week-6
Sneha
2024-10-08
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 errorsastro <- 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.Creating new variables for analysis:
Age at First Mission: This can be derived by subtracting the year of birth from the year of the first mission.
Total Time in Space (hours): This can be a sum of hours_mission and total_eva_hrs (extra-vehicular activity hours).
astro <- astro |>
mutate(age_at_first_mission = year_of_mission - year_of_birth,
total_time_in_space = hours_mission + total_eva_hrs)astro## # A tibble: 1,277 × 25
## id number nationwide_number name sex year_of_birth nationality
## <dbl> <dbl> <dbl> <chr> <chr> <dbl> <chr>
## 1 1 1 1 Gagarin, Yuri male 1934 U.S.S.R/Ru…
## 2 2 2 2 Titov, Gherman male 1935 U.S.S.R/Ru…
## 3 3 3 1 Glenn, John H… male 1921 U.S.
## 4 4 3 1 Glenn, John H… male 1921 U.S.
## 5 5 4 2 Carpenter, M.… male 1925 U.S.
## 6 6 5 2 Nikolayev, An… male 1929 U.S.S.R/Ru…
## 7 7 5 2 Nikolayev, An… male 1929 U.S.S.R/Ru…
## 8 8 6 4 Popovich, Pav… male 1930 U.S.S.R/Ru…
## 9 9 6 4 Popovich, Pav… male 1930 U.S.S.R/Ru…
## 10 10 7 3 Schirra, Walt… male 1923 U.S.
## # ℹ 1,267 more rows
## # ℹ 18 more variables: military_civilian <chr>, selection <chr>,
## # year_of_selection <dbl>, mission_number <dbl>,
## # total_number_of_missions <dbl>, occupation <chr>, year_of_mission <dbl>,
## # mission_title <chr>, ascend_shuttle <chr>, in_orbit <chr>,
## # descend_shuttle <chr>, hours_mission <dbl>, total_hrs_sum <dbl>,
## # field21 <dbl>, eva_hrs_mission <dbl>, total_eva_hrs <dbl>, …Proposed Numeric Variable Pairs:
Pair 1: Age at First Mission (explanatory) V/S Total Hours in Space (response)
Pair 2: Age at First Mission (explanatory) V/S EVA (spacewalk) hours (response)
Plot 1: Age at First Mission vs Total Time in Space
ggplot(astro, aes(x = age_at_first_mission, y = total_time_in_space)) +
geom_point(color = "blue") +
labs(title = "Age at First Mission vs Total Time in Space",
x = "Age at First Mission",
y = "Total Time in Space (hours)") +
theme_minimal()
Age at First Mission vs. Total Time in Space:
The scatter plot shows a weak positive relationship between the age of astronauts at their first mission and the total time they spent in space.
The low correlation between Age at First Mission and Total Time in Space suggests that the age at which an astronaut embarks on their first mission does not have a strong or linear relationship with how long they spend in space.
The total time an astronaut spends in space can vary greatly depending on the type of missions they are assigned to. Some missions are short-term (lasting days or weeks), while others, like those on the International Space Station (ISS), can last for months. These mission durations are often determined by factors unrelated to age, such as the mission’s objectives, duration of specific research experiments, or the astronaut’s role.
Calculate correlation coefficient:
cor_age_time <- cor(astro$age_at_first_mission, astro$total_time_in_space)
cor_age_time## [1] 0.1560771The correlation coefficient is 0.156, indicating a weak positive correlation. This makes sense given that the points are scattered with no clear linear trend.
Outliers:
One potential outlier in the dataset is an astronaut who embarked on their first mission after the age of 70. This stands out as unusual because most astronauts tend to retire before reaching this age, given the physical demands of space travel.
While this data point could indeed indicate a data entry error, there are a few other possibilities
Exceptional Circumstance: The astronaut might be an exceptional individual, such as a highly experienced or specialized scientist, selected for a unique mission that required their expertise, even at an advanced age.
Data Collection Anomaly: This could also be the result of a data anomaly or human error during data collection.
Special Roles: It’s also possible that this individual had a non-astronaut role, like a space tourist.
- From the dataset, we could see that the astronaut was on PSP role(special role)
Plot 2: Age at First Mission vs EVA Hours
ggplot(astro, aes(x = age_at_first_mission, y = total_eva_hrs)) +
geom_point(color = "red") +
labs(title = "Age at First Mission vs EVA Hours",
x = "Age at First Mission",
y = "EVA Hours") +
theme_minimal()
Age at First Mission vs. EVA Hours:
The second scatter plot shows a weak positive relationship between the age of astronauts at their first mission and the total EVA (spacewalk) hours, this can be because the total total EVA hours in space often depends on an astronaut’s specialization rather than their age. Certain astronauts, such as those with specific scientific or engineering expertise, may be assigned to longer missions to complete specific tasks, irrespective of when they first flew.
cor_age_eva <- cor(astro$age_at_first_mission, astro$total_eva_hrs)
cor_age_eva## [1] 0.1280116The correlation coefficient is 0.128, also indicating a weak positive correlation, suggesting that EVA hours do not strongly depend on the astronaut’s age during their first mission.
Outliers: We could see the same outlier as the graph before
Building confidence intervals for the response variables
Total Time in Space
The 95% confidence interval for the total time astronauts spent in space is between 969 hours and 1159 hours.
This interval suggests that, on average, astronauts spend roughly 1061 hours in space. The relatively wide range reflects some variability in total time across astronauts.
library(boot)boot_ci <- function (v, func = median, conf = 0.95, n_iter = 1000) {
# the `boot` library needs the function in this format
boot_func <- \(x, i) func(x[i])
b <- boot(v, boot_func, R = n_iter)
boot.ci(b, conf = conf, type = "perc")
}boot_ci(astro$total_time_in_space, mean, 0.95)## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = b, conf = conf, type = "perc")
##
## Intervals :
## Level Percentile
## 95% ( 969, 1155 )
## Calculations and Intervals on Original ScaleEVA Hours (Spacewalk Hours):
The 95% confidence interval for EVA hours is between 9.89 hours and 11.65 hours.
This narrower range indicates that astronauts typically spend about 10-12 hours in spacewalks, with less variation compared to the total time in space.
boot_ci(astro$total_eva_hrs, mean, 0.95)## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = b, conf = conf, type = "perc")
##
## Intervals :
## Level Percentile
## 95% ( 9.88, 11.66 )
## Calculations and Intervals on Original ScaleInsights:
While age at the first mission seems to have a weak relationship with both total time in space and EVA hours, the confidence intervals show meaningful estimates of time astronauts typically spend in these activities. We could understand that astronauts typically have less EVA hours in their whole space travel.
Factors such as nationality, mission availability, health, and even political decisions can influence an astronaut’s total time in space. These factors introduce randomness into the relationship between age and total time in space, further weakening the correlation.
Further investigation could examine other factors influencing time in space, such as nationality, mission type, or selection group. Additionally, potential outliers suggest that these could be because of specific missions or data anomaly.
In summary, the weak correlation between Age at First Mission and Total Time in Space likely arises because time spent in space is driven by factors like mission type, specialization, career, and space program needs, which do not necessarily depend on when the astronaut first flew. Therefore, age at the first mission has little predictive power over the total time an astronaut will spend in space.