Last updated: 2020-11-01

Checks: 7 0

Knit directory: r4ds_book/

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html bf15f3b sciencificity 2020-11-01 Build site.
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Rmd beacfd5 sciencificity 2020-10-26 added Ch21


Overview of commands

Text formatting 

*italic*  or _italic_
**bold**   __bold__
superscript^2^ and subscript~2~


# 1st Level Header

## 2nd Level Header

### 3rd Level Header


*   Bulleted list item 1

*   Item 2

    * Item 2a

    * Item 2b

1.  Numbered list item 1

1.  Item 2. The numbers are incremented automatically in the output.

Links and images


[linked phrase](

![optional caption text](path/to/img.png)


First Header  | Second Header
------------- | -------------
Content Cell  | Content Cell
Content Cell  | Content Cell

title: "Mock CV"
author: "Vebash Naidoo"
date: "25/10/2020"
output: html_document

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = FALSE,
                     message = FALSE)

## CV of Vebash Naidoo

### Education

- BSc Computer Science and Applied Mathematics
- BSc Computer Science Honours [^1]
- Bachelor of Banking


### Employment

1. Denel Aviation: Software Engineer in the Weapons Division
1. Rand Merchant Bank: Quantitative Business Analyst specialising in Credit
and Market Risk.

> “Why, sometimes I've believed as many as six impossible things before breakfast.”
> <U+2015> Lewis Carroll, Alice in Wonderland

[^1] Here is a footnote

Mock CV


The following table summarises which types of output each option suppresses:

Option Run code Show code Output Plots Messages Warnings
eval = FALSE - - - - -
include = FALSE - - - - -
echo = FALSE -
results = "hide" - = "hide" -
message = FALSE -
warning = FALSE -

The code below generates Table @ref(tab:kable).

  mtcars[1:5, ],
  caption = "A knir table"
A knir table
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2


Each knit of a document starts from a completely clean slate.

  • Pro: great for reproducibility.

  • Con: It can be painful if you have some computations that take a long time.

  • The solution is cache = TRUE. When set, the output of the chunk is saved to a specially named file on disk. On subsequent runs, knitr will check to see if the code has changed, and if it hasn’t, it will reuse the cached results.

  • Note: The caching system must be used with care, because by default it is based on the code only, not its dependencies. For example, here the processed_data chunk depends on the raw_data chunk:

        ```{r raw_data}
        rawdata <- readr::read_csv("a_very_large_file.csv")
        ```{r processed_data, cache = TRUE}
        processed_data <- rawdata %>% 
          filter(! %>% 
          mutate(new_variable = complicated_transformation(x, y, z))

    Caching the processed_data chunk means that it will get re-run if the dplyr pipeline is changed, but it won’t get rerun if the read_csv() call changes. You can avoid that problem with the dependson chunk option:

        ```{r processed_data, cache = TRUE, dependson = "raw_data"}
        processed_data <- rawdata %>% 
          filter(! %>% 
          mutate(new_variable = complicated_transformation(x, y, z))

    dependson should contain a character vector of every chunk that the cached chunk depends on.

  • Note that the chunks won’t update if a_very_large_file.csv changes, because knitr caching only tracks changes within the .Rmd file. Use the cache.extra option to track changes to the file. The R expression will invalidate the cache whenever it changes. A good function to use is it returns a bunch of information about the file including when it was last modified.

        ```{r raw_data, cache.extra ="a_very_large_file.csv")}
        rawdata <- readr::read_csv("a_very_large_file.csv")
  • It’s a good idea to regularly clear out all your caches with knitr::clean_cache().

Inline code

comma <- function(x) format(x, digits = 2, big.mark = ',')
# `r comma(3452345)` to produce the inline result

This year has felt 3,452,345 days long. Formatting decimals is a breeze using the comma function: 0.12.


  1. Set up a network of chunks where d depends on c and b, and both b and c depend on a. Have each chunk print lubridate::now(), set cache = TRUE, then verify your understanding of caching.

    title: "Ch21 Exercise"
    author: "Vebash Naidoo"
    date: "26/10/2020"
    output: html_document
      date_now: !r lubridate::now()
    ```{r setup, include=FALSE}
    knitr::opts_chunk$set(echo = TRUE,
                          cache = TRUE)
    ```{r a}
    x <- 20
    y <- rnorm(10)
    z <- tibble(x, y)
    # comment added after 05:37:05
    ```{r b, dependson = "a"}
    x <- 20
    y <- rnorm(10)
    z <- tibble(x, y)
    ```{r c, dependson = c("a")}
    x <- 20
    y <- rnorm(10)
    z <- tibble(x, y)
    ```{r d, dependson = c("c","b")}
    x <- 20
    y <- rnorm(10)
    z <- tibble(x, y)

    Rendered file

#> R version 3.6.3 (2020-02-29)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 18363)
#> Matrix products: default
#> locale:
#> [1] LC_COLLATE=English_South Africa.1252  LC_CTYPE=English_South Africa.1252   
#> [3] LC_MONETARY=English_South Africa.1252 LC_NUMERIC=C                         
#> [5] LC_TIME=English_South Africa.1252    
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> other attached packages:
#>  [1] tidyquant_1.0.0            quantmod_0.4.17           
#>  [3] TTR_0.23-6                 PerformanceAnalytics_2.0.4
#>  [5] xts_0.12-0                 zoo_1.8-7                 
#>  [7] magrittr_1.5               lubridate_1.7.8           
#>  [9] emo_0.0.0.9000             flair_0.0.2               
#> [11] forcats_0.5.0              stringr_1.4.0             
#> [13] dplyr_1.0.0                purrr_0.3.4               
#> [15] readr_1.3.1                tidyr_1.1.0               
#> [17] tibble_3.0.3               ggplot2_3.3.0             
#> [19] tidyverse_1.3.0            workflowr_1.6.2           
#> loaded via a namespace (and not attached):
#>  [1] Rcpp_1.0.4.6     lattice_0.20-38  assertthat_0.2.1 rprojroot_1.3-2 
#>  [5] digest_0.6.25    R6_2.4.1         cellranger_1.1.0 backports_1.1.6 
#>  [9] reprex_0.3.0     evaluate_0.14    highr_0.8        httr_1.4.2      
#> [13] pillar_1.4.6     rlang_0.4.7      curl_4.3         readxl_1.3.1    
#> [17] rstudioapi_0.11  whisker_0.4      rmarkdown_2.4    munsell_0.5.0   
#> [21] broom_0.5.6      compiler_3.6.3   httpuv_1.5.2     modelr_0.1.6    
#> [25] xfun_0.13        pkgconfig_2.0.3  htmltools_0.5.0  tidyselect_1.1.0
#> [29] quadprog_1.5-8   fansi_0.4.1      crayon_1.3.4     dbplyr_1.4.3    
#> [33] withr_2.2.0      later_1.0.0      Quandl_2.10.0    grid_3.6.3      
#> [37] nlme_3.1-144     jsonlite_1.7.0   gtable_0.3.0     lifecycle_0.2.0 
#> [41] DBI_1.1.0        git2r_0.26.1     scales_1.1.0     cli_2.0.2       
#> [45] stringi_1.4.6    fs_1.4.1         promises_1.1.0   xml2_1.3.2      
#> [49] ellipsis_0.3.1   generics_0.0.2   vctrs_0.3.2      tools_3.6.3     
#> [53] glue_1.4.1       hms_0.5.3        yaml_2.2.1       colorspace_1.4-1
#> [57] rvest_0.3.5      knitr_1.28       haven_2.2.0