Week 7: Apply it to your data 6

Import data

## # A tibble: 45,090 × 10
##    stock_symbol date                 open  high   low close adj_close    volume
##    <chr>        <dttm>              <dbl> <dbl> <dbl> <dbl>     <dbl>     <dbl>
##  1 AAPL         2010-01-04 00:00:00  7.62  7.66  7.58  7.64      6.52 493729600
##  2 AAPL         2010-01-05 00:00:00  7.66  7.70  7.62  7.66      6.53 601904800
##  3 AAPL         2010-01-06 00:00:00  7.66  7.69  7.53  7.53      6.42 552160000
##  4 AAPL         2010-01-07 00:00:00  7.56  7.57  7.47  7.52      6.41 477131200
##  5 AAPL         2010-01-08 00:00:00  7.51  7.57  7.47  7.57      6.45 447610800
##  6 AAPL         2010-01-11 00:00:00  7.6   7.61  7.44  7.50      6.40 462229600
##  7 AAPL         2010-01-12 00:00:00  7.47  7.49  7.37  7.42      6.32 594459600
##  8 AAPL         2010-01-13 00:00:00  7.42  7.53  7.29  7.52      6.41 605892000
##  9 AAPL         2010-01-14 00:00:00  7.50  7.52  7.46  7.48      6.38 432894000
## 10 AAPL         2010-01-15 00:00:00  7.53  7.56  7.35  7.35      6.27 594067600
## # ℹ 45,080 more rows
## # ℹ 2 more variables: Column1 <lgl>, HPR <dbl>

Questions

Variation

Visualizing distributions

Typical values

Unusual values

Missing Values

## # A tibble: 2 × 10
##   stock_symbol date    open  high   low close adj_close volume
##   <chr>        <dttm> <dbl> <dbl> <dbl> <dbl>     <dbl>  <dbl>
## 1 <NA>         NA        NA    NA    NA    NA        NA     NA
## 2 <NA>         NA        NA    NA    NA    NA        NA     NA
## # ℹ 2 more variables: Column1 <lgl>, HPR <dbl>

Covariation

A categorical and continuous variable

Two categorical variables

Two continous variables

Patterns and models