A list of at least 3 columns (or values) in your data which are unclear untit you read the documentation.

  1. field21 - In the astronaut dataset field21 refer to “Instances of EVA by mission”. Which would have remained unclear until referring the documentation. The context doesn’t explicitly mention the nature of Field21, it might have an indirect or direct relationship to Instances of EVA (Extravehicular Activity) by mission.

Why do you think they chose to encode the data the way they did?

According to my analysis, there might be some relationship with EVA in someway. some points i can think of are :

Mission duration: Longer missions might have more EVAs as astronauts have more time and requirements to perform spacewalks.

Mission type: Different mission types (e.g., space station maintenance, exploration) could determine how often EVAs occur.

Crew size: Larger crews might allow for more EVA activities due to more available astronauts.

What could have happened if you didn’t read the documentation?

If I hadn’t referred to the documentation, it would have been nearly impossible to understand what ‘field21’ represents. There is no explanation available on the internet either, which strongly suggests that the dataset’s author may have named the column based on their own intuition or internal conventions.

Why do you think they chose to encode the data this way?

Astronauts may be recruited from the navy or military for specific missions, or they could be scientists selected for space exploration based on their research expertise. Additionally, civilians such as tourists, journalists, or other non-military personnel may also travel to space. This diversity in backgrounds likely explains why the author labeled the column “military_civilian” to distinguish between military personnel and civilians.

What could have happened if you didn’t read the documentation?

Without reading the documentation, understanding the exact meaning of this column would have been confusing. However, by analyzing the dataset in more detail, we might have inferred that it refers to a distinction between military and civilian astronauts based on context clues in other columns.

Why do you think they chose to encode the data this way?

The likely reason for this encoding is that the dataset author simply abbreviated “extravehicular activities during the mission” without much consideration for clarity. However, this could easily cause confusion for users, as many might assume it represents the count or number of EVA instances rather than the duration.

What could have happened if you didn’t read the documentation?

Without consulting the documentation, this column could easily be misinterpreted. Since “field21” already captures the number of EVA instances per mission, and this column measures EVA duration, the two could be confused, leading to ambiguity in understanding the dataset.

At least one element or your data that is unclear even after reading the documentation

  1. The column I found to be unclear is “nationwide_number.” The documentation describes it as “Number within country,” but this explanation is vague and doesn’t provide a clear understanding of what it represents. I explored the additional articles and databases referenced in the documentation, but none provided any further information on nationwide_number.

    The documentation fails to clarify the data’s significance or context. Based on my analysis of the dataset, it appears that this column represents a unique identifier assigned to each astronaut within their respective country, likely based on their rank or order of selection. This assumption can be supported by the fact that no two astronauts share the same number within their nation. We will be visualizing this later below.

  1. Another unclear element in the dataset is the abbreviation “MSP” in the column “occupation.”

    The dataset documentation does not provide any explanation of what MSP stands for, and the references linked in the documentation also offer no clarification. Given that it falls under the astronaut’s occupation, I believe MSP likely stands for “Mission Specialist Pilot,” based on my research into space travel terminology. It seems the author may have abbreviated the term for simplicity, but unfortunately, they did not include sufficient details in the dataset to make this clear.

LOADING DATASET:

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
astro <- read_delim('/Users/sneha/H510-Statistics/astronaut-data.csv')
## Rows: 1277 Columns: 23
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): name, sex, nationality, military_civilian, selection, occupation, ...
## dbl (13): id, number, nationwide_number, year_of_birth, year_of_selection, m...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

VISUALIZATION:

Getting the count of particular occupation :

occupation_counts <- astro |>
  count(occupation) |>
  arrange(desc(n))
occupation_counts
## # A tibble: 9 × 2
##   occupation                  n
##   <chr>                   <int>
## 1 MSP                       498
## 2 commander                 315
## 3 flight engineer           196
## 4 pilot                     196
## 5 PSP                        59
## 6 Space tourist              10
## 7 Other (Journalist)          1
## 8 Pilot                       1
## 9 spaceflight participant     1
ggplot(occupation_counts, aes(x = occupation, y = n)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  theme_minimal() +
  labs(title = "Occupation Counts of Astronauts",
       x = "Occupation",
       y = "Count") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

The occupation MSP has the highest count in the dataset, prompting us to question what this occupation actually represents, as many astronauts fall under this category. After further analysis and research, I conclude that MSP likely stands for Mission Specialist. According to various sources, Mission Specialists are astronauts assigned specific duties related to their mission. There are several sub-categories of Mission Specialists, such as Educator Mission Specialist, International Mission Specialist, and USAF Manned Spaceflight Engineer. Unfortunately, all of these distinct roles seem to have been grouped under the umbrella term MSP, which limits the accuracy and granularity needed for proper data analysis.

lets analyse if nationwide_number is a unique number given to astronauts in nation.

MY ASSUMPTION : nationwide_number : Unique identifier assigned to each astronaut within their respective country, likely based on their rank or order of selection

Order the dataset by the name column, below tibble has unique astronauts grouped by nation and nationwide_number

uniqueness_check <- astro |>
  distinct(nationality, nationwide_number,name) |>
  arrange(name)
uniqueness_check
## # A tibble: 566 × 3
##    nationality    nationwide_number name                            
##    <chr>                      <dbl> <chr>                           
##  1 U.S.                         314 Acaba, Joseph M.                
##  2 U.S.                         104 Acton, Loren Wilbur             
##  3 U.S.                         131 Adamson, James C.               
##  4 U.S.S.R/Russia                70 Afanasyev, Viktor Mikhaylovich  
##  5 Kazakhstan                     1 Aidyn (Aydyn) Akanovich Aimbetov
##  6 U.S.                         144 Akers, Thomas D.                
##  7 Japan                          1 Akiyama, Toyohiro               
##  8 U.S.S.R/Russia                36 Aksyonov, Vladimir              
##  9 UAE                            1 Al Mansoori, Hazzaa             
## 10 Saudi Arabia                   1 Al-saud, Sultan bin Salman      
## # ℹ 556 more rows

Now, lets visualize this with a graph : if its a unique number, then the graph would show a line in increasing order where a single astronaut would not have 2 values.

ggplot(uniqueness_check, aes(x = reorder(name, nationwide_number), y = nationwide_number)) +
  geom_point(stat = "identity", fill = "skyblue", size = 3) +  
  theme_minimal() +
  labs(title = "Astronauts Nationwide Number",
       x = "Name",
       y = "Nationwide Number") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

IGNORE THE MESSY NAME IN X-AXIS(there are a lot of unique values which cannot be plotted here, but this graph helps us focus on visualization to get the clear picture) I have plotted a better graph below.

Here we could see that there are multiple values plotted for two astronauts. On deeper analysis(analyzing values in the tibble : uniqueness_check) it can be seen as below :

Lets visualize this: 
Unique_below_100 <- uniqueness_check |>
  distinct(nationality, nationwide_number,name) |>
  filter(nationwide_number >= 0, nationwide_number <= 60 ) 
  
Unique_below_100
## # A tibble: 213 × 3
##    nationality    nationwide_number name                            
##    <chr>                      <dbl> <chr>                           
##  1 Kazakhstan                     1 Aidyn (Aydyn) Akanovich Aimbetov
##  2 Japan                          1 Akiyama, Toyohiro               
##  3 U.S.S.R/Russia                36 Aksyonov, Vladimir              
##  4 UAE                            1 Al Mansoori, Hazzaa             
##  5 Saudi Arabia                   1 Al-saud, Sultan bin Salman      
##  6 U.S.                          18 Aldrin, Edwin Eugene, Jr.       
##  7 U.S.S.R/Russia                55 Aleksandrov, Aleksandr          
##  8 Bulgaria                       2 Aleksandrov, Aleksandr          
##  9 U.S.                          50 Allen, Joseph P.                
## 10 U.S.                          21 Anders, William Alison          
## # ℹ 203 more rows
ggplot(Unique_below_100, aes(x = reorder(name, nationwide_number), y = nationwide_number)) +
  geom_point(stat = "identity", fill = "skyblue", size = 3) +  
  theme_minimal() +
  labs(title = "Astronauts Nationwide Number below 100",
       x = "Name",
       y = "Nationwide Number") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Here we can see that “Aleksandrov, Aleksandr” is a common name which is listed in two nations(Russia and Bulgaria) and not an issue which contradicts our assumption.
U.S.S.R/Russia 55 Aleksandrov, Aleksandr
Bulgaria 2 Aleksandrov, Aleksandr

Lets visualize the unclear data closely: Filtering the data where nationwide_number is more than 310 to visualize the anomaly clearly:

US_Unique_above_300 <- uniqueness_check |>
  distinct(nationality, nationwide_number,name) |>
  filter(nationwide_number > 310 ) |>
  filter(nationality =='U.S.')
US_Unique_above_300
## # A tibble: 39 × 3
##    nationality nationwide_number name                    
##    <chr>                   <dbl> <chr>                   
##  1 U.S.                      314 Acaba, Joseph M.        
##  2 U.S.                      313 Antonelli, Dominic A.   
##  3 U.S.                      315 Arnold, Richard R., II  
##  4 U.S.                      343 Auñón-Chancellor, Serena
##  5 U.S.                      316 Barratt, Michael R.     
##  6 U.S.                      311 Bowen, Stephen G.       
##  7 U.S.                      329 Bresnik, Randolph J.    
##  8 U.S.                      322 Cassidy, Christopher J. 
##  9 U.S.                      331 Creamer, Timothy J.     
## 10 U.S.                      333 Dutton, James P.        
## # ℹ 29 more rows
ggplot(US_Unique_above_300, aes(x = reorder(name, nationwide_number), y = nationwide_number)) +
  geom_point(stat = "identity", fill = "skyblue", size = 3) +  
  theme_minimal() +
  labs(title = "US Astronauts Nationwide Number more than 310",
       x = "Name",
       y = "Nationwide Number") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

We could see below astronaut Hague Tyler has two nation-wide numbers listed, which is unclear and contradicts our assumption. but below anomaly looks like a human-made error while creating the dataset.

U.S. 344 Hague, Tyler
U.S. 433 Hague, Tyler

As mentioned earlier, Hague Taylor has two different nationwide_number entries, making this column unclear and confusing. Without proper documentation or explanation, it’s challenging to draw definitive conclusions. However, I believe the nationwide_number represents the order in which astronauts were selected within their respective countries (e.g the first astronaut in the U.S. receives the number 1, and so on).

I don’t see any significant risk here, as this appears to be a human error that can be corrected. Additionally, the assumption that nationwide_number represents a sequential designation seems valid, as the following observations support this interpretation:

The astronaut who is given nationwide_number :1 in Cuba is the first astronaut in Cuba:

Cuba 1 Arnaldo Tamayo Mendez

The astronaut who is given nationwide_number :1 in Slovakia is the first astronaut in Slovakia:

Slovakia 1 Bella, Ivan

Similarly it matches the rest of the nation.

While this seems like a simple error that can be corrected, understanding the true meaning of this column is crucial for drawing accurate insights. For instance, this column could be extremely useful for identifying the first astronauts from different nations, as well as tracking the most recent astronauts to go to space. Without clarity on the column’s purpose, making reliable inferences would be difficult. Therefore, having clear documentation or an explanation of nationwide_number is essential for any meaningful analysis.