Overview
Teaching: 10 min Exercises: 5 minQuestions
How do I import data into the R environment?
What is the Gapminder data structure?
Objectives
To read Gapminder data to R
To evaluate Gapminder data structure
data <- read.csv("gapminder_all.csv")
head(data)
continent country gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 gdpPercap_1967 gdpPercap_1972
1 Africa Algeria 2449.0082 3013.9760 2550.8169 3246.9918 4182.6638
2 Africa Angola 3520.6103 3827.9405 4269.2767 5522.7764 5473.2880
3 Africa Benin 1062.7522 959.6011 949.4991 1035.8314 1085.7969
4 Africa Botswana 851.2411 918.2325 983.6540 1214.7093 2263.6111
5 Africa Burkina Faso 543.2552 617.1835 722.5120 794.8266 854.7360
6 Africa Burundi 339.2965 379.5646 355.2032 412.9775 464.0995
gdpPercap_1977 gdpPercap_1982 gdpPercap_1987 gdpPercap_1992 gdpPercap_1997 gdpPercap_2002 gdpPercap_2007
1 4910.4168 5745.1602 5681.3585 5023.2166 4797.2951 5288.0404 6223.3675
2 3008.6474 2756.9537 2430.2083 2627.8457 2277.1409 2773.2873 4797.2313
3 1029.1613 1277.8976 1225.8560 1191.2077 1232.9753 1372.8779 1441.2849
4 3214.8578 4551.1421 6205.8839 7954.1116 8647.1423 11003.6051 12569.8518
5 743.3870 807.1986 912.0631 931.7528 946.2950 1037.6452 1217.0330
6 556.1033 559.6032 621.8188 631.6999 463.1151 446.4035 430.0707
lifeExp_1952 lifeExp_1957 lifeExp_1962 lifeExp_1967 lifeExp_1972 lifeExp_1977 lifeExp_1982 lifeExp_1987
1 43.077 45.685 48.303 51.407 54.518 58.014 61.368 65.799
2 30.015 31.999 34.000 35.985 37.928 39.483 39.942 39.906
3 38.223 40.358 42.618 44.885 47.014 49.190 50.904 52.337
4 47.622 49.618 51.520 53.298 56.024 59.319 61.484 63.622
5 31.975 34.906 37.814 40.697 43.591 46.137 48.122 49.557
6 39.031 40.533 42.045 43.548 44.057 45.910 47.471 48.211
lifeExp_1992 lifeExp_1997 lifeExp_2002 lifeExp_2007 pop_1952 pop_1957 pop_1962 pop_1967 pop_1972 pop_1977
1 67.744 69.152 70.994 72.301 9279525 10270856 11000948 12760499 14760787 17152804
2 40.647 40.963 41.003 42.731 4232095 4561361 4826015 5247469 5894858 6162675
3 53.919 54.777 54.406 56.728 1738315 1925173 2151895 2427334 2761407 3168267
4 62.745 52.556 46.634 50.728 442308 474639 512764 553541 619351 781472
5 50.260 50.324 50.650 52.295 4469979 4713416 4919632 5127935 5433886 5889574
6 44.736 45.326 47.360 49.580 2445618 2667518 2961915 3330989 3529983 3834415
pop_1982 pop_1987 pop_1992 pop_1997 pop_2002 pop_2007
1 20033753 23254956 26298373 29072015 31287142 33333216
2 7016384 7874230 8735988 9875024 10866106 12420476
3 3641603 4243788 4981671 6066080 7026113 8078314
4 970347 1151184 1342614 1536536 1630347 1639131
5 6634596 7586551 8878303 10352843 12251209 14326203
6 4580410 5126023 5809236 6121610 7021078 8390505
srt(data)
'data.frame': 142 obs. of 38 variables:
$ continent : Factor w/ 5 levels "Africa","Americas",..: 1 1 1 1 1 1 1 1 1 1 ...
$ country : Factor w/ 142 levels "Afghanistan",..: 3 4 11 14 17 18 20 22 23 27 ...
$ gdpPercap_1952: num 2449 3521 1063 851 543 ...
$ gdpPercap_1957: num 3014 3828 960 918 617 ...
$ gdpPercap_1962: num 2551 4269 949 984 723 ...
$ gdpPercap_1967: num 3247 5523 1036 1215 795 ...
$ gdpPercap_1972: num 4183 5473 1086 2264 855 ...
$ gdpPercap_1977: num 4910 3009 1029 3215 743 ...
$ gdpPercap_1982: num 5745 2757 1278 4551 807 ...
$ gdpPercap_1987: num 5681 2430 1226 6206 912 ...
$ gdpPercap_1992: num 5023 2628 1191 7954 932 ...
$ gdpPercap_1997: num 4797 2277 1233 8647 946 ...
$ gdpPercap_2002: num 5288 2773 1373 11004 1038 ...
$ gdpPercap_2007: num 6223 4797 1441 12570 1217 ...
$ lifeExp_1952 : num 43.1 30 38.2 47.6 32 ...
$ lifeExp_1957 : num 45.7 32 40.4 49.6 34.9 ...
$ lifeExp_1962 : num 48.3 34 42.6 51.5 37.8 ...
$ lifeExp_1967 : num 51.4 36 44.9 53.3 40.7 ...
$ lifeExp_1972 : num 54.5 37.9 47 56 43.6 ...
$ lifeExp_1977 : num 58 39.5 49.2 59.3 46.1 ...
$ lifeExp_1982 : num 61.4 39.9 50.9 61.5 48.1 ...
$ lifeExp_1987 : num 65.8 39.9 52.3 63.6 49.6 ...
$ lifeExp_1992 : num 67.7 40.6 53.9 62.7 50.3 ...
$ lifeExp_1997 : num 69.2 41 54.8 52.6 50.3 ...
$ lifeExp_2002 : num 71 41 54.4 46.6 50.6 ...
$ lifeExp_2007 : num 72.3 42.7 56.7 50.7 52.3 ...
$ pop_1952 : num 9279525 4232095 1738315 442308 4469979 ...
$ pop_1957 : num 10270856 4561361 1925173 474639 4713416 ...
$ pop_1962 : num 11000948 4826015 2151895 512764 4919632 ...
$ pop_1967 : num 12760499 5247469 2427334 553541 5127935 ...
$ pop_1972 : num 14760787 5894858 2761407 619351 5433886 ...
$ pop_1977 : num 17152804 6162675 3168267 781472 5889574 ...
$ pop_1982 : num 20033753 7016384 3641603 970347 6634596 ...
$ pop_1987 : num 23254956 7874230 4243788 1151184 7586551 ...
$ pop_1992 : num 26298373 8735988 4981671 1342614 8878303 ...
$ pop_1997 : num 29072015 9875024 6066080 1536536 10352843 ...
$ pop_2002 : int 31287142 10866106 7026113 1630347 12251209 7021078 15929988 4048013 8835739 614382 ...
$ pop_2007 : int 33333216 12420476 8078314 1639131 14326203 8390505 17696293 4369038 10238807 710960 ...
names(data)
[1] "continent" "country" "gdpPercap_1952" "gdpPercap_1957" "gdpPercap_1962" "gdpPercap_1967"
[7] "gdpPercap_1972" "gdpPercap_1977" "gdpPercap_1982" "gdpPercap_1987" "gdpPercap_1992" "gdpPercap_1997"
[13] "gdpPercap_2002" "gdpPercap_2007" "lifeExp_1952" "lifeExp_1957" "lifeExp_1962" "lifeExp_1967"
[19] "lifeExp_1972" "lifeExp_1977" "lifeExp_1982" "lifeExp_1987" "lifeExp_1992" "lifeExp_1997"
[25] "lifeExp_2002" "lifeExp_2007" "pop_1952" "pop_1957" "pop_1962" "pop_1967"
[31] "pop_1972" "pop_1977" "pop_1982" "pop_1987" "pop_1992" "pop_1997"
[37] "pop_2002" "pop_2007"
Data Structures Challenge
How many rows are in the Gapminder Data?
A. 6 B. 38 C. 142 D. 2007
Answer
C. 142
How many columns are in the Gapminder Data?
A. 6 B. 38 C. 142 D. 2007
Answer
B. 38
Key Points
Be sure to
setwd()to point to your data file before importing it.Import data using
read.csv().Familiarize yourself with your data and its structure prior to analysis.