Functions and Iterations

Writing functions

In the gapminder dataset calculate the range of population, gdp, and life expectancy

round(max(gapminder$lifeExp) - min(gapminder$lifeExp),1)

## [1] 59

round(max(gapminder$pop) - min(gapminder$pop),1)

## [1] 1318623085

round(max(gapminder$pop) - min(gapminder$pop),1)

## [1] 1318623085

Make a function for calculating range

max_minus_min <- function(x){
  round((max(x) - min(x)), 1)
  }

Test-run your function

max_minus_min(gapminder$lifeExp)

## [1] 59

Iterations using purrr package

Apply class() function to each column of gampminder data

gapminder %>% 
  map(class)

## $country
## [1] "factor"
## 
## $continent
## [1] "factor"
## 
## $year
## [1] "integer"
## 
## $lifeExp
## [1] "numeric"
## 
## $pop
## [1] "integer"
## 
## $gdpPercap
## [1] "numeric"

The default output of map() is a list. If you want a character vector as ouput use map_chr()

gapminder %>% 
  map_chr(class)

##   country continent      year   lifeExp       pop gdpPercap 
##  "factor"  "factor" "integer" "numeric" "integer" "numeric"

Another example, What is the number of distinct values in each column? Hint: use n_distinct()

gapminder %>% 
  map_int(n_distinct) # using map_int for integer output

##   country continent      year   lifeExp       pop gdpPercap 
##       142         5        12      1626      1704      1704

What is the median of all numeric columns?

gapminder %>% 
  dplyr::select_if(is.numeric) %>% 
  map_dbl(median)

##         year      lifeExp          pop    gdpPercap 
##    1979.5000      60.7125 7023595.5000    3531.8470

Using ~ with map()

~ helps reduce the amount of typing when you want to pass complex functions through map()

Example

my_vector <- c(1, 2, 3)
map_dbl(my_vector, function(x){x+10})

## [1] 11 12 13

Shortcut of the same code using ~

my_vector <- c(1, 2, 3)
map_dbl(my_vector, ~(.+10))

## [1] 11 12 13

More complex example:

Fitting a linear model with different groups of the data

gapminder %>% 
  split(.$continent) %>%  # split dataset by continent
  map(function(df) lm(lifeExp ~ pop, data = df)) # linear model for each group

## $Africa
## 
## Call:
## lm(formula = lifeExp ~ pop, data = df)
## 
## Coefficients:
## (Intercept)          pop  
##   4.816e+01    7.150e-08  
## 
## 
## $Americas
## 
## Call:
## lm(formula = lifeExp ~ pop, data = df)
## 
## Coefficients:
## (Intercept)          pop  
##   6.353e+01    4.587e-08  
## 
## 
## $Asia
## 
## Call:
## lm(formula = lifeExp ~ pop, data = df)
## 
## Coefficients:
## (Intercept)          pop  
##   5.992e+01    1.901e-09  
## 
## 
## $Europe
## 
## Call:
## lm(formula = lifeExp ~ pop, data = df)
## 
## Coefficients:
## (Intercept)          pop  
##   7.162e+01    1.650e-08  
## 
## 
## $Oceania
## 
## Call:
## lm(formula = lifeExp ~ pop, data = df)
## 
## Coefficients:
## (Intercept)          pop  
##   7.207e+01    2.545e-07

Shortcut of the same code using ~

gapminder %>% 
  split(.$continent) %>% # split dataset by continent
  map(~lm(lifeExp ~ pop, data = .)) # linear model for each group

## $Africa
## 
## Call:
## lm(formula = lifeExp ~ pop, data = .)
## 
## Coefficients:
## (Intercept)          pop  
##   4.816e+01    7.150e-08  
## 
## 
## $Americas
## 
## Call:
## lm(formula = lifeExp ~ pop, data = .)
## 
## Coefficients:
## (Intercept)          pop  
##   6.353e+01    4.587e-08  
## 
## 
## $Asia
## 
## Call:
## lm(formula = lifeExp ~ pop, data = .)
## 
## Coefficients:
## (Intercept)          pop  
##   5.992e+01    1.901e-09  
## 
## 
## $Europe
## 
## Call:
## lm(formula = lifeExp ~ pop, data = .)
## 
## Coefficients:
## (Intercept)          pop  
##   7.162e+01    1.650e-08  
## 
## 
## $Oceania
## 
## Call:
## lm(formula = lifeExp ~ pop, data = .)
## 
## Coefficients:
## (Intercept)          pop  
##   7.207e+01    2.545e-07

Reading multiple files using purrr()

Download the data here - Google drive link

This is data from diffrent countries. “_gm” suffix is for gapminder from where data is borrowed. In the example below, the files in a folder called “data”

Step 1 and 2: Make a list of all .csv files with _gm suffix

my_files <- dir(here("data"), # specify file path
                pattern = "*_gm.csv",  # look for .csv files with _gm suffix
                full.names = TRUE)  # preserve file path
my_files

## [1] "/Users/meenakshikushwaha/Dropbox/R projects/github/CSTEP_R_course/data/china_gm.csv"
## [2] "/Users/meenakshikushwaha/Dropbox/R projects/github/CSTEP_R_course/data/india_gm.csv"
## [3] "/Users/meenakshikushwaha/Dropbox/R projects/github/CSTEP_R_course/data/japan_gm.csv"
## [4] "/Users/meenakshikushwaha/Dropbox/R projects/github/CSTEP_R_course/data/nepal_gm.csv"

Step 3: Read and combine all files using map_dfr()

my_df <- my_files %>% 
  map_dfr(read_csv) 

my_df

## # A tibble: 16 × 6
##    country continent  year lifeExp       pop gdpPercap
##    <chr>   <chr>     <dbl>   <dbl>     <dbl>     <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 India   Asia       1952    37.4 372000000      547.
##  6 India   Asia       1957    40.2 409000000      590.
##  7 India   Asia       1962    43.6 454000000      658.
##  8 India   Asia       1967    47.2 506000000      701.
##  9 Japan   Asia       1952    63.0  86459025     3217.
## 10 Japan   Asia       1957    65.5  91563009     4318.
## 11 Japan   Asia       1962    68.7  95831757     6577.
## 12 Japan   Asia       1967    71.4 100825279     9848.
## 13 Nepal   Asia       1952    36.2   9182536      546.
## 14 Nepal   Asia       1957    37.7   9682338      598.
## 15 Nepal   Asia       1962    39.4  10332057      652.
## 16 Nepal   Asia       1967    41.5  11261690      676.