Iyer Response

Read in crime data

crime <- read.csv("nycrime.csv", stringsAsFactors = FALSE)
head(crime)

##             Crime Comp_Model_PS_327 Angelo_Patri Bronx_House_Bronx_Green
## 1        Burglary               140          168                     121
## 2    Fel_ Assault               472          390                     197
## 3          G.L.A.                70           64                      86
## 4       GrLarceny               485          327                     299
## 5         Housing                36           27                      47
## 6 Misd_Sex_Crimes               779          692                     353
##   Jackie_Robinson Franklin_K_Lane Rockaway_Collegiate Kappa_Vi
## 1             246             644                  96      111
## 2             112             263                  56      254
## 3              48             151                  18       27
## 4             289             709                 151      164
## 5               3             203                  20       85
## 6             415            1133                 162      356

library(tidyr)
crime_long <- crime %>% gather(location, incidents, -Crime)
head(crime_long)

##             Crime          location incidents
## 1        Burglary Comp_Model_PS_327       140
## 2    Fel_ Assault Comp_Model_PS_327       472
## 3          G.L.A. Comp_Model_PS_327        70
## 4       GrLarceny Comp_Model_PS_327       485
## 5         Housing Comp_Model_PS_327        36
## 6 Misd_Sex_Crimes Comp_Model_PS_327       779

Go back to wide

head(crime_long %>% spread(location, incidents))

##             Crime Angelo_Patri Bronx_House_Bronx_Green Comp_Model_PS_327
## 1        Burglary          168                     121               140
## 2    Fel_ Assault          390                     197               472
## 3          G.L.A.           64                      86                70
## 4       GrLarceny          327                     299               485
## 5         Housing           27                      47                36
## 6 Misd_Sex_Crimes          692                     353               779
##   Franklin_K_Lane Jackie_Robinson Kappa_Vi Rockaway_Collegiate
## 1             644             246      111                  96
## 2             263             112      254                  56
## 3             151              48       27                  18
## 4             709             289      164                 151
## 5             203               3       85                  20
## 6            1133             415      356                 162

But you don’t need to convert back to wide to transpose, you can take the long data frame and spread on crime back to wide.

head(crime_long %>% spread(Crime, incidents))

##                  location Burglary Fel_ Assault G.L.A. GrLarceny Housing
## 1            Angelo_Patri      168          390     64       327      27
## 2 Bronx_House_Bronx_Green      121          197     86       299      47
## 3       Comp_Model_PS_327      140          472     70       485      36
## 4         Franklin_K_Lane      644          263    151       709     203
## 5         Jackie_Robinson      246          112     48       289       3
## 6                Kappa_Vi      111          254     27       164      85
##   Misd_Sex_Crimes MisdAssault Murder Petit_Larceny Rape Robbery
## 1             692          28     10           759   24     303
## 2             353          23      2           635   18     138
## 3             779          55      9          1177   16     311
## 4            1133          51     16          1352   43     423
## 5             415          28      3           613    7     150
## 6             356          21      5           342   12     126
##   Shooting_Vic. ShootingInc Total_Violent_Crime
## 1            26          31                1286
## 2             5           5                 861
## 3            24          27                1503
## 4            38          45                2249
## 5            13          17                 855
## 6            18          21                 699