#str () provides a method for displaying the structurees of a data frame
# or any R structire including vectors and lists 
# it can be used to create a basic outline for our data dictionaries
library(ggplot2)
library(dplyr)
## 
## 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(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ lubridate 1.9.3     ✔ tibble    3.2.1
## ✔ purrr     1.0.2     ✔ tidyr     1.3.1
## ✔ readr     2.1.5
## ── 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 errors
diamonds
## # A tibble: 53,940 × 10
##    carat cut       color clarity depth table price     x     y     z
##    <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
##  1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
##  2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
##  3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
##  4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
##  5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
##  6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
##  7  0.24 Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
##  8  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
##  9  0.22 Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
## 10  0.23 Very Good H     VS1      59.4    61   338  4     4.05  2.39
## # ℹ 53,930 more rows
str(diamonds)
## tibble [53,940 × 10] (S3: tbl_df/tbl/data.frame)
##  $ carat  : num [1:53940] 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
##  $ cut    : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
##  $ color  : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
##  $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
##  $ depth  : num [1:53940] 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
##  $ table  : num [1:53940] 55 61 65 58 58 57 57 55 61 61 ...
##  $ price  : int [1:53940] 326 326 327 334 335 336 336 337 337 338 ...
##  $ x      : num [1:53940] 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
##  $ y      : num [1:53940] 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
##  $ z      : num [1:53940] 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...
view(diamonds)
summary(diamonds$carat)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.2000  0.4000  0.7000  0.7979  1.0400  5.0100
val <-c (46,34,87,22,91)
mean (val)
## [1] 56
summary(diamonds$price)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     326     950    2401    3933    5324   18823
ggplot(data=diamonds)+geom_point(mapping = aes(x=carat, y=price))

ggplot(data=diamonds, mapping = aes(x=carat, y=price))+ geom_point(mapping = aes(color=cut))+ geom_smooth()
## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

#Histograms 
# a way to depict the spread of a numeric value 
hist(diamonds$carat, main= "histogram of diamonds carat weight", xlab = "carat")

hist(diamonds$price, main= "histogram of diamonds price weight", xlab = "price")

var(diamonds$carat)
## [1] 0.2246867
var(diamonds$price)
## [1] 15915629
sd(diamonds$carat)
## [1] 0.4740112
sd(diamonds$price)
## [1] 3989.44
table(diamonds$cut)
## 
##      Fair      Good Very Good   Premium     Ideal 
##      1610      4906     12082     13791     21551
ggplot(data = diamonds)+ geom_bar(mapping = aes(x=cut))

# for stat summary 
ggplot(data = diamonds)+ stat_summary(mapping = aes(x=cut, y=depth),fun.min = min, fun.max = max, fun = median)

# to add color
ggplot(data = diamonds)+ geom_bar(mapping = aes(x=cut, color = cut))

ggplot(data = diamonds)+ geom_bar(mapping = aes(x=cut, fill=cut ))

# stacking variables 
ggplot(data = diamonds)+ geom_bar(mapping = aes(x=cut, fill=clarity))

# position dodge 
ggplot(data = diamonds)+ geom_bar(mapping = aes(x=cut, fill=clarity), position= "dodge")

#map data
usa <-map_data("usa") 
ggplot(usa, aes(long,lat,group = group))+geom_polygon(fill="white", color="black")

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