R Data Frames
Learning objectives
data.frame objects allow to store data of different basic types into a single tabular structure. I will use the “Data frame” spelling.
Data frames are very handy to work with survey data since they are usually stored in tabular form, with many columns corresponding to the responses to the survey and many rows (usually, but not always, one row corresponding to one survey).
In this section, you will learn:
- What is a data frame
- How to create a data.frame object
- How to access the data
- How to sort the data
- How to quickly summarize the data
- Other classes of data frames
What is a data frame?
A data frame is a table or a two-dimensional array-like structure in which each column contains values of one variable and each row contains one set of values from each column.
Following are the characteristics of a data frame.
- The column names should be non-empty;
- The row names should be unique;
- The data stored in a data frame can be of numeric, factor or character type;
- Each column should contain same number of data items.
Data frames are particularly useful because we can combine different types into one single object and are easier to handle than lists.
We can use built-in dataframes in R to understand this. For example, here is a built-in data frame in R, called mtcars. You can access it by just simplying typing its name.
mtcars
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
The top line of the table, called the header, contains the column names. Each horizontal line afterward denotes a data row, which begins with the name of the row, and then followed by the actual data. Each data member of a row is called a cell.
You can easily interpret this as a data frame describing cars. Each row corresponds to a unique model of car, each column corresponds to the different characteristics of the cars.
If you want to know the exact structure of the object mtcars, you can use the function str()
str(mtcars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
We are learning that mtcars is an R object of type data.frame, that contains 32 observations (i.e. 32 rows) of 11 variables (i.e., 11 columns). Then each column is described (name, type, first values stored).
Create a Data Frame
Typically, data.frames are imported from other programs rather than created from scratch (refer to the Import Data section for details). Nevertheless, it is important to be aware that you can generate a data.frame on your own.
You create a data frame by supplying name-vector pairs to the function data.frame():
df1 <- data.frame(
emp_id = c (1:5),
emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
salary = c(623.3, 515.2, 611.0, 729.0, 843.25),
start_date = as.Date(c("2012-01-01", "2013-09-23", "2021-11-15", "2014-05-11", "2015-03-27"))
)
str(df1)
## 'data.frame': 5 obs. of 4 variables:
## $ emp_id : int 1 2 3 4 5
## $ emp_name : chr "Rick" "Dan" "Michelle" "Ryan" ...
## $ salary : num 623 515 611 729 843
## $ start_date: Date, format: "2012-01-01" "2013-09-23" ...
How to retrieve the data?
Data in a cell
To retrieve data in a cell, you will enter its row and column coordinates in the single square bracket [ ] operator. The two coordinates are separated by a comma. In other words, the coordinates begins with row position, then followed by a comma, and ends with the column position. The order is important.
Here is the cell value from the first row, second column of mtcars.
mtcars[1, 2]
## [1] 6
Moreover, we can use the row and column names instead of the numeric coordinates.
mtcars["Mazda RX4", "cyl"]
## [1] 6
A note on row and column names
If you do not remember the names of the columns you can use either names() or colnames():
names(mtcars)
## [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
## [11] "carb"
# colnames(mtcars) #colnames give the same results
If you do not remember the names of the rows you can use rownames():
rownames(mtcars)
## [1] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710"
## [4] "Hornet 4 Drive" "Hornet Sportabout" "Valiant"
## [7] "Duster 360" "Merc 240D" "Merc 230"
## [10] "Merc 280" "Merc 280C" "Merc 450SE"
## [13] "Merc 450SL" "Merc 450SLC" "Cadillac Fleetwood"
## [16] "Lincoln Continental" "Chrysler Imperial" "Fiat 128"
## [19] "Honda Civic" "Toyota Corolla" "Toyota Corona"
## [22] "Dodge Challenger" "AMC Javelin" "Camaro Z28"
## [25] "Pontiac Firebird" "Fiat X1-9" "Porsche 914-2"
## [28] "Lotus Europa" "Ford Pantera L" "Ferrari Dino"
## [31] "Maserati Bora" "Volvo 142E"
Data contained in a column
There are several ways to retrieve the data contained into the columns.
First, we can retrieve a single column with the “$” operator:
mtcars$hp
## [1] 110 110 93 110 175 105 245 62 95 123 123 180 180 180 205 215 230 66 52
## [20] 65 97 150 150 245 175 66 91 113 264 175 335 109
class(mtcars$hp)
## [1] "numeric"
Note that you need to know the name of the column you want to retrieve. However, if using R Studio, if you once you have typed mtcars$ a list of possible names will be suggested to you. (yet another good reason to use R Studio instead of R)
Second, we can retrieve the same column with the single square bracket “[]” operator. To do this, we have to prepend the column name (or column number) with a comma character, which signals that we want to take consider all the rows:
mtcars[,"hp"]
## [1] 110 110 93 110 175 105 245 62 95 123 123 180 180 180 205 215 230 66 52
## [20] 65 97 150 150 245 175 66 91 113 264 175 335 109
Alternatively, we can use the column number.
mtcars[,4]
## [1] 110 110 93 110 175 105 245 62 95 123 123 180 180 180 205 215 230 66 52
## [20] 65 97 150 150 245 175 66 91 113 264 175 335 109
However, it is often clearer to use the column name than the column number. Besides, if you change the structure of the data frame, the ordering of the columns may change, and you may unknowingly refer to a different number if you use column numbers.
In both cases:
- the object
mtcars$hpormtcars[,4]is a vector containing 32 numbers (i.e., the number of rows). - the order of the entries in the
mtcars$hpvector preserves the order of the rows in our data frame. This is important as this allows us to manipulate one variable based on the results of another.
Data contained in several columns
This use of brackets is becoming especially useful when you want to retrieve several columns. Try this command:
carsub <- mtcars[,2:4]
head(carsub) # the function head() displays the first six rows of the table
## cyl disp hp
## Mazda RX4 6 160 110
## Mazda RX4 Wag 6 160 110
## Datsun 710 4 108 93
## Hornet 4 Drive 6 258 110
## Hornet Sportabout 8 360 175
## Valiant 6 225 105
This example shows that you can extract the columns 2, 3 and 4 of the table mtcars and store them in a new data.frame called carsub.
If you want to show several columns that are not in the same sequence of numbers, you can use the function c().
For example, if you want to select the columns 3,7 and 11:
carsub <- mtcars[,c(3,7,11)]
head(carsub)
## disp qsec carb
## Mazda RX4 160 16.46 4
## Mazda RX4 Wag 160 17.02 4
## Datsun 710 108 18.61 1
## Hornet 4 Drive 258 19.44 1
## Hornet Sportabout 360 17.02 2
## Valiant 225 20.22 1
Again, you can use a vector of column names instead of column numbers:
carsub <- mtcars[ ,c("disp", "qsec","carb")]
Data contained in the rows
We retrieve rows from a data frame with the single square bracket operator, just like what we did with columns. However, in additional to an index vector of row positions, we append an extra comma character. This is important, as the extra comma signals a wildcard match for the second coordinate for column positions.
To extract rows from a data frame, we use the single square bracket operator, just as we did with columns. However, instead of just providing a vector of row positions, we add a comma after it.
mtcars[1:2, ]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21 6 160 110 3.9 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4
This comma is crucial because it indicates a wildcard match for the second coordinate for column positions. In other words, the comma tells R to include all columns when sub-setting the data frame. Without the comma, R would interpret the index vector as selecting columns, which is not what you wanted. To convince yourself, evaluate the following command:
mtcars[1:2]
You can also select rows that are not part of a sequence:
mtcars[c(1,3,10), ]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.62 16.46 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.32 18.61 1 1 4 1
## Merc 280 19.2 6 167.6 123 3.92 3.44 18.30 1 0 4 4
Once you understand this syntax and what was said in the previous section about selection of columns, you should be able to select different row selections using the c() function and either row number or row names.
Sub-set of rows and columns
mtcars[c(1,3,10), c(2, 4:5) ]
## cyl hp drat
## Mazda RX4 6 110 3.90
## Datsun 710 4 93 3.85
## Merc 280 6 123 3.92
Subset based on logical criteria
When looking to create subsets based on a logical condition, you can use the subset() function.
subset(mtcars, hp < 90)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Note that:
- the first argument you pass to
subset()is the name of the dataframe you want to subset - you shouldn’t use quotes around hp
- you should use the traditional comparators to contrusct your conditions:
==,>, etc.
How to sort a data.frame?
To sort a data.frame, use the order( ) function. By default, sorting is ASCENDING. Prepend the sorting variable by a minus sign to indicate DESCENDING order.
#order the cars using the column mpg (ascending order)
mtcars[order(mtcars$mpg),]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#sort the cars using two keys
mtcars[order(mtcars$mpg, -mtcars$cyl),]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Note that you need to repeat the mtcars$ part within the order function; not doing so will produce an error, because the order function will expect a variable that does not exist.
mtcars[order(mpg),]
## Error in order(mpg): object 'mpg' not found
How to quickly summarize the data
The statistical summary and nature of the data can be obtained by applying summary() function.
summary(carsub)
## disp qsec carb
## Min. : 71.1 Min. :14.50 Min. :1.000
## 1st Qu.:120.8 1st Qu.:16.89 1st Qu.:2.000
## Median :196.3 Median :17.71 Median :2.000
## Mean :230.7 Mean :17.85 Mean :2.812
## 3rd Qu.:326.0 3rd Qu.:18.90 3rd Qu.:4.000
## Max. :472.0 Max. :22.90 Max. :8.000
Other classes of data frames
Tibbles
Tibble is a new class of data frames (developed by RStudio). They are also capturing data in tabular forms, and have internal features that make working with some packages a little easier.
You can work with tibbles by installing the suite of packages tidyverse or the specific package tibble.
# install a suite of related packages
install.packages("tidyverse")
# Alternatively, install just tibble:
install.packages("tibble")
Then load the package tibble
library(tibble)
Creation
Like data.frame, you can create a new tibble from individual vectors with tibble().
However, there are a few differences:
tibble() will:
- keep strings as characters (and not convert them automatically into factors)
- allows you to refer to variables that you just created
- automatically recycle inputs of length 1
tibble(
x = 1:5,
y = 1, # this will be automatically transformed into a vector with length 5
z = x^2 + y, # it will recognize x and y that were just created
t = letters[1:5] # strings will not be converted into factors
)
## # A tibble: 5 × 4
## x y z t
## <int> <dbl> <dbl> <chr>
## 1 1 1 2 a
## 2 2 1 5 b
## 3 3 1 10 c
## 4 4 1 17 d
## 5 5 1 26 e
It also checks that columns have the same lengths (except, as seen above for unique values)
tibble(
x = 1:5,
t = letters[1:3]
)
## Error in `tibble()`:
## ! Tibble columns must have compatible sizes.
## • Size 5: Existing data.
## • Size 3: Column `t`.
## ℹ Only values of size one are recycled.
Display
With large data frames, it will show only the first 10 rows, and all the columns that fit on screen and in addition to its name, each column reports its type.
ans <- tibble(
x = 1:50,
t = letters[1:50],
x2 = x^2,
e = sample(letters, 50, replace = TRUE)
)
ans
## # A tibble: 50 × 4
## x t x2 e
## <int> <chr> <dbl> <chr>
## 1 1 a 1 v
## 2 2 b 4 l
## 3 3 c 9 i
## 4 4 d 16 x
## 5 5 e 25 u
## 6 6 f 36 m
## 7 7 g 49 d
## 8 8 h 64 b
## 9 9 i 81 f
## 10 10 j 100 w
## # … with 40 more rows
Subsetting
Compared to a data.frame, tibbles are stricter; in particular they never do partial matching, and they will generate a warning if the column you are trying to access does not exist.
Compare the two codes:
df <- data.frame(
x1 = runif(5),
y1 = rnorm(5),
y2 = rnorm(5)
)
df$x # Extract by name
## [1] 0.4185595 0.7090068 0.8417981 0.4979531 0.7715919
df$y
## NULL
df[, 1] # Extract by position
## [1] 0.4185595 0.7090068 0.8417981 0.4979531 0.7715919
df <- tibble(
x1 = runif(5),
y1 = rnorm(5),
y2 = rnorm(5)
)
df$x # Extract by name
## Warning: Unknown or uninitialised column: `x`.
## NULL
df$x1
## [1] 0.82821475 0.02363167 0.99581781 0.88277135 0.64373937
df$y
## Warning: Unknown or uninitialised column: `y`.
## NULL
df[, 1 ] # Extract by position
## # A tibble: 5 × 1
## x1
## <dbl>
## 1 0.828
## 2 0.0236
## 3 0.996
## 4 0.883
## 5 0.644
data.table
data.table is a package that extends data.frames. Two of its most notable features are speed and cleaner syntax. I will give much more details about data.table in a separate section.
Exercises
Exercise 1:
- Create the following vectors corresponding to Name, Age, Height, Weigth and Sex. Make sure Sex is a factor.
## [1] 25 31 23 52 76 49 26
## [1] 177 163 190 179 163 183 164
## [1] 57 69 83 75 70 83 53
## [1] F F M M F M F
## Levels: F M
- Using these vectors, create the following data frame. (Be careful the vector Name must be used for the creation of row names)
## Age Height Weight Sex
## Alex 25 177 57 F
## Moses 31 163 69 F
## Stephan 23 190 83 M
## Zakhele 52 179 75 M
## Leane 76 163 70 F
## Lucas 49 183 83 M
## Nobhule 26 164 53 F
Show the answer
Question 1:
Name <- c("Alex", "Moses", "Stephan", "Zakhele", "Leane", "Lucas", "Nobhule")
Age <- c(25, 31, 23, 52, 76, 49, 26)
Height <- c(177, 163, 190, 179, 163, 183, 164)
Weight <- c(57, 69, 83, 75, 70, 83, 53)
Sex <- as.factor(c("F", "F", "M", "M", "F", "M", "F"))
Age
## [1] 25 31 23 52 76 49 26
Height
## [1] 177 163 190 179 163 183 164
Weight
## [1] 57 69 83 75 70 83 53
Sex
## [1] F F M M F M F
## Levels: F M
Question 2:
df <- data.frame (row.names = Name, Age, Height, Weight, Sex)
df
## Age Height Weight Sex
## Alex 25 177 57 F
## Moses 31 163 69 F
## Stephan 23 190 83 M
## Zakhele 52 179 75 M
## Leane 76 163 70 F
## Lucas 49 183 83 M
## Nobhule 26 164 53 F
Exercise 2
Using the dataframe mtcars, create a new data frame that:
- only includes cars with a horse power (hp) greater or equal to 120 and lower or equal to 200,
- only contains the columns
disp,dratandhp, - is sorted by decreasing
hp
Show the answer
sub <- subset(mtcars[, c("disp", "drat", "hp")], hp >= 120 & hp <=200)
sub2 <- sub[order(-sub$hp),]
sub2
## disp drat hp
## Merc 450SE 275.8 3.07 180
## Merc 450SL 275.8 3.07 180
## Merc 450SLC 275.8 3.07 180
## Hornet Sportabout 360.0 3.15 175
## Pontiac Firebird 400.0 3.08 175
## Ferrari Dino 145.0 3.62 175
## Dodge Challenger 318.0 2.76 150
## AMC Javelin 304.0 3.15 150
## Merc 280 167.6 3.92 123
## Merc 280C 167.6 3.92 123