ggplot2practice

Load Data

library (readr)

## Warning: package 'readr' was built under R version 4.0.3

Data1<-read_csv("EconData.csv")

## 
## -- Column specification --------------------------------------------------------
## cols(
##   meaning_code = col_character(),
##   code = col_character(),
##   Year = col_double(),
##   Numbers = col_number(),
##   sales = col_number(),
##   payroll = col_number(),
##   paid_employee = col_number()
## )

## Warning: 109 parsing failures.
##  row           col expected actual           file
## 1475 sales         a number      D 'EconData.csv'
## 1475 payroll       a number      D 'EconData.csv'
## 1475 paid_employee a number      i 'EconData.csv'
## 1477 sales         a number      D 'EconData.csv'
## 1477 payroll       a number      D 'EconData.csv'
## .... ............. ........ ...... ..............
## See problems(...) for more details.

library (dplyr)

## Warning: package 'dplyr' was built under R version 4.0.3

## 
## 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

Data2<-select(Data1, meaning_code, Year, sales)
Data2<-filter(Data2, 
              meaning_code == "Metal ore mining")
library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.0.3

ggplot(Data2, aes(x = Year, y = sales)) +
  geom_point() +
  labs(title = "Metal Ore Mining Sales Comparison Between Years")

library(readxl)

## Warning: package 'readxl' was built under R version 4.0.3

Data3 <- read_excel("C:/Users/tyang/Desktop/Fat_Supply_Quantity_Data.xlsx")

ggplot(Data3, aes(x = Country, y=Eggs)) + 
  geom_point()

library(readr)
Data4 <- read_csv("C:/Users/tyang/Desktop/timeseries.csv")

## 
## -- Column specification --------------------------------------------------------
## cols(
##   Month = col_character(),
##   Year = col_double(),
##   Wholesalers = col_number(),
##   Durable_Goods = col_number(),
##   Motors = col_number(),
##   Furniture = col_number(),
##   Lumber = col_number(),
##   Equipment = col_number(),
##   computer = col_number(),
##   metals = col_number(),
##   appliances = col_number(),
##   Hardwares = col_number(),
##   Machinary = col_number()
## )

ggplot(Data4, aes(x = Month, y = Wholesalers)) +
  geom_line() +
  labs(titile = "wholesale over days",
       x = "Time",
       y = "Sales")

## Warning: Removed 1 row(s) containing missing values (geom_path).

library(ggplot2)
data(economics, package = "ggplot2")
ggplot(economics, aes(x = date, y = psavert)) +
  geom_line() +
  labs(title = "Personal Savings Rate",
       x = "Date",
       y = "Personal Savings Rate")

library(ggplot2)
library(scales)

## Warning: package 'scales' was built under R version 4.0.3

## 
## Attaching package: 'scales'

## The following object is masked from 'package:readr':
## 
##     col_factor

Data5 <- ggplot(economics, aes(x = date, y = psavert)) +
  geom_line(color = "indianred3", 
            size=1 ) +
  geom_smooth() +
  scale_x_date(date_breaks = '5 years', 
               labels = date_format("%b-%y")) +
  labs(title = "Personal Savings Rate",
       subtitle = "1967 to 2015",
       x = "",
       y = "Personal Savings Rate") +
  theme_minimal()
Data5

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'