R Notebook

library(“gapminder”) head(gapminder)

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gapminder %>%
  filter(country == "China")

# A tibble: 12 x 6
   country continent  year lifeExp        pop gdpPercap
   <fct>   <fct>     <int>   <dbl>      <int>     <dbl>
 1 China   Asia       1952    44    556263527      400.
 2 China   Asia       1957    50.5  637408000      576.
 3 China   Asia       1962    44.5  665770000      488.
 4 China   Asia       1967    58.4  754550000      613.
 5 China   Asia       1972    63.1  862030000      677.
 6 China   Asia       1977    64.0  943455000      741.
 7 China   Asia       1982    65.5 1000281000      962.
 8 China   Asia       1987    67.3 1084035000     1379.
 9 China   Asia       1992    68.7 1164970000     1656.
10 China   Asia       1997    70.4 1230075000     2289.
11 China   Asia       2002    72.0 1280400000     3119.
12 China   Asia       2007    73.0 1318683096     4959.

gapminder %>%
  filter(country=="India")

# A tibble: 12 x 6
   country continent  year lifeExp        pop gdpPercap
   <fct>   <fct>     <int>   <dbl>      <int>     <dbl>
 1 India   Asia       1952    37.4  372000000      547.
 2 India   Asia       1957    40.2  409000000      590.
 3 India   Asia       1962    43.6  454000000      658.
 4 India   Asia       1967    47.2  506000000      701.
 5 India   Asia       1972    50.7  567000000      724.
 6 India   Asia       1977    54.2  634000000      813.
 7 India   Asia       1982    56.6  708000000      856.
 8 India   Asia       1987    58.6  788000000      977.
 9 India   Asia       1992    60.2  872000000     1164.
10 India   Asia       1997    61.8  959000000     1459.
11 India   Asia       2002    62.9 1034172547     1747.
12 India   Asia       2007    64.7 1110396331     2452.

gapminder %>%
  filter(gdpPercap>500)

# A tibble: 1,641 x 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# ... with 1,631 more rows

gapminder%>%
  filter(year == 1997)

# A tibble: 142 x 6
   country     continent  year lifeExp       pop gdpPercap
   <fct>       <fct>     <int>   <dbl>     <int>     <dbl>
 1 Afghanistan Asia       1997    41.8  22227415      635.
 2 Albania     Europe     1997    73.0   3428038     3193.
 3 Algeria     Africa     1997    69.2  29072015     4797.
 4 Angola      Africa     1997    41.0   9875024     2277.
 5 Argentina   Americas   1997    73.3  36203463    10967.
 6 Australia   Oceania    1997    78.8  18565243    26998.
 7 Austria     Europe     1997    77.5   8069876    29096.
 8 Bahrain     Asia       1997    73.9    598561    20292.
 9 Bangladesh  Asia       1997    59.4 123315288      973.
10 Belgium     Europe     1997    77.5  10199787    27561.
# ... with 132 more rows

gapminder%>%
  filter(continent=="Europe" & year=="1997")

# A tibble: 30 x 6
   country                continent  year lifeExp      pop gdpPercap
   <fct>                  <fct>     <int>   <dbl>    <int>     <dbl>
 1 Albania                Europe     1997    73.0  3428038     3193.
 2 Austria                Europe     1997    77.5  8069876    29096.
 3 Belgium                Europe     1997    77.5 10199787    27561.
 4 Bosnia and Herzegovina Europe     1997    73.2  3607000     4766.
 5 Bulgaria               Europe     1997    70.3  8066057     5970.
 6 Croatia                Europe     1997    73.7  4444595     9876.
 7 Czech Republic         Europe     1997    74.0 10300707    16049.
 8 Denmark                Europe     1997    76.1  5283663    29804.
 9 Finland                Europe     1997    77.1  5134406    23724.
10 France                 Europe     1997    78.6 58623428    25890.
# ... with 20 more rows

gapminder%>%
  filter(continent=="Asia" & year=="2007")

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