title: “MiniLesson1” author: “Tin Le” date: “September 14, 2017” output: html_document —

Mini Lesson 1 Tin Le 9/15/2017

Creating a pie graph using coord_polar() function

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

glimpse(mpg)

## Observations: 234
## Variables: 11
## $ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "...
## $ model        <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 qua...
## $ displ        <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0,...
## $ year         <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1...
## $ cyl          <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6...
## $ trans        <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)...
## $ drv          <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4",...
## $ cty          <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 1...
## $ hwy          <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 2...
## $ fl           <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p",...
## $ class        <chr> "compact", "compact", "compact", "compact", "comp...

theme_set(theme_classic())

#Creating Frequency table for transmission

df <- as.data.frame(table(mpg$trans))
colnames(df) <- c("trans", "count") #Creating column names

#creating ggplot
pie <- ggplot(df, aes(x = "", y = count, fill = factor(trans))) +
  geom_bar(width = 1, stat = "identity") +
  #adding simple changes to graph for readability
  theme(axis.line = element_blank(),
        plot.title = element_text(hjust=0.5)) +
  #adding athestics for graph
  labs(fill = "trans",
       x = NULL,
       Y = NULL,
       title = "Pie Chart of Trans",
       caption = "Source: mpg")

print(pie)

The current graph is not what was wanted but has good information. Data seems to be stacked. and adding percentages.

#dplyr
df2 <-  mpg %>%
  group_by(trans) %>% #
  summarise(count = n()) %>%
  mutate(rel.percentage = paste0(round(100 * count/sum(count), 0), "%")) #source: stackoverflow

print(df2)

## # A tibble: 10 x 3
##         trans count rel.percentage
##         <chr> <int>          <chr>
##  1   auto(av)     5             2%
##  2   auto(l3)     2             1%
##  3   auto(l4)    83            35%
##  4   auto(l5)    39            17%
##  5   auto(l6)     6             3%
##  6   auto(s4)     3             1%
##  7   auto(s5)     3             1%
##  8   auto(s6)    16             7%
##  9 manual(m5)    58            25%
## 10 manual(m6)    19             8%

coord_polar function only has a few arguments: theta: the variable to maple angles to(x or Y) start: where at the point the pie chart begins and ends. Defaults to 12 o’clock direction: clockwise(1) or counter clockwise(-1)

#creating ggplot
g1 <- ggplot(transform(transform(df2, count = count/sum(count)), labPos=cumsum(count)-count/2),aes(x = "", y = count, fill = trans)) + #Changes count numbers to percentages #stackoverflow
  geom_bar(width = 1, stat = "identity") +
  #adding simple changes to graph for readability
  theme(axis.line = element_blank(),
        plot.title = element_text(hjust=0.5)) +
  #adding athestics for graph
  labs(fill = "trans",
       x = NULL,
       Y = NULL,
       title = "Pie Chart of Trans",
       caption = "Source: mpg"
       ) 
  

print(pie)

#creates a pie chart
pie1 = g1 + coord_polar(theta = "y")
print(pie1)