library(rmarkdown)
library(gapminder)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
gapminder
## # A tibble: 1,704 × 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.
## # ℹ 1,694 more rows

select the columns

gapminder_x<-gapminder%>%
select(country,year,pop)
gapminder_x%>%
  arrange(desc(pop))
## # A tibble: 1,704 × 3
##    country  year        pop
##    <fct>   <int>      <int>
##  1 China    2007 1318683096
##  2 China    2002 1280400000
##  3 China    1997 1230075000
##  4 China    1992 1164970000
##  5 India    2007 1110396331
##  6 China    1987 1084035000
##  7 India    2002 1034172547
##  8 China    1982 1000281000
##  9 India    1997  959000000
## 10 China    1977  943455000
## # ℹ 1,694 more rows

filter for gapminder with a pop above 1000000

gapminder_x%>%
  filter(pop>1000000)
## # A tibble: 1,524 × 3
##    country      year      pop
##    <fct>       <int>    <int>
##  1 Afghanistan  1952  8425333
##  2 Afghanistan  1957  9240934
##  3 Afghanistan  1962 10267083
##  4 Afghanistan  1967 11537966
##  5 Afghanistan  1972 13079460
##  6 Afghanistan  1977 14880372
##  7 Afghanistan  1982 12881816
##  8 Afghanistan  1987 13867957
##  9 Afghanistan  1992 16317921
## 10 Afghanistan  1997 22227415
## # ℹ 1,514 more rows
gapminder_x%>%
  filter(country=="Nigeria",
          pop>1000000)
## # A tibble: 12 × 3
##    country  year       pop
##    <fct>   <int>     <int>
##  1 Nigeria  1952  33119096
##  2 Nigeria  1957  37173340
##  3 Nigeria  1962  41871351
##  4 Nigeria  1967  47287752
##  5 Nigeria  1972  53740085
##  6 Nigeria  1977  62209173
##  7 Nigeria  1982  73039376
##  8 Nigeria  1987  81551520
##  9 Nigeria  1992  93364244
## 10 Nigeria  1997 106207839
## 11 Nigeria  2002 119901274
## 12 Nigeria  2007 135031164