Last updated: 2020-11-21
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Knit directory: r4ds_book/
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| Rmd | beacfd5 | sciencificity | 2020-10-26 | added Ch21 |
options(scipen=10000)
library(tidyverse)
library(flair)
library(emo)
library(lubridate)
library(magrittr)
library(tidyquant)
theme_set(theme_tq())Text formatting
------------------------------------------------------------
*italic* or _italic_
**bold** __bold__
`code`
superscript^2^ and subscript~2~
Headings
------------------------------------------------------------
# 1st Level Header
## 2nd Level Header
### 3rd Level Header
Lists
------------------------------------------------------------
* 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
------------------------------------------------------------
<http://example.com>
[linked phrase](http://example.com)
Tables
------------------------------------------------------------
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
- MBA
***
### 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.”
>
> - Lewis Carroll, Alice in Wonderland
[^1] Here is a footnoteThe 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" |
- | |||||
fig.show = "hide" |
- | |||||
message = FALSE |
- | |||||
warning = FALSE |
- |
The code below generates Table @ref(tab:kable).
knitr::kable(
mtcars[1:5, ],
caption = "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(!is.na(import_var)) %>%
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(!is.na(import_var)) %>%
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 file.info(): it returns a bunch of information about the file including when it was last modified.
```{r raw_data, cache.extra = file.info("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().
comma <- function(x) format(x, digits = 2, big.mark = ',')
# `r comma(3452345)` to produce the inline resultThis year has felt 3,452,345 days long. Formatting decimals is a breeze using the comma function: 0.12.
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
params:
date_now: !r lubridate::now()
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,
cache = TRUE)
library(tidyverse)
```
```{r a}
print(params$date_now)
print(lubridate::now())
x <- 20
y <- rnorm(10)
z <- tibble(x, y)
z
# comment added after 05:37:05
```
```{r b, dependson = "a"}
print(lubridate::now())
x <- 20
y <- rnorm(10)
z <- tibble(x, y)
z
```
```{r c, dependson = c("a")}
print(lubridate::now())
x <- 20
y <- rnorm(10)
z <- tibble(x, y)
z
```
```{r d, dependson = c("c","b")}
print(lubridate::now())
x <- 20
y <- rnorm(10)
z <- tibble(x, y)
z
```
sessionInfo()
#> R version 3.6.3 (2020-02-29)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19042)
#>
#> 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_1.7.9
#> [9] emo_0.0.0.9000 flair_0.0.2
#> [11] forcats_0.5.0 stringr_1.4.0
#> [13] dplyr_1.0.2 purrr_0.3.4
#> [15] readr_1.4.0 tidyr_1.1.2
#> [17] tibble_3.0.3 ggplot2_3.3.2
#> [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 ps_1.3.2 assertthat_0.2.1
#> [5] rprojroot_1.3-2 digest_0.6.27 R6_2.4.1 cellranger_1.1.0
#> [9] backports_1.1.6 reprex_0.3.0 evaluate_0.14 highr_0.8
#> [13] httr_1.4.2 pillar_1.4.6 rlang_0.4.8 curl_4.3
#> [17] readxl_1.3.1 rstudioapi_0.11 whisker_0.4 rmarkdown_2.4
#> [21] munsell_0.5.0 broom_0.7.2 compiler_3.6.3 httpuv_1.5.2
#> [25] modelr_0.1.8 xfun_0.13 pkgconfig_2.0.3 htmltools_0.5.0
#> [29] tidyselect_1.1.0 quadprog_1.5-8 fansi_0.4.1 crayon_1.3.4
#> [33] dbplyr_2.0.0 withr_2.2.0 later_1.0.0 Quandl_2.10.0
#> [37] grid_3.6.3 jsonlite_1.7.1 gtable_0.3.0 lifecycle_0.2.0
#> [41] DBI_1.1.0 git2r_0.26.1 scales_1.1.0 cli_2.1.0
#> [45] stringi_1.5.3 fs_1.5.0 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_1.4.2 hms_0.5.3 yaml_2.2.1 colorspace_1.4-1
#> [57] rvest_0.3.6 knitr_1.28 haven_2.3.1