1
I took some data because it is 1704 Obs. of 6 variables. So I selected the first 80 lines. Follow the code below:
structure(list(country = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L), .Label = c("Afghanistan",
"Albania", "Algeria", "Angola", "Argentina", "Australia", "Austria",
"Bahrain", "Bangladesh", "Belgium", "Benin", "Bolivia", "Bosnia and Herzegovina",
"Botswana", "Brazil", "Bulgaria", "Burkina Faso", "Burundi",
"Cambodia", "Cameroon", "Canada", "Central African Republic",
"Chad", "Chile", "China", "Colombia", "Comoros", "Congo, Dem. Rep.",
"Congo, Rep.", "Costa Rica", "Cote d'Ivoire", "Croatia", "Cuba",
"Czech Republic", "Denmark", "Djibouti", "Dominican Republic",
"Ecuador", "Egypt", "El Salvador", "Equatorial Guinea", "Eritrea",
"Ethiopia", "Finland", "France", "Gabon", "Gambia", "Germany",
"Ghana", "Greece", "Guatemala", "Guinea", "Guinea-Bissau", "Haiti",
"Honduras", "Hong Kong, China", "Hungary", "Iceland", "India",
"Indonesia", "Iran", "Iraq", "Ireland", "Israel", "Italy", "Jamaica",
"Japan", "Jordan", "Kenya", "Korea, Dem. Rep.", "Korea, Rep.",
"Kuwait", "Lebanon", "Lesotho", "Liberia", "Libya", "Madagascar",
"Malawi", "Malaysia", "Mali", "Mauritania", "Mauritius", "Mexico",
"Mongolia", "Montenegro", "Morocco", "Mozambique", "Myanmar",
"Namibia", "Nepal", "Netherlands", "New Zealand", "Nicaragua",
"Niger", "Nigeria", "Norway", "Oman", "Pakistan", "Panama", "Paraguay",
"Peru", "Philippines", "Poland", "Portugal", "Puerto Rico", "Reunion",
"Romania", "Rwanda", "Sao Tome and Principe", "Saudi Arabia",
"Senegal", "Serbia", "Sierra Leone", "Singapore", "Slovak Republic",
"Slovenia", "Somalia", "South Africa", "Spain", "Sri Lanka",
"Sudan", "Swaziland", "Sweden", "Switzerland", "Syria", "Taiwan",
"Tanzania", "Thailand", "Togo", "Trinidad and Tobago", "Tunisia",
"Turkey", "Uganda", "United Kingdom", "United States", "Uruguay",
"Venezuela", "Vietnam", "West Bank and Gaza", "Yemen, Rep.",
"Zambia", "Zimbabwe"), class = "factor"), continent = structure(c(3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("Africa",
"Americas", "Asia", "Europe", "Oceania"), class = "factor"),
year = c(1952L, 1957L, 1962L, 1967L, 1972L, 1977L, 1982L,
1987L, 1992L, 1997L, 2002L, 2007L, 1952L, 1957L, 1962L, 1967L,
1972L, 1977L, 1982L, 1987L, 1992L, 1997L, 2002L, 2007L, 1952L,
1957L, 1962L, 1967L, 1972L, 1977L, 1982L, 1987L, 1992L, 1997L,
2002L, 2007L, 1952L, 1957L, 1962L, 1967L, 1972L, 1977L, 1982L,
1987L, 1992L, 1997L, 2002L, 2007L, 1952L, 1957L, 1962L, 1967L,
1972L, 1977L, 1982L, 1987L, 1992L, 1997L, 2002L, 2007L, 1952L,
1957L, 1962L, 1967L, 1972L, 1977L, 1982L, 1987L, 1992L, 1997L,
2002L, 2007L, 1952L, 1957L, 1962L, 1967L, 1972L, 1977L, 1982L,
1987L), lifeExp = c(28.801, 30.332, 31.997, 34.02, 36.088,
38.438, 39.854, 40.822, 41.674, 41.763, 42.129, 43.828, 55.23,
59.28, 64.82, 66.22, 67.69, 68.93, 70.42, 72, 71.581, 72.95,
75.651, 76.423, 43.077, 45.685, 48.303, 51.407, 54.518, 58.014,
61.368, 65.799, 67.744, 69.152, 70.994, 72.301, 30.015, 31.999,
34, 35.985, 37.928, 39.483, 39.942, 39.906, 40.647, 40.963,
41.003, 42.731, 62.485, 64.399, 65.142, 65.634, 67.065, 68.481,
69.942, 70.774, 71.868, 73.275, 74.34, 75.32, 69.12, 70.33,
70.93, 71.1, 71.93, 73.49, 74.74, 76.32, 77.56, 78.83, 80.37,
81.235, 66.8, 67.48, 69.54, 70.14, 70.63, 72.17, 73.18, 74.94
), pop = c(8425333L, 9240934L, 10267083L, 11537966L, 13079460L,
14880372L, 12881816L, 13867957L, 16317921L, 22227415L, 25268405L,
31889923L, 1282697L, 1476505L, 1728137L, 1984060L, 2263554L,
2509048L, 2780097L, 3075321L, 3326498L, 3428038L, 3508512L,
3600523L, 9279525L, 10270856L, 11000948L, 12760499L, 14760787L,
17152804L, 20033753L, 23254956L, 26298373L, 29072015L, 31287142L,
33333216L, 4232095L, 4561361L, 4826015L, 5247469L, 5894858L,
6162675L, 7016384L, 7874230L, 8735988L, 9875024L, 10866106L,
12420476L, 17876956L, 19610538L, 21283783L, 22934225L, 24779799L,
26983828L, 29341374L, 31620918L, 33958947L, 36203463L, 38331121L,
40301927L, 8691212L, 9712569L, 10794968L, 11872264L, 13177000L,
14074100L, 15184200L, 16257249L, 17481977L, 18565243L, 19546792L,
20434176L, 6927772L, 6965860L, 7129864L, 7376998L, 7544201L,
7568430L, 7574613L, 7578903L), gdpPercap = c(779.4453145,
820.8530296, 853.10071, 836.1971382, 739.9811058, 786.11336,
978.0114388, 852.3959448, 649.3413952, 635.341351, 726.7340548,
974.5803384, 1601.056136, 1942.284244, 2312.888958, 2760.196931,
3313.422188, 3533.00391, 3630.880722, 3738.932735, 2497.437901,
3193.054604, 4604.211737, 5937.029526, 2449.008185, 3013.976023,
2550.81688, 3246.991771, 4182.663766, 4910.416756, 5745.160213,
5681.358539, 5023.216647, 4797.295051, 5288.040382, 6223.367465,
3520.610273, 3827.940465, 4269.276742, 5522.776375, 5473.288005,
3008.647355, 2756.953672, 2430.208311, 2627.845685, 2277.140884,
2773.287312, 4797.231267, 5911.315053, 6856.856212, 7133.166023,
8052.953021, 9443.038526, 10079.02674, 8997.897412, 9139.671389,
9308.41871, 10967.28195, 8797.640716, 12779.37964, 10039.59564,
10949.64959, 12217.22686, 14526.12465, 16788.62948, 18334.19751,
19477.00928, 21888.88903, 23424.76683, 26997.93657, 30687.75473,
34435.36744, 6137.076492, 8842.59803, 10750.72111, 12834.6024,
16661.6256, 19749.4223, 21597.08362, 23687.82607)), row.names = c(NA,
80L), class = "data.frame")
I want number of countries per continent in the R. Thank you.
If you have the message with 132 more Rows, can you list all the results? When a data set is too large, it shows only a few.
– Fidel Henrique Fernandes
I edited the answer. See if it fits what you want.
– neves
Oh I got it. I couldn’t remember the view. Much better now. Thank you. thanks
– Fidel Henrique Fernandes