Week 4: Apply 3

Import data

# excel file
data <- read_excel("data/myData.xlsx")
data

## # A tibble: 125 × 27
##    EmployerName  Emplo…¹ Address PostC…² Compa…³ SicCo…⁴ DiffM…⁵ DiffM…⁶ DiffM…⁷
##    <chr>           <dbl> <chr>   <chr>   <chr>   <chr>     <dbl>   <dbl>   <dbl>
##  1 ABACUS EMPLO…     767 1 Cheq… RG21 7… 026383… "78200"    -3       3.2    NA  
##  2 AFH INDEPEND…   19364 Afh Ho… B60 4JE 040491… "64999"    25      32     -33  
##  3 Allerdale Bo…   17194 Allerd… CA14 3… <NA>    "1,\r\…     9.5    17      NA  
##  4 ALTRAD EMPLO…   21061 Buildi… S63 0JF 116311… "78300"    15      25      -1  
##  5 APPLUS UK LTD    1343 Block … FK3 8YE SC2361… "71200"    50.6    56      26.6
##  6 ARCHANT COMM…    1374 Loudwa… HP10 9… 000193… "58130…    14.5    15.8    21.8
##  7 ARRK EUROPE …      87 Caldwe… CV11 4… 034186… "32990"    -1.8    12.9   -58.1
##  8 ASSOCIATION …   21453 2nd Fl… EC4R 9… 037443… "86900…     4.2    -7.3    NA  
##  9 ATLAS PROFES…   19368 10th F… BS1 4XE 057889… "78109"   -10      16      NA  
## 10 Bank of Engl…   14475 Thread… EC2R 8… <NA>    "1,\r\…    18.5    20.1    23.1
## # … with 115 more rows, 18 more variables: DiffMedianBonusPercent <dbl>,
## #   MaleBonusPercent <dbl>, FemaleBonusPercent <dbl>, MaleLowerQuartile <dbl>,
## #   FemaleLowerQuartile <dbl>, MaleLowerMiddleQuartile <dbl>,
## #   FemaleLowerMiddleQuartile <dbl>, MaleUpperMiddleQuartile <dbl>,
## #   FemaleUpperMiddleQuartile <dbl>, MaleTopQuartile <dbl>,
## #   FemaleTopQuartile <dbl>, CompanyLinkToGPGInfo <chr>,
## #   ResponsiblePerson <chr>, EmployerSize <chr>, CurrentName <chr>, …

Plot prices

data %>%
    
    ggplot(aes(DiffMeanHourlyPercent)) +
    geom_bar()