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ch4_exercise.Rmd
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ch4_exercise.Rmd
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---
title: "基本資料結構 - 隨堂練習"
author: "Yao-Jen Kuo"
date: "`r Sys.Date()`"
output:
html_document:
self_contained: false
---
```{r, include=FALSE}
tutorial::go_interactive()
```
## 草帽海賊團的年紀向量
我們已經預先幫你建好一個向量 `age` 紀錄草帽海賊團成員的年紀，利用邏輯值向量找出海賊團成員中未滿 25 歲的人數總和。
```{r ex="vector_exercise", type="pre-exercise-code"}
# Create the age factor
age <- c(19, 21, 20, 19, 21, 17, 30, 36, 90)
```
```{r ex="vector_exercise", type="sample-code"}
# Print the vector: age
# Generate a logical vector indicating which scalar is less than 25
is_under_25 <- ___ < ___
# Print is_under_25
# Use the logical vector above to create a new vector under_25
under_25 <- age[___]
# Calculate the length of under_25
length(___)
```
```{r ex="vector_exercise", type="solution"}
# Print the vector: age
age
# Generate a logical vector indicating which scalar is less than 25
is_under_25 <- age < 25
# Print is_under_25
is_under_25
# Use the logical vector above to create a new vector under_25
under_25 <- age[is_under_25]
# Calculate the length of under_25
length(under_25)
```
```{r ex="vector_exercise", type="sct"}
test_output_contains("age", incorrect_msg = "Did you print the vector `age`?")
test_object("is_under_25", incorrect_msg = "Did you generate the vector `is_under_25` correctly?")
test_output_contains("is_under_25", incorrect_msg = "Did you print the vector `is_under_25`?")
test_object("under_25", incorrect_msg = "Did you generate the vector `under_25` correctly?")
test_output_contains("length(under_25)", incorrect_msg = "Did you print the length of vector `under_25` using `length()`?")
```
## 草帽海賊團的資料框
我們已經預先幫你建好一個資料框 `one_piece_df` 紀錄草帽海賊團成員的姓名，年紀與性別。
```{r ex="dataframe_exercise", type="pre-exercise-code"}
name <- c("Monkey D. Luffy", "Roronoa Zoro", "Nami", "Usopp", "Vinsmoke Sanji", "Tony Tony Chopper", "Nico Robin", "Franky", "Brook")
age <- c(19, 21, 20, 19, 21, 17, 30, 36, 90)
is_female <- c(FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE)
one_piece_df <- data.frame(name, age, is_female)
```
```{r ex="dataframe_exercise", type="sample-code"}
# Use `str()` function to examine the structure of `one_piece_df`
# Use `summary()` function to examine the summary of `one_piece_df`
# Select the `name` variable using $ or indexing
# Select the rows where name equals to "Usopp" and "Franky"
one_piece_df[one_piece_df$name == "___" | one_piece_df$name == "___", ]
```
```{r ex="dataframe_exercise", type="solution"}
# Use `str()` function to examine the structure of `one_piece_df`
str(one_piece_df)
# Use `summary()` function to examine the summary of `one_piece_df`
summary(one_piece_df)
# Select the `name` variable using $ or indexing
one_piece_df$name
# Select the rows where name equals to "Usopp" and "Franky"
one_piece_df[one_piece_df$name == "Usopp" | one_piece_df$name == "Franky", ]
```
```{r ex="dataframe_exercise", type="sct"}
test_output_contains("str(one_piece_df)", incorrect_msg = "Did you use `str()` to examine the structure of `one_piece_df`?")
test_output_contains("summary(one_piece_df)", incorrect_msg = "Did you use `summary()` to examine the summary of `one_piece_df`?")
test_output_contains("one_piece_df$name", incorrect_msg = "Did you select the `name` variable from `one_piece_df` using $ or indexing?")
test_output_contains("one_piece_df[one_piece_df$name == \"Usopp\" | one_piece_df$name == \"Franky\", ]", incorrect_msg = "Did you select the rows for Usopp and Franky?")
```