Problem 1

my_vector <- c(2, 4, 5, 7, 10)
sum(my_vector)
## [1] 28

Problem 2

my_dat <- tibble(
  alpha = 1:5,
  beta = 6:10
)
my_dat
## # A tibble: 5 × 2
##   alpha  beta
##   <int> <int>
## 1     1     6
## 2     2     7
## 3     3     8
## 4     4     9
## 5     5    10

Problem 3

summary(my_dat$beta)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       6       7       8       8       9      10

Problem 4

boxplot(dat$faminc_new, 
        main = "Sylwester Lichosik", 
        xlab = "Family Income")

Problem 5

problem5 <- dat %>%
  filter(gender == 1,
         region %in% c(1, 2),
         marstat == 1,
         newsint == 1)

str(problem5)
## 'data.frame':    75 obs. of  25 variables:
##  $ caseid      : int  420208067 412948037 411855595 414480371 416806180 414729651 412021973 412348831 412929385 412047867 ...
##  $ region      : int  2 1 1 1 2 2 1 2 1 2 ...
##  $ gender      : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ educ        : int  3 5 6 5 3 2 6 5 5 5 ...
##  $ edloan      : int  2 1 2 1 1 2 2 2 2 2 ...
##  $ race        : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ hispanic    : int  2 2 2 2 2 2 2 2 2 2 ...
##  $ employ      : int  1 1 1 1 1 5 5 1 1 1 ...
##  $ marstat     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ pid7        : int  4 1 4 4 6 2 1 1 1 7 ...
##  $ ideo5       : int  5 1 3 3 3 1 2 3 1 5 ...
##  $ pew_religimp: int  4 4 4 1 2 4 4 2 4 1 ...
##  $ newsint     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ faminc_new  : int  10 11 10 9 10 2 13 8 7 11 ...
##  $ union       : int  1 2 3 3 1 3 2 2 2 3 ...
##  $ investor    : int  2 1 1 1 1 2 1 2 1 2 ...
##  $ CC18_308a   : int  4 4 4 1 2 3 4 4 4 1 ...
##  $ CC18_310a   : int  2 3 3 3 2 2 3 2 3 2 ...
##  $ CC18_310b   : int  2 3 3 3 2 3 3 5 3 3 ...
##  $ CC18_310c   : int  3 3 3 3 3 3 2 5 3 3 ...
##  $ CC18_310d   : int  2 5 3 3 3 2 2 2 3 3 ...
##  $ CC18_325a   : int  1 2 1 1 1 2 2 2 2 1 ...
##  $ CC18_325b   : int  2 2 1 1 1 1 2 1 2 2 ...
##  $ CC18_325c   : int  1 2 2 1 1 2 2 1 2 2 ...
##  $ CC18_325d   : int  1 1 1 1 1 1 1 1 2 1 ...

Problem 6

problem6 <- dat %>%
  filter(gender == 1,
         region %in% c(1, 2),
         marstat == 1,
         newsint == 1)
table(problem6$investor)
## 
##  1  2 
## 57 18

Problem 7

problem7 <- dat %>%
  mutate(tax_scale = recode(CC18_325a, `1` = 1, `2` = 0) + 
                     recode(CC18_325d, `1` = 1, `2` = 0))

head(problem7$tax_scale, 20)
##  [1] 2 1 1 1 2 2 2 1 0 1 0 2 0 1 0 1 1 1 2 1

Problem 8

table(problem7$tax_scale)
## 
##   0   1   2 
## 130 408 331

Problem 9

problem9 <- dat %>%
  group_by(region) %>%
  summarise(mean_9 = mean(CC18_308a))

problem9
## # A tibble: 4 × 2
##   region mean_9
##    <int>  <dbl>
## 1      1   2.77
## 2      2   2.76
## 3      3   2.71
## 4      4   3.03

Problem 10

problem10 <- dat %>%
  filter(investor == 2, faminc_new >= 5 & faminc_new <= 10) %>%
  summarise(
    mean_religion = mean(pew_religimp),
    median_religion = median(pew_religimp),
    sd_religion = sd(pew_religimp)
  )

problem10
##   mean_religion median_religion sd_religion
## 1      2.325893               2    1.188906

Problem 11

dat %>%
  summarise(
    mean = mean(faminc_new),
    median = median(faminc_new),
    sd = sd(faminc_new)
  ) %>%
knitr::kable(caption = "Summarise Family Income")
Summarise Family Income
mean median sd
6.581128 6 3.247035

Problem 12

qplot(pid7, data = dat, geom = "histogram", 
      xlab = "Seven Point Party ID", 
      ylab = "Count")
## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.