Data Science Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface Data Science Introducing R R Constructs R Tasks R Strings R Read, Write, and Create Data Template Data Exploration Data Wrangling Data Visualisation Statistics ML Template ML Scenarios ML Activities ML Applications ML Algorithms Cluster Analysis Decision Trees Computer Vision Graph Data Privacy Literate Data Science Coding with Style Resources Bibliography Index

CLICK HERE TO VISIT THE UPDATED SURVIVAL GUIDE

Combine Rows

20201026 We can utilise dplyr::bind_rows() to combine the rows of two datasets with the same columns:

bind_rows(head(ds), tail(ds))

## # A tibble: 12 x 24
##    date       location min_temp max_temp rainfall evaporation sunshine
##    <date>     <chr>       <dbl>    <dbl>    <dbl>       <dbl>    <dbl>
##  1 2008-12-01 Albury       13.4     22.9      0.6          NA       NA
##  2 2008-12-02 Albury        7.4     25.1      0            NA       NA
##  3 2008-12-03 Albury       12.9     25.7      0            NA       NA
##  4 2008-12-04 Albury        9.2     28        0            NA       NA
##  5 2008-12-05 Albury       17.5     32.3      1            NA       NA
##  6 2008-12-06 Albury       14.6     29.7      0.2          NA       NA
##  7 2020-04-24 Uluru        23       36.7      0            NA       NA
##  8 2020-04-25 Uluru        18.4     37.4      0            NA       NA
##  9 2020-04-26 Uluru        21.4     32.7      0            NA       NA
## 10 2020-04-27 Uluru        19.4     32.2      0            NA       NA
## 11 2020-04-28 Uluru        16.6     32.6      0            NA       NA
## 12 2020-04-29 Uluru        16.7     25.7      0            NA       NA
## # ... with 17 more variables: wind_gust_dir <ord>, wind_gust_speed <dbl>,
## #   wind_dir_9am <ord>, wind_dir_3pm <ord>, wind_speed_9am <dbl>,
## #   wind_speed_3pm <dbl>, humidity_9am <int>, humidity_3pm <int>,
## #   pressure_9am <dbl>, pressure_3pm <dbl>, cloud_9am <int>, cloud_3pm <...
## #   temp_9am <dbl>, temp_3pm <dbl>, rain_today <fct>, risk_mm <dbl>,
## #   rain_tomorrow <fct>

The operation performed by dplyr::bind_rows() is also useful to convert a named list (or vector if all values are of the same type) into a tibble:

ds %>%
  head() %>%
  select(2:5) %>%
  bind_rows(list(location="Somewhere", min_temp=-10,
                 max_temp=30, rainfall=10))

## # A tibble: 7 x 4
##   location  min_temp max_temp rainfall
##   <chr>        <dbl>    <dbl>    <dbl>
## 1 Albury        13.4     22.9      0.6
## 2 Albury         7.4     25.1      0  
## 3 Albury        12.9     25.7      0  
## 4 Albury         9.2     28        0  
## 5 Albury        17.5     32.3      1  
## 6 Albury        14.6     29.7      0.2
## 7 Somewhere    -10       30       10