Last updated: 2020-05-20

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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

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"

Group by - summarize

heights %>%
    group_by(sex) %>%
    summarize(average = mean(height), standard_deviation = sd(height))
# A tibble: 2 x 3
  sex    average standard_deviation
  <fct>    <dbl>              <dbl>
1 Female    64.9               3.76
2 Male      69.3               3.61

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.

Version Author Date
2da8824 KaranSShakya 2020-05-20

Graph


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       utf8_1.1.4       rlang_0.4.6      later_1.0.0     
[13] pillar_1.4.4     withr_2.2.0      glue_1.4.1       DBI_1.1.0       
[17] dbplyr_1.4.3     modelr_0.1.7     readxl_1.3.1     lifecycle_0.2.0 
[21] munsell_0.5.0    gtable_0.3.0     cellranger_1.1.0 rvest_0.3.5     
[25] evaluate_0.14    knitr_1.28       httpuv_1.5.2     fansi_0.4.1     
[29] broom_0.5.6      Rcpp_1.0.4.6     promises_1.1.0   backports_1.1.6 
[33] scales_1.1.1     jsonlite_1.6.1   fs_1.4.1         hms_0.5.3       
[37] digest_0.6.25    stringi_1.4.6    grid_4.0.0       rprojroot_1.3-2 
[41] cli_2.0.2        tools_4.0.0      magrittr_1.5     crayon_1.3.4    
[45] whisker_0.4      pkgconfig_2.0.3  ellipsis_0.3.0   xml2_1.3.2      
[49] reprex_0.3.0     lubridate_1.7.8  rstudioapi_0.11  assertthat_0.2.1
[53] rmarkdown_2.1    httr_1.4.1       R6_2.4.1         nlme_3.1-147    
[57] git2r_0.27.1     compiler_4.0.0