In-Class Exercise 1: Each Chick Weight and Time

Load data file

dta <- as.data.frame(datasets::ChickWeight)
head(dta)

##   weight Time Chick Diet
## 1     42    0     1    1
## 2     51    2     1    1
## 3     59    4     1    1
## 4     64    6     1    1
## 5     76    8     1    1
## 6     93   10     1    1

Extract separate slope estimates of regressing weight onto Time for each chick

m0 <- lapply(split(dta[, 1:2], 
                   list(dta$Chick, dta$Time)), 
             function(x) lm(weight ~ Time, data=dta))
sapply(m0, coef)

##                  18.0      16.0      15.0      13.0       9.0      20.0
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  10.0       8.0      17.0      19.0       4.0       6.0
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  11.0       3.0       1.0      12.0       2.0       5.0
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  14.0       7.0      24.0      30.0      22.0      23.0
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  27.0      28.0      26.0      25.0      29.0      21.0
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  33.0      37.0      36.0      31.0      39.0      38.0
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  32.0      40.0      34.0      35.0      44.0      45.0
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  43.0      41.0      47.0      49.0      46.0      50.0
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  42.0      48.0      18.2      16.2      15.2      13.2
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                   9.2      20.2      10.2       8.2      17.2      19.2
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                   4.2       6.2      11.2       3.2       1.2      12.2
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                   2.2       5.2      14.2       7.2      24.2      30.2
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  22.2      23.2      27.2      28.2      26.2      25.2
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  29.2      21.2      33.2      37.2      36.2      31.2
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  39.2      38.2      32.2      40.2      34.2      35.2
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  44.2      45.2      43.2      41.2      47.2      49.2
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  46.2      50.2      42.2      48.2      18.4      16.4
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  15.4      13.4       9.4      20.4      10.4       8.4
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  17.4      19.4       4.4       6.4      11.4       3.4
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                   1.4      12.4       2.4       5.4      14.4       7.4
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  24.4      30.4      22.4      23.4      27.4      28.4
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  26.4      25.4      29.4      21.4      33.4      37.4
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  36.4      31.4      39.4      38.4      32.4      40.4
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  34.4      35.4      44.4      45.4      43.4      41.4
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  47.4      49.4      46.4      50.4      42.4      48.4
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  18.6      16.6      15.6      13.6       9.6      20.6
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  10.6       8.6      17.6      19.6       4.6       6.6
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  11.6       3.6       1.6      12.6       2.6       5.6
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  14.6       7.6      24.6      30.6      22.6      23.6
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  27.6      28.6      26.6      25.6      29.6      21.6
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  33.6      37.6      36.6      31.6      39.6      38.6
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  32.6      40.6      34.6      35.6      44.6      45.6
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  43.6      41.6      47.6      49.6      46.6      50.6
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  42.6      48.6      18.8      16.8      15.8      13.8
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                   9.8      20.8      10.8       8.8      17.8      19.8
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                   4.8       6.8      11.8       3.8       1.8      12.8
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                   2.8       5.8      14.8       7.8      24.8      30.8
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  22.8      23.8      27.8      28.8      26.8      25.8
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  29.8      21.8      33.8      37.8      36.8      31.8
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  39.8      38.8      32.8      40.8      34.8      35.8
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  44.8      45.8      43.8      41.8      47.8      49.8
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  46.8      50.8      42.8      48.8     18.10     16.10
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 15.10     13.10      9.10     20.10     10.10      8.10
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 17.10     19.10      4.10      6.10     11.10      3.10
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  1.10     12.10      2.10      5.10     14.10      7.10
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 24.10     30.10     22.10     23.10     27.10     28.10
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 26.10     25.10     29.10     21.10     33.10     37.10
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 36.10     31.10     39.10     38.10     32.10     40.10
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 34.10     35.10     44.10     45.10     43.10     41.10
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 47.10     49.10     46.10     50.10     42.10     48.10
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 18.12     16.12     15.12     13.12      9.12     20.12
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 10.12      8.12     17.12     19.12      4.12      6.12
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 11.12      3.12      1.12     12.12      2.12      5.12
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 14.12      7.12     24.12     30.12     22.12     23.12
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 27.12     28.12     26.12     25.12     29.12     21.12
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 33.12     37.12     36.12     31.12     39.12     38.12
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 32.12     40.12     34.12     35.12     44.12     45.12
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 43.12     41.12     47.12     49.12     46.12     50.12
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 42.12     48.12     18.14     16.14     15.14     13.14
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  9.14     20.14     10.14      8.14     17.14     19.14
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  4.14      6.14     11.14      3.14      1.14     12.14
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  2.14      5.14     14.14      7.14     24.14     30.14
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 22.14     23.14     27.14     28.14     26.14     25.14
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 29.14     21.14     33.14     37.14     36.14     31.14
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 39.14     38.14     32.14     40.14     34.14     35.14
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 44.14     45.14     43.14     41.14     47.14     49.14
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 46.14     50.14     42.14     48.14     18.16     16.16
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 15.16     13.16      9.16     20.16     10.16      8.16
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 17.16     19.16      4.16      6.16     11.16      3.16
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  1.16     12.16      2.16      5.16     14.16      7.16
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 24.16     30.16     22.16     23.16     27.16     28.16
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 26.16     25.16     29.16     21.16     33.16     37.16
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 36.16     31.16     39.16     38.16     32.16     40.16
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 34.16     35.16     44.16     45.16     43.16     41.16
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 47.16     49.16     46.16     50.16     42.16     48.16
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 18.18     16.18     15.18     13.18      9.18     20.18
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 10.18      8.18     17.18     19.18      4.18      6.18
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 11.18      3.18      1.18     12.18      2.18      5.18
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 14.18      7.18     24.18     30.18     22.18     23.18
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 27.18     28.18     26.18     25.18     29.18     21.18
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 33.18     37.18     36.18     31.18     39.18     38.18
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 32.18     40.18     34.18     35.18     44.18     45.18
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 43.18     41.18     47.18     49.18     46.18     50.18
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 42.18     48.18     18.20     16.20     15.20     13.20
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  9.20     20.20     10.20      8.20     17.20     19.20
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  4.20      6.20     11.20      3.20      1.20     12.20
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  2.20      5.20     14.20      7.20     24.20     30.20
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 22.20     23.20     27.20     28.20     26.20     25.20
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 29.20     21.20     33.20     37.20     36.20     31.20
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 39.20     38.20     32.20     40.20     34.20     35.20
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 44.20     45.20     43.20     41.20     47.20     49.20
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 46.20     50.20     42.20     48.20     18.21     16.21
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 15.21     13.21      9.21     20.21     10.21      8.21
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 17.21     19.21      4.21      6.21     11.21      3.21
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                  1.21     12.21      2.21      5.21     14.21      7.21
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 24.21     30.21     22.21     23.21     27.21     28.21
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 26.21     25.21     29.21     21.21     33.21     37.21
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 36.21     31.21     39.21     38.21     32.21     40.21
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 34.21     35.21     44.21     45.21     43.21     41.21
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039
##                 47.21     49.21     46.21     50.21     42.21     48.21
## (Intercept) 27.467425 27.467425 27.467425 27.467425 27.467425 27.467425
## Time         8.803039  8.803039  8.803039  8.803039  8.803039  8.803039

Plot the Weight across time by individualy chick

library(lattice)
xyplot(weight ~ Time | Chick, data=dta, type=c("p", "r", "g"))

In-Class Exercise 2: What does the code do (lapply(lapply(search(), ls), length))

The search function shows that ‘Gives a list of attached packages (see library), and R objects, usually data.frames.’

?search

List the pachage and R objects

search()

##  [1] ".GlobalEnv"        "package:lattice"   "package:stats"    
##  [4] "package:graphics"  "package:grDevices" "package:utils"    
##  [7] "package:datasets"  "package:methods"   "Autoloads"        
## [10] "package:base"

Show the number of function or datasets by each package.

lapply(lapply(search(), ls), length)

## [[1]]
## [1] 2
## 
## [[2]]
## [1] 151
## 
## [[3]]
## [1] 448
## 
## [[4]]
## [1] 87
## 
## [[5]]
## [1] 109
## 
## [[6]]
## [1] 215
## 
## [[7]]
## [1] 104
## 
## [[8]]
## [1] 218
## 
## [[9]]
## [1] 0
## 
## [[10]]
## [1] 1229

In-Class Exercise 3: Cushings example

library(pacman)
pacman::p_load(MASS, tidyverse)

Calculate mean Tetrahydrocortisone and Pregnanetriol by each type

method 1

aggregate( . ~ Type, data = Cushings, mean)

##   Type Tetrahydrocortisone Pregnanetriol
## 1    a            2.966667          2.44
## 2    b            8.180000          1.12
## 3    c           19.720000          5.50
## 4    u           14.016667          1.20

## The command is more straightforward
## The output is a data frame, which allowed us to easily operate (long format).

method 2

sapply(split(Cushings[,-3], Cushings$Type), function(x) apply(x, 2, mean))

##                            a    b     c        u
## Tetrahydrocortisone 2.966667 8.18 19.72 14.01667
## Pregnanetriol       2.440000 1.12  5.50  1.20000

## The output is a matrix and more like a wide format.

method 3

do.call("rbind", as.list(
  by(Cushings, list(Cushings$Type), function(x) {
    y <- subset(x, select =  -Type)
    apply(y, 2, mean)
  }
  )))

## The output is a matrix and more like a long format.
## The code chunck is a little bit complicated for me.

method 4

Cushings %>%
  group_by(Type) %>%
  summarize( t_m = mean(Tetrahydrocortisone), p_m = mean(Pregnanetriol))

## # A tibble: 4 x 3
##   Type    t_m   p_m
##   <fct> <dbl> <dbl>
## 1 a      2.97  2.44
## 2 b      8.18  1.12
## 3 c     19.7   5.5 
## 4 u     14.0   1.2

## The output is a tibble and more like a long format.
## We can set up the variable name as we expected to make reader more easily read.

method 5

Cushings %>%
  nest(-Type) %>%
  mutate(avg = map(data, ~ apply(., 2, mean)), 
         res_1 = map_dbl(avg, "Tetrahydrocortisone"), 
         res_2 = map_dbl(avg, "Pregnanetriol"))

## Warning: All elements of `...` must be named.
## Did you want `data = c(Tetrahydrocortisone, Pregnanetriol)`?

## # A tibble: 4 x 5
##   Type  data              avg       res_1 res_2
##   <fct> <list>            <list>    <dbl> <dbl>
## 1 a     <tibble [6 × 2]>  <dbl [2]>  2.97  2.44
## 2 b     <tibble [10 × 2]> <dbl [2]>  8.18  1.12
## 3 c     <tibble [5 × 2]>  <dbl [2]> 19.7   5.5 
## 4 u     <tibble [6 × 2]>  <dbl [2]> 14.0   1.2

## The output is a tibble and more like a long format.
## We can set up the variable name as we expected to make reader more easily read.

In-Class Exercise 4: A Case Study

keep the school names with white spaces

dta <- read.csv("/Users/haolunfu/Documents/資料管理/week8/nzSchools.csv", as.is=2)

Show the data structure

str(dta)

## 'data.frame':    2571 obs. of  6 variables:
##  $ ID  : int  1015 1052 1062 1092 1130 1018 1029 1030 1588 1154 ...
##  $ Name: chr  "Hora Hora School" "Morningside School" "Onerahi School" "Raurimu Avenue School" ...
##  $ City: Factor w/ 541 levels "Ahaura","Ahipara",..: 533 533 533 533 533 533 533 533 533 533 ...
##  $ Auth: Factor w/ 4 levels "Other","Private",..: 3 3 3 3 3 3 3 3 4 3 ...
##  $ Dec : int  2 3 4 2 4 8 5 5 6 1 ...
##  $ Roll: int  318 200 455 86 577 329 637 395 438 201 ...

Show the numbers of obsevations and variables in the data

dim(dta)

## [1] 2571    6

binning

Create a new variable to discriminate the school size by median of roll

dta$Size <- ifelse(dta$Roll > median(dta$Roll), "Large", "Small")

Delete the Size column

dta$Size <- NULL

Show the first 6 rows

head(dta)

##     ID                  Name      City  Auth Dec Roll
## 1 1015      Hora Hora School Whangarei State   2  318
## 2 1052    Morningside School Whangarei State   3  200
## 3 1062        Onerahi School Whangarei State   4  455
## 4 1092 Raurimu Avenue School Whangarei State   2   86
## 5 1130      Whangarei School Whangarei State   4  577
## 6 1018       Hurupaki School Whangarei State   8  329

Create a new variable to discriminate the school size by each 1/3 of roll

dta$Size <- cut(dta$Roll, 3, labels=c("Small", "Mediam", "Large"))

Show the numbers of school by each size

table(dta$Size)

## 
##  Small Mediam  Large 
##   2555     15      1

sorting

Create a new variable of RollOrd to show the order of roll and order the data by roll

dta$RollOrd <- order(dta$Roll, decreasing=T)

Show the first six rows of the data ordered by RollOrd

head(dta[dta$RollOrd, ])

##       ID                  Name         City  Auth Dec Roll   Size RollOrd
## 1726 498 Correspondence School   Wellington State  NA 5546  Large     753
## 301   28     Rangitoto College     Auckland State  10 3022 Mediam     353
## 376   78      Avondale College     Auckland State   4 2613 Mediam     712
## 2307 319  Burnside High School Christchurch State   8 2588 Mediam     709
## 615   41      Macleans College     Auckland State  10 2476 Mediam    1915
## 199   43    Massey High School     Auckland State   5 2452 Mediam    1683

Show the lase six rows of the data ordered by RollOrd

tail(dta[dta$RollOrd, ])

##        ID                    Name                  City    Auth Dec Roll  Size
## 2401 1641  Amana Christian School               Dunedin Private   9    7 Small
## 1590 2461       Tangimoana School              Manawatu   State   4    6 Small
## 1996 3598         Woodbank School              Kaikoura   State   4    6 Small
## 2112 3386     Jacobs River School          Jacobs River   State   5    6 Small
## 1514 2407     Ngamatapouri School Sth Taranaki District   State   9    5 Small
## 1575 2420 Papanui Junction School               Taihape   State   5    5 Small
##      RollOrd
## 2401    2562
## 1590     266
## 1996    2478
## 2112    1501
## 1514    2377
## 1575    1542

Show the first six rows of the data ordered by City and Roll

head(dta[order(dta$City, dta$Roll, decreasing=T), ])

##        ID                      Name      City  Auth Dec Roll  Size RollOrd
## 2548  401           Menzies College   Wyndham State   4  356 Small     859
## 2549 4054            Wyndham School   Wyndham State   5   94 Small    1163
## 1611 2742          Woodville School Woodville State   3  147 Small     726
## 1630 2640           Papatawa School Woodville State   7   27 Small    2273
## 2041 3600            Woodend School   Woodend State   9  375 Small    1401
## 1601  399 Central Southland College    Winton State   7  549 Small     450

Show the last six rows of the data ordered by City and Roll

tail(dta[order(dta$City, dta$Roll, decreasing=T), ])

##        ID                         Name    City  Auth Dec Roll  Size RollOrd
## 2169 3273                Albury School  Albury State   8   30 Small    1010
## 2018  350           Akaroa Area School  Akaroa State   8  125 Small    1051
## 2023 3332           Duvauchelle School  Akaroa State   9   41 Small     749
## 335  1200                Ahuroa School  Ahuroa State   7   22 Small     193
## 99   1000               Ahipara School Ahipara State   3  241 Small    1963
## 2117 2105 Awahono School - Grey Valley  Ahaura State   4  119 Small     364

counting

Show the numbers of school by each Auth

table(dta$Auth)

## 
##            Other          Private            State State Integrated 
##                1               99             2144              327

Create a table to show the numbers of school by each Auth

authtbl <- table(dta$Auth); authtbl

## 
##            Other          Private            State State Integrated 
##                1               99             2144              327

Show the authtbl character

class(authtbl)

## [1] "table"

Show the data which Auth is Other

dta[dta$Auth == "Other", ]

##       ID            Name         City  Auth Dec Roll  Size RollOrd
## 2315 518 Kingslea School Christchurch Other   1   51 Small    1579

Show the numbers of school by each Auth and Dec

xtabs(~ Auth + Dec, data=dta)

##                   Dec
## Auth                 1   2   3   4   5   6   7   8   9  10
##   Other              1   0   0   0   0   0   0   0   0   0
##   Private            0   0   2   6   2   2   6  11  12  38
##   State            259 230 208 219 214 215 188 200 205 205
##   State Integrated  12  22  35  28  38  34  45  45  37  31

aggregating

Calculate the mean of roll

mean(dta$Roll)

## [1] 295.4737

Calculate the mean of roll which Auth is Private

mean(dta$Roll[dta$Auth == "Private"])

## [1] 308.798

Calculate the mean of roll by each Auth

aggregate(dta["Roll"], by=list(dta$Auth), FUN=mean)

##            Group.1     Roll
## 1            Other  51.0000
## 2          Private 308.7980
## 3            State 300.6301
## 4 State Integrated 258.3792

Create a new variable to distinguish the rich by Dec is larger than 5

dta$Rich <- dta$Dec > 5; dta$Rich

##    [1] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
##   [13] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE
##   [25] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
##   [37] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
##   [49] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE
##   [61] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##   [73] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##   [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##   [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
##  [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [133] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
##  [145] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
##  [157] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
##  [169]  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
##  [181] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
##  [193]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
##  [205]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [217]  TRUE  TRUE  TRUE  TRUE    NA  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
##  [229] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
##  [241]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [253]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [265] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
##  [277] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
##  [289]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE    NA    NA
##  [301]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
##  [313] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
##  [325]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
##  [337]  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [349]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [361]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
##  [373]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
##  [385]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
##  [397]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
##  [409]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [421]  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE
##  [433] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE
##  [445]  TRUE FALSE  TRUE  TRUE FALSE    NA  TRUE  TRUE FALSE  TRUE FALSE FALSE
##  [457]  TRUE  TRUE FALSE  TRUE  TRUE    NA  TRUE FALSE    NA  TRUE  TRUE FALSE
##  [469]  TRUE    NA FALSE  TRUE  TRUE FALSE  TRUE FALSE    NA  TRUE FALSE  TRUE
##  [481] FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE
##  [493] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
##  [505] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE
##  [517]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [529]  TRUE  TRUE  TRUE  TRUE  TRUE    NA  TRUE  TRUE  TRUE FALSE FALSE FALSE
##  [541] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [553]    NA FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [565] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [577] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
##  [589]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
##  [601]  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE
##  [613]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE    NA FALSE FALSE    NA FALSE
##  [625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE    NA FALSE  TRUE
##  [661] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
##  [673] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [685] FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [697] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
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##  [721] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
##  [733] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
##  [745] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE
##  [757]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
##  [769]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
##  [781] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
##  [793] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE
##  [805]  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
##  [817]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
##  [829]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
##  [841] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
##  [853]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
##  [877] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
##  [889] FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
##  [901] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE    NA  TRUE FALSE  TRUE  TRUE
##  [913] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
##  [925]  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [937]  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
##  [949] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE
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##  [985]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [1045] FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
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## [2557] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [2569] FALSE  TRUE  TRUE

Calculate the mean of roll by each Auth and show whether the school rich

aggregate(dta["Roll"], by=list(dta$Auth, dta$Rich), FUN=mean)

##            Group.1 Group.2     Roll
## 1            Other   FALSE  51.0000
## 2          Private   FALSE 151.4000
## 3            State   FALSE 261.7487
## 4 State Integrated   FALSE 183.2370
## 5          Private    TRUE 402.5362
## 6            State    TRUE 338.8243
## 7 State Integrated    TRUE 311.2135

Calculate the range of roll by each Auth

by(dta["Roll"], INDICES=list(dta$Auth), FUN=range)

## : Other
## [1] 51 51
## ------------------------------------------------------------ 
## : Private
## [1]    7 1663
## ------------------------------------------------------------ 
## : State
## [1]    5 5546
## ------------------------------------------------------------ 
## : State Integrated
## [1]   18 1475

In-Clasee Exercise 5: A Case Study - II

Load data file

dta2 <- read.table("/Users/haolunfu/Documents/資料管理/week8/NCEA2007.txt", sep=":", quote="", h=T, as.is=T)

Show the numbers of obsevations and variables in the data

dim(dta2)

## [1] 88  4

Show the data structure

str(dta2)

## 'data.frame':    88 obs. of  4 variables:
##  $ Name  : chr  "Al-Madinah School" "Alfriston College" "Ambury Park Centre for Riding Therapy" "Aorere College" ...
##  $ Level1: num  61.5 53.9 33.3 39.5 71.2 22.1 50.8 57.3 89.3 59.8 ...
##  $ Level2: num  75 44.1 20 50.2 78.9 30.8 34.8 49.8 89.7 65.7 ...
##  $ Level3: num  0 0 0 30.6 55.5 26.3 48.9 44.6 88.6 50.4 ...

Show the first 6 rows

head(dta2)

##                                    Name Level1 Level2 Level3
## 1                     Al-Madinah School   61.5   75.0    0.0
## 2                     Alfriston College   53.9   44.1    0.0
## 3 Ambury Park Centre for Riding Therapy   33.3   20.0    0.0
## 4                        Aorere College   39.5   50.2   30.6
## 5        Auckland Girls' Grammar School   71.2   78.9   55.5
## 6                      Auckland Grammar   22.1   30.8   26.3

Calculate the mean expect for the first columns (table)

apply(dta2[, -1], MARGIN=2, FUN=mean)

##   Level1   Level2   Level3 
## 62.26705 61.06818 47.97614

list apply

Calculate the mean expect for the first columns (list)

lapply(dta2[, -1], FUN=mean)

## $Level1
## [1] 62.26705
## 
## $Level2
## [1] 61.06818
## 
## $Level3
## [1] 47.97614

simplify the list apply

Calculate the mean expect for the first columns (matrix)

sapply(dta2[, -1], FUN=mean)

##   Level1   Level2   Level3 
## 62.26705 61.06818 47.97614

Calculate the range expect for the first columns (table)

apply(dta2[, -1], MARGIN=2, FUN=range)

##      Level1 Level2 Level3
## [1,]    2.8    0.0    0.0
## [2,]   97.4   95.7   95.7

Calculate the range expect for the first columns (list)

lapply(dta2[, -1], FUN=range)

## $Level1
## [1]  2.8 97.4
## 
## $Level2
## [1]  0.0 95.7
## 
## $Level3
## [1]  0.0 95.7

Calculate the range expect for the first columns (matrix)

sapply(dta2[, -1], FUN=range)

##      Level1 Level2 Level3
## [1,]    2.8    0.0    0.0
## [2,]   97.4   95.7   95.7

splitting

Split the data by roll and auth (list)

rollsByAuth <- split(dta$Roll, dta$Auth)

Show the data structure

str(rollsByAuth)

## List of 4
##  $ Other           : int 51
##  $ Private         : int [1:99] 255 39 154 73 83 25 95 85 94 729 ...
##  $ State           : int [1:2144] 318 200 455 86 577 329 637 395 201 267 ...
##  $ State Integrated: int [1:327] 438 26 191 560 151 114 126 171 211 57 ...

Show the data character

class(rollsByAuth)

## [1] "list"

Calculate the mean roll by each Auth (list)

lapply(split(dta$Roll, dta$Auth), mean)

## $Other
## [1] 51
## 
## $Private
## [1] 308.798
## 
## $State
## [1] 300.6301
## 
## $`State Integrated`
## [1] 258.3792

Calculate the mean roll by each Auth (matrix)

sapply(split(dta$Roll, dta$Auth), mean)

##            Other          Private            State State Integrated 
##          51.0000         308.7980         300.6301         258.3792

Week 8 In-class exercise (Function)

Hao-Lun Fu

2020-05-11

In-Class Exercise 1: Each Chick Weight and Time

Load data file

Extract separate slope estimates of regressing weight onto Time for each chick

Plot the Weight across time by individualy chick

In-Class Exercise 2: What does the code do (lapply(lapply(search(), ls), length))

The search function shows that ‘Gives a list of attached packages (see library), and R objects, usually data.frames.’

List the pachage and R objects

Show the number of function or datasets by each package.

In-Class Exercise 3: Cushings example

Calculate mean Tetrahydrocortisone and Pregnanetriol by each type

method 1

method 2

method 3

method 4

method 5

In-Class Exercise 4: A Case Study

keep the school names with white spaces

Show the data structure

Show the numbers of obsevations and variables in the data

binning

Create a new variable to discriminate the school size by median of roll

Delete the Size column

Show the first 6 rows

Create a new variable to discriminate the school size by each 1/3 of roll

Show the numbers of school by each size

sorting

Create a new variable of RollOrd to show the order of roll and order the data by roll

Show the first six rows of the data ordered by RollOrd

Show the lase six rows of the data ordered by RollOrd

Show the first six rows of the data ordered by City and Roll

Show the last six rows of the data ordered by City and Roll

counting

Show the numbers of school by each Auth

Create a table to show the numbers of school by each Auth

Show the authtbl character

Show the data which Auth is Other

Show the numbers of school by each Auth and Dec

aggregating

Calculate the mean of roll

Calculate the mean of roll which Auth is Private

Calculate the mean of roll by each Auth

Create a new variable to distinguish the rich by Dec is larger than 5

Calculate the mean of roll by each Auth and show whether the school rich

Calculate the range of roll by each Auth

In-Clasee Exercise 5: A Case Study - II

Load data file

Show the numbers of obsevations and variables in the data

Show the data structure

Show the first 6 rows

Calculate the mean expect for the first columns (table)

list apply

Calculate the mean expect for the first columns (list)

simplify the list apply

Calculate the mean expect for the first columns (matrix)

Calculate the range expect for the first columns (table)

Calculate the range expect for the first columns (list)

Calculate the range expect for the first columns (matrix)

splitting

Split the data by roll and auth (list)

Show the data structure

Show the data character

Calculate the mean roll by each Auth (list)

Calculate the mean roll by each Auth (matrix)