| output |
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github_document |
00070_informalexample_5.1_of_section_5.1.1.R
# informalexample 5.1 of section 5.1.1
# (informalexample 5.1 of section 5.1.1) : Data engineering and data shaping : Data selection : Sub-setting rows and columns
library("ggplot2") # Note: 1
summary(iris) # Note: 2 ## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
##
## Species
## setosa :50
## versicolor:50
## virginica :50
# Note 1:
# Attach the ggplot2 package for later plotting.
# Note 2:
# Take a look at the built-in iris data. 00071_informalexample_5.2_of_section_5.1.1.R
# informalexample 5.2 of section 5.1.1
# (informalexample 5.2 of section 5.1.1) : Data engineering and data shaping : Data selection : Sub-setting rows and columns
head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
ggplot(iris,
aes(x = Petal.Length, y = Petal.Width,
shape = Species, color = Species)) +
geom_point(size =2 ) +
ggtitle("Petal dimensions by iris species: all measurements")
00072_informalexample_5.3_of_section_5.1.1.R
# informalexample 5.3 of section 5.1.1
# (informalexample 5.3 of section 5.1.1) : Data engineering and data shaping : Data selection : Sub-setting rows and columns
columns_we_want <- c("Petal.Length", "Petal.Width", "Species")
rows_we_want <- iris$Petal.Length > 2
# before
head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
iris_base <- iris[rows_we_want, columns_we_want, drop = FALSE]
# after
head(iris_base)## Petal.Length Petal.Width Species
## 51 4.7 1.4 versicolor
## 52 4.5 1.5 versicolor
## 53 4.9 1.5 versicolor
## 54 4.0 1.3 versicolor
## 55 4.6 1.5 versicolor
## 56 4.5 1.3 versicolor
## Petal.Length Petal.Width Species
## 51 4.7 1.4 versicolor
## 52 4.5 1.5 versicolor
## 53 4.9 1.5 versicolor
## 54 4.0 1.3 versicolor
## 55 4.6 1.5 versicolor
## 56 4.5 1.3 versicolor00073_informalexample_5.4_of_section_5.1.1.R
# informalexample 5.4 of section 5.1.1
# (informalexample 5.4 of section 5.1.1) : Data engineering and data shaping : Data selection : Sub-setting rows and columns
library("data.table")
iris_data.table <- as.data.table(iris) # Note: 1
columns_we_want <- c("Petal.Length", "Petal.Width", "Species")
rows_we_want <- iris_data.table$Petal.Length > 2
iris_data.table <- iris_data.table[rows_we_want , ..columns_we_want] # Note: 2
head(iris_data.table)## Petal.Length Petal.Width Species
## 1: 4.7 1.4 versicolor
## 2: 4.5 1.5 versicolor
## 3: 4.9 1.5 versicolor
## 4: 4.0 1.3 versicolor
## 5: 4.6 1.5 versicolor
## 6: 4.5 1.3 versicolor
## Petal.Length Petal.Width Species
## 1: 4.7 1.4 versicolor
## 2: 4.5 1.5 versicolor
## 3: 4.9 1.5 versicolor
## 4: 4.0 1.3 versicolor
## 5: 4.6 1.5 versicolor
## 6: 4.5 1.3 versicolor
# Note 1:
# Convert to data.table class to get data.table semantics.
# Note 2:
# The “..” notation tells data.table that “columns_we_want” isn’t itself the name of a column
# but a variable referring to names of columns. 00074_informalexample_5.5_of_section_5.1.1.R
# informalexample 5.5 of section 5.1.1
# (informalexample 5.5 of section 5.1.1) : Data engineering and data shaping : Data selection : Sub-setting rows and columns
library("data.table")
df <- data.frame(x = 1:2, y = 3:4) # Note: 1
df[, x] # Note: 2 ## Error in `[.data.frame`(df, , x): object 'x' not found
## Error in `[.data.frame`(df, , x) : object 'x' not found
x <- "y" # Note: 3
dt <- data.table(df)
dt[, x] # Note: 4 ## [1] 1 2
## [1] 1 2
dt[, ..x] # Note: 5 ## y
## 1: 3
## 2: 4
## y
## 1: 3
## 2: 4
# Note 1:
# Example data.frame.
# Note 2:
# Notice writing df[, x] instead of df[, "x"] is an error (assuming x is not bound to a value in
# our environment).
# Note 3:
# Set up data.table example.
# Note 4:
# Notice this returns the column x much like d$x would.
# Note 5:
# This uses data.table’s “look up” idiom to get a data.table of columns referred to by the
# variable x. 00075_informalexample_5.6_of_section_5.1.1.R
# informalexample 5.6 of section 5.1.1
# (informalexample 5.6 of section 5.1.1) : Data engineering and data shaping : Data selection : Sub-setting rows and columns
library("dplyr")##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:data.table':
##
## between, first, last
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
iris_dplyr <- iris %>%
select(.,
Petal.Length, Petal.Width, Species) %>%
filter(.,
Petal.Length > 2)
head(iris_dplyr)## Petal.Length Petal.Width Species
## 1 4.7 1.4 versicolor
## 2 4.5 1.5 versicolor
## 3 4.9 1.5 versicolor
## 4 4.0 1.3 versicolor
## 5 4.6 1.5 versicolor
## 6 4.5 1.3 versicolor
## Petal.Length Petal.Width Species
## 1 4.7 1.4 versicolor
## 2 4.5 1.5 versicolor
## 3 4.9 1.5 versicolor
## 4 4.0 1.3 versicolor
## 5 4.6 1.5 versicolor
## 6 4.5 1.3 versicolor00076_informalexample_5.7_of_section_5.1.2.R
# informalexample 5.7 of section 5.1.2
# (informalexample 5.7 of section 5.1.2) : Data engineering and data shaping : Data selection : Removing records with incomplete data
library("ggplot2")
data(msleep) # Note: 1
str(msleep)## Classes 'tbl_df', 'tbl' and 'data.frame': 83 obs. of 11 variables:
## $ name : chr "Cheetah" "Owl monkey" "Mountain beaver" "Greater short-tailed shrew" ...
## $ genus : chr "Acinonyx" "Aotus" "Aplodontia" "Blarina" ...
## $ vore : chr "carni" "omni" "herbi" "omni" ...
## $ order : chr "Carnivora" "Primates" "Rodentia" "Soricomorpha" ...
## $ conservation: chr "lc" NA "nt" "lc" ...
## $ sleep_total : num 12.1 17 14.4 14.9 4 14.4 8.7 7 10.1 3 ...
## $ sleep_rem : num NA 1.8 2.4 2.3 0.7 2.2 1.4 NA 2.9 NA ...
## $ sleep_cycle : num NA NA NA 0.133 0.667 ...
## $ awake : num 11.9 7 9.6 9.1 20 9.6 15.3 17 13.9 21 ...
## $ brainwt : num NA 0.0155 NA 0.00029 0.423 NA NA NA 0.07 0.0982 ...
## $ bodywt : num 50 0.48 1.35 0.019 600 ...
## Classes 'tbl_df', 'tbl' and 'data.frame': 83 obs. of 11 variables:
## $ name : chr "Cheetah" "Owl monkey" "Mountain beaver" "Greater short-tailed shrew" ...
## $ genus : chr "Acinonyx" "Aotus" "Aplodontia" "Blarina" ...
## $ vore : chr "carni" "omni" "herbi" "omni" ...
## $ order : chr "Carnivora" "Primates" "Rodentia" "Soricomorpha" ...
## $ conservation: chr "lc" NA "nt" "lc" ...
## $ sleep_total : num 12.1 17 14.4 14.9 4 14.4 8.7 7 10.1 3 ...
## $ sleep_rem : num NA 1.8 2.4 2.3 0.7 2.2 1.4 NA 2.9 NA ...
## $ sleep_cycle : num NA NA NA 0.133 0.667 ...
## $ awake : num 11.9 7 9.6 9.1 20 9.6 15.3 17 13.9 21 ...
## $ brainwt : num NA 0.0155 NA 0.00029 0.423 NA NA NA 0.07 0.0982 ...
## $ bodywt : num 50 0.48 1.35 0.019 600 ...
# Note 1:
# Copy the msleep from the ggplot2 package into our workspace. 00077_informalexample_5.8_of_section_5.1.2.R
# informalexample 5.8 of section 5.1.2
# (informalexample 5.8 of section 5.1.2) : Data engineering and data shaping : Data selection : Removing records with incomplete data
summary(msleep)## name genus vore
## Length:83 Length:83 Length:83
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## order conservation sleep_total sleep_rem
## Length:83 Length:83 Min. : 1.90 Min. :0.100
## Class :character Class :character 1st Qu.: 7.85 1st Qu.:0.900
## Mode :character Mode :character Median :10.10 Median :1.500
## Mean :10.43 Mean :1.875
## 3rd Qu.:13.75 3rd Qu.:2.400
## Max. :19.90 Max. :6.600
## NA's :22
## sleep_cycle awake brainwt bodywt
## Min. :0.1167 Min. : 4.10 Min. :0.00014 Min. : 0.005
## 1st Qu.:0.1833 1st Qu.:10.25 1st Qu.:0.00290 1st Qu.: 0.174
## Median :0.3333 Median :13.90 Median :0.01240 Median : 1.670
## Mean :0.4396 Mean :13.57 Mean :0.28158 Mean : 166.136
## 3rd Qu.:0.5792 3rd Qu.:16.15 3rd Qu.:0.12550 3rd Qu.: 41.750
## Max. :1.5000 Max. :22.10 Max. :5.71200 Max. :6654.000
## NA's :51 NA's :27
## name genus vore
## Length:83 Length:83 Length:83
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## order conservation sleep_total sleep_rem
## Length:83 Length:83 Min. : 1.90 Min. :0.100
## Class :character Class :character 1st Qu.: 7.85 1st Qu.:0.900
## Mode :character Mode :character Median :10.10 Median :1.500
## Mean :10.43 Mean :1.875
## 3rd Qu.:13.75 3rd Qu.:2.400
## Max. :19.90 Max. :6.600
## NA's :22
## sleep_cycle awake brainwt bodywt
## Min. :0.1167 Min. : 4.10 Min. :0.00014 Min. : 0.005
## 1st Qu.:0.1833 1st Qu.:10.25 1st Qu.:0.00290 1st Qu.: 0.174
## Median :0.3333 Median :13.90 Median :0.01240 Median : 1.670
## Mean :0.4396 Mean :13.57 Mean :0.28158 Mean : 166.136
## 3rd Qu.:0.5792 3rd Qu.:16.15 3rd Qu.:0.12550 3rd Qu.: 41.750
## Max. :1.5000 Max. :22.10 Max. :5.71200 Max. :6654.000
## NA's :51 NA's :2700078_informalexample_5.9_of_section_5.1.2.R
# informalexample 5.9 of section 5.1.2
# (informalexample 5.9 of section 5.1.2) : Data engineering and data shaping : Data selection : Removing records with incomplete data
clean_base_1 <- msleep[complete.cases(msleep), , drop = FALSE]
summary(clean_base_1)## name genus vore
## Length:20 Length:20 Length:20
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## order conservation sleep_total sleep_rem
## Length:20 Length:20 Min. : 2.900 Min. :0.600
## Class :character Class :character 1st Qu.: 8.925 1st Qu.:1.300
## Mode :character Mode :character Median :11.300 Median :2.350
## Mean :11.225 Mean :2.275
## 3rd Qu.:13.925 3rd Qu.:3.125
## Max. :19.700 Max. :4.900
## sleep_cycle awake brainwt bodywt
## Min. :0.1167 Min. : 4.30 Min. :0.00014 Min. : 0.0050
## 1st Qu.:0.1792 1st Qu.:10.07 1st Qu.:0.00115 1st Qu.: 0.0945
## Median :0.2500 Median :12.70 Median :0.00590 Median : 0.7490
## Mean :0.3458 Mean :12.78 Mean :0.07882 Mean : 72.1177
## 3rd Qu.:0.4167 3rd Qu.:15.07 3rd Qu.:0.03670 3rd Qu.: 6.1250
## Max. :1.0000 Max. :21.10 Max. :0.65500 Max. :600.0000
## name genus vore
## Length:20 Length:20 Length:20
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## order conservation sleep_total sleep_rem
## Length:20 Length:20 Min. : 2.900 Min. :0.600
## Class :character Class :character 1st Qu.: 8.925 1st Qu.:1.300
## Mode :character Mode :character Median :11.300 Median :2.350
## Mean :11.225 Mean :2.275
## 3rd Qu.:13.925 3rd Qu.:3.125
## Max. :19.700 Max. :4.900
## sleep_cycle awake brainwt bodywt
## Min. :0.1167 Min. : 4.30 Min. :0.00014 Min. : 0.0050
## 1st Qu.:0.1792 1st Qu.:10.07 1st Qu.:0.00115 1st Qu.: 0.0945
## Median :0.2500 Median :12.70 Median :0.00590 Median : 0.7490
## Mean :0.3458 Mean :12.78 Mean :0.07882 Mean : 72.1177
## 3rd Qu.:0.4167 3rd Qu.:15.07 3rd Qu.:0.03670 3rd Qu.: 6.1250
## Max. :1.0000 Max. :21.10 Max. :0.65500 Max. :600.0000
nrow(clean_base_1)## [1] 20
## [1] 20
clean_base_2 = na.omit(msleep)
nrow(clean_base_2)## [1] 20
## [1] 2000079_informalexample_5.10_of_section_5.1.2.R
# informalexample 5.10 of section 5.1.2
# (informalexample 5.10 of section 5.1.2) : Data engineering and data shaping : Data selection : Removing records with incomplete data
library("data.table")
msleep_data.table <- as.data.table(msleep)
clean_data.table = msleep_data.table[complete.cases(msleep_data.table), ]
nrow(clean_data.table)## [1] 20
## [1] 2000080_informalexample_5.11_of_section_5.1.2.R
# informalexample 5.11 of section 5.1.2
# (informalexample 5.11 of section 5.1.2) : Data engineering and data shaping : Data selection : Removing records with incomplete data
library("dplyr")
clean_dplyr <- msleep %>%
filter(., complete.cases(.))
nrow(clean_dplyr)## [1] 20
## [1] 2000081_informalexample_5.12_of_section_5.1.3.R
# informalexample 5.12 of section 5.1.3
# (informalexample 5.12 of section 5.1.3) : Data engineering and data shaping : Data selection : Ordering rows
purchases <- wrapr::build_frame( # Note: 1
"day", "hour", "n_purchase" |
1 , 9 , 5 |
2 , 9 , 3 |
2 , 11 , 5 |
1 , 13 , 1 |
2 , 13 , 3 |
1 , 14 , 1 )
# Note 1:
# Use wrapr::build_frame to type data in directly in legible column order. 00082_informalexample_5.13_of_section_5.1.3.R
# informalexample 5.13 of section 5.1.3
# (informalexample 5.13 of section 5.1.3) : Data engineering and data shaping : Data selection : Ordering rows
order_index <- with(purchases, order(day, hour)) # Note: 1
purchases_ordered <- purchases[order_index, , drop = FALSE]
purchases_ordered$running_total <- cumsum(purchases_ordered$n_purchase) # Note: 2
purchases_ordered## day hour n_purchase running_total
## 1 1 9 5 5
## 4 1 13 1 6
## 6 1 14 1 7
## 2 2 9 3 10
## 3 2 11 5 15
## 5 2 13 3 18
## day hour n_purchase running_total
## 1 1 9 5 5
## 4 1 13 1 6
## 6 1 14 1 7
## 2 2 9 3 10
## 3 2 11 5 15
## 5 2 13 3 18
# Note 1:
# with() executes the code in its second argument as if the columns of the first argument were
# variables. This lets us write “x” instead of “order$x”.
# Note 2:
# Compute the running sum. 00083_informalexample_5.14_of_section_5.1.3.R
# informalexample 5.14 of section 5.1.3
# (informalexample 5.14 of section 5.1.3) : Data engineering and data shaping : Data selection : Ordering rows
library("data.table")
DT_purchases <- as.data.table(purchases)
order_cols <- c("day", "hour") # Note: 1
setorderv(DT_purchases, order_cols)
DT_purchases[ , running_total := cumsum(n_purchase)]
# print(DT_purchases)
# Note 1:
# Re-order data 00084_informalexample_5.15_of_section_5.1.3.R
# informalexample 5.15 of section 5.1.3
# (informalexample 5.15 of section 5.1.3) : Data engineering and data shaping : Data selection : Ordering rows
library("dplyr")
res <- purchases %>%
arrange(., day, hour) %>%
mutate(., running_total = cumsum(n_purchase))
# print(res)00085_informalexample_5.16_of_section_5.1.3.R
# informalexample 5.16 of section 5.1.3
# (informalexample 5.16 of section 5.1.3) : Data engineering and data shaping : Data selection : Ordering rows
order_index <- with(purchases, order(day, hour)) # Note: 1
purchases_ordered <- purchases[order_index, , drop = FALSE]
data_list <- split(purchases_ordered, purchases_ordered$day) # Note: 2
data_list <- lapply( # Note: 3
data_list,
function(di) {
di$running_total <- cumsum(di$n_purchase)
di
})
purchases_ordered <- do.call(base::rbind, data_list) # Note: 4
rownames(purchases_ordered) <- NULL # Note: 5
purchases_ordered## day hour n_purchase running_total
## 1 1 9 5 5
## 2 1 13 1 6
## 3 1 14 1 7
## 4 2 9 3 3
## 5 2 11 5 8
## 6 2 13 3 11
## day hour n_purchase running_total
## 1 1 9 5 5
## 2 1 13 1 6
## 3 1 14 1 7
## 4 2 9 3 3
## 5 2 11 5 8
## 6 2 13 3 11
# Note 1:
# First: sort the data.
# Note 2:
# Now split data into a list of groups.
# Note 3:
# Apply the cumsum to each group.
# Note 4:
# Put the results back to together into a single data.frame.
# Note 5:
# R often keeps annotations in the rownames(). In this case it is storing the original row
# numbers of the pieces we are assembling. This can confuse users when printing, so
# it is good practice to remove these annotations as we do here. 00086_informalexample_5.17_of_section_5.1.3.R
# informalexample 5.17 of section 5.1.3
# (informalexample 5.17 of section 5.1.3) : Data engineering and data shaping : Data selection : Ordering rows
library("data.table")
# new copy for result solution
DT_purchases <- as.data.table(purchases)[order(day, hour),
.(hour = hour,
n_purchase = n_purchase,
running_total = cumsum(n_purchase)),
by = "day"] # Note: 1
# print(DT_purchases) # Note: 2
# in-place solution
DT_purchases <- as.data.table(purchases)
order_cols <- c("day", "hour")
setorderv(DT_purchases, order_cols)
DT_purchases[ , running_total := cumsum(n_purchase), by = day]
# print(DT_purchases) # Note: 3
# don't reorder the actual data variation!
DT_purchases <- as.data.table(purchases)
DT_purchases[order(day, hour),
`:=`(hour = hour,
n_purchase = n_purchase,
running_total = cumsum(n_purchase)),
by = "day"]
# print(DT_purchases) # Note: 4
# Note 1:
# Adding the “by” keyword converts the calculation into a per-group calculation..
# Note 2:
# First solution: result is a second copy of the data .(=) notation. Only columns used in the
# calculation (such as “day”) and those explicitly assigned to are in the
# result.
# Note 3:
# Second solution: result is computed in-place by ordering the table before the grouped
# calculation.
# Note 4:
# Third solution: result is in same order as the original table, but the cumulative some is
# computed as if we sorted the table, computed the grouped
# running sum, and then returned the table to the original order. 00087_informalexample_5.18_of_section_5.1.3.R
# informalexample 5.18 of section 5.1.3
# (informalexample 5.18 of section 5.1.3) : Data engineering and data shaping : Data selection : Ordering rows
library("dplyr")
res <- purchases %>%
arrange(., day, hour) %>%
group_by(., day) %>%
mutate(., running_total = cumsum(n_purchase)) %>%
ungroup(.)
# print(res)00088_informalexample_5.19_of_section_5.2.1.R
# informalexample 5.19 of section 5.2.1
# (informalexample 5.19 of section 5.2.1) : Data engineering and data shaping : Basic data transforms : Add new columns
library("datasets")
library("ggplot2")
summary(airquality)## Ozone Solar.R Wind Temp
## Min. : 1.00 Min. : 7.0 Min. : 1.700 Min. :56.00
## 1st Qu.: 18.00 1st Qu.:115.8 1st Qu.: 7.400 1st Qu.:72.00
## Median : 31.50 Median :205.0 Median : 9.700 Median :79.00
## Mean : 42.13 Mean :185.9 Mean : 9.958 Mean :77.88
## 3rd Qu.: 63.25 3rd Qu.:258.8 3rd Qu.:11.500 3rd Qu.:85.00
## Max. :168.00 Max. :334.0 Max. :20.700 Max. :97.00
## NA's :37 NA's :7
## Month Day
## Min. :5.000 Min. : 1.0
## 1st Qu.:6.000 1st Qu.: 8.0
## Median :7.000 Median :16.0
## Mean :6.993 Mean :15.8
## 3rd Qu.:8.000 3rd Qu.:23.0
## Max. :9.000 Max. :31.0
##
## Ozone Solar.R Wind Temp
## Min. : 1.00 Min. : 7.0 Min. : 1.700 Min. :56.00
## 1st Qu.: 18.00 1st Qu.:115.8 1st Qu.: 7.400 1st Qu.:72.00
## Median : 31.50 Median :205.0 Median : 9.700 Median :79.00
## Mean : 42.13 Mean :185.9 Mean : 9.958 Mean :77.88
## 3rd Qu.: 63.25 3rd Qu.:258.8 3rd Qu.:11.500 3rd Qu.:85.00
## Max. :168.00 Max. :334.0 Max. :20.700 Max. :97.00
## NA's :37 NA's :7
## Month Day
## Min. :5.000 Min. : 1.0
## 1st Qu.:6.000 1st Qu.: 8.0
## Median :7.000 Median :16.0
## Mean :6.993 Mean :15.8
## 3rd Qu.:8.000 3rd Qu.:23.0
## Max. :9.000 Max. :31.0
##00089_informalexample_5.20_of_section_5.2.1.R
# informalexample 5.20 of section 5.2.1
# (informalexample 5.20 of section 5.2.1) : Data engineering and data shaping : Basic data transforms : Add new columns
library("lubridate")##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:data.table':
##
## hour, isoweek, mday, minute, month, quarter, second, wday,
## week, yday, year
## The following object is masked from 'package:base':
##
## date
library("ggplot2")
# create a function to make the date string.
datestr = function(day, month, year) {
paste(day, month, year, sep="-")
}00090_informalexample_5.21_of_section_5.2.1.R
# informalexample 5.21 of section 5.2.1
# (informalexample 5.21 of section 5.2.1) : Data engineering and data shaping : Basic data transforms : Add new columns
airquality_with_date <- airquality # Note: 1
airquality_with_date$date <- with(airquality_with_date, # Note: 2
dmy(datestr(Day, Month, 1973)))
airquality_with_date <- airquality_with_date[, # Note: 3
c("Ozone", "date"),
drop = FALSE]
head(airquality_with_date) # Note: 4 ## Ozone date
## 1 41 1973-05-01
## 2 36 1973-05-02
## 3 12 1973-05-03
## 4 18 1973-05-04
## 5 NA 1973-05-05
## 6 28 1973-05-06
## Ozone date
## 1 41 1973-05-01
## 2 36 1973-05-02
## 3 12 1973-05-03
## 4 18 1973-05-04
## 5 NA 1973-05-05
## 6 28 1973-05-06
ggplot(airquality_with_date, aes(x = date, y = Ozone)) + # Note: 5
geom_point() +
geom_line() +
xlab("Date") +
ggtitle("New York ozone readings, May 1 - Sept 30, 1973")## Warning: Removed 37 rows containing missing values (geom_point).
# Note 1:
# Build a copy of the data.
# Note 2:
# Add the date column, with “with()” to refer to columns without needing the table name.
# Note 3:
# Limit down to columns of interest.
# Note 4:
# Show the results.
# Note 5:
# Plot the results. 00091_informalexample_5.22_of_section_5.2.1.R
# informalexample 5.22 of section 5.2.1
# (informalexample 5.22 of section 5.2.1) : Data engineering and data shaping : Basic data transforms : Add new columns
library("wrapr") # Note: 1 ##
## Attaching package: 'wrapr'
## The following object is masked from 'package:dplyr':
##
## coalesce
## The following object is masked from 'package:data.table':
##
## :=
airquality %.>% # Note: 2
transform(., date = dmy(datestr(Day, Month, 1973))) %.>%
subset(., !is.na(Ozone), select = c("Ozone", "date")) %.>%
head(.)## Ozone date
## 1 41 1973-05-01
## 2 36 1973-05-02
## 3 12 1973-05-03
## 4 18 1973-05-04
## 6 28 1973-05-06
## 7 23 1973-05-07
## Ozone date
## 1 41 1973-05-01
## 2 36 1973-05-02
## 3 12 1973-05-03
## 4 18 1973-05-04
## 6 28 1973-05-06
## 7 23 1973-05-07
# Note 1:
# Attach the wrapr package to define the wrapr dot arrow pipe: %.>%. The dot arrow pipe is
# another R pipe and is described in the R Journal at
# https://journal.r-project.org/archive/2018/RJ-2018-042/index.html
# Note 2:
# Run all the steps as before using transform() and subset(), adding an extra step of filtering
# down to rows that do not have missing Ozone values. 00092_informalexample_5.23_of_section_5.2.1.R
# informalexample 5.23 of section 5.2.1
# (informalexample 5.23 of section 5.2.1) : Data engineering and data shaping : Basic data transforms : Add new columns
library("data.table")
DT_airquality <-
as.data.table(airquality)[ # Note: 1
, date := dmy(datestr(Day, Month, 1973)) ][ # Note: 2
, c("Ozone", "date")] # Note: 3
head(DT_airquality)## Ozone date
## 1: 41 1973-05-01
## 2: 36 1973-05-02
## 3: 12 1973-05-03
## 4: 18 1973-05-04
## 5: NA 1973-05-05
## 6: 28 1973-05-06
## Ozone date
## 1: 41 1973-05-01
## 2: 36 1973-05-02
## 3: 12 1973-05-03
## 4: 18 1973-05-04
## 5: NA 1973-05-05
## 6: 28 1973-05-06
# Note 1:
# Build a data.table copy of the data.
# Note 2:
# Add the date column.
# Note 3:
# Limit down to columns of interest. 00093_informalexample_5.24_of_section_5.2.1.R
# informalexample 5.24 of section 5.2.1
# (informalexample 5.24 of section 5.2.1) : Data engineering and data shaping : Basic data transforms : Add new columns
library("dplyr")
airquality_with_date2 <- airquality %>%
mutate(., date = dmy(datestr(Day, Month, 1973))) %>%
select(., Ozone, date)
head(airquality_with_date2)## Ozone date
## 1 41 1973-05-01
## 2 36 1973-05-02
## 3 12 1973-05-03
## 4 18 1973-05-04
## 5 NA 1973-05-05
## 6 28 1973-05-06
## Ozone date
## 1 41 1973-05-01
## 2 36 1973-05-02
## 3 12 1973-05-03
## 4 18 1973-05-04
## 5 NA 1973-05-05
## 6 28 1973-05-0600094_informalexample_5.25_of_section_5.2.1.R
# informalexample 5.25 of section 5.2.1
# (informalexample 5.25 of section 5.2.1) : Data engineering and data shaping : Basic data transforms : Add new columns
library("zoo")##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
airquality_corrected <- airquality_with_date
airquality_corrected$OzoneCorrected <-
na.locf(airquality_corrected$Ozone, na.rm = FALSE)
summary(airquality_corrected)## Ozone date OzoneCorrected
## Min. : 1.00 Min. :1973-05-01 Min. : 1.00
## 1st Qu.: 18.00 1st Qu.:1973-06-08 1st Qu.: 16.00
## Median : 31.50 Median :1973-07-16 Median : 30.00
## Mean : 42.13 Mean :1973-07-16 Mean : 39.78
## 3rd Qu.: 63.25 3rd Qu.:1973-08-23 3rd Qu.: 52.00
## Max. :168.00 Max. :1973-09-30 Max. :168.00
## NA's :37
## Ozone date OzoneCorrected
## Min. : 1.00 Min. :1973-05-01 Min. : 1.00
## 1st Qu.: 18.00 1st Qu.:1973-06-08 1st Qu.: 16.00
## Median : 31.50 Median :1973-07-16 Median : 30.00
## Mean : 42.13 Mean :1973-07-16 Mean : 39.78
## 3rd Qu.: 63.25 3rd Qu.:1973-08-23 3rd Qu.: 52.00
## Max. :168.00 Max. :1973-09-30 Max. :168.00
## NA's :37
ggplot(airquality_corrected, aes(x = date, y = Ozone)) +
geom_point(aes(y=Ozone)) +
geom_line(aes(y=OzoneCorrected)) +
ggtitle("New York ozone readings, May 1 - Sept 30, 1973",
subtitle = "(corrected)") +
xlab("Date")## Warning: Removed 37 rows containing missing values (geom_point).
00095_informalexample_5.26_of_section_5.2.1.R
# informalexample 5.26 of section 5.2.1
# (informalexample 5.26 of section 5.2.1) : Data engineering and data shaping : Basic data transforms : Add new columns
library("data.table")
library("zoo")
DT_airquality[, OzoneCorrected := na.locf(Ozone, na.rm=FALSE)]
summary(DT_airquality)## Ozone date OzoneCorrected
## Min. : 1.00 Min. :1973-05-01 Min. : 1.00
## 1st Qu.: 18.00 1st Qu.:1973-06-08 1st Qu.: 16.00
## Median : 31.50 Median :1973-07-16 Median : 30.00
## Mean : 42.13 Mean :1973-07-16 Mean : 39.78
## 3rd Qu.: 63.25 3rd Qu.:1973-08-23 3rd Qu.: 52.00
## Max. :168.00 Max. :1973-09-30 Max. :168.00
## NA's :37
## Ozone date OzoneCorrected
## Min. : 1.00 Min. :1973-05-01 Min. : 1.00
## 1st Qu.: 18.00 1st Qu.:1973-06-08 1st Qu.: 16.00
## Median : 31.50 Median :1973-07-16 Median : 30.00
## Mean : 42.13 Mean :1973-07-16 Mean : 39.78
## 3rd Qu.: 63.25 3rd Qu.:1973-08-23 3rd Qu.: 52.00
## Max. :168.00 Max. :1973-09-30 Max. :168.00
## NA's :3700096_informalexample_5.27_of_section_5.2.1.R
# informalexample 5.27 of section 5.2.1
# (informalexample 5.27 of section 5.2.1) : Data engineering and data shaping : Basic data transforms : Add new columns
library("dplyr")
library("zoo")
airquality_with_date %>%
mutate(.,
OzoneCorrected = na.locf(Ozone, na.rm = FALSE)) %>%
summary(.)## Ozone date OzoneCorrected
## Min. : 1.00 Min. :1973-05-01 Min. : 1.00
## 1st Qu.: 18.00 1st Qu.:1973-06-08 1st Qu.: 16.00
## Median : 31.50 Median :1973-07-16 Median : 30.00
## Mean : 42.13 Mean :1973-07-16 Mean : 39.78
## 3rd Qu.: 63.25 3rd Qu.:1973-08-23 3rd Qu.: 52.00
## Max. :168.00 Max. :1973-09-30 Max. :168.00
## NA's :37
## Ozone date OzoneCorrected
## Min. : 1.00 Min. :1973-05-01 Min. : 1.00
## 1st Qu.: 18.00 1st Qu.:1973-06-08 1st Qu.: 16.00
## Median : 31.50 Median :1973-07-16 Median : 30.00
## Mean : 42.13 Mean :1973-07-16 Mean : 39.78
## 3rd Qu.: 63.25 3rd Qu.:1973-08-23 3rd Qu.: 52.00
## Max. :168.00 Max. :1973-09-30 Max. :168.00
## NA's :3700097_informalexample_5.28_of_section_5.2.2.R
# informalexample 5.28 of section 5.2.2
# (informalexample 5.28 of section 5.2.2) : Data engineering and data shaping : Basic data transforms : Other simple operations
d <- data.frame(x = 1:2, y = 3:4)
print(d)## x y
## 1 1 3
## 2 2 4
#> x y
#> 1 1 3
#> 2 2 4
colnames(d) <- c("BIGX", "BIGY")
print(d)## BIGX BIGY
## 1 1 3
## 2 2 4
#> BIGX BIGY
#> 1 1 3
#> 2 2 4
d$BIGX <- NULL
print(d)## BIGY
## 1 3
## 2 4
#> BIGY
#> 1 3
#> 2 400098_informalexample_5.29_of_section_5.3.1.R
# informalexample 5.29 of section 5.3.1
# (informalexample 5.29 of section 5.3.1) : Data engineering and data shaping : Aggregating transforms : Combining many rows into summary rows
library("datasets")
library("ggplot2")
head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa00099_informalexample_5.30_of_section_5.3.1.R
# informalexample 5.30 of section 5.3.1
# (informalexample 5.30 of section 5.3.1) : Data engineering and data shaping : Aggregating transforms : Combining many rows into summary rows
iris_summary <- aggregate(
cbind(Petal.Length, Petal.Width) ~ Species,
data = iris,
FUN = mean)
print(iris_summary)## Species Petal.Length Petal.Width
## 1 setosa 1.462 0.246
## 2 versicolor 4.260 1.326
## 3 virginica 5.552 2.026
# Species Petal.Length Petal.Width
# 1 setosa 1.462 0.246
# 2 versicolor 4.260 1.326
# 3 virginica 5.552 2.026
library(ggplot2)
ggplot(mapping = aes(x = Petal.Length, y = Petal.Width,
shape = Species, color = Species)) +
geom_point(data = iris, # raw data
alpha = 0.5) +
geom_point(data = iris_summary, # per-group summaries
size = 5) +
ggtitle("Average Petal dimensions by iris species\n(with raw data for reference)")
00100_informalexample_5.31_of_section_5.3.1.R
# informalexample 5.31 of section 5.3.1
# (informalexample 5.31 of section 5.3.1) : Data engineering and data shaping : Aggregating transforms : Combining many rows into summary rows
library("data.table")
iris_data.table <- as.data.table(iris)
iris_data.table <- iris_data.table[,
.(Petal.Length = mean(Petal.Length),
Petal.Width = mean(Petal.Width)),
by = .(Species)]
# print(iris_data.table)00101_informalexample_5.32_of_section_5.3.1.R
# informalexample 5.32 of section 5.3.1
# (informalexample 5.32 of section 5.3.1) : Data engineering and data shaping : Aggregating transforms : Combining many rows into summary rows
library("dplyr")
iris_summary <- iris %>% group_by(., Species) %>%
summarize(.,
Petal.Length = mean(Petal.Length),
Petal.Width = mean(Petal.Width)) %>%
ungroup(.)
# print(iris_summary)00102_informalexample_5.33_of_section_5.3.1.R
# informalexample 5.33 of section 5.3.1
# (informalexample 5.33 of section 5.3.1) : Data engineering and data shaping : Aggregating transforms : Combining many rows into summary rows
iris_copy <- iris
iris_copy$mean_Petal.Length <- ave(iris$Petal.Length, iris$Species, FUN = mean)
iris_copy$mean_Petal.Width <- ave(iris$Petal.Width, iris$Species, FUN = mean)
# head(iris_copy)
# tail(iris_copy)00103_informalexample_5.34_of_section_5.3.1.R
# informalexample 5.34 of section 5.3.1
# (informalexample 5.34 of section 5.3.1) : Data engineering and data shaping : Aggregating transforms : Combining many rows into summary rows
library("data.table")
iris_data.table <- as.data.table(iris)
iris_data.table[ ,
`:=`(mean_Petal.Length = mean(Petal.Length),
mean_Petal.Width = mean(Petal.Width)),
by = "Species"]
# print(iris_data.table)00104_informalexample_5.35_of_section_5.3.1.R
# informalexample 5.35 of section 5.3.1
# (informalexample 5.35 of section 5.3.1) : Data engineering and data shaping : Aggregating transforms : Combining many rows into summary rows
library("dplyr")
iris_dplyr <- iris %>%
group_by(., Species) %>%
mutate(.,
mean_Petal.Length = mean(Petal.Length),
mean_Petal.Width = mean(Petal.Width)) %>%
ungroup(.)
# head(iris_dplyr)00105_informalexample_5.36_of_section_5.4.1.R
# informalexample 5.36 of section 5.4.1
# (informalexample 5.36 of section 5.4.1) : Data engineering and data shaping : Multi-table data transforms : Combining two or more ordered data frames quickly
productTable <- wrapr::build_frame(
"productID", "price" |
"p1" , 9.99 |
"p2" , 16.29 |
"p3" , 19.99 |
"p4" , 5.49 |
"p5" , 24.49 )
salesTable <- wrapr::build_frame(
"productID", "sold_store", "sold_online" |
"p1" , 6 , 64 |
"p2" , 31 , 1 |
"p3" , 30 , 23 |
"p4" , 31 , 67 |
"p5" , 43 , 51 )
productTable2 <- wrapr::build_frame(
"productID", "price" |
"n1" , 25.49 |
"n2" , 33.99 |
"n3" , 17.99 )
productTable$productID <- factor(productTable$productID)
productTable2$productID <- factor(productTable2$productID)00106_informalexample_5.37_of_section_5.4.1.R
# informalexample 5.37 of section 5.4.1
# (informalexample 5.37 of section 5.4.1) : Data engineering and data shaping : Multi-table data transforms : Combining two or more ordered data frames quickly
rbind_base = rbind(productTable,
productTable2)00107_informalexample_5.38_of_section_5.4.1.R
# informalexample 5.38 of section 5.4.1
# (informalexample 5.38 of section 5.4.1) : Data engineering and data shaping : Multi-table data transforms : Combining two or more ordered data frames quickly
str(rbind_base)## 'data.frame': 8 obs. of 2 variables:
## $ productID: Factor w/ 8 levels "p1","p2","p3",..: 1 2 3 4 5 6 7 8
## $ price : num 9.99 16.29 19.99 5.49 24.49 ...
## 'data.frame': 8 obs. of 2 variables:
## $ productID: Factor w/ 8 levels "p1","p2","p3",..: 1 2 3 4 5 6 7 8
## $ price : num 9.99 16.29 19.99 5.49 24.49 ...00108_informalexample_5.39_of_section_5.4.1.R
# informalexample 5.39 of section 5.4.1
# (informalexample 5.39 of section 5.4.1) : Data engineering and data shaping : Multi-table data transforms : Combining two or more ordered data frames quickly
library("data.table")
rbindlist(list(productTable,
productTable2))## productID price
## 1: p1 9.99
## 2: p2 16.29
## 3: p3 19.99
## 4: p4 5.49
## 5: p5 24.49
## 6: n1 25.49
## 7: n2 33.99
## 8: n3 17.99
## productID price
## 1: p1 9.99
## 2: p2 16.29
## 3: p3 19.99
## 4: p4 5.49
## 5: p5 24.49
## 6: n1 25.49
## 7: n2 33.99
## 8: n3 17.9900109_informalexample_5.40_of_section_5.4.1.R
# informalexample 5.40 of section 5.4.1
# (informalexample 5.40 of section 5.4.1) : Data engineering and data shaping : Multi-table data transforms : Combining two or more ordered data frames quickly
library("dplyr")
bind_rows(list(productTable,
productTable2))## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## productID price
## 1 p1 9.99
## 2 p2 16.29
## 3 p3 19.99
## 4 p4 5.49
## 5 p5 24.49
## 6 n1 25.49
## 7 n2 33.99
## 8 n3 17.99
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## productID price
## 1 p1 9.99
## 2 p2 16.29
## 3 p3 19.99
## 4 p4 5.49
## 5 p5 24.49
## 6 n1 25.49
## 7 n2 33.99
## 8 n3 17.9900110_informalexample_5.41_of_section_5.4.1.R
# informalexample 5.41 of section 5.4.1
# (informalexample 5.41 of section 5.4.1) : Data engineering and data shaping : Multi-table data transforms : Combining two or more ordered data frames quickly
# add an extra column telling us which table
# each row comes from
productTable_marked <- productTable
productTable_marked$table <- "productTable"
productTable2_marked <- productTable2
productTable2_marked$table <- "productTable2"
# combine the tables
rbind_base <- rbind(productTable_marked,
productTable2_marked)
rbind_base## productID price table
## 1 p1 9.99 productTable
## 2 p2 16.29 productTable
## 3 p3 19.99 productTable
## 4 p4 5.49 productTable
## 5 p5 24.49 productTable
## 6 n1 25.49 productTable2
## 7 n2 33.99 productTable2
## 8 n3 17.99 productTable2
## productID price table
## 1 p1 9.99 productTable
## 2 p2 16.29 productTable
## 3 p3 19.99 productTable
## 4 p4 5.49 productTable
## 5 p5 24.49 productTable
## 6 n1 25.49 productTable2
## 7 n2 33.99 productTable2
## 8 n3 17.99 productTable2
# split them apart
tables <- split(rbind_base, rbind_base$table)
tables## $productTable
## productID price table
## 1 p1 9.99 productTable
## 2 p2 16.29 productTable
## 3 p3 19.99 productTable
## 4 p4 5.49 productTable
## 5 p5 24.49 productTable
##
## $productTable2
## productID price table
## 6 n1 25.49 productTable2
## 7 n2 33.99 productTable2
## 8 n3 17.99 productTable2
## $productTable
## productID price table
## 1 p1 9.99 productTable
## 2 p2 16.29 productTable
## 3 p3 19.99 productTable
## 4 p4 5.49 productTable
## 5 p5 24.49 productTable
##
## $productTable2
## productID price table
## 6 n1 25.49 productTable2
## 7 n2 33.99 productTable2
## 8 n3 17.99 productTable200111_informalexample_5.42_of_section_5.4.1.R
# informalexample 5.42 of section 5.4.1
# (informalexample 5.42 of section 5.4.1) : Data engineering and data shaping : Multi-table data transforms : Combining two or more ordered data frames quickly
library("data.table")
# convert to data.table
dt <- as.data.table(rbind_base)
# arbitrary user defined function
f <- function(.BY, .SD) {
max(.SD$price)
}
# apply the function to each group
# and collect results
dt[ , max_price := f(.BY, .SD), by = table]
print(dt)## productID price table max_price
## 1: p1 9.99 productTable 24.49
## 2: p2 16.29 productTable 24.49
## 3: p3 19.99 productTable 24.49
## 4: p4 5.49 productTable 24.49
## 5: p5 24.49 productTable 24.49
## 6: n1 25.49 productTable2 33.99
## 7: n2 33.99 productTable2 33.99
## 8: n3 17.99 productTable2 33.99
## productID price table max_price
## 1: p1 9.99 productTable 24.49
## 2: p2 16.29 productTable 24.49
## 3: p3 19.99 productTable 24.49
## 4: p4 5.49 productTable 24.49
## 5: p5 24.49 productTable 24.49
## 6: n1 25.49 productTable2 33.99
## 7: n2 33.99 productTable2 33.99
## 8: n3 17.99 productTable2 33.9900112_informalexample_5.43_of_section_5.4.1.R
# informalexample 5.43 of section 5.4.1
# (informalexample 5.43 of section 5.4.1) : Data engineering and data shaping : Multi-table data transforms : Combining two or more ordered data frames quickly
library("data.table")
dt <- as.data.table(rbind_base)
grouping_column <- "table"
dt[ , max_price := max(price), by = eval(grouping_column)]
print(dt)## productID price table max_price
## 1: p1 9.99 productTable 24.49
## 2: p2 16.29 productTable 24.49
## 3: p3 19.99 productTable 24.49
## 4: p4 5.49 productTable 24.49
## 5: p5 24.49 productTable 24.49
## 6: n1 25.49 productTable2 33.99
## 7: n2 33.99 productTable2 33.99
## 8: n3 17.99 productTable2 33.99
## productID price table max_price
## 1: p1 9.99 productTable 24.49
## 2: p2 16.29 productTable 24.49
## 3: p3 19.99 productTable 24.49
## 4: p4 5.49 productTable 24.49
## 5: p5 24.49 productTable 24.49
## 6: n1 25.49 productTable2 33.99
## 7: n2 33.99 productTable2 33.99
## 8: n3 17.99 productTable2 33.9900113_informalexample_5.44_of_section_5.4.1.R
# informalexample 5.44 of section 5.4.1
# (informalexample 5.44 of section 5.4.1) : Data engineering and data shaping : Multi-table data transforms : Combining two or more ordered data frames quickly
rbind_base %>%
group_by(., table) %>%
mutate(., max_price = max(price)) %>%
ungroup(.)## # A tibble: 8 x 4
## productID price table max_price
## <fct> <dbl> <chr> <dbl>
## 1 p1 9.99 productTable 24.5
## 2 p2 16.3 productTable 24.5
## 3 p3 20.0 productTable 24.5
## 4 p4 5.49 productTable 24.5
## 5 p5 24.5 productTable 24.5
## 6 n1 25.5 productTable2 34.0
## 7 n2 34.0 productTable2 34.0
## 8 n3 18.0 productTable2 34.0
## # A tibble: 8 x 4
## productID price table max_price
## <fct> <dbl> <chr> <dbl>
## 1 p1 9.99 productTable 24.5
## 2 p2 16.3 productTable 24.5
## 3 p3 20.0 productTable 24.5
## 4 p4 5.49 productTable 24.5
## 5 p5 24.5 productTable 24.5
## 6 n1 25.5 productTable2 34.0
## 7 n2 34.0 productTable2 34.0
## 8 n3 18.0 productTable2 34.000114_informalexample_5.45_of_section_5.4.1.R
# informalexample 5.45 of section 5.4.1
# (informalexample 5.45 of section 5.4.1) : Data engineering and data shaping : Multi-table data transforms : Combining two or more ordered data frames quickly
cbind(productTable, salesTable[, -1])## productID price sold_store sold_online
## 1 p1 9.99 6 64
## 2 p2 16.29 31 1
## 3 p3 19.99 30 23
## 4 p4 5.49 31 67
## 5 p5 24.49 43 51
## productID price sold_store sold_online
## 1 p1 9.99 6 64
## 2 p2 16.29 31 1
## 3 p3 19.99 30 23
## 4 p4 5.49 31 67
## 5 p5 24.49 43 5100115_informalexample_5.46_of_section_5.4.1.R
# informalexample 5.46 of section 5.4.1
# (informalexample 5.46 of section 5.4.1) : Data engineering and data shaping : Multi-table data transforms : Combining two or more ordered data frames quickly
library("data.table")
cbind(as.data.table(productTable),
as.data.table(salesTable[, -1]))## productID price sold_store sold_online
## 1: p1 9.99 6 64
## 2: p2 16.29 31 1
## 3: p3 19.99 30 23
## 4: p4 5.49 31 67
## 5: p5 24.49 43 51
## productID price sold_store sold_online
## 1: p1 9.99 6 64
## 2: p2 16.29 31 1
## 3: p3 19.99 30 23
## 4: p4 5.49 31 67
## 5: p5 24.49 43 5100116_informalexample_5.47_of_section_5.4.1.R
# informalexample 5.47 of section 5.4.1
# (informalexample 5.47 of section 5.4.1) : Data engineering and data shaping : Multi-table data transforms : Combining two or more ordered data frames quickly
library("dplyr")
# list of data frames calling convention
dplyr::bind_cols(list(productTable, salesTable[, -1]))## productID price sold_store sold_online
## 1 p1 9.99 6 64
## 2 p2 16.29 31 1
## 3 p3 19.99 30 23
## 4 p4 5.49 31 67
## 5 p5 24.49 43 51
## productID price sold_store sold_online
## 1 p1 9.99 6 64
## 2 p2 16.29 31 1
## 3 p3 19.99 30 23
## 4 p4 5.49 31 67
## 5 p5 24.49 43 5100117_informalexample_5.48_of_section_5.4.2.R
# informalexample 5.48 of section 5.4.2
# (informalexample 5.48 of section 5.4.2) : Data engineering and data shaping : Multi-table data transforms : Principled methods to combine data from multiple tables
productTable <- wrapr::build_frame(
"productID", "price" |
"p1" , 9.99 |
"p3" , 19.99 |
"p4" , 5.49 |
"p5" , 24.49 )
salesTable <- wrapr::build_frame(
"productID", "unitsSold" |
"p1" , 10 |
"p2" , 43 |
"p3" , 55 |
"p4" , 8 )00118_informalexample_5.49_of_section_5.4.2.R
# informalexample 5.49 of section 5.4.2
# (informalexample 5.49 of section 5.4.2) : Data engineering and data shaping : Multi-table data transforms : Principled methods to combine data from multiple tables
merge(productTable, salesTable, by = "productID", all.x = TRUE)## productID price unitsSold
## 1 p1 9.99 10
## 2 p3 19.99 55
## 3 p4 5.49 8
## 4 p5 24.49 NA
## productID price unitsSold
## 1 p1 9.99 10
## 2 p3 19.99 55
## 3 p4 5.49 8
## 4 p5 24.49 NA00119_informalexample_5.50_of_section_5.4.2.R
# informalexample 5.50 of section 5.4.2
# (informalexample 5.50 of section 5.4.2) : Data engineering and data shaping : Multi-table data transforms : Principled methods to combine data from multiple tables
library("data.table")
productTable_data.table <- as.data.table(productTable)
salesTable_data.table <- as.data.table(salesTable)
# index notation for join
# idea is rows are produced for each row inside the []
salesTable_data.table[productTable_data.table, on = "productID"]## productID unitsSold price
## 1: p1 10 9.99
## 2: p3 55 19.99
## 3: p4 8 5.49
## 4: p5 NA 24.49
## productID unitsSold price
## 1: p1 10 9.99
## 2: p3 55 19.99
## 3: p4 8 5.49
## 4: p5 NA 24.49
# data.table also overrides merge()
merge(productTable, salesTable, by = "productID", all.x = TRUE)## productID price unitsSold
## 1 p1 9.99 10
## 2 p3 19.99 55
## 3 p4 5.49 8
## 4 p5 24.49 NA
## productID price unitsSold
## 1 p1 9.99 10
## 2 p3 19.99 55
## 3 p4 5.49 8
## 4 p5 24.49 NA00120_informalexample_5.51_of_section_5.4.2.R
# informalexample 5.51 of section 5.4.2
# (informalexample 5.51 of section 5.4.2) : Data engineering and data shaping : Multi-table data transforms : Principled methods to combine data from multiple tables
library("data.table")
joined_table <- productTable
joined_table$unitsSold <- salesTable$unitsSold[match(joined_table$productID,
salesTable$productID)]
print(joined_table)## productID price unitsSold
## 1 p1 9.99 10
## 2 p3 19.99 55
## 3 p4 5.49 8
## 4 p5 24.49 NA
## productID price unitsSold
## 1 p1 9.99 10
## 2 p3 19.99 55
## 3 p4 5.49 8
## 4 p5 24.49 NA00121_informalexample_5.52_of_section_5.4.2.R
# informalexample 5.52 of section 5.4.2
# (informalexample 5.52 of section 5.4.2) : Data engineering and data shaping : Multi-table data transforms : Principled methods to combine data from multiple tables
library("dplyr")
left_join(productTable, salesTable, by = "productID")## productID price unitsSold
## 1 p1 9.99 10
## 2 p3 19.99 55
## 3 p4 5.49 8
## 4 p5 24.49 NA
## productID price unitsSold
## 1 p1 9.99 10
## 2 p3 19.99 55
## 3 p4 5.49 8
## 4 p5 24.49 NA00122_informalexample_5.53_of_section_5.4.2.R
# informalexample 5.53 of section 5.4.2
# (informalexample 5.53 of section 5.4.2) : Data engineering and data shaping : Multi-table data transforms : Principled methods to combine data from multiple tables
merge(productTable, salesTable, by = "productID")## productID price unitsSold
## 1 p1 9.99 10
## 2 p3 19.99 55
## 3 p4 5.49 8
## productID price unitsSold
## 1 p1 9.99 10
## 2 p3 19.99 55
## 3 p4 5.49 800123_informalexample_5.54_of_section_5.4.2.R
# informalexample 5.54 of section 5.4.2
# (informalexample 5.54 of section 5.4.2) : Data engineering and data shaping : Multi-table data transforms : Principled methods to combine data from multiple tables
library("data.table")
productTable_data.table <- as.data.table(productTable)
salesTable_data.table <- as.data.table(salesTable)
merge(productTable, salesTable, by = "productID")## productID price unitsSold
## 1 p1 9.99 10
## 2 p3 19.99 55
## 3 p4 5.49 8
## productID price unitsSold
## 1 p1 9.99 10
## 2 p3 19.99 55
## 3 p4 5.49 800124_informalexample_5.55_of_section_5.4.2.R
# informalexample 5.55 of section 5.4.2
# (informalexample 5.55 of section 5.4.2) : Data engineering and data shaping : Multi-table data transforms : Principled methods to combine data from multiple tables
library("dplyr")
inner_join(productTable, salesTable, by = "productID")## productID price unitsSold
## 1 p1 9.99 10
## 2 p3 19.99 55
## 3 p4 5.49 8
## productID price unitsSold
## 1 p1 9.99 10
## 2 p3 19.99 55
## 3 p4 5.49 800125_informalexample_5.56_of_section_5.4.2.R
# informalexample 5.56 of section 5.4.2
# (informalexample 5.56 of section 5.4.2) : Data engineering and data shaping : Multi-table data transforms : Principled methods to combine data from multiple tables
# note that merge orders the result by key column by default
# use sort=FALSE to skip the sorting
merge(productTable, salesTable, by = "productID", all=TRUE)## productID price unitsSold
## 1 p1 9.99 10
## 2 p2 NA 43
## 3 p3 19.99 55
## 4 p4 5.49 8
## 5 p5 24.49 NA
## productID price unitsSold
## 1 p1 9.99 10
## 2 p2 NA 43
## 3 p3 19.99 55
## 4 p4 5.49 8
## 5 p5 24.49 NA00126_informalexample_5.57_of_section_5.4.2.R
# informalexample 5.57 of section 5.4.2
# (informalexample 5.57 of section 5.4.2) : Data engineering and data shaping : Multi-table data transforms : Principled methods to combine data from multiple tables
library("data.table")
productTable_data.table <- as.data.table(productTable)
salesTable_data.table <- as.data.table(salesTable)
merge(productTable_data.table, salesTable_data.table,
by = "productID", all = TRUE)## productID price unitsSold
## 1: p1 9.99 10
## 2: p2 NA 43
## 3: p3 19.99 55
## 4: p4 5.49 8
## 5: p5 24.49 NA
## productID price unitsSold
## 1: p1 9.99 10
## 2: p2 NA 43
## 3: p3 19.99 55
## 4: p4 5.49 8
## 5: p5 24.49 NA00127_informalexample_5.58_of_section_5.4.2.R
# informalexample 5.58 of section 5.4.2
# (informalexample 5.58 of section 5.4.2) : Data engineering and data shaping : Multi-table data transforms : Principled methods to combine data from multiple tables
library("dplyr")
full_join(productTable, salesTable, by = "productID")## productID price unitsSold
## 1 p1 9.99 10
## 2 p3 19.99 55
## 3 p4 5.49 8
## 4 p5 24.49 NA
## 5 p2 NA 43
## productID price unitsSold
## 1 p1 9.99 10
## 2 p3 19.99 55
## 3 p4 5.49 8
## 4 p5 24.49 NA
## 5 p2 NA 4300128_informalexample_5.59_of_section_5.4.2.R
# informalexample 5.59 of section 5.4.2
# (informalexample 5.59 of section 5.4.2) : Data engineering and data shaping : Multi-table data transforms : Principled methods to combine data from multiple tables
library("data.table")
quotes <- data.table(
bid = c(5, 5, 7, 8),
ask = c(6, 6, 8, 10),
bid_quantity = c(100, 100, 100, 100),
ask_quantity = c(100, 100, 100, 100),
when = as.POSIXct(strptime(
c("2018-10-18 1:03:17",
"2018-10-18 2:12:23",
"2018-10-18 2:15:00",
"2018-10-18 2:17:51"),
"%Y-%m-%d %H:%M:%S")))
print(quotes)## bid ask bid_quantity ask_quantity when
## 1: 5 6 100 100 2018-10-18 01:03:17
## 2: 5 6 100 100 2018-10-18 02:12:23
## 3: 7 8 100 100 2018-10-18 02:15:00
## 4: 8 10 100 100 2018-10-18 02:17:51
## bid ask bid_quantity ask_quantity when
## 1: 5 6 100 100 2018-10-18 01:03:17
## 2: 5 6 100 100 2018-10-18 02:12:23
## 3: 7 8 100 100 2018-10-18 02:15:00
## 4: 8 10 100 100 2018-10-18 02:17:5100129_informalexample_5.60_of_section_5.4.2.R
# informalexample 5.60 of section 5.4.2
# (informalexample 5.60 of section 5.4.2) : Data engineering and data shaping : Multi-table data transforms : Principled methods to combine data from multiple tables
trades <- data.table(
trade_id = c(32525, 32526),
price = c(5.5, 9),
quantity = c(100, 200),
when = as.POSIXct(strptime(
c("2018-10-18 2:13:42",
"2018-10-18 2:19:20"),
"%Y-%m-%d %H:%M:%S")))
print(trades)## trade_id price quantity when
## 1: 32525 5.5 100 2018-10-18 02:13:42
## 2: 32526 9.0 200 2018-10-18 02:19:20
## trade_id price quantity when
## 1: 32525 5.5 100 2018-10-18 02:13:42
## 2: 32526 9.0 200 2018-10-18 02:19:2000130_informalexample_5.61_of_section_5.4.2.R
# informalexample 5.61 of section 5.4.2
# (informalexample 5.61 of section 5.4.2) : Data engineering and data shaping : Multi-table data transforms : Principled methods to combine data from multiple tables
quotes[, quote_time := when]
trades[ , trade_time := when ]
quotes[ trades, on = "when", roll = TRUE ][
, .(quote_time, bid, price, ask, trade_id, trade_time) ]## quote_time bid price ask trade_id trade_time
## 1: 2018-10-18 02:12:23 5 5.5 6 32525 2018-10-18 02:13:42
## 2: 2018-10-18 02:17:51 8 9.0 10 32526 2018-10-18 02:19:20
## quote_time bid price ask trade_id trade_time
## 1: 2018-10-18 02:12:23 5 5.5 6 32525 2018-10-18 02:13:42
## 2: 2018-10-18 02:17:51 8 9.0 10 32526 2018-10-18 02:19:2000131_informalexample_5.62_of_section_5.5.1.R
# informalexample 5.62 of section 5.5.1
# (informalexample 5.62 of section 5.5.1) : Data engineering and data shaping : Reshaping transforms : Moving data from wide to tall form
library("datasets")
library("xts")## Registered S3 method overwritten by 'xts':
## method from
## as.zoo.xts zoo
##
## Attaching package: 'xts'
## The following objects are masked from 'package:dplyr':
##
## first, last
## The following objects are masked from 'package:data.table':
##
## first, last
# move the date index into a column
dates <- index(as.xts(time(Seatbelts)))
Seatbelts <- data.frame(Seatbelts)
Seatbelts$date <- dates
# restrict down to 1982 and 1983
Seatbelts <- Seatbelts[ (Seatbelts$date >= as.yearmon("Jan 1982")) &
(Seatbelts$date <= as.yearmon("Dec 1983")),
, drop = FALSE]
Seatbelts$date <- as.Date(Seatbelts$date)
# mark if the seatbelt law was in effect
Seatbelts$law <- ifelse(Seatbelts$law==1, "new law", "pre-law")
# limit down to the columns we want
Seatbelts <- Seatbelts[, c("date", "DriversKilled", "front", "rear", "law")]
head(Seatbelts)## date DriversKilled front rear law
## 157 1982-01-01 115 595 238 pre-law
## 158 1982-02-01 104 673 285 pre-law
## 159 1982-03-01 131 660 324 pre-law
## 160 1982-04-01 108 676 346 pre-law
## 161 1982-05-01 103 755 410 pre-law
## 162 1982-06-01 115 815 411 pre-law
## date DriversKilled front rear law
## 157 1982-01-01 115 595 238 pre-law
## 158 1982-02-01 104 673 285 pre-law
## 159 1982-03-01 131 660 324 pre-law
## 160 1982-04-01 108 676 346 pre-law
## 161 1982-05-01 103 755 410 pre-law
## 162 1982-06-01 115 815 411 pre-law00132_informalexample_5.63_of_section_5.5.1.R
# informalexample 5.63 of section 5.5.1
# (informalexample 5.63 of section 5.5.1) : Data engineering and data shaping : Reshaping transforms : Moving data from wide to tall form
# let's give an example of the kind of graph we have in mind, using just driver deaths
library("ggplot2")
ggplot(Seatbelts,
aes(x = date, y = DriversKilled, color = law, shape = law)) +
geom_point() +
geom_smooth(se=FALSE) +
ggtitle("UK car driver deaths by month")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
00133_informalexample_5.64_of_section_5.5.1.R
# informalexample 5.64 of section 5.5.1
# (informalexample 5.64 of section 5.5.1) : Data engineering and data shaping : Reshaping transforms : Moving data from wide to tall form
library("data.table")
seatbelts_long2 <-
melt.data.table(as.data.table(Seatbelts),
id.vars = NULL,
measure.vars = c("DriversKilled", "front", "rear"),
variable.name = "victim_type",
value.name = "nvictims")00134_informalexample_5.65_of_section_5.5.1.R
# informalexample 5.65 of section 5.5.1
# (informalexample 5.65 of section 5.5.1) : Data engineering and data shaping : Reshaping transforms : Moving data from wide to tall form
library("cdata")
seatbelts_long3 <- unpivot_to_blocks(
Seatbelts,
nameForNewKeyColumn = "victim_type",
nameForNewValueColumn = "nvictims",
columnsToTakeFrom = c("DriversKilled", "front", "rear"))00135_informalexample_5.66_of_section_5.5.1.R
# informalexample 5.66 of section 5.5.1
# (informalexample 5.66 of section 5.5.1) : Data engineering and data shaping : Reshaping transforms : Moving data from wide to tall form
library("tidyr")
seatbelts_long1 <- gather(
Seatbelts,
key = victim_type,
value = nvictims,
DriversKilled, front, rear)
head(seatbelts_long1)## date law victim_type nvictims
## 1 1982-01-01 pre-law DriversKilled 115
## 2 1982-02-01 pre-law DriversKilled 104
## 3 1982-03-01 pre-law DriversKilled 131
## 4 1982-04-01 pre-law DriversKilled 108
## 5 1982-05-01 pre-law DriversKilled 103
## 6 1982-06-01 pre-law DriversKilled 115
## date law victim_type nvictims
## 1 1982-01-01 pre-law DriversKilled 115
## 2 1982-02-01 pre-law DriversKilled 104
## 3 1982-03-01 pre-law DriversKilled 131
## 4 1982-04-01 pre-law DriversKilled 108
## 5 1982-05-01 pre-law DriversKilled 103
## 6 1982-06-01 pre-law DriversKilled 115
ggplot(seatbelts_long1,
aes(x = date, y = nvictims, color = law, shape = law)) +
geom_point() +
geom_smooth(se=FALSE) +
facet_wrap(~victim_type, ncol=1, scale="free_y") +
ggtitle("UK auto fatalities by month and seating position")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
00136_informalexample_5.67_of_section_5.5.2.R
# informalexample 5.67 of section 5.5.2
# (informalexample 5.67 of section 5.5.2) : Data engineering and data shaping : Reshaping transforms : Moving data from tall to wide form
library("datasets")
library("data.table")
library("ggplot2")
ChickWeight <- data.frame(ChickWeight) # get rid of attributes
ChickWeight$Diet <- NULL # remove the diet label
# pad names with zeros
padz <- function(x, n=max(nchar(x))) gsub(" ", "0", formatC(x, width=n))
# append "Chick" to the chick ids
ChickWeight$Chick <- paste0("Chick", padz(as.character(ChickWeight$Chick)))
head(ChickWeight)## weight Time Chick
## 1 42 0 Chick01
## 2 51 2 Chick01
## 3 59 4 Chick01
## 4 64 6 Chick01
## 5 76 8 Chick01
## 6 93 10 Chick01
## weight Time Chick
## 1 42 0 Chick01
## 2 51 2 Chick01
## 3 59 4 Chick01
## 4 64 6 Chick01
## 5 76 8 Chick01
## 6 93 10 Chick0100137_informalexample_5.68_of_section_5.5.2.R
# informalexample 5.68 of section 5.5.2
# (informalexample 5.68 of section 5.5.2) : Data engineering and data shaping : Reshaping transforms : Moving data from tall to wide form
# aggregate count and mean weight by time
ChickSummary <- as.data.table(ChickWeight)
ChickSummary <- ChickSummary[,
.(count = .N,
weight = mean(weight),
q1_weight = quantile(weight, probs = 0.25),
q2_weight = quantile(weight, probs = 0.75)),
by = Time]
head(ChickSummary)## Time count weight q1_weight q2_weight
## 1: 0 50 41.06000 41 42
## 2: 2 50 49.22000 48 51
## 3: 4 49 59.95918 57 63
## 4: 6 49 74.30612 68 80
## 5: 8 49 91.24490 83 102
## 6: 10 49 107.83673 93 124
## Time count weight q1_weight q2_weight
## 1: 0 50 41.06000 41 42
## 2: 2 50 49.22000 48 51
## 3: 4 49 59.95918 57 63
## 4: 6 49 74.30612 68 80
## 5: 8 49 91.24490 83 102
## 6: 10 49 107.83673 93 12400138_informalexample_5.69_of_section_5.5.2.R
# informalexample 5.69 of section 5.5.2
# (informalexample 5.69 of section 5.5.2) : Data engineering and data shaping : Reshaping transforms : Moving data from tall to wide form
library("ggplot2")
ChickSummary <- cdata::unpivot_to_blocks( # Note: 1
ChickSummary,
nameForNewKeyColumn = "measurement",
nameForNewValueColumn = "value",
columnsToTakeFrom = c("count", "weight"))## Warning in data.table::melt.data.table(data = data, id.vars =
## columnsToCopy, : 'measure.vars' [count, weight] are not all of the same
## type. By order of hierarchy, the molten data value column will be of type
## 'double'. All measure variables not of type 'double' will be coerced too.
## Check DETAILS in ?melt.data.table for more on coercion.
ChickSummary$q1_weight[ChickSummary$measurement=="count"] <- NA # Note: 2
ChickSummary$q2_weight[ChickSummary$measurement=="count"] <- NA
CW <- ChickWeight
CW$measurement <- "weight"
ggplot(ChickSummary, aes(x = Time, y = value, color = measurement)) + # Note: 3
geom_line(data = CW, aes(x = Time, y = weight, group = Chick),
color="LightGray") +
geom_line(size=2) +
geom_ribbon(aes(ymin = q1_weight, ymax = q2_weight),
alpha = 0.3, colour = NA) +
facet_wrap(~measurement, ncol=1, scales = "free_y") +
theme(legend.position = "none") +
ylab(NULL) +
ggtitle("Chick Weight and Count Measurements by Time",
subtitle = "25% through 75% quartiles of weight shown shaded around mean")
# Note 1:
# Unpivot into tall form for plotting.
# Note 2:
# Make sure we have the exact set of columns needed for plotting.
# Note 3:
# Make the plot. 00139_informalexample_5.70_of_section_5.5.2.R
# informalexample 5.70 of section 5.5.2
# (informalexample 5.70 of section 5.5.2) : Data engineering and data shaping : Reshaping transforms : Moving data from tall to wide form
library("data.table")
ChickWeight_wide2 <- dcast.data.table(
as.data.table(ChickWeight),
Chick ~ Time,
value.var = "weight")00140_informalexample_5.71_of_section_5.5.2.R
# informalexample 5.71 of section 5.5.2
# (informalexample 5.71 of section 5.5.2) : Data engineering and data shaping : Reshaping transforms : Moving data from tall to wide form
library("cdata")
ChickWeight_wide3 <- pivot_to_rowrecs(
ChickWeight,
columnToTakeKeysFrom = "Time",
columnToTakeValuesFrom = "weight",
rowKeyColumns = "Chick")00141_informalexample_5.72_of_section_5.5.2.R
# informalexample 5.72 of section 5.5.2
# (informalexample 5.72 of section 5.5.2) : Data engineering and data shaping : Reshaping transforms : Moving data from tall to wide form
library("tidyr")
ChickWeight_wide1 <- spread(ChickWeight,
key = Time,
value = weight)
head(ChickWeight_wide1)## Chick 0 2 4 6 8 10 12 14 16 18 20 21
## 1 Chick01 42 51 59 64 76 93 106 125 149 171 199 205
## 2 Chick02 40 49 58 72 84 103 122 138 162 187 209 215
## 3 Chick03 43 39 55 67 84 99 115 138 163 187 198 202
## 4 Chick04 42 49 56 67 74 87 102 108 136 154 160 157
## 5 Chick05 41 42 48 60 79 106 141 164 197 199 220 223
## 6 Chick06 41 49 59 74 97 124 141 148 155 160 160 157
## Chick 0 2 4 6 8 10 12 14 16 18 20 21
## 1 Chick01 42 51 59 64 76 93 106 125 149 171 199 205
## 2 Chick02 40 49 58 72 84 103 122 138 162 187 209 215
## 3 Chick03 43 39 55 67 84 99 115 138 163 187 198 202
## 4 Chick04 42 49 56 67 74 87 102 108 136 154 160 157
## 5 Chick05 41 42 48 60 79 106 141 164 197 199 220 223
## 6 Chick06 41 49 59 74 97 124 141 148 155 160 160 157