Accessing and Manipulating Data - Data Science for Lawyers

There are different commands you can use to access data in your R objects, and their use differs slightly between object classes.

                            # To access an item in a list, just indicate its place within the vector through [ ].

character_vector <- c("Days", "Months", "Year")

character_vector[2]

## [1] "Months"

# If you are unsure about the location of given information in a list, use the which() function.

which(character_vector == "Months")

 ##[1] 2

Most of the time we work with dataframes. Dataframes have two dimensions – rows and columns. When using [ ] to access values in dataframes, R has an in-built ordering of placing rows before columns i.e. dataframe[row,columm].

# So when you want to access data on "Arkansas" (4th row) on "Murder" (1st column), you would write:

USArrests[4,1]

 ## [1] 8.8


# If you are interested in all columns for Arkansas just leave the space behind the comma blank.

USArrests[4,]

##

State	Murder	Assault	Urban Population	Rape
Arkansas	8.8	190	50	19.5


# Similarly, if you are interested in murder rates for all states just leave the space in front of the comma blank.

USArrests[,1]

## [1] 13.2 10.0 8.1 8.8 9.0 7.9 3.3 5.9 15.4 17.4 5.3 2.6 10.4 7.2 2.2 6.0 9.7 15.4 2.1 11.3 4.4 12.1 2.7 16.1 9.0 6.0

[27] 4.3 12.2 2.1 7.4 11.4 11.1 13.0 0.8 7.3 6.6 4.9 6.3 3.4 14.4 3.8 13.2 12.7 3.2 2.2 8.5 4.0 5.7 2.6 6.8

There are alternative ways to get to the same information.

                            # To access only the murder rates in the USArrests dataframe, you can call the column name:

USArrests["Murder"]

##

State	Murder
Alabama	13.2
Alaska	10.0
Arizona	8.1
Arkansas	8.8
California	9.0
Colorado	7.9
Connecticut	3.3
Delaware	5.9
Florida	15.4
Georgia	17.4
Hawaii	5.3
Idaho	2.6
Illinois	10.4
Indiana	7.2
Iowa	2.2
Kansas	6.0
Kentucky	9.7
Louisiana	15.4
Maine	2.1
Maryland	11.3
Massachusetts	4.4
Michigan	12.1
Minnesota	2.7
Mississippi	16.1
Missouri	9.0
Montana	6.0
Nebraska	4.3
Nevada	12.2
New Hampshire	2.1
New Jersey	7.4
New Mexico	11.4
New York	11.1
North Carolina	13.0
North Dakota	0.8
Ohio	7.3
Oklahoma	6.6
Oregan	4.9
Pennsylvania	6.3
Rhode Island	3.4
South Carolina	14.4
South Dakota	3.8
Tennessee	13.2
Texas	12.7
Utah	3.2
Vermont	2.2
Virginia	8.5
Washington	4.0
West Virginia	5.7
Wisconsin	2.6
Wyoming	6.8


# Alternatively, to access the data in a column in a dataframe we can also use the $ character:

USArrests$Murder

## [1] 13.2 10.0 8.1 8.8 9.0 7.9 3.3 5.9 15.4 17.4 5.3 2.6 10.4 7.2 2.2 6.0 9.7 15.4 2.1 11.3 4.4 12.1 2.7 16.1 9.0 6.0

[27] 4.3 12.2 2.1 7.4 11.4 11.1 13.0 0.8 7.3 6.6 4.9 6.3 3.4 14.4 3.8 13.2 12.7 3.2 2.2 8.5 4.0 5.7 2.6 6.8

You can combine the above $ and [ ] approaches.

                            # For instance, to again get the murder rates in Arkansas, you can write:

USArrests$Murder[4]

 ## [1] 8.8

Sometimes we are only interested in subsets of the data, we then create objects from larger dataframes.

                            # For instance, we want to create a new dataframe that only has the murder rate data.

murder_rate <- USArrests["Murder"]


# Or we want to exclude murder rates from our dataset. Then we can simply write [,-1] which will eliminate the first column.

other_crimes <- USArrests[,-1]

print(other_crimes)

##

State	Assault	Urban Population	Rape
Alabama	236	58	21.2
Alaska	263	48	44.5
Arizona	294	80	31.0
Arkansas	190	50	19.5
California	276	91	40.6
Colorado	204	78	38.7
Connecticut	110	77	11.1
Delaware	238	72	15.8
Florida	335	80	31.9
Georgia	211	60	25.8
Hawaii	46	83	20.2
Idaho	120	54	14.2
Illinois	249	83	24.0
Indiana	113	65	21.0
Iowa	56	57	11.3
Kansas	115	66	18.0
Kentucky	109	52	16.3
Louisiana	249	66	22.2
Maine	83	51	7.8
Maryland	300	67	27.8
Massachusetts	149	85	16.3
Michigan	255	74	35.1
Minnesota	72	66	14.9
Mississippi	259	44	17.1
Missouri	178	70	28.2
Montana	109	53	16.4
Nebraska	102	62	16.5
Nevada	252	81	46.0
New Hampshire	57	56	9.5
New Jersey	159	89	18.8
New Mexico	285	70	32.1
New York	254	86	26.1
North Carolina	337	45	16.1
North Dakota	45	44	7.3
Ohio	120	75	21.4
Oklahoma	151	68	20.0
Oregan	159	67	29.3
Pennsylvania	106	72	14.9
Rhode Island	174	87	8.3
South Carolina	279	48	22.5
South Dakota	86	45	12.8
Tennessee	188	59	26.9
Texas	201	80	25.5
Utah	120	80	22.9
Vermont	48	32	11.2
Virginia	156	63	20.7
Washington	145	73	26.2
West Virginia	81	39	9.3
Wisconsin	53	66	10.8
Wyoming	161	60	15.6

In order to combine datasets, we use two functions. The function rbind() glues two datasets together by their rows, while cbind() connects them by their columns.

                            # So if we want to merge the two datasets again, we can do:

full_dataset <- cbind(murder_rate,other_crimes)

print(full_dataset)

##

State	Murder	Assault	Urban Population	Rape
Alabama	13.2	236	58	21.2
Alaska	10.0	263	48	44.5
Arizona	8.1	294	80	31.0
Arkansas	8.8	190	50	19.5
California	9.0	276	91	40.6
Colorado	7.9	204	78	38.7
Connecticut	3.3	110	77	11.1
Delaware	5.9	238	72	15.8
Florida	15.4	335	80	31.9
Georgia	17.4	211	60	25.8
Hawaii	5.3	46	83	20.2
Idaho	2.6	120	54	14.2
Illinois	10.4	249	83	24.0
Indiana	7.2	113	65	21.0
Iowa	2.2	56	57	11.3
Kansas	6.0	115	66	18.0
Kentucky	9.7	109	52	16.3
Louisiana	15.4	249	66	22.2
Maine	2.1	83	51	7.8
Maryland	11.3	300	67	27.8
Massachusetts	4.4	149	85	16.3
Michigan	12.1	255	74	35.1
Minnesota	2.7	72	66	14.9
Mississippi	16.1	259	44	17.1
Missouri	9.0	178	70	28.2
Montana	6.0	109	53	16.4
Nebraska	4.3	102	62	16.5
Nevada	12.2	252	81	46.0
New Hampshire	2.1	57	56	9.5
New Jersey	7.4	159	89	18.8
New Mexico	11.4	285	70	32.1
New York	11.1	254	86	26.1
North Carolina	13.0	337	45	16.1
North Dakota	0.8	45	44	7.3
Ohio	7.3	120	75	21.4
Oklahoma	6.6	151	68	20.0
Oregan	4.9	159	67	29.3
Pennsylvania	6.3	106	72	14.9
Rhode Island	3.4	174	87	8.3
South Carolina	14.4	279	48	22.5
South Dakota	3.8	86	45	12.8
Tennessee	13.2	188	59	26.9
Texas	12.7	201	80	25.5
Utah	3.2	120	80	22.9
Vermont	2.2	48	32	11.2
Virginia	8.5	156	63	20.7
Washington	4.0	145	73	26.2
West Virginia	5.7	81	39	9.3
Wisconsin	2.6	53	66	10.8
Wyoming	6.8	161	60	15.6

Last update January 7, 2021.