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add some additional excercises for part one

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Natalie Elphick 2024-01-19 10:47:39 -08:00
parent 86064336f5
commit 799ac0bb45
1 changed files with 28 additions and 6 deletions
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34
intro-r-data-analysis/Intro_to_R_workshop_materials/part_1.R
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@ -93,7 +93,7 @@ which(abc == "b")
num <- 1:10 num <- 1:10
num num
# Defining an unnamed list # Defining an unnamed list (note that here we overwrite the original contents of abc)
abc <- list("a", "b", 3) abc <- list("a", "b", 3)
# Accessing elements of unnamed list # Accessing elements of unnamed list
@ -110,6 +110,8 @@ abc[[2]] # Numeric index with brackets
# Range of elements # Range of elements
abc[2:3] abc[2:3]
# Numeric index to exclude elements
abc[-3]
# Defining a matrix # Defining a matrix
numeric_matrix <- matrix(1:9, nrow = 3, ncol = 3) numeric_matrix <- matrix(1:9, nrow = 3, ncol = 3)
@ -154,16 +156,36 @@ mixed_df["letters"] # Returns the column as a single column data frame
nrow(mixed_df) nrow(mixed_df)
ncol(mixed_df) ncol(mixed_df)
# Similar to matrices, elements in a data frame can also be accessed by # Like matrices, elements in a data frame can also be accessed by
# combinations of numeric index and column and row names. # numeric index or column and row names.
mixed_df[2, 3]
mixed_df["B", "letters"]
mixed_df[1:2, "letters"]
# **Check your understanding** ######################## **Check your understanding** ########################
# Write 1 line of code that accesses only elements B, C, FALSE and NA of ## Data Types:
# mixed_df #
# 1) Write 2 lines of code that converts the following character vector into a
# numeric vector and then checks the type of that vector.
vec <- c("1", "2", "3", "4", "5")
## Data Structures:
#
# 2) Write 1 line of code that accesses only elements B, C, FALSE and NA of
# mixed_df.
# #
# Hint: line 134 # Hint: line 134
# 3) Write the two ways to access only elements "2" and 3 from the list abc.
#