data <- read.csv("candy_production.csv")
summary(data)
##   observation_date    IPG3113N     
##  Length   :548     Min.   : 50.67  
##  N.unique :548     1st Qu.: 87.86  
##  N.blank  :  0     Median :102.28  
##  Min.nchar: 10     Mean   :100.66  
##  Max.nchar: 10     3rd Qu.:114.69  
##                    Max.   :139.92

Including Plots

You can also embed plots, for example:

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

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

data <- read.csv("candy_production.csv")
summary(data)
##   observation_date    IPG3113N     
##  Length   :548     Min.   : 50.67  
##  N.unique :548     1st Qu.: 87.86  
##  N.blank  :  0     Median :102.28  
##  Min.nchar: 10     Mean   :100.66  
##  Max.nchar: 10     3rd Qu.:114.69  
##                    Max.   :139.92
library(ggplot2)
library(ggplot2)

ggplot(data, aes(x = IPG3113N)) +
  geom_histogram(binwidth = 10, fill = "green")

data <- read.csv("candy_production.csv")
summary(data)
##   observation_date    IPG3113N     
##  Length   :548     Min.   : 50.67  
##  N.unique :548     1st Qu.: 87.86  
##  N.blank  :  0     Median :102.28  
##  Min.nchar: 10     Mean   :100.66  
##  Max.nchar: 10     3rd Qu.:114.69  
##                    Max.   :139.92
library(ggplot2)
hist(data$IPG3113N,main='histogram plot for candy production data',xlab='candy_production',prob=T) 
lines(density(data$IPG3113N),lty='dashed',lwd=2.5, col='blue')