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dplyr中的exp函数mutate返回1'；s

r

dplyr中的exp函数mutate返回1'；s,r,R,我试图使用指数函数转换变量，但是我获得了1的值 dat %>% mutate(estimate_exp = exp(estimate)) 其中： # A tibble: 30 x 3 # Groups: .id [30] .id estimate estimate_exp <chr> <dbl> <dbl> 1 COP 0.000363 1.00 2 D 0.0001

我试图使用指数函数转换变量，但是我获得了
1
的值

dat %>% 
  mutate(estimate_exp = exp(estimate))
其中：

# A tibble: 30 x 3
# Groups:   .id [30]
   .id    estimate estimate_exp
   <chr>     <dbl>        <dbl>
 1 COP    0.000363         1.00
 2 D      0.000128         1.00
 3 MKC    0.000661         1.00
 4 XRAY  -0.000220         1.00
 5 AZO    0.000646         1.00

 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
编辑：

我重新启动了R会话，现在得到以下输出： 当我跑步时：

models %>% 
  unnest(tidymodels) %>% 
  mutate(estimate_exp = exp(estimate)) %>% 
  select(estimate_exp)

# A tibble: 60 x 2
# Groups:   .id [30]
   .id   estimate_exp
   <chr>        <dbl>
 1 LEN      40.1     
 2 LEN       1.00    
 3 USB       8.47    
 4 USB       1.00    
 5 KMI       2.64    
 6 KMI       1.00    
 7 CSX       0.00592 
 8 CSX       1.00    
 9 AMT       0.000845
10 AMT       1.00

models2 %>% 
  unnest(tidymodels) %>% 
  filter(term == "date") %>% 
  mutate(estimate_exp = exp(estimate)) %>% 
  select(estimate_exp)
返回：

# A tibble: 30 x 2
# Groups:   .id [30]
   .id   estimate_exp
   <chr>        <dbl>
 1 LEN           1.00
 2 USB           1.00
 3 KMI           1.00
 4 CSX           1.00
 5 AMT           1.00
 6 VRSK          1.00
 7 CTSH          1.00
这给了我输出：

Adding missing grouping variables: `.id`
# A tibble: 30 x 2
# Groups:   .id [30]
   .id   estimate_exp
   <chr>        <dbl>
 1 AES          1.00 
 2 AMT          1.00 
 3 ANET         1.00 
 4 BSX          1.00 
 5 CAH          0.999
 6 CSX          1.00
编辑：通过刷新R会话，我运行以下操作：

library(dplyr)
library(broom)
library(purrr)


models2 <- d %>% 
  group_by(.id) %>% 
  nest() %>% 
  mutate(models = map(data, ~lm(logPrice ~ date, data = .x))) %>% 
  mutate(
    tidymodels = map(models, ~tidy(.x)),
    glancemodels = map(models, ~glance(.x)),
    augmentmodels = map(models, ~augment(.x))
  )


mod1 <- models2 %>% unnest(tidymodels) %>% filter(term == "date")
exp(mod1$estimate)
会话信息：

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] rvest_0.3.5                xml2_1.2.2                 tibbletime_0.1.3          
 [4] tsfeatures_1.0.1           gsubfn_0.7                 proto_1.0.0               
 [7] ggmap_3.0.0                ggrepel_0.8.1              directlabels_2018.05.22   
[10] patchwork_1.0.0            cowplot_1.0.0              gtable_0.3.0              
[13] stargazer_5.2.2            e1071_1.7-3                lightgbm_2.3.2            
[16] R6_2.4.1                   tensorflow_2.0.0           keras_2.2.5.0             
[19] drlib_0.1.1                PRROC_1.3.1                pROC_1.16.1               
[22] caret_6.0-85               lattice_0.20-38            Matrix_1.2-17             
[25] xgboostExplainer_0.1       broom_0.5.3                imputeTS_3.0              
[28] data.table_1.12.8          viridis_0.5.1              viridisLite_0.3.0         
[31] CVXR_0.99-7                timetk_0.1.2               forcats_0.4.0             
[34] readr_1.3.1                tibble_2.99.99.9014        tidyverse_1.3.0           
[37] tsibble_0.8.5              rsample_0.0.5.9000         kableExtra_1.1.0          
[40] knitr_1.27                 xgboost_0.90.0.2           reticulate_1.14-9001      
[43] formatR_1.7                tidyquant_0.5.9            quantmod_0.4-15           
[46] TTR_0.23-6                 PerformanceAnalytics_1.5.3 xts_0.12-0                
[49] zoo_1.8-7                  ggplot2_3.3.0.9000         purrr_0.3.3               
[52] lubridate_1.7.4            tidyr_1.0.2                future.apply_1.4.0        
[55] furrr_0.1.0                future_1.16.0              stringr_1.4.0             
[58] stringi_1.4.5              dplyr_0.8.99.9000         

loaded via a namespace (and not attached):
  [1] readxl_1.3.1           backports_1.1.5        scs_1.3-2             
  [4] selectr_0.4-2          plyr_1.8.5             splines_3.6.1         
  [7] gmp_0.5-13.6           listenv_0.8.0          tfruns_1.4            
 [10] digest_0.6.23          foreach_1.4.7          htmltools_0.4.0       
 [13] fansi_0.4.1            magrittr_1.5           recipes_0.1.9         
 [16] globals_0.12.5         modelr_0.1.5           gower_0.2.1           
 [19] R.utils_2.9.2          anytime_0.3.7          forecast_8.10         
 [22] tseries_0.10-47        jpeg_0.1-8.1           waterfalls_0.1.2      
 [25] colorspace_1.4-1       lobstr_1.1.1           haven_2.2.0           
 [28] xfun_0.12              tcltk_3.6.1            crayon_1.3.4          
 [31] jsonlite_1.6           zeallot_0.1.0          survival_2.44-1.1     
 [34] iterators_1.0.12       glue_1.3.1             ipred_0.9-9           
 [37] webshot_0.5.2          Quandl_2.10.0          ECOSolveR_0.5.3       
 [40] Rmpfr_0.8-1            scales_1.1.0           stinepack_1.4         
 [43] DBI_1.1.0              Rcpp_1.0.3             bit_1.1-15.1          
 [46] stats4_3.6.1           lava_1.6.6             prodlim_2019.11.13    
 [49] httr_1.4.1             ellipsis_0.3.0         farver_2.0.3          
 [52] pkgconfig_2.0.3        R.methodsS3_1.7.1      nnet_7.3-12           
 [55] dbplyr_1.4.2           utf8_1.1.4             labeling_0.3          
 [58] tidyselect_0.2.99.9000 rlang_0.4.4.9000       reshape2_1.4.3        
 [61] munsell_0.5.0          cellranger_1.1.0       tools_3.6.1           
 [64] cli_2.0.1              generics_0.0.2         evaluate_0.14         
 [67] ModelMetrics_1.2.2.1   bit64_0.9-7            fs_1.3.1              
 [70] RgoogleMaps_1.4.5.2    nlme_3.1-142           whisker_0.4           
 [73] R.oo_1.23.0            compiler_3.6.1         rstudioapi_0.10       
 [76] png_0.1-7              curl_4.3               testthat_2.3.1        
 [79] reprex_0.3.0           desc_1.2.0             urca_1.3-0            
 [82] vctrs_0.2.99.9005      pillar_1.4.3.9000      lifecycle_0.1.0       
 [85] lmtest_0.9-37          bitops_1.0-6           gridExtra_2.3         
 [88] codetools_0.2-16       pkgload_1.0.2          MASS_7.3-51.4         
 [91] assertthat_0.2.1       rprojroot_1.3-2        rjson_0.2.20          
 [94] withr_2.1.2            fracdiff_1.5-1         parallel_3.6.1        
 [97] hms_0.5.3              quadprog_1.5-8         rpart_4.1-15          
[100] timeDate_3043.102      class_7.3-15           rmarkdown_2.1         
[103] base64enc_0.1-3

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gsubfn_0.7                 proto_1.0.0                ggmap_3.0.0               
 [4] ggrepel_0.8.1              directlabels_2018.05.22    patchwork_1.0.0           
 [7] cowplot_1.0.0              gtable_0.3.0               stargazer_5.2.2           
[10] e1071_1.7-3                lightgbm_2.3.2             R6_2.4.1                  
[13] tensorflow_2.0.0           keras_2.2.5.0              drlib_0.1.1               
[16] PRROC_1.3.1                pROC_1.16.1                caret_6.0-85              
[19] lattice_0.20-38            Matrix_1.2-17              xgboostExplainer_0.1      
[22] broom_0.5.3                imputeTS_3.0               data.table_1.12.8         
[25] viridis_0.5.1              viridisLite_0.3.0          CVXR_0.99-7               
[28] timetk_0.1.2               forcats_0.4.0              readr_1.3.1               
[31] tibble_2.99.99.9014        tidyverse_1.3.0            tsibble_0.8.5             
[34] rsample_0.0.5.9000         kableExtra_1.1.0           knitr_1.27                
[37] xgboost_0.90.0.2           reticulate_1.14-9001       formatR_1.7               
[40] tidyquant_0.5.9            quantmod_0.4-15            TTR_0.23-6                
[43] PerformanceAnalytics_1.5.3 xts_0.12-0                 zoo_1.8-7                 
[46] ggplot2_3.3.0.9000         purrr_0.3.3                lubridate_1.7.4           
[49] tidyr_1.0.2                future.apply_1.4.0         furrr_0.1.0               
[52] future_1.16.0              stringr_1.4.0              stringi_1.4.5             
[55] dplyr_0.8.99.9000         

loaded via a namespace (and not attached):
 [1] readxl_1.3.1           backports_1.1.5        scs_1.3-2             
 [4] plyr_1.8.5             splines_3.6.1          gmp_0.5-13.6          
 [7] listenv_0.8.0          tfruns_1.4             digest_0.6.23         
[10] foreach_1.4.7          htmltools_0.4.0        fansi_0.4.1           
[13] magrittr_1.5           recipes_0.1.9          globals_0.12.5        
[16] modelr_0.1.5           gower_0.2.1            R.utils_2.9.2         
[19] anytime_0.3.7          forecast_8.10          tseries_0.10-47       
[22] jpeg_0.1-8.1           waterfalls_0.1.2       colorspace_1.4-1      
[25] rvest_0.3.5            lobstr_1.1.1           haven_2.2.0           
[28] xfun_0.12              tcltk_3.6.1            crayon_1.3.4          
[31] jsonlite_1.6           zeallot_0.1.0          survival_2.44-1.1     
[34] iterators_1.0.12       glue_1.3.1             ipred_0.9-9           
[37] webshot_0.5.2          Quandl_2.10.0          ECOSolveR_0.5.3       
[40] Rmpfr_0.8-1            scales_1.1.0           stinepack_1.4         
[43] DBI_1.1.0              Rcpp_1.0.3             bit_1.1-15.1          
[46] stats4_3.6.1           lava_1.6.6             prodlim_2019.11.13    
[49] httr_1.4.1             ellipsis_0.3.0         pkgconfig_2.0.3       
[52] R.methodsS3_1.7.1      nnet_7.3-12            dbplyr_1.4.2          
[55] utf8_1.1.4             tidyselect_0.2.99.9000 rlang_0.4.4.9000      
[58] reshape2_1.4.3         munsell_0.5.0          cellranger_1.1.0      
[61] tools_3.6.1            cli_2.0.1              generics_0.0.2        
[64] evaluate_0.14          ModelMetrics_1.2.2.1   bit64_0.9-7           
[67] fs_1.3.1               RgoogleMaps_1.4.5.2    nlme_3.1-142          
[70] whisker_0.4            R.oo_1.23.0            xml2_1.2.2            
[73] compiler_3.6.1         rstudioapi_0.10        png_0.1-7             
[76] curl_4.3               reprex_0.3.0           urca_1.3-0            
[79] vctrs_0.2.99.9005      pillar_1.4.3.9000      lifecycle_0.1.0       
[82] lmtest_0.9-37          bitops_1.0-6           gridExtra_2.3         
[85] codetools_0.2-16       MASS_7.3-51.4          assertthat_0.2.1      
[88] rjson_0.2.20           withr_2.1.2            fracdiff_1.5-1        
[91] parallel_3.6.1         hms_0.5.3              quadprog_1.5-8        
[94] rpart_4.1-15           timeDate_3043.102      class_7.3-15          
[97] rmarkdown_2.1          base64enc_0.1-3
没有什么不对的。如果我们提取列中的值，它将给出完整值，而不是
print
格式化的tbl_df列值

library(dplyr)
dat %>% 
   mutate(estimate_exp = exp(estimate)) %>%
   pull(estimate_exp)
#[1] 1.0003633 1.0001284 1.0006611 0.9997798 1.0006460 1.0003745 1.0004764 1.0002343 1.0002678 0.9998316
#[11] 0.9999734 1.0006664 1.0005484 1.0003239 0.9998811 0.9999193 1.0002518 0.9999877 1.0006545 1.0003430
#[21] 1.0005299 1.0004024 1.0004834 1.0008319 1.0000060 1.0003500 1.0003589 1.0001489 1.0004167 1.0003802
此外，我们不需要按属性分组

dat %>%
    ungroup %>%
    mutate(estimate_exp = exp(estimate)) %>% 
    pull(estimate_exp)
#[1] 1.0003633 1.0001284 1.0006611 0.9997798 1.0006460 1.0003745 1.0004764 1.0002343 1.0002678 0.9998316
#[11] 0.9999734 1.0006664 1.0005484 1.0003239 0.9998811 0.9999193 1.0002518 0.9999877 1.0006545 1.0003430
#[21] 1.0005299 1.0004024 1.0004834 1.0008319 1.0000060 1.0003500 1.0003589 1.0001489 1.0004167 1.0003802
使现代化 使用新数据“d”

models2 %>% 
   unnest(tidymodels) %>%
   ungroup %>%
   filter(term == "date") %>% 
   mutate(estimate_exp = exp(estimate)) %>% 
   select(estimate_exp) %>% pull(estimate_exp)
#[1] 1.0006405 1.0007140 0.9999498 1.0003551 0.9994210 1.0004718 0.9997622 1.0003188 1.0001203 1.0003742
#[11] 1.0001127 1.0005580 1.0000700 0.9990263 0.9997404 1.0001405 1.0000650 1.0002640 1.0004100 1.0009941
#[21] 1.0000995 1.0006812 1.0009634 1.0002974 0.9998595 0.9999282 1.0006958 1.0004811 1.0005498 1.0004972
没有什么不对的。如果我们提取列中的值，它将给出完整值，而不是
print
格式化的tbl_df列值

library(dplyr)
dat %>% 
   mutate(estimate_exp = exp(estimate)) %>%
   pull(estimate_exp)
#[1] 1.0003633 1.0001284 1.0006611 0.9997798 1.0006460 1.0003745 1.0004764 1.0002343 1.0002678 0.9998316
#[11] 0.9999734 1.0006664 1.0005484 1.0003239 0.9998811 0.9999193 1.0002518 0.9999877 1.0006545 1.0003430
#[21] 1.0005299 1.0004024 1.0004834 1.0008319 1.0000060 1.0003500 1.0003589 1.0001489 1.0004167 1.0003802
此外，我们不需要按属性分组

dat %>%
    ungroup %>%
    mutate(estimate_exp = exp(estimate)) %>% 
    pull(estimate_exp)
#[1] 1.0003633 1.0001284 1.0006611 0.9997798 1.0006460 1.0003745 1.0004764 1.0002343 1.0002678 0.9998316
#[11] 0.9999734 1.0006664 1.0005484 1.0003239 0.9998811 0.9999193 1.0002518 0.9999877 1.0006545 1.0003430
#[21] 1.0005299 1.0004024 1.0004834 1.0008319 1.0000060 1.0003500 1.0003589 1.0001489 1.0004167 1.0003802
使现代化 使用新数据“d”

models2 %>% 
   unnest(tidymodels) %>%
   ungroup %>%
   filter(term == "date") %>% 
   mutate(estimate_exp = exp(estimate)) %>% 
   select(estimate_exp) %>% pull(estimate_exp)
#[1] 1.0006405 1.0007140 0.9999498 1.0003551 0.9994210 1.0004718 0.9997622 1.0003188 1.0001203 1.0003742
#[11] 1.0001127 1.0005580 1.0000700 0.9990263 0.9997404 1.0001405 1.0000650 1.0002640 1.0004100 1.0009941
#[21] 1.0000995 1.0006812 1.0009634 1.0002974 0.9998595 0.9999282 1.0006958 1.0004811 1.0005498 1.0004972
哦，我明白了！这是有道理的。如何使输出具有更多的小数位数？当我应用你的代码时，我得到：
11111
我有
选项（scipen=999）
@user113156是因为软件包版本不同，我无法复制吗that@user113156有一个grup_by属性，您也可以删除它
dat%>%ungroup%>%mutate（estimate_exp=exp（估计值））%%>%pull（估计值）
使用
dat%%>%ungroup（.id）%%>%mutate（估计值=exp（估计值））%%>%pull（估计值）
仍然会给我
1…
输出。我也在使用这个版本。可能是因为
dplyr
，我不知道。但是添加
数据。帧
解决了我的问题。再次感谢！哦，我明白了！这很有意义。我如何使我的输出有更多的小数位？当我应用你的代码时，我得到：
1 11 11 11 11 11
我有
选项（scipen=999）
@user113156是因为软件包版本不同，我无法复制吗that@user113156有一个grup_by属性，您也可以删除它
dat%>%ungroup%>%mutate（估计值=exp（估计值））%>%pull（估计值）
使用
dat%%>%ungroup（.id）%%>%mutate（estimate\u exp=exp（estimate））%%>%pull（estimate\u exp）
仍然会给我
1…
输出。我也在使用这个版本。也许这与
dplyr有关，我不知道。但是添加
数据。框架解决了我的问题。再次感谢！我也有相同的包版本
packageVersion（'dplyr'）#[1]“0.8.99.9000”
关于
R 3.6.2
你能检查
exp（dat$estimate）的输出吗
即，我们直接提取列并获取
exp
当我应用
过滤器时出错-我添加了一个编辑。由于您加载了很多包，是否有可能
mutate
或
exp
被另一个包中的相同函数屏蔽了我现在必须离开。稍后还会检查吗在
R3.6.2
上有相同的软件包版本
packageVersion（'dplyr'）#[1]“0.8.99.9000”
您能检查
exp（dat$estimate）的输出吗
即，我们直接提取列并获取
exp
当我应用
过滤器时出错-我添加了一个编辑。由于您加载了很多包，是否有可能
mutate
或
exp
被另一个包中的相同函数屏蔽我现在必须离开。稍后将检查

dat %>%
    ungroup %>%
    mutate(estimate_exp = exp(estimate)) %>% 
    pull(estimate_exp)
#[1] 1.0003633 1.0001284 1.0006611 0.9997798 1.0006460 1.0003745 1.0004764 1.0002343 1.0002678 0.9998316
#[11] 0.9999734 1.0006664 1.0005484 1.0003239 0.9998811 0.9999193 1.0002518 0.9999877 1.0006545 1.0003430
#[21] 1.0005299 1.0004024 1.0004834 1.0008319 1.0000060 1.0003500 1.0003589 1.0001489 1.0004167 1.0003802



models2 %>% 
   unnest(tidymodels) %>%
   ungroup %>%
   filter(term == "date") %>% 
   mutate(estimate_exp = exp(estimate)) %>% 
   select(estimate_exp) %>% pull(estimate_exp)
#[1] 1.0006405 1.0007140 0.9999498 1.0003551 0.9994210 1.0004718 0.9997622 1.0003188 1.0001203 1.0003742
#[11] 1.0001127 1.0005580 1.0000700 0.9990263 0.9997404 1.0001405 1.0000650 1.0002640 1.0004100 1.0009941
#[21] 1.0000995 1.0006812 1.0009634 1.0002974 0.9998595 0.9999282 1.0006958 1.0004811 1.0005498 1.0004972