ImportTitanicTest.Rmd

library("psych") 
describe(df) # summary stats of data set

##             vars   n    mean     sd median trimmed    mad    min     max  range
## PassengerId    1 332 1100.06 122.76 1099.5 1100.41 161.60 892.00 1307.00 415.00
## Pclass         2 332    2.14   0.85    2.0    2.18   1.48   1.00    3.00   2.00
## Name*          3 332  166.50  95.98  166.5  166.50 123.06   1.00  332.00 331.00
## Sex*           4 332    1.62   0.49    2.0    1.65   0.00   1.00    2.00   1.00
## Age            5 332   30.27  14.18   27.0   29.64  11.86   0.17   76.00  75.83
## SibSp          6 332    0.48   0.87    0.0    0.32   0.00   0.00    8.00   8.00
## Parch          7 332    0.40   0.81    0.0    0.20   0.00   0.00    6.00   6.00
## Ticket*        8 332  141.68  84.33  142.5  141.53 112.68   1.00  285.00 284.00
## Fare           9 331   40.98  61.23   16.0   25.63  12.41   0.00  512.33 512.33
## Cabin*        10 332   10.41  18.93    1.0    5.59   0.00   1.00   73.00  72.00
## Embarked*     11 332    2.44   0.86    3.0    2.55   0.00   1.00    3.00   2.00
##              skew kurtosis   se
## PassengerId -0.01    -1.25 6.74
## Pclass      -0.28    -1.55 0.05
## Name*        0.00    -1.21 5.27
## Sex*        -0.48    -1.77 0.03
## Age          0.45     0.05 0.78
## SibSp        3.53    20.02 0.05
## Parch        2.76    10.56 0.04
## Ticket*      0.00    -1.29 4.63
## Fare         3.26    13.84 3.37
## Cabin*       1.92     2.37 1.04
## Embarked*   -0.97    -0.95 0.05

#install.packages("visdat")

library(visdat)

# vis_miss(df)

vis_dat(df)

#install.packages("stargazer") 
library(stargazer)

## 
## Please cite as:

##  Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.

##  R package version 5.2.3. https://CRAN.R-project.org/package=stargazer

stargazer(df, type = "text" )

## 
## ================================================
## Statistic    N    Mean    St. Dev.  Min    Max  
## ------------------------------------------------
## PassengerId 332 1,100.063 122.763   892   1,307 
## Pclass      332   2.145    0.846     1      3   
## Age         332  30.273    14.181  0.170 76.000 
## SibSp       332   0.482    0.874     0      8   
## Parch       332   0.398    0.811     0      6   
## Fare        331  40.982    61.229  0.000 512.329
## ------------------------------------------------

variable_labels <- c("Passenger Id", 
                     "Passenger Class", 
                     "Age", 
                     "# of Siblings/Spouses", 
                     "# of Children/Parents", 
                     "Fare")

data_notes <- c("N = 332, because 418 responses omitted due to missing values.",                  "Age has 86 missing values.", 
                "Fare has 1 missing value.")

class(variable_labels)

## [1] "character"

length(variable_labels)

## [1] 6

stargazer(df, 
          type              = "text", 
          title             = "Summary Statistics", 
          covariate.labels  = variable_labels,
          notes             = data_notes, 
          omit.summary.stat = "n", 
          digits            = 2)

## 
## Summary Statistics
## =================================================================
## Statistic                    Mean      St. Dev.     Min     Max  
## -----------------------------------------------------------------
## Passenger Id               1,100.06     122.76      892    1,307 
## Passenger Class              2.14        0.85        1       3   
## Age                          30.27       14.18     0.17    76.00 
## # of Siblings/Spouses        0.48        0.87        0       8   
## # of Children/Parents        0.40        0.81        0       6   
## Fare                         40.98       61.23     0.00   512.33 
## -----------------------------------------------------------------
## N = 332, because 418 responses omitted due to missing values.    
## Age has 86 missing values.                                       
## Fare has 1 missing value.

Notes:

• The average age was about 30, so, with the maximum being 76 and the minimum being 0.17, the distribution of passenger’s ages is skewed-right.

• Similarly, a large number of passenger fares must have been relatively low / the distribution was most likely skewed-right because the mean is closer t the minimum than the maximum.

data <- df$Age # Name the sub category

median(data) 

## [1] 27

mode(data)

## [1] "numeric"

mean(data)

## [1] 30.27259

hist(data)

?sd

sd(x = data)

## [1] 14.18121

var(x = data)

## [1] 201.1067

var(data) == sd(x = data)^2

## [1] FALSE

max(data)

## [1] 76

min(data)

## [1] 0.17

range(data)

## [1]  0.17 76.00

max(data) - min(data)

## [1] 75.83

round(x = sd(x = data), digits = 2)

## [1] 14.18

?boxplot
boxplot(data, horizontal = T)

?quartile

## No documentation for 'quartile' in specified packages and libraries:
## you could try '??quartile'

summary(data)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.17   21.00   27.00   30.27   39.00   76.00

IQR(data)

## [1] 18

boxplot(data, horizontal = T, range = 3)

?boxplot

boxplot(data, 
        vertical = TRUE, 
        col = "blue", 
        range = 3)

boxplot(data, 
        horizontal = TRUE, 
        col = "pink", 
        range = 1.5, 
        xlab = "Passenger Ages",
        border = "orange")

End Notes:

• The distribution of responses/ages is skewed-right, meaning the median age was closer to the minimum age rather than the maximum age.