Class 3 Attempt 2
Alex Law
2023-07-19
read this help file Help
My first pic:
1 Setup:
setwd("C:/Users/Alex Law/Documents/BC Data Analysis")install all libraries: make sure you have the packages
#install.packages("tidyverse")
#install.packages("visdat")
#install.packages("stargazer")
library("psych")
library("tidyverse")## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ ggplot2::%+%() masks psych::%+%()
## ✖ ggplot2::alpha() masks psych::alpha()
## ✖ 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 errorslibrary("visdat") #for visualizing the data
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=stargazer1.1 Set my working directory
2 import Data:
now I will import my data, make sure you comment out
df<- read.csv("train (1).csv")
head(df)## PassengerId Survived Pclass
## 1 1 0 3
## 2 2 1 1
## 3 3 1 3
## 4 4 1 1
## 5 5 0 3
## 6 6 0 3
## Name Sex Age SibSp Parch
## 1 Braund, Mr. Owen Harris male 22 1 0
## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
## 3 Heikkinen, Miss. Laina female 26 0 0
## 4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
## 5 Allen, Mr. William Henry male 35 0 0
## 6 Moran, Mr. James male NA 0 0
## Ticket Fare Cabin Embarked
## 1 A/5 21171 7.2500 S
## 2 PC 17599 71.2833 C85 C
## 3 STON/O2. 3101282 7.9250 S
## 4 113803 53.1000 C123 S
## 5 373450 8.0500 S
## 6 330877 8.4583 Qtail(df)## PassengerId Survived Pclass Name Sex
## 886 886 0 3 Rice, Mrs. William (Margaret Norton) female
## 887 887 0 2 Montvila, Rev. Juozas male
## 888 888 1 1 Graham, Miss. Margaret Edith female
## 889 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female
## 890 890 1 1 Behr, Mr. Karl Howell male
## 891 891 0 3 Dooley, Mr. Patrick male
## Age SibSp Parch Ticket Fare Cabin Embarked
## 886 39 0 5 382652 29.125 Q
## 887 27 0 0 211536 13.000 S
## 888 19 0 0 112053 30.000 B42 S
## 889 NA 1 2 W./C. 6607 23.450 S
## 890 26 0 0 111369 30.000 C148 C
## 891 32 0 0 370376 7.750 Qstr(df)## 'data.frame': 891 obs. of 12 variables:
## $ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...
## $ Survived : int 0 1 1 1 0 0 0 0 1 1 ...
## $ Pclass : int 3 1 3 1 3 3 1 3 3 2 ...
## $ Name : chr "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
## $ Sex : chr "male" "female" "female" "female" ...
## $ Age : num 22 38 26 35 35 NA 54 2 27 14 ...
## $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
## $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
## $ Ticket : chr "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
## $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
## $ Cabin : chr "" "C85" "" "C123" ...
## $ Embarked : chr "S" "C" "S" "S" ...3 Summary statistics:
here is my table from stargazer
Split the arguments into different lines within a function
Align your code equal to(=)
df_drop <- na.omit(df)
#embellished command
stargazer(df,
type = "text", #controls HTML
notes = "N=891, but age has 177 missing values",
summary.stat = c("mean","sd", "min", "max"),
title = "Titanic Stats",
digits = 1 #controls decimal places
)##
## Titanic Stats
## =========================================
## Statistic Mean St. Dev. Min Max
## -----------------------------------------
## PassengerId 446.0 257.4 1 891
## Survived 0.4 0.5 0 1
## Pclass 2.3 0.8 1 3
## Age 29.7 14.5 0.4 80.0
## SibSp 0.5 1.1 0 8
## Parch 0.4 0.8 0 6
## Fare 32.2 49.7 0.0 512.3
## -----------------------------------------
## N=891, but age has 177 missing values4 Visualization
Age missing data is 1.7% of the dataset
vis_dat(df)
Read up on indexing:
| Variable | Type | Level of Measurement |
|---|---|---|
| Passenger ID | Qualitative | Nominal |
| Survived | Qualitiative | Nominal |
| Passenger Class: | Qualitative | Ordinal |
| Name | Qualitative | Nominal |
| Sex | Qualititative | Nominal |
| Age | Quantitative | Ratio |
| Number of siblings/ spouses aboard | Quantitative | Ratio |
| Number of parents/ children aboard | Quantitative | Ratio |
| Ticket | Qualitative | Nominal |
| Fare | Quantitative | Ratio |
| Cabin | Qualitative | Nominal |
| Embark | Qualitative | Nominal |
5 Treating Missing Data
I cna drop all observations corresponding to missing age values
{r- drop missing values} df_drop <- na.omit(df)
#replace missing values of age with the median
########STEP 1 FIND THE MEAN
mean(df$Age, na.rm = TRUE)## [1] 29.69912########STEP 2 DUPLICATE THE ORIGINAL DATA
df_imputed <- df
#######STEP 3 CHANGE THE VARIABLE VALUES
#describe(df_imputed$Age) #mean before and after imputing
#df_imputed$Age[is.na(df$Age)] <- mean(df$Age, na.rm = TRUE)
#describe[is.na(df_imputed$Age)6 Homework:
test <- read.csv("train (1).csv")
df_clean <- na.omit(test)
vis_dat(df_clean)
stargazer(df_clean, type = "text")##
## ==============================================
## Statistic N Mean St. Dev. Min Max
## ----------------------------------------------
## PassengerId 714 448.583 259.120 1 891
## Survived 714 0.406 0.491 0 1
## Pclass 714 2.237 0.838 1 3
## Age 714 29.699 14.526 0.420 80.000
## SibSp 714 0.513 0.930 0 5
## Parch 714 0.431 0.853 0 6
## Fare 714 34.695 52.919 0.000 512.329
## ----------------------------------------------boxplot(df_clean$Age ,
horizontal = TRUE,
ylim = c(0, 100),
xaxt = "n" ,
col = rgb(0.9, 0.5, 0,0.3) ,
frame = F
)
hist(df_clean$Age ,
breaks = 10 ,
col = rgb(0.2,0.8,0.5,0.5) ,
border = F ,
main = "" ,
xlab = "Age",
xlim = c(0,100)
)