# DS4100 group coding week 2
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# 0. determine your current working directory. Set your working directory to another directory

# 1. investigate the functions c, data.frame, and setwd with the help function, for example ?c

# 2. create  a numeric vector named object consisting of the values
#1,NA,3,4,67,98,43,NA,NA

# 3. create another numberic vector named object2 consisting of the values 1,NA,3,4,67,98,43,NA,13

# 4. Investigate NA, the mode and the length of the 2 vectors, create a factor variable containing the following levels "small", "medium", "large"

# 5. combine the 2 numeric vectors into a data frame named frame

# 6. install the tidyverse packages using install.packages

# 7. access the dataset mpg read about the data

#8. determine the average number of city miles per gallon for the data

# 9. use the is.na function to find/locate NAs in object and object2

# 10. use the na.omit function to remove the NAs from object and object2

# 11. use the logical function to create a logical vector named tt of length 5

# 12. calculate the sum of object

# 13. use the seq function to create the following sequence 5, 10, 15, 20

# 14. learn about the diamonds dataset using the help facility

# 15. determine the number of columns and rows in the diamonds data set

# dplyr practice
# 16. use the select function from dplyr to create a dataframe containing the caret and price column from the diamonds data set

# 17. select all columns from the diamonds dataset BUT the x,y,z and price columns

# 18. use the filter function to select diamonds with a caret size > .5

# 19. Create at least 2 other filter expressions for diamonds dataset
filter(diamonds,x > 4)
filter(diamonds, z < 2.5)

# 20. write at least 1 filter containing the %in% keyword
filter(diamonds, cut %in% c('Ideal', 'Good'))
# 21. use the head function with diamonds and the dataframe object frame - notice the difference in the output

# 22. use the summary function on the mpg data frame and frame dataframe compare the results