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Welcome to the second part of this two-part series on data manipulation in R. This article aims to present the reader with different ways of data aggregation and sorting. Here is the composition of this article.

1. Aggregation in R
1. Why aggregate data?
3. Aggregation using base R
1. Aggregation using the aggregate function
2. Assigning a name to an aggregated column in aggregate function in R
3. Aggregating multiple columns using an aggregate function in R
4. Aggregating multiple columns data by multiple functions using the aggregate function in R
5. Aggregation using by function
6. Aggregation using sweep function
4. Aggregation using dplyr
1. Aggregation using group_by and summarize functions
2. Aggregation by group_by and summarize functions with multiple variables and functions
3. Aggregation using group_by and summarize functions with a range of variables
4. Aggregation using group_by and summarize_if function
5. Aggregation using group_by and summarize_at function
6. Aggregation using group_by and summarize_all function
5. Aggregation using data.table
1. Converting data frame to data table
2. Aggregating single variable using data table
3. Assigning a custom name to aggregated variable in data table
4. Aggregating multiple variables using multiple aggregation functions in data table
5. Aggregating variables as well as filtering observations in data table
6. Counting the number of observations within a group in data table
6. Aggregation using sqldf
2. Sorting in R
1. Why sort data?
2. Sorting using base R
1. Sorting data using order function
2. Sorting data in descending order using order function
3. Sorting using dplyr
1. Sorting data using arrange function
2. Sorting data in descending order using arrange function
4. Sorting using data.table
1. Sorting data using order function
2. Sorting data in descending order using order function
3. Sorting data using setorder function
4. Sorting data in descending order using setorder function
5. Sorting using sqldf

## Aggregation in R

To begin with, let’s look at the term aggregation. As per the Cambridge dictionary, “the process of combining things or amounts into a single group or total” is aggregation.

### Why aggregate data?

Usually, the data has two types, qualitative and quantitative. These two are statistical terms. Qualitative data defines the characteristics of the data. Labels, properties, attributes, and categories are all examples of qualitative data. As the name suggests, data that express the quality is qualitative data. On the other hand, quantitative data represent numbers. In other disciplines like data warehousing or business intelligence, qualitative data is equivalent to dimensions and quantitative data to measures.

Data analysis is a complex process and involves several steps. Some of these steps may include data to be examined by its quality. Usually, the qualitative data’s granularity is higher. Granularity is the level of detail.

For example, a dataset that contains all countries of the world may have multiple variables describing qualitative data. The name of the country would be at the most granular level, as all countries’ names would be unique and, no two countries will have the same name. Whereas, granularity level would rise as we look at the countries by continents and then by the hemisphere.

Similarly, in an analysis, data is examined on various levels by its different qualities. At this point, aggregation comes into the picture. It is required if you want to explore quantitative data elements by its quality that sits higher than the most granular level.

In this article, we will practice different aggregation functions and options offered by base R and other packages like dplyr and data.table. Note that this article does not aim to list all the functions that (if used in a way) can aggregate data, whereas the aim here is to explain the concept of data aggregation. R is a rich language that offers different ways of doing the same thing.

We will use the data that we have prepared in our previous post How to prepare data for analysis in R. Click on this link to download the original data. This data is available under the PDDL license. However, to save space and make it cleaner, we will remove a few columns from the data frame. It’s an opportunity for us to revise how to remove a column from the data frame. Let’s first create the data frame and understand the data.

financials <- read.csv("constituents-financials_csv.csv")
str(financials)
## 'data.frame':    505 obs. of  14 variables:
##  $Symbol : chr "MMM" "AOS" "ABT" "ABBV" ... ##$ Name          : chr  "3M Company" "A.O. Smith Corp" "Abbott Laboratories" "AbbVie Inc." ...
##  $Sector : chr "Industrials" "Industrials" "Health Care" "Health Care" ... ##$ Price         : num  222.9 60.2 56.3 108.5 150.5 ...
##  $Price.Earnings: num 24.3 27.8 22.5 19.4 25.5 ... ##$ Dividend.Yield: num  2.33 1.15 1.91 2.5 1.71 ...
##  $Earnings.Share: num 7.92 1.7 0.26 3.29 5.44 1.28 7.43 3.39 6.19 0.03 ... ##$ X52.Week.Low  : num  259.8 68.4 64.6 125.9 162.6 ...
##  $X52.Week.High : num 175.5 48.9 42.3 60 114.8 ... ##$ Market.Cap    : num  1.39e+11 1.08e+10 1.02e+11 1.81e+11 9.88e+10 ...
##  $EBITDA : num 9.05e+09 6.01e+08 5.74e+09 1.03e+10 5.64e+09 ... ##$ Price.Sales   : num  4.39 3.58 3.74 6.29 2.6 ...
##  $Price.Book : num 11.34 6.35 3.19 26.14 10.62 ... ##$ SEC.Filings   : chr  "http://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=MMM" "http://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=AOS" "http://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=ABT" "http://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=ABBV" ...
Now we have our data frame ready, let's reshape our data frame by selecting a few columns to keep the results clean and tidy.

financials <- financials %>% select(Symbol, Sector, Price, X52.Week.Low, X52.Week.High)

##    Symbol                 Sector  Price X52.Week.Low X52.Week.High
## 1     MMM            Industrials 222.89      259.770       175.490
## 2     AOS            Industrials  60.24       68.390        48.925
## 3     ABT            Health Care  56.27       64.600        42.280
## 4    ABBV            Health Care 108.48      125.860        60.050
## 5     ACN Information Technology 150.51      162.600       114.820
## 6    ATVI Information Technology  65.83       74.945        38.930
## 7     AYI            Industrials 145.41      225.360       142.000
## 8    ADBE Information Technology 185.16      204.450       114.451
## 9     AAP Consumer Discretionary 109.63      169.550        78.810
## 10    AMD Information Technology  11.22       15.650         9.700
Our final data now has five variables and 505 observations.

### Aggregation using base R

#### Aggregation using the aggregate function

When talking about aggregation, the aggregate function is the most obvious choice. We can use the aggregate function with data frames and time series. We can be specific with the signature if we are using the data frame by writing aggregate.data.frame. Both aggregate and aggregate.data.frame will result in the same data.

Aggregate is a generic function that can be used for both data frames and time series.

We will look at this function by different scenarios. Before the use of any aggregation, the use case must exist. It means why you want to aggregate data. This information is crucial as this will guide you to extract correctly summarized data that will answer your question.

So the scenario here is to "find out the total price of all shares by all sectors". If you recall from the section above on qualitative and quantitative data, the variables sector and price represents qualitative and quantitative data, respectively. This information is crucial to put the variables into their correct place within the function.

aggregate(financials$Price, by = list(financials$Sector), FUN = sum)
##                       Group.1        x
## 1      Consumer Discretionary 10418.90
## 2            Consumer Staples  2711.98
## 3                      Energy  1852.40
## 4                  Financials  6055.81
## 5                 Health Care  8083.46
## 6                 Industrials  7831.47
## 7      Information Technology  8347.00
## 8                   Materials  2559.67
## 9                 Real Estate  2927.52
## 10 Telecommunication Services   100.81
## 11                  Utilities  1545.45

#### Assigning a name to an aggregated column in aggregate function in R

The result above shows that the function aggregate split the original data into small subsets by sector variable and applied sum function over the price variable. The result above shows x as the name of the summarised column. If you want to rename it, a little tweak in the code using setName function will do the trick. Here is an example.

setNames(aggregate(financials$Price, by = list(financials$Sector), FUN = sum),c("Sector","Total.Price"))
##                        Sector Total.Price
## 1      Consumer Discretionary    10418.90
## 2            Consumer Staples     2711.98
## 3                      Energy     1852.40
## 4                  Financials     6055.81
## 5                 Health Care     8083.46
## 6                 Industrials     7831.47
## 7      Information Technology     8347.00
## 8                   Materials     2559.67
## 9                 Real Estate     2927.52
## 10 Telecommunication Services      100.81
## 11                  Utilities     1545.45

#### Aggregating multiple columns using the aggregate function in R

Now to apply the same function on multiple variables, all you have to do is to supply an expression to subset the required columns from the data frame as an argument. Here is an example, we have used setName function on top to assign meaningful names to the variables.
The scenario here is to "find out the average price, average 52-weeks low price, and the average 52-week high price of all shares by all sectors".
setNames(aggregate(financials[,c("Price","X52.Week.Low","X52.Week.High")], by = list(financials$Sector), FUN = mean), c("Sector","Average.Price","Average.Low","Average.High")) ## Sector Average.Price Average.Low Average.High ## 1 Consumer Discretionary 124.03452 146.93143 96.09236 ## 2 Consumer Staples 79.76412 92.83229 68.92944 ## 3 Energy 57.88750 72.58969 48.14123 ## 4 Financials 89.05603 101.82185 72.69447 ## 5 Health Care 132.51574 160.75853 103.71925 ## 6 Industrials 116.88761 134.57948 90.83702 ## 7 Information Technology 119.24286 138.77864 91.89142 ## 8 Materials 102.38680 118.03885 85.58325 ## 9 Real Estate 88.71273 110.55045 82.87809 ## 10 Telecommunication Services 33.60333 41.69333 29.50367 ## 11 Utilities 55.19464 68.49732 52.80232 #### Aggregating multiple columns data by multiple functions using the aggregate function in R Moving on to the next level of the scenario, we would like to apply multiple functions over the different variables. It is possible by defining a custom function. The requirement now is to "find out the minimum and maximum values of price, 52-weeks low price of all shares by all sectors". Here is an example. aggregate(financials[c("Price","X52.Week.Low")], by = list(financials$Sector), FUN = function(x) c(min(x),max(x)))
##                       Group.1 Price.1 Price.2 X52.Week.Low.1 X52.Week.Low.2
## 1      Consumer Discretionary   10.43 1806.06         13.480       2067.990
## 2            Consumer Staples   19.96  208.73         21.175        229.500
## 3                      Energy    2.82  169.16          6.590        199.830
## 4                  Financials   13.38  509.38         16.530        594.520
## 5                 Health Care   25.20  601.00         29.930        697.260
## 6                 Industrials   14.45  334.30         30.590        361.790
## 7      Information Technology   11.22 1007.71         15.650       1198.000
## 8                   Materials   17.16  387.65         20.250        435.150
## 9                 Real Estate   14.01  409.98         21.530        495.345
## 10 Telecommunication Services   16.20   49.04         27.610         54.770
## 11                  Utilities   10.06  145.29         12.050        159.640
The above command can be written in different ways. Here are a couple of examples. Note the use of cbind and formula (~).
aggregate(cbind(financials$Price,financials$X52.Week.Low) ~financials$Sector, FUN = function(x) c(min(x),max(x))) ## financials$Sector    V1.1    V1.2     V2.1     V2.2
## 1      Consumer Discretionary   10.43 1806.06   13.480 2067.990
## 2            Consumer Staples   19.96  208.73   21.175  229.500
## 3                      Energy    2.82  169.16    6.590  199.830
## 4                  Financials   13.38  509.38   16.530  594.520
## 5                 Health Care   25.20  601.00   29.930  697.260
## 6                 Industrials   14.45  334.30   30.590  361.790
## 7      Information Technology   11.22 1007.71   15.650 1198.000
## 8                   Materials   17.16  387.65   20.250  435.150
## 9                 Real Estate   14.01  409.98   21.530  495.345
## 10 Telecommunication Services   16.20   49.04   27.610   54.770
## 11                  Utilities   10.06  145.29   12.050  159.640
aggregate(cbind(Price,X52.Week.Low) ~ Sector, data = financials, FUN = function(x) c(min(x),max(x)))
##                        Sector Price.1 Price.2 X52.Week.Low.1 X52.Week.Low.2
## 1      Consumer Discretionary   10.43 1806.06         13.480       2067.990
## 2            Consumer Staples   19.96  208.73         21.175        229.500
## 3                      Energy    2.82  169.16          6.590        199.830
## 4                  Financials   13.38  509.38         16.530        594.520
## 5                 Health Care   25.20  601.00         29.930        697.260
## 6                 Industrials   14.45  334.30         30.590        361.790
## 7      Information Technology   11.22 1007.71         15.650       1198.000
## 8                   Materials   17.16  387.65         20.250        435.150
## 9                 Real Estate   14.01  409.98         21.530        495.345
## 10 Telecommunication Services   16.20   49.04         27.610         54.770
## 11                  Utilities   10.06  145.29         12.050        159.640
With the aggregate function, possibilities are endless. We can do a lot using the aggregate function. What we have seen in the sections above is just a part. Here are some other arguments which you can add to the aggregate function based on your requirements.
 Argument Use na.action Use this to specify if your data contain NA or missing values and how you want to handle them simplify If used results will be presented in vector or matrix form formula Depending on subsetting requirement formulas like y ~ x or cbind(y1,y2) ~ x1 + x2 can be used data The data frame in question which has the variables to subset and aggregate drop Use if you want to drop unused combination of grouping values subset Use it to limit the operation to certain observations

Lastly, to name a few, you can use sum, count, first, last, mean, median, min, max, and sd functions with the aggregate function. If you think this list should have more function names, then please add a comment in the comment box below.

#### Aggregation using by function

The by function is different from aggregate as it is a wrapper for tapply function, this means it also encapsulates the functionality of tapply function. The return value from "by" function depends on the usage of simplify argument. If the value of simplify argument is TRUE then it returns a list or array otherwise it always returns a list. Here is an example of by function. In this example, we are trying to find the sum of price variable by sector.

All other arguments in this function are self-explanatory except INDICES that represent factors or a list of factors.

by(data=financials$Price, INDICES = financials$Sector, FUN = sum, simplify = TRUE)
## financials$Sector: Consumer Discretionary ## [1] 10418.9 ## ------------------------------------------------------------ ## financials$Sector: Consumer Staples
## [1] 2711.98
## ------------------------------------------------------------
## financials$Sector: Energy ## [1] 1852.4 ## ------------------------------------------------------------ ## financials$Sector: Financials
## [1] 6055.81
## ------------------------------------------------------------
## financials$Sector: Health Care ## [1] 8083.46 ## ------------------------------------------------------------ ## financials$Sector: Industrials
## [1] 7831.47
## ------------------------------------------------------------
## financials$Sector: Information Technology ## [1] 8347 ## ------------------------------------------------------------ ## financials$Sector: Materials
## [1] 2559.67
## ------------------------------------------------------------
## financials$Sector: Real Estate ## [1] 2927.52 ## ------------------------------------------------------------ ## financials$Sector: Telecommunication Services
## [1] 100.81
## ------------------------------------------------------------
## financials\$Sector: Utilities
## [1] 1545.45

#### Aggregation using sweep function

There is one more function I would like to mention here that you can use to aggregate data. Function sweep returns an array by sweeping out summary statistics. This specific function may not fit into all kinds of aggregation requirements but worth mentioning if you want to do some operation while summarising data. This function wouldn't be my first choice though it may fit into yours. Here is an example.

sweep(financials["Price"], MARGIN = 1, STATS = 0,FUN = sum)
## [1] 52434.47
The function above is returning the sum of variable Price. Over the years, I didn't use the sweep function for my work. If you know the aggregation scenario where this function would be most useful, then please comment in the comment box below. All other arguments are obvious except STATS, my understanding of this argument is that this is the value that you would like to apply to the variable in conjunction with the function argument. So, I have set STATS to 0 and used sum as the function, which results in the total Price variable. If I set STATS to 1, then all the values of variable Price would be increased with one, and the total will be different.

Though sweep function doesn't result in grouped or subsetted data as others, I thought this function is worth mentioning. Similarly, other functions from the apply family are available to aggregate data, but you may encounter limitations on grouping or performance requirements. If you want, you can achieve aggregation results using these functions, but there are better and faster options available, and from this point onwards, we will concentrate on those.

### Aggregation using dplyr

#### Aggregation using group_by and summarize functions

The three functions we have seen above may fit into different aggregation scenarios, but in terms of ease of use, I consider the group_by and summarize functions of the dplyr package. The performance of functions is subjective and dependent on the size and operation of the data. I personally never evaluated the performance though wherever I have read, group_by is praised for its performance. Let's look at an example of aggregating data using the group by function.

The scenario here is to "find out the total price of all shares by all sectors".

financials %>% group_by(Sector) %>% summarize(total.price = sum(Price))
## summarise() ungrouping output (override with .groups argument)
## # A tibble: 11 x 2
##    Sector                     total.price
##
##  1 Consumer Discretionary          10419.
##  2 Consumer Staples                 2712.
##  3 Energy                           1852.
##  4 Financials                       6056.
##  5 Health Care                      8083.
##  6 Industrials                      7831.
##  7 Information Technology           8347
##  8 Materials                        2560.
##  9 Real Estate                      2928.
## 10 Telecommunication Services        101.
## 11 Utilities                        1545.
Simply supplying the variable name within the group_by function is enough to set the grouping of observations by the value of the selected variable. The summarize function is the engine where the data is operated upon using the function of your choice. In the example above, I have chosen the sum function which is applied over grouped observation. This strategy or method of aggregation is known as split-apply-combine. First, we are splitting the observations into groups followed by applying a function and then combining results to present.

If you were observant, then you may have noticed a warning as well. This warning is a hint to remind you how you want to control the grouping of data. You can suppress this warning by using options(dplyr.summarise.inform = FALSE). For more information, please click this link.

Also, did you notice how the results are printed above?

Nothing wrong with that, it is because the return value is in the form of tibble. You can print.data.frame() function to print is in a nice format. Here is an example.

financials %>% group_by(Sector) %>% summarize(total.price = sum(Price)) %>% print.data.frame()
## summarise() ungrouping output (override with .groups argument)
##                        Sector total.price
## 1      Consumer Discretionary    10418.90
## 2            Consumer Staples     2711.98
## 3                      Energy     1852.40
## 4                  Financials     6055.81
## 5                 Health Care     8083.46
## 6                 Industrials     7831.47
## 7      Information Technology     8347.00
## 8                   Materials     2559.67
## 9                 Real Estate     2927.52
## 10 Telecommunication Services      100.81
## 11                  Utilities     1545.45

Lastly, did you notice that the summarised variable is now named total.price? By simply putting the new name in front of the aggregation function will do the trick. It is not mandatory, but I am sure you wouldn't like the default name.

#### Aggregation by group_by and summarize functions with multiple variables and functions

Let's progress ahead with our scenario and find out the total price, minimum 52-week low and maximum 52-week high price of all shares and by all sectors. The aim here is to show the use of multiple variables with multiple aggregation functions.

financials %>% group_by(Sector) %>% summarize(total.price = sum(Price), min.52.week.low = min(X52.Week.Low), max.52.week.high = max(X52.Week.High)) %>% print.data.frame()
## summarise() ungrouping output (override with .groups argument)
##                        Sector total.price min.52.week.low max.52.week.high
## 1      Consumer Discretionary    10418.90          13.480        1589.0000
## 2            Consumer Staples     2711.98          21.175         152.0100
## 3                      Energy     1852.40           6.590         125.4600
## 4                  Financials     6055.81          16.530         368.0000
## 5                 Health Care     8083.46          29.930         459.3400
## 6                 Industrials     7831.47          30.590         256.4000
## 7      Information Technology     8347.00          15.650         824.3000
## 8                   Materials     2559.67          20.250         302.0101
## 9                 Real Estate     2927.52          21.530         361.9000
## 10 Telecommunication Services      100.81          27.610          42.8000
## 11                  Utilities     1545.45          12.050         124.1800
Also, we can supply more than one variable in the group_by function if you want to group data by multiple variables.

#### Aggregation using group_by and summarize functions with a range of variables

You can use the summarize function in a variety of ways. If you have several variables to aggregate, then the following type of command would help.

financials %>% group_by(Sector) %>% summarize(across(Price:X52.Week.High, ~sum(.x))) %>% print.data.frame()
## summarise() ungrouping output (override with .groups argument)
##                        Sector    Price X52.Week.Low X52.Week.High
## 1      Consumer Discretionary 10418.90    12342.240      8071.759
## 2            Consumer Staples  2711.98     3156.298      2343.601
## 3                      Energy  1852.40     2322.870      1540.519
## 4                  Financials  6055.81     6923.886      4943.224
## 5                 Health Care  8083.46     9806.271      6326.874
## 6                 Industrials  7831.47     9016.825      6086.081
## 7      Information Technology  8347.00     9714.505      6432.399
## 8                   Materials  2559.67     2950.971      2139.581
## 9                 Real Estate  2927.52     3648.165      2734.977
## 10 Telecommunication Services   100.81      125.080        88.511
## 11                  Utilities  1545.45     1917.925      1478.465

#### Aggregation using group_by and summarize_if function

Or, if you want to aggregate all of the numeric variables from your data frame, then the following version of summarize function would help.

financials %>% group_by(Sector) %>% summarize_if(is.numeric,sum) %>% print.data.frame()
##                        Sector    Price X52.Week.Low X52.Week.High
## 1      Consumer Discretionary 10418.90    12342.240      8071.759
## 2            Consumer Staples  2711.98     3156.298      2343.601
## 3                      Energy  1852.40     2322.870      1540.519
## 4                  Financials  6055.81     6923.886      4943.224
## 5                 Health Care  8083.46     9806.271      6326.874
## 6                 Industrials  7831.47     9016.825      6086.081
## 7      Information Technology  8347.00     9714.505      6432.399
## 8                   Materials  2559.67     2950.971      2139.581
## 9                 Real Estate  2927.52     3648.165      2734.977
## 10 Telecommunication Services   100.81      125.080        88.511
## 11                  Utilities  1545.45     1917.925      1478.465

#### Aggregation using group_by and summarize_at function

Summarize_at, on the other hand, helps you to aggregate more than one variable in one go. The requirement is that the variables are supplied as vector or vars.
financials %>% group_by(Sector) %>% summarize_at(c("Price","X52.Week.High"),sum) %>% print.data.frame()
##                        Sector    Price X52.Week.High
## 1      Consumer Discretionary 10418.90      8071.759
## 2            Consumer Staples  2711.98      2343.601
## 3                      Energy  1852.40      1540.519
## 4                  Financials  6055.81      4943.224
## 5                 Health Care  8083.46      6326.874
## 6                 Industrials  7831.47      6086.081
## 7      Information Technology  8347.00      6432.399
## 8                   Materials  2559.67      2139.581
## 9                 Real Estate  2927.52      2734.977
## 10 Telecommunication Services   100.81        88.511
## 11                  Utilities  1545.45      1478.465

#### Aggregation using group_by and summarize_all function

Lastly, summarize_all function helps to aggregate all the values from the data frame, and we can use multiple aggregation functions as well. Note in our data frame we have two character variables, let's prepare the data for using summarize_all function and aggregate data to show total and minimum values of all variable across the data frame.
financials %>% select(-Symbol) %>% group_by(Sector) %>% summarize_all(c(sum,min))
## # A tibble: 11 x 7
##    Sector Price_fn1 X52.Week.Low_fn1 X52.Week.High_f… Price_fn2 X52.Week.Low_fn2
##
##  1 Consu…    10419.           12342.           8072.      10.4             13.5
##  2 Consu…     2712.            3156.           2344.      20.0             21.2
##  3 Energy     1852.            2323.           1541.       2.82             6.59
##  4 Finan…     6056.            6924.           4943.      13.4             16.5
##  5 Healt…     8083.            9806.           6327.      25.2             29.9
##  6 Indus…     7831.            9017.           6086.      14.4             30.6
##  7 Infor…     8347             9715.           6432.      11.2             15.6
##  8 Mater…     2560.            2951.           2140.      17.2             20.2
##  9 Real …     2928.            3648.           2735.      14.0             21.5
## 10 Telec…      101.             125.             88.5     16.2             27.6
## 11 Utili…     1545.            1918.           1478.      10.1             12.0
## # … with 1 more variable: X52.Week.High_fn2 

### Aggregation using data.table

#### Converting data frame to data table

Aggregation using the data table is a step further from dplyr in terms of simplicity. We write the aggregation code as if we are subsetting the data frame. Before we progress, let's convert our data frame into a data table.

financials <- setDT(financials)
class(financials)
## [1] "data.table" "data.frame"

#### Aggregating single variable using data table

Let's look at an example where the scenario is to find out the total price of all shares by all sectors.

financials[,sum(Price), by=Sector]
##                         Sector       V1
##  1:                Industrials  7831.47
##  2:                Health Care  8083.46
##  3:     Information Technology  8347.00
##  4:     Consumer Discretionary 10418.90
##  5:                  Utilities  1545.45
##  6:                 Financials  6055.81
##  7:                  Materials  2559.67
##  8:                Real Estate  2927.52
##  9:           Consumer Staples  2711.98
## 10:                     Energy  1852.40
## 11: Telecommunication Services   100.81

#### Assigning a custom name to an aggregated variable in data table

The result above shows the aggregated value by the selected variable. However, the aggregated variable's name is not friendly. Let's give it a proper name.

financials[,list(total.price=sum(Price)), by=Sector]
Or

financials[,.(total.price=sum(Price)), by=Sector]
##                         Sector total.price
##  1:                Industrials     7831.47
##  2:                Health Care     8083.46
##  3:     Information Technology     8347.00
##  4:     Consumer Discretionary    10418.90
##  5:                  Utilities     1545.45
##  6:                 Financials     6055.81
##  7:                  Materials     2559.67
##  8:                Real Estate     2927.52
##  9:           Consumer Staples     2711.98
## 10:                     Energy     1852.40
## 11: Telecommunication Services      100.81

#### Aggregating multiple variables using multiple aggregation functions in data table

What we have seen until now is one custom-named variable aggregated by a different variable. Let take it further and see if we can apply more than one aggregation function over more than one variable. The scenario is to find out the total price and minimum 52-week low price from all shares by all sectors.

financials[,.(total.price=sum(Price), mim.52.week.low=min(X52.Week.Low)), by=Sector]
Or

financials[,list(total.price=sum(Price), mim.52.week.low=min(X52.Week.Low)), by=Sector]
##                         Sector total.price mim.52.week.low
##  1:                Industrials     7831.47          30.590
##  2:                Health Care     8083.46          29.930
##  3:     Information Technology     8347.00          15.650
##  4:     Consumer Discretionary    10418.90          13.480
##  5:                  Utilities     1545.45          12.050
##  6:                 Financials     6055.81          16.530
##  7:                  Materials     2559.67          20.250
##  8:                Real Estate     2927.52          21.530
##  9:           Consumer Staples     2711.98          21.175
## 10:                     Energy     1852.40           6.590
## 11: Telecommunication Services      100.81          27.610

#### Aggregating variables as well as filtering observations in data table

What if we want to aggregate as well as filter the observations by supplying specific conditions. The data table offers a solution to this. Here is an example.

financials[Sector == "Industrials",list(total.price=sum(Price), mim.52.week.low=min(X52.Week.Low)), by=Sector]
##         Sector total.price mim.52.week.low
## 1: Industrials     7831.47           30.59

#### Counting the number of observations within a group in data table

Although we can use different aggregation functions, what if we want to count the observations within a group? We can do this using .N special variable.

financials[,.N, by=Sector]
##                         Sector  N
##  1:                Industrials 67
##  2:                Health Care 61
##  3:     Information Technology 70
##  4:     Consumer Discretionary 84
##  5:                  Utilities 28
##  6:                 Financials 68
##  7:                  Materials 25
##  8:                Real Estate 33
##  9:           Consumer Staples 34
## 10:                     Energy 32
## 11: Telecommunication Services  3
If the requirement is to group observations using multiple variables, then the following syntax can be used for by argument.

by=.(variable to group 1, variable to group 2)

### Aggregation using sqldf

I love sqldf, it makes things easy, but if you are not familiar with SQL then it may be a little challenging. Aggregation in sqldf require us to understand SQL, and hence I would just list one example here. SQL is an easy language to learn and I will soon be posing articles on SQL.

Let's define our scenario, find out the total price and minimum 52-week low price from all shares by all sectors where aggregated total price is greater than 5000. This scenario includes more than one variable with the use of more than one aggregation function as well as filtration criterion over grouped observations.

sqldf('select Sector, sum(Price), min("X52.Week.Low") from financials group by Sector having sum(Price) > 5000')
##                   Sector sum(Price) min("X52.Week.Low")
## 1 Consumer Discretionary   10418.90               13.48
## 2             Financials    6055.81               16.53
## 3            Health Care    8083.46               29.93
## 4            Industrials    7831.47               30.59
## 5 Information Technology    8347.00               15.65
Let's give aggregated columns a custom name.

sqldf('select Sector, sum(Price) as "total.price", min("X52.Week.Low") as "min.52.week.low" from financials group by Sector having sum(Price) > 5000')
##                   Sector total.price min.52.week.low
## 1 Consumer Discretionary    10418.90           13.48
## 2             Financials     6055.81           16.53
## 3            Health Care     8083.46           29.93
## 4            Industrials     7831.47           30.59
## 5 Information Technology     8347.00           15.65

## Sorting in R

### Why sort data?

It is a fact that the human brain looks for patterns. Identifying patterns is crucial while performing analysis, and sorted data helps a lot in finding these patterns. Sort help understanding the data, moreover, in profiling the data. Sort functionality help put data into an order which can provide information to help your analysis.

### Sorting data using base R

#### Sorting data using order function

In base R, function order help sort the data. The usage of the order function is straight forward. Here is an example.

financials[order(Symbol),c("Symbol","Price")]
##      Symbol  Price
##   1:      A  65.05
##   2:    AAL  48.60
##   3:    AAP 109.63
##   4:   AAPL 155.15
##   5:   ABBV 108.48
##  ---
## 501:    XYL  70.24
## 502:    YUM  76.30
## 503:    ZBH 115.53
## 504:   ZION  50.71
## 505:    ZTS  71.51

#### Sorting data in descending order using order function

We are subsetting variables Symbol and Price from data frame financials and sorting the output by Symbol variable's values. If we want to sort the data in descending order, then function desc comes to our rescue.

financials[order(desc(Symbol)),c("Symbol","Price")]
##      Symbol  Price
##   1:    ZTS  71.51
##   2:   ZION  50.71
##   3:    ZBH 115.53
##   4:    YUM  76.30
##   5:    XYL  70.24
##  ---
## 501:   ABBV 108.48
## 502:   AAPL 155.15
## 503:    AAP 109.63
## 504:    AAL  48.60
## 505:      A  65.05

### Sorting data using dplyr

#### Sorting data using the arrange function

dplyr package has a function arrange which help sort data. Here is an example.

financials %>% group_by(Sector) %>% summarize(sum(Price)) %>% arrange(Sector) %>% print.data.frame()
## summarise() ungrouping output (override with .groups argument)
##                        Sector sum(Price)
## 1      Consumer Discretionary   10418.90
## 2            Consumer Staples    2711.98
## 3                      Energy    1852.40
## 4                  Financials    6055.81
## 5                 Health Care    8083.46
## 6                 Industrials    7831.47
## 7      Information Technology    8347.00
## 8                   Materials    2559.67
## 9                 Real Estate    2927.52
## 10 Telecommunication Services     100.81
## 11                  Utilities    1545.45

#### Sorting data in descending order using arrange function

Let's reorder the data in descending order.

financials %>% group_by(Sector) %>% summarize(sum(Price)) %>% arrange(desc(Sector)) %>% print.data.frame()
## summarise() ungrouping output (override with .groups argument)
##                        Sector sum(Price)
## 1                   Utilities    1545.45
## 2  Telecommunication Services     100.81
## 3                 Real Estate    2927.52
## 4                   Materials    2559.67
## 5      Information Technology    8347.00
## 6                 Industrials    7831.47
## 7                 Health Care    8083.46
## 8                  Financials    6055.81
## 9                      Energy    1852.40
## 10           Consumer Staples    2711.98
## 11     Consumer Discretionary   10418.90

### Sorting data using data table

#### Sorting data using order function

Data table has two functions order and setorder to help sort the data. Here is an example but first convert the data frame to data table and then sort.

financials <- setDT(financials)
financials[order(Sector),sum(Price), by=Sector]
##                         Sector       V1
##  1:     Consumer Discretionary 10418.90
##  2:           Consumer Staples  2711.98
##  3:                     Energy  1852.40
##  4:                 Financials  6055.81
##  5:                Health Care  8083.46
##  6:                Industrials  7831.47
##  7:     Information Technology  8347.00
##  8:                  Materials  2559.67
##  9:                Real Estate  2927.52
## 10: Telecommunication Services   100.81
## 11:                  Utilities  1545.45

#### Sorting data in descending order using order function

Let's reverse the order now using desc function.

financials[order(desc(Sector)),sum(Price), by=Sector]
##                         Sector       V1
##  1:                  Utilities  1545.45
##  2: Telecommunication Services   100.81
##  3:                Real Estate  2927.52
##  4:                  Materials  2559.67
##  5:     Information Technology  8347.00
##  6:                Industrials  7831.47
##  7:                Health Care  8083.46
##  8:                 Financials  6055.81
##  9:                     Energy  1852.40
## 10:           Consumer Staples  2711.98
## 11:     Consumer Discretionary 10418.90

#### Sorting data using setorder function

Now let's look at the sorting using setorder function.

order.data <- setorder(financials[,c("Symbol","Price")],Symbol)
order.data
##      Symbol  Price
##   1:      A  65.05
##   2:    AAL  48.60
##   3:    AAP 109.63
##   4:   AAPL 155.15
##   5:   ABBV 108.48
##  ---
## 501:    XYL  70.24
## 502:    YUM  76.30
## 503:    ZBH 115.53
## 504:   ZION  50.71
## 505:    ZTS  71.51

#### Sorting data in descending order using setorder function

Same setorder function can be used to reverse the order of the data by simply putting a minus sign in the front. Here is an example.

order.data <- setorder(financials[,c("Symbol","Price")],-Symbol)
order.data
##      Symbol  Price
##   1:    ZTS  71.51
##   2:   ZION  50.71
##   3:    ZBH 115.53
##   4:    YUM  76.30
##   5:    XYL  70.24
##  ---
## 501:   ABBV 108.48
## 502:   AAPL 155.15
## 503:    AAP 109.63
## 504:    AAL  48.60
## 505:      A  65.05

### Sorting data using sqldf

Data sorting using sqldf is simply achieved using the "order by" clause in the SQL command.

financials <- read.csv("constituents-financials_csv.csv")
sqldf('select Sector, sum(Price), min("X52.Week.Low") from financials group by Sector order by Sector')
##                        Sector sum(Price) min("X52.Week.Low")
## 1      Consumer Discretionary   10418.90              13.480
## 2            Consumer Staples    2711.98              21.175
## 3                      Energy    1852.40               6.590
## 4                  Financials    6055.81              16.530
## 5                 Health Care    8083.46              29.930
## 6                 Industrials    7831.47              30.590
## 7      Information Technology    8347.00              15.650
## 8                   Materials    2559.67              20.250
## 9                 Real Estate    2927.52              21.530
## 10 Telecommunication Services     100.81              27.610
## 11                  Utilities    1545.45              12.050
Reversing the order of the data using desc clause. Here is an example.

sqldf('select Sector, sum(Price), min("X52.Week.Low") from financials group by Sector order by Sector desc')
##                        Sector sum(Price) min("X52.Week.Low")
## 1                   Utilities    1545.45              12.050
## 2  Telecommunication Services     100.81              27.610
## 3                 Real Estate    2927.52              21.530
## 4                   Materials    2559.67              20.250
## 5      Information Technology    8347.00              15.650
## 6                 Industrials    7831.47              30.590
## 7                 Health Care    8083.46              29.930
## 8                  Financials    6055.81              16.530
## 9                      Energy    1852.40               6.590
## 10           Consumer Staples    2711.98              21.175
## 11     Consumer Discretionary   10418.90              13.480

Concludingly, you may have noticed that we have not discussed NA values at all. Almost all aggregation function allows na.rm argument. Please try and comment if you find out that any particular function did not support it.

Well done, you've made to the end. Thank you for reading this article. I hope you've liked it. Please share it with your friends and if you are agreeing or disagreeing with any point above then please comment in the comment box below.