風中一匹狼

Use the built-in data set "airquality".

1. Extract the first 4 rows of the data frame and print them to the console.

> write.table(airquality, "airquality.txt")

> x <- read.table("airquality.txt", nrow = 4)

> x

  Ozone Solar.R Wind Temp Month Day

1    41     190  7.4   67     5   1

2    36     118  8.0   72     5   2

3    12     149 12.6   74     5   3

4    18     313 11.5   62     5   4

方法二

> head(airquality, n=4)

  Ozone Solar.R Wind Temp Month Day

1    41     190  7.4   67     5   1

2    36     118  8.0   72     5   2

3    12     149 12.6   74     5   3

4    18     313 11.5   62     5   4

 2. Extract the last 3 rows of the data frame and print them to the console.

方法一

> x <- read.table("airquality.txt", skip = 151 , nrow = 3)

> x

   V1 V2  V3   V4 V5 V6 V7

1 151 14 191 14.3 75  9 28

2 152 18 131  8.0 76  9 29

3 153 20 223 11.5 68  9 30

方法二

> tail(airquality, n=3)

    Ozone Solar.R Wind Temp Month Day

151    14     191 14.3   75     9  28

152    18     131  8.0   76     9  29

153    20     223 11.5   68     9  30

3. What is the value of "Wind" in the 24th row?

> x$Wind[24]

[1] 12

4. What is the mean of "Temp" when "Month" is equal to 8?

方法一

> c<- data.table(airquality)

> c[Month==8,.(y=mean(Temp))]

          y

1: 83.96774

方法二

> mean(x$Temp[x$Month==8])

[1] 83.96774

5. Extract the subset of rows of the data frame where "Ozone" values are above 30 and "Temp" values are above 80. What is the mean of "Solar.R" in this subset?

> mean(x$Solar.R[x$Ozone>30 &x$Temp>80], na.rm = T)

[1] 220.6512

6. How many missing values are in the "Solar.R" column of this data frame?

> length(x$Solar.R[is.na(x$Solar.R)])

[1] 7