In [2]:
library(DataComputing)
library(magrittr)
library(ggplot2)
library(dplyr)
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# built-in dataset in R
head(airquality)
Out[4]:
Change data from wide to narrow format (above) using gather function.
Group Ozone, Solar.R, Wind, Temp into one variable called type and create another column called value to store their values. Your output should look like this:
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# Data frame for questions
data <- data.frame(V1 = rep(c("a","b","c"), each = 2),
V2 = rep(c(1:2), times = 3),
V3 = rep(c("alpha", "beta", "gamma")),
V4 = seq(10, 60, by = 10))
print(data)
Assuming that the tidyr and dplyr libraries are already loaded, write down what the output for the following code. The final result is enough for full credit, but partial credit will be given for writing out and labelling intermediate steps.
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data %>%
filter(V1 == "a") %>% # Step 1
select(V2, V4) %>% # Step 2
gather(key = Apple, value = Banana, V2, V4) %>% # Step 3
mutate(Apple = Banana) # Step 4
Suppose you have a data frame, data, as given below.
a) Write a function called fix_missing_99 that takes one argument: x, a numeric vector. The function should replace every component of x equal to -99 with NA.
b) Write a loop that replaces every -99 in data with NA. For full credit, your code must use the function in part (a) and it should continue to work without modification if additional columns are added to the data frame.
c) Write down an appropriate call from the apply family of functionals to perform the same task as in part (b).
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set.seed(1014)
data <- data.frame(replicate(6, sample(c(1:10, -99), 6, rep = TRUE)))
names(data) <- letters[1:6]
data
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head(diamonds)
What command in 'ggplot' that you will use to generate the graph given below?