Import your data

data("mtcars")
mtcars <- as_tibble(mtcars)
mydata <- read_csv('Worldmap - Sheet1.csv')
## Rows: 233 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Country, Continents, World%
## dbl (1): #
## num (2): Population, Land Area Km2
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Repeat the same operation over different columns of a data frame

Case of numeric variables

mtcars %>% map_dbl(.x = ., .f = ~mean(x = .x))
##        mpg        cyl       disp         hp       drat         wt       qsec 
##  20.090625   6.187500 230.721875 146.687500   3.596563   3.217250  17.848750 
##         vs         am       gear       carb 
##   0.437500   0.406250   3.687500   2.812500
mtcars %>% map_dbl(.f = ~mean(x = .x))
##        mpg        cyl       disp         hp       drat         wt       qsec 
##  20.090625   6.187500 230.721875 146.687500   3.596563   3.217250  17.848750 
##         vs         am       gear       carb 
##   0.437500   0.406250   3.687500   2.812500
mtcars %>% map_dbl(.f = mean)
##        mpg        cyl       disp         hp       drat         wt       qsec 
##  20.090625   6.187500 230.721875 146.687500   3.596563   3.217250  17.848750 
##         vs         am       gear       carb 
##   0.437500   0.406250   3.687500   2.812500
mtcars %>% map_dbl(mean)
##        mpg        cyl       disp         hp       drat         wt       qsec 
##  20.090625   6.187500 230.721875 146.687500   3.596563   3.217250  17.848750 
##         vs         am       gear       carb 
##   0.437500   0.406250   3.687500   2.812500
# Adding an argument
mtcars %>% map_dbl(.x = ., .f = ~mean(x = .x, trim = 0.1))
##         mpg         cyl        disp          hp        drat          wt 
##  19.6961538   6.2307692 222.5230769 141.1923077   3.5792308   3.1526923 
##        qsec          vs          am        gear        carb 
##  17.8276923   0.4230769   0.3846154   3.6153846   2.6538462
mtcars %>% map_dbl(mean, trim = 0.1)
##         mpg         cyl        disp          hp        drat          wt 
##  19.6961538   6.2307692 222.5230769 141.1923077   3.5792308   3.1526923 
##        qsec          vs          am        gear        carb 
##  17.8276923   0.4230769   0.3846154   3.6153846   2.6538462
mtcars %>% select(.data = ., mpg)
## # A tibble: 32 × 1
##      mpg
##    <dbl>
##  1  21  
##  2  21  
##  3  22.8
##  4  21.4
##  5  18.7
##  6  18.1
##  7  14.3
##  8  24.4
##  9  22.8
## 10  19.2
## # ℹ 22 more rows
mtcars %>% select(mpg)
## # A tibble: 32 × 1
##      mpg
##    <dbl>
##  1  21  
##  2  21  
##  3  22.8
##  4  21.4
##  5  18.7
##  6  18.1
##  7  14.3
##  8  24.4
##  9  22.8
## 10  19.2
## # ℹ 22 more rows

Create your own function

#double values in columns
double_by_factor <- function(x,factor) {x*factor}
10 %>% double_by_factor(factor = 2)
## [1] 20
mtcars %>% map_dfr(.x =., .f = ~double_by_factor(x= .x, factor = 10))
## # A tibble: 32 × 11
##      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
##    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1   210    60  1600  1100  39    26.2  165.     0    10    40    40
##  2   210    60  1600  1100  39    28.8  170.     0    10    40    40
##  3   228    40  1080   930  38.5  23.2  186.    10    10    40    10
##  4   214    60  2580  1100  30.8  32.2  194.    10     0    30    10
##  5   187    80  3600  1750  31.5  34.4  170.     0     0    30    20
##  6   181    60  2250  1050  27.6  34.6  202.    10     0    30    10
##  7   143    80  3600  2450  32.1  35.7  158.     0     0    30    40
##  8   244    40  1467   620  36.9  31.9  200     10     0    40    20
##  9   228    40  1408   950  39.2  31.5  229     10     0    40    20
## 10   192    60  1676  1230  39.2  34.4  183     10     0    40    40
## # ℹ 22 more rows
mtcars %>% map_dfr(double_by_factor, factor = 10)
## # A tibble: 32 × 11
##      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
##    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1   210    60  1600  1100  39    26.2  165.     0    10    40    40
##  2   210    60  1600  1100  39    28.8  170.     0    10    40    40
##  3   228    40  1080   930  38.5  23.2  186.    10    10    40    10
##  4   214    60  2580  1100  30.8  32.2  194.    10     0    30    10
##  5   187    80  3600  1750  31.5  34.4  170.     0     0    30    20
##  6   181    60  2250  1050  27.6  34.6  202.    10     0    30    10
##  7   143    80  3600  2450  32.1  35.7  158.     0     0    30    40
##  8   244    40  1467   620  36.9  31.9  200     10     0    40    20
##  9   228    40  1408   950  39.2  31.5  229     10     0    40    20
## 10   192    60  1676  1230  39.2  34.4  183     10     0    40    40
## # ℹ 22 more rows

Repeat the same operation over different elements of a list

When you have a grouping variable (factor)

mtcars %>% lm(formula = mpg ~ wt, data =.)
## 
## Call:
## lm(formula = mpg ~ wt, data = .)
## 
## Coefficients:
## (Intercept)           wt  
##      37.285       -5.344
mtcars %>% distinct(cyl)
## # A tibble: 3 × 1
##     cyl
##   <dbl>
## 1     6
## 2     4
## 3     8
reg_coeff_tbl <- mtcars %>%
    
    #split into a list of data frames
    split(.$cyl) %>%

    #repeat regression over each group
    map(~lm(formula = mpg ~ wt, data =.)) %>%
    map(broom::tidy, conf.int = TRUE) %>%
    
    #convert to tibble
    bind_rows(.id = "cyl") %>%
    
    #filter to wt coefficients
    filter(term == "wt")

reg_coeff_tbl %>%
    
    mutate(estimate = -estimate,
           conf.low = -conf.low,
           conf.high = -conf.high) %>%
    ggplot(aes(x =estimate, y = cyl)) +
    geom_point() +
    geom_errorbar(aes(xmin = conf.low, xmax = conf.high))

Create your own

Choose either one of the two cases above and apply it to your data

mydata %>%
  mutate(
    Land_Area_Km2  = `Land Area Km2`)
## # A tibble: 233 × 7
##      `#` Country    Population Continents `Land Area Km2` `World%` Land_Area_Km2
##    <dbl> <chr>           <dbl> <chr>                <dbl> <chr>            <dbl>
##  1     1 India      1463865525 Asia               2973190 17.78%         2973190
##  2     2 China      1416096094 Asia               9388211 17.20%         9388211
##  3     3 United St…  347275807 North Ame…         9147420 4.22%          9147420
##  4     4 Indonesia   285721236 Asia               1811570 3.47%          1811570
##  5     5 Pakistan    255219554 Asia                770880 3.10%           770880
##  6     6 Nigeria     237527782 Africa              910770 2.89%           910770
##  7     7 Brazil      212812405 South Ame…         8358140 2.59%          8358140
##  8     8 Bangladesh  175686899 Asia                130170 2.13%           130170
##  9     9 Russia      143997393 Europe/As…        16376870 1.75%         16376870
## 10    10 Ethiopia    135472051 Africa             1000000 1.65%          1000000
## # ℹ 223 more rows
mydata %>% 
  lm(formula = Population ~ `Land Area Km2`, data = .)
## 
## Call:
## lm(formula = Population ~ `Land Area Km2`, data = .)
## 
## Coefficients:
##     (Intercept)  `Land Area Km2`  
##       1.475e+07        3.684e+01
mydata %>% 
  distinct(Continents)
## # A tibble: 8 × 1
##   Continents   
##   <chr>        
## 1 Asia         
## 2 North America
## 3 Africa       
## 4 South America
## 5 Europe/Asia  
## 6 Europe       
## 7 Oceania      
## 8 Other
 reg_coeff_tbl <- mydata %>%
  split(.$Continents) %>%                                    
  map(~ lm(Population ~ `Land Area Km2`, data = .x)) %>%  
  map(broom::tidy, conf.int = TRUE) %>%                     
  bind_rows(.id = "Continents") %>%                 
  filter(term == "`Land Area Km2`") 
## Warning in qt(a, object$df.residual): NaNs produced
reg_coeff_tbl %>%
  ggplot(aes(x = estimate, y = Continents)) +
  geom_point() +
  geom_errorbar(aes(xmin = conf.low, xmax = conf.high))