Fiore proposed the idea of the Three Ages. For Fiore, history was divided into three parts: The Age of the Father, the Age of the Son, and the Age of the Holy Spirit. The Age of the Father is associated with the Old Testament and was a time of focusing on obedience, running from creation until the birth of Christ. The Age of the Son is linked with the New Testament, which was the time from the birth of Christ until the 13th century. Lastly, the Age of the Holy Spirit began in the 13th century and is a time of universal love that would transcend the letter of the law. During this final age, man’s material body would disappear.

Splinter Groups

Fiore would inspire many apocalyptic Christian groups. The age of the Holy Spirit provided ideas for several groups that would expand on Fiore’s ideas in particular. For example, the Almaricians, an early 13th-century group, believed each of the three stages of Fiore was an incarnation. Incarnation is the belief that Christ came in the flesh. Almaricians believe they were the incarnation of the Holy Spirit. This idea implies the Almaricians were claiming to be gods and showed signs of pantheism.

Brethren of the Free Spirit

The Brethren of the Free Spirit, a group that began in the late 13th century, believed that the “Elect” would not die and would be gods on earth. Since there is no death, it implies there is no law, which makes the Brethren supporters of antinomianism (against the law). The Brethren were also supporters of taking the property of the non-elect. Seizing property is a key component of Communism.

Taborites

The Taborites emerged during the 15th century, originating from the Hussites. Their beliefs were based on the Brethren of the Free Spirit, but they believed not just in taking the property of the non-elect but in violently destroying them. This is similar to various communist purges that have taken place throughout history. The taborites did not believe in private property, believing that all things should be held in common. Strangely enough, the idea of no personal property included sexual relationships with women, which meant people were free to sleep with whoever they pleased.. Marx was a married man, but he was also critical of marriage and the family, viewing these institutions as tools that supported bourgeois society.

Adamites

The Brethren of the Free Spirit also inspired the Admites. They not only believed they were living gods but were superior to Christ. Their thought process was that since Christ died while they lived, this made them superior.

As with the Taborites, the Adamites shared all goods in common while having conflicting views on chastity. There was no marriage, and people in theory could sleep with whoever they wanted. In practice, sex was restricted because everyone had to get permission from the leader to sleep with each other.

Another unusual belief of this group is the practice of walking around naked. Adamites believe that walking around naked, as Adam and Eve did, is important to reflect the perfect love of the original couple. However, walking around naked did not discourage the belief in destroying the non-Elect. The Adamits were eventually destroyed due in part to their heretical beliefs.

Conclusion

The motivation behind each of these groups was that, by stripping people of autonomy and sacrificing individual desires for the group, it would lead to a heaven on earth. Of course, autonomy and personal desire are what fuel progress. Therefore, by removing this, you bring a form of peace without the necessary motivation to maintain the utopia. Individualism is a two-edged sword that brings the benefits of ambition with the downside of selfishness and oppression.

Furthermore, one thing Christians and Communists have in common is a desire for a better world here. The difference is in whether or not freedom will be a part of this new world.

Definition

Reabsorption theology posits that the end of humanity will culminate in its reabsorption into the essence or nature of divinity. In other words, man must return to God. Another key term is alienation. Alienation, as defined in Communism, is separation not only from one’s work and fellow man but also from god. Therefore, the ultimate purpose of Communism is to end alienation, unite man with man, and humanity with god. As such, Communism is much bigger than just the emancipation of the proletariat.

Kolakowski states that God created the world and separated it from himself because he was lonely. He continues by stating that there are three stages to existence. The first is pre-creation, when God is alone. The second is post-creation, when there is a separation between God and the rest of reality. Finally, there is a reunion when man is reabsorbed into God.

The Problem

The problem with creation is that once it was separated from God, it became evil. The logic behind this thought is that if God is good, being separate from him is bad or evil. As such, as soon as man was separated from God, man was evil. The idea of an inherently evil creation is in stark contrast to Christianity, which states explicitly that creation was good. With this assumption of an evil of creation, Communism is seen as the solution to this corruption of man, and it will be the process used to reunite man with God.

The idea that man must reunite with God is not unique to Communism and is found in such religions as Hinduism and faintly in Buddhism. The first man emerged from Brahma in Hinduism. The motivation of Brahma to create humans was due to loneliness, but there are various interpretations of this.

Buddhism skips explaining creation and focuses on the endless cycle of life, birth, and death. The purpose of this process is almost a form of purification. As an individual lives over and over, hopefully they eventually awaken (reach enlightenment) and achieve Nirvana, which is challenging to define but involves the extinction of desire and perhaps removal from this plane of existence.

Christianity does not suggest that man will be reabsorbed, but it does state that the relationship between man and God will be reestablished like a husband and wife reconciling after a severe conflict. In Christianity, the problem is not that man is separate from God but that the relationship between man and God has been strained by sin. By reestablishing this broken relationship, man is united with God in a way that a family is united. One family, but individual members have personal autonomy.

The Solution

The solution of unification, as determined by Communism, is for all of mankind and not for the individual. What this means is that people on the individual level do not have a choice in this process. True “freedom” can only happen through the submergence of self into the state and the removal of diversity. Everyone must conform, or nobody gets the reward. Therefore, the elimination of non-conformers is a necessary sacrifice for the greater good. This has led to the murder of millions in various iterations of Communism.

Individuality is the origin and source of greed and strife because people are thinking of themselves over the group. Communism will always have issues with individualism, as individualistic people are materialistic in their eyes. In other words, individuality leads to greed and strife as these behaviors contribute to alienation and separation from God. By destroying individuality, the fruit of this behavior is also destroyed, and reabsorption can transpire.

The idea that man and God need to reunite suggests that man and God are essentially equal and that neither is perfect without the other. This idea is not generally associated with mainline Christian thought, which views God as self-sufficient with a desire to save fallen man if they are willing to accept his help.

Conclusion

Reabsorption theology is an interesting idea that attempts to explain the motivations of Communism. However, it is an outsider’s perspective on the motivations of people who hold a particular ideology. Kołakowski was anti-communist, and it would be hard for his opinion to be unbiased. Despite this, his ideas concerning reabsorption provide an interesting insight into understanding Communism.

Restrictions on Property

One of the most common tenets of Communism is restrictions on property. Commonly, this has been interpreted as no private property. Several attempts at Communism have removed all private property rights, such as in the Soviet Union. Marx did dislike private property, but he truly hated individual ownership of the means of production. Anything that could produce wealth should be owned by the people, in Marx’s opinion.

Therefore, and much to many people’s surprise, Marx may not have had issues with people owning homes, computers, phones, or cars, but he would challenge a person’s right to own farmland, factories, or businesses. Consumption was fine as long as production was controlled centrally.

Loss of Individualism

Many interpretations of Communism involve the sacrifice of the individual for the collective. Individualism is often seen as a threat because, to have a communist society, everyone must go along with it. In other words, for true Communism to arise, everyone must support it so that the state withers away. Particularly for Communists who ascribe and yearn for a utopia, this heaven on earth cannot transpire until dissent is removed.

This desire for a man-made, secular heaven explains in part the tremendous amount of persecution and death that is associated with Communism. Unlike capitalism, which may abuse power to make more money, Communism will abuse power to bring about a new earth in which there is no more strife. In other words, the sacrifice of the few to save the many.

Examples of the destruction of countless lives in the pursuit of Communism can be found in the millions who died in the Soviet Union, China, and Cambodia. Dissenters and even apparent dissenters were systematically destroyed or “reeducated.” All this was done in the name of the people to bring about a better world.

Upheaval of Social Order

Communism brings about a total upheaval of the social order. Marx makes it clear that the working class, or proletariat, needs to rise and overthrow the bourgeoisie. Later, Communist thinkers such as Marcuse included minorities (whether sexual, racial, gender, etc.) as part of the revolution. Eventually, everyone is included as oppressed, thanks to the splintering of people into oppressed groups that encompass anyone who is not part of the normalized society.

The destruction of the current oppressors creates a vacuum that the rising Communist leaders fill. Essentially, Communism throws out one corrupt government to bring in another. The new leaders claim to be for the people, but eventually become accustomed to doing whatever it takes to maintain their power. An example would be what has happened in Cuba, China, and North Korea over the past 80 years. Each of these governments used Communism to take power and has used it to maintain power.

Religious Undertones

Even though Marx despised religion, Communism is often treated as a religion. Some adherents of Communism truly believe that implementing this belief system will lead to peace and prosperity on Earth in a way that believers in Christianity believe in heaven.

Even with all the evidence to the contrary that Communism does not work, believers fight to preserve the idea of Communism. A common counterargument is that Communism has never been implemented properly or that the famous leaders of Communism misunderstood it.

For example, the focus of Communism was primarily economic, with an emphasis on the means of production. However, as the middle class rose and became content, many communist thought leaders moved from attacking the means of production to critiquing the cultural reproduction of society. This is why there is so much criticism of Judeo-Christian-Heterosexual-White norms in the West today. Pulling down these norms today is the equivalent of seizing the factories of the bourgeoisie in the 19th century.

Communism seeks to displace other religious systems to generate a religion in which man is God rather than the gods of various religions. Marx viewed religion as a tool that kept people asleep and ignorant of their condition. This has been interpreted as the need to destroy religion by many so that the masses are awakened or “woke.” Evangelism is performed with protesting in the streets and or the barrel of a gun rather than with the persuasion of the Bible.

Conclusion

Communism is a complex ideology that has had a major influence on the world. For better or for worse, people believe that the ideas of Communism will make the world a better place. As such, there have been attempts to realize the ideas of the philosophy with mixed results. Despite the implementation, the traits described here are generally present when Communists take power.

Packages and Data Preparation

In the code below, we load our library data.table. Next, we prepare our data set mtcars and convert it into a data.table of the same name. Note that mtcars is preloaded within R.

library(data.table)
mtcars<-data.table(mtcars)

Below is a preview of the mtcars dataset.

> head(mtcars)
     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
   <num> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
1:  21.0     6   160   110  3.90 2.620 16.46     0     1     4     4
2:  21.0     6   160   110  3.90 2.875 17.02     0     1     4     4
3:  22.8     4   108    93  3.85 2.320 18.61     1     1     4     1
4:  21.4     6   258   110  3.08 3.215 19.44     1     0     3     1
5:  18.7     8   360   175  3.15 3.440 17.02     0     0     3     2
6:  18.1     6   225   105  2.76 3.460 20.22     1     0     3     1

Selecting Columns

Below is an example of how to select columns. You can do this by using brackets and placing the columns you want inside the c function. Remember to place a comma in front of the c function, as this indicates to take all rows of data, while the information after the comma indicates which columns to take.

# Select mpg and cyl using a character vector
> mtcars_select <- mtcars[,c("mpg","cyl")]
> head(mtcars_select)
   mpg cyl
1 21.0   6
2 21.0   6
3 22.8   4
4 21.4   6
5 18.7   8
6 18.1   6

By indicating which columns we wanted, we were able to pull only what we wanted. If you want to leave out columns, you just need to place a minus sign in front of the c function as shown below.

> # Deselect mph and cyl columns
> mtcars_drop <- mtcars[,-c("mpg","cyl")]
> head(mtcars_drop)
    disp    hp  drat    wt  qsec    vs    am  gear  carb
   <num> <num> <num> <num> <num> <num> <num> <num> <num>
1:   160   110  3.90 2.620 16.46     0     1     4     4
2:   160   110  3.90 2.875 17.02     0     1     4     4
3:   108    93  3.85 2.320 18.61     1     1     4     1
4:   258   110  3.08 3.215 19.44     1     0     3     1
5:   360   175  3.15 3.440 17.02     0     0     3     2
6:   225   105  2.76 3.460 20.22     1     0     3     1

In the example above, the columns left out are mpg and cyl, as we indicated. Next, we will look at performing calculations.

Performing Calculations

It is also possible to perform specific calculations. In the example below, we calculate the median mpg of all cars in the dataset.

> # Calculate median mpg using the j argument
> median_mpg <- mtcars[,median(mpg)]
> median_mpg
[1] 19.2

As you can see, to perform a calculation, you must place the function inside the brackets and after the comma. The column you want to perform the calculation on must be inside the formula, as usual.

It is also possible to give names to your output. In the example below, we provide the output of our calculation, the name “mean_mpg”. Notice also the use of the period right in front of the parentheses, which is needed when performing this type of calculation

> # Calculate the average mpg as mean_mpg 
> mean_mpg <- mtcars[,.(mean_mpg=mean(mpg))]
> mean_mpg
   mean_mpg
       <num>
1:  20.09062

In our example above, we can see that the average mpg of all the cars in our dataset is 20.09.

Multiple Calculations

By employing the same dot notation, it is possible to perform multiple calculations at once. In the example below, we find the minimum and maximum values of mpg for all cars.

> # Get the min and max mpg values
> min_max_mpg <- mtcars[, .(min(mpg),max(mpg))]
> min_max_mpg
      V1    V2
   <num> <num>
1:  10.4  33.9

There is nothing unique here except for the inclusion of a second function. Notice how each function is separated by a comma.

Just as before, you can also name each output from your results. Below is the mean weight and the max hp from the dataset.

> # Calculate the average wt and the max hp
> other_stats <- mtcars[, .(mean_wt=mean(wt),max_hp=max(hp))]
> other_stats
   mean_wt max_hp
     <num>  <num>
1: 3.21725    335

Filtering and Calculations

So far, we have not made any adjustments to the input before the comma when performing calculations. In the example below, we are filtering for cars with 6 cylinders and hp that is less than 120. Once this is filtered, we then want to calculate the minimum and maximum mpg.

> #filter for two or more variables then statistics
> mpg_stats <- mtcars[cyl==6 & hp<120, .(min_dur=min(mpg), 
+                             max_dur=max(mpg))]
> mpg_stats
   min_dur max_dur
     <num>   <num>
1:    18.1    21.4

The output speaks for itself. Normally, when subsetting data, the information before the comma indicates the rows. However, when performing calculations, the information before the comma can be used to filter the data as appropriate.

In the example below, we make a histogram based on the same filtering criteria.

mtcars[cyl==6 & hp<120, 
                    hist(mpg)]

As you can see, the use of data.table is almost endless

Conclusion

The data.table library provides you with several beneficial tools for conveniently slicing data. Data analysis can use these tools as needed to provide insights for their audience.

Packages and Data Preparation

We will begin by loading our package data.table and converting our datasets mtcars and iris, into data tables. Both mtcars and iris are preinstalled on R. Below is the code.

library(data.table)
mtcars<-data.table(mtcars)
iris<-data.table(iris)

Next, we will quickly examine both datasets using the head() function to understand what each one is about.

We now move to filtering.

Filtering for Not

Our first exercise is the use of NOT logic in filtering. With NOT logic, you are filtering for what is not included in your code. For example, in the code below, we are telling R to display all cars that do not have a transmission. The code for NOT is != which means “does not equal”. Below is the code and example.

> # Filter all rows where am is not 0
> not_0_am <- mtcars[am !=0]
> not_0_am
      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
    <num> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
 1:  21.0     6 160.0   110  3.90 2.620 16.46     0     1     4     4
 2:  21.0     6 160.0   110  3.90 2.875 17.02     0     1     4     4
 3:  22.8     4 108.0    93  3.85 2.320 18.61     1     1     4     1
 4:  32.4     4  78.7    66  4.08 2.200 19.47     1     1     4     1
 5:  30.4     4  75.7    52  4.93 1.615 18.52     1     1     4     2
 6:  33.9     4  71.1    65  4.22 1.835 19.90     1     1     4     1
 7:  27.3     4  79.0    66  4.08 1.935 18.90     1     1     4     1
 8:  26.0     4 120.3    91  4.43 2.140 16.70     0     1     5     2
 9:  30.4     4  95.1   113  3.77 1.513 16.90     1     1     5     2
10:  15.8     8 351.0   264  4.22 3.170 14.50     0     1     5     4
11:  19.7     6 145.0   175  3.62 2.770 15.50     0     1     5     6
12:  15.0     8 301.0   335  3.54 3.570 14.60     0     1     5     8
13:  21.4     4 121.0   109  4.11 2.780 18.60     1     1     4     2
>

Of course, you can have more than one argument within your code, as we will see in the next example.

Multiple Commands for Not

It is also possible to include multiple commands. In the example below, we are filtering for cars with an automatic transmission (am==1) but do not have 6 cylinders (cyl != 6). The output matches the criteria that were set

> # Filter all rows where am is 0 AND cyl is not 6
> am_cyl <- mtcars[am==1 & cyl != 6]
> am_cyl
      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
    <num> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
 1:  22.8     4 108.0    93  3.85 2.320 18.61     1     1     4     1
 2:  32.4     4  78.7    66  4.08 2.200 19.47     1     1     4     1
 3:  30.4     4  75.7    52  4.93 1.615 18.52     1     1     4     2
 4:  33.9     4  71.1    65  4.22 1.835 19.90     1     1     4     1
 5:  27.3     4  79.0    66  4.08 1.935 18.90     1     1     4     1
 6:  26.0     4 120.3    91  4.43 2.140 16.70     0     1     5     2
 7:  30.4     4  95.1   113  3.77 1.513 16.90     1     1     5     2
 8:  15.8     8 351.0   264  4.22 3.170 14.50     0     1     5     4
 9:  15.0     8 301.0   335  3.54 3.570 14.60     0     1     5     8
10:  21.4     4 121.0   109  4.11 2.780 18.60     1     1     4     2

Searching Text

It is also possible to search for text and even numbers. In the code below, we are searching the iris dataset for the species “setosa” and for petal lengths that are less than 1.3

> #with text
> iris[Species=="setosa" & Petal.Length<1.3]
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <num>       <num>        <num>       <num>  <fctr>
1:          4.3         3.0          1.1         0.1  setosa
2:          5.8         4.0          1.2         0.2  setosa
3:          4.6         3.6          1.0         0.2  setosa
4:          5.0         3.2          1.2         0.2  setosa

We can also search for text when unsure what we are looking for. In the example below, we use the %like% argument to search the Specias column for text containing the letter v. Since the results are rather long, we use the head() function to see the first few rows.

> # Filter all rows where Species contains "V"
> any_v <- iris[Species %like% "v"]
> head(any_v)
   Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
          <num>       <num>        <num>       <num>     <fctr>
1:          7.0         3.2          4.7         1.4 versicolor
2:          6.4         3.2          4.5         1.5 versicolor
3:          6.9         3.1          4.9         1.5 versicolor
4:          5.5         2.3          4.0         1.3 versicolor
5:          6.5         2.8          4.6         1.5 versicolor
6:          5.7         2.8          4.5         1.3 versicolor

Another way to search text is by looking for words that end with something. In the example below, we are looking for words in the Species column that end with the word “color.” We indicate this to are by using the %like% argument again and the word “color” with a dollar sign at the end of it. The dollar sign tells R to look for this word at the end of a word in the Species column.

> # Filter all rows where Species ends with "color"
> end_flowers <- iris[Species %like% "color$"]
> head(end_flowers)
   Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
          <num>       <num>        <num>       <num>     <fctr>
1:          7.0         3.2          4.7         1.4 versicolor
2:          6.4         3.2          4.5         1.5 versicolor
3:          6.9         3.1          4.9         1.5 versicolor
4:          5.5         2.3          4.0         1.3 versicolor
5:          6.5         2.8          4.6         1.5 versicolor
6:          5.7         2.8          4.5         1.3 versicolor

Multiple Numerical Arguments

Multiple numerical arguments are also possible. In the example shown below, we are looking for all cars in the mtcars dataset that are 4 or 6 cylinders. We achieve this by listing the variable we are searching “cyl” followed by the %in% argument, and lastly we use the c() function and include our values inside it. Below is the code and output.

> # Filter all rows where cyl is 4 or 6
> filter_cyl <- mtcars[cyl %in% c(4, 6)]
> filter_cyl
      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
    <num> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
 1:  21.0     6 160.0   110  3.90 2.620 16.46     0     1     4     4
 2:  21.0     6 160.0   110  3.90 2.875 17.02     0     1     4     4
 3:  22.8     4 108.0    93  3.85 2.320 18.61     1     1     4     1
 4:  21.4     6 258.0   110  3.08 3.215 19.44     1     0     3     1
 5:  18.1     6 225.0   105  2.76 3.460 20.22     1     0     3     1
 6:  24.4     4 146.7    62  3.69 3.190 20.00     1     0     4     2
 7:  22.8     4 140.8    95  3.92 3.150 22.90     1     0     4     2
 8:  19.2     6 167.6   123  3.92 3.440 18.30     1     0     4     4
 9:  17.8     6 167.6   123  3.92 3.440 18.90     1     0     4     4
10:  32.4     4  78.7    66  4.08 2.200 19.47     1     1     4     1
11:  30.4     4  75.7    52  4.93 1.615 18.52     1     1     4     2
12:  33.9     4  71.1    65  4.22 1.835 19.90     1     1     4     1
13:  21.5     4 120.1    97  3.70 2.465 20.01     1     0     3     1
14:  27.3     4  79.0    66  4.08 1.935 18.90     1     1     4     1
15:  26.0     4 120.3    91  4.43 2.140 16.70     0     1     5     2
16:  30.4     4  95.1   113  3.77 1.513 16.90     1     1     5     2
17:  19.7     6 145.0   175  3.62 2.770 15.50     0     1     5     6
18:  21.4     4 121.0   109  4.11 2.780 18.60     1     1     4     2

In this last example, we learn to find data that meets a range rather than just specific values. In the code below, we are looking for cars that have an mpg between 20 and 22. The new argument in this example is the %between% argument, which is used to tell R to search for a range of values. Below is the code, followed by the output

> # Filter all rows where mpg is between [20, 22]
> mpg_20_22 <- mtcars[mpg %between% c(20,22)]
> mpg_20_22
     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
   <num> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
1:  21.0     6 160.0   110  3.90 2.620 16.46     0     1     4     4
2:  21.0     6 160.0   110  3.90 2.875 17.02     0     1     4     4
3:  21.4     6 258.0   110  3.08 3.215 19.44     1     0     3     1
4:  21.5     4 120.1    97  3.70 2.465 20.01     1     0     3     1
5:  21.4     4 121.0   109  4.11 2.780 18.60     1     1     4     2

Conclusion

Data tables provide a different way of pulling insights from data. The value of this approach becomes clearer when dealing with large datasets in which speed becomes important.