messy data more structured by gathering all the information into key-value pairs. It takes in a data frame and duplicates the columns as required and then organizes the data into key-value pairs. This is an important task when information about the same attributes is spread across multiple columns in a data frame.
Usage:
gather(data, key, value, ..., na.rm = FALSE, convert = FALSE, factor_key = FALSE)
OR
data %>% gather( key, value, ..., na.rm = FALSE, convert = FALSE, factor_key = FALSE)
key, value | names of the columns to be created in output |
‘…’ | indicates the specification regarding the columns to be included while gathering information. Either single variable names can be used to specify the column names or a list of column names using “:” operator can also be mentioned. To exclude a column from a list, “-“ operator has to be used before the name of that particular column |
na.rm | takes a Boolean value and is an indicator whether to discard the rows with missing values in output. If set TRUE, the rows with missing values are discarded |
convert | takes a Boolean value and is used to activate type conversion on key value column |
factor_key | when set to FALSE treats the key values as character vector. Else they are perceived as factors and the original ordering of the columns are retained |
> gather(stocks, stock, price, -time) # A tibble: 30 x 3 time stock price <date> <chr> <dbl> 1 2009-01-01 X 0.1223951 2 2009-01-02 X -1.6043815 3 2009-01-03 X 1.3915413 4 2009-01-04 X -0.3061332 5 2009-01-05 X -0.9642182 6 2009-01-06 X 0.9444028 7 2009-01-07 X 0.7419676 8 2009-01-08 X -1.0001474 9 2009-01-09 X 2.1008638 10 2009-01-10 X 0.5348490 # ... with 20 more rows
spread()
This operation does the opposite of gather(). It breaks the key-value pair structure and spreads them across multiple columns.
Usage
spread(data, key, value, fill = NA, convert = FALSE, drop = TRUE, sep = NULL)
key, value | meanings remain same |
fill | the value with which both explicit and implicit missing values will be filled in the final output |
drop | takes a Boolean value and when set to FALSE, will retain the factors with missing values by filling them with “fill” value |
sep | when set to NULL has no special effect on the names of the columns but when it is not NULL, the column values are given by “key_name”+ “sep” + “key_value” |
> gather(stocks, stock, price, X:Z) %>% spread(stock, price) # A tibble: 10 x 4 time X Y Z * <date> <dbl> <dbl> <dbl> 1 2009-01-01 0.1223951 -0.3321443 -2.4538854 2 2009-01-02 -1.6043815 -0.6105896 7.8490067 3 2009-01-03 1.3915413 1.0549784 -3.9146167 4 2009-01-04 -0.3061332 1.8282398 8.6643264 5 2009-01-05 -0.9642182 2.6306931 5.1099932 6 2009-01-06 0.9444028 -0.8407392 1.2753129 7 2009-01-07 0.7419676 -1.4761008 5.1857155 8 2009-01-08 -1.0001474 1.2178356 -1.9425926 9 2009-01-09 2.1008638 1.1833792 -0.2438280 10 2009-01-10 0.5348490 0.6174628 -0.1822071
separate()
This operation is used when a single column contains multiple information. It breaks the column information and builds two independent columns for easier access of data.
unite()
This operation performs the negation of separate().
> unite(stocks,time_X, time, X, sep = " // ") %>% separate(time_X, c("time", "X"), sep = " // ") # A tibble: 10 x 4 time X Y Z * <chr> <chr> <dbl> <dbl> 1 2009-01-01 0.122395115962805 -0.3321443 -2.4538854 2 2009-01-02 -1.60438146101809 -0.6105896 7.8490067 3 2009-01-03 1.39154128073248 1.0549784 -3.9146167 4 2009-01-04 -0.306133216903522 1.8282398 8.6643264 5 2009-01-05 -0.964218188445317 2.6306931 5.1099932 6 2009-01-06 0.944402751900942 -0.8407392 1.2753129 7 2009-01-07 0.741967646727019 -1.4761008 5.1857155 8 2009-01-08 -1.00014741958864 1.2178356 -1.9425926 9 2009-01-09 2.10086382498772 1.1833792 -0.2438280 10 2009-01-10 0.53484896281827 0.6174628 -0.1822071
dplyr
The operations and internal methods (that act as facilitators) of the dplyr library include the following:
1. select()
This method is used to select a specific subset of the variables that are present in the library. A smaller set of original data can be separated by using the names of the columns :
> select(stocks,X,time) # A tibble: 10 x 2 X time <dbl> <date> 1 0.1223951 2009-01-01 2 -1.6043815 2009-01-02 3 1.3915413 2009-01-03 4 -0.3061332 2009-01-04 5 -0.9642182 2009-01-05 6 0.9444028 2009-01-06 7 0.7419676 2009-01-07 8 -1.0001474 2009-01-08 9 2.1008638 2009-01-09 10 0.5348490 2009-01-10 > > > select(stocks,X,contains('i')) # A tibble: 10 x 2 X time <dbl> <date> 1 0.1223951 2009-01-01 2 -1.6043815 2009-01-02 3 1.3915413 2009-01-03 4 -0.3061332 2009-01-04 5 -0.9642182 2009-01-05 6 0.9444028 2009-01-06 7 0.7419676 2009-01-07 8 -1.0001474 2009-01-08 9 2.1008638 2009-01-09 10 0.5348490 2009-01-10 > > > > select(stocks,-Z) # A tibble: 10 x 3 time X Y <date> <dbl> <dbl> 1 2009-01-01 0.1223951 -0.3321443 2 2009-01-02 -1.6043815 -0.6105896 3 2009-01-03 1.3915413 1.0549784 4 2009-01-04 -0.3061332 1.8282398 5 2009-01-05 -0.9642182 2.6306931 6 2009-01-06 0.9444028 -0.8407392 7 2009-01-07 0.7419676 -1.4761008 8 2009-01-08 -1.0001474 1.2178356 9 2009-01-09 2.1008638 1.1833792 10 2009-01-10 0.5348490 0.6174628
2. Extracting data by using observations through the following:
> > sample_frac(stocks,0.5,replace = TRUE) # A tibble: 5 x 4 time X Y Z <date> <dbl> <dbl> <dbl> 1 2009-01-06 0.9444028 -0.8407392 1.275313 2 2009-01-06 0.9444028 -0.8407392 1.275313 3 2009-01-01 0.1223951 -0.3321443 -2.453885 4 2009-01-08 -1.0001474 1.2178356 -1.942593 5 2009-01-01 0.1223951 -0.3321443 -2.453885 > > sample_frac(stocks,0.5,replace = FALSE) # A tibble: 5 x 4 time X Y Z <date> <dbl> <dbl> <dbl> 1 2009-01-04 -0.3061332 1.8282398 8.664326 2 2009-01-09 2.1008638 1.1833792 -0.243828 3 2009-01-06 0.9444028 -0.8407392 1.275313 4 2009-01-02 -1.6043815 -0.6105896 7.849007 5 2009-01-03 1.3915413 1.0549784 -3.914617 > > sample_frac(stocks,0.5,replace = TRUE) %>% distinct() # A tibble: 3 x 4 time X Y Z <date> <dbl> <dbl> <dbl> 1 2009-01-10 0.5348490 0.6174628 -0.1822071 2 2009-01-07 0.7419676 -1.4761008 5.1857155 3 2009-01-04 -0.3061332 1.8282398 8.6643264 > sample_frac(stocks,0.5,replace = TRUE) %>% slice(1:3) %>% distinct() # A tibble: 3 x 4 time X Y Z <date> <dbl> <dbl> <dbl> 1 2009-01-10 0.5348490 0.6174628 -0.1822071 2 2009-01-06 0.9444028 -0.8407392 1.2753129 3 2009-01-01 0.1223951 -0.3321443 -2.4538854
3. group_by(df, variable): Groups the data based on the given variable. This is a silent function and does not show any visual effect on the data, however, it is effective when applied in the pipeline along with other functions.
4. summarise(): This operation is used to collect the summary of the data frame.
Usage:
summarise(df, col_name = sum_func(column_set))
> gather(stocks, stock, price, -time) %>% group_by(time) Source: local data frame [30 x 3] Groups: time [10] time stock price <date> <chr> <dbl> 1 2009-01-01 X 0.1223951 2 2009-01-02 X -1.6043815 3 2009-01-03 X 1.3915413 4 2009-01-04 X -0.3061332 5 2009-01-05 X -0.9642182 6 2009-01-06 X 0.9444028 7 2009-01-07 X 0.7419676 8 2009-01-08 X -1.0001474 9 2009-01-09 X 2.1008638 10 2009-01-10 X 0.5348490 # ... with 20 more rows > gather(stocks, stock, price, -time) %>% group_by(time) %>% summarise(avg = mean(price)) # A tibble: 10 x 2 time avg <date> <dbl> 1 2009-01-01 -0.8878782 2 2009-01-02 1.8780119 3 2009-01-03 -0.4893657 4 2009-01-04 3.3954776 5 2009-01-05 2.2588227 6 2009-01-06 0.4596588 7 2009-01-07 1.4838608 8 2009-01-08 -0.5749682 9 2009-01-09 1.0134717 10 2009-01-10 0.3233682
5. mutate() is used to compute a new value and add it as an additional variable or to simply add a new variable to the data frame.
Usage:
mutate(new_col = col1/col2).
There are various window functions present in dplyr and in R that can used along with mutate to compute a new value for each vector in data frame.
> gather(stocks, stock, price, -time) %>% group_by(time) %>% summarise(avg = mean(price)) # A tibble: 10 x 2 time avg <date> <dbl> 1 2009-01-01 -0.8878782 2 2009-01-02 1.8780119 3 2009-01-03 -0.4893657 4 2009-01-04 3.3954776 5 2009-01-05 2.2588227 6 2009-01-06 0.4596588 7 2009-01-07 1.4838608 8 2009-01-08 -0.5749682 9 2009-01-09 1.0134717 10 2009-01-10 0.3233682 > > > mutate(stocks, avg = (X + Y+ Z)/3) # A tibble: 10 x 5 time X Y Z avg <date> <dbl> <dbl> <dbl> <dbl> 1 2009-01-01 0.1223951 -0.3321443 -2.4538854 -0.8878782 2 2009-01-02 -1.6043815 -0.6105896 7.8490067 1.8780119 3 2009-01-03 1.3915413 1.0549784 -3.9146167 -0.4893657 4 2009-01-04 -0.3061332 1.8282398 8.6643264 3.3954776 5 2009-01-05 -0.9642182 2.6306931 5.1099932 2.2588227 6 2009-01-06 0.9444028 -0.8407392 1.2753129 0.4596588 7 2009-01-07 0.7419676 -1.4761008 5.1857155 1.4838608 8 2009-01-08 -1.0001474 1.2178356 -1.9425926 -0.5749682 9 2009-01-09 2.1008638 1.1833792 -0.2438280 1.0134717 10 2009-01-10 0.5348490 0.6174628 -0.1822071 0.3233682 > >
6. dplyr provides methods to perform various types of join functions too.
Usage:
left_join(tab_1, tab_2, by = “col_1”)
Function list
The 4 join operations, which are mentioned above, are known as mutating joins (that add some data to the existing ones). semi_join(), anti_join() are known as filter joins as they filter out the data based on the given condition and refine it out. There are other merging functions available like intersect(), union(), setdiff(), bind_rows(), bind_cols() and combine().
> semi_join(sample_frac(select(stocks, time, X), 0.5, replace = TRUE), sample_frac(select(stocks, time, Y), 0.5, replace = TRUE), by = "time") # A tibble: 2 x 2 time X <date> <dbl> 1 2009-01-02 -1.604381 2 2009-01-02 -1.604381 > anti_join(sample_frac(select(stocks, time, X), 0.5, replace = TRUE), sample_frac(select(stocks, time, Y), 0.5, replace = TRUE), by = "time") # A tibble: 3 x 2 time X <date> <dbl> 1 2009-01-02 -1.6043815 2 2009-01-07 0.7419676 3 2009-01-07 0.7419676 > setdiff(sample_frac(select(stocks, time, X), 0.5, replace = TRUE), sample_frac(select(stocks, time, X), 0.5, replace = TRUE)) # A tibble: 3 x 2 time X <date> <dbl> 1 2009-01-05 -0.9642182 2 2009-01-04 -0.3061332 3 2009-01-06 0.9444028
7. Another important feature of dplyr is the arrange() function that lets us sort the data in ascending order based on a specific column value. If required, the data can be sorted in descending order using desc() function.
Using functions in ggplot2() will help us visualize the data in a more efficient way through plotting them.
The examples below explain how raw data can be processed by using the functions mentioned above and then be plotted to visualize the average change in value of stock for every month of the year.
Example
For bar graph visualization with time as color coding parameter
ggplot(data=gather(stocks, stock, price, -time) %>% group_by(time) %>% summarise(avg = mean(price)), aes(x=time, y=avg, fill=time)) + geom_bar(stat="identity")
For scatter plot of same data
qplot(time,avg, data = gather(stocks, stock, price, -time) %>% group_by(time) %>% summarise(avg = mean(price)), colour = 'red', main = "Avg change of stock price for each month", xlab = "month", ylab = "avg price")
Regression models are designed to understand patterns that cannot be observed easily in the data. R equips you with libraries similar to those that we discussed in this article. If you think deeper, they are not only data-visualization tools, but also tools to improve your aptitude towards understanding granular aspects of data.
In the next article, we’ll explore more data-visualization libraries.
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