Week_1_codigo.R

# From the course Making Sense of Data
# Video at: C:\Users\GATEWAY\Videos\Making_sense_of_data
setwd("C:\\Users\\GATEWAY\\Documents")
LifeExpRegion <- read.table("~/making_sense_ofData/LifeExpRegion.txt", quote="\"", comment.char="")
head(LifeExpRegion)

##            V1     V2   V3
## 1 Afghanistan 48.673  SAs
## 2     Albania 76.918 EuCA
## 3     Algeria 73.131 MENA
## 4      Angola 51.093  SSA
## 5   Argentina 75.901 Amer
## 6     Armenia 74.241 EuCA

library(magrittr)
lifexp <- set_colnames(LifeExpRegion,c("country","years","region"))
head(lifexp)

##       country  years region
## 1 Afghanistan 48.673    SAs
## 2     Albania 76.918   EuCA
## 3     Algeria 73.131   MENA
## 4      Angola 51.093    SSA
## 5   Argentina 75.901   Amer
## 6     Armenia 74.241   EuCA

str(lifexp)

## 'data.frame':    197 obs. of  3 variables:
##  $ country: Factor w/ 197 levels "Afghanistan",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ years  : num  48.7 76.9 73.1 51.1 75.9 ...
##  $ region : Factor w/ 6 levels "Amer","EAP","EuCA",..: 5 3 4 6 1 3 1 2 3 3 ...

table(lifexp$region)

## 
## Amer  EAP EuCA MENA  SAs  SSA 
##   39   30   50   21    8   49

counts <- table(lifexp$region)
relfreq <- counts/sum(counts)
region_names <- c("Americas","E.Asia&Pc","Eur&C.As","M.E&Afr",
                  "S.Asia","S-S.Africa")
barplot(counts,col = rainbow(6),names.arg = region_names,main = "World Regions: Bar
        Chart(counts")

barplot(relfreq,col = rainbow(6),names.arg=region_names,main="World Regions:Bar
        Chart(relative frequencies")

pie(counts,col = rainbow(6),labels = region_names,main = "World Regions: Pie Chart")

SkeletonData <- read.csv("~/making_sense_ofData/SkeletonData.txt", sep="")
summary(SkeletonData$BMI)

##      normal       obese  overweight underweight 
##         225          20          81          74

with(SkeletonData,{
  sex_counts = table(Sex)
  sex_relfreq = sex_counts/sum(sex_counts)
  sex_names=c("Male","Female")
  barplot(sex_counts,col = rainbow(2),names.arg = sex_names,main = "Skeleton Sex Bar
 Relative Frequencies")
  barplot(sex_relfreq,col = rainbow(2),names.arg = sex_names,main = "Skeleton Sex Bar Chart")
  pie(sex_counts,col = rainbow(2),labels = sex_names,main = "Skeleton Sex Pie Chart")
     })

with(SkeletonData,{
  bmi_counts = table(BMI)
  bmi_relfreq = bmi_counts/sum(bmi_counts)
  bmi_names = c("underweigth","normal","obese","overweight")
  barplot(bmi_counts,col = rainbow(4),names.arg = bmi_names,main = "BMI Bar Chart")
  barplot(bmi_relfreq,col = rainbow(4),names.arg = bmi_names,main = "BMI Bar Chart Relative Freq.")
  pie(bmi_counts,col = rainbow(4),labels = bmi_names,main = "BMI Pie Chart")
})