Data frames with base R

Working with data frames using “Base R”

Let’s load the gapminder dataset

gapminder <- read.csv("data/gapminder.csv")

To extract individual entries from a data frame using base R

# extract the entry in the 3rd row and 4th column
gapminder[3, 4]

[1] 31.997

# extract the entries in the 3rd and 4th rows and 4th column
gapminder[c(3, 4), 4]

[1] 31.997 34.020

# extract the entry in the 3rd and 4th rows and 4th and 5th columns
gapminder[c(3, 4), c(4, 5)]

  lifeExp      pop
3  31.997 10267083
4  34.020 11537966

What type of object are these?

Extracting entire columns from a data frame

There are many ways to extract a single column from a data frame:

# Extract the 4th column
head(gapminder[, 4])

[1] 28.801 30.332 31.997 34.020 36.088 38.438

# extract the lifeExp column (using [,] syntax)
head(gapminder[, "lifeExp"])

[1] 28.801 30.332 31.997 34.020 36.088 38.438

# extract the lifeExp column (using $ syntax)
head(gapminder$lifeExp)

[1] 28.801 30.332 31.997 34.020 36.088 38.438

What type of object are these?

What do you think the output of the following code will be

head(gapminder[3])

head(gapminder["year"])

A data frame can be thought of as a collection (technically a “list”) of vectors, so the third entry, is the third vector.

Notice the difference in the output between these two ways of extracting the third column:

head(gapminder[, 3])

[1] 1952 1957 1962 1967 1972 1977

head(gapminder[3])

To extract the third vector/column directly, you can use double square parentheses [[]]. This is actually list notation.

# extract the third column with `[[]]` using both numbered indexing and named indexing
head(gapminder[[3]])

[1] 1952 1957 1962 1967 1972 1977

head(gapminder[["year"]])

[1] 1952 1957 1962 1967 1972 1977

Exercise

Extract the gdpPercap entry for the fourth and fifth rows

gapminder[c(4, 5), "gdpPercap"]

[1] 836.1971 739.9811

gapminder[c(4, 5), 6]

[1] 836.1971 739.9811

gapminder[c(4, 5), ncol(gapminder)]

[1] 836.1971 739.9811

Extract the entire lifeExp column in as many different ways as you can (you may want to just look at the head() of your outputs).

# 7 ways of extracting the lifeExp column
head(gapminder[, 4])

[1] 28.801 30.332 31.997 34.020 36.088 38.438

head(gapminder[, "lifeExp"])

[1] 28.801 30.332 31.997 34.020 36.088 38.438

head(gapminder$lifeExp)

[1] 28.801 30.332 31.997 34.020 36.088 38.438

head(gapminder[4])

head(gapminder["lifeExp"])

head(gapminder[[4]])

[1] 28.801 30.332 31.997 34.020 36.088 38.438

head(gapminder[["lifeExp"]])

[1] 28.801 30.332 31.997 34.020 36.088 38.438

Using logical indexing

Let’s create a logical vector, called is_aus that is TRUE when country is “Australia” and FALSE otherwise.

# create vector is_aus
is_aus <- gapminder$country == "Australia"
# test that is_aus contains at least some TRUE values
head(is_aus, 100)

  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [61]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [97] FALSE FALSE FALSE FALSE

sum(is_aus)

[1] 12

Use is_aus to filter to just the rows for Australia.

# use is_aus to filter to just the rows for Australia
gapminder[is_aus, ]

     country continent year lifeExp      pop gdpPercap
61 Australia   Oceania 1952  69.120  8691212  10039.60
62 Australia   Oceania 1957  70.330  9712569  10949.65
63 Australia   Oceania 1962  70.930 10794968  12217.23
64 Australia   Oceania 1967  71.100 11872264  14526.12
65 Australia   Oceania 1972  71.930 13177000  16788.63
66 Australia   Oceania 1977  73.490 14074100  18334.20
67 Australia   Oceania 1982  74.740 15184200  19477.01
68 Australia   Oceania 1987  76.320 16257249  21888.89
69 Australia   Oceania 1992  77.560 17481977  23424.77
70 Australia   Oceania 1997  78.830 18565243  26997.94
71 Australia   Oceania 2002  80.370 19546792  30687.75
72 Australia   Oceania 2007  81.235 20434176  34435.37

Removing columns using negative indexing

You can use negative indexing to remove columns

# remove the third column from gapminder (don't overwrite gapminder)
head(gapminder[-3])

      country continent lifeExp      pop gdpPercap
1 Afghanistan      Asia  28.801  8425333  779.4453
2 Afghanistan      Asia  30.332  9240934  820.8530
3 Afghanistan      Asia  31.997 10267083  853.1007
4 Afghanistan      Asia  34.020 11537966  836.1971
5 Afghanistan      Asia  36.088 13079460  739.9811
6 Afghanistan      Asia  38.438 14880372  786.1134

# if you wanted to update the gapminder dataset:
# gapminder <- gapminder[-3]

Adding columns

You can also use the above syntaxes to add new columns

# create a copy of gapminder called gapminder_tmp
gapminder_tmp <- gapminder
# add a new column to gapminder_tmp called gap, which is the product of pop and gdpPercap
gapminder_tmp$gdp <- gapminder_tmp$gdpPercap * gapminder_tmp$pop
# look at the head of gapminder
head(gapminder_tmp)

      country continent year lifeExp      pop gdpPercap         gdp
1 Afghanistan      Asia 1952  28.801  8425333  779.4453  6567086330
2 Afghanistan      Asia 1957  30.332  9240934  820.8530  7585448670
3 Afghanistan      Asia 1962  31.997 10267083  853.1007  8758855797
4 Afghanistan      Asia 1967  34.020 11537966  836.1971  9648014150
5 Afghanistan      Asia 1972  36.088 13079460  739.9811  9678553274
6 Afghanistan      Asia 1977  38.438 14880372  786.1134 11697659231

Exercise

Modify the lifeExp column of gapminder_tmp so that it is rounded to the nearest integer (use round()).

Challenge: do this JUST for the Australia rows. Check your output for the just the country and lifeExp columns for the first 100 rows

Hint: to undo any changes to gapminder_tmp, reassign it to the original gapminder object: gapminder_tmp <- gapminder

# for all rows:
gapminder_tmp$lifeExp <- round(gapminder_tmp$lifeExp)
head(gapminder_tmp)

      country continent year lifeExp      pop gdpPercap         gdp
1 Afghanistan      Asia 1952      29  8425333  779.4453  6567086330
2 Afghanistan      Asia 1957      30  9240934  820.8530  7585448670
3 Afghanistan      Asia 1962      32 10267083  853.1007  8758855797
4 Afghanistan      Asia 1967      34 11537966  836.1971  9648014150
5 Afghanistan      Asia 1972      36 13079460  739.9811  9678553274
6 Afghanistan      Asia 1977      38 14880372  786.1134 11697659231

# reset gapminder_tmp 
gapminder_tmp <- gapminder
gapminder_tmp[gapminder_tmp == "Australia", ]$lifeExp <- round(gapminder_tmp[gapminder_tmp == "Australia", ]$lifeExp)
# look at just the country and lifeExp columns for the first 100 rows
head(gapminder_tmp, 100)[, c("country", "lifeExp")]

        country lifeExp
1   Afghanistan  28.801
2   Afghanistan  30.332
3   Afghanistan  31.997
4   Afghanistan  34.020
5   Afghanistan  36.088
6   Afghanistan  38.438
7   Afghanistan  39.854
8   Afghanistan  40.822
9   Afghanistan  41.674
10  Afghanistan  41.763
11  Afghanistan  42.129
12  Afghanistan  43.828
13      Albania  55.230
14      Albania  59.280
15      Albania  64.820
16      Albania  66.220
17      Albania  67.690
18      Albania  68.930
19      Albania  70.420
20      Albania  72.000
21      Albania  71.581
22      Albania  72.950
23      Albania  75.651
24      Albania  76.423
25      Algeria  43.077
26      Algeria  45.685
27      Algeria  48.303
28      Algeria  51.407
29      Algeria  54.518
30      Algeria  58.014
31      Algeria  61.368
32      Algeria  65.799
33      Algeria  67.744
34      Algeria  69.152
35      Algeria  70.994
36      Algeria  72.301
37       Angola  30.015
38       Angola  31.999
39       Angola  34.000
40       Angola  35.985
41       Angola  37.928
42       Angola  39.483
43       Angola  39.942
44       Angola  39.906
45       Angola  40.647
46       Angola  40.963
47       Angola  41.003
48       Angola  42.731
49    Argentina  62.485
50    Argentina  64.399
51    Argentina  65.142
52    Argentina  65.634
53    Argentina  67.065
54    Argentina  68.481
55    Argentina  69.942
56    Argentina  70.774
57    Argentina  71.868
58    Argentina  73.275
59    Argentina  74.340
60    Argentina  75.320
61    Australia  69.000
62    Australia  70.000
63    Australia  71.000
64    Australia  71.000
65    Australia  72.000
66    Australia  73.000
67    Australia  75.000
68    Australia  76.000
69    Australia  78.000
70    Australia  79.000
71    Australia  80.000
72    Australia  81.000
73      Austria  66.800
74      Austria  67.480
75      Austria  69.540
76      Austria  70.140
77      Austria  70.630
78      Austria  72.170
79      Austria  73.180
80      Austria  74.940
81      Austria  76.040
82      Austria  77.510
83      Austria  78.980
84      Austria  79.829
85      Bahrain  50.939
86      Bahrain  53.832
87      Bahrain  56.923
88      Bahrain  59.923
89      Bahrain  63.300
90      Bahrain  65.593
91      Bahrain  69.052
92      Bahrain  70.750
93      Bahrain  72.601
94      Bahrain  73.925
95      Bahrain  74.795
96      Bahrain  75.635
97   Bangladesh  37.484
98   Bangladesh  39.348
99   Bangladesh  41.216
100  Bangladesh  43.453