Day 5

Daily Visualisations and Analysis
Author

Khushi

Introduction

Today, we are looking at groups.

library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── 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
library(mosaic)
Registered S3 method overwritten by 'mosaic':
  method                           from   
  fortify.SpatialPolygonsDataFrame ggplot2

The 'mosaic' package masks several functions from core packages in order to add 
additional features.  The original behavior of these functions should not be affected by this.

Attaching package: 'mosaic'

The following object is masked from 'package:Matrix':

    mean

The following objects are masked from 'package:dplyr':

    count, do, tally

The following object is masked from 'package:purrr':

    cross

The following object is masked from 'package:ggplot2':

    stat

The following objects are masked from 'package:stats':

    binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
    quantile, sd, t.test, var

The following objects are masked from 'package:base':

    max, mean, min, prod, range, sample, sum
library(ggformula)
library(palmerpenguins)

Look at the wages dataset

wages <- read_csv("https://vincentarelbundock.github.io/Rdatasets/csv/stevedata/gss_wages.csv")
Rows: 61697 Columns: 12
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): occrecode, wrkstat, gender, educcat, maritalcat
dbl (7): rownames, year, realrinc, age, occ10, prestg10, childs

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
wages
# A tibble: 61,697 × 12
   rownames  year realrinc   age occ10 occrecode  prestg10 childs wrkstat gender
      <dbl> <dbl>    <dbl> <dbl> <dbl> <chr>         <dbl>  <dbl> <chr>   <chr> 
 1        1  1974     4935    21  5620 Office an…       25      0 School  Male  
 2        2  1974    43178    41  2040 Professio…       66      3 Full-T… Male  
 3        3  1974       NA    83    NA <NA>             NA      2 Housek… Female
 4        4  1974       NA    69    NA <NA>             NA      2 Housek… Female
 5        5  1974    18505    58  5820 Office an…       37      0 Full-T… Female
 6        6  1974    22206    30   910 Business/…       45      0 School  Male  
 7        7  1974    55515    48   230 Business/…       59      2 Full-T… Male  
 8        8  1974       NA    67  6355 Construct…       49      1 Retired Male  
 9        9  1974       NA    51  4720 Sales            28      2 Housek… Female
10       10  1974     4935    54  3940 Service          38      2 Full-T… Female
# ℹ 61,687 more rows
# ℹ 2 more variables: educcat <chr>, maritalcat <chr>

Glimpse - wages

glimpse(wages)
Rows: 61,697
Columns: 12
$ rownames   <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, …
$ year       <dbl> 1974, 1974, 1974, 1974, 1974, 1974, 1974, 1974, 1974, 1974,…
$ realrinc   <dbl> 4935, 43178, NA, NA, 18505, 22206, 55515, NA, NA, 4935, NA,…
$ age        <dbl> 21, 41, 83, 69, 58, 30, 48, 67, 51, 54, 89, 71, 27, 30, 22,…
$ occ10      <dbl> 5620, 2040, NA, NA, 5820, 910, 230, 6355, 4720, 3940, 4810,…
$ occrecode  <chr> "Office and Administrative Support", "Professional", NA, NA…
$ prestg10   <dbl> 25, 66, NA, NA, 37, 45, 59, 49, 28, 38, 47, 45, 50, 29, 33,…
$ childs     <dbl> 0, 3, 2, 2, 0, 0, 2, 1, 2, 2, 3, 1, 4, 3, 0, 1, 2, 3, 4, 8,…
$ wrkstat    <chr> "School", "Full-Time", "Housekeeper", "Housekeeper", "Full-…
$ gender     <chr> "Male", "Male", "Female", "Female", "Female", "Male", "Male…
$ educcat    <chr> "High School", "Bachelor", "Less Than High School", "Less T…
$ maritalcat <chr> "Married", "Married", "Widowed", "Widowed", "Never Married"…

Inspect - wages

inspect(wages)

categorical variables:  
        name     class levels     n missing
1  occrecode character     11 58136    3561
2    wrkstat character      8 61676      21
3     gender character      2 61697       0
4    educcat character      5 61562     135
5 maritalcat character      5 61670      27
                                   distribution
1 Professional (19%), Service (16.9%) ...      
2 Full-Time (49.4%), Housekeeper (15.1%) ...   
3 Female (56.1%), Male (43.9%)                 
4 High School (51.5%) ...                      
5 Married (51.7%), Never Married (21.8%) ...   

quantitative variables:  
      name   class  min    Q1 median    Q3      max         mean           sd
1 rownames numeric    1 15425  30849 46273  61697.0 30849.000000 17810.534116
2     year numeric 1974  1985   1996  2006   2018.0  1996.073715    12.794470
3 realrinc numeric  227  8156  16563 27171 480144.5 22326.359234 28581.794499
4      age numeric   18    32     44    59     89.0    46.176177    17.561065
5    occ10 numeric   10  2710   4720  6230   9997.0  4695.774081  2627.724076
6 prestg10 numeric   16    33     42    50     80.0    43.060701    12.987526
7   childs numeric    0     0      2     3      8.0     1.923457     1.763569
      n missing
1 61697       0
2 61697       0
3 37887   23810
4 61478     219
5 58136    3561
6 57511    4186
7 61508     189

Skim - wages

skimr::skim(wages)
Data summary
Name wages
Number of rows 61697
Number of columns 12
_______________________
Column type frequency:
character 5
numeric 7
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
occrecode 3561 0.94 5 37 0 11 0
wrkstat 21 1.00 5 23 0 8 0
gender 0 1.00 4 6 0 2 0
educcat 135 1.00 8 21 0 5 0
maritalcat 27 1.00 7 13 0 5 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
rownames 0 1.00 30849.00 17810.53 1 15425 30849 46273 61697.0 ▇▇▇▇▇
year 0 1.00 1996.07 12.79 1974 1985 1996 2006 2018.0 ▆▇▇▇▇
realrinc 23810 0.61 22326.36 28581.79 227 8156 16563 27171 480144.5 ▇▁▁▁▁
age 219 1.00 46.18 17.56 18 32 44 59 89.0 ▇▇▆▅▂
occ10 3561 0.94 4695.77 2627.72 10 2710 4720 6230 9997.0 ▃▅▇▂▃
prestg10 4186 0.93 43.06 12.99 16 33 42 50 80.0 ▃▇▇▃▁
childs 189 1.00 1.92 1.76 0 0 2 3 8.0 ▇▇▂▁▁

Data Dictionary

wages_clean <-
  wages %>%
  tidyr::drop_na(realrinc)

Question-1: What is the basic distribution of realrinc?

wages_clean %>%
  gf_boxplot(realrinc ~ "Income") %>% # Dummy X-axis "variable"
  gf_labs(
    title = "Plot 1A: Income",
    subtitle = "Many outliers on the high side"
  )

## A boxplot visually summarizes the distribution of a dataset by displaying its quartiles. It highlights the median, the interquartile range (IQR), and potential outliers. The whiskers extend to the smallest and largest values within 1.5 times the IQR, while any points outside this range are considered outliers.
## A skewed distribution occurs when data is unevenly spread, with one tail longer than the other. In right-skewed data, the tail is longer on the right; in left-skewed data, it's longer on the left. The mean, median, and mode are different in skewed distributions.

Question-2: Is realrinc affected by gender?

wages_clean %>%
  gf_boxplot(gender ~ realrinc) %>%
  gf_labs(title = "Plot 2A: Income by Gender")