Section 4.15

1. Load the murders dataset. Which of the following is true?

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(dslabs)
murders
##                   state abb        region population total
## 1               Alabama  AL         South    4779736   135
## 2                Alaska  AK          West     710231    19
## 3               Arizona  AZ          West    6392017   232
## 4              Arkansas  AR         South    2915918    93
## 5            California  CA          West   37253956  1257
## 6              Colorado  CO          West    5029196    65
## 7           Connecticut  CT     Northeast    3574097    97
## 8              Delaware  DE         South     897934    38
## 9  District of Columbia  DC         South     601723    99
## 10              Florida  FL         South   19687653   669
## 11              Georgia  GA         South    9920000   376
## 12               Hawaii  HI          West    1360301     7
## 13                Idaho  ID          West    1567582    12
## 14             Illinois  IL North Central   12830632   364
## 15              Indiana  IN North Central    6483802   142
## 16                 Iowa  IA North Central    3046355    21
## 17               Kansas  KS North Central    2853118    63
## 18             Kentucky  KY         South    4339367   116
## 19            Louisiana  LA         South    4533372   351
## 20                Maine  ME     Northeast    1328361    11
## 21             Maryland  MD         South    5773552   293
## 22        Massachusetts  MA     Northeast    6547629   118
## 23             Michigan  MI North Central    9883640   413
## 24            Minnesota  MN North Central    5303925    53
## 25          Mississippi  MS         South    2967297   120
## 26             Missouri  MO North Central    5988927   321
## 27              Montana  MT          West     989415    12
## 28             Nebraska  NE North Central    1826341    32
## 29               Nevada  NV          West    2700551    84
## 30        New Hampshire  NH     Northeast    1316470     5
## 31           New Jersey  NJ     Northeast    8791894   246
## 32           New Mexico  NM          West    2059179    67
## 33             New York  NY     Northeast   19378102   517
## 34       North Carolina  NC         South    9535483   286
## 35         North Dakota  ND North Central     672591     4
## 36                 Ohio  OH North Central   11536504   310
## 37             Oklahoma  OK         South    3751351   111
## 38               Oregon  OR          West    3831074    36
## 39         Pennsylvania  PA     Northeast   12702379   457
## 40         Rhode Island  RI     Northeast    1052567    16
## 41       South Carolina  SC         South    4625364   207
## 42         South Dakota  SD North Central     814180     8
## 43            Tennessee  TN         South    6346105   219
## 44                Texas  TX         South   25145561   805
## 45                 Utah  UT          West    2763885    22
## 46              Vermont  VT     Northeast     625741     2
## 47             Virginia  VA         South    8001024   250
## 48           Washington  WA          West    6724540    93
## 49        West Virginia  WV         South    1852994    27
## 50            Wisconsin  WI North Central    5686986    97
## 51              Wyoming  WY          West     563626     5
class(murders)
## [1] "data.frame"

*murders is in tidy format and is stored in a data frame.

2. Use as_tibble to convert the murders data table into a tibble and save it in an object called murders_tibble.

murders_tibble<-as_tibble(murders)

3. Use the group_by function to convert murders into a tibble that is grouped by region.

group_by(murders_tibble, region)
## # A tibble: 51 × 5
## # Groups:   region [4]
##    state                abb   region    population total
##    <chr>                <chr> <fct>          <dbl> <dbl>
##  1 Alabama              AL    South        4779736   135
##  2 Alaska               AK    West          710231    19
##  3 Arizona              AZ    West         6392017   232
##  4 Arkansas             AR    South        2915918    93
##  5 California           CA    West        37253956  1257
##  6 Colorado             CO    West         5029196    65
##  7 Connecticut          CT    Northeast    3574097    97
##  8 Delaware             DE    South         897934    38
##  9 District of Columbia DC    South         601723    99
## 10 Florida              FL    South       19687653   669
## # ℹ 41 more rows

4. Write tidyverse code that is equivalent to this code:

exp(mean(log(murders$population)))

Write it using the pipe so that each function is called without arguments. Use the dot operator to access the population. Hint: The code should start with murders |>.

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ readr     2.1.4
## ✔ ggplot2   3.4.4     ✔ stringr   1.5.1
## ✔ lubridate 1.9.3     ✔ tibble    3.2.1
## ✔ purrr     1.0.2     ✔ tidyr     1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
murders |>  _$population |> log() |> mean() |> exp()
## [1] 3675209

5. Use the map_df to create a data frame with three columns named n, s_n, and s_n_2. The first column should contain the numbers 1 through 100. The second and third columns should each contain the sum of 1 through \(n\) with  \(n\)  the row number.

library(purrr)
library(dplyr)
compute_s_n <- function(n){
  x <- 1:n
  tibble(sum = sum(x))
}
n<-c(seq(1, 100, by=1))
sno<-map_df(n, compute_s_n)
snox<-mutate(sno, n)
compute_s_n_2<-map_df(n, compute_s_n)
final<-full_join(snox, compute_s_n_2)
## Joining with `by = join_by(sum)`