Question

数据

df <- read.csv(url("https://www.dropbox.com/s/uaivja22czx2pe8/df_stats_question.csv?raw=1"))

为EVT

创建不同级别

#for example "0-15", "15-30", "30-60", ">60"
library(dplyr)
df <- df %>% 
  mutate(EVT_mod = ifelse (EVT <= 15, "0-15", 
                           ifelse(EVT <= 30, "15-30",
                                  ifelse(EVT <= 60, "30-60", ">60"))))

我想做什么？

对于每个区域（Zone1到Zone5），我想获得param1和param2的不同组合的总区域面积百分比以及percent_area

中每个级别的EVT_mod分布

示例输出

#I want the output to be as below
#ID      param1   param2    percent_area      0-15  15-30  30-60   >60 
#zone1   High     High      10                2     3      4       1
#zone1   High     Medium    5                 0.5   2      0.5     2
#zone1   High     Low       15                3     4      5       3
#zone1   Medium   High      9                 3     2      3       1
#zone1   Medium   Medium    11                2     3      4       2
#zone1   Medium   Low       8                 0.7   0.3    3       4
#zone1   Low      High      7                 0.9   1.1    3       2
#zone1   Low      Medium    23                8     7      5       3
#zone1   Low      Low       12                7     2      1       2

我做了什么？

#I got the percent of area for each zone like below
df1 <- df %>% 
  dplyr::select(ID, param1, param2, area) %>% 
  dplyr::arrange(ID, param1, param2) %>% 
  dplyr::group_by(ID, param1, param2) %>%
  dplyr::summarise(area = sum(area)) %>% 
  dplyr::group_by(ID) %>% 
  dplyr::mutate(percent_area = area/sum(area) * 100)

head(df1)
#      ID param1 param2        area percent_area
#  <fctr> <fctr> <fctr>       <dbl>        <dbl>
#1  Zone1   High   High  1247.26891   1.60636374
#2  Zone1   High    Low  4725.57502   6.08609125
#3  Zone1   High Medium    10.06087   0.01295744
#4  Zone1    Low   High  1432.38859   1.84478029
#5  Zone1 Medium   High 44907.15570  57.83614608
#6  Zone1 Medium    Low 22036.19702  28.38052622

问题

如何为每个percent_area级别分发EVT_mod的任何建议，我们将不胜感激？

Answer 1

这个怎么样？首先按EVT_mod进行分组，然后在列上进行分组，然后以类似的方式结束。

首先，我改变了这一行：

df <- df %>% 
  mutate(EVT_mod = ifelse (EVT <= 15, 'cat1', 
                           ifelse(EVT <= 30, 'cat2',
                                  ifelse(EVT <= 60, 'cat3', 'cat4'))))

由于这些将成为列名，并且0-15作为列名这样的内容很痛苦，尤其是，其NSE为dplyr。

df %>% 
  select(ID, param1, param2, area, EVT_mod) %>%
  group_by(ID, param1, param2, EVT_mod) %>%
  summarise(area = sum(area)) %>% 
  tidyr::spread(EVT_mod, area, fill = 0) %>% 
  mutate(area = sum(c(cat1, cat2, cat3, cat4))) %>% 
  group_by(ID) %>% 
  mutate(cat1 = cat1 / sum(area) * 100,
         cat2 = cat2 / sum(area) * 100,
         cat3 = cat3 / sum(area) * 100,
         cat4 = cat4 / sum(area) * 100,
         percent_area = area / sum(area) * 100) %>% 
  arrange(ID, param1, param2)

# Source: local data frame [61 x 9]
# Groups: ID [5]
# 
#        ID param1 param2        cat1        cat2       cat3  cat4        area percent_area
#    <fctr> <fctr> <fctr>       <dbl>       <dbl>      <dbl> <dbl>       <dbl>        <dbl>
# 1   Zone1   High   High  1.34705031  0.25931343 0.00000000     0  1247.26891   1.60636374
# 2   Zone1   High    Low  5.59184841  0.49424283 0.00000000     0  4725.57502   6.08609125
# 3   Zone1   High Medium  0.01262533  0.00033211 0.00000000     0    10.06087   0.01295744
# 4   Zone1    Low   High  1.84478029  0.00000000 0.00000000     0  1432.38859   1.84478029
# 5   Zone1 Medium   High 56.31313681  1.52300927 0.00000000     0 44907.15570  57.83614608
# 6   Zone1 Medium    Low 18.64165645  9.73886978 0.00000000     0 22036.19702  28.38052622
# 7   Zone1 Medium Medium  4.06436687  0.16876810 0.00000000     0  3286.83815   4.23313497
# 8   Zone2   High   High 30.03120766 10.13084134 0.01099552     0 11522.80578  40.17304453
# 9   Zone2   High    Low  6.91574950  1.58340654 0.04628919     0  2451.08397   8.54544522
# 10  Zone2   High Medium  0.88955660  0.05981439 0.00000000     0   272.30741   0.94937100
# # ... with 51 more rows

dplyr：获取不同级别的区域和区域分布

1 个答案: