hk0330_inclass3_An example with dplyr

The data set concerns species and weight of animals caught in plots in a study area in Arizona over time.

Each row holds information for a single animal, and the columns represent:

• record_id: Unique id for the observation
• month: month of observation
• day: day of observation
• year: year of observation
• plot_id: ID of a particular plot
• species_id: 2-letter code
• sex: sex of animal (“M”, “F”)
• hindfoot_length: length of the hindfoot in mm
• weight: weight of the animal in grams
• genus: genus of animal
• species: species of animal
• taxa: e.g. Rodent, Reptile, Bird, Rabbit
• plot_type: type of plot

使用pacman讀取套件

pacman::p_load(tidyverse)

讀取csv資料

dta <- read_csv("http://kbroman.org/datacarp/portal_data_joined.csv")

## Parsed with column specification:
## cols(
##   record_id = col_double(),
##   month = col_double(),
##   day = col_double(),
##   year = col_double(),
##   plot_id = col_double(),
##   species_id = col_character(),
##   sex = col_character(),
##   hindfoot_length = col_double(),
##   weight = col_double(),
##   genus = col_character(),
##   species = col_character(),
##   taxa = col_character(),
##   plot_type = col_character()
## )

檢視資料

glimpse(dta)

## Observations: 34,786
## Variables: 13
## $ record_id       <dbl> 1, 72, 224, 266, 349, 363, 435, 506, 588, 661, 748,...
## $ month           <dbl> 7, 8, 9, 10, 11, 11, 12, 1, 2, 3, 4, 5, 6, 8, 9, 10...
## $ day             <dbl> 16, 19, 13, 16, 12, 12, 10, 8, 18, 11, 8, 6, 9, 5, ...
## $ year            <dbl> 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1978, 197...
## $ plot_id         <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...
## $ species_id      <chr> "NL", "NL", "NL", "NL", "NL", "NL", "NL", "NL", "NL...
## $ sex             <chr> "M", "M", NA, NA, NA, NA, NA, NA, "M", NA, NA, "M",...
## $ hindfoot_length <dbl> 32, 31, NA, NA, NA, NA, NA, NA, NA, NA, NA, 32, NA,...
## $ weight          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 218, NA, NA, 204, 2...
## $ genus           <chr> "Neotoma", "Neotoma", "Neotoma", "Neotoma", "Neotom...
## $ species         <chr> "albigula", "albigula", "albigula", "albigula", "al...
## $ taxa            <chr> "Rodent", "Rodent", "Rodent", "Rodent", "Rodent", "...
## $ plot_type       <chr> "Control", "Control", "Control", "Control", "Contro...

檢視列、行(數)

dim(dta)

## [1] 34786    13

預覽 head (plot_id, species_id, weight) the data

dplyr::select(dta, plot_id, species_id, weight) %>% head()

預覽(不要顯示record、pecies)

dplyr::select(dta, -record_id, -species_id) %>% head()

預覽year=1995的資料

dplyr::filter(dta, year == 1995) %>% head()

選取weight在5以內，依species、sex、weight預覽

head(dplyr::select(dplyr::filter(dta, weight <= 5), species_id, sex, weight))

選取weight在5以內，依species、sex、weight預覽(將語言分開來寫)

dta %>% 
  dplyr::filter(weight <= 5) %>% 
  dplyr::select(species_id, sex, weight) %>% 
  head

加入新變項：weight_kg、weight_lb

dta %>% 
  mutate(weight_kg = weight / 1000,
         weight_lb = weight_kg * 2.2) %>% 
  head()

排除missing , group by(sex, species_id), 算 mean_weight ,排列描述 mean_weight , head the data

dta %>% 
  filter(!is.na(weight)) %>%
  group_by(sex, species_id) %>%
  summarize(mean_weight = mean(weight)) %>%
  arrange(desc(mean_weight)) %>% 
  head()

按照sex計算筆數

dta %>%
  group_by(sex) %>%
  tally

按照sex計數

dta %>%
  count(sex)

按照sex統整總數； n()的功能類似tally

dta %>%
  group_by(sex) %>%
  summarize(count = n())

去除遺漏值得總數

dta %>%
  group_by(sex) %>%
  summarize(count = sum(!is.na(year)))

刪除遺漏值，按照genus、plot，mean_weight分類；新資料名為dta_gw

dta_gw <- dta %>% 
  filter(!is.na(weight)) %>%
  group_by(genus, plot_id) %>%
  summarize(mean_weight = mean(weight))

檢視dta_gw

glimpse(dta_gw)

## Observations: 196
## Variables: 3
## Groups: genus [10]
## $ genus       <chr> "Baiomys", "Baiomys", "Baiomys", "Baiomys", "Baiomys", ...
## $ plot_id     <dbl> 1, 2, 3, 5, 18, 19, 20, 21, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...
## $ mean_weight <dbl> 7.000000, 6.000000, 8.611111, 7.750000, 9.500000, 9.533...

資料長寬倒轉，依照genus顯示mean_weight；新資料名為dta_w

dta_w <- dta_gw %>%
  spread(key = genus, value = mean_weight)

檢視資料

glimpse(dta_w)

## Observations: 24
## Variables: 11
## $ plot_id         <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, ...
## $ Baiomys         <dbl> 7.000000, 6.000000, 8.611111, NA, 7.750000, NA, NA,...
## $ Chaetodipus     <dbl> 22.19939, 25.11014, 24.63636, 23.02381, 17.98276, 2...
## $ Dipodomys       <dbl> 60.23214, 55.68259, 52.04688, 57.52454, 51.11356, 5...
## $ Neotoma         <dbl> 156.2222, 169.1436, 158.2414, 164.1667, 190.0370, 1...
## $ Onychomys       <dbl> 27.67550, 26.87302, 26.03241, 28.09375, 27.01695, 2...
## $ Perognathus     <dbl> 9.625000, 6.947368, 7.507812, 7.824427, 8.658537, 7...
## $ Peromyscus      <dbl> 22.22222, 22.26966, 21.37037, 22.60000, 21.23171, 2...
## $ Reithrodontomys <dbl> 11.375000, 10.680556, 10.516588, 10.263158, 11.1545...
## $ Sigmodon        <dbl> NA, 70.85714, 65.61404, 82.00000, 82.66667, 68.7777...
## $ Spermophilus    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...

長寬倒轉，顯示genus, mean_weight，用0填補缺少的值

dta_gw %>%
  spread(genus, mean_weight, fill = 0) %>%
  head()

長寬倒轉，依照plot_id、genus、mean_weight顯示

dta_l <- dta_w %>%
  gather(key = genus, value = mean_weight, -plot_id)

檢視資料

glimpse(dta_l)

## Observations: 240
## Variables: 3
## $ plot_id     <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, ...
## $ genus       <chr> "Baiomys", "Baiomys", "Baiomys", "Baiomys", "Baiomys", ...
## $ mean_weight <dbl> 7.000000, 6.000000, 8.611111, NA, 7.750000, NA, NA, NA,...

顯示genus類別Baiomys~Spermophilus的mean_weight

dta_w %>%
  gather(key = genus, value = mean_weight, Baiomys:Spermophilus) %>%
  head()

篩選完整的資料(去除NA)

dta_complete <- dta %>%
  filter(!is.na(weight),           
         !is.na(hindfoot_length),  
         !is.na(sex))                

篩選n小於50的species，個別記數

species_counts <- dta_complete %>%
    count(species_id) %>% 
    filter(n >= 50)

篩選species_counts所包含的species_id資料

dta_complete <- dta_complete %>%
  filter(species_id %in% species_counts$species_id)