⇒ Assignment4_ACJ.Rmd

State by State

numstates = ZipGeography%>%
  group_by(CityName,State)%>%
  summarise(states=n())%>%
  arrange(desc(states))%>%
  na.omit()

Babies and the Bible

BibleNames <- read.csv("http://tiny.cc/dcf/BibleNames.csv")
BibleCount <- merge(BibleNames, BabyNames) %>%
  group_by(year, sex) %>%
  summarise(total=n())
ggplot(data = BibleCount, 
       aes(x = sex,y = total, fill = sex)) + 
  geom_bar(stat='identity',position="stack") #position_stack(width=.9)) 

BibleGirls=merge(BibleNames,BabyNames)%>%
  group_by(name, sex)%>%
  summarise(total=n())%>%
  spread(key=sex, value=total, fill=0)%>%
  mutate(ratio=pmin(M/F))%>%
  filter(ratio<1)

Gender-Neutral Names

BothSexes <-
  BabyNames %>%
  spread(key=sex,value=count) %>%
  filter( F>1, M>1)
Balance = BothSexes%>%
  mutate(balance=abs(log(F/M)))%>%
  select(name, F, M, balance)%>%
  group_by(name)%>%
  summarise(totalF=sum(F), totalM=sum(M), totalBalance=mean(balance))%>%
  arrange(totalBalance)
head(Balance,10)
## Source: local data frame [10 x 4]
## 
##       name totalF totalM totalBalance
##     (fctr)  (int)  (int)        (dbl)
## 1    Tiney      5      5            0
## 2    Lenzy     11     11            0
## 3     Caro      5      5            0
## 4     Acey      5      5            0
## 5   Gustie      5      5            0
## 6  Quinnie      5      5            0
## 7     Dela      5      5            0
## 8     Ebba      5      5            0
## 9     Emer      5      5            0
## 10    Hart      7      7            0
Balance1 = Balance%>%
    filter(totalF>100)
head(Balance1,10)
## Source: local data frame [10 x 4]
## 
##         name totalF totalM totalBalance
##       (fctr)  (int)  (int)        (dbl)
## 1     Infant   3749   4036    0.1574909
## 2     Eldean    107    102    0.1874680
## 3   Notnamed    643    717    0.1885897
## 4      Sevyn    111    113    0.1936266
## 5     Paxtyn    125    111    0.1976434
## 6     Lakota   1207   1089    0.2070751
## 7  Tennessee    146    145    0.2128245
## 8     Velmer    169    154    0.2200691
## 9       Dail    125    134    0.2255523
## 10     Riyan    261    253    0.2288037