Import your data

data("mtcars")
mtcars<- as_tibble(mtcars)
myData <-read_csv ("../00_data/myData.csv")
## Rows: 27 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): film, film_rating
## dbl  (2): number, run_time
## date (1): release_date
## 
## 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(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 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 colums
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 list
    split(.$cyl) %>%

    #repeat regression
    map(~lm(formula= mpg~ wt, data= .x))%>%

    # extract coefficients
    map(broom::tidy, conf.int= TRUE)%>%

    #convert to tibble
    bind_rows(.id = "cyl")%>%
    
    #filter for WT
    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%>% lm(formula= run_time~ release_date, data= .)
## 
## Call:
## lm(formula = run_time ~ release_date, data = .)
## 
## Coefficients:
##  (Intercept)  release_date  
##    59.669348      0.002968
myData%>% distinct(film_rating)
## # A tibble: 4 × 1
##   film_rating
##   <chr>      
## 1 G          
## 2 PG         
## 3 N/A        
## 4 Not Rated
reg_results<- myData%>%
    
    #split into list
    split(.$film_rating) %>%

    #repeat regression
    map(~lm(formula= run_time~ release_date, data= .x))%>%

    # extract coefficients
    map(broom::tidy, conf.int= TRUE)%>%

    #convert to tibble
    bind_rows(.id = "film_rating")%>%
    
    #filter for WT
    filter(term== "release_date")
## Warning in qt(a, object$df.residual): NaNs produced
## Warning in qt(a, object$df.residual): NaNs produced
reg_results%>%
    
    mutate(estimate= -estimate, 
           conf.low= -conf.low,
           conf.high= -conf.high)%>%
    
    ggplot(aes(x= estimate, y= film_rating))+
    geom_point()+
    geom_errorbar(aes(xmin= conf.low, xmax= conf.high))
## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).