Homework One

0.1 Intro

In this assignment we are using the USArrests data set.

• Description: This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

• Notes: USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule that R’s round() uses), as found by Daniel S Coven (Arizona). See the example below on how to correct the error and improve accuracy for the ‘.5’ percentages.

• Source: World Almanac and Book of facts 1975. (Crime rates).

1 Create a scatterplot matrix for the data.

USArrests <-rownames_to_column(USArrests, var="State")
pairs(USArrests[2:5])

2 Create a numerical summary of the data.

summary(USArrests)

##     State               Murder          Assault         UrbanPop    
##  Length:50          Min.   : 0.800   Min.   : 45.0   Min.   :32.00  
##  Class :character   1st Qu.: 4.075   1st Qu.:109.0   1st Qu.:54.50  
##  Mode  :character   Median : 7.250   Median :159.0   Median :66.00  
##                     Mean   : 7.788   Mean   :170.8   Mean   :65.54  
##                     3rd Qu.:11.250   3rd Qu.:249.0   3rd Qu.:77.75  
##                     Max.   :17.400   Max.   :337.0   Max.   :91.00  
##       Rape      
##  Min.   : 7.30  
##  1st Qu.:15.07  
##  Median :20.10  
##  Mean   :21.23  
##  3rd Qu.:26.18  
##  Max.   :46.00

3 Sort the data by population and call the new data “sortedUSAArrests”

sortedUSAArrests <-arrange(USArrests,UrbanPop)
sortedUSAArrests

4 Change the variable name “UrbanPop” to “UrbanPopulation” without changing the dataset name.

colnames(USArrests)[4] <-"UrbanPopulation"
USArrests

5 Make a subset from the sorted data that only includes the top 10 states with the highest murder rates, then

HighMurder <- arrange(USArrests,desc(Murder)) %>% top_n(10,Murder)
HighMurder

6 Create a visual of the murder rate for the 10 states.

df <- data.frame(
  id = 1:10
  , Coolness_Level = 1:10
  , Coolness_Color = NA
  , stringsAsFactors = FALSE)
color.function <- colorRampPalette(c("#104E8B","#CCCCCC"))
color.ramp <- color.function(n = nrow(x = df))
df$Coolness_Color <-
  as.character(
    x = cut(
      x = rank( x = df$Coolness_Level )  # used to assign order in the event of ties
      , breaks = nrow( x = df )  # same as the 'n' supplied in color.function()
      , labels = color.ramp  # label the groups with the color in color.ramp
    )
  )

barplot(HighMurder$Murder,
        names.arg = HighMurder$State,
        las = 3,
        ylab = "Murders",
        col = df$Coolness_Color, 
        border = "black",
        main = "Top 10 Murder Rate by State")

7 Create a subset from the original data such that the subset only contains the observations with population greater than 80 and the murder rate greater than 10.

LastQuestion <- filter(USArrests,UrbanPopulation>80,Murder>10)
LastQuestion