Module 2

Packages and Data

setwd("~/mth250")
library(tidyverse)

df <- read.csv("Dropout Final Project Data.csv",skip=1,
               stringsAsFactors=FALSE,na.strings=c("","‡")) %>%
  slice(-c(1:3)) %>%
  slice(-(61:nrow(.))) %>%
  select(-starts_with("X")) %>%
  rename(state=State.or.jurisdiction) %>%
  filter(!is.na(state)) %>%
  mutate(state=gsub("2","",state)) %>%
  mutate_at(vars(starts_with("Grade")),
            .funs=function(x) as.numeric(gsub(",","",x)))

#R> Warning in (function (x) : NAs introduced by coercion

#R> Warning in (function (x) : NAs introduced by coercion

#R> Warning in (function (x) : NAs introduced by coercion

#R> Warning in (function (x) : NAs introduced by coercion

#R> Warning in (function (x) : NAs introduced by coercion

#R> Warning in (function (x) : NAs introduced by coercion

head(df)

#R>        state Grade.7 Grade.8 Grade.9 Grade.10 Grade.11 Grade.12
#R> 1    Alabama      NA      NA   64569    53604    47538    42672
#R> 2     Alaska   10886   10857   11934    10664     9625     8766
#R> 3    Arizona   80011   78747   92028    82686    75384    71161
#R> 4   Arkansas      NA      NA   38359    35869    31995    28698
#R> 5 California  496757  502927  554935   501661   462940   412605
#R> 6   Colorado   59548   59397   64446    57678    52770    50387

df2 <- pivot_longer(df,-state,names_to="Grade",values_to="Dropouts")%>%
  mutate(Grade=as.numeric(gsub("Grade.","",Grade)))
head(df2)

#R> # A tibble: 6 x 3
#R>   state   Grade Dropouts
#R>   <chr>   <dbl>    <dbl>
#R> 1 Alabama     7       NA
#R> 2 Alabama     8       NA
#R> 3 Alabama     9    64569
#R> 4 Alabama    10    53604
#R> 5 Alabama    11    47538
#R> 6 Alabama    12    42672

Dropouts per State in 2004-2005 School Year

p <- ggplot(data=df2,mapping=aes(x=state, y=Dropouts))+
  geom_bar(stat="identity" ,color="grey", alpha=0.75, fill="wheat")+
  scale_x_discrete(name="State")+
  scale_y_continuous(name="Total Number of Student Dropouts", breaks=seq(0,100000,15000),limits=c(0,100000),expand=expansion(mult=c(0,0.01)))+
  theme(legend.position = "none")+
  theme_bw() + 
  theme(panel.grid.major.x=element_blank(), axis.text.x=element_text(angle=90))+
  scale_fill_manual() +
  scale_color_manual()+
  labs(title="Dropouts per State in 2004-2005", x=element_blank(), y=element_blank())
p

#R> Warning: Removed 94 rows containing missing values (position_stack).

#R> Warning: Removed 101 rows containing missing values (geom_bar).

#Dropouts in the US per Grade

p2 <- ggplot(data=df2, mapping=aes(x=Grade, y=Dropouts)) +
  geom_bar(stat="identity", fill="grey") +
 scale_x_continuous(name="Grade", breaks=seq(7,12,1)) +
  scale_y_continuous(name="Total Dropouts",breaks=seq(0,4000000,100000)) +
  theme_bw()+
  theme(axis.text.y=element_text(hjust=.25))+
    labs(title="US Dropouts per Grade in 2004-2005", x=element_blank(), y=element_blank())
  geom_hline(yintercept=0)

#R> mapping: yintercept = ~yintercept 
#R> geom_hline: na.rm = FALSE
#R> stat_identity: na.rm = FALSE
#R> position_identity

p2

#R> Warning: Removed 37 rows containing missing values (position_stack).