Last updated: 2020-05-22

Checks: 6 1

Knit directory: R-codes/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(20200515) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 9b03ebb. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .RData
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Unstaged changes:
    Modified:   analysis/base_r.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/base_r.Rmd) and HTML (docs/base_r.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html 25d343a KaranSShakya 2020-05-20 ggplot faceting
Rmd 2da8824 KaranSShakya 2020-05-20 adding to base r stat.
html 2da8824 KaranSShakya 2020-05-20 adding to base r stat.
html e1a5784 KaranSShakya 2020-05-19 link on all pages
Rmd 9097ee4 KaranSShakya 2020-05-19 aes erros SOLVED + links
html 9097ee4 KaranSShakya 2020-05-19 aes erros SOLVED + links
Rmd 1a7c01c KaranSShakya 2020-05-19 bug commit
Rmd a286ee6 KaranSShakya 2020-05-18 normal dist
html a286ee6 KaranSShakya 2020-05-18 normal dist
Rmd c42059f KaranSShakya 2020-05-16 base r analysis - first drafts
html c42059f KaranSShakya 2020-05-16 base r analysis - first drafts

Home

Generating sequences

Normal sequence

x <- seq(1:10)
x
 [1]  1  2  3  4  5  6  7  8  9 10

Odd numbers only

x.odd <- seq(1,10,2)
x.odd
[1] 1 3 5 7 9

Even distribution between ranges. Example: 3 equal division between 1 and 10.

y.divide <- seq(1,10, length.out = 3)
y.divide
[1]  1.0  5.5 10.0

Monte Carlo Simulations

Monte Carlo simulations model the probability of different outcomes by repeating a random process a large enough number of times that the results are similar to what would be observed if the process were repeated forever. First, create an box with data. This can be done through rep (2 red, 3 blue balls:

beads <- rep(c("red", "blue"), times = c(2,3))
beads
[1] "red"  "red"  "blue" "blue" "blue"

Lets pick a bead from this box 10,000 times. To do this, we set up B and run it.

B <- 100000    
events <- replicate(B, sample(beads, 1))    # draw 1 bead, B times
tab <- table(events)    # make a table of outcome counts
prop.table(tab)    # view table of outcome proportio
events
   blue     red 
0.60104 0.39896 

Mean / Sd

For mean, median, sd, min, max:

heights %>%
    filter(sex == "Male") %>%
    summarize(average=mean(height), sd=sd(height), median = median(height),
              minimum = min(height), maximum = max(height))

Observing Columns

murders is the dataset. To observe the column types,

str(murders)
'data.frame':   51 obs. of  5 variables:
 $ state     : chr  "Alabama" "Alaska" "Arizona" "Arkansas" ...
 $ abb       : chr  "AL" "AK" "AZ" "AR" ...
 $ region    : Factor w/ 4 levels "Northeast","South",..: 2 4 4 2 4 4 1 2 2 2 ...
 $ population: num  4779736 710231 6392017 2915918 37253956 ...
 $ total     : num  135 19 232 93 1257 ...

To observe all column numbers. Easy to see which column is which number.

names(murders)
[1] "state"      "abb"        "region"     "population" "total"     

To observe the first 5 data data of a column.

murders$state[1:5]
[1] "Alabama"    "Alaska"     "Arizona"    "Arkansas"   "California"

Data Frames (Vectors)

Associating vectors only

city <- c("Tokyo", "Lile", "Dover")
area <- c(10, 8, 13)
names(area) <- city
area
Tokyo  Lile Dover 
   10     8    13 

Creating Data Frames. Extra column created because of previous code.

data <- data.frame(name=city, value=area)
data
       name value
Tokyo Tokyo    10
Lile   Lile     8
Dover Dover    13

Normal Distribution

q-q graph

To see if male dataset follow a normal distribution. First define p as quantile ranges from 0.05 to 0.95.

p <- seq(0.05, 0.95, 0.05)

The observed_q is the real quantile of your dataset. The theory_q is the expected quantiles of a normal distribution.

observed_q <- quantile(male$height, p)
theory_q <- qnorm(p, mean=mean(male$height), sd=sd(male$height))

The see closely they match, simply plot them. This shows that points almost match, so male dataset is a good approximation for normal distribution. ***



sessionInfo()
R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.4

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] dslabs_0.7.3    forcats_0.5.0   stringr_1.4.0   dplyr_0.8.5    
 [5] purrr_0.3.4     readr_1.3.1     tidyr_1.0.3     tibble_3.0.1   
 [9] ggplot2_3.3.0   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0 xfun_0.13        haven_2.2.0      lattice_0.20-41 
 [5] colorspace_1.4-1 vctrs_0.3.0      generics_0.0.2   htmltools_0.4.0 
 [9] yaml_2.2.1       rlang_0.4.6      later_1.0.0      pillar_1.4.4    
[13] withr_2.2.0      glue_1.4.1       DBI_1.1.0        dbplyr_1.4.3    
[17] modelr_0.1.7     readxl_1.3.1     lifecycle_0.2.0  munsell_0.5.0   
[21] gtable_0.3.0     cellranger_1.1.0 rvest_0.3.5      evaluate_0.14   
[25] knitr_1.28       httpuv_1.5.2     fansi_0.4.1      broom_0.5.6     
[29] Rcpp_1.0.4.6     promises_1.1.0   backports_1.1.6  scales_1.1.1    
[33] jsonlite_1.6.1   fs_1.4.1         hms_0.5.3        digest_0.6.25   
[37] stringi_1.4.6    grid_4.0.0       rprojroot_1.3-2  cli_2.0.2       
[41] tools_4.0.0      magrittr_1.5     crayon_1.3.4     whisker_0.4     
[45] pkgconfig_2.0.3  ellipsis_0.3.0   xml2_1.3.2       reprex_0.3.0    
[49] lubridate_1.7.8  rstudioapi_0.11  assertthat_0.2.1 rmarkdown_2.1   
[53] httr_1.4.1       R6_2.4.1         nlme_3.1-147     git2r_0.27.1    
[57] compiler_4.0.0