Base R Statistics
Last updated: 2020-05-22
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Knit directory: R-codes/
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| 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 |
Generating sequences
Normal sequence
x <- seq(1:10)
x [1] 1 2 3 4 5 6 7 8 9 10Odd numbers only
x.odd <- seq(1,10,2)
x.odd[1] 1 3 5 7 9Even 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 10.0Monte 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 proportioevents
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$state[1:5][1] "Alabama" "Alaska" "Arizona" "Arkansas" "California"Data Frames (Vectors)
Associating vectors only
city <- c("Tokyo", "Lile", "Dover")
area <- c(10, 8, 13)
names(area) <- city
areaTokyo Lile Dover
10 8 13 Creating Data Frames. Extra column created because of previous code.
data <- data.frame(name=city, value=area)
data name value
Tokyo Tokyo 10
Lile Lile 8
Dover Dover 13Normal Distribution
q-q graph
To see if male dataset follow a normal distribution. First define p as quantile ranges from 0.05 to 0.95.
p <- seq(0.05, 0.95, 0.05)The observed_q is the real quantile of your dataset. The theory_q is the expected quantiles of a normal distribution.
observed_q <- quantile(male$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