This page describes how to upload and look at data in the R package. First, we will downlaod a CSV of UN population estimates on central African cities to import into R: Central Africa West.
The first thing to do is to let R know where to find the CSV. You can do this by navigating through the bottom right window in R Studio (Files/More/Set As Working Directory) or setting your working directory using the setwd command.
Then read the CSV file: read.csv(“Central Africa West.csv”)
Note how the data are read directly printed on the command console. Next we will create a new object called pop so that we can call on the data more easily and interact with it.
Try: pop <- read.csv(“Central Africa West.csv”)
NB you can use = instead of <- but it is better coding practice to save the = sign for other uses.
To look at the data, try cutting and pasting the following commands into the command console and hitting enter.
str(pop)
## 'data.frame': 17 obs. of 12 variables:
## $ Year : int 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 ...
## $ Period : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Douala : int 95 117 153 205 298 433 571 740 940 1184 ...
## $ Yaounde : int 32 49 75 112 183 292 415 578 777 1025 ...
## $ Libreville : int 15 17 29 49 77 155 234 293 366 439 ...
## $ Brazzaville : int 83 92 124 172 238 329 446 596 704 830 ...
## $ Pointe_Noire : int 16 35 64 89 116 154 217 300 363 439 ...
## $ Sub_Saharan_300kplus : int 3923 4909 7083 10779 16335 24143 34813 47767 62327 75996 ...
## $ Central_Africa_300kplus: int 3660 4521 5651 7047 8921 11495 14465 18043 22566 28525 ...
## $ Cameroon_urban : int 417 557 747 1011 1375 2112 2851 3761 4787 5930 ...
## $ Congo_urban : int 201 253 320 408 522 672 860 1086 1295 1535 ...
## $ Gabon_urban : int 54 68 87 126 189 279 397 515 655 814 ...
This tells you the structure of the data. In this case, all of the variables are integers, but it is also common to see characters, factors, and other types of data.
names(pop)
## [1] "Year" "Period"
## [3] "Douala" "Yaounde"
## [5] "Libreville" "Brazzaville"
## [7] "Pointe_Noire" "Sub_Saharan_300kplus"
## [9] "Central_Africa_300kplus" "Cameroon_urban"
## [11] "Congo_urban" "Gabon_urban"
This command gives the names of all the variables in the data. The country, city, and regions contain population estimates in thousands.
summary(pop)
## Year Period Douala Yaounde
## Min. :1950 Min. : 1 Min. : 95.0 Min. : 32.0
## 1st Qu.:1970 1st Qu.: 5 1st Qu.: 205.0 1st Qu.: 112.0
## Median :1990 Median : 9 Median : 571.0 Median : 415.0
## Mean :1990 Mean : 9 Mean : 804.8 Mean : 693.8
## 3rd Qu.:2010 3rd Qu.:13 3rd Qu.:1184.0 3rd Qu.:1025.0
## Max. :2030 Max. :17 Max. :2361.0 Max. :2349.0
## NA's :4 NA's :4
## Libreville Brazzaville Pointe_Noire Sub_Saharan_300kplus
## Min. : 15.0 Min. : 83.0 Min. : 16.0 Min. : 3923
## 1st Qu.: 49.0 1st Qu.: 172.0 1st Qu.: 89.0 1st Qu.: 16335
## Median :234.0 Median : 446.0 Median :217.0 Median : 62327
## Mean :258.5 Mean : 575.3 Mean :293.1 Mean : 91980
## 3rd Qu.:439.0 3rd Qu.: 830.0 3rd Qu.:439.0 3rd Qu.:137283
## Max. :631.0 Max. :1574.0 Max. :815.0 Max. :300153
## NA's :4 NA's :4 NA's :4
## Central_Africa_300kplus Cameroon_urban Congo_urban Gabon_urban
## Min. : 3660 Min. : 417 Min. : 201 Min. : 54.0
## 1st Qu.: 8921 1st Qu.: 1375 1st Qu.: 522 1st Qu.: 189.0
## Median : 22566 Median : 4787 Median :1295 Median : 655.0
## Mean : 34795 Mean : 6835 Mean :1723 Mean : 819.6
## 3rd Qu.: 51883 3rd Qu.:10625 3rd Qu.:2600 3rd Qu.:1334.0
## Max. :107747 Max. :20492 Max. :4804 Max. :2122.0
##
Another good way to get a sense for the data is to look at the first or last entries, using the head or tail commands.
head(pop)
## Year Period Douala Yaounde Libreville Brazzaville Pointe_Noire
## 1 1950 1 95 32 15 83 16
## 2 1955 2 117 49 17 92 35
## 3 1960 3 153 75 29 124 64
## 4 1965 4 205 112 49 172 89
## 5 1970 5 298 183 77 238 116
## 6 1975 6 433 292 155 329 154
## Sub_Saharan_300kplus Central_Africa_300kplus Cameroon_urban Congo_urban
## 1 3923 3660 417 201
## 2 4909 4521 557 253
## 3 7083 5651 747 320
## 4 10779 7047 1011 408
## 5 16335 8921 1375 522
## 6 24143 11495 2112 672
## Gabon_urban
## 1 54
## 2 68
## 3 87
## 4 126
## 5 189
## 6 279
For help with any of these commands, use the help function by typing ? before the command name. For example, try typing ?head into the command console
EXERCISE
Walk through this brief introduction to R and R Studio.