Merging updates to Day 2&3 Material

exercises/day1part1_R-intro_exercises.R

@@ -6,46 +6,46 @@
 #### Arithmetic ####
 
 # Pick a number; save it as x
-
+x <- 6
 
 # Multiply x by 3
-
+x*3
 
 # Take the log of the above (Hint, you need the function log() here)
-
+log(x)
 
 # Subtract 4 from the above
-
+log(x)-4
 
 # Square the above
-
+(log(x)-4)^2
 
 #### Comparisons and Logical Operators ####
 
 # Check if 1 is bigger than 2
-
+1>2
 
 # Check if 1 + 1 is equal to 2
-
+(1+1) == 2
 
 # Check if it is true that the strings "eat" and "drink" are not equal to each other
-
+"eat" != "drink"
 
 # Check if it is true that 1 is equal to 1 *AND* 1 is equal to 2 
 # (Hint: remember what the operators & and | do)
-
+(1==1) $ (1==2)
 
 # Check if it is true that 1 is equal to 1 *OR* 1 is equal to 2
-
+(1==1) | (1==2)
 
 #### Packages and Functions ####
 
 # Load the package tidyverse
-
+install.packages("tidyverse")  #load it every time you use: library(tidyverse)
 
 # Open the help file for the function recode 
 # (Hint: remember what ? does)
-
+?recode
 
 #### REVIEW: DATA STRUCTURES ####
 
@@ -57,134 +57,146 @@ x1 <- rnorm(5)
 x2 <- rnorm(20, mean=0.5)
 
 # Select the 3rd element in x1
-
+x1[3]
 
 # Select the elements of x1 that are less than 0
-
+x1[x1 < 0]
 
 # Select the elements of x2 that are greater than 1
-
+x2[x2 > 1]
 
 # Create x3 containing the first five elements of x2
-
+x3 <- x2[1:5]
 
 # Select all but the third element of x1
-
+x1[-3]
 
 #### Missing values ####
 
 # Generate a vector
 vec <- c(1, 8, NA, 7, 3)
 
 # Calculate the mean of vec, excluding the NA value
-
+mean(vec)
+mean(vec, na.rm=TRUE)
 
 # Count the number of missing values in vec
-
+is.na(vec)
+sum(is.na(vec))
+mean(is.na(vec))
 
 #### Factors ####
 
 # See lecture notes and DataCamp for guidance and practice
-
+#Create factors with factor(), which includes an argument for levels =
 
 #### Lists ####
 
 # See lecture notes and DataCamp for guidance and practice
-
+#index an element in a list using double brackets: [[1]]
 
 #### Matricies ####
 
 # Generate a matrix
 mat <- matrix(c(1:51, rep(NA,4)), ncol=5)
 
 # Select row 4, column 5
-
+mat(4,5)
 
 # Select column 3
-
+mat(,3)
 
 # Bonus: How many NA values are there in this matrix?
 
 
 #### Data frames ####
+#data.frame(), or by combining vectors with cbind() or rbind()
+#df$var or df["var"]
 
 # Load one of R's example data frames, mtcars
 data(mtcars)
 
 # Identify the number of observations (rows) and number of variables (columns)
-
+dim(mtcars)
+ncol(mtcars)
+nrow(mtcars)
 
 # Identify the names of the variables
-
+names(mtcars)  # mtcars$var 
 
 # Select the variable 'mpg'
-
+mtcars["mpg"]
 
 # Select the 4th row
-
+mtcars[4,]
 
 # Square the value of the 'cyl' variable and store this as a new variable 'cylsq'
-
+mtcars$cylsq <- (mtcars$cyl)^2
 
 #### READING FILES ####
 
 # Check your working directory. It should be the root folder where you downloaded the boot camp materials. If that's not the case, set your working directory accordingly.
-
+getwd()
 
 # Read gapminder data with read.csv()
 gapminder <- read.csv("data/gapminder5.csv", stringsAsFactors=FALSE)
 
 # Load the readr package
-
+library(readr)
 
 # Read gapminder data with read_csv()
-
+gapminder <- read.csv("data/gapminder5.csv")  # read_csv("data/gapminder5.csv")
 
 #### DATA MANIPULATION ####
 
 #### Exploring data frames ####
 
 # Run summary() on the gapminder data
-
+summary(gapminder)
 
 # Find the mean of the variable pop
-
+mean(gapminder$pop)
 
 # Create a frequency table of the variable 'year'
 # Hint: use table()
-
+table(gapminder$year)
 
 # Create a proportion table of the variable 'continent'
 # Hint: use prop.table()
-
+prop.table(table(gapminder$continent))
 
 #### Subsetting and Sorting ####
 
 # Create a new data frame called gapminder07 contaning only those rows in the gapminder data where year is 2007
-
+gapminder07 <- gapminder[gapminder$year==2007,]
+ggapminder07 <- subset(gapminder, subset = year==2007)
 
 # Created a sorted frequency table of the variable continent in gapminder07
-
+sort(table(gapminder07$continent))
 
 # Print out the population of Mexico in 2007
-
+gapminder07$pop[gapminder07$country=="Mexico"]
 
 # BONUS: Print out the rows represnting the 5 countries with the highest population in 2007
 # Hint: Use order(), which we learned about, and head(), which prints out the first 5 rows of a data frame
-
+head(gapminder07[order(gapminder07$pop, decreasing=TRUE),])
 
 #### Adding and removing columns ####
 
 # See lecture notes for more guidance. We will practice this skill in the next section.
+gapminder$newvar <- newvar
+#or gapminder <- cbind(gapminder, newvar)
 
+gapminder$newvar <- NULL
+#or gapminder <- gapminder[-"newvar"]
 
 #### Recoding variables ####
 
 # Round the values of the variable `lifeExp` using `round()` and store this as a new variable `lifeExp_round`
-
+gapminder07$lifeExp_round <- round(gapminder07$lifeExp)
 
 # Print out the new variable to see what it looks like
-
+head(gapminder07$lifeExp_round)
 
 # This code creates the new variable 'lifeExp_over70'. Try to understand what it does.
 gapminder07$lifeExp_over70 <- NA  # Initialize a variable containing all "NA" values
@@ -195,71 +207,81 @@ table(gapminder07$lifeExp_over70)
 # Try to create a new variable 'lifeExp_highlow' that has the value 
 # "High" when life expectancy is over the mean and the value "Low" 
 # when it is below the mean. When you are done, print a frequency table.
-
-
-
+gapminder07$lifeExp_highlow <- NA  # Initialize a variable containing all "NA" values
+gapminder07$lifeExp_highlow[gapminder07$lifeExp>mean(gapminder07$lifeExp)] <- "High"
+gapminder07$lifeExp_highlow[gapminder07$lifeExp<mean(gapminder07$lifeExp)] <- "Low"
+table(gapminder07$lifeExp_highlow)
 
 
 #### Aggregating ####
 
 # Find the mean of life expectancy in 2007 for each continent
 # Hint: use the aggregate() function
-
+aggregate(gapminder07$lifeExp ~ gapminder07$continent, FUN = mean)
+#or aggregate(lifeExp ~ continent, data = gapminder07, FUN = mean)
 
 #### Statistics, part 1 ####
 
 # Calculate the correlation between 'lifeExp' and 'gdpPercap'.
-
+cor(gapminder07$lifeExp,gapminder07$gdpPercap)
 
 # Use a t-test to evaluate the difference between 'gdpPercap' in "high" and "low" life expectancy countries. Store the results as t1, and then print out t1.
-
-
+t1 <- t.test(gapminder07$gdpPercap ~ gapminder07$lifeExp_highlow)
+#or t1 <- t.test(gdpPercap ~ lifeExp_highlow, data =gapminder07)
+t1
 
 #### Statistics, part 2 ####
 
 # Conduct a linear regression predicting 'lifeExp' as a function of 'gdpPercap' and 'pop', and store the results as reg1.
-
+reg1 <- lm(gapminder07$lifeExp ~ gapminder07$gdpPercap +gapminder07$pop)
 
 # Print out reg1.
-
+reg1
 
 # Run summary() on reg1.
-
+summary(reg1)
 
 #### WRITING FILES ####
 
 #### Writing a data file ####
 
 # Save the gapminder07 data frame as a CSV file using write.csv() in the "data" subfolder within the working directory
 # Set the argument `row.names = FALSE`.
-
+write.csv(gapminder07, file = "data/gapminder07.csv", row.names = FALSE)
 
 #### Save R objects ####
 
 # See lecture notes for guidance and more examples.
-
+#???Save??? icon in the Environment tab
 
 #### DATA VISUALIZATION ####
 
 #### Histograms ####
 
 # Create a histogram of the variable 'lifeExp' in gapminder07
-
+hist(gapminder07$lifeExp)
 
 # Re-create the histogram with a title and axis labels
-
+hist(gapminder07$lifeExp,breaks = 15, main="Distribution of life expectancy across countries in 2007", 
+              xlab="Life expectancy", ylab="Frequency")
 
 # Bonus: Change the `breaks = ` argument from its default setting and see what happens.
-
+hist(gapminder07$lifeExp,breaks = 5, main="Distribution of life expectancy across countries in 2007", 
+              xlab="Life expectancy", ylab="Frequency")
 
 #### Scatterplots ####
 
 # Create a scatterplot with `lifeExp` on the y-axis and `gdpPercap` on the x-axis.
-
+plot(gapminder07$lifeExp ~ gapminder07$gdpPercap)
 
 # Add a title and axis labels.
-
+plot(gapminder07$lifeExp ~ gapminder07$gdpPercap,
+     main="Relationship between life expectancy and GDP per capita in 2007", 
+     ylab="Life expectancy", xlab="GDP per capita")
 
 # Bonus: Add a horizontal line indicating the mean of `lifeExp` onto the plot using `abline()`.
+plot(gapminder07$lifeExp ~ gapminder07$gdpPercap,
+                 main="Relationship between life expectancy and GDP per capita in 2007", 
+                 ylab="Life expectancy", xlab="GDP per capita")
 
-
+abline(h = mean(gapminder07$lifeExp))