Data frame primer
Kristen Sosulski
4/7/2021
Importing data
library(readr)
gdp <- read_csv("gdp.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## `Country Name` = col_character(),
## `Country Code` = col_character()
## )## See spec(...) for full column specifications.Accessing columns and rows
Use the square brackets. Separate by rows and columns
gdp[1,] #Shows first row with all variables## # A tibble: 1 x 60
## `Country Name` `Country Code` `1960` `1961` `1962` `1963` `1964` `1965` `1966`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Aruba ABW NA NA NA NA NA NA NA
## # … with 51 more variables: `1967` <dbl>, `1968` <dbl>, `1969` <dbl>,
## # `1970` <dbl>, `1971` <dbl>, `1972` <dbl>, `1973` <dbl>, `1974` <dbl>,
## # `1975` <dbl>, `1976` <dbl>, `1977` <dbl>, `1978` <dbl>, `1979` <dbl>,
## # `1980` <dbl>, `1981` <dbl>, `1982` <dbl>, `1983` <dbl>, `1984` <dbl>,
## # `1985` <dbl>, `1986` <dbl>, `1987` <dbl>, `1988` <dbl>, `1989` <dbl>,
## # `1990` <dbl>, `1991` <dbl>, `1992` <dbl>, `1993` <dbl>, `1994` <dbl>,
## # `1995` <dbl>, `1996` <dbl>, `1997` <dbl>, `1998` <dbl>, `1999` <dbl>,
## # `2000` <dbl>, `2001` <dbl>, `2002` <dbl>, `2003` <dbl>, `2004` <dbl>,
## # `2005` <dbl>, `2006` <dbl>, `2007` <dbl>, `2008` <dbl>, `2009` <dbl>,
## # `2010` <dbl>, `2011` <dbl>, `2012` <dbl>, `2013` <dbl>, `2014` <dbl>,
## # `2015` <dbl>, `2016` <dbl>, `2017` <dbl>gdp[,1] #shows Country Name variable and all rows## # A tibble: 264 x 1
## `Country Name`
## <chr>
## 1 Aruba
## 2 Afghanistan
## 3 Angola
## 4 Albania
## 5 Andorra
## 6 Arab World
## 7 United Arab Emirates
## 8 Argentina
## 9 Armenia
## 10 American Samoa
## # … with 254 more rowsTo do an unordered selection of columns and rows, use c()
gdp[c(1:6),c(1,2,57:60)] #rows 1:6, columns 1,2, and 57 through 60 inclusive## # A tibble: 6 x 6
## `Country Name` `Country Code` `2014` `2015` `2016` `2017`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Aruba ABW NA NA NA NA
## 2 Afghanistan AFG 2.06e10 1.92e10 1.95e10 2.08e10
## 3 Angola AGO 1.27e11 1.03e11 9.53e10 1.24e11
## 4 Albania ALB 1.32e10 1.14e10 1.19e10 1.30e10
## 5 Andorra AND 3.35e 9 2.81e 9 2.88e 9 3.01e 9
## 6 Arab World ARB 2.91e12 2.55e12 2.50e12 2.59e12Adding a new column way 1
gdp$newcolumn <- NA #
print(gdp$newcolumn)## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [101] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [126] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [151] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [176] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [201] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [226] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [251] NA NA NA NA NA NA NA NA NA NA NA NA NA NACheck to see that a new variable was added
dim(gdp)## [1] 264 61Adding a new column way 2
gdp["anothercolumn"] <- NA #
print(gdp$anothercolumn)## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [101] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [126] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [151] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [176] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [201] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [226] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [251] NA NA NA NA NA NA NA NA NA NA NA NA NA NAdim(gdp)## [1] 264 62Reordering data frame columns
http://www.sthda.com/english/wiki/reordering-data-frame-columns-in-r
gdp2 <- gdp[, c(61, 62, 1:60)]
gdp2## # A tibble: 264 x 62
## newcolumn anothercolumn `Country Name` `Country Code` `1960` `1961`
## <lgl> <lgl> <chr> <chr> <dbl> <dbl>
## 1 NA NA Aruba ABW NA NA
## 2 NA NA Afghanistan AFG 5.38e8 5.49e8
## 3 NA NA Angola AGO NA NA
## 4 NA NA Albania ALB NA NA
## 5 NA NA Andorra AND NA NA
## 6 NA NA Arab World ARB NA NA
## 7 NA NA United Arab E… ARE NA NA
## 8 NA NA Argentina ARG NA NA
## 9 NA NA Armenia ARM NA NA
## 10 NA NA American Samoa ASM NA NA
## # … with 254 more rows, and 56 more variables: `1962` <dbl>, `1963` <dbl>,
## # `1964` <dbl>, `1965` <dbl>, `1966` <dbl>, `1967` <dbl>, `1968` <dbl>,
## # `1969` <dbl>, `1970` <dbl>, `1971` <dbl>, `1972` <dbl>, `1973` <dbl>,
## # `1974` <dbl>, `1975` <dbl>, `1976` <dbl>, `1977` <dbl>, `1978` <dbl>,
## # `1979` <dbl>, `1980` <dbl>, `1981` <dbl>, `1982` <dbl>, `1983` <dbl>,
## # `1984` <dbl>, `1985` <dbl>, `1986` <dbl>, `1987` <dbl>, `1988` <dbl>,
## # `1989` <dbl>, `1990` <dbl>, `1991` <dbl>, `1992` <dbl>, `1993` <dbl>,
## # `1994` <dbl>, `1995` <dbl>, `1996` <dbl>, `1997` <dbl>, `1998` <dbl>,
## # `1999` <dbl>, `2000` <dbl>, `2001` <dbl>, `2002` <dbl>, `2003` <dbl>,
## # `2004` <dbl>, `2005` <dbl>, `2006` <dbl>, `2007` <dbl>, `2008` <dbl>,
## # `2009` <dbl>, `2010` <dbl>, `2011` <dbl>, `2012` <dbl>, `2013` <dbl>,
## # `2014` <dbl>, `2015` <dbl>, `2016` <dbl>, `2017` <dbl>Removing a column
library(dplyr )##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniongdp2 <- select(gdp2, -anothercolumn)
dim(gdp2)## [1] 264 61Learning more about our data
str(gdp2)## tibble [264 × 61] (S3: tbl_df/tbl/data.frame)
## $ newcolumn : logi [1:264] NA NA NA NA NA NA ...
## $ Country Name: chr [1:264] "Aruba" "Afghanistan" "Angola" "Albania" ...
## $ Country Code: chr [1:264] "ABW" "AFG" "AGO" "ALB" ...
## $ 1960 : num [1:264] NA 5.38e+08 NA NA NA ...
## $ 1961 : num [1:264] NA 5.49e+08 NA NA NA ...
## $ 1962 : num [1:264] NA 5.47e+08 NA NA NA ...
## $ 1963 : num [1:264] NA 7.51e+08 NA NA NA ...
## $ 1964 : num [1:264] NA 8e+08 NA NA NA ...
## $ 1965 : num [1:264] NA 1.01e+09 NA NA NA ...
## $ 1966 : num [1:264] NA 1.4e+09 NA NA NA ...
## $ 1967 : num [1:264] NA 1.67e+09 NA NA NA ...
## $ 1968 : num [1:264] NA 1.37e+09 NA NA NA ...
## $ 1969 : num [1:264] NA 1.41e+09 NA NA NA ...
## $ 1970 : num [1:264] NA 1.75e+09 NA NA 7.86e+07 ...
## $ 1971 : num [1:264] NA 1.83e+09 NA NA 8.94e+07 ...
## $ 1972 : num [1:264] NA 1.60e+09 NA NA 1.13e+08 ...
## $ 1973 : num [1:264] NA 1.73e+09 NA NA 1.51e+08 ...
## $ 1974 : num [1:264] NA 2.16e+09 NA NA 1.87e+08 ...
## $ 1975 : num [1:264] NA 2.37e+09 NA NA 2.20e+08 ...
## $ 1976 : num [1:264] NA 2.56e+09 NA NA 2.27e+08 ...
## $ 1977 : num [1:264] NA 2.95e+09 NA NA 2.54e+08 ...
## $ 1978 : num [1:264] NA 3.30e+09 NA NA 3.08e+08 ...
## $ 1979 : num [1:264] NA 3.70e+09 NA NA 4.12e+08 ...
## $ 1980 : num [1:264] NA 3.64e+09 5.93e+09 NA 4.46e+08 ...
## $ 1981 : num [1:264] NA 3.48e+09 5.55e+09 NA 3.89e+08 ...
## $ 1982 : num [1:264] NA NA 5.55e+09 NA 3.76e+08 ...
## $ 1983 : num [1:264] NA NA 5.78e+09 NA 3.28e+08 ...
## $ 1984 : num [1:264] NA NA 6.13e+09 1.92e+09 3.30e+08 ...
## $ 1985 : num [1:264] NA NA 7.55e+09 1.97e+09 3.47e+08 ...
## $ 1986 : num [1:264] NA NA 7.07e+09 2.17e+09 4.82e+08 ...
## $ 1987 : num [1:264] NA NA 8.08e+09 2.16e+09 6.11e+08 ...
## $ 1988 : num [1:264] NA NA 8.77e+09 2.13e+09 7.21e+08 ...
## $ 1989 : num [1:264] NA NA 1.02e+10 2.34e+09 7.95e+08 ...
## $ 1990 : num [1:264] NA NA 1.12e+10 2.10e+09 1.03e+09 ...
## $ 1991 : num [1:264] NA NA 1.06e+10 1.14e+09 1.11e+09 ...
## $ 1992 : num [1:264] NA NA 8.31e+09 7.09e+08 1.21e+09 ...
## $ 1993 : num [1:264] NA NA 5.77e+09 1.23e+09 1.01e+09 ...
## $ 1994 : num [1:264] 1.33e+09 NA 4.44e+09 1.99e+09 1.02e+09 ...
## $ 1995 : num [1:264] 1.32e+09 NA 5.54e+09 2.42e+09 1.18e+09 ...
## $ 1996 : num [1:264] 1.38e+09 NA 7.53e+09 3.31e+09 1.22e+09 ...
## $ 1997 : num [1:264] 1.53e+09 NA 7.65e+09 2.36e+09 1.18e+09 ...
## $ 1998 : num [1:264] 1.67e+09 NA 6.51e+09 2.71e+09 1.21e+09 ...
## $ 1999 : num [1:264] 1.72e+09 NA 6.15e+09 3.41e+09 1.24e+09 ...
## $ 2000 : num [1:264] 1.87e+09 NA 9.13e+09 3.63e+09 1.43e+09 ...
## $ 2001 : num [1:264] 1.92e+09 2.46e+09 8.94e+09 4.06e+09 1.50e+09 ...
## $ 2002 : num [1:264] 1.94e+09 4.13e+09 1.25e+10 4.44e+09 1.73e+09 ...
## $ 2003 : num [1:264] 2.02e+09 4.58e+09 1.42e+10 5.75e+09 2.40e+09 ...
## $ 2004 : num [1:264] 2.23e+09 5.29e+09 1.96e+10 7.31e+09 2.94e+09 ...
## $ 2005 : num [1:264] 2.33e+09 6.28e+09 2.82e+10 8.16e+09 3.26e+09 ...
## $ 2006 : num [1:264] 2.42e+09 7.06e+09 4.18e+10 8.99e+09 3.54e+09 ...
## $ 2007 : num [1:264] 2.62e+09 9.84e+09 6.04e+10 1.07e+10 4.02e+09 ...
## $ 2008 : num [1:264] 2.79e+09 1.02e+10 8.42e+10 1.29e+10 4.01e+09 ...
## $ 2009 : num [1:264] 2.50e+09 1.25e+10 7.55e+10 1.20e+10 3.66e+09 ...
## $ 2010 : num [1:264] 2.47e+09 1.59e+10 8.25e+10 1.19e+10 3.36e+09 ...
## $ 2011 : num [1:264] 2.58e+09 1.79e+10 1.04e+11 1.29e+10 3.44e+09 ...
## $ 2012 : num [1:264] NA 2.05e+10 1.14e+11 1.23e+10 3.16e+09 ...
## $ 2013 : num [1:264] NA 2.03e+10 1.25e+11 1.28e+10 3.28e+09 ...
## $ 2014 : num [1:264] NA 2.06e+10 1.27e+11 1.32e+10 3.35e+09 ...
## $ 2015 : num [1:264] NA 1.92e+10 1.03e+11 1.14e+10 2.81e+09 ...
## $ 2016 : num [1:264] NA 1.95e+10 9.53e+10 1.19e+10 2.88e+09 ...
## $ 2017 : num [1:264] NA 2.08e+10 1.24e+11 1.30e+10 3.01e+09 ...Summary
summary(gdp2)## newcolumn Country Name Country Code 1960
## Mode:logical Length:264 Length:264 Min. :1.201e+07
## NA's:264 Class :character Class :character 1st Qu.:5.357e+08
## Mode :character Mode :character Median :2.761e+09
## Mean :7.419e+10
## 3rd Qu.:2.992e+10
## Max. :1.366e+12
## NA's :131
## 1961 1962 1963
## Min. :1.159e+07 Min. :9.123e+06 Min. :1.084e+07
## 1st Qu.:5.489e+08 1st Qu.:5.604e+08 1st Qu.:6.323e+08
## Median :3.034e+09 Median :3.287e+09 Median :3.601e+09
## Mean :7.658e+10 Mean :8.110e+10 Mean :8.768e+10
## 3rd Qu.:3.108e+10 3rd Qu.:3.368e+10 3rd Qu.:3.862e+10
## Max. :1.421e+12 Max. :1.526e+12 Max. :1.643e+12
## NA's :131 NA's :129 NA's :129
## 1964 1965 1966
## Min. :1.271e+07 Min. :1.359e+07 Min. :1.447e+07
## 1st Qu.:6.505e+08 1st Qu.:5.998e+08 1st Qu.:6.500e+08
## Median :3.459e+09 Median :3.120e+09 Median :3.157e+09
## Mean :9.616e+10 Mean :9.872e+10 Mean :1.059e+11
## 3rd Qu.:3.768e+10 3rd Qu.:3.724e+10 3rd Qu.:3.748e+10
## Max. :1.800e+12 Max. :1.961e+12 Max. :2.127e+12
## NA's :129 NA's :119 NA's :118
## 1967 1968 1969
## Min. :1.584e+07 Min. :1.460e+07 Min. :1.585e+07
## 1st Qu.:6.212e+08 1st Qu.:6.412e+08 1st Qu.:6.680e+08
## Median :3.371e+09 Median :3.910e+09 Median :4.461e+09
## Mean :1.101e+11 Mean :1.155e+11 Mean :1.274e+11
## 3rd Qu.:3.165e+10 3rd Qu.:3.266e+10 3rd Qu.:3.662e+10
## Max. :2.263e+12 Max. :2.442e+12 Max. :2.689e+12
## NA's :115 NA's :111 NA's :111
## 1970 1971 1972
## Min. :1.430e+07 Min. :1.528e+07 Min. :1.894e+07
## 1st Qu.:5.238e+08 1st Qu.:5.376e+08 1st Qu.:5.820e+08
## Median :4.179e+09 Median :4.476e+09 Median :5.710e+09
## Mean :1.339e+11 Mean :1.469e+11 Mean :1.693e+11
## 3rd Qu.:4.114e+10 3rd Qu.:4.582e+10 3rd Qu.:5.333e+10
## Max. :2.957e+12 Max. :3.267e+12 Max. :3.768e+12
## NA's :102 NA's :101 NA's :101
## 1973 1974 1975
## Min. :2.420e+07 Min. :3.151e+07 Min. :3.251e+07
## 1st Qu.:7.128e+08 1st Qu.:1.032e+09 1st Qu.:1.105e+09
## Median :7.002e+09 Median :8.894e+09 Median :9.397e+09
## Mean :2.079e+11 Mean :2.408e+11 Mean :2.630e+11
## 3rd Qu.:6.732e+10 3rd Qu.:9.086e+10 3rd Qu.:9.761e+10
## Max. :4.591e+12 Max. :5.296e+12 Max. :5.896e+12
## NA's :101 NA's :100 NA's :96
## 1976 1977 1978
## Min. :3.004e+07 Min. :3.414e+07 Min. :4.157e+07
## 1st Qu.:1.065e+09 1st Qu.:9.354e+08 1st Qu.:1.196e+09
## Median :9.649e+09 Median :1.103e+10 Median :1.287e+10
## Mean :2.831e+11 Mean :3.126e+11 Mean :3.673e+11
## 3rd Qu.:1.013e+11 3rd Qu.:1.049e+11 3rd Qu.:1.194e+11
## Max. :6.415e+12 Max. :7.257e+12 Max. :8.543e+12
## NA's :95 NA's :91 NA's :92
## 1979 1980 1981
## Min. :4.262e+07 Min. :3.872e+07 Min. :3.102e+07
## 1st Qu.:1.289e+09 1st Qu.:1.378e+09 1st Qu.:1.390e+09
## Median :1.507e+10 Median :1.346e+10 Median :1.335e+10
## Mean :4.274e+11 Mean :4.540e+11 Mean :4.561e+11
## 3rd Qu.:1.347e+11 3rd Qu.:1.401e+11 3rd Qu.:1.298e+11
## Max. :9.925e+12 Max. :1.117e+13 Max. :1.146e+13
## NA's :91 NA's :79 NA's :76
## 1982 1983 1984
## Min. :3.492e+07 Min. :3.784e+07 Min. :4.125e+07
## 1st Qu.:1.295e+09 1st Qu.:1.256e+09 1st Qu.:1.367e+09
## Median :1.365e+10 Median :1.003e+10 Median :9.701e+09
## Mean :4.480e+11 Mean :4.502e+11 Mean :4.615e+11
## 3rd Qu.:1.259e+11 3rd Qu.:1.107e+11 3rd Qu.:1.079e+11
## Max. :1.136e+13 Max. :1.163e+13 Max. :1.207e+13
## NA's :75 NA's :74 NA's :73
## 1985 1986 1987
## Min. :3.213e+07 Min. :3.209e+07 Min. :3.361e+07
## 1st Qu.:1.418e+09 1st Qu.:1.719e+09 1st Qu.:2.003e+09
## Median :1.001e+10 Median :1.062e+10 Median :1.136e+10
## Mean :4.795e+11 Mean :5.588e+11 Mean :6.215e+11
## 3rd Qu.:1.078e+11 3rd Qu.:1.251e+11 3rd Qu.:1.371e+11
## Max. :1.268e+13 Max. :1.502e+13 Max. :1.710e+13
## NA's :71 NA's :69 NA's :65
## 1988 1989 1990
## Min. :4.297e+07 Min. :4.112e+07 Min. :8.824e+06
## 1st Qu.:2.153e+09 1st Qu.:2.248e+09 1st Qu.:2.561e+09
## Median :1.058e+10 Median :1.039e+10 Median :1.229e+10
## Mean :6.865e+11 Mean :7.179e+11 Mean :7.554e+11
## 3rd Qu.:1.333e+11 3rd Qu.:1.651e+11 3rd Qu.:1.665e+11
## Max. :1.915e+13 Max. :2.008e+13 Max. :2.257e+13
## NA's :63 NA's :59 NA's :43
## 1991 1992 1993
## Min. :9.365e+06 Min. :9.743e+06 Min. :9.631e+06
## 1st Qu.:2.653e+09 1st Qu.:2.317e+09 1st Qu.:2.348e+09
## Median :1.134e+10 Median :1.138e+10 Median :1.314e+10
## Mean :7.981e+11 Mean :8.388e+11 Mean :8.380e+11
## 3rd Qu.:1.646e+11 3rd Qu.:1.574e+11 3rd Qu.:1.801e+11
## Max. :2.392e+13 Max. :2.541e+13 Max. :2.582e+13
## NA's :43 NA's :42 NA's :38
## 1994 1995 1996
## Min. :1.089e+07 Min. :1.103e+07 Min. :1.233e+07
## 1st Qu.:2.163e+09 1st Qu.:2.512e+09 1st Qu.:2.786e+09
## Median :1.291e+10 Median :1.358e+10 Median :1.390e+10
## Mean :8.917e+11 Mean :9.619e+11 Mean :9.868e+11
## 3rd Qu.:1.688e+11 3rd Qu.:1.734e+11 3rd Qu.:1.830e+11
## Max. :2.775e+13 Max. :3.085e+13 Max. :3.154e+13
## NA's :36 NA's :28 NA's :27
## 1997 1998 1999
## Min. :1.270e+07 Min. :1.276e+07 Min. :1.369e+07
## 1st Qu.:2.910e+09 1st Qu.:2.981e+09 1st Qu.:3.035e+09
## Median :1.492e+10 Median :1.509e+10 Median :1.566e+10
## Mean :9.901e+11 Mean :9.793e+11 Mean :1.001e+12
## 3rd Qu.:1.829e+11 3rd Qu.:1.775e+11 3rd Qu.:1.788e+11
## Max. :3.143e+13 Max. :3.135e+13 Max. :3.251e+13
## NA's :28 NA's :26 NA's :25
## 2000 2001 2002
## Min. :1.374e+07 Min. :1.320e+07 Min. :1.545e+07
## 1st Qu.:2.905e+09 1st Qu.:2.794e+09 1st Qu.:3.020e+09
## Median :1.376e+10 Median :1.318e+10 Median :1.428e+10
## Mean :1.009e+12 Mean :1.007e+12 Mean :1.032e+12
## 3rd Qu.:1.895e+11 3rd Qu.:1.905e+11 3rd Qu.:1.915e+11
## Max. :3.357e+13 Max. :3.337e+13 Max. :3.464e+13
## NA's :19 NA's :19 NA's :15
## 2003 2004 2005
## Min. :1.823e+07 Min. :2.153e+07 Min. :2.184e+07
## 1st Qu.:3.446e+09 1st Qu.:3.875e+09 1st Qu.:4.530e+09
## Median :1.720e+10 Median :2.015e+10 Median :2.285e+10
## Mean :1.165e+12 Mean :1.318e+12 Mean :1.443e+12
## 3rd Qu.:2.176e+11 3rd Qu.:2.550e+11 3rd Qu.:3.047e+11
## Max. :3.888e+13 Max. :4.379e+13 Max. :4.741e+13
## NA's :15 NA's :14 NA's :14
## 2006 2007 2008
## Min. :2.290e+07 Min. :2.043e+07 Min. :3.029e+07
## 1st Qu.:4.710e+09 1st Qu.:5.761e+09 1st Qu.:6.110e+09
## Median :2.583e+10 Median :3.235e+10 Median :3.914e+10
## Mean :1.577e+12 Mean :1.807e+12 Mean :2.030e+12
## 3rd Qu.:3.451e+11 3rd Qu.:4.085e+11 3rd Qu.:5.102e+11
## Max. :5.134e+13 Max. :5.783e+13 Max. :6.343e+13
## NA's :13 NA's :13 NA's :15
## 2009 2010 2011
## Min. :2.710e+07 Min. :3.182e+07 Min. :3.871e+07
## 1st Qu.:5.833e+09 1st Qu.:6.960e+09 1st Qu.:7.674e+09
## Median :3.744e+10 Median :4.028e+10 Median :4.586e+10
## Mean :1.925e+12 Mean :2.141e+12 Mean :2.395e+12
## 3rd Qu.:4.291e+11 3rd Qu.:4.835e+11 3rd Qu.:5.298e+11
## Max. :6.014e+13 Max. :6.596e+13 Max. :7.330e+13
## NA's :15 NA's :15 NA's :14
## 2012 2013 2014
## Min. :3.767e+07 Min. :3.751e+07 Min. :3.729e+07
## 1st Qu.:8.681e+09 1st Qu.:8.995e+09 1st Qu.:1.019e+10
## Median :5.039e+10 Median :5.447e+10 Median :5.673e+10
## Mean :2.490e+12 Mean :2.569e+12 Mean :2.655e+12
## 3rd Qu.:5.552e+11 3rd Qu.:5.587e+11 3rd Qu.:5.712e+11
## Max. :7.497e+13 Max. :7.705e+13 Max. :7.913e+13
## NA's :17 NA's :16 NA's :17
## 2015 2016 2017
## Min. :3.556e+07 Min. :3.657e+07 Min. :3.973e+07
## 1st Qu.:8.758e+09 1st Qu.:9.412e+09 1st Qu.:1.151e+10
## Median :5.317e+10 Median :5.324e+10 Median :5.945e+10
## Mean :2.506e+12 Mean :2.586e+12 Mean :2.848e+12
## 3rd Qu.:5.786e+11 3rd Qu.:6.449e+11 3rd Qu.:7.175e+11
## Max. :7.484e+13 Max. :7.594e+13 Max. :8.068e+13
## NA's :18 NA's :23 NA's :30Converting to log scale
This is the basic logarithm function with 9 as the value and 3 as the base. The results are 2 because 9 is the square of 3.
# log in r - core syntax
log(9,3)## [1] 2Here, the second parameter has been omitted resulting in a base of e producing the natural logarithm of 5.
log(5)## [1] 1.609438Inverse
exp(1.609438)## [1] 5Log transformation
A log transformation is a process of applying a logarithm to data to reduce its skew. This is usually done when the numbers are highly skewed to reduce the skew so the data can be understood easier. Log transformation in R is accomplished by applying the log() function to vector, data-frame or other data set. Before the logarithm is applied, 1 is added to the base value to prevent applying a logarithm to a 0 value. The resulting presentation of the data is less skewed than the original making it easier to understand.
myvector = c(100,10,5,2,1,0.5,0.1,0.05,0.01,0.001,0.0001)
transformedvector=log(myvector+1)
plot(myvector)
plot(transformedvector)
data frame
Log transforming your data in R for a data frame is a little trickier because getting the log requires separating the data. Taking the log of the entire dataset get you the log of each data point. However, you usually need the log from only one column of data.
ChickWeight$logweight=log(ChickWeight$weight)
#head(ChickWeight)
plot(head(ChickWeight$Time),head(ChickWeight$logweight))
plot(head(ChickWeight$Time),head(ChickWeight$weight))
Visualize it
boxplot(gdp$`2017`[1:10])
#boxplot(gdp$`2017`)Let's apply this to a column in GDP
gdp[,3:60] <- log(gdp[,3:60] + 1)
#(or use log1p(x) computes log(1+x) accurately)Learn more about using the natural logarithm with economic data: https://econbrowser.com/archives/2014/02/use-of-logarithms-in-economics
Subsetting
gdp <- read_csv("gdp.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## `Country Name` = col_character(),
## `Country Code` = col_character()
## )## See spec(...) for full column specifications.uae <- subset(gdp, `Country Code` == "ARE")library(dplyr)
uae2 = filter(gdp, `Country Code` == "ARE")Single column - Multiple values
gdp_multiple= subset(gdp, `Country Code` %in% c("ARE", "CHN", "GBR"))gdp_multiple2 = filter(gdp, `Country Code` == "ARE" | `Country Code` == "CHN" | `Country Code` == "GBR")