d2t-chenzhu.rmd

# Dare To Tinker (R markdown file)
- Chen Zhu (Birmingham Law School)
- Code demonstration for UoB Msc Responsible Data Science (RDS) students (October 2022)


## exploring law-related datasets in R 
### US Judges Ratings (base-R)

```{r} 
library(skimr)

data()

str (USJudgeRatings) #==> a dataframe
?USJudgeRatings
require(graphics)
pairs(USJudgeRatings, main = "USJudgeRatings data")

head (USJudgeRatings, n = 20)
tail (USJudgeRatings)
colnames (USJudgeRatings)

USJudgeRatings[,1]

## list Judges' names by using the ~rownames~ function
rownames (USJudgeRatings)

## list all the 12 variables (i.e. column names)
colnames (USJudgeRatings)


skim (USJudgeRatings) #==> skim through the dataset
```

### exploring `historydata` (not a base-R package)

- Lincoln Mullen, ‘Data Sets for Historians [R Package Historydata Version 0.1]’ (24 December 2014) <https://CRAN.R-project.org/package=historydata>

 
```{r , eval = TRUE, echo = FALSE }
install.packages (historydata)

library (tidyverse)
library (historydata)

# description of the datasets
?historydata
?judges
                                        #==>  Federal judges in the United States of America since 1789; two dataframes ‘judges_people’ and ‘judges_appointments’.

#---------------------------
# how many variables in `judges_people`?
colnames (judges_people) # 13 variables

# how to find judges' age range? 
judges_people$birth_date 

## two ways of doing this
sort (judges_people$birth_date)

judges_people$birth_date %>% sort() # using pipe (from `tidyverse`)

unique (judges_people$birth_date) %>% sort()

# visualisation: histogram of judges birth years
judges_people$birth_date %>% hist()
#==> where is the peak of this histogram? (most judges were born in which decade?)

# find a particular judge: [eg] Is Judge Alsup (Oracle vs Google) in this data?
"Alsup" %in% judges_people$name_last

alsup <- judges_people[judges_people$name_last == "Alsup", ]

str (alsup) 
alsup %>% str # use pipe (from "tidyverse")

#---------------------------
# race and gender of judges
judges_people$race
judges_people$gender

# how diverse judges' ethinical background? how many "races" in this dataset?
judges_people$race 
judges_people$race %>% unique() 


# white
judges_people[judges_people$race == "White",]

# white and female
judges_people[judges_people$race == "White" & judges_people$gender == "F", ] # white female judges

# “Black” judges (African American)
judges_people[judges_people$race == "African American",]

# female black judges
judges_people[judges_people$race == "African American"  & judges_people$gender == "F", ]

```
# shell command
## date and calendar 

```{shell}
date

cal Oct 2022
cal sep 1752


```
## for loop
```{shell}
for i in $(seq 1 10); do echo "RDS$i"; done
```

## grep
```{shell}
grep -ri law *
```