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library(ggplot2)

## Warning: package 'ggplot2' was built under R version 3.4.2

library(dplyr)

## Warning: package 'dplyr' was built under R version 3.4.2

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(mosaic)

## Warning: package 'mosaic' was built under R version 3.4.2

## Loading required package: lattice

## Loading required package: ggformula

## Warning: package 'ggformula' was built under R version 3.4.2

## 
## New to ggformula?  Try the tutorials: 
##  learnr::run_tutorial("introduction", package = "ggformula")
##  learnr::run_tutorial("refining", package = "ggformula")

## Loading required package: mosaicData

## Warning: package 'mosaicData' was built under R version 3.4.2

## Loading required package: Matrix

## 
## The 'mosaic' package masks several functions from core packages in order to add 
## additional features.  The original behavior of these functions should not be affected by this.
## 
## Note: If you use the Matrix package, be sure to load it BEFORE loading mosaic.

## 
## Attaching package: 'mosaic'

## The following object is masked from 'package:Matrix':
## 
##     mean

## The following objects are masked from 'package:dplyr':
## 
##     count, do, tally

## The following objects are masked from 'package:stats':
## 
##     binom.test, cor, cor.test, cov, fivenum, IQR, median,
##     prop.test, quantile, sd, t.test, var

## The following objects are masked from 'package:base':
## 
##     max, mean, min, prod, range, sample, sum

cancer <- read.csv("Rdatasets-master/csv/survival/cancer.csv")
head(cancer)

##   X inst time status age sex ph.ecog ph.karno pat.karno meal.cal wt.loss
## 1 1    3  306      2  74   1       1       90       100     1175      NA
## 2 2    3  455      2  68   1       0       90        90     1225      15
## 3 3    3 1010      1  56   1       0       90        90       NA      15
## 4 4    5  210      2  57   1       1       90        60     1150      11
## 5 5    1  883      2  60   1       0      100        90       NA       0
## 6 6   12 1022      1  74   1       1       50        80      513       0

ggplot(cancer, aes(time)) +
geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(cancer, aes(time, fill = cut(time, 100))) +
geom_histogram(show.legend = FALSE) +
scale_fill_discrete(h = c(240, 10))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

p <- ggplot(cancer, aes(time, fill = cut(time, 100))) +
geom_histogram(show.legend = FALSE) +
theme_minimal() +
labs(time = "Variable X", y = "age") +
ggtitle("Histogram of Time")
p + scale_fill_discrete(h = c(180, 360), c = 150, l = 80)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(cancer, aes(time, fill = cut(time, 100))) +
geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

fill="chartreuse"