# List any packages you need to use here
packages <- c("ggplot2", "readr", "tidyverse", "dplyr", "ggpubr")

#Check to see if any of your listed packages need installed
check_install_packages <- function(pkg){
  if (!require(pkg, character.only = TRUE)) {
    install.packages(pkg, dependencies = TRUE)
    library(pkg, character.only = TRUE)
  }
}

# Download the packages and read in the libraries if necessary
sapply(packages, check_install_packages)
## $ggplot2
## NULL
## 
## $readr
## NULL
## 
## $tidyverse
## NULL
## 
## $dplyr
## NULL
## 
## $ggpubr
## NULL
## Practice Assignment 1

data("USArrests")
head(USArrests, 50)
##                Murder Assault UrbanPop Rape
## Alabama          13.2     236       58 21.2
## Alaska           10.0     263       48 44.5
## Arizona           8.1     294       80 31.0
## Arkansas          8.8     190       50 19.5
## California        9.0     276       91 40.6
## Colorado          7.9     204       78 38.7
## Connecticut       3.3     110       77 11.1
## Delaware          5.9     238       72 15.8
## Florida          15.4     335       80 31.9
## Georgia          17.4     211       60 25.8
## Hawaii            5.3      46       83 20.2
## Idaho             2.6     120       54 14.2
## Illinois         10.4     249       83 24.0
## Indiana           7.2     113       65 21.0
## Iowa              2.2      56       57 11.3
## Kansas            6.0     115       66 18.0
## Kentucky          9.7     109       52 16.3
## Louisiana        15.4     249       66 22.2
## Maine             2.1      83       51  7.8
## Maryland         11.3     300       67 27.8
## Massachusetts     4.4     149       85 16.3
## Michigan         12.1     255       74 35.1
## Minnesota         2.7      72       66 14.9
## Mississippi      16.1     259       44 17.1
## Missouri          9.0     178       70 28.2
## Montana           6.0     109       53 16.4
## Nebraska          4.3     102       62 16.5
## Nevada           12.2     252       81 46.0
## New Hampshire     2.1      57       56  9.5
## New Jersey        7.4     159       89 18.8
## New Mexico       11.4     285       70 32.1
## New York         11.1     254       86 26.1
## North Carolina   13.0     337       45 16.1
## North Dakota      0.8      45       44  7.3
## Ohio              7.3     120       75 21.4
## Oklahoma          6.6     151       68 20.0
## Oregon            4.9     159       67 29.3
## Pennsylvania      6.3     106       72 14.9
## Rhode Island      3.4     174       87  8.3
## South Carolina   14.4     279       48 22.5
## South Dakota      3.8      86       45 12.8
## Tennessee        13.2     188       59 26.9
## Texas            12.7     201       80 25.5
## Utah              3.2     120       80 22.9
## Vermont           2.2      48       32 11.2
## Virginia          8.5     156       63 20.7
## Washington        4.0     145       73 26.2
## West Virginia     5.7      81       39  9.3
## Wisconsin         2.6      53       66 10.8
## Wyoming           6.8     161       60 15.6
ggplot(USArrests, aes(x = Murder, y = Assault))+
  geom_point(color = "black")+
  geom_smooth(method = lm, se = FALSE, color = "red")+
  labs(
    title = "Scatter Plot of Assault vs. Murder Rates", 
    x = "Murder Rate", y = "Assault Rate"
  )+
  theme_classic()

## Practice Assignment 2
USArrests$State <- rownames(USArrests)
USArrests$AverageCrimeRate <- rowMeans(USArrests[c("Murder", "Assault", "Rape")], na.rm = TRUE)
USArrests$AverageCrimeRate <- round(USArrests$AverageCrimeRate, 2)

head(USArrests)
##            Murder Assault UrbanPop Rape      State AverageCrimeRate
## Alabama      13.2     236       58 21.2    Alabama            90.13
## Alaska       10.0     263       48 44.5     Alaska           105.83
## Arizona       8.1     294       80 31.0    Arizona           111.03
## Arkansas      8.8     190       50 19.5   Arkansas            72.77
## California    9.0     276       91 40.6 California           108.53
## Colorado      7.9     204       78 38.7   Colorado            83.53
ggplot(USArrests, aes(x = State, y = AverageCrimeRate, group = 1))+
  geom_line(color = "#298c9c", size = 1)+
  geom_point(color = "#800074", size = 2.5)+
  theme_classic()+
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.3),
        panel.grid.major.x = element_line(),
        panel.grid.major.y = element_line(),
        )+
  labs(
    title = "Line Plot of Average Crime Rate by State",
    x = "State", y = "Average Crime Rate"
  )

Question: When I run my code my graph comes out very choppy and not as smooth as the graph I saw when I went to check my answers. Is that something on my end or does it look fine after export. Is there something I can do to improve the quality?