data-science-collection's Introduction

Data Exploration

Jiaqi Yin 07 May, 2019

titanic data

knitr::opts_chunk$set(echo = TRUE,nclude=FALSE, cache=TRUE, include=TRUE, message=FALSE, warning=FALSE)
rm(list = ls()) # clean all the data

library(ggplot2)
library(dplyr)
library(tidyr)

library(rms)
library(rmarkdown)

dplyr, tidyr, and ggplots are excellent tools dealing with data. There are so many usefull functions in those packages and sometimes we (I) may forget how to implement those functions. Therefore, I try to make a notebook where I collect some representative work from my previous/current projects. There are no certain orders here. However, once it (this notebook) grows large enough, I will organize it and maybe have blogs for it.

`titanic data`

getHdata(titanic3)

dat = titanic3 %>% select(survived, pclass, sex, age) # only select those variables

dat$dead = 1 - dat$survived

# dead vs. passeneger class
dat %>%
      group_by(pclass, dead) %>%
      summarise(n = n()) %>% # count number
      mutate(N = sum(n), prop = n / sum(n)) %>% # add prop column
      filter(dead == 1) %>% ggplot(aes(x = pclass, y = prop)) + 
      geom_point(aes(size = N), alpha = 0.7, color = "red") +
      scale_y_continuous(breaks = seq(0, 1, 0.2), limits = c(0, 1)) +
      scale_size_continuous(breaks = c(300, 500, 700), range = c(10,25), name = "Size") + 
      xlab("Passenger Class") +
      ylab("Probability of Death") +
      theme_bw() + # white backgroud
      theme(axis.text.x = element_text(color = "grey20", size = 10, face = "plain"),
            axis.text.y = element_text(color = "grey20", size = 10, face = "plain"),
            axis.title.x = element_text(color = "grey20", size = 15, face = "bold"),
            axis.title.y = element_text(color = "grey20", size = 15, face = "bold"),
            legend.text = element_text(size = 10),
            legend.title = element_text(size = 10, face = "bold"),
            legend.position="bottom")