R

The first language I learned was R, which I had past familiarity with. R is useful for data analysis and working with large data sets. I actually use R frequently in my research lab where we study gut and vaginal microbiomes. You can check out two projects I did using R, under “Projects” in the navigation bar.

Python

The second language I learned was python. I learned how to do basic functions with python such as indexing and slicing, as seen below.

list1=[2,4,6,8,10]
list2=['a','b','c','d', 'e']
list1[-2] #second to last element

## 8

plist=list(range(20))
plist

## [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]

plist[::-5] #Grab every fifth element in reverse

## [19, 14, 9, 4]

I also learned how to create loops using python.

#loop with an index
mylist = ['dog', 'cat', 'bunny', 'frog', 'turtle', 'bird', 'mouse']
for i in range(len(mylist)):
    print(mylist[i])

## dog
## cat
## bunny
## frog
## turtle
## bird
## mouse

#loop over a string
for letter in "hi my name is tien":
    print(letter)

## h
## i
##  
## m
## y
##  
## n
## a
## m
## e
##  
## i
## s
##  
## t
## i
## e
## n

I also learned how to read in datasets, view them and analyze them using python. Below, I used the figure skating dataset from my Project 1.

library(reticulate)

import pandas as pd
import numpy as np
skate=pd.read_csv("https://raw.githubusercontent.com/BuzzFeedNews/2018-02-olympic-figure-skating-analysis/master/data/judged-aspects.csv",index_col=0)
skate.head()

##            performance_id     section  aspect_num  ...   goe ref scores_of_panel
## aspect_id                                          ...                          
## 004e382688     648ff2cbff    elements         2.0  ...  0.80 NaN            6.60
## 005bdf4588     5458eddc1d    elements         3.0  ... -2.71 NaN            5.49
## 0070f9cc40     c39eade62e  components         NaN  ...   NaN NaN            6.29
## 0071f2e3ae     cb67dacba3  components         NaN  ...   NaN NaN            7.25
## 007ae3fc4b     9e771ce55d    elements         3.0  ...  1.90 NaN            8.50
## 
## [5 rows x 11 columns]

skate.shape

## (3405, 11)

skate["goe"] > 2

## aspect_id
## 004e382688    False
## 005bdf4588    False
## 0070f9cc40    False
## 0071f2e3ae    False
## 007ae3fc4b    False
##               ...  
## ff96152a6c    False
## ffa5ee8f76    False
## ffac9b2f6a    False
## ffc8eba079    False
## fff0940a94    False
## Name: goe, Length: 3405, dtype: bool

Finally, I used the reticulate package in R to demonstrate how python and R can talk to each other and share information across code chunks.

By using alternating R and python code chunks, I created a joke exchange between the two. This is a more simple use of reticulate and much more complex things can be done using a combination of R and python. Regardless, please enjoy!

library(reticulate)
hi <- "Good"

hi="afternoon!"
print(r.hi, hi)

## Good afternoon!

cat(c(hi,py$hi))

## Good afternoon!

knock <- "knock"

knock = "knock"
print(r.knock, knock)

## knock knock

who = "who's there?"

cat(c(py$who))

## who's there?

python <- "python"

python = "python"
print(r.python)

## python

snake = "ah! a snake!"

cat(c(py$snake))

## ah! a snake!