Assignment-1
Sneha
2024-09-19
Assignment : 1
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 errorsLoading the data : Using astronaut dataset.
Loading the CSV file to astro
astro <- read_delim('/Users/sneha/H510-Statistics/astronaut.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.#head(astro, 5)Numeric Summary
Total number of mission’s completed based on sex
astro |>
group_by(sex) |>
summarise(count_mission = sum(total_number_of_missions))## # A tibble: 2 × 2
## sex count_mission
## <chr> <dbl>
## 1 female 428
## 2 male 3381There is a significant disparity between the number of missions completed by female and male astronauts. Male astronauts have completed far more missions than female astronauts.The data suggests that male astronauts are currently more represented in space missions compared to female astronauts.
Analyzing year of selection
astro |>
summarise(year_of_selection)## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
## always returns an ungrouped data frame and adjust accordingly.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## # A tibble: 1,277 × 1
## year_of_selection
## <dbl>
## 1 1960
## 2 1960
## 3 1959
## 4 1959
## 5 1959
## 6 1960
## 7 1960
## 8 1960
## 9 1960
## 10 1959
## # ℹ 1,267 more rowsastro |>
summarise(min_year= min(year_of_selection))## # A tibble: 1 × 1
## min_year
## <dbl>
## 1 1959From the data, we observe that space missions began around 1959, which aligns with historical records, as the Soviet Union launched the first satellite, Sputnik, into space in 1957. Consequently, it’s possible that the dataset may not include data for the years 1957-1958.
Mean mission years
astro |>
group_by(nationality) |>
summarise(mean_mission = mean(year_of_mission))## # A tibble: 40 × 2
## nationality mean_mission
## <chr> <dbl>
## 1 Afghanistan 1988
## 2 Australia 1997.
## 3 Austria 1991
## 4 Belgium 2001
## 5 Brazil 2006
## 6 Bulgaria 1984.
## 7 Canada 2001.
## 8 China 2010.
## 9 Cuba 1980
## 10 Czechoslovakia 1978
## # ℹ 30 more rowsWe could see most of the missions are between the year 1980 - 2010. We could conclude that space missions became more popular between these years for majority of the countries.
counting the unique occupation of people
astro |>
pull(occupation) |>
table()##
## commander flight engineer Flight engineer
## 315 192 4
## MSP Other (Journalist) Other (space tourist)
## 498 1 5
## Other (Space tourist) pilot Pilot
## 3 196 1
## PSP Space tourist spaceflight participant
## 59 2 1It is interesting to know that journalist and tourist have also been to space. highlighting how space travel is gradually expanding beyond professional astronauts and scientists. Maybe advancements in technology is making space accessible to civilians.
occupation_counts <- astro |>
count(occupation) |>
arrange(desc(n))occupation_counts## # A tibble: 12 × 2
## occupation n
## <chr> <int>
## 1 MSP 498
## 2 commander 315
## 3 pilot 196
## 4 flight engineer 192
## 5 PSP 59
## 6 Other (space tourist) 5
## 7 Flight engineer 4
## 8 Other (Space tourist) 3
## 9 Space tourist 2
## 10 Other (Journalist) 1
## 11 Pilot 1
## 12 spaceflight participant 1It is evident that the majority of the occupations fall under the MSP (Mission Specialist) title, and interestingly, there are more commanders than pilots. As of now the number of space tourists are less, but it is interesting to know that people are going to space which will be more in the coming future.
Distribution of Nationalities in the Astronaut Dataset
astro |>
group_by(nationality) |>
summarize(count_nation = n())## # A tibble: 40 × 2
## nationality count_nation
## <chr> <int>
## 1 Afghanistan 1
## 2 Australia 4
## 3 Austria 1
## 4 Belgium 3
## 5 Brazil 1
## 6 Bulgaria 2
## 7 Canada 18
## 8 China 14
## 9 Cuba 1
## 10 Czechoslovakia 1
## # ℹ 30 more rowsFiltering nationality - with count more than 100
astro |>
group_by(nationality) |>
summarize(count_nation = n()) |>
filter(count_nation >=100)## # A tibble: 2 × 2
## nationality count_nation
## <chr> <int>
## 1 U.S. 854
## 2 U.S.S.R/Russia 273It can be observed that US and Russia are the only nations that has been listed more than 100 times in the dataset, which mean most of the astronauts in the dataset are either from the US or Russia.
3 novel questions to investigate
Question 1 : How does the number of missions completed vary across different nationalities ?
Question 2 : In which year did maximum number of missions happen?
Question 3 : What are the patterns in Duration of mission in hours based on the spacecraft used in the orbit?
Data Documentation
This dataset contains publically available information about all astronauts who participated in space missions before 15 January 2020 collected from NASA, Roscosmos, and fun-made websites. The provided information includes full astronaut name, sex, date of birth, nationality, military status, a title and year of a selction program, and information about each mission completed by a particular astronaut such as a year, ascend and descend shuttle names, mission and extravehicular activity (EVAs) durations.
Task : Address at least one of the above questions using an aggregation function
Question addressed : How does the number of missions completed vary across different nationalities ?
astro %>%
aggregate(total_number_of_missions ~ nationality, data = ., FUN = sum) ## nationality total_number_of_missions
## 1 Afghanistan 1
## 2 Australia 16
## 3 Austria 1
## 4 Belgium 5
## 5 Brazil 1
## 6 Bulgaria 2
## 7 Canada 38
## 8 China 22
## 9 Cuba 1
## 10 Czechoslovakia 1
## 11 Denmark 1
## 12 France 38
## 13 Germany 28
## 14 Hungry 1
## 15 India 1
## 16 Israel 1
## 17 Italy 29
## 18 Japan 42
## 19 Kazakhstan 1
## 20 Korea 1
## 21 Malysia 1
## 22 Mexico 1
## 23 Mongolia 1
## 24 Netherland 5
## 25 Poland 1
## 26 Republic of South Africa 1
## 27 Romania 1
## 28 Saudi Arabia 1
## 29 Slovakia 1
## 30 Spain 4
## 31 Sweden 4
## 32 Switzerland 16
## 33 Syria 1
## 34 U.K. 2
## 35 U.K./U.S. 14
## 36 U.S. 2734
## 37 U.S.S.R/Russia 787
## 38 U.S.S.R/Ukraine 1
## 39 UAE 1
## 40 Vietnam 1Finding out which all nations has more than 20 total missions
astro %>%
aggregate(total_number_of_missions ~ nationality, data = ., FUN = sum) |>
filter( total_number_of_missions >= 20 ) |>
pluck("nationality")## [1] "Canada" "China" "France" "Germany"
## [5] "Italy" "Japan" "U.S." "U.S.S.R/Russia"Canada, China, France, Germany, Italy, Japan, US, USSR/Russia are the nations which has more than 20 missions completed.
Question 2 : In which year did maximum number of missions happen?
astro |>
group_by(year_of_selection) |>
summarize(count_missions = sum(total_number_of_missions)) |>
filter(count_missions == max(count_missions))## # A tibble: 1 × 2
## year_of_selection count_missions
## <dbl> <dbl>
## 1 1978 444From above output, we could understand that the year 1978 had the most number of missions to space.
Question 3 : What are the patterns in Duration of mission in hours based on the spacecraft used in the orbit?
patterns_in_duration <- astro |>
group_by(in_orbit) |>
summarize(
mean_duration = mean(total_hrs_sum),
median_duration = median(total_hrs_sum),
min_duration = min(total_hrs_sum),
max_duration = max(total_hrs_sum)
)
patterns_in_duration## # A tibble: 289 × 5
## in_orbit mean_duration median_duration min_duration max_duration
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 ASTP 490. 508 217. 746
## 2 Apollo 10 636. 565 508 836.
## 3 Apollo 11 254. 266 206 289
## 4 Apollo 12 1056. 1179 316. 1672.
## 5 Apollo 13 333. 142 142 715.
## 6 Apollo 14 216. 216 216 216.
## 7 Apollo 15 379. 295. 295 546
## 8 Apollo 16 536. 508 265 836.
## 9 Apollo 17 389 301 301 565
## 10 Apollo 7 272. 260. 260 295.
## # ℹ 279 more rowsAbove summarize() method Summarizes the data for each group (spacecraft), calculating
mean_duration: The average duration of missions.
median_duration: The median duration of missions.
min_duration: The shortest mission duration.
max_duration: The longest mission duration.
Visual summaries : Including Plots
Analysing patterns in Duration of mission in hours based on Military/Civilian status
status <- astro |>
group_by(military_civilian) |>
summarise(mean_hours = mean(total_hrs_sum))
status## # A tibble: 2 × 2
## military_civilian mean_hours
## <chr> <dbl>
## 1 civilian 3043.
## 2 military 2919.ggplot(astro, aes(x = military_civilian, y = total_hrs_sum, fill = military_civilian)) +
geom_boxplot() +
labs(title = "Mission Duration Distribution by Military/Civilian Status",
x = "Status (Military/Civilian)",
y = "Mission Duration (Hours)") +
theme_minimal()
From the above graph we could understand that Military /civilian status doesnt have much significance on the mission completion duration. Both of their mean hours are nearly close.
Bar Plot of Number of Missions by Nationality
Grouping by nationality and counting the number of missions completed and filtering them based on more number of missions(>=100)
missions_by_nationality <- astro |>
group_by(nationality) |>
summarise(total_missions = sum(total_number_of_missions)) |>
filter( total_missions >= 100 )
missions_by_nationality## # A tibble: 2 × 2
## nationality total_missions
## <chr> <dbl>
## 1 U.S. 2734
## 2 U.S.S.R/Russia 787ggplot(missions_by_nationality, aes(x = nationality, y = total_missions, fill = nationality)) +
geom_bar(stat = "identity") +
labs(title = "Total Missions Completed by Nationality (More Than 100 Missions)",
x = "Nationality",
y = "Total Missions Completed") +
theme_minimal()
The graph above indicates that the US and Russia have completed most missions, with US leading with a count of 2734 missions.