A Python Flipbook

In flipbookr: Parses Code, Creates Partial Code Builds, Delivers Code Movie

This is pretty experimental work. The python "parser" (basically no parsing) means there are strong constraints on code. Each new line must yield complete code statement...

# This is the recommended set up for flipbooks
# you might think about setting cache to TRUE as you gain practice --- building flipbooks from scratch can be time consuming
knitr::opts_chunk$set(fig.width = 6, message = FALSE, warning = FALSE, comment = "", cache = F, dev = "svg", fig.ext = "svg")
library(flipbookr)
library(tidyverse)

try(source("../../../../../R/a_create_test_code.R"))
try(source("../../../../../R/b_parsing.R"))
try(source("../../../../../R/c_prep_sequences.R"))
try(source("../../../../../R/d_prep_rmd_chunks.R"))
try(source("../../../../../R/e_define_css.R"))
try(source("../../../../../R/f_chunk_expand.R"))
try(source("../../../../../R/g_exported_functions.R"))
try(source("../../../../../R/h_write_instant_flipbook.R.R"))

---

r chunk_reveal("hello_python", lang = "python")

```{python hello_python, include = F} [1, 4] * 8

3 + 4 * 8

2 + 6

4 * 8

[1,2] + [3,4,5,6]

["hello", "world!", 1, 2, 3] * 2

import numpy as np

np.reshape(np.arange(1,25), (4,3,2), "F")

---

`r chunk_reveal("simple_plot", lang = "python", widths = c(59,40))`


```{python simple_plot, include = F}
import matplotlib.pyplot as plt
year = [1950, 1970, 
  1990, 2010]
pop = [2.519, 3, 5, 6]
plt.plot(year, pop); plt.show()

---

r chunk_reveal("numpy", lang = "python")

```{python numpy, include = F}

import pandas as pd

import matplotlib.pyplot as plt import numpy as np t = np.arange(0, 2, .05) t s = np.sin(2np.pit) s

---

`r chunk_reveal("numpy2", lang = "python")`


```{python numpy2, include = F}
plt.plot(t, s)
plt.xlabel('time (s)')
plt.ylabel('voltage (mV)')
plt.grid(True); plt.show()

---

Credit to matplotlib.org for this example!

---

r chunk_reveal("prep", lang = "python", break_type = 1)

```{python prep, include = F} import numpy as np
import pandas as pd

student_scores = pd.DataFrame( {"student": ["Andy", "Bernie", "Cindy", "Deb"], "sex": ["M", "M", "F", "F"], "english": [98, 95, 70, 40], # eng grades "math": [66, 89, 60, 70], # math grades "physics": [78, 90, 92, 88] # physics grades })

student_scores

---

## Now lets find the highest three grades among all of these grades.  This is done by reshaping the data, sorting it decending order, and selecting the top three rows. 

---

`r chunk_reveal("bam", lang = "python", widths = c(1,1))`

```{python bam, include = F}
student_scores \
  .melt(id_vars=['student', "sex"], 
        var_name="subject", 
        value_name="final_grade") \
  .sort_values(by=['final_grade'], ascending=False) \
  .head(3)

---

The last example owes thanks Suraj Thapa for sketching out ideas these ideas!

---

r chunk_reveal("def", lang = "python", title = "## Code by Malia!")

```{python def, include = F} def flower(like): print("I like flowers " + like)

flower("that are red.") flower("that smell good.")

def strawberry(sweet): print("strawberrys are sweet" + sweet)

strawberry(", and red")

```{css, eval = TRUE, echo = FALSE}
.remark-code{line-height: 1.5; font-size: 80%}

@media print {
  .has-continuation {
    display: block;
  }
}

flipbookr documentation built on May 31, 2021, 5:10 p.m.